Miracle-dz/UniFAR-FTUs huggingface dataset:
Presentation of COVID-19 in a liver transplant recipient

Diarrhea is common after liver transplant. Although the majority of episodes are experienced during the first year of transplantation, they can occur any time after the procedure. Diarrhea can impose a major risk of morbidity and mortality on transplanted patients; so a careful evaluation is required to manage it. There are few studies highlighting the gastrointestinal manifestations of COVID-19, in particular among immunosuppressed patients. The predominant respiratory symptoms of coronavirus cause GI aspects of the virus to be overlooked. This study represents a young woman with a history of liver transplant who was referred to the hospital because of diarrhea, fever, and abdominal pain attributed to coronavirus infection.
Distributed Reinforcement Learning is a Dataflow Problem

Researchers and practitioners in the field of reinforcement learning (RL) frequently leverage parallel computation, which has led to a plethora of new algorithms and systems in the last few years. In this paper, we re-examine the challenges posed by distributed RL and try to view it through the lens of an old idea: distributed dataflow. We show that viewing RL as a dataflow problem leads to highly composable and performant implementations. We propose AnonFlow, a hybrid actor-dataflow programming model for distributed RL, and validate its practicality by porting the full suite of algorithms in AnonLib, a widely-adopted distributed RL library.
Perceived Barriers and Preventive Measures of COVID-19 Among Healthcare Providers in Debretabor, North Central Ethiopia, 2020

Background Healthcare professionals are at the frontline facing the pandemic. Since the pandemic is new with sophisticated needs of resources, identifying perceived barriers of the healthcare institutions has an important input for the government in slowing the viral spread and prevents healthcare professionals from infection. Objective The study was aimed to assessperceived barriers and preventive measures of corona virus disease among healthcare providers in Debretabor Town, north central Ethiopia. Methods An institution-based cross-sectional survey was conducted among healthcare providers working in Debretabor town, north central Ethiopia from May 1 to 15, 2020. Data were collected using a self-administered questionnaire. Data were coded, entered, cleaned, and checked using Epi data statistical software version 4.2. 0.0 and analysis using STATA Version 14 statistical software. Descriptive statistics of different variables were presented in figures, text, and tables. Chi-square (chi2) test of independence was considered to examine the nonparametric association of factors with preventive practice, and association was considered at a P-value of less than 0.05. Results A total of 183 healthcare providers participated in the survey, of which 67.76% were males, and 80.87% were in the age range of 25–35 years. About 45% of participants were nurses. In the current study, overall preventive practice was 68.3%. Despite recommendations by the World Health Organization, avoidance of outdoor, physical distancing, and avoiding meeting a person with a coughwere not implemented by 32.24%, 24.18%, and 13.11% of healthcare providers, respectively. Insufficient training, lack of policy, less commitment to infection control, and limitations of resources were perceived as major barriers for practicing preventive measures during the pandemic. Conclusion Preventive measures taken by healthcare providers were poor. Multiple barriers, such as insufficient training related to the pandemic, limitation of resources, and less commitment of healthcare providers to infection prevention measures were reported. So, the government should work on reducing the pandemic propagation by fulfilling the barriers using different means or strategies.
Recent Progress in Appearance-based Action Recognition

Action recognition, which is formulated as a task to identify various human actions in a video, has attracted increasing interest from computer vision researchers due to its importance in various applications. Recently, appearance-based methods have achieved promising progress towards accurate action recognition. In general, these methods mainly fulfill the task by applying various schemes to model spatial and temporal visual information effectively. To better understand the current progress of appearance-based action recognition, we provide a comprehensive review of recent achievements in this area. In particular, we summarise and discuss several dozens of related research papers, which can be roughly divided into four categories according to different appearance modelling strategies. The obtained categories include 2D convolutional methods, 3D convolutional methods, motion representation-based methods, and context representation-based methods. We analyse and discuss representative methods from each category, comprehensively. Empirical results are also summarised to better illustrate cutting-edge algorithms. We conclude by identifying important areas for future research gleaned from our categorisation.
Midinfrared GaInSb∕AlGaInSb quantum well laser diodes grown on GaAs

The aluminium-gallium-indium-antimonide (Al x Ga y In\({}_{1-x-y}\)Sb) material system offers great promise for efficient laser diode operation across the 3 to 5 μm wavelength range. It offers an excellent compromise between the requirements for good electronic and optical confinement and those for low series resistance. In addition, the use of an active region comprising compressively strained Type-I quantum wells (QWs) is predicted to lead to increased gain, which leads to lower threshold current densities and hence reduced non-radiative Auger recombination. In this paper a review of recent progress in the development of this material system is given, including the demonstration of multi-quantum well samples exhibiting photoluminescence up to room temperature, and laser diodes operating up to 219 K.
Iterative Detection and Decoding for Multiuser MIMO Systems with Low Resolution Precoding and PSK Modulation

Low-resolution precoding techniques have gained considerable attention in the wireless communications area recently. Vital but hardly discussed in literature, discrete precoding in conjunction with channel coding is the subject of this study. Unlike prior studies, we propose three different soft detection methods and an iterative detection and decoding scheme that allow the utilization of channel coding in conjunction with low-resolution precoding. Besides an exact approach for computing the extrinsic information, we propose two approximations with reduced computational complexity. Numerical results based on PSK modulation and an LDPC block code indicate a superior performance as compared to the system design based on the common AWGN channel model in terms of bit-error-rate.
Impossibility of creating a superposition of unknown quantum states

