Miracle-dz/UniFAR-FTUs huggingface dataset:
Death and Cardiac Arrest in U.S. Triathlon Participants

TO THE EDITOR: I believe that Harris and colleagues (1) overlook the most obvious cause of deaths in triathlons: panic attacks followed by drowning or fatal arrhythmia. This was the conclusion I drew in an article I wrote in The Washington Post in 2011 that explored the unusual number of deathsat least 9that racing season (2). All but 1 of the deaths occurred in the swim segment. In my role as a science reporter for that publication, I attempted to contact relatives of all the victims. Survivors of 5 of the 8 swim-related victims were willing to talk. Four said that the victim had expressed anxiety about the swim segment. My hypothesis that panic attacks and not previously asymptomatic cardiac disease were the underlying cause of the deaths brought a flood of confirmatory messages from triathletes. Many said that they had experienced unprovoked feelings of panic during the swim segment even after years of racing (3). Given the rarity of these deaths, a firm explanation will never be found. However, circumstantial evidence strongly favors panic attacks. After doing triathlons for 22 years, I can say with confidence that most competitors train before races. They are not sedentary persons for whom vigorous exercise suddenly unmasks fatal heart disease. Further, most deaths (70% in Harris and colleagues' study) occur in the first, shortest, and least exertional eventthe swim segmentmaking arrhythmogenic ischemia from either coronary disease or myocardial hypertrophy an implausible explanation. Of note, 82% of deaths and cardiac arrests occur in water where the swimmer probably cannot see the bottom or easily get out of the water and is unlikely to have help nearby (lake/reservoir and ocean/gulf/bay/harbor in Harris and colleagues' study). These conditions are new and threatening for persons who have done all or most of their training in swimming pools. That more than one half of deaths occur in the shortest triathlons (sprint distance) and the death rate in triathlons is much higher than that in arguably more taxing marathons also suggest that something other than unrecognized heart disease is taking place. Rather than looking to prerace cardiovascular screening, researchers and race directors should study the incidence of panic attacks in triathlons and counsel competitors in self-rescue strategies as a way to prevent these tragic events.
Antimicrobial and Chemotactic Activity of Scorpion-Derived Peptide, ToAP2, against Mycobacterium massiliensis

Mycobacterium massiliense is a rapid growing, multidrug-resistant, non-tuberculous mycobacteria that is responsible for a wide spectrum of skin and soft tissue infections, as well as other organs, such as the lungs. Antimicrobial peptides had been described as broad-spectrum antimicrobial, chemotactic, and immunomodulator molecules. In this study we evaluated an antimicrobial peptide derived from scorpion Tityus obscurus as an anti-mycobacterial agent in vitro and in vivo. Bioinformatics analyses demonstrated that the peptide ToAP2 have a conserved region similar to several membrane proteins, as well as mouse cathelicidin. ToAP2 inhibited the growth of four M. massiliense strains (GO01, GO06, GO08, and CRM0020) at a minimal bactericidal concentration (MBC) of 200 µM. MBC concentration used to treat infected macrophages was able to inhibit 50% of the bacterial growth of all strains. ToAP2 treatment of infected mice with bacilli reduced the bacterial load in the liver, lung, and spleen, similarly to clarithromycin levels (90%). ToAP2 alone recruited monocytes (F4/80low Gr1), neutrophils (F4/80− Gr1), and eosinophils (F4/80+ Gr1+). ToAP2, together with M. massiliense infection, was able to increase F4/80low and reduce the percentage of F4/80high macrophages when compared with infected and untreated mice. ToAP2 has in vitro anti-microbial activity that is improved in vivo due to chemotactic activity.
Antitrypanosomal 8-Hydroxy-Naphthyridines Are Chelators of Divalent Transition Metals

