Abstract: This paper provides an overview and a tutorial of the BPP program, which is a Bayesian MCMC program for analyzing multi-locus genomic sequence data under the multispecies coalescent model. An example dataset of five nuclear loci from the East Asian brown frogs is used to illustrate four different analyses, including estimation of species divergence time and population size parameters under the multispecies coalescent model on a fixed species phylogeny (A00), species tree estimation when the assignment and species delimitation are fixed (A01), species delimitation using a fixed guide tree (A10), and joint species delimitation and species-tree estimation or unguided species delimitation (A11). For the joint analysis (A11), two new priors are introduced, which assign uniform probabilities for the different numbers of delimited species, which may be useful when assignment, species delimitation, and species phylogeny are all inferred in one joint analysis. The paper ends with a discussion of the assumptions, the strengths and weaknesses of the BPP analysis [Current Zoology 61 (5) : – , 2015 ].

Abstract: The United Nations declared 2016 as the International Year of Pulses (grain legumes) under the banner ‘nutritious seeds for a sustainable future’. A second green revolution is required to ensure food and nutritional security in the face of global climate change. Grain legumes provide an unparalleled solution to this problem because of their inherent capacity for symbiotic atmospheric nitrogen fixation, which provides economically sustainable advantages for farming. In addition, a legume-rich diet has health benefits for humans and livestock alike. However, grain legumes form only a minor part of most current human diets, and legume crops are greatly under-used. Food security and soil fertility could be significantly improved by greater grain legume usage and increased improvement of a range of grain legumes. The current lack of coordinated focus on grain legumes has compromised human health, nutritional security and sustainable food production.

Abstract: The development of a vaccine against SARS-CoV-2 infection is on the way. To prepare for public availability, the acceptability of a hypothetical COVID-19 vaccine and willingness to pay (WTP) were a...

Abstract: Purpose: A first-in-human pilot safety and feasibility trial evaluating chimeric antigen receptor (CAR)–engineered, autologous primary human CD8 + cytotoxic T lymphocytes (CTL) targeting IL13Rα2 for the treatment of recurrent glioblastoma (GBM). Experimental Design: Three patients with recurrent GBM were treated with IL13(E13Y)-zetakine CD8 + CTL targeting IL13Rα2. Patients received up to 12 local infusions at a maximum dose of 10 8 CAR-engineered T cells via a catheter/reservoir system. Results: We demonstrate the feasibility of manufacturing sufficient numbers of autologous CTL clones expressing an IL13(E13Y)-zetakine CAR for redirected HLA-independent IL13Rα2-specific effector function for a cohort of patients diagnosed with GBM. Intracranial delivery of the IL13-zetakine + CTL clones into the resection cavity of 3 patients with recurrent disease was well-tolerated, with manageable temporary brain inflammation. Following infusion of IL13-zetakine + CTLs, evidence for transient anti-glioma responses was observed in 2 of the patients. Analysis of tumor tissue from 1 patient before and after T-cell therapy suggested reduced overall IL13Rα2 expression within the tumor following treatment. MRI analysis of another patient indicated an increase in tumor necrotic volume at the site of IL13-zetakine + T-cell administration. Conclusions: These findings provide promising first-in-human clinical experience for intracranial administration of IL13Rα2-specific CAR T cells for the treatment of GBM, establishing a foundation on which future refinements of adoptive CAR T-cell therapies can be applied. Clin Cancer Res; 21(18); 4062–72. ©2015 AACR .

Abstract: This review addresses the recent developments of the, processing of cellulose nanocomposites, focusing on the most used techniques, including solution casting, melt-processing of thermoplastic cell ...

