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Scientists Implant New Memories Into A Songbird’s Brain
DR TODD ROBERTS, STUDY AUTHOR, AND A ZEBRA FINCH IN HIS LABORATORY. UTSWADVERTISMENT
Like an oddly quaint sci-fi story, scientists have managed to encode “false” memories and parts of a song into the brain of a tiny bird.
Researchers at the University of Texas Southwestern taught a zebra finch new information using optogenetics, a technique that uses light to control living neurons that have been genetically modified to be light-sensitive. This information was then used to create a behavior response in the form of a chirpy song.
Zebra finches, a small songbird native to Central Australia renowned for their musical chirps, typically learn songs by listening and mimicking as their father sings. Previous research from the same team has shown that this process is linked to a network of neurons firing between the HVC, a brain area known to be tightly linked to learning from auditory experience, and the NIf, a region linked to forming syllable-specific memories.
Reporting in the journal Science, the researchers found that artificially activating the neurons in the HVC-NIf network could simulate a similar effect of experiencing and learning. Optogenetic techniques were used to manipulate neuron activity between the NIf and HVC brain regions, thereby encoding memories into a bird that had not experienced “tutoring” from its father. These false memories were then used to learn “syllables” of the species’ song.
“This is the first time we have confirmed brain regions that encode behavioral-goal memories – those memories that guide us when we want to imitate anything from speech to learning the piano,” Dr Todd Roberts, a neuroscientist with UT Southwestern’s O’Donnell Brain Institute, said in a statement. “The findings enabled us to implant these memories into the birds and guide the learning of their song.”
However, the birds were not able to learn the whole song because these two brain regions only deal with certain parts of the song-learning process in birds. The method was only able to teach them the “syllables” of their song, with shorter bursts of light exposure to the neurons resulting in a shorter note and vice versa.
“If we figure out those other pathways, we could hypothetically teach a bird to sing its song without any interaction from its father,” Dr Roberts said. “But we’re a long way from being able to do that.”
As for implanting songs into human brains, that is a long way off. Dr Roberts explained that the human brain and the networks associated with speech are “immensely complicated” compared to those found in a songbird.
Nevertheless, this field of optogenetics could hold some real applications for humans very shortly. For example, the researchers argue their work could be used to deepen our understanding of the neural processes and genes linked to human speech disorders. It could also answer questions about why some genes related to speech are disrupted in people with autism or other neurodevelopmental conditions.Sponsored Stories
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Volume 115Issue 131 November 2019Article Contents
- 1. Introduction
- 2. Small animal models of HFrEF
- 3. Small animal models of HFpEF
- 4. General advantages and limitations using small animal models
- 5. General considerations for the use of small and large animal models
- 6. Summary and conclusion
Small animal models of heart failure
Heart disease is a major cause of death worldwide with increasing prevalence, which urges the development of new therapeutic strategies. Over the last few decades, numerous small animal models have been generated to mimic various pathomechanisms contributing to heart failure (HF). Despite some limitations, these animal models have greatly advanced our understanding of the pathogenesis of the different aetiologies of HF and paved the way to understanding the underlying mechanisms and development of successful treatments. These models utilize surgical techniques, genetic modifications, and pharmacological approaches. The present review discusses the strengths and limitations of commonly used small animal HF models, which continue to provide crucial insight and facilitate the development of new treatment strategies for patients with HF.Animal models, Rodents, Heart failure, HFpEF, HFrEFTopic:
- heart failure
- animal model
- surgical procedures, operative
- heart failure with preserved ejection fraction
- heart failure with reduced ejection fraction
Issue Section: Reviews
Heart failure (HF) is the leading cause of death worldwide. The mortality rate of HF is high, with about 50% of patients dying within 5 years after the initial diagnosis, which exceeds most types of cancer (www.who.int). Furthermore, the prevalence of HF in industrialized nations is increasing, which results in an enormous economic burden. The increase is attributable, at least in part, to the improved treatment following acute myocardial infarction (MI), which has decreased the mortality rate, but not morbidity, and is based on the number of surviving patients. Additional factors comprise an increased prevalence of comorbidities, which predispose and accelerate the development of HF. Therefore, there is an urgent need to modify these risk factors and to develop new therapeutic strategies for HF patients.
Based on left ventricular (LV) ejection fraction (LVEF), HF can be categorized as heart failure with preserved ejection fraction (HFpEF; LVEF ≥50%), heart failure with mid-range ejection fraction (HFmrEF; LVEF 40–49%), or heart failure with reduced ejection fraction (HFrEF; LVEF < 40%).1 About 50% of HF patients are afflicted with HFpEF and exhibit HF symptoms, which include exercise intolerance, congestion, and oedema that are associated with cardiac hypertrophy, increased fibrosis, and decreased capillary content. Common risk factors for the development of HFpEF include arterial hypertension, obesity, diabetes mellitus, atrial fibrillation, and renal dysfunction (Figure 1). This implies that impaired cardiac compliance and contractile dysfunction found in HFpEF can be triggered by associated comorbidities. Importantly, the postulation that diastolic dysfunction is equivalent to HFpEF is an oversimplification and only partially correct. This emanates from the observation that diastolic dysfunction has also been detected in normal subjects without clinical HFpEF symptoms.2 In contrast, HFrEF is typically associated with loss of cardiomyocytes, which can be a consequence of myocardial damage of different aetiologies (Figure 1) and may increase wall stress, as reflected by higher levels of natriuretic peptides compared to HFpEF.2Figure 1
Schematic depicting selected risk factors for the development of heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF). Note that some risk factors increase the risk of both HFrEF and HFpEF, and HFpEF may precede the later onset of HFrEF. COPD, chronic obstructive pulmonary disease; LVEF, left ventricular ejection fraction.
Small animal models, including mice, rats, and guinea pigs,3 continue to improve our understanding of the various aspects and aetiologies of HF and help to develop novel treatment strategies. Mice and rats are the most commonly used animal models and share a high degree of homology to the human genome, with ∼30 000 protein-coding genes each. Major advantages of rodent models include relatively short breeding cycles and low housing costs. Numerous small animal models have been generated as tools to decipher HF aetiologies and develop new HF treatment strategies. These models typically utilize genetic modifications, pharmacological and surgical approaches, which can also be combined. The pathogenesis of HFpEF and HFrEF is multifactorial. Thus, it is often impossible to discern the underlying mechanisms, which can be overlapping and interconnected. This provides a challenge to investigate co-existing risk factors for HF development in a single model organism,4,5 especially in models of diabetic cardiomyopathy and HFpEF,6 necessitating the thoughtful selection of the best animal model for a given hypothesis. However, small animal HF models enable the study of specific risk factors without the confounding effect of comorbidities. Over the last few decades, numerous small animal models have greatly advanced our understanding of the pathogenesis of HFrEF and HFpEF, many of which will be highlighted in this article and are summarized in Table 1.
Characteristics of selected small animal models of heart failure
|Model||HF stimulus||Advantage||Limitation||Species||Selected references|
|LV pressure overload||TAC||Reliable model to induce cardiac hypertrophy and HF.||The acute increase in afterload does not reflect the gradual progression of arterial hypertension and aortic valve stenosis in patients.||Mouse||7–16|
|Ascending aortic constriction||Gradual onset of pressure overload, which mimics the gradual progression of arterial hypertension in patients.||Limited relevance to human disease as pressure overload is induced in young animals, whereas arterial hypertension is primarily observed in elderly patients.||Rat||19,20|
|Temporary LV pressure overload||TAC + removal of the stenosis||Reliable model of cardiac hypertrophy followed by removal of stressor to study reverse cardiac remodelling.||Two surgeries required.||Mouse||21–25|
|Technically challenging technique to remove suture or clip.|
|MI||LAD ligation||Reliable model to induce tissue damage and HF.||Model does not reflect the clinical setting with reperfusion of the occluded vessel during coronary angiography performed after an acute MI.||Mouse||26–33|
|Ischaemia/reperfusion injury||Temporary LAD ligation||Close to clinical scenario with reperfusion of the occluded vessel during coronary angiography performed after an acute MI.||Surgery is more time consuming and more complex than placement of permanent LAD ligation.||Mouse||41–44|
|MI (neonatal)||LAD ligation||Identification and characterization of pathways involved in cardiac regeneration.||Limited relevance to human disease. MI and coronary artery disease primarily occur in elderly patients.||Mouse||46|
|Pressure overload + MI||TAC + LAD ligation||Model mimics the relevant co-morbidities of arterial hypertension and ischaemic heart disease.||The acute increase in afterload does not reflect the gradual progression of arterial hypertension in patients.||Mouse||47|
|Gradual and predictable progression of HF.|
|Ascending aortic constriction + LAD ligation||Same as for mouse model of TAC + LAD ligation.||Same as for mouse model of TAC + LAD ligation.||Rat||48|
|Abdominal aortic constriction + LAD ligation||Same as for mouse model of TAC + LAD ligation.||Same as for mouse model of TAC + LAD ligation.||Rat||49|
|Pulmonary hypertension||Pulmonary artery constriction||Model mimics characteristics of RV HF, including increased liver weight and peripheral oedema.||The acute increase in afterload does not reflect the gradual progression of pulmonary hypertension in patients.||Mouse||50–52|
|Volume overload||Aorto-caval fistula (shunt)||Model of chronic volume overload as observed in patients with mitral valve regurgitation.||The acute increase in volume overload does not reflect the gradual progression of mitral valve regurgitation in patients.||Mouse||12|
|Reproducible model of volume overload-induced HF.||Shunt creates an artificial mix of arterial with venous blood.||Rat||54,55|
|Toxic cardiomyopathy||Doxorubicin||Potent stimulus to induce dilated cardiomyopathy.||Systemic toxic effects, especially on bone marrow cells, and gastrointestinal system.||Mouse||56–59|
|Isoproterenol||Potent stimulus to induce cardiac hypertrophy.||Chronic activation of adrenergic signalling is only one contributing factor to the development of HF in patients.||Mouse||8,61|
|Drug is easy to administer (i.p. injection or osmotic mini pump).||Rat||62|
|Monocrotaline||Model of predominantly RV hypertrophy and RV failure.||Toxicity on other organ systems, i.e. pulmonary and kidney injury.||Rat||63,64|
|Homocysteine||Potential clinical relevance; hyperhomocysteinaemia is a risk factor for HF.||Hyperhomocysteinaemia represents only one aspect in the development of HF in humans, which is conversely discussed.||Rat||65–67|
|Non-specific side effects and toxicity on other organ systems, especially vasculature.|
|Ethanol||Non-specific side effects and toxicity on other organ systems, especially liver.||Rat||68|
|Angiotensin II infusion||Chronic stimulation of angiotensin II Type 1 receptor (AT1R) signalling||Reliable model of cardiac hypertrophy.||Unspecific side effects on organ systems, especially kidney.||Mouse||69|
|Technically easy surgery to implant osmotic minipumps.||Rat||70|
|Dahl salt-sensitive rat||Inbred strain of Sprague-Dawley rats, which are susceptible to hypertension following a high-salt diet||Induction of hypertension and HF by high-salt diet feeding without additional surgery.||High housing costs based on the slow progression of hypertension and HF.||Rat||71,72|
|Slow progression of hypertension and HF development as observed in patients.|
|Spontaneously hypertensive rat (SHR)||Inbred strain of Wistar-Kyoto rats with hypertension||Slow progression of hypertension and HF development as observed in patients.||High housing costs based on the slow progression of hypertension and HF.||Rat||73|
|Akita (Ins2Akita+/–)||Pancreatic β-cell failure based on mutation in the Insulin2 gene||Robust model for T1D.||High housing costs based on the time-dependent progression of the phenotype.||Mouse||74–76|
|High-dose STZ||Pancreatic β-cell toxin||Robust model for T1D.||Does not capture the autoimmune contribution to the development of T1D in patients.||Mouse/rat||6|
|ob/ob||Hyperphagia based on leptin deficiency||Robust phenotype of obesity and T2D.||High housing costs based on the time-dependent progression of the phenotype.||Mouse||77,78|
|Potentially confounding effects of altered leptin-mediated signalling.|
|db/db||Hyperphagia based on leptin resistance||Robust phenotype of obesity and T2D.||High housing costs based on the time-dependent progression of the phenotype.||Mouse||79,80|
|Potentially confounding effects of altered leptin-mediated signalling.|
|ZF/ZDF rats||Hyperphagia based on leptin resistance||Model of metabolic syndrome with increased levels of circulating lipids and cholesterol.||High housing costs based on the time-dependent progression of the phenotype.||Rat||81,82|
|Potentially confounding effects of altered leptin-mediated signalling.|
|High-caloric diet (± low-dose STZ)||High caloric intake (± pancreatic β-cell toxin)||Additional low-dose STZ treatment mimics β-cell failure and late stage T2D.||High housing costs based on the time-dependent progression of the phenotype. Additional low-dose STZ treatment mimics mixture of T1D and T2D.||Rat/mouse||6|
Note that housing costs for mice are typically lower than for rats. Another advantage of mouse models is the availability of numerous transgenic strains available. General advantages of rat models are that surgical techniques are easier to perform than in mice.
