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Webinar: Machine Learning and AI - Opportunities and Challenges for Corporates


The development of the internet over the last few decades has resulted in a massive increase in the production of data and the unprecedented availability of computing power for corporate applications. Machine Learning and artificial intelligence (AI) techniques have been fuelled by these revolutions to emerge from being purely academic topics of investigation to be the basis for a new wave of products and services for the digital age. The paradigm-shifting opportunities presented to corporates by this emerging technology range from the ability to expose and extract insights and patterns from data lakes to replacing human beings in critical decision-making scenarios. However, with these opportunities also come novel risks and concerns that must be considered when contemplating the development and deployment of AI and machine learning agents. These include understanding how their trustworthiness may be measured, the ethics and policies required for their deployment and the cybersecurity implications of their widespread adoption.

Machine learning models predict how much time aging mice have left -


How old are you for your age? Scientists who study aging have begun to distinguish chronological age: how long it's been since a person was born, and so-called biological age: how much a body is "aged" and how close it is to the end of life. These researchers are uncovering ways to measure biological age, from grip strength to the lengths of protective caps on the ends of chromosomes, known as telomeres. Their goal: to construct a comprehensive set of metrics that predicts an individual's life span and health span -- the number of healthy years they have left -- and illuminates the drivers of, and treatments for, age-related diseases. A team led by David Sinclair, professor of genetics in the Blavatnik Institute at Harvard Medical School, has just taken another step toward this goal by developing two artificial intelligence-based clocks that use established measures of frailty to gauge both chronological and biological age in mice.

Harvard researchers developed an AI to determine how medical treatments affect life spans


A new AI system that predicts the health spans of mice could help develop life-extension interventions for humans, according to the tool's inventors. The system analyzes established measures of frailty to gauge a mouse's chronological age and their so-called biological age -- the condition of their physical and mental functions. It was created by researchers from Harvard Medical School's Sinclair Lab, who say it's the first study to track a mouse's frailty for the duration of its life. They plan to use the predictions to quickly test interventions intended to extend the mice's lives and move towards doing the same in humans. "It can take up to three years to complete a longevity study in mice to see if a particular drug or diet slows the aging process," said study co-first author Alice Kane, a research fellow in genetics at Harvard Medical School's Sinclair Lab.

Digital phenotyping and machine learning can help assess severe mental illness


Digital phenotyping approaches that collect and analyze Smartphone-user data on locations, activities, and even feelings - combined with machine learning to recognize patterns and make predictions from the data - have emerged as promising tools for monitoring patients with psychosis spectrum illnesses, according to a report in the September/October issue of Harvard Review of Psychiatry. The journal is published in the Lippincott portfolio by Wolters Kluwer.John Tourous, MD, MBI, of Harvard Medical School and colleagues reviewed available evidence on digital phenotyping and machine learning to improve care for people living with schizophrenia, bipolar disorder, and related illnesses. Digital phenotyping provides a much-needed bridge between patients' symptomatology and the behaviors that can be used to assess and monitor psychiatric disorders." "Digital phenotyping is the use of data from smartphones and wearables collected in situ for capturing a digital expression of human behaviors," according to the authors. Psychiatry researchers think that collecting and analyzing this kind of behavioral information might be useful in understanding how patients with severe mental illness are functioning in everyday life outside of the clinic or lab - in particular, to assess symptoms and predict clinical relapses.

Coronavirus US: Boston Dynamics' robot dog detects symptoms

Daily Mail - Science & tech

A hospital in Massachusetts has found another job for Spot, Boston Dynamics' dog-like robot: Doctor. The yellow-and-black quadruped has been proven able to take patients' vital signs from a distance of over six feet. That could allow healthcare workers to keep a safe distance from patients who may be infected with the coronavirus or other contagion. So far, Spot has only been tested on healthy patients at Harvard Medical School's Brigham and Women's Hospital - the next step would be to try it out in an emergency room setting. Researchers at MIT say they've developed cameras that allow Spot, Boston Dynamics' dog-like robot, to take vital signs from more than six feet away.

Covid-19 is boosting the use of AI triage in emergency rooms


Healthcare systems have adopted artificial intelligence in fits and starts. For years, emergency rooms have haltingly tested AI systems that collect information on patients' symptoms and medical histories, weigh it against data about similar cases, and make recommendations about who should be rushed in for treatment first. Doctors see the potential, but are wary of algorithms that don't have years of medical training. But the risk of Covid-19 transmission in ERs, along with shortages of staff and resources, have left some hospitals with no choice. The pandemic has dramatically accelerated the use of AI triage.