Abstract: Importance Delirium is defined as an acute disorder of attention and cognition. It is a common, serious, and often fatal condition among older patients. Although often underrecognized, delirium has serious adverse effects on the individual’s function and quality of life, as well as broad societal effects with substantial health care costs. Objective To summarize the current state of the art in diagnosis and treatment of delirium and to highlight critical areas for future research to advance the field. Evidence Review Search of Ovid MEDLINE, Embase, and the Cochrane Library for the past 6 years, from January 1, 2011, until March 16, 2017, using a combination of controlled vocabulary and keyword terms. Since delirium is more prevalent in older adults, the focus was on studies in elderly populations; studies based solely in the intensive care unit (ICU) and non–English-language articles were excluded. Findings Of 127 articles included, 25 were clinical trials, 42 cohort studies, 5 systematic reviews and meta-analyses, and 55 were other categories. A total of 11 616 patients were represented in the treatment studies. Advances in diagnosis have included the development of brief screening tools with high sensitivity and specificity, such as the 3-Minute Diagnostic Assessment; 4 A’s Test; and proxy-based measures such as the Family Confusion Assessment Method. Measures of severity, such as the Confusion Assessment Method–Severity Score, can aid in monitoring response to treatment, risk stratification, and assessing prognosis. Nonpharmacologic approaches focused on risk factors such as immobility, functional decline, visual or hearing impairment, dehydration, and sleep deprivation are effective for delirium prevention and also are recommended for delirium treatment. Current recommendations for pharmacologic treatment of delirium, based on recent reviews of the evidence, recommend reserving use of antipsychotics and other sedating medications for treatment of severe agitation that poses risk to patient or staff safety or threatens interruption of essential medical therapies. Conclusions and Relevance Advances in diagnosis can improve recognition and risk stratification of delirium. Prevention of delirium using nonpharmacologic approaches is documented to be effective, while pharmacologic prevention and treatment of delirium remains controversial.

Abstract: The Coronavirus disease 2019 (COVID-19) pandemic has significantly impacted and devastated the world. As the infection spreads, the projected mortality and economic devastation are unprecedented. In particular, racial and ethnic minorities may be at a particular disadvantage as many already assume the status of a marginalized group. Black Americans have a long-standing history of disadvantage and are in a vulnerable position to experience the impact of this crisis and the myth of Black immunity to COVID-19 is detrimental to promoting and maintaining preventative measures. We are the first to present the earliest available data in the peer-reviewed literature on the racial and ethnic distribution of COVID-19-confirmed cases and fatalities in the state of Connecticut. We also seek to explode the myth of Black immunity to the virus. Finally, we call for a National Commission on COVID-19 Racial and Ethnic Health Disparities to further explore and respond to the unique challenges that the crisis presents for Black and Brown communities.

Abstract: Metagenomics experiments often characterize microbial communities by sequencing the ribosomal 16S and ITS regions. Taxonomy prediction is a fundamental step in such studies. The SINTAX algorithm predicts taxonomy by using k-mer similarity to identify the top hit in a reference database and provides bootstrap confidence for all ranks in the prediction. SINTAX achieves comparable or better accuracy to the RDP Naive Bayesian Classifier with a simpler algorithm that does not require training. Most tested methods are shown to have high rates of over-classification errors where novel taxa are incorrectly predicted to have known names.

Abstract: The physical realization of a quantum computer requires built-in error-correcting codes that compensate the disruption of quantum information arising from noise. Here, the authors demonstrate a quantum error detection scheme for arbitrary single-qubit errors on a four superconducting qubit lattice.

Abstract: In this paper we discuss the anatomy of frequency-domain gravitational-wave signals from nonprecessing black-hole coalescences with the goal of constructing accurate phenomenological waveform models. We first present new numerical-relativity simulations for mass ratios up to 18, including spins. From a comparison of different post-Newtonian approximants with numerical-relativity data we select the uncalibrated SEOBNRv2 model as the most appropriate for the purpose of constructing hybrid post-Newtonian/numerical-relativity waveforms, and we discuss how we prepare time-domain and frequency-domain hybrid data sets. We then use our data together with results in the literature to calibrate simple explicit expressions for the final spin and radiated energy. Equipped with our prediction for the final state we then develop a simple and accurate merger-ringdown model based on modified Lorentzians in the gravitational-wave amplitude and phase, and we discuss a simple method to represent the low frequency signal augmenting the TaylorF2 post-Newtonian approximant with terms corresponding to higher orders in the post-Newtonian expansion. We finally discuss different options for modelling the small intermediate frequency regime between inspiral and merger ringdown. A complete phenomenological model based on the present work is presented in a companion paper

Abstract: BackgroundThe cardiovascular safety of celecoxib, as compared with nonselective nonsteroidal antiinflammatory drugs (NSAIDs), remains uncertain. MethodsPatients who required NSAIDs for osteoarthritis or rheumatoid arthritis and were at increased cardiovascular risk were randomly assigned to receive celecoxib, ibuprofen, or naproxen. The goal of the trial was to assess the noninferiority of celecoxib with regard to the primary composite outcome of cardiovascular death (including hemorrhagic death), nonfatal myocardial infarction, or nonfatal stroke. Noninferiority required a hazard ratio of 1.12 or lower, as well as an upper 97.5% confidence limit of 1.33 or lower in the intention-to-treat population and of 1.40 or lower in the on-treatment population. Gastrointestinal and renal outcomes were also adjudicated. ResultsA total of 24,081 patients were randomly assigned to the celecoxib group (mean [±SD] daily dose, 209±37 mg), the naproxen group (852±103 mg), or the ibuprofen group (2045±246 mg) for a mean tr...