HF, heart failure; i.p. intraperitoneal; LAD, left anterior descending artery; LV, left ventricular; MI, myocardial infarction; RV, right ventricular; STZ, streptozotocin; T1D, Type 1 diabetes; T2D, Type 2 diabetes; TAC, transverse aortic constriction; ZDF, Zucker diabetic fatty; ZF, Zucker fatty.Open in new tab
2. Small animal models of HFrEF
The following sections discuss rodent models, which typically provoke HFrEF (Figure 2). It is important to note that some of these models induce HFpEF, which precedes the later onset of systolic dysfunction and HFrEF.Figure 2
Schematic depicting selected stressors to induce heart failure with reduced ejection fraction (HFrEF) in small animal models. Note that these models may also induce heart failure with preserved ejection fraction (HFpEF), which precedes the later onset of HFrEF. Additional animal models with temporary exposure to drugs and temporary genetic gain-of-function or loss-of-function modifications have been developed. DOX, doxorubicin; EtOH, ethanol; Hcy, homocysteine; I/R, ischaemia/reperfusion injury; ISO, isoproterenol; LAD, left anterior descending artery; LV, left ventriclular; MCT, monocrotaline; RV, right ventriclular.
2.1 Surgical models
2.1.1 LV pressure overload
Chronic LV pressure overload causes HF in mice7–16 and rats,7,17,18 which is accomplished by various surgical approaches to mimic the adaptations associated with hypertension and aortic valve stenosis in patients. Transverse aortic constriction (TAC) in mice was first described by Rockman et al.15 and has been subsequently used as a method for LV pressure overload by numerous laboratories. TAC increases LV afterload, which results in concentric cardiac hypertrophy and, ultimately, HFrEF. Several surgical techniques for TAC have been developed, including minimally invasive approaches by a small incision in the proximal sternum7,9,10,14 and placement of surgical clips or sutures to impede blood flow across the aortic arch. Recently, a novel method using o-rings with fixed inner diameters has been described, which are placed around the ascending aorta in mice.83 Measurement of the peak flow velocity difference of the right relative to the left carotid artery enables the quantification of the pressure gradient post-surgery.7 Important parameters for the hypertrophic response and progression of HF include sex, weight, age, and the genetic background of the species used. Mice with the C57BL/6J genetic background develop HF more rapidly post-TAC compared to the 129S1/SvImJ strain11 and have similar gene expression patterns of human dilated cardiomyopathy compared to 129S1/SvImJ mice.85 The identified pathways contributing to accelerated HF in C57BL/6J mice include periostin, angiotensin, and IGF1 signalling. Different adaptations for the response to pressure overload have also been reported for the different C57BL/6 substrains, i.e. C57BL/6NCrl (maintained by the Charles River Laboratories), C57BL/6NTac (maintained by the Taconic Laboratories), and C57BL/6J (maintained by the Jackson Laboratory).84,86 C57BL/6J mice have a mutation in the nicotinamide nucleotide transhydrogenase (Nnt) gene, which regenerates NADPH from NADH. This mutation protects C57BL/6J mice from oxidative stress and HF post-TAC compared to the inbred C57BL/6N strain.84
One important limitation of TAC is the immediate onset of pressure overload, which is in contrast to the slow progression of hypertension and aortic valve stenosis in patients. To overcome this potential drawback, constriction of the ascending aorta has been performed in 3- to 4-week-old rats. In this model, LV hypertrophy is observed by 6 weeks and overt HF by 18 weeks post-surgery.19,20 Aortic constriction in rats has also been performed around the abdominal aorta both in the infrarenal and suprarenal position, the latter of which induces renal hypoperfusion, hypertension, and LV hypertrophy. Abdominal aortic constriction typically contributes to a slower progression of the HF phenotype.87 Recently, additional models have been developed that facilitate the study of reverse cardiac remodelling. The models described use different surgical approaches to remove the TAC stenosis and subsequently decrease cardiac workload.21–25
2.1.2 Ischaemic injury
Coronary artery ligation is a commonly used, small animal HF model88 that was initially established by Pfeffer et al.34 in rats and has been subsequently used by numerous groups.35–38 The Pfeffer group performed groundbreaking studies and demonstrated that infarct size, post-MI LV chamber dilatation and LV function are correlated. They subsequently showed that treatment with the angiotensin-converting enzyme (ACE) inhibitor captopril improves contractile function and survival following MI in rats.39,40 The impact of ACE inhibition was subsequently tested in large clinical trials in patients post-MI, which improved contractile function and survival.89 These studies established pharmacological ACE inhibition for patients with MI, which is now a commonly used, standard treatment. Coronary artery ligation has also been performed in mouse models.26–33 Ligation of the left anterior descending (LAD) artery typically results in HF 4 weeks post-surgery and strongly depends on the genetic background of the mice used.32 One potential limitation of these animal models with permanent coronary artery occlusion is the differences in their observed phenotypes relative to those observed in patients; atherosclerosis of the coronary arteries in patients results in ischaemic heart disease that slowly progresses and coronary artery blood flow can eventually be re-established during coronary angiography performed after an acute MI. To overcome this limitation, ischaemia/reperfusion (I/R) models have been established, which facilitate the investigation of molecular mechanisms and tissue damage following temporary LAD occlusion.41–45,90 The I/R model typically exhibits less tissue damage compared to permanent LAD occlusion. Importantly, ischaemic injury is typically induced in young rodent models. This is in contrast to the patient population, in which elderly and multimorbid patients exhibit the greatest risk of coronary artery disease and acute MI.
MI has also been induced in neonatal mice to identify and characterize pathways that are involved in cardiac regeneration. Ischaemic injury in neonatal mice is provoked by LAD ligation and complete recovery is observed by 3 weeks of age. The regenerative potential decreases as the mice age and the abundance of proliferating cardiomyocytes diminishes.46 Similar to TAC, the adaptations post-MI have been compared across the most commonly used mouse strains and are dependent on genetic background. While infarct rupture was most frequently observed in 129S6 mice, cardiac dilatation was most prominent in Swiss mice.32 Therefore, the genetic background should be an important consideration in study designs.
2.1.3 Combined LV pressure overload/ischaemic injury
To explore the coexistence of clinically relevant morbidities of arterial hypertension and coronary artery disease present in patients, recent surgical HF models combine the techniques of TAC surgery and LAD ligation. This combined surgical approach was first described in rats48 and has since been modified for mouse models.47 Various models have been published with different locations for the placement of the aortic stenosis, all of which exhibit adverse LV remodelling and rapid HF progression.49 Recently, a mouse model with combined MI and temporary TAC was developed,91 which has enabled the elucidation of the impact of mechanical unloading following ischaemic injury.
2.1.4 Right ventricular pressure overload
Similarly to TAC surgery, which increases LV afterload, pulmonary artery banding increases right ventricular (RV) afterload, and mimics pulmonary hypertension in mice50–52 and rats.53 Pulmonary artery banding results in RV hypertrophy and pathological remodelling.50 As reported for TAC, the severity of the pulmonary artery stenosis correlates with the progression of contractile dysfunction and mortality.51 Notably, the acute increase in RV afterload does not reflect the gradual progression of pulmonary hypertension in patients.
2.1.5 Volume overload
Chronic volume overload in small animal models reproduces the pathologies observed in patients with mitral valve regurgitation, which typically increase diastolic wall stress and cause eccentric cardiac hypertrophy.12 Cardiac volume overload is accomplished in rodents by creating a surgical aorto-caval shunt and has been reported for rats54,55 and mice.12 Volume overload in rats initially decreases LV function. The subsequent compensatory hypertrophy normalizes contractile function at one month post-surgery,54 with the time course of HF development strongly depending on the shunt volume and being less predictable compared to TAC models. The shunt creates an artificial mix of arterial with venous blood, which is in contrast to the clinical setting in patients with mitral valve regurgitation. Volume overload in mice causes minimal apoptosis in the absence of pathological remodelling, which is in contrast to the increased afterload following TAC surgery. This indicates that increased preload, i.e. aorto-caval shunt, and increased afterload, i.e. TAC, contribute to different morphological phenotypes, which is important for the design of future HF therapies.
