Miracle-dz/UniFAR-FTUs huggingface dataset:
A Randomized, Controlled Trial of ZMapp for Ebola Virus Infection.

BACKGROUND Data from studies in nonhuman primates suggest that the triple monoclonal antibody cocktail ZMapp is a promising immune-based treatment for Ebola virus disease (EVD). METHODS Beginning in March 2015, we conducted a randomized, controlled trial of ZMapp plus the current standard of care as compared with the current standard of care alone in patients with EVD that was diagnosed in West Africa by polymerase-chain-reaction (PCR) assay. Eligible patients of any age were randomly assigned in a 1:1 ratio to receive either the current standard of care or the current standard of care plus three intravenous infusions of ZMapp (50 mg per kilogram of body weight, administered every third day). Patients were stratified according to baseline PCR cycle-threshold value for the virus (≤22 vs. >22) and country of enrollment. Oral favipiravir was part of the current standard of care in Guinea. The primary end point was mortality at 28 days. RESULTS A total of 72 patients were enrolled at sites in Liberia, Sierra Leone, Guinea, and the United States. Of the 71 patients who could be evaluated, 21 died, representing an overall case fatality rate of 30%. Death occurred in 13 of 35 patients (37%) who received the current standard of care alone and in 8 of 36 patients (22%) who received the current standard of care plus ZMapp. The observed posterior probability that ZMapp plus the current standard of care was superior to the current standard of care alone was 91.2%, falling short of the prespecified threshold of 97.5%. Frequentist analyses yielded similar results (absolute difference in mortality with ZMapp, -15 percentage points; 95% confidence interval, -36 to 7). Baseline viral load was strongly predictive of both mortality and duration of hospitalization in all age groups. CONCLUSIONS In this randomized, controlled trial of a putative therapeutic agent for EVD, although the estimated effect of ZMapp appeared to be beneficial, the result did not meet the prespecified statistical threshold for efficacy. (Funded by the National Institute of Allergy and Infectious Diseases and others; PREVAIL II ClinicalTrials.gov number, NCT02363322 .).
Adding Intelligence to Virtual Reality

Virtual reality systems have reached a certain level of maturity in terms of visual and aural quality, but the sense of presence also requires believably behaving environment. A new field of adding intelligence into virtual environments is emerging. We describe shortly the field of virtual reality, and how AI can be applied there. We outline the possibilities of using AI to enhance existing applications, like a training environment for astronauts
Dendrite Complexity of Sympathetic Neurons Is Controlled during Postnatal Development by BMP Signaling

Dendrite development is controlled by the interplay of intrinsic and extrinsic signals affecting initiation, growth, and maintenance of complex dendrites. Bone morphogenetic proteins (BMPs) stimulate dendrite growth in cultures of sympathetic, cortical, and hippocampal neurons but it was unclear whether BMPs control dendrite morphology in vivo. Using a conditional knock-out strategy to eliminate Bmpr1a and Smad4 in immature noradrenergic sympathetic neurons we now show that dendrite length, complexity, and neuron cell body size are reduced in adult mice deficient of Bmpr1a. The combined deletion of Bmpr1a and Bmpr1b causes no further decrease in dendritic features. Sympathetic neurons devoid of Bmpr1a/1b display normal Smad1/5/8 phosphorylation, which suggests that Smad-independent signaling paths are involved in dendritic growth control downstream of BMPR1A/B. Indeed, in the Smad4 conditional knock-out dendrite and cell body size are not affected and dendrite complexity and number are increased. Together, these results demonstrate an in vivo function for BMPs in the generation of mature sympathetic neuron dendrites. BMPR1 signaling controls dendrite complexity postnatally during the major dendritic growth period of sympathetic neurons.
Emergent Semantics and the Multimedia Semantic Webi

It is well known that context plays an important role in the meaning of a work of art. This paper addresses the dynamic context of a collection of linked multimedia documents, of which the web is a perfect example. Contextual document semantics emerge through identification of various users’ browsing paths though this multimedia collection. In this paper, we present techniques that use multimedia information as part of this determination. Some implications of our approach are that the author of a webpage cannot completely define that document’s semantics and that semantics emerge through use.
Somaesthetics and the Revival of Aesthetics