Abstract: Background The time to initiate intravenous thrombolysis for acute ischemic stroke is generally limited to within 4.5 hours after the onset of symptoms. Some trials have suggested that the...

Abstract: European Union Seventh Framework Programme [FP7/2007-2013, 306000]; Spanish Ministry of Science and Innovation [MICINN, BIO2008-04638-E], in the framework of ERA-Net Pathogenomics; MICINN [DPI2008-06880-C03-03/DPI]. Funding for open access charge: University of Florida, Publication Funds.

Abstract: Robust perception-action models should be learned from training data with diverse visual appearances and realistic behaviors, yet current approaches to deep visuomotor policy learning have been generally limited to in-situ models learned from a single vehicle or a simulation environment. We advocate learning a generic vehicle motion model from large scale crowd-sourced video data, and develop an end-to-end trainable architecture for learning to predict a distribution over future vehicle egomotion from instantaneous monocular camera observations and previous vehicle state. Our model incorporates a novel FCN-LSTM architecture, which can be learned from large-scale crowd-sourced vehicle action data, and leverages available scene segmentation side tasks to improve performance under a privileged learning paradigm.

Abstract: Purpose: This article illustrates how successful leaders combine the too often dichotomized practices of transformational and instructional leadership in different ways across different phases of their schools’ development in order to progressively shape and “layer” the improvement culture in improving students’ outcomes. Research Methods: Empirical data were drawn from a 3-year mixed-methods national study (“Impact Study”) that investigated associations between the work of principals in effective and improving primary and secondary schools in England and student outcomes as defined (but not confined) by their national examination and assessment results over 3 years. The research began with a critical survey of the extant literature, followed by a national survey that explored principals’ and key staff’s perceptions of school improvement strategies and actions that they believed had helped foster better student attainment. This was complemented by multiperspective in-depth case studies of a subsample of 20 schools. Findings: The research provides new empirical evidence of how successful principals directly and indirectly achieve and sustain improvement over time through combining both transformational and instructional leadership strategies. The findings show that schools’ abilities to improve and sustain effectiveness over the long term are not primarily the result of the principals’ leadership style but of their understanding and diagnosis of the school’s needs and their application of clearly articulated, organizationally shared educational values through multiple combinations and accumulations of time and context-sensitive strategies that are “layered” and progressively embedded in the school’s work, culture, and achievements. Implications: Mixed-methods research designs are likely to provide finer grained, more nuanced evidence-based understandings of the leadership roles and behaviors of principals who achieve and sustain educational outcomes in schools than single lens quantitative analyses, meta-analyses, or purely qualitative approaches. The findings themselves provide support for more differentiated, context sensitive training and development for aspiring and serving principals.

Abstract: Huntington's disease (HD) is a fully penetrant neurodegenerative disease caused by a dominantly inherited CAG trinucleotide repeat expansion in the huntingtin gene on chromosome 4. In Western populations HD has a prevalence of 10.6-13.7 individuals per 100 000. It is characterized by cognitive, motor and psychiatric disturbance. At the cellular level mutant huntingtin results in neuronal dysfunction and death through a number of mechanisms, including disruption of proteostasis, transcription and mitochondrial function and direct toxicity of the mutant protein. Early macroscopic changes are seen in the striatum with involvement of the cortex as the disease progresses. There are currently no disease modifying treatments; therefore supportive and symptomatic management is the mainstay of treatment. In recent years there have been significant advances in understanding both the cellular pathology and the macroscopic structural brain changes that occur as the disease progresses. In the last decade there has been a large growth in potential therapeutic targets and clinical trials. Perhaps the most promising of these are the emerging therapies aimed at lowering levels of mutant huntingtin. Antisense oligonucleotide therapy is one such approach with clinical trials currently under way. This may bring us one step closer to treating and potentially preventing this devastating condition.