2.2 Toxic cardiomyopathy
The anthracycline compound Doxorubicin (DOX) is a standard anti-cancer therapeutic agent. DOX causes dilated cardiomyopathy in a dose-dependent manner92 that is typically irreversible and progressive. DOX has been administered to numerous small animal models56–60 and promotes the formation of free radicals and mitochondrial dysfunction.93,94 Juvenile DOX exposure in mice results in no immediate contractile dysfunction, however, impairs the ability to adapt to angiotensin II-induced hypertension later in life, which is restored by co-treatment with resveratrol.95 Notably, cancer cachexia increases the risk of HF and decreases systemic insulin levels. Chronic insulin supplementation decreases glucose usage by the tumour, normalizes cancer-mediated impairment in cardiac Akt signalling and attenuates contractile dysfunction.96 Conversely, HF following MI increases tumour growth as reported for APCMin mice that have a mutation in the tumour suppressor gene Adenomatosis polyposis coli (Apc) and are prone to multiple intestinal neoplasia (Min) and cancer development.97
Chronic stimulation of G-protein-coupled ß-adrenergic receptor signalling with isoproterenol provokes cardiomyocyte hypertrophy and fibrosis in mice8,61 and rats,62 which is similar to the progressive HF development in mice with cardiac-specific overexpression of β1-adrenergic receptors.98 The mechanisms responsible include an imbalance between the increased energy demand, which is based on the hypercontractility of the myocardium relative to the oxygen and nutrients provided.
Monocrotaline (MCT) is a pyrrolizidine alkaloid obtained from the plant species Crotalaria spectabilis, which induces pulmonary hypertension and RV hypertrophy. MCT is converted in the liver to MCT pyrrole and circulates to the lung parenchyma to increase capillary permeability and to trigger interstitial oedema and smooth muscle hypertrophy.99 These alterations increase pulmonary vascular resistance, RV pressure overload, and RV failure. MCT has been used in rats63,64 and larger animal models. Importantly, non-specific side effects for MCT have been reported, such as lung and kidney injury,64,99 which are important to consider when designing future studies.
As previously reviewed, high circulating homocysteine levels are a risk factor for the future onset of HF.100 Similarly, dietary supplementation with homocysteine increases inflammation, collagen remodelling, and oxidative stress,65,66,100 and provokes contractile dysfunction in both normotensive and spontaneously hypertensive rats.65–67 Chronic ethanol ingestion contributes to dilated cardiomyopathy in both rodent models and humans.101 The underlying mechanisms comprise decreased myocardial contractility as a consequence of altered myofibrillar Mg2+-ATPase activity and cardiomyocyte loss.68
2.3 Genetically engineered models
Numerous transgenic animal models of HF have been generated to investigate the impact of genetic modifications, typically gain-of-function or loss-of-function modifications, on cellular and molecular processes contributing to clinically relevant phenotypes.102,103 The complex topic of genetic modification for the generation of transgenic mouse models has been reviewed in detail.104 Transgenic mice with whole body gene deletions have been developed (constitutive knockouts). Confounders that emanate from the deletion of a gene throughout the entire organism resulted in the development of tools to generate conditional knockouts with spatial and temporal gene deletion. Gene deletion can be facilitated by Cre/loxP- or Flippase/FRT-mediated recombination, and tissue-specific Cre and Flippase recombination are achieved by the use of a specific promoter (e.g. myosin heavy chain 6, Myh6). Recently, engineered nucleases have been developed to modulate DNA sequences and to generate transgenic mice. The nucleases used for genome editing include transcription activator-like effector nuclease (TALEN), zinc-finger nuclease (ZFN), and clustered regularly interspaced short palindromic repeat (CRISPR)/-associated protein 9 (Cas9). Compared to other nucleases, the CRISPR/Cas9 system is more efficient and the design of constructs easier to perform.104 Important limitations of the CRISPR/Cas9 system are non-specific off-target effects, which can affect the phenotype of the model generated and necessitate whole-genome sequencing of the generated mouse model. Genetic modification can also be facilitated by adeno-associated virus (AAV)-mediated delivery of DNA constructs, which can be performed by the use of a combination of specific viral serotypes and promotors (e.g. AAV9 and Myh6).105 Compared to the generation of transgenic animals, virus-mediated approaches are usually more time- and cost-efficient. Potential drawbacks include side effects in other tissues following systemic injection and badge-to-badge variability of the virus construct.
3. Small animal models of HFpEF
In the following sections, we will discuss the most common models to investigate classical risk factors for the development of HFpEF, which include hypertension, obesity, diabetes mellitus, and aging. Importantly, systolic contractile dysfunction may also be present in these models, which additionally enables their use as HFrEF models. Additional risk factors for the development of HFpEF in humans include renal dysfunction, chronic obstructive pulmonary disease (COPD) and atrial fibrillation, which have not been studied in detail in small animal models.
The Dahl salt-sensitive rat, which was generated by inbreeding Sprague-Dawley rats,71 is one of the most commonly used HFpEF models. When fed with a high-salt diet containing 8% NaCl, this model rapidly develops hypertension, diastolic dysfunction, and HFrEF.72 Spontaneously hypertensive rat is an inbred strain of Wistar-Kyoto rats with hypertension.73 Chronic infusion with angiotensin II causes hypertension and cardiomyocyte hypertrophy in mice and rats.69,70 Major advantages of these models are the slow progression of hypertension and HF, which is also observed in patients with hypertension and is in contrast to the immediate increase in LV workload following TAC surgery.
3.2 Obesity and diabetes mellitus
Numerous small animal models have been generated to investigate the impact of Type 1 (T1D) and Type 2 diabetes (T2D) on the heart.6 A commonly used model for T1D is the Akita mouse (Ins2Akita+/−), which exhibits a mutation in the Insulin2 encoding gene. This results in misfolding of the insulin protein, endoplasmic reticulum stress, and β-cell failure.74 Hearts from Akita mice show increased inflammation75 and diastolic dysfunction in the presence of normal systolic function.76
The glucosamine-nitrosourea streptozotocin (STZ) is toxic to pancreatic β-cells and has been used to study both T1D and T2D. Because of its structural similarity to glucose, STZ enters pancreatic β-cells via the glucose transporter 2 (GLUT2), causing cellular damage, and impairing insulin production. The STZ-mediated effects on β-cell destruction and hyperglycaemia are dose-dependent. High-dose STZ treatment induces T1D in rodents. In contrast, low-dose STZ protocols have been used to overcome the low penetrance of some high-calorie dietary regimens and to mimic β-cell failure and late stage T2D. Therefore, low-dose STZ treatment has been added to the high-fat diet (HFD) protocols.6
Ob/ob77 and db/db79 mice are commonly used models of obesity and T2D that are based on leptin resistance or deficiency, respectively. Diastolic dysfunction has been reported for both models.78,80 Additional models for T2D and insulin resistance include Zucker fatty (ZF) rats, which express non-functional leptin receptors81 and Zucker diabetic fatty rats, which are an inbred strain of ZF rats with high serum glucose levels.82
Numerous dietary treatment regimens are used to induce insulin resistance and T2D in rodents. HFD chow usually contains a total fat content of up to 60%. Rodent ‘Western’ diets typically contain a high content in fat and sucrose, which makes them a useful tool to study pathologies that have been described by the ‘Western’ dietary pattern in humans.106 Depending on the total fat content and duration these dietary treatments may induce contractile dysfunction in rodents. The proposed mechanisms have been recently discussed in detail.6,102
HFpEF is primarily found in elderly patients. Senescence-accelerated prone (SAMP) mice have been generated by selective inbreeding of AKR/mice with inherited senescence107 and have been subsequently used to study various effects of aging. SAMP mice develop age-dependent diastolic dysfunction, adverse remodelling, endothelial cell dysfunction, and HFpEF when subjected to a high-salt, HFD. These studies suggest endothelial cell dysfunction as one potential mechanism contributing to the age-dependent increase in HFpEF in patients.108,109
4. General advantages and limitations using small animal models
General advantages of small animal models include a lower housing cost compared to large animals, shorter gestation times and reduced costs for pharmacological treatments, which is typically administered proportionally to body weight. The potential increase in sample size improves statistical power. Recent advancements in magnetic resonance imaging, high-resolution transthoracic echocardiography, and micromanometer conductance catheters enable a detailed assessment of contractile function even in small rodents. Major advantages of mouse models compared to rats are the availability of a variety of already existing transgenic strains and readily available tools to generate novel transgenic lines. Therefore, transgenic mouse models also facilitate the investigation of specific genetic modifications in the context of superimposed stressors, for example using a surgical model or dietary treatment. Genetic modifications using viral vectors are typically easier to introduce into the genome of small rodents compared to larger animals. This mainly results from the amount of virus required for sufficient transduction, which is typically proportional to body weight. Intravenous delivery of AAV9, for example, results in a very low transduction rate of cardiomyocytes in dogs.110 Based on the technical challenge to transduce myocardial tissue of large animals, surgical and catheter-based approaches have been developed to overcome this limitation. In contrast, AAVs that target the myocardium of small rodents can easily be delivered by intravenous and intraperitoneal injection. Using transgenic gain-of-function models, it is important to consider that high levels of overexpression can cause HF per se, as reported for transgenic overexpression of the biologically inactive green fluorescent protein (GFP).111
Despite these advantages, several limitations using small animal models warrant attention. Rodents are typically on the same or very similar genetic background, which does not reflect the genetic heterogeneity of the patient population. Another limitation of small animal models, especially surgical models, is the rapid induction of the stressor, which is in contrast to the typically slow disease progression in patients. HF and coronary artery disease are often associated with atherosclerosis in patients, which is difficult to induce in most small rodent models. Several differences comparing the murine and human heart exist that result from the difference in heart rate. In general, the heart rate and the size of the species are inversely correlated, with about 500–600 beats per minute (b.p.m.) in mice, 350 b.p.m. in rats, 60–80 b.p.m. in humans, 30 b.p.m. in elephants, and 6 b.p.m. in blue whales.112,113 Human ventricular myocytes predominately express β-myosin heavy chain (MHC). Adult murine cardiomyocytes mainly express α-MHC with rapid ATPase activity, which facilitates a contraction rate of up to ∼600 b.p.m. Action potentials in murine cardiomyocytes exhibit a rapid repolarization phase, lack a prominent plateau phase and have a shorter total duration compared to human cardiomyocytes. This facilitates faster contraction/relaxation compared to larger mammals, which is required to sustain cardiac output (calculation: stroke volume × heart rate) at high heart rates. Based on these contractile kinetics, the ability to increase heart rates in small animal models is impaired relative to humans, which can typically increase by up to approximately three-fold. In contrast, the heart rate of mice can increase by about 30–40% under exercise conditions, which limits cardiac reserve and is an important consideration in the design of exercise studies.