This paper examines the ten-year history of somaesthetics – describing the field's origins and genealogical roots, explaining its terminology, analyzing its structure, tracing its reception, exploring its most interesting applications, and responding to the most important criticisms that have been directed at it. Somaesthetics, as the paper shows, emerges from the framework of my work in pragmatist aesthetics which sought to revive aesthetics by bringing art closer to life and bridging the presumed divide between the aesthetic and the practical while also advocating an idea of ethics as an art of living. One way to bring more life to aesthetics is to emphasize the role of the living body in art and aesthetic experience. Another strategy of revival is to expand the field of aesthetics to make it more relevant to more people by including practices of somatic stylization and enhanced somatic perception (aisthesis). Somaesthetics, a discipline of both theory and practice, deploys these means of revival, and seems to have had some positive influence. It has been applied by diverse authors in many different fields – from performance art and computer design, to sports, feminist issues of identity, and the use of technological body enhancements in the art of living. The three main branches of somaesthetics are introduced, and the complex structure of the field is used in refuting some of the typical criticisms made against it. Somaesthetics is not a blanket apology for the dangerous excesses of our body culture, but rather a site for the ideological critique of the dominant somatic ideals. Its interest in the body is not a celebration of irrationality or the abandoning of critical reflection.
Quantifying Transient Hypoxia in Human Tumor Xenografts by Flow Cytometry

Transient hypoxia is a poorly understood and potentially important factor that may limit tumor response to various forms of therapy. We assessed transient hypoxia on a global scale in two different human tumor xenografts by sequentially administering two hypoxia markers followed by quantification of hypoxic cells using flow cytometry. High levels of the first hypoxia marker (pimonidazole) were maintained in the circulation over an 8-hour period by multiple hourly injections, providing a “time-integrated” hypoxia measure showing an asymptotic increase in the total number of hypoxic cells. Subsequent administration of a second hypoxia marker (CCI-103F) showed that substantial numbers of the previously pimonidazole-labeled cells were no longer hypoxic during the circulation lifetime of the second marker. The overall fraction of tumor cells that demonstrated changes in hypoxic status with time increased with different kinetics and by different magnitudes in the two xenograft systems. Specifically, up to 20% of the cells in SiHa (human cervical squamous cell carcinoma) tumors and up to 8% of the cells in WiDr (human colon adenocarcinoma) tumors were intermittently hypoxic over an 8-hour period. Also, the tumor cells that demonstrated transient hypoxia were typically not adjacent to functional tumor blood vessels. Similar approaches could be used in the clinic to provide information on the duration of intermittent hypoxia episodes and the fraction of transiently hypoxic tumor cells, which would, in turn, have important implications for the strategic improvement of cancer therapy.
Discovery of New Inducible Genes in in vitroDecidualized Human Endometrial Stromal Cells Using Microarray Technology.

A prerequisite for implantation in humans is differentiation (decidualization) of stromal cells in the endometrium, believed to be stimulated by progesterone (P) and/or cAMP. In the current study, advances in microarray technology have allowed us to investigate genes differentially expressed in human endometrial stromal cells decidualized in vitro in response to P or cAMP, compared to nondecidualized cells. Endometrial stromal cells were isolated from endometrial biopsy tissue and cultured without steroid hormones, with 1 microM P (after E2 priming), or 1 mM 8-bromo-cAMP. Total RNA was isolated and reverse transcribed to synthesize 32P-labeled cDNA probes using primers corresponding to genes represented on the Clontech Human Atlas cDNA Expression Array. After hybridization, signals were quantified by phosphor imaging densitometry and were normalized to GAPDH and ubiquitin. Of the 588 genes screened, marked upregulation was observed of cytokines, growth factors, nuclear transcription factors, members of the cyclin family, and mediators of the cAMP signal transduction pathway. Additional mRNAs expressed unexpectedly and regulated by P and cAMP, include the insulin receptor, some neurotransmitter receptors, neuromodulators, the FSH receptor, inhibin/activin betaA subunit, inhibin alpha, and TNF-related apoptosis-inducing ligand (TRAIL). Expression of previously unrecognized genes regulated in decidualized human endometrial stromal cells suggests mechanisms not yet appreciated in the endometrium during decidualization. In addition, marked upregulation of cytokines, chemokines, growth factors, apoptosis modulators, and their receptors in decidualized stromal cells supports a major role for paracrine interactions between the stroma and other endogenous and transient cell populations within the endometrium and during early pregnancy.
Desiderata for an authoritative Representation of MeSH in RDF

The Semantic Web provides a framework for the integration of resources on the web, which facilitates information integration and interoperability. RDF is the main representation format for Linked Open Data (LOD). However, datasets are not always made available in RDF by their producers and the Semantic Web community has had to convert some of these datasets to RDF in order for these datasets to participate in the LOD cloud. As a result, the LOD cloud sometimes contains outdated, partial and even inaccurate RDF datasets. We review the LOD landscape for one of these resources, MeSH, and analyze the characteristics of six existing representations in order to identify desirable features for an authoritative version, for which we create a prototype. We illustrate the suitability of this prototype on three common use cases. NLM intends to release an authoritative representation of MeSH in RDF (beta version) in the Fall of 2014.
Trends and Challenges in Neuroengineering: Toward “Intelligent” Neuroprostheses through Brain-“Brain Inspired Systems” Communication