Personalized 3D printed models in optimizing cardiac computed tomography imaging protocols


Patient-specific or personalised 3D printed models created from cardiac imaging data can be applied to research areas beyond the current domains of 3D printing in cardiovascular disease, which mainly focuses on pre-surgical planning and simulation, medical education and training, as well as doctor-patient communication. These areas represent the most commonly used applications of 3D printed models, in particular, use of 3D printing models on congenital heart disease is a very promising field with sufficient evidence provided by randomised controlled trials. Further, 3D printed heart models are shown to play an important role in guiding patient's surgical planning and treatment as reported by single and multi-center studies. In addition to these reported applications, the realistic physical models serve as a valuable tool in studying appropriate cardiac CT protocols for the purpose of optimizing CT scanning techniques. Zhonghua Sun, a professor and medical imaging researcher from Curtin University, Australia has been in search of new ways to acquire accurate and efficient medical images.

Orthopedic field awaits impact of artificial intelligence


Since the 1950s when the term artificial intelligence was coined, its application and use has increased through rapid technological advances and has found their way into the health care sector, including orthopedics. A study published in 2018 showed the amount of orthopedic literature on machine learning, which is one type of artificial intelligence (AI), had an approximate tenfold increase since 2010, with the most frequently applied machine learning algorithms found in spine pathology, osteoarthritis detection and prediction, and imaging of bone and cartilage. "I think there has definitely been an increase in our understanding but also our attraction or fascination with how [artificial intelligence] may shift care in orthopedics going forward," Atul F. Kamath, MD, director of the Hip Preservation Center, staff in the Orthopedic and Rheumatologic Institute and professor of orthopedic surgery at Cleveland Clinic, told Orthopedics Today. "I think qualitatively, whether you are a lay person or someone in the medical field, you know artificial intelligence is integrated into multiple facets of daily life with autonomous cars and Siri, but also has merged into the medical world with projects like IBM Watson and Google platforms." An increase in larger datasets along with the convergence of cloud-based computing and graphical processing units (GPUs) with other areas of technology have allowed AI to become what it is today, according to Joseph H. Schwab, MD, chief of spine surgery and associate professor of orthopedic surgery at Harvard Medical School and Massachusetts General Hospital.