In humans, HF is typically observed in older patients, in contrast to most rodent models, in which HF is commonly induced by various stressors starting at young ages to reduce experimental costs. Depending on the extent and duration of the stressor, rodent models with HFpEF may also develop HFrEF. In contrast, several disease conditions are associated with HFpEF in humans, which typically do not progress to HFrEF, such as hypertensive heart disease.
5. General considerations for the use of small and large animal models
Despite the specific limitations and differences outlined above, myocardial energetics and contraction are overall relatively similar between small rodents and humans. Consequently, numerous proteins share functions across species, which makes small rodent models inevitable tools to rapidly conduct proof-of-principle studies at a large scale and to test for different druggable targets and genetic modifications over a relatively short-time period. However, despite their widespread use and acceptance, studies performed in small rodent models should be interpreted with caution. Different phenotypes for humans with genetic mutations and transgenic mice recapitulating diseases were observed. For example, patients with Duchenne Muscular Dystrophy, which lack the expression of the dystrophin protein, have an average survival rate of about 40 years, with about 10-20% of patients developing HF. In contrast, dystrophin-deficient mice have a normal life span and relatively mild cardiomyopathy (reviewed in Ref.114). Another example is the nonsense T116G mutation in the phospholamban (PLB) gene in patients with dilated cardiomyopathy, which results in severe HF. Conversely, PLB deficient-mice exhibit enhanced cardiac contractility and a normal life span.115 Several drugs have also been tested in small animal models with beneficial effects observed, despite their failure in humans.116 Examples include the phosphodiesterase (PDE) 5 inhibitor Sildenafil, which attenuated the onset of HF post-TAC in mice117 but showed no benefit in chronic HF patients in the RELAX trial.118 Relaxin and the recombinant protein Serelaxin also attenuated adverse remodelling post-MI in mice119 but showed no beneficial effects in humans with acute HF in the RELAX-AHF-2 trial.120
These examples highlight different adaptations of small animal models with genetic modifications and pharmacological treatments compared to patients. As a result, results from small animal models should be validated in large animals prior to Phase I trials in humans. Swine is a prototypical pre-clinical large animal model. Advantages of swine are a similar expression pattern of MHC isoforms and a similar reserve in heart rate and cardiac output compared to humans. Importantly, animal models are typically subjected to an single-drug treatment in the context of a HF stressor and beneficial effects for a specific drug tested might be observed. However, these effects may not be observed in later clinical trials, in which patients typically receive the drug in addition to the well-established standard treatment for chronic HF. This also provides a potential explanation for the successful bench-to-bedside translation of the very early studies performed by the Pfeffer group using ACE inhibitors and the failure of numerous later clinical studies, which showed efficacy in animal models, but not in patients.
6. Summary and conclusion
Small animal models, especially mice and rats, mimic various aspects of the pathogenesis of HF and help to decipher various underlying contributing mechanisms of the disease. Several limitations for small animal studies exist that warrant the interpretation of the results of the studies performed with caution. Despite these specific limitations, small animal models serve as invaluable tools that have greatly advanced our understanding of the pathogenesis of HF. Based on recent advancements in genome editing, numerous novel transgenic models are likely to be generated in the near future. These models will continue to facilitate the identification of new targets and to develop novel treatment strategies for HF patients.
Conflict of interest: none declared.
This work was supported by the German Research Foundation (DFG), Clinical Research Unit (KFO) 311.
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Lab Invest 2011;91:675–690.Google ScholarCrossrefPubMed 120Teerlink JR, Voors AA, Ponikowski P, Pang PS, Greenberg BH, Filippatos G, Felker GM, Davison BA, Cotter G, Gimpelewicz C, Boer-Martins L, Wernsing M, Hua TA, Severin T, Metra M. Serelaxin in addition to standard therapy in acute heart failure: rationale and design of the RELAX-AHF-2 study. Eur J Heart Fail 2017;19:800–809.Google ScholarCrossrefPubMed © The Author(s) 2019. Published by Oxford University Press on behalf of the European Society of Cardiology.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact email@example.comView Metrics
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New synthesis method yields degradable polymers
Materials could be useful for delivering drugs or imaging agents in the body; may offer alternative to some industrial plastics.
Anne Trafton | MIT News Office
October 28, 2019
MIT chemists have devised a way to synthesize polymers that can break down more readily in the body and in the environment.
A chemical reaction called ring-opening metathesis polymerization, or ROMP, is handy for building novel polymers for various uses such as nanofabrication, high-performance resins, and delivering drugs or imaging agents. However, one downside to this synthesis method is that the resulting polymers do not naturally break down in natural environments, such as inside the body.
The MIT research team has come up with a way to make those polymers more degradable by adding a novel type of building block to the backbone of the polymer. This new building block, or monomer, forms chemical bonds that can be broken down by weak acids, bases, and ions such as fluoride.
“We believe that this is the first general way to produce ROMP polymers with facile degradability under biologically relevant conditions,” says Jeremiah Johnson, an associate professor of chemistry at MIT and the senior author of the study. “The nice part is that it works using the standard ROMP workflow; you just need to sprinkle in the new monomer, making it very convenient.”
This building block could be incorporated into polymers for a wide variety of uses, including not only medical applications but also synthesis of industrial polymers that would break down more rapidly after use, the researchers say.
The lead author of the paper, which appears in Nature Chemistry today, is MIT postdoc Peyton Shieh. Postdoc Hung VanThanh Nguyen is also an author of the study.
The most common building blocks of ROMP-generated polymers are molecules called norbornenes, which contain a ring structure that can be easily opened up and strung together to form polymers. Molecules such as drugs or imaging agents can be added to norbornenes before the polymerization occurs.
Johnson’s lab has used this synthesis approach to create polymers with many different structures, including linear polymers, bottlebrush polymers, and star-shaped polymers. These novel materials could be used for delivering many cancer drugs at once, or carrying imaging agents for magnetic resonance imaging (MRI) and other types of imaging.
“It’s a very robust and powerful polymerization reaction,” Johnson says. “But one of the big downsides is that the backbone of the polymers produced entirely consists of carbon-carbon bonds, and as a result, the polymers are not readily degradable. That’s always been something we’ve kept in the backs of our minds when thinking about making polymers for the biomaterials space.”
To circumvent that issue, Johnson’s lab has focused on developing small polymers, on the order of about 10 nanometers in diameter, which could be cleared from the body more easily than larger particles. Other chemists have tried to make the polymers degradable by using building blocks other than norbornenes, but these building blocks don’t polymerize as efficiently. It’s also more difficult to attach drugs or other molecules to them, and they often require harsh conditions to degrade.
“We prefer to continue to use norbornene as the molecule that enables us to polymerize these complex monomers,” Johnson says. “The dream has been to identify another type of monomer and add it as a co-monomer into a polymerization that already uses norbornene.”
The researchers came upon a possible solution through work Shieh was doing on another project. He was looking for new ways to trigger drug release from polymers, when he synthesized a ring-containing molecule that is similar to norbornene but contains an oxygen-silicon-oxygen bond. The researchers discovered that this kind of ring, called a silyl ether, can also be opened up and polymerized with the ROMP reaction, leading to polymers with oxygen-silicon-oxygen bonds that degrade more easily. Thus, instead of using it for drug release, the researchers decided to try to incorporate it into the polymer backbone to make it degradable.
They found that by simply adding the silyl-ether monomer in a 1:1 ratio with norbornene monomers, they could create similar polymer structures to what they have previously made, with the new monomer incorporated fairly uniformly throughout the backbone. But now, when exposed to a slightly acidic pH, around 6.5, the polymer chain begins to break apart.
“It’s quite simple,” Johnson says. “It’s a monomer we can add to widely used polymers to make them degradable. But as simple as that is, examples of such an approach are surprisingly rare.”
In tests in mice, the researchers found that during the first week or two, the degradable polymers showed the same distribution through the body as the original polymers, but they began to break down soon after that. After six weeks, the concentrations of the new polymers in the body were between three and 10 times less than the concentrations of the original polymers, depending on the exact chemical composition of the silyl-ether monomers that the researchers used.
The findings suggest that adding this monomer to polymers for drug delivery or imaging could help them get cleared from the body more quickly.
“We are excited about the prospect of using this technology to precisely tune the breakdown of ROMP-based polymers in biological tissues, which we believe could be leveraged to control biodistribution, drug release kinetics, and many other features,” Johnson says.
The researchers have also started working on adding the new monomers to industrial resins, such as plastics or adhesives. They believe it would be economically feasible to incorporate these monomers into the manufacturing processes of industrial polymers, to make them more degradable, and they are working with Millipore-Sigma to commercialize this family of monomers and make them available for research.
The research was funded by the National Institutes of Health, the American Cancer Society, and the National Science Foundation.
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Northwestern Medicine study finds that stem cell transplants can reverse autoimmune diseases
Photo courtesy Northwestern Now News
A study conducted by Northwestern Medicine and Mayo Clinic has found that stem cell transplants can reverse a debilitating autoimmune diseases.
A new study from Northwestern Medicine and the Mayo Clinic found that a stem cell transplant is capable of reversing autoimmune diseases like neuromyelitis optica, an aggressive neurological disease that causes many patients to lose their sight and ability to walk within five years following diagnosis.
Formerly classified as a subtype of multiple sclerosis, neuromyelitis optica is now categorized as a separate disease. What sets it apart from MS and other autoimmune diseases is that it has a biological marker known as AQP4, which increases the chances of a relapse. Researchers discovered that following a stem cell transplant, AQP4 disappeared in the blood of patients.
The transplant procedure involves collecting stem cells from patients before “knocking down” their immune system and then giving the stem cells back to them after a few days of drugs, when the immune system resets.
Dr. Richard Burt pioneered this approach, also known as hematopoietic stem cell transplantation. The professor of medicine at Feinberg said the idea came to him when he was a fellow at Johns Hopkins, and patients had to be re-immunized for childhood vaccines after receiving transplants for cancer.
