Annotating Synapses in Large EM Datasets

Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics. To date, electron microscopy (EM) is the most proven technique for identifying and quantifying synaptic connections. As advances in EM make acquiring larger datasets possible, subsequent manual synapse identification ({\em i.e.}, proofreading) for deciphering a connectome becomes a major time bottleneck. Here we introduce a large-scale, high-throughput, and semi-automated methodology to efficiently identify synapses. We successfully applied our methodology to the Drosophila medulla optic lobe, annotating many more synapses than previous connectome efforts. Our approaches are extensible and will make the often complicated process of synapse identification accessible to a wider-community of potential proofreaders

Prevention of suicidal behaviour in prisons: an overview of initiatives based on a systematic review of research on near-lethal suicide attempts

Background: Worldwide, prisoners are at high risk of suicide. Research on near-lethal suicide attempts can provide important insights into risk and protective factors, and inform suicide prevention initiatives in prison. Aims: To synthesize findings of research on near-lethal attempts in prisons, and consider their implications for suicide prevention policies and practice, in the context of other research in custody and other settings. Method: We searched two bibliographic indexes for studies in any language on near-lethal and severe self-harm in prisoners, supplemented by targeted searches over the period 2000–2014. We extracted information on risk factors descriptively. Data were not meta-analyzed owing to heterogeneity of samples and methods. Results: We identified eight studies reporting associations between prisoner near-lethal attempts and specific factors. The latter included historical, prison-related, and clinical factors, including psychiatric morbidity and comorbidity, trauma, social isolation, and bullying. These factors were also identified as important in prisoners' own accounts of what may have contributed to their attempts (presented in four studies). Conclusion: Factors associated with prisoners' severe suicide attempts include a range of potentially modifiable clinical, psychosocial, and environmental factors. We make recommendations to address these factors in order to improve detection, management, and prevention of suicide risk in prisoners

Five-State Study of ACA Marketplace Competition

The health insurance marketplaces created by the Affordable Care Act (ACA) were intended to broaden health insurance coverage by making it relatively easy for the uninsured, armed with income-related federal subsidies, to choose health plans that met their needs from an array of competing options. The further hope was that competition among health plans on the exchanges would lead to lower costs and higher value for consumers, because inefficient, low-value plans would lose out in the competitive market place. This study sought to understand the diverse experience in five states under the ACA in order to gain insights for improving competition in the private health insurance industry and the implementation of the ACA.In spring 2016, the insurance marketplaces had been operating for nearly three full years. There were numerous press stories of plans' decisions to enter or leave selected states or market areas within states and to narrow provider networks by including fewer choices among hospitals, medical specialists, and other providers. There were also beginning to be stories of insurer requests for significant premium increases. However, there was no clear understanding of how common these practices were, nor how and why practices differed across carriers, markets, and state regulatory settings.This project used the ACA Implementation Research Network to conduct field research in California, Michigan, Florida, North Carolina, and Texas. In each state, expert field researchers engaged directly with marketplace stakeholders, including insurance carriers, provider groups, state regulators, and consumer engagement organizations, to identify and understand their various decisions. This focus included an effort to understand why carriers choose to enter or exit markets and the barriers they faced, how provider networks were built, and how state regulatory decisions affected decision-making. Ultimately, it sought to find where and why certain markets are successful and competitive and how less competitive markets might be improved.The study of five states was not intended to provide statistically meaningful generalizations about the functioning of the marketplace exchanges. Rather, it was intended to accomplish two other objectives. First, the study was designed to generate hypotheses about the development and evolution of the exchanges that might be tested with "harder" data from all the exchanges. Second, it sought to describe the potentially idiosyncratic nature of the marketplaces in each of the five states. Political and economic circumstances may differ substantially across markets. Policymakers and market participants need to appreciate the nuances of different local settings if programs are to be successful. What works in Michigan may not work in Texas and vice versa. Field research of this sort can give researchers and policymakers insight into how idiosyncratic local factors matter in practice.In brief, our five states had four years of experience in the open enrollment periods from 2014 through 2017. The states array themselves in a continuum of apparent success in enhancing and maintaining competition among insurers. California and Michigan appear to have had success in nurturing insurer competition, in at least the urban areas of their states. Florida, North Carolina, and Texas were less successful. This divergence is recent, however. As recently as the 2015 and 2016 open enrollment periods, all of the states had what appeared to be promising, if not always robust, insurance competition. Large changes occurred in the run-up to the 2017 open enrollment period