Future technologies aiming at restoring and enhancing organs function will intimately rely on near-physiological and energy-efficient communication between living and artificial biomimetic systems. Interfacing brain-inspired devices with the real brain is at the forefront of such emerging field, with the term “neurobiohybrids” indicating all those systems where such interaction is established. We argue that achieving a “high-level” communication and functional synergy between natural and artificial neuronal networks in vivo, will allow the development of a heterogeneous world of neurobiohybrids, which will include “living robots” but will also embrace “intelligent” neuroprostheses for augmentation of brain function. The societal and economical impact of intelligent neuroprostheses is likely to be potentially strong, as they will offer novel therapeutic perspectives for a number of diseases, and going beyond classical pharmaceutical schemes. However, they will unavoidably raise fundamental ethical questions on the intermingling between man and machine and more specifically, on how deeply it should be allowed that brain processing is affected by implanted “intelligent” artificial systems. Following this perspective, we provide the reader with insights on ongoing developments and trends in the field of neurobiohybrids. We address the topic also from a “community building” perspective, showing through a quantitative bibliographic analysis, how scientists working on the engineering of brain-inspired devices and brain-machine interfaces are increasing their interactions. We foresee that such trend preludes to a formidable technological and scientific revolution in brain-machine communication and to the opening of new avenues for restoring or even augmenting brain function for therapeutic purposes.
Gene coexpression measures in large heterogeneous samples using count statistics

Significance Coexpression analysis is one of the earliest tools for inferring gene associations using expression data but faces new challenges in this “big data” era. In a large heterogeneous dataset, it is likely that gene relationships may change or only exist in a subset of the samples, and they can be nonlinear or nonfunctional. We propose two new robust count statistics to account for local patterns in gene expression profiles. The statistics are generalizable to detect statistical dependence in other application domains. The performance of the statistics is evaluated against a number of popular bivariate dependence measures, showing favorable results. The asymptotic studies of the statistics provide an interesting addition to the combinatorics literature. With the advent of high-throughput technologies making large-scale gene expression data readily available, developing appropriate computational tools to process these data and distill insights into systems biology has been an important part of the “big data” challenge. Gene coexpression is one of the earliest techniques developed that is still widely in use for functional annotation, pathway analysis, and, most importantly, the reconstruction of gene regulatory networks, based on gene expression data. However, most coexpression measures do not specifically account for local features in expression profiles. For example, it is very likely that the patterns of gene association may change or only exist in a subset of the samples, especially when the samples are pooled from a range of experiments. We propose two new gene coexpression statistics based on counting local patterns of gene expression ranks to take into account the potentially diverse nature of gene interactions. In particular, one of our statistics is designed for time-course data with local dependence structures, such as time series coupled over a subregion of the time domain. We provide asymptotic analysis of their distributions and power, and evaluate their performance against a wide range of existing coexpression measures on simulated and real data. Our new statistics are fast to compute, robust against outliers, and show comparable and often better general performance.
Automated Concept Location Using Independent Component Analysis

Concept location techniques are designed to help isolate sections of source code that relate to specific concepts. Blind Signal Separation techniques like Singular Value Decomposition and Latent Semantic Indexing can be used as a way to identify related sections of source code. This paper explores a related technique called Independent Component Analysis that has the added benefit of identifying statistically independent signals in text, as opposed to ones that are just decorrelated. We describe a tool that we have developed to explore how ICA performs when analysing source code, and show how the technique can be used to perform unsupervised concept location.
Characterization of human rotavirus strains with G12 and P[9] detected in Japan

Two G12 human rotavirus strains, CP727 and CP1030, were isolated from the respective diarrheic stools of an infant and an adult in Japan. VP7 gene sequences of strains CP727 and CP1030 showed high identity with that of the G12 prototype strain L26, and with those of G12 strains reported recently from Thailand, the United States, and India. VP4 gene sequences of strains CP727 and CP1030 showed the highest identity with those of P[9] rotaviruses. In Northern blot hybridization, strains CP727 and CP1030 were found to be closely related to strain AU‐1 (G3P[9]); nine RNA segments hybridized to each other. Moreover, all segments each of the two Japanese G12 strains hybridized to those of the Thai G12 strain T152. These results suggest that Japanese G12 strains detected in this study are reassortants between a L26‐like strain and a strain in the AU‐1 genogroup. A similar reassortant was found in the Thai G12 strain T152. J. Med. Virol. 73:612–616, 2004. © 2004 Wiley‐Liss, Inc.
Image Augmentation-Based Food Recognition with Convolutional Neural Networks