“It occurred to me that losing an immune response to self-antigens in an autoimmune disease is exactly what you want,” Burt said. “We first did this in animal models of autoimmune diseases such as (experimental autoimmune encephalomyelitis), an animal model of multiple sclerosis, and long story short, it worked.”
Northwestern researchers find possible link between disrupted sleep-cycles and protection against neurodegenerative diseases
Burt said that is why HSCT isn’t actually a stem cell-based therapy, but rather an immune-based therapy.
HSCT is not only effective in reversing neuromyelitis optica — it is also a very cost-efficient treatment method so patients can avoid paying for expensive drugs every year.
“The only official treatment for neuromyelitis optica is eculizumab, which is a drug that costs about half a million dollars per year,” Burt said. “The transplant, however, is a one time treatment that costs $100,000. For multiple sclerosis, drugs cost $70,000 per year for the rest of the patient’s life.”
Dr. Roumen Balabanov, an associate professor of neurology at Feinberg, said the future implications of this study are that chronic autoimmune diseases will be able to be treated through “a single, radical approach.”
“The point of this treatment being radical is that the patients will actually have normal lives,” Balabanov said. “They don’t have to take those lifelong medications.”
According to Burt’s January 2019 research, HSCT reverses diseases like neuromyelitis optica, systemic sclerosis, chronic inflammatory demyelinating polyneuropathy and multiple sclerosis. Researchers added they hope it can be used to treat even more autoimmune diseases.
- chronic inflammatory demyelinating polyneuropathy
- hematopoietic stem cell transplantation
- Mayo Clinic
- multiple sclerosis
- neuromyelitis optica
- Northwestern Feinberg School of Medicine
- Richard Burt
- Roumen Balabanov
- systemic sclerosis
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FOCUS | PERSPECTIVE1Mila, Montréal, Quebec, Canada. 2School of Computer Science, McGill University, Montréal, Quebec, Canada. 3Department of Neurology & Neurosurgery, McGill University, Montréal, Quebec, Canada. 4Canadian Institute for Advanced Research, Toronto, Ontario, Canada. 5DeepMind, Inc., London, UK. 6Centre for Computation, Mathematics and Physics in the Life Sciences and Experimental Biology, University College London, London, UK. 7Element AI, Montréal, QC, Canada. 8Université de Montréal, Montréal, Quebec, Canada. 9MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK. 10Department of Electrical Engineering, Stanford University, Stanford, CA, USA. 11Department of Bioengineering, Imperial College London, London, UK. 12Computational Neuroscience Unit, School of Computer Science, Electrical and Electronic Engineering, and Engineering Maths, University of Bristol, Bristol, UK. 13Department of Physiology, Universität Bern, Bern, Switzerland. 14Department of Applied Physics, Stanford University, Stanford, CA, USA. 15Google Brain, Mountain View, CA, USA. 16Department of Biological Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada. 17Department of Cell & Systems Biology, University of Toronto, Toronto, Ontario, Canada. 18Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. 19Vector Institute, Toronto, Ontario, Canada. 20Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. 21Department of Psychology and Neuroscience, Columbia University, New York, NY, USA. 22Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, USA. 23Gatsby Computational Neuroscience Unit, University College London, London, UK. 24Center for Theoretical Neuroscience, Columbia University, New York, NY, USA. 25Department of Neuroscience, College of Physicians and Surgeons, Columbia University, New York, NY, USA. 26University of Ottawa Brain and Mind Institute, Ottawa, Ontario, Canada. 27Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada. 28Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece. 29Department of Vision & Cognition, Netherlands Institute for Neuroscience, Amsterdam, Netherlands. 30Institute of Neuroinformatics, ETH Zürich and University of Zürich, Zürich, Switzerland. 31Department of Experimental Psychology, University of Oxford, Oxford, UK. 32Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA. 33Department of Psychology, Stanford University, Stanford, CA, USA. 34Department of Computer Science, Stanford University, Stanford, CA, USA. 35Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA. 36Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland. 37Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK. 38Department of Physics and Astronomy York University, Toronto, Ontario, Canada. 39Center for Vision Research, York University, Toronto, Ontario, Canada. 40Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA. 41Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA. 42These authors contributed equally: Blake A. Richards, Timothy P. Lillicrap, Denis Therien, Konrad P Kording. *e-mail: firstname.lastname@example.orgMajor technical advances are revolutionizing our ability to observe and manipulate brains at a large scale and to quantify complex behaviors1,2. How should we use this data to develop models of the brain? When the classical framework for systems neuroscience was developed, we could only record from small sets of neurons. In this framework, a researcher observes neu-ral activity, develops a theory of what individual neurons compute, then assembles a circuit-level theory of how the neurons combine their operations. This approach has worked well for simple com-putations. For example, we know how central pattern generators control rhythmic movements3, how the vestibulo-ocular reflex pro-motes gaze stabilization4 and how the retina computes motion5. But can this classical framework scale up to recordings of thousands of neurons and all of the behaviors that we may wish to account for? Arguably, we have not had as much success with the classical approach in large neural circuits that perform a multitude of func-tions, like the neocortex or hippocampus. In such circuits, research-ers often find neurons with response properties that are difficult to summarize in a succinct manner6,7.The limitations of the classical framework suggest that new approaches are needed to take advantage of experimental advances. A promising framework is emerging from the interactions between neuroscience and artificial intelligence (AI)8–10. The rise of deep learning as a leading machine-learning method invites us to revisit artificial neural networks (ANNs). At their core, ANNs model neural computation using simplified units that loosely mimic the integration and activation properties of real neurons11. Units are implemented with varying degrees of abstraction, ranging from A deep learning framework for neuroscienceBlake A. Richards1,2,3,4,42*, Timothy P. Lillicrap 5,6,42, Philippe Beaudoin7, Yoshua Bengio1,4,8, Rafal Bogacz9, Amelia Christensen10, Claudia Clopath 11, Rui Ponte Costa12,13, Archy de Berker7, Surya Ganguli14,15, Colleen J. Gillon 16,17, Danijar Hafner 15,18,19, Adam Kepecs20, Nikolaus Kriegeskorte21,22, Peter Latham 23, Grace W. Lindsay22,24, Kenneth D. Miller 22,24,25, Richard Naud26,27, Christopher C. Pack3, Panayiota Poirazi 28, Pieter Roelfsema 29, João Sacramento30, Andrew Saxe31, Benjamin Scellier1,8, Anna C. Schapiro 32, Walter Senn13, Greg Wayne5, Daniel Yamins33,34,35, Friedemann Zenke36,37, Joel Zylberberg4,38,39, Denis Therien 7,42 and Konrad P. Kording4,40,41,42Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artifi-cial neural networks, the three components specified by design are the objective functions, the learning rules and the architec-tures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.FOCUS | PERSPECTIVEhttps://doi.org/10.1038/s41593-019-0520-2NATURE NEUROSCIENCE | VOL 22 | NOVEMBER 2019 | 1761–1770 | http://www.nature.com/natureneuroscience1761PERSPECTIVE | FOCUSNATURE NEUROSCIENCEhighly simplified linear operations to relatively complex models with multiple compartments, spikes, and so on11–14. Importantly, the specific computations performed by ANNs are not designed, but learned15.However, human design still plays a role in determining three essential components in ANNs: the learning goal, expressed as an objective function (or loss function) to be maximized or mini-mized; a set of learning rules, expressed as synaptic weight updates; and the network architecture, expressed as the pathways and con-nections for information flow (Fig. 1)15. Within this framework, we do not seek to summarize how a computation is performed, but we do summarize what objective functions, learning rules and architec-tures would enable learning of that computation.Deep learning can be seen as a rebranding of long-standing ANN ideas11. Deep ANNs possess multiple layers, either feedforward or recurrent over time. The ‘layers’ are best thought of as being analo-gous to brain regions, rather than as specific laminae in biological brains16,17. ‘Deep’ learning specifically refers to training hierarchical ANNs in an end-to-end manner, such that plasticity in each layer of the hierarchy contributes to the learning goals15, which requires a solution to the ‘credit assignment problem’ (Box 1)18,19. In recent years, progress in deep learning has come from the use of bigger ANNs, trained with bigger datasets using graphics processing units (GPUs) that can efficiently handle the required computations. Such developments have produced solutions for many new problems, including image20 and speech21 classification and generation, lan-guage processing and translation22, haptics and grasping23, naviga-tion24, sensory prediction25, game playing26 and reasoning27.Many recent findings suggest that deep learning can inform our theories of the brain. First, it has been shown that deep ANNs can mimic, closely in some cases, the representational transformations in primate perceptual systems17,28 and thereby can be leveraged to manipulate neural activity29. Second, many well-known behavioral and neurophysiological phenomena, including grid cells24, shape tuning30, temporal receptive fields31, visual illusions32 and appar-ent model-based reasoning33, have been shown to emerge in deep ANNs trained on tasks similar to those solved by animals. Third, many modeling studies have demonstrated that the apparent bio-logical implausibility of end-to-end learning rules, such as learning Box 1 | Learning and the credit assignment problemA natural definition of learning is ‘a change to a system that improves its performance’. Suppose we have an objective func-tion, F(W), which measures how well a system is currently per-forming, given the N-dimensional vector of its current synaptic weights, W. If the synaptic weights change from W to W + ΔW, then the change in performance is ΔF = F(W + ΔW) − F(W). If we make small changes to W and F is locally smooth, then ΔF is given approximately by the inner product between the weight change and the gradient41,ΔF ΔWT ∇WFðWÞwhere ∇WF(W) is the gradient of F with respect to W and T indicates the transpose. Suppose we want to guarantee improved performance, i.e., we want to ensure ΔF ≥ 0. We know that there is an (N – 1)-dimensional manifold of local changes in W that all lead to the same improvement. Which one should we choose? Gradient-based algorithms derive from the intuition that we want to take small steps in the direction that gives us the greatest level of improvement for that step size. The gradient points in the steepest direction of the objective, so if we choose a small step size, η, times the gradient ∇WF, then we will improve as much as possible for that step size. Thus, we haveΔF η ∇WFðWÞT ∇WFðWÞ ≥ 0In other words, the objective function value increases with every step (when η is small) according to the length of the gradient vector.The concept of credit assignment refers to the problem of determining how much ‘credit’ or ‘blame’ a given neuron or synapse should get for a given outcome. More specifically, it is a way of determining how each parameter in the system (for example, each synaptic weight) should change to ensure that ΔF ≥ 0. In its simplest form, the credit assignment problem refers to the difficulty of assigning credit in complex networks. Updating weights using the gradient of the objective function, ∇WF(W), has proven to be an excellent means of solving the credit assignment problem in ANNs. A question that systems neuroscience faces is whether the brain also approximates something like gradient-based methods.The most common method for calculating gradients in deep ANNs is backprop15. It uses the chain rule to recursively calculate gradients backwards from the output11. But backprop rests on biologically implausible assumptions, such as symmetric feedback weights and distinct forward and backward passes of information14. Many different learning algorithms, not just backprop, can provide estimates of a gradient, and some of these do not suffer from backprop’s biological implausibility12,14,34–38,91,93,94. However, algorithms differ in their variance and bias properties (Fig. 2)36,92. Algorithms such as weight/node perturbation, which reinforce random changes in synaptic weights through rewards, have high variance in their path along the gradient92. Algorithms that use random feedback weights to communicate gradient information have high bias36,95. Various proposals have been made to minimize bias and variance in algorithms while maintaining their biological realism37,38.Learning rulesObjective functions ArchitecturesLearning rule updateObjective function gradientBetterObjectiveWorseSynapse 1Synapse 2StrengthStrengthFig. 