Abstract: MicroRNAs (miRNAs) represent a class of small, non-coding RNAs with the main roles of regulating mRNA through its degradation and adjusting protein levels. In recent years, extraordinary progress has been made in terms of identifying the origin and exact functions of miRNA, focusing on their potential use in both the research and the clinical field. This review aims at improving the current understanding of these molecules and their applicability in the medical field. A thorough analysis of the literature consulting resources available in online databases such as NCBI, PubMed, Medline, ScienceDirect, and UpToDate was performed. There is promising evidence that in spite of the lack of standardized protocols regarding the use of miRNAs in current clinical practice, they constitute a reliable tool for future use. These molecules meet most of the required criteria for being an ideal biomarker, such as accessibility, high specificity, and sensitivity. Despite present limitations, miRNAs as biomarkers for various conditions remain an impressive research field. As current techniques evolve, we anticipate that miRNAs will become a routine approach in the development of personalized patient profiles, thus permitting more specific therapeutic interventions.

Abstract: Rationale: Tropospheric ozone (O3) is potentially associated with cardiovascular disease risk and premature death. Results from long-term epidemiological studies on O3 are scarce and inconclusive.Objectives: In this study, we examined associations between chronic ambient O3 exposure and all-cause and cause-specific mortality in a large cohort of U.S. adults.Methods: Cancer Prevention Study II participants were enrolled in 1982. A total of 669,046 participants were analyzed, among whom 237,201 deaths occurred through 2004. We obtained estimates of O3 concentrations at the participant’s residence from a hierarchical Bayesian space–time model. Estimates of fine particulate matter (particulate matter with an aerodynamic diameter of up to 2.5 μm [PM2.5]) and NO2 concentrations were obtained from land use regression. Cox proportional hazards regression models were used to examine mortality associations adjusted for individual- and ecological-level covariates.Measurements and Main Results: In single-pollutant mo...

Abstract: The systemic expression of the bile acid (BA) sensor farnesoid X receptor (FXR) has led to promising new therapies targeting cholesterol metabolism, triglyceride production, hepatic steatosis and biliary cholestasis. In contrast to systemic therapy, bile acid release during a meal selectively activates intestinal FXR. By mimicking this tissue-selective effect, the gut-restricted FXR agonist fexaramine (Fex) robustly induces enteric fibroblast growth factor 15 (FGF15), leading to alterations in BA composition, but does so without activating FXR target genes in the liver. However, unlike systemic agonism, we find that Fex reduces diet-induced weight gain, body-wide inflammation and hepatic glucose production, while enhancing thermogenesis and browning of white adipose tissue (WAT). These pronounced metabolic improvements suggest tissue-restricted FXR activation as a new approach in the treatment of obesity and metabolic syndrome.

Abstract: Nearly 60 years ago thalidomide was prescribed to treat morning sickness in pregnant women. What followed was the biggest man‐made medical disaster ever, where over 10,000 children were born with a range of severe and debilitating malformations. Despite this, the drug is now used successfully to treat a range of adult conditions, including multiple myeloma and complications of leprosy. Tragically, a new generation of thalidomide damaged children has been identified in Brazil. Yet, how thalidomide caused its devastating effects in the forming embryo remains unclear. However, studies in the past few years have greatly enhanced our understanding of the molecular mechanisms the drug. This review will look at the history of the drug, and the range and type of damage the drug caused, and outline the mechanisms of action the drug uses including recent molecular advances and new findings. Some of the remaining challenges facing thalidomide biologists are also discussed. Birth Defects Research (Part C) 105:140–156, 2015. © 2015 The Authors Birth Defects Research Part C: Embryo Today: Reviews Published by Wiley Periodicals, Inc.

Abstract: Dye-sensitized solar cells (DSSCs) belong to the group of thin-film solar cells which have been under extensive research for more than two decades due to their low cost, simple preparation methodology, low toxicity and ease of production. Still, there is lot of scope for the replacement of current DSSC materials due to their high cost, less abundance, and long-term stability. The efficiency of existing DSSCs reaches up to 12%, using Ru(II) dyes by optimizing material and structural properties which is still less than the efficiency offered by first- and second-generation solar cells, i.e., other thin-film solar cells and Si-based solar cells which offer ~ 20–30% efficiency. This article provides an in-depth review on DSSC construction, operating principle, key problems (low efficiency, low scalability, and low stability), prospective efficient materials, and finally a brief insight to commercialization.