The lack of information regarding the mechanisms of action (MoA) or specific molecular targets of phenotypically active compounds can prove a barrier to their development as chemotherapeutic agents. Here, we report the results of our orthogonal genetic, molecular, and biochemical studies to determine the MoA of a novel 7-substituted 8-hydroxy-1,6-naphthyridine (8-HNT) series that displays promising activity against Trypanosoma brucei and Leishmania donovani. ABSTRACT The lack of information regarding the mechanisms of action (MoA) or specific molecular targets of phenotypically active compounds can prove a barrier to their development as chemotherapeutic agents. Here, we report the results of our orthogonal genetic, molecular, and biochemical studies to determine the MoA of a novel 7-substituted 8-hydroxy-1,6-naphthyridine (8-HNT) series that displays promising activity against Trypanosoma brucei and Leishmania donovani. High-throughput loss-of-function genetic screens in T. brucei highlighted two probable zinc transporters associated with resistance to these compounds. These transporters localized to the parasite Golgi apparatus. Directed by these findings, the role of zinc and other divalent cations in the MoA of these compounds was investigated. 8-HNT compounds were found to directly deplete intracellular levels of Zn2+, while the addition of exogenous Zn2+ and Fe2+ reduced the potency of compounds from this series. Detailed biochemical analyses confirmed that 8-HNT compounds bind directly to a number of divalent cations, predominantly Zn2+, Fe2+, and Cu2+, forming 2:1 complexes with one of these cations. Collectively, our studies demonstrate transition metal depletion, due to chelation, as the MoA of the 8-HNT series of compounds. Strategies to improve the selectivity of 8-HNT compounds are discussed.
Blind Source Separation Using Temporal Correlation, Non-Gaussianity and Conditional Heteroscedasticity

Independent component analysis separates latent sources from a linear mixture by assuming sources are statistically independent. In real world applications, hidden sources are usually non-Gaussian and have dependence among samples. In such case, both attributes should be considered jointly to obtain a successful separation. To capture sample dependence, a latent source is sometimes modeled by autoregressive or moving average models with an independent and identically distributed error or residual. However, these models are limited by assuming only linear dependence among a source’s samples. This paper proposes a new blind source separation algorithm based on an autoregressive-autoregressive conditional heteroscedasticity (AR-ARCH) model, which captures linear correlations, non-Gaussianity, and squared residuals’ dependence. The AR part of the AR-ARCH model captures the correlation among samples. The ARCH part of the model captures the non-Gaussianity and nonlinear dependence among samples. The ARCH model also assumes the time-varying conditional variances for sources. We derive the Cramér Rao lower bound (CRLB) for the mixing matrix based on the AR-ARCH model. We perform simulations on both synthetic and real data. The results show that the proposed method outperforms the baseline algorithms especially for a small number of samples and approaches the CRLB.
Synchronous prediction of arousal and valence using LSTM network for affective video content analysis

The affect embedded in video data conveys high-level semantic information about the content and has direct impact on the understanding and perception of reviewers, as well as their emotional responses. Affective Video Content Analysis (AVCA) attempts to generate a direct mapping between video content and the corresponding affective states such as arousal and valence dimensions. Most existing studies establish the mapping for each dimension separately using knowledge-based rules or traditional classifiers such as Support Vector Machine (SVM). The inherent correlations between affective dimensions have largely been unexploited, which are anticipated to include important information for accurate prediction of affective dimensions. To address this issue, this paper presents an approach to predict arousal and valance dimensions synchronously using the Long Short Term Memory (LSTM) network. The approach extracts a set of low-level audio and visual features from video data and projects them synchronously into pairs of arousal and valence values using the LSTM network which automatically incorporates the correlations between arousal and valance dimensions. We evaluate the performance of the proposed approach on a dataset comprising video clips segmented from real-world resources such as film, drama, and news, and demonstrate its superior performance over the traditional SVM based method. The results provide one of the earliest preliminary evidence to the benefit of considering correlations between affective dimensions towards accurate AVCA.
Detecting Multiclass Emotions from Labeled Movie Scripts