: Image retrieval for food ingredients is important work, tremendously tiring, uninteresting, and expensive. Computer vision systems have extraordinary advancements in image retrieval with CNNs skills. But it is not feasible for small-size food datasets using convolutional neural networks directly. In this study, a novel image retrieval approach is presented for small and medium-scale food datasets, which both augments images utilizing image transformation techniques to enlarge the size of datasets, and promotes the average accuracy of food recognition with state-of-the-art deep learning technologies. First, typical image transformation techniques are used to augment food images. Then transfer learning technology based on deep learning is applied to extract image features. Finally, a food recognition algorithm is leveraged on extracted deep-feature vectors. The presented image-retrieval architecture is analyzed based on a small-scale food dataset which is composed of forty-one categories of food ingredients and one hundred pictures for each category. Extensive experimental results demonstrate the advantages of image-augmentation architecture for small and medium datasets using deep learning. The novel approach combines image augmentation, ResNet feature vectors, and SMO classification, and shows its superiority for food detection of small/medium-scale datasets with comprehensive experiments.
Ionic Channels as Targets for Drug Design: A Review on Computational Methods

Ion channels are involved in a broad range of physiological and pathological processes. The implications of ion channels in a variety of diseases, including diabetes, epilepsy, hypertension, cancer and even chronic pain, have signaled them as pivotal drug targets. Thus far, drugs targeting ion channels were developed without detailed knowledge of the molecular interactions between the lead compounds and the target channels. In recent years, however, the emergence of high-resolution structures for a plethora of ion channels paves the way for computer-assisted drug design. Currently, available functional and structural data provide an attractive platform to generate models that combine substrate-based and protein-based approaches. In silico approaches include homology modeling, quantitative structure-activity relationships, virtual ligand screening, similarity and pharmacophore searching, data mining, and data analysis tools. These strategies have been frequently used in the discovery and optimization of novel molecules with enhanced affinity and specificity for the selected therapeutic targets. In this review we summarize recent applications of in silico methods that are being used for the development of ion channel drugs.
Predicting Land Use Change

This paper proposes to measure the extent of the influence of transportation systems on land use change. With a set of high-resolution land use data for the Twin Cities, Minnesota, metropolitan region, logistic regression models of land use change are estimated for a 10-year period from 1990 to 2000. The models account for existing land use types, neighboring land uses, and transportation network variables that measure the physical proximity of highway networks, as well as the level of accessibility associated with a specific location. The models are estimated with and without the transportation variables and compared to assess the extent of their influence. Transportation-related variables exert some influence on changes to land use patterns, though not as much as variables representing existing and neighboring land uses.
Performance evaluation of battery balancing hardware

In this paper we evaluate the performance of seven proposed hardware topologies for balancing the cells in a battery pack. We consider four classes of battery balancing hardware; shunting, cell-to-stack, storage element, and dissipative hardware. We present models of these hardware topologies that capture the steady-state behavior of the balancing hardware dynamics. We evaluate the hardware topologies based on time required to balance the cells and energy dissipated during balancing. A linear programming based method for calculating the worst-case time to balance and energy dissipated during balance is provided. The number of linear programs required to compute both metrics grows exponentially with the number of cells. We show how to use symmetries to efficiently compute both metrics. Our approach scales well for large-scale battery packs and provides non intuitive solutions.
Missing clinical and behavioral health data in a large electronic health record (EHR) system

OBJECTIVE Recent massive investment in electronic health records (EHRs) was predicated on the assumption of improved patient safety, research capacity, and cost savings. However, most US health systems and health records are fragmented and do not share patient information. Our study compared information available in a typical EHR with more complete data from insurance claims, focusing on diagnoses, visits, and hospital care for depression and bipolar disorder. METHODS We included insurance plan members aged 12 and over, assigned throughout 2009 to a large multispecialty medical practice in Massachusetts, with diagnoses of depression (N = 5140) or bipolar disorder (N = 462). We extracted insurance claims and EHR data from the primary care site and compared diagnoses of interest, outpatient visits, and acute hospital events (overall and behavioral) between the 2 sources. RESULTS Patients with depression and bipolar disorder, respectively, averaged 8.4 and 14.0 days of outpatient behavioral care per year; 60% and 54% of these, respectively, were missing from the EHR because they occurred offsite. Total outpatient care days were 20.5 for those with depression and 25.0 for those with bipolar disorder, with 45% and 46% missing, respectively, from the EHR. The EHR missed 89% of acute psychiatric services. Study diagnoses were missing from the EHR's structured event data for 27.3% and 27.7% of patients. CONCLUSION EHRs inadequately capture mental health diagnoses, visits, specialty care, hospitalizations, and medications. Missing clinical information raises concerns about medical errors and research integrity. Given the fragmentation of health care and poor EHR interoperability, information exchange, and usability, priorities for further investment in health IT will need thoughtful reconsideration.