Abstract: If our overuse and misuse of antibiotics is not halted now, about 10 million people will die annually from drug-resistant bacterial infections within 35 years. The hammer blow will fall hardest on Africa and Asia, accounting for 4.1 and 4.7 million deaths, respec tively, and the world’s economy will lose more than 7% of its gross domestic product (USD210 trillion) by 2050. These numbers should make people sit up, listen and change behaviour.

Abstract: Automatically describing an image with a natural language has been an emerging challenge in both fields of computer vision and natural language processing. In this paper, we present Long Short-Term Memory with Attributes (LSTM-A) - a novel architecture that integrates attributes into the successful Convolutional Neural Networks (CNNs) plus Recurrent Neural Networks (RNNs) image captioning framework, by training them in an end-to-end manner. Particularly, the learning of attributes is strengthened by integrating inter-attribute correlations into Multiple Instance Learning (MIL). To incorporate attributes into captioning, we construct variants of architectures by feeding image representations and attributes into RNNs in different ways to explore the mutual but also fuzzy relationship between them. Extensive experiments are conducted on COCO image captioning dataset and our framework shows clear improvements when compared to state-of-the-art deep models. More remarkably, we obtain METEOR/CIDEr-D of 25.5%/100.2% on testing data of widely used and publicly available splits in [10] when extracting image representations by GoogleNet and achieve superior performance on COCO captioning Leaderboard.

Abstract: Dye-sensitized solar cells (DSSCs) are regarded as prospective solar cells for the next generation of photovoltaic technologies and have become research hotspots in the PV field. The counter electrode, as a crucial component of DSSCs, collects electrons from the external circuit and catalyzes the redox reduction in the electrolyte, which has a significant influence on the photovoltaic performance, long-term stability and cost of the devices. Solar cells, dye-sensitized solar cells, as well as the structure, principle, preparation and characterization of counter electrodes are mentioned in the introduction section. The next six sections discuss the counter electrodes based on transparency and flexibility, metals and alloys, carbon materials, conductive polymers, transition metal compounds, and hybrids, respectively. The special features and performance, advantages and disadvantages, preparation, characterization, mechanisms, important events and development histories of various counter electrodes are presented. In the eighth section, the development of counter electrodes is summarized with an outlook. This article panoramically reviews the counter electrodes in DSSCs, which is of great significance for enhancing the development levels of DSSCs and other photoelectrochemical devices.

Abstract: It was suggested more than thirty years ago that Watson-Crick base pairing might be used for the rational design of nanometre-scale structures from nucleic acids. Since then, and especially since the introduction of the origami technique, DNA nanotechnology has enabled increasingly more complex structures. But although general approaches for creating DNA origami polygonal meshes and design software are available, there are still important constraints arising from DNA geometry and sense/antisense pairing, necessitating some manual adjustment during the design process. Here we present a general method of folding arbitrary polygonal digital meshes in DNA that readily produces structures that would be very difficult to realize using previous approaches. The design process is highly automated, using a routeing algorithm based on graph theory and a relaxation simulation that traces scaffold strands through the target structures. Moreover, unlike conventional origami designs built from close-packed helices, our structures have a more open conformation with one helix per edge and are therefore stable under the ionic conditions usually used in biological assays.

Abstract: Sequences have become first class citizens in supervised learning thanks to the resurgence of recurrent neural networks. Many complex tasks that require mapping from or to a sequence of observations can now be formulated with the sequence-to-sequence (seq2seq) framework which employs the chain rule to efficiently represent the joint probability of sequences. In many cases, however, variable sized inputs and/or outputs might not be naturally expressed as sequences. For instance, it is not clear how to input a set of numbers into a model where the task is to sort them; similarly, we do not know how to organize outputs when they correspond to random variables and the task is to model their unknown joint probability. In this paper, we first show using various examples that the order in which we organize input and/or output data matters significantly when learning an underlying model. We then discuss an extension of the seq2seq framework that goes beyond sequences and handles input sets in a principled way. In addition, we propose a loss which, by searching over possible orders during training, deals with the lack of structure of output sets. We show empirical evidence of our claims regarding ordering, and on the modifications to the seq2seq framework on benchmark language modeling and parsing tasks, as well as two artificial tasks -- sorting numbers and estimating the joint probability of unknown graphical models.

Abstract: With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand, and anticipate human behavior becomes increasingly important. Spec...