Detecting human emotions will likely become a key component in future artificial intelligence (AI) systems, where the challenge lies in the precise discerning of negative emotions that require delicate responses such as anger and sadness. Existing sentiment tools, however, are mostly limited to dichotomous affect scales and are subject to positivity bias. To infer diverse negative emotions, this paper presents a multiclass emotion classifier that focus on negative emotions. By utilizing a rich set of both content and meta information from a labeled movie transcript, we make a novel finding that while negative emotions are hardly distinguishable from each other based on standard approaches, our non-lexical meta features remarkably increase the recall performance by 52% to 113% among the negative emotions. Our model evaluated in cross-validation studies and via human tagging demonstrate an improved performance compared to traditional baselines. This research presents a pilot study, based on small yet rich dataset, which envisions AI systems that can understand the complex negative feelings to better assist human-robot interactions.
Modeling Global Trade in Phosphate Rock within a Partial Equilibrium Framework

Against the background of combined population and consumption growth, the global sustainable development agenda foresees limits to the expansion of agricultural land. The application of fertilizer is necessary to replenish soil nutrients and keep crop yields high. Phosphate rock (PR) is the main raw material for the production of commercial phosphorus fertilizers. The international PR market is highly concentrated in terms of reserves and supply: a few countries export the major share of all PR traded globally. As many countries are highly dependent on phosphorus import, the modeling of international PR trade and thus exploration of what-if scenarios is of great interest. For modeling purposes, we employ the partial equilibrium framework. The model is driven by a subset of the United Nations (UN) Comtrade database at a yearly time step spanning the period 1997–2016. The only inputs to the model are slope coefficients of demand–supply curves. The transportation costs are internalized by creating a costs ensemble on the basis of historical data. While reasonably sensitive to its inputs, the model fits very well to reported global annual traded quantities and prices and considerably improves per-trade-partner quantity estimates as compared to simple period-averaging approaches. This is the first application of a partial equilibrium approach to global PR market modeling, including validation.
Modeling Math Identity and Math Success through Sentiment Analysis and Linguistic Features

A number of studies have demonstrated strong links between students' language features (as found in spoken and written production) and their math performance. However, no studies have examined links between the students’ language features and measures of their Math Identity. This project extends prior studies that use natural language processing (NLP) features to examine student language features and math performance, replicating their analyses. The study then uses NLP features to model students’ Math Identity. Specifically, the study compares performance on basic math skills within an online math tutoring system to both student language (as captured in emails to a virtual pedagogical agent) and to survey measures of Math Identity (math self concept, interest, and value). Language features were analyzed by a number of NLP tools that extracted information related to text cohesion, lexical sophistication, and sentiment. The findings indicate weak to medium relationships between math scores and Math Identity and language features were able to predict a significant amount of the variance in each Math Identity variable and in math scores. The potential for these measures to inform interventions for students with lower Math Identity is discussed.
Developing a Model

Model development may seem a daunting task for the novice. The purpose of this article is to illustrate the steps of model development applied to a real-life phenomenon using an inductive theory-generating research approach. The value of the illustration is that nurse researchers can follow the application of the process as a point of departure for their own work. A logical stepwise discussion is followed for empirical theory development. The logical thought process from identifying the phenomenon to describing the model as a visual metaphor of the phenomenon is illustrated.
Analytical evaluation of matrix elements of electromagnetic integral equations with RWG basis functions for arbitrarily oriented pairs of triangular surface elements

We present an approach to calculation of matrix elements for arbitrarily oriented planar surface elements, based on reduction of the surface integrals to line integrals over perimeters of the elements. The described method is a generalization of our previous developments applicable to parallel geometry elements; it utilizes a representation of the Helmholtz-equation Green function in terms of an auxiliary function acted upon by a differential operator involving tangential gradients in two different planes. Integration by parts allows then reduction of surface-to line integrals with nonsingular integrands. The latter integrals can be either evaluated as standard quadratures or expressed analytically, in every order of expansion in the wave number k, in closed form. The method can be applied to all operators arising in Maxwell equations and in similar problems in acoustics and elastodynamics, offering significant advantages in accuracy and the computational cost.
TomoEED: fast edge‐enhancing denoising of tomographic volumes