Expanding the brain researcher's toolkit


Despite the wealth and quality of basic neuroscience research, there is still little we can do to treat or prevent most brain disorders. Industry efforts, meanwhile, have shied away from this field, particularly after a series of major drug candidates for the treatment of Alzheimer's disease failed to meet expectations ([ 1 ][1]). My previous research, which entailed developing and using optogenetics ([ 2 ][2], [ 3 ][3]) to understand how deep brain stimulation works in Parkinson's disease (PD) ([ 4 ][4], [ 5 ][5]), resulted in two key insights: We need to look and intervene earlier in brain disease progression, and we need to be able to access relevant cell populations with noninvasive yet precise tools to investigate, prevent, contain, or even reverse the course of disease. Accumulating evidence has highlighted a third insight: We may need to look beyond the brain to fully understand brain disorders ([ 6 ][6], [ 7 ][7]). My goal has been to develop an effective toolkit for neuromodulation so we can start to bridge the gap between what we know and what we can do to treat the brain. To achieve minimally invasive optogenetic-mediated modulation, we need to be able to penetrate the blood–brain barrier (BBB) so that vectors can be delivered systemically rather than through intracranial injections and address the poor reach of visible light through tissue so that large tissue volumes can be recruited without implantation of optical fibers. For early intervention, we need to get past the neuronal and brain-centric view of neurological disease. The adult brain is protected from compounds circulating in the blood by the BBB. Gene delivery to the brain requires surgery that is not only invasive but also results in limited tissue coverage and nonuniform gene expression. To achieve sufficient coverage for conditions characterized by broad dysfunction, such as neurodegeneration, multiple injections are needed, each of which creates local inflammation and damage. Systemic delivery would therefore be preferable to focal delivery because it does not require surgery and achieves broader tissue coverage. We have pioneered a powerful strategy that allows for the generation and selection of adeno-associated viral (AAV) vectors with optimized properties through Cre-recombination-dependent AAV-targeted evolution (CREATE) ([ 8 ][8]). My lab has used CREATE to synthesize AAVs that cross the BBB and transduce most cells in the adult mouse brain (see the first figure). These systemically delivered AAVs enable noninvasive brainwide transduction of specific cell types and regions in rodents when used with gene regulatory elements ([ 9 ][9]). During our quest to achieve systemic delivery of AAVs with opsin cargoes, we learned that the lower per-cell transgene copy number produced by systemic delivery led to ineffective overall opsin conductance. We needed better, high-performance opsins to make this method viable. To achieve such opsins, we built diversity into channelrhodopsin (ChR) using structure-guided SCHEMA ([ 10 ][10]) protein recombination from distinct opsin parents and then measured membrane localization and photocurrents. However, the dominant method for testing opsin properties—whole-cell patch clamping—has low throughput, so we used machine learning with limited training data to efficiently explore the vast sequence space and restrict the number of opsins to be tested. With Gaussian-process models trained on a limited experimental set of 102 functionally characterized ChRs, we selected ChR sequences that the models predicted would express, localize, and function. The result was a panel of high-photocurrent ChRs with exceptional light sensitivity (ChRgers) ([ 11 ][11]). These high-fluxing opsins not only overcome the low–copy number limitation of systemic delivery but also allow the light source to be placed at a distance from the transduced cells (for example, on a thinned mouse skull rather than implanted directly in the brain). The net effect of these advances is more effective coverage by both light and transgenes. This may be particularly useful for advancing optogenetic studies in nonhuman primates (NHPs), a key model relevant to human health. Optogenetics has had a relatively limited impact in NHP research, compared to its impact in smaller model organisms, predominantly owing to coverage problems stemming from the delivery limitations for genes and photons. Using systemic delivery of high-performance opsins to transduce and recruit large brain regions in NHPs could enable better neuromodulation in these important animal models. Neurodegeneration research has focused mainly on compromised neurons and circuits in the brain. Nevertheless, evidence points to roles for inflammation mediated by non-neuronal brain cells and body-to-brain connections (by way of the peripheral nervous system and/or a compromised BBB) ([ 6 ][6], [ 7 ][7]). Engineering gene-delivery tools that specifically target non-neuronal brain cells relevant to neurodegeneration, such as the brain endothelial cells that constitute the vasculature and the BBB, may be paradigm shifting because an impaired BBB can initiate and/or precipitate neurodegeneration. The ability to perform both positive and negative selection is key to yielding vectors with desired organ, cell type, and cell region tropisms. ![Figure][12] Multiplexed CREATE (Cre-recombination–based AAV-targeted evolution) Engineered systemic AAV capsids with improved tropism for peripheral sensory neurons (AAV-PHP.S, left) or the central nervous system (AAV-PHP.B and AAV-PHP.eB, right) upon systemic delivery in adult mice. Panels show native eGFP (enchanced green fluorescent protein) in the intact brain. Scale bar represents 1 mm. GRAPHIC: ADAPTED FROM CHAN ET AL. ([ 9 ][9]) BY N. DESAI/ SCIENCE To enable this, we transformed our CREATE platform into multiplexed M-CREATE ([ 12 ][13]), an in vivo screening strategy that incorporates next-generation sequencing, synthetic library generation, and a dedicated analysis pipeline. Using M-CREATE, we identified capsid variants that exhibited bias toward vascular cells or that targeted neurons with greater specificity, as well as capsids that transduced the central nervous system broadly or crossed the BBB in diverse murine strains. As a weak BBB can allow pathological factors into the brain ([ 13 ][14], [ 14 ][15]), functionally targeting BBB permeability by means of engineered AAVs can affect body-to-brain access through the circulation. Modulating permeability