1 | The three core components of ANN design. When designing ANNs, researchers do not craft the specific computations performed by the network. Instead they specify these three components. Objective functions quantify the performance of the network on a task, and learning involves finding synaptic weights that maximize or minimize the objective function. (Often, these are referred to as ‘loss’ or ‘cost’ functions.) Learning rules provide a recipe for updating the synaptic weights. This can lead to ascent of the objective, even if the explicit gradient of the objective function isn’t followed. Architectures specify the arrangement of units in the network and determine the flow of information, as well as the computations that are or are not possible for the network to learn.PERSPECTIVE | FOCUSNATURE NEUROSCIENCENATURE NEUROSCIENCE | VOL 22 | NOVEMBER 2019 | 1761–1770 | http://www.nature.com/natureneuroscience1762FOCUS | PERSPECTIVENATURE NEUROSCIENCEalgorithms that can mimic the power of the canonical backprop-agation-of-error algorithm (backprop) (Fig. 2 and Box 1), is over-stated. Relatively simple assumptions about cellular and subcellular electrophysiology, inhibitory microcircuits, patterns of spike tim-ing, short-term plasticity and feedback connections can enable biological systems to approximate backprop-like learning in deep ANNs12,14,34–39. Hence, ANN-based models of the brain may not be as unrealistic as previously thought, and simultaneously, they appear to explain a lot of neurobiological data.With these developments, it is the right time to consider a deep-learning-inspired framework for systems neuroscience8,19,40. We have a growing understanding of the key principles that under-lie ANNs, and there are theoretical reasons to believe that these insights apply generally41,42. Concomitantly, our ability to monitor and manipulate large neural populations opens the door to new ways of testing hypotheses derived from the deep learning litera-ture. Here we sketch the scaffolding of a deep learning framework for modern systems neuroscience.Constraining learning in artificial neural networks and the brain with ‘task sets’The ‘No Free Lunch Theorems’ demonstrated broadly that no learning algorithm can perform well on all possible problems43. ANN researchers in the first decade of the 21st century thus argued that AI should be primarily concerned with the set of tasks that “…most animals can perform effortlessly, such as perception and control, as well as … long-term prediction, reasoning, planning, and [communication]”44. This set of tasks has been termed the ‘AI Set’, and the focus on building computers with capabilities that are similar to those of humans and animals is what distinguishes AI tasks from other tasks in computer science44 (note that the word ‘tasks’ here refers broadly to any computation, including those that are unsupervised.)Much of the success of deep learning can be attributed to the consideration given to learning in the AI Set15,44. Designing ANNs that are well-suited to learn specific tasks is an example of incorpo-rating ‘inductive biases’ (Box 2): assumptions that one makes about the nature of the solutions to a given optimization problem. Deep learning works so well, in part, because it uses appropriate induc-tive biases for the AI Set15,45, particularly hierarchical architectures. For example, images can be well described by composing them into a hierarchical set of increasingly complex features: from edges to simple combinations of edges to larger configurations that form objects. Language too can be considered a hierarchical construc-tion, with phonemes assembled into words, words into sentences, sentences into narratives. However, deep learning also eschews hand-engineering, allowing the function computed by the system to emerge during learning15. Thus, despite the common belief that deep learning relies solely on increases in computational power, or that it represents a ‘blank slate’ approach to intelligence, many of the successes of deep learning have grown out of a balance between use-ful inductive biases and emergent computation, echoing the blend of nature and nurture which underpins the adult brain.Similarly, neuroscientists focus on the behaviors or tasks that a species evolved to perform. This set of tasks overlaps with the AI Set, though possibly not completely, since different species have evolved strong inductive biases for their ecological niches. By considering this ‘Brain Set’ for specific species—the tasks that are important for survival and reproduction for that species—research-ers can focus on the features most likely to be key to learning. Just as departing from a pure blank slate was the key to the success of modern ANNs—e.g., by focusing on ANN designs with inductive biases that are useful for the AI Set—so we suspect that it will also be crucial to the development of a deep learning framework for sys-tems neuroscience to focus on how a given animal might solve tasks in its appropriate Brain Set.Recognizing the importance of inductive biases in deep learning also helps address some existing misconceptions. Deep networks are Box 2 | What are inductive biases?Learning is easier when we have prior knowledge about the kind of problems that we will have to solve43. Inductive biases are a means of embedding such prior knowledge into an optimization system. Such inductive biases may be generic (such as hierarchy) or specific (such as convolutions). Importantly, the inductive bi-ases that exist in the brain will have been shaped by evolution to increase an animal’s fitness in both the broad context of life on Earth (for example, life in a three-dimensional world where one needs to obtain food, water, shelter, etc.), and in specific ecologi-cal niches. Examples of inductive biases are:Simple explanations: When attempting to make sense of the world, simple explanations may be preferred, as articulated by Occam’s razor96. We can build this into ANNs using either Bayesian frameworks or by other mechanisms, such as sparse representations59.Object permanence: The world is organized into objects, which are spatiotemporally constant. We can build this into ANNs by learning representations that assume consistent movement in sensory space97.Visual translation invariance: A visual feature tends to have the same meaning regardless of its location. We can build this into ANNs using convolution operations98.Focused attention: Some aspects of the information coming into a system are more important than others. We can build this into ANNs through attention mechanisms99.Biasi.e.,(gradient, weight change)Variancei.e., randomness inweight changesErrorbackpropagationFeedback alignmentNodeperturbationWeightperturbation?Contrastive learning,predictive coding,dendritic errorRDD??AGRELFig. 2 | Bias and variance in learning rules. Many learning rules provide an estimate of the gradient of an objective function, even if they are not explicitly gradient-based. However, as with any estimator, these learning rules can exhibit different degrees of variance and bias in their estimates of the gradient. Here we provide a rough illustration of how much bias and variance some of the proposed biologically plausible learning rules may have relative to backprop. It is important to note that the exact bias and variance properties of many of the learning rules are unknown, and this is just a sketch. As such, for some of the learning rules shown here, for example, contrastive Hebbian learning, predictive coding35, dendritic error learning14, regression discontinuity design (RDD)91 and attention-gated reinforcement learning (AGREL)37, we have indicated their location with a question mark. For others, namely backpropagation, feedback alignment36 and node/weight perturbation92, we show their known relative positions.FOCUS | PERSPECTIVENATURE NEUROSCIENCENATURE NEUROSCIENCE | VOL 22 | NOVEMBER 2019 | 1761–1770 | http://www.nature.com/natureneuroscience1763PERSPECTIVE | FOCUSNATURE NEUROSCIENCEoften considered different from brains because they depend on large amounts of data. However, it is worth noting that (i) many species, especially humans, develop slowly with large quantities of experien-tial data and (ii) deep networks can work well in low-data regimes if they have good inductive biases46. For example, deep networks can learn how to learn quickly47. In the case of brains, evolution could be one means by which such inductive biases are acquired48,49.The three core components of a deep learning framework for the brainDeep learning combines human design with automatic learn-ing to solve a task. The design comprises not the computa-tions (i.e., the specific input–output functions of the ANNs), but three components: (i) objective functions, (ii) learning rules and (iii) architectures (Fig. 1). ‘Objective functions’ describe the goals of the learning system. They are functions of the synaptic weights of a neural network and the data it receives, but they can be defined without making reference to a specific task or dataset. For example, the cross-entropy objective function, which is common in machine learning, specifies a means of calculating performance on any cat-egorization task, from distinguishing different breeds of dog in the ImageNet dataset to classifying the sentiment behind a tweet. We will return to some of the specific objective functions proposed for the brain below50–53. ‘Learning rules’ describe how the parameters in a model are updated. In ANNs, these rules are generally used to improve on the objective function. Notably, this is true not only for supervised learning (in which an agent receives an explicit target to mimic), but also for unsupervised learning (in which an agent must learn without any instruction) and reinforcement learning systems (in which an agent must learn using only rewards or pun-ishments). Finally, ‘architectures’ describe how the units in an ANN are arranged and what operations they can perform. For example, convolutional networks impose a connectivity pattern whereby the same receptive fields are applied repeatedly over the spatial extent of an input.Why do so many AI researchers now focus on objective func-tions, learning rules and architectures instead of designing specific computations? The short answer is that this appears to be the most tractable way to solve real-world problems. Originally, AI practi-tioners believed that intelligent systems could be hand-designed by piecing together elementary computations54, but results on the AI Set were underwhelming11. It now seems clear that solving com-plex problems with predesigned computations (for example, hand-crafted features) is usually too difficult and practically unworkable. In contrast, specifying objective functions, architectures and learn-ing rules works well.There is, though, a drawback: the computations that emerge in large-scale ANNs trained on high-dimensional datasets can be diffi-cult to interpret. We can construct a neural network in a few lines of code, and for each unit in an ANN we can specify the equations that determine their responses to stimuli or relationships to behavior. However, after training, a network is characterized by millions of weights that collectively encode what the network has learned, and it is hard to imagine how we could describe such a system with only a small number of parameters, let alone in words55.Such considerations of complexity are informative for neurosci-ence. For small circuits comprising only tens of neurons, it may be possible to build compact models of individual neural responses and computations (i.e., to develop models that can be communicated using a small number of free parameters or words)3–5. But consider-ing that animals are solving many AI Set problems, it is likely that the brain uses solutions that are as complex as the solutions used by ANNs. This suggests that a normative framework explaining why neural responses are as they are might be best obtained by view-ing neural responses as an emergent consequence of the