Summary: TomoEED is an optimized software tool for fast feature‐preserving noise filtering of large 3D tomographic volumes on CPUs and GPUs. The tool is based on the anisotropic nonlinear diffusion method. It has been developed with special emphasis in the reduction of the computational demands by using different strategies, from the algorithmic to the high performance computing perspectives. TomoEED manages to filter large volumes in a matter of minutes in standard computers. Availability and implementation: TomoEED has been developed in C. It is available for Linux platforms at http://www.cnb. csic.es/%7ejjfernandez/tomoeed. Supplementary information: Supplementary data are available at Bioinformatics online.
Defining the Role of Stress Granules in Innate Immune Suppression by the Herpes Simplex Virus 1 Endoribonuclease VHS

ABSTRACT In response to virus-induced shutoff host protein synthesis, dynamic aggregates containing mRNA, RNA-binding proteins and translation factors termed stress granules (SGs) often accumulate within the cytoplasm. SGs typically form following phosphorylation and inactivation of the eukaryotic translation initiation factor 2α (eIF2α), a substrate of the double-stranded RNA (dsRNA)-activated kinase protein kinase R (PKR). The detection of innate immune sensors and effectors like PKR at SGs suggests a role in pathogen nucleic acid sensing. However, the functional importance of SGs in host innate responses is unclear and has primarily been examined in response to infection with select RNA viruses. During infection with the DNA virus herpes simplex virus 1 (HSV-1), the virus-encoded virion host shutoff (VHS) endoribonuclease is required to restrict interferon production, PKR activation, and SG formation, although the relationship between these activities remains incompletely understood. Here, we show that in cells infected with a VHS-deficient HSV-1 (ΔVHS) dsRNA accumulated and localized to SGs. Surprisingly, formation of dsRNA and its concentration at SGs was not required for beta interferon mRNA induction, indicating that suppression of type I interferon induction by VHS does not stem from its control of dsRNA accumulation. Instead, STING signaling downstream of cGMP-AMP synthase (cGAS)-dependent DNA sensing is required for beta interferon induction. In contrast, significantly less PKR activation is observed when SG assembly is disrupted by ISRIB, an inhibitor of phosphorylated eIF2α-mediated translation repression, or depleting SG scaffolding proteins G3BP1 or TIA1. This demonstrates that PKR activation is intimately linked to SG formation and that SGs form important hubs to potentiate PKR activation during infection. IMPORTANCE Formation of cytoplasmic stress granules that are enriched for innate immune sensors and effectors is suppressed during many viral infections. It is unclear, however, to what extent this is a side effect of viral efforts to maintain protein synthesis or intentional disruption of a hub for innate immune sensing. In this study, we utilize a herpes simplex virus 1 mutant lacking the RNA nuclease VHS which upon infection induces SGs, PKR activation, and beta interferon to address this question. We show that dsRNA is localized to SGs and that SGs can function to promote PKR activation in the context of a DNA virus infection, but we find no evidence to support their importance for interferon induction during HSV-1 infection.
Soybean genomics: Efforts to reveal the complex genome

Soybean (Glycine max (L.) Merrill) is the most important leguminous crop in the world due to its highest content of high-quality protein for food and feed, and its capacity of oil production for food and industrial materials. Physiologically functional constituents in soybean seeds contribute significantly to human health, and because of its characteristic symbiosis with root bacteroids, soybean supplies nitrogen to soil. Although soybean has a relatively large and complex genome, several methods for genome analysis have been applied to obtain improved genotypes and to elucidate the special function of soybean. Research activities undertaken to analyze soybean genome structure and function include the construction of high density linkage maps, integration of trait loci into linkage maps, development of bacterial artificial chromosome libraries, physical mapping, large collection of expressed sequence tags, development of large populations of mutagenized plants and large-scale sequencing of gene-rich regions.
A Temporal and Topic-Aware Recommender Model

