Jason Yang, an IMES research scientist, is the lead author of the paper, which appears in the May 9 issue of Cell. Other authors include Sarah Wright, a recent MIT MEng recipient; Meagan Hamblin, a former Broad Institute research technician; Miguel Alcantar, an MIT graduate student; Allison Lopatkin, an IMES postdoc; Douglas McCloskey and Lars Schrubbers of the Novo Nordisk Foundation Center for Biosustainability; Sangeeta Satish and Amir Nili, both recent graduates of Boston University; Bernhard Palsson, a professor of bioengineering at the University of California at San Diego; and Graham Walker, an MIT professor of biology.
Measles, once thought to have been eliminated in the U.S., is popping up in isolated outbreaks as a result of skipped well-child visits and parents' fears that the measles-mumps-rubella (MMR) vaccine is linked to autism. Though some 350 measles cases occurred in 15 states in the first three months of 2019, more than half were in Brooklyn, N.Y., and nearby Rockland County, N.Y., where large religious communities have adopted anti-vaccine positions. Rockland County responded by pulling 6,000 unvaccinated children out of schools and barring them from public places. The county's actions were effective; in just a few months, 17,500 doses of MMR were administered to area children. Yet, wouldn't it have been better to contain the outbreak before it got started?
It may be possible to spot if your relative, friend or colleague is ill just by looking at them, research suggests. Scientists injected volunteers with either E.coli or a placebo before asking others how sick they looked two hours later. The infected patients were judged to look'significantly worse', with people noticing their drooping eyelids and mouths. They also showed more negative facial expressions, which may be brought on by inflammation as the immune system fights off the infection. Researchers believe humans may have evolved the ability to pick up on subtle cues that suggest someone is contagious to avoid getting ill.
Data from all 1010 patients enrolled in the ART were analysed. Partitioning suggested that three clusters were present in the ART population. The largest cluster (Cluster 1) was characterised by patients with pneumonia and requiring vasopressor support. Recruitment manoeuvres with PEEP titration were associated with higher mortality in Cluster 1 (probability of harm of 98%), but this association was absent in Clusters 2 and 3 (probability of harm of 45% and 68%, respectively). Higher baseline driving pressure was associated with a progressive reduction in the association between alveolar recruitment with PEEP titration and mortality.
Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated "white-box" biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics.
Harnessing machine learning to improve health is a major ambition for both medical practitioners and the healthcare industry. If the two can join forces on a global scale in 2019, with the right investment and the right approach, AI could propel a revolution to democratise global health and to leapfrog access to health services in low- and middle-income countries. A chronic shortage of human resources is one of the major obstacles to better health and healthcare in many resource-poor settings. When it comes to global health, artificial intelligence offers huge opportunities to fill the gap left by critical healthcare worker shortages, particularly if combined with mobile phone technology. For example, social enterprises such as Peek Vision can use smart-phone based technology to enable healthcare providers to deliver cost-effective and targeted treatment to people with eyesight problems.
Last November Chinese scientist He Jiankui announced the birth of twin babies whose germline he claimed to have altered to reduce their susceptibility to contracting HIV. The news of embryo editing and gene-edited babies prompted immediate condemnation both within and beyond the scientific community. An ABC News headline asked: "Genetically edited babies--scientific advancement or playing God?" The answer may be "both." He's application of gene-editing technology to human embryos flouted norms of scientific transparency and oversight, but even less controversial scientific developments sometimes provoke the reaction that humans are overstepping their appropriate sphere of influence.
The humanity has been facing a plethora of challenges associated with infectious diseases, which kill more than 6 million people a year. Although continuous efforts have been applied to relieve the potential damages from such misfortunate events, it is unquestionable that there are many persisting challenges yet to overcome. One related issue we particularly address here is the assessment and prediction of such epidemics. In this field of study, traditional and ad-hoc models frequently fail to provide proper predictive situation awareness (PSAW), characterized by understanding the current situations and predicting the future situations. Comprehensive PSAW for infectious disease can support decision making and help to hinder disease spread. In this paper, we develop a computing system platform focusing on collective intelligence causal modeling, in order to support PSAW in the domain of infectious disease. Analyses of global epidemics require integration of multiple different data and models, which can be originated from multiple independent researchers. These models should be integrated to accurately assess and predict the infectious disease in terms of holistic view. The system shall provide three main functions: (1) collaborative causal modeling, (2) causal model integration, and (3) causal model reasoning. These functions are supported by subject-matter expert and artificial intelligence (AI), with uncertainty treatment. Subject-matter experts, as collective intelligence, develop causal models and integrate them as one joint causal model. The integrated causal model shall be used to reason about: (1) the past, regarding how the causal factors have occurred; (2) the present, regarding how the spread is going now; and (3) the future, regarding how it will proceed. Finally, we introduce one use case of predictive situation awareness for the Ebola virus disease.
A man who almost died from meningitis has revealed how he began to look forward to having his limbs amputated. Mike Davies, 60, from Brighton, spent 70 days in intensive care with meningococcal meningitis and septicaemia. During this time, he said he knew his hands and feet were "dead" and he would recover better without them. Now he says he is in a positive place and "can even hold a pint of beer". With the help of prosthetic limbs, Mr Davies can drive a specially-adapted car and said he was living life to the full.
Reprinted with permission from Quanta Magazine's Abstractions blog. Social organisms come in all shapes and sizes, from the obviously gregarious ones like mammals and birds down to the more cryptic socializers like bacteria. Evolutionary biologists often puzzle over altruistic behaviors among them, because self-sacrificing individuals would at first seem to be at a severe disadvantage under natural selection. William D. Hamilton, one of the 20th century's most prominent evolutionary theorists, developed a mathematical theory to explain the evolution of altruism through kin selection--for instance, why most individual ants, bees and wasps forgo the ability to reproduce and instead pour all their efforts into raising their siblings. Bacteriologists developed game-theory models to explain why bacteria in groups produce metabolites for their neighbors, even though some cheaters take advantage of the situation.