Learning Arbitrary Statistical Mixtures of Discrete Distributions

We study the problem of learning from unlabeled samples very general statistical mixture models on large finite sets. Specifically, the model to be learned, $\vartheta$, is a probability distribution over probability distributions $p$, where each such $p$ is a probability distribution over $[n] = \{1,2,\dots,n\}$. When we sample from $\vartheta$, we do not observe $p$ directly, but only indirectly and in very noisy fashion, by sampling from $[n]$ repeatedly, independently $K$ times from the distribution $p$. The problem is to infer $\vartheta$ to high accuracy in transportation (earthmover) distance. We give the first efficient algorithms for learning this mixture model without making any restricting assumptions on the structure of the distribution $\vartheta$. We bound the quality of the solution as a function of the size of the samples $K$ and the number of samples used. Our model and results have applications to a variety of unsupervised learning scenarios, including learning topic models and collaborative filtering.Comment: 23 pages. Preliminary version in the Proceeding of the 47th ACM Symposium on the Theory of Computing (STOC15

Why the fair innings argument is not persuasive

The fair innings argument (FIA) is frequently put forward as a justification for denying elderly patients treatment when they are in competition with younger patients and resources are scarce. In this paper I will examine some arguments that are used to support the FIA. My conclusion will be that they do not stand up to scrutiny and therefore, the FIA should not be used to justify the denial of treatment to elderly patients, or to support rationing of health care by age. There are six issues arising out of the FIA which are to be addressed. First, the implication that there is such a thing as a fair share of life. Second, whether it makes sense to talk of a fair share of resources in the context of health care and the FIA. Third, that 'fairness' is usually only mentioned with regard to the length of a person's life, and not to any other aspect of it. Fourth, if it is sensible to discuss the merits of the FIA without taking account of the 'all other things being equal' argument. Fifth, the difference between what is unfair and what is unfortunate. Finally, that it is tragic if a young person dies, but only unfortunate if an elderly person does

Multiphoton radiative recombination of electron assisted by laser field

In the presence of an intensive laser field the radiative recombination of the continuum electron into an atomic bound state generally is accompanied by absorption or emission of several laser quanta. The spectrum of emitted photons represents an equidistant pattern with the spacing equal to the laser frequency. The distribution of intensities in this spectrum is studied employing the Keldysh-type approximation, i.e. neglecting interaction of the impact electron with the atomic core in the initial continuum state. Within the adiabatic approximation the scale of emitted photon frequencies is subdivided into classically allowed and classically forbidden domains. The highest intensities correspond to emission frequencies close to the edges of classically allowed domain. The total cross section of electron recombination summed over all emitted photon channels exhibits negligible dependence on the laser field intensity.Comment: 14 pages, 5 figures (Figs.2-5 have "a" and "b" parts), Phys.Rev.A accepted for publication. Fig.2b is presented correctl

Preserving Differential Privacy in Convolutional Deep Belief Networks

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing epsilon-differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions

The unfolded protein response affects readthrough of premature termination codons

One-third of monogenic inherited diseases result from premature termination codons (PTCs). Readthrough of in-frame PTCs enables synthesis of full-length functional proteins. However, extended variability in the response to readthrough treatment is found among patients, which correlates with the level of nonsense transcripts. Here, we aimed to reveal cellular pathways affecting this inter-patient variability. We show that activation of the unfolded protein response (UPR) governs the response to readthrough treatment by regulating the levels of transcripts carrying PTCs. Quantitative proteomic analyses showed substantial differences in UPR activation between patients carrying PTCs, correlating with their response. We further found a significant inverse correlation between the UPR and nonsense-mediated mRNA decay (NMD), suggesting a feedback loop between these homeostatic pathways. We uncovered and characterized the mechanism underlying this NMD-UPR feedback loop, which augments both UPR activation and NMD attenuation. Importantly, this feedback loop enhances the response to readthrough treatment, highlighting its clinical importance. Altogether, our study demonstrates the importance of the UPR and its regulatory network for genetic diseases caused by PTCs and for cell homeostasis under normal conditions

What do we know about dynamic glucose-enhanced (DGE) MRI and how close is it to the clinics? Horizon 2020 GLINT consortium report

Cancer is one of the most devastating diseases that the world is currently facing, accounting for 10 million deaths in 2020 (WHO). In the last two decades, advanced medical imaging has played an ever more important role in the early detection of the disease, as it increases the chances of survival and the potential for full recovery. To date, dynamic glucose-enhanced (DGE) MRI using glucose-based chemical exchange saturation transfer (glucoCEST) has demonstrated the sensitivity to detect both D-glucose and glucose analogs, such as 3-oxy-methyl-D-glucose (3OMG) uptake in tumors. As one of the recent international efforts aiming at pushing the boundaries of translation of the DGE MRI technique into clinical practice, a multidisciplinary team of eight partners came together to form the "glucoCEST Imaging of Neoplastic Tumors (GLINT)" consortium, funded by the Horizon 2020 European Commission. This paper summarizes the progress made to date both by these groups and others in increasing our knowledge of the underlying mechanisms related to this technique as well as translating it into clinical practice