interplay between objective functions, learning rules and architecture. With such a framework in hand, one could then train ANN models that do, in fact, predict neural responses well29. Of course, those ANN mod-els would likely be non-compact, involving millions, billions or even trillions of free parameters, and being nigh indescribable with words. Hence, our claim is not that we could ever hope to predict neural responses with a compact model, but rather that we could explain the emergence of neural responses within a compact framework.A question that naturally arises is whether the environment, or data, that an animal encounters should be a fourth essential com-ponent for neuroscience. Determining the Brain Set for an animal necessarily involves consideration of its evolutionary and ontogenic milieu. Efforts to efficiently describe naturalistic stimuli and iden-tify ethologically relevant behaviors are crucial to neuroscience and have shaped many aspects of nervous systems. However, the core issue we are addressing in this Perspective is how to develop models of complex, hierarchical brain circuits, so we view the environment as a crucial consideration to anchor the core components, but not as one of the components itself.Once the appropriate Brain Set has been identified, the first ques-tion is: what is the architecture of the circuits? This involves descrip-tions of the cell types and their connectivity (micro-, meso- and macroscopic). Thus, uncontroversially, we propose that circuit-level descriptions of the brain are a crucial topic for systems neuroscien-tists. Thanks to modern techniques for circuit tracing and genetic lineage determination, rapid progress is being made56,57. But, to reiterate, we would argue that understanding the architecture is not sufficient for understanding the circuit; rather, it should be comple-mented by knowledge of learning rules and objective functions.Many neuroscientists recognize the importance of learning rules and architecture. But identifying the objective functions that have shaped the brain, either during learning or evolution, is less com-mon. Unlike architectures and learning rules, objective functions may not be directly observable in the brain (Fig. 3). Nonetheless, we can define them mathematically and without making reference to a specific environment or task. For example, predictive coding models minimize an objective function known as the descrip-tion length, which measures how much information is required to encode sensory data using the neural representations. Several other objective functions have been proposed for the brain (Box 3). In this Perspective, we are not advocating for any of these specific objec-tive functions in the brain, as we are articulating a framework, not a model. One of our key claims is that, even though we must infer them, objective functions are an attainable part of a complete theory of how the architectures or learning rules help to achieve a compu-tational goal.This optimization framework has an added benefit: as with ANNs, the architectures, learning rules and objective functions of the brain are likely relatively simple and compact, at least in com-parison to the list of computations performed by individual neu-rons58. The reason is that these three components must presumably be conveyed to offspring through a limited information bottleneck, i.e., the genome (which may not have sufficient capacity to fully specify the wiring of large vertebrate brains48). In contrast, the envi-ronment in which we live can convey vast amounts of complex and changing information that dwarf the capacity of the genome.Since the responses of individual neurons are shaped by the environment, their computations should reflect this massive infor-mation source. We can see evidence of this in the ubiquity of neu-rons in the brain that have high entropy in their activity and that do not exhibit easy-to-describe correlations with the multitude of stimuli and behaviors that experimentalists have explored to date6,7. To clarify our claim, we are suggesting that identifying a normative explanation using the three components may be a fruitful way to go on to develop better, non-compact models of the response prop-erties of neurons in a circuit, as shown by recent studies that use task-optimized deep ANNs to determine the optimal stimuli for PERSPECTIVE | FOCUSNATURE NEUROSCIENCENATURE NEUROSCIENCE | VOL 22 | NOVEMBER 2019 | 1761–1770 | http://www.nature.com/natureneuroscience1764FOCUS | PERSPECTIVENATURE NEUROSCIENCEactivating specific neurons29. As an analogy, the theory of evolution by natural selection provides a compact explanation for why spe-cies emerge as they do, one which can be stated in relatively few words. This compact explanation of the emergence of species can then be used to develop more complex, non-compact models of the phylogeny of specific species. Our suggestion is that normative explanations based on the three components could provide similar high-level theories for generating our lower-level models of neural responses and that this would bring us one step closer to the form of ‘understanding’ that many scientists seek.It is worth recognizing that researchers have long postulated objective functions and plasticity rules to explain the function of neural circuits59–62. Many of them, however, have sidestepped the question of hierarchical credit assignment, which is key to deep learning15. There are clear experimental success stories too, includ-ing work on predictive coding31,63, reinforcement learning64,65 and hierarchical sensory processing17,28. Thus, the optimization-based framework that we articulate here can, and has, operated alongside studies of individual neuron response properties. But we believe that we will see even greater success if a framework focused on the three core components is adopted more widely.Architectures, learning rules and objective functions in the wet labHow can the framework articulated here engage with experimental work? One way to make progress is to build working models using the three core components, then compare the models with the brain. Such models should ideally check out on all levels: (i) they should solve the complex tasks from the Brain Set under consideration, (ii) they should be informed by our knowledge of anatomy and plastic-ity, and (iii) they should reproduce the representations and changes in representation we observe in brains (Fig. 4). Of course, checking each of these criteria will be non-trivial. It may require many new experimental paradigms. Checking that a model can solve a given task is relatively straightforward, but representational and anatomi-cal matches are not straightforward to establish, and this is an area of active research66,67. Luckily, the modularity of the optimization framework allows researchers to attempt to study each of the three components in isolation.Empirical studies of architecture in the brainTo be able to identify the architecture that defines the inductive biases of the brain, we need to continue performing experiments that explore neuroanatomy at the circuit level. To really frame neu-roanatomy within an optimization framework, we must also be able to identify what information is available to a circuit, including where signals about action outcomes may come from. Ultimately, we want to be able to relate these aspects of anatomy to concrete biological markers that guide the developmental processes respon-sible for learning.There is considerable experimental effort already underway toward describing the anatomy of the nervous system. We are using a range of imaging techniques to quantify the anatomy and develop-ment of circuits57,68. Extensive work is also conducted in mapping out the projections of neural circuits with cell-type-specificity56. Research attempting to map out the hierarchy of the brain has long existed69, but several groups are now probing which parts of deep ANN hierarchies may best reflect which brain areas17,70. For example, representations in striate cortex (as measured, for example, by dissimilarity matri-ces) better match early layers of a deep ANN, while those in infero-temporal cortex better match later layers8,71. This strain of work also involves optimizing the architecture of deep ANNs so that they pro-vide a closer fit to representation dynamics in the brain, for example, by exploring different recurrent connectivity motifs66. Confronted yxxyzz: objective function ( minimum, maximum) x, y: synaptic weightsPartially following a gradientNot following a gradientFollowing a gradientFig. 3 | Learning rules that don’t follow gradients. Learning should ultimately lead to some form of improvement that could be measured with an objective function. But not all synaptic plasticity rules need to follow a gradient. Here we illustrate this idea by showing three different hypothetical learning rules, characterized as vector fields in synaptic weight space. The x and y dimensions correspond to synaptic weights, and the z dimension corresponds to an objective function. Any vector field can be decomposed into a gradient and the directions orthogonal to it. On the left is a plasticity rule that adheres to the gradient of an objective function, directly bringing the system up to the maximum. In the middle is a plasticity rule that is orthogonal to the gradient and, as such, never brings the system closer to the maximum. On the right is a learning rule that only partially follows the gradient, bringing the system toward the maximum, but indirectly. Theoretically, any of these situations may hold in the brain, though learning goals would only be met in the cases where the gradient is fully or partially followed (left and right).FOCUS | PERSPECTIVENATURE NEUROSCIENCENATURE NEUROSCIENCE | VOL 22 | NOVEMBER 2019 | 1761–1770 | http://www.nature.com/natureneuroscience1765Add Note FiguresMetricsRelated/ 10 Add to Library PDF151%
Virgin Galactic CEO says millions of tourists will want to fly to space in the next decade
- Virgin Galactic and Blue Origin represent the top two space tourism companies.
- “More people are going to want to go to space than either of us can bring in terms of service,” Virgin Galactic CEO George Whitesides told CNBC.
- With tickets priced at $250,000 per person, Whitesides expects there is a market for about 2 million space tourists.
WATCH NOWVIDEO14:25Watch CNBC’s full interview with Sir Richard Branson, Chamath Palihapitiya and George Whitesides
Virgin Galactic’s leadership expects millions of space tourists will want to fly in the next decade, more demand than can be fulfilled by both Richard Branson’s company or Jeff Bezos’ Blue Origin, its closest competitor.
“Ultimately we think this is going to be a capacity constrained market – more people are going to want to go to space than either of us can bring in terms of service,” Virgin Galactic CEO George Whitesides told CNBC’s Morgan Brennan on “Squawk on the Street.”
Virgin Galactic and Blue Origin represent the top two space tourism companies in the industry, with rocket-powered vehicles that will send passengers to the edge of space on zero gravity joyrides. The former is in the lead so far, as Virgin Galactic flew its first test passenger in February, while Blue Origin doesn’t expect to launch people for the first time until next year at the earliest.Virgin Galactic co-founder Sir Richard Branson, CEO George Whitesides and Social Capital CEO Chamath Palihapitiya pose together outside of the New York Stock Exchange (NYSE) ahead of Virgin Galactic (SPCE) trading in New York, U.S., October 28, 2019.Brendan McDermid | Reuters
With Virgin Galactic now a publicly-traded company, Whitesides spoke at the New York Stock Exchange alongside founder Sir Richard Branson with investor and chairman Chamath Palihapitiya. The trio touted Virgin Galactic’s position as an “out-of-home luxury experience,” which Whitesides noted is the fastest growing part of the luxury market.
“Globally we think around 2 million people can experience this over the coming years at this price point. Over time we’ll be able to reduce that price point and at that point the market just explodes, it’s 10 times as many at 40 million people,” Whitesides said.
UBS, in a March report, estimated that space tourism has a potential market of $3 billion a decade from now, even though it’s “still at a nascent phase.”
Virgin Galactic is in the final stages of testing its reusable spacecraft, which can carry six passengers on a 90 minute trip to the edge of space. Tickets go for $250,000 per person, with 603 people having already paid deposits for Virgin Galactic’s first flights. The company expects to begin commercial operations next year and aims to be profitable by 2021.
“Understanding that demand will be really important for people to get comfortable around the long term projections,” Palihapitiya said.