Individuals' interests and concerning topics are generally changing over time, with a strong impact on their behaviors in social media. Accordingly, designing an intelligent recommender system which can adapt with the temporal characters of both factors becomes a significant research task. In this paper, we suppose that users' current interests and topics are transferred from the previous time step with a Markov property. Based on this idea, we focus on designing a dynamic recommender model based on collective factorization, named Temporal and Topic-Aware Recommender Model (TTARM), which can express the transition process of both user interests and relevant topics in fine granularity. It is a hybrid recommender model which joint Collaborative Filtering (CF) and Content-based recommender method, thus can produce promising recommendations about both existing and newly published items. Experimental results on two real-life data sets from CiteULike and MovieLens demonstrate the effectiveness of our proposed model.
Atlantic Tropical Cyclogenetic Processes During Sop-3 Namma in the Geos-5 Global Data Assimilation and Forecast System

Abstract This article investigates the role of the Saharan air layer (SAL) in tropical cyclogenetic processes associated with a nondeveloping and a developing African easterly wave observed during the Special Observation Period (SOP-3) phase of the 2006 NASA African Monsoon Multidisciplinary Analyses (NAMMA). The two waves are chosen because they both interact heavily with Saharan air. A global data assimilation and forecast system, the NASA Goddard Earth Observing System, version 5 (GEOS-5), is being run to produce a set of high-quality global analyses, inclusive of all observations used operationally but with additional satellite information. In particular, following previous works by the same authors, the quality-controlled data from the Atmospheric Infrared Sounder (AIRS) used to produce these analyses have a better coverage than the one adopted by operational centers. From these improved analyses, two sets of 31 five-day high-resolution forecasts, at horizontal resolutions of both half and quarter de.
Population status and diet of the Yellow-legged Gull in the Azores.

During 2004 a census of the Yellow-legged Gull (Larus michahellis atlantis) was conducted in the Azores to assess its present status in the archipelago. The census yielded an estimate of 4249 breeding pairs, an increase by almost 60% since the previous survey conducted in 1984. Gulls were detected on a total of 14 islets and/or sea stacks and are probably limiting the distribution of terns in the archipelago. Out of 37 gull breeding sites found during the 1984 and 2004 surveys, 24 were located within 1 km of tern colonies. All the gull colonies were coastal except Lagoa do Fogo on Sao Miguel Island. Pellet analysis indicated that gulls from all the colonies feed on refuse, but the percentage of pellets containing refuse on islands with small human populations was less than half than that of islands with more than 55000 inhabitants. Gulls have no direct competitors in the Azores and benefit from an increase in refuse production. The growing number and size of rubbish dumps over the last two decades has probably contributed to the large increase in the Azorean population.
State-ofthe-Art in Immobilized Polymer Stationary Phases for High-Performance Liquid Chromatography

O aumento da estabilidade química e a redução da alta atividade de grupos residuais têm sido, nas últimas quatro décadas, os principais focos de pesquisas na preparação de fases estacionárias para cromatografia líquida de alta eficiência na modalidade de fase reversa (CLAE-FR). Novas e eficientes estratégias de modificação da superfície da sílica e a introdução de novos materiais como suportes cromatográficos têm minimizado estas limitações e possibilitado uma crescente popularização da CLAE-FR. Neste trabalho será apresentada uma visão geral do estado da arte de uma das estratégias de preparo de fases estacionárias que vem contornando tais limitações da CLAE-FR; as fases estacionárias baseadas na imobilização de polímeros orgânicos pré-sintetizados sobre partículas de sílica e sílica metalizada (zirconizada e titanizada). Uma consideração especial será dada ao desenvolvimento destas fases estacionárias ocorrido no Brasil nos últimos 15 anos.