affords the opportunity to study and/or repair a barrier that can be weakened in disease or to deliver therapies to the brain by way of the bloodstream. Synucleinopathies are neurodegenerative diseases characterized by the aggregation of insoluble amyloid α-synuclein (αSyn) fibrils. PD is a synucleinopathy characterized by death of selected midbrain and brainstem neuronal populations and motor dysfunction. Roughly 90% of Parkinson's cases arise sporadically, making the study of its etiology difficult. Emerging findings suggest that nonmotor features such as loss of smell and gastrointestinal deficits may precede clinical diagnosis ([ 15 ][16], [ 16 ][17]). Postmortem biopsies from asymptomatic PD-diagnosed individuals have revealed the presence of pathologic αSyn assemblies in gastrointestinal tissue, leading Braak and colleagues to suggest that αSyn aggregation may originate in peripheral tissues such as the gut and progress to the brain by way of autonomic fibers ([ 17 ][18], [ 18 ][19]). Understanding the role of the peripheral nervous system in propagating pathology may therefore aid our understanding of neurodegeneration and help prevent it. Recognizing the lack of tools and methods available to study peripheral nervous systems, we developed whole-body tissue clearing and a tunable and rapid vector expression system that we used to evaluate network connectivity in the enteric nervous system (ENS) ([ 19 ][20], [ 20 ][21]). We injected a modest amount of αSyn preformed fibrils into the gut lining of mice (specifically the highly innervated duodenal wall) and observed subsequent gastrointestinal inflammation and physiological changes to the ENS (measured by optogenetics, calcium imaging, and changes in fecal production) ([ 21 ][22]). ENS pathology was also associated with a severe deficit in the lysosomal enzyme glucocerebrosidase, encoded by the gene GBA1 , known to be involved in Gaucher disease and PD. We therefore delivered GBA1 by means of the AAV-PHP.S capsid, which efficiently transduces the peripheral nervous system, to noninvasively restore glucocerebrosidase in the periphery. This led to a reduction in αSyn pathology and hints at a possible therapeutic target for early PD. Lastly, we demonstrated that inoculation of αSyn fibrils in aged mice, but not younger mice, resulted in progression of αSyn histopathology to the midbrain and decreased dopamine in the striatum, and subsequent motor defects. Taken together, this work (summarized in the second figure) shifts the focus of neurodegenerative disease etiology to the peripheral nervous system and expands our understanding of the role played by the ENS in prodromal synucleinopathy. ![Figure][12] αSyn fibrils can disrupt the enteric nervous system and aging increases susceptibility to the progression of αSyn pathology from the gut to the brain, leading to motor dysfunction. These deleterious effects were mitigated by peripheral gene transfer of GBA1 , through the systemic administration of specially engineered AAVs. GRAPHIC: ADAPTED FROM CHALLIS ET AL. ([ 21 ][22]) AND CHAN ET AL. ([ 9 ][9]) BY N. DESAI/ SCIENCE We aim to bridge the gap between what is currently feasible in neuromodulation and what is needed to meaningfully improve the lives of those with neuropathologies. We have used protein engineering principles to noninvasively, effectively, and specifically deliver effector genomes to nervous tissues and associated cell types ([ 22 ][23]). We have made advances in the creation of systemic viruses that cross the BBB, which open up the potential for noninvasive modulation of targets deep in the brain. In addition, we have used our gene delivery and optogenetic tools to modulate the peripheral nervous system in a mouse model of PD, demonstrating the potential utility of neuromodulation beyond the brain for the treatment of brain disorders. A number of relevant barriers remain, including the need to expand the AAV modest packaging limit, to penetrate the BBB in a variety of species, and to carefully consider and mitigate AAV side effects with better vectors, delivery methods, and immune avoidance strategies. Nevertheless, the findings and resources generated by this work represent a step forward with implications for neurological disorders and will be generalizable across neurological and psychiatric indications. GRAND PRIZE WINNER Viviana Gradinaru Viviana Gradinaru received her B.S. from the California Institute of Technology (CalTech) and a Ph.D. in neuroscience from Stanford Medical School. After a year in industry, Gradinaru started her lab in the Division of Biology and Biological Engineering at Caltech in 2012, where she is now a professor of neuroscience and biological engineering. Gradinaru's research group specializes in developing neuroscience tools and methods, including engineering of microbial opsins and viral vectors with optimized brain tropism with systemic delivery. Her research uses mouse models of neurodevelopmental and neurodegenerative disorders combined with electrophysiology and optogenetics to understand the cellular basis of dysfunction with the goal of developing new strategies for intervention. FINALIST Guosong Hong Guosong Hong received his undergraduate degree from Peking University and a Ph.D. from Stanford University. After completing his postdoctoral fellowship at Harvard University, Guosong started his lab in the Department of Materials Science and Engineering at Stanford University in 2018. His research aims to develop new materials-enabled neurotechnologies to interrogate and manipulate the brain with high spatiotemporal resolution, minimal invasiveness, and targeted neural specificity. [ ][24] 1. [↵][25]1. K. Servick , Science 10.1126/science.aax4236 (2019). 2. [↵][26]1. V. Gradinaru et al ., Cell 141, 154 (2010). [OpenUrl][27][CrossRef][28][PubMed][29][Web of Science][30] 3. [↵][31]1. V. Gradinaru et al ., J. 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Fruit flies have special neurons that sense the wind to aid navigation

New Scientist

Specific neurons in fruit flies fire according to wind direction, helping them form a neural map of their surroundings. Algorithms inspired by this may be able to help robots to better navigate their environment. Tatsuo Okubo at Harvard Medical School and his colleagues wanted to determine how wind direction was characterised by a fruit fly's brain. While it is well known that wind direction affects the behaviour of insects, no one had yet developed a map of the neurons involved in this phenomenon for any animal.