Whitesides noted that he and his wife bought tickets for flights before he became CEO, adding that Palihapitiya is also signed on to fly. Palihapitiya helped take Virgin Galactic public, by merging the company with his special purpose vehicle called Social Capital Hedosophia. He took a 49% stake in Virgin Galactic, resulting in the company’s debut Monday on public markets. While Virgin Galactic isn’t Palihapitiya’s first space investment, he has high expectations for the space tourism venture’s earnings in the years ahead.
“I think the profitability of this company is going to look as good as one of the best software companies around,” Palihapitiya said. “This is a business at scale that will have almost 70% operating margin.”
With the merger closed, and fresh capital injected into Virgin Galactic, the company’s founder expects it’s raised all the money it needs to become profitable.
“We’ve managed to completely fund Virgin Galactic through to when it breaks even,” Branson said.
Long term business potential: Hypersonic space travel
If a profitable space tourism business wasn’t ambitious enough, Virgin Galactic also has its eye on an even bigger market: Hypersonic travel, also known as point-to-point space travel. Although a different method, SpaceX has similarly expressed interest in applying its reusable rocket technology to long haul travel.
“A hypersonic plane can take a 10 hour flight and reduce it to 90 minutes,” Palihapitiya said.VIDEO09:06SpaceX wants to go New York to Shanghai in 40 minutes
While Virgin Galactic’s current spacecraft design is not capable of hypersonic travel, the company is looking to take what it’s learned and develop a vehicle that could fly at five times the speed of sound from one destination to another. Boeing’s venture arm HorizonX announced on earlier this month it was investing $20 million in Virgin Galactic, to help develop the technologies needed.
“What we’re buying is a really interesting, very lucrative, super high margin space tourism business where we’ll be able to take some of the profits to iterate on this other idea,” Palihapitiya said.
He expects it will take Virgin Galactic between five to ten years to prove out the technology. But, if successful, a hypersonic spaceplane would unlock a market many times more lucrative than space tourism.
“We will be able to directly disrupt a $300 plus billion revenue business for the airlines,” Palihapitiya said.TRENDING NOW
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List of Nobel laureates
From Wikipedia, the free encyclopediaJump to navigationJump to searchNobel laureates receive a gold medal together with a diploma and (as of 2017) 9 million SEK (roughly US$1.0 million, €0.87 million).Nobel laureates of 2012 Alvin E. Roth, Brian Kobilka, Robert J. Lefkowitz, David J. Wineland, and Serge Haroche during the ceremony
The Nobel Prizes (Swedish: Nobelpriset, Norwegian: Nobelprisen) are prizes awarded annually by the Royal Swedish Academy of Sciences, the Swedish Academy, the Karolinska Institutet, and the Norwegian Nobel Committee to individuals and organizations who make outstanding contributions in the fields of chemistry, physics, literature, peace, and physiology or medicine. They were established by the 1895 will of Alfred Nobel, which dictates that the awards should be administered by the Nobel Foundation. The Nobel Memorial Prize in Economic Sciences was established in 1968 by the Sveriges Riksbank, the central bank of Sweden, for contributions to the field of economics. Each recipient, or “laureate”, receives a gold medal, a diploma, and a sum of money, which is decided annually by the Nobel Foundation.
Each prize is awarded by a separate committee; the Royal Swedish Academy of Sciences awards the Prizes in Physics, Chemistry, and Economics; the Karolinska Institute awards the Prize in Physiology or Medicine; and the Norwegian Nobel Committee awards the Prize in Peace. Each recipient receives a medal, a diploma and a monetary award that has varied throughout the years. In 1901, the recipients of the first Nobel Prizes were given 150,782 SEK, which is equal to 8,402,670 SEK in December 2017. In 2017, the laureates were awarded a prize amount of 9,000,000 SEK. The awards are presented in Stockholm in an annual ceremony on December 10, the anniversary of Nobel’s death.
In years in which the Nobel Prize is not awarded due to external events or a lack of nominations, the prize money is returned to the funds delegated to the relevant prize. The Nobel Prize was not awarded between 1940 and 1942 due to the outbreak of World War II.
Between 1901 and 2017, the Nobel Prizes and the Nobel Memorial Prize in Economic Sciences were awarded 585 times to 923 people and organizations. With some receiving the Nobel Prize more than once, this makes a total of 892 individuals (including 844 men, 48 women) and 24 organizations. Four Nobel laureates were not permitted by their governments to accept the Nobel Prize. Adolf Hitler forbade three Germans, Richard Kuhn (Chemistry, 1938), Adolf Butenandt (Chemistry, 1939), and Gerhard Domagk (Physiology or Medicine, 1939), from accepting their Nobel Prizes, and the government of the Soviet Union pressured Boris Pasternak (Literature, 1958) to decline his award. Two Nobel laureates, Jean-Paul Sartre (Literature, 1964) and Lê Ðức Thọ (Peace, 1973), declined the award; Sartre declined the award as he declined all official honors, and Lê declined the award due to the situation Vietnam was in at the time.
Six laureates have received more than one prize; of the six, the International Committee of the Red Cross has received the Nobel Peace Prize three times, more than any other. UNHCR has been awarded the Nobel Peace Prize twice. Also the Nobel Prize in Physics was awarded to John Bardeen twice, and the Nobel Prize in Chemistry to Frederick Sanger. Two laureates have been awarded twice but not in the same field: Marie Curie (Physics and Chemistry) and Linus Pauling (Chemistry and Peace). Among the 892 Nobel laureates, 48 have been women; the first woman to receive a Nobel Prize was Marie Curie, who received the Nobel Prize in Physics in 1903. She was also the first person (male or female) to be awarded two Nobel Prizes, the second award being the Nobel Prize in Chemistry, given in 1911.
List of laureates
- List of Nobel laureates by country
- List of Nobel laureates by university affiliation
- Nobel Prize laureates by secondary school affiliation
- ^ Jump up to:a b c In 1938 and 1939, the government of Germany did not allow three German Nobel nominees to accept their Nobel Prizes. The three were Richard Kuhn, Nobel laureate in Chemistry in 1938; Adolf Butenandt, Nobel laureate in Chemistry in 1939; and Gerhard Domagk, Nobel laureate in Physiology or Medicine in 1939. They were later awarded the Nobel Prize diploma and medal, but not the money.
- ^ In 1948, the Nobel Prize in Peace was not awarded. The Nobel Foundation’s website suggests that it would have been awarded to Mohandas Karamchand Gandhi, however, due to his assassination earlier that year, it was left unassigned in his honor.
- ^ In 1958, Russian-born Boris Pasternak, under pressure from the government of the Soviet Union, was forced to decline the Nobel Prize in Literature.
- ^ In 1964, Jean-Paul Sartre refused to accept the Nobel Prize in Literature, as he had consistently refused all official honors in the past.
- ^ In 1973, Lê Ðức Thọ declined the Nobel Peace Prize. His reason was that he felt he did not deserve it because although he helped negotiate the Paris Peace Accords (a cease-fire in the Vietnam War), there had been no actual peace agreement.
- ^ In 2010, Liu Xiaobo was unable to receive the Nobel Peace Prize as he was sentenced to 11 years of imprisonment by the Chinese authorities.
- ^ The 2018 Nobel Prize in Literature was awarded in 2019, as scandals within the Swedish Academy forced it to postpone the ceremony.
- “All Nobel Laureates in Physics”. Nobel Foundation. Retrieved 2008-11-25.
- “All Nobel Laureates in Chemistry”. Nobel Foundation. Retrieved 2008-11-25.
- “All Nobel Laureates in Medicine”. Nobel Foundation. Retrieved 2008-11-25.
- “All Nobel Laureates in Literature”. Nobel Foundation. Retrieved 2008-11-25.
- “All Nobel Peace Prize Laureates”. Nobel Foundation. Retrieved 2008-11-25.
- “All Laureates in Economics”. Nobel Foundation. Retrieved 2008-11-25.
- ^ “Alfred Nobel – The Man Behind the Nobel Prize”. Nobel Foundation. Archived from the original on 2007-10-25. Retrieved 2008-11-27.
- ^ Jump up to:a b “The Nobel Prize”. Nobel Foundation. Archived from the original on 2008-10-15. Retrieved 2008-11-27.
- ^ “The Nobel Prize Awarders”. Nobel Foundation. Archived from the original on 2008-10-15. Retrieved 2008-11-27.
- ^ “The Nobel Prize Amounts” (PDF). Nobel Foundation. Archived from the original (PDF) on 2018-06-15. Retrieved 2018-06-23.
- ^ “The Nobel Prize Award Ceremonies”. Nobel Foundation. Archived from the original on 2008-08-22. Retrieved 2008-11-27.
- ^ “List of All Nobel Laureates 1942”. Nobel Foundation. Archived from the original on 2008-12-08. Retrieved 2008-11-30.
- ^ Jump up to:a b Lundestad, Geir (2001-03-15). “The Nobel Peace Prize 1901-2000”. Nobel Foundation. Archived from the original on 2008-12-19. Retrieved 2008-11-30.
- ^ “All Nobel Prizes”. http://www.nobelprize.org. Archived from the original on 6 April 2018. Retrieved 14 March 2018.
- ^ Jump up to:a b c d e f “Nobel Prize Facts”. Nobel Foundation. Archivedfrom the original on 2017-07-08. Retrieved 2015-10-11.
- ^ “Women Nobel Laureates”. Nobel Foundation. Archivedfrom the original on 2008-09-28. Retrieved 2011-10-11.
- ^ “Official Website of Nobel Prize”.
- ^ Tønnesson, Øyvind (December 1, 1999). “Mahatma Gandhi, the Missing Laureates”. Nobel Foundation. Archived from the original on January 9, 2010. Retrieved January 3, 2010.
Later, there have been speculations that the committee members could have had another deceased peace worker than Gandhi in mind when they declared that there was “no suitable living candidate”, namely the Swedish UN envoy to Palestine, Count Bernadotte, who was murdered in September 1948. Today, this can be ruled out; Bernadotte had not been nominated in 1948. Thus it seems reasonable to assume that Gandhi would have been invited to Oslo to receive the Nobel Peace Prize had he been alive one more year.
- ^ “The Nobel Peace Prize 2010 – Presentation Speech”. Nobel Foundation. Archived from the original on November 5, 2011. Retrieved October 10, 2011.
- ^ “2016 Nobel Prizes – Prize Announcement Dates”. Nobelprize.org. Nobel Media AB 2014. Archived from the original on 29 October 2016. Retrieved 3 October 2016.
- ^ Henley, Jon (10 October 2019). “Two Nobel literature prizes to be awarded after sexual assault scandal”. The Guardian. Retrieved 10 October 2019.
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