The superposition principle is fundamental to quantum theory. Yet a recent no-go theorem has proved that quantum theory forbids superposition of unknown quantum states, even with nonzero probability. The implications of this result, however, remain poorly understood so far. In this paper we show that the existence of a protocol that superposes two unknown pure states with nonzero probability (allowed to vary over input states) leads to violation of other no-go theorems. In particular, such a protocol can be used to perform certain state discrimination and cloning tasks that are forbidden not only in quantum theory but in no-signaling theories as well.
Maximum Covering Subtrees for Phylogenetic Networks

Tree-based phylogenetic networks, which may be roughly defined as leaf-labeled networks built by adding arcs only between the original tree edges, have elegant properties for modeling evolutionary histories. We answer an open question of Francis, Semple, and Steel about the complexity of determining how far a phylogenetic network is from being tree-based, including non-binary phylogenetic networks. We show that finding a phylogenetic tree covering the maximum number of nodes in a phylogenetic network can be computed in polynomial time via an encoding into a minimum-cost flow problem.
Automatically Evolving Texture Image Descriptors Using the Multitree Representation in Genetic Programming Using Few Instances

Abstract The performance of image classification is highly dependent on the quality of the extracted features that are used to build a model. Designing such features usually requires prior knowledge of the domain and is often undertaken by a domain expert who, if available, is very costly to employ. Automating the process of designing such features can largely reduce the cost and efforts associated with this task. Image descriptors, such as local binary patterns, have emerged in computer vision, and aim at detecting keypoints, for example, corners, line-segments, and shapes, in an image and extracting features from those keypoints. In this article, genetic programming (GP) is used to automatically evolve an image descriptor using only two instances per class by utilising a multitree program representation. The automatically evolved descriptor operates directly on the raw pixel values of an image and generates the corresponding feature vector. Seven well-known datasets were adapted to the few-shot setting and used to assess the performance of the proposed method and compared against six handcrafted and one evolutionary computation-based image descriptor as well as three convolutional neural network (CNN) based methods. The experimental results show that the new method has significantly outperformed the competitor image descriptors and CNN-based methods. Furthermore, different patterns have been identified from analysing the evolved programs.
Systemic cancer therapy with engineered adenovirus that evades innate immunity

Safe and effective “designer” adenoviruses target tumors after systemic administration. De-livering an oncolytic virus Oncolytic viruses, optimized for killing tumor cells, are a promising modality in cancer immunotherapy. However, there are some challenges that these viruses must overcome, including antiviral immune responses that can affect both the efficacy and the safety of these viruses. For example, naturally occurring immunoglobulin M (IgM) antibodies bind to human species C adenovirus, resulting in trapping of the virus in the liver. To overcome this, Atasheva et al. engineered a modified version of this adenovirus, with mutations in the binding site for IgM. This modified virus was not trapped in the liver and effectively suppressed tumor growth in mouse models without safety concerns. Oncolytic virus therapy is a cancer treatment modality that has the potential to improve outcomes for patients with currently incurable malignancies. Although intravascular delivery of therapeutic viruses provides access to disseminated tumors, this delivery route exposes the virus to opsonizing and inactivating factors in the blood, which limit the effective therapeutic virus dose and contribute to activation of systemic toxicities. When human species C adenovirus HAdv-C5 is delivered intravenously, natural immunoglobulin M (IgM) antibodies and coagulation factor X rapidly opsonize HAdv-C5, leading to virus sequestration in tissue macrophages and promoting infection of liver cells, triggering hepatotoxicity. Here, we showed that natural IgM antibody binds to the hypervariable region 1 (HVR1) of the main HAdv-C5 capsid protein hexon. Using compound targeted mutagenesis of hexon HVR1 loop and other functional sites that mediate virus-host interactions, we engineered and obtained a high-resolution cryo–electron microscopy structure of an adenovirus vector, Ad5-3M, which resisted inactivation by blood factors, avoided sequestration in liver macrophages, and failed to trigger hepatotoxicity after intravenous delivery. Systemic delivery of Ad5-3M to mice with localized or disseminated lung cancer led to viral replication in tumor cells, suppression of tumor growth, and prolonged survival. Thus, compound targeted mutagenesis of functional sites in the virus capsid represents a generalizable approach to tailor virus interactions with the humoral and cellular arms of the immune system, enabling generation of “designer” viruses with improved therapeutic properties.
Neurotoxicants and neurobiological function : effects of organoheavy metals

Organomercury, Lead and Tin Compounds in the Environment and the Potential for Human Exposure Neuropathological Changes Associated with Accidental or Experimental Exposure to Organometallic Compounds: CNS Effects Neuropathology of Organometallic Compounds: Review of Selected Literature Macrophysiological Assessment of Organometal Neurotoxicity Selective and Nonselective Effects of Organometals on Brain Neurochemistry Unconditioned Behavioral Measures of Neurotoxicity Modification of Conditioned Responding by Exposure to Organometals Learning and Memory Dysfunction as Selective Neurotoxic Effects But Where is the Path? A Comment on the State of the Art and Suggestions for Future Research Strategies Index.
Feces Metagenomes and Metagenome-Assembled Genome Sequences from Two Separate Dogs (Canis lupus familiaris) with Multiple Diarrheal Episodes

This study reports two feces metagenomes (D84 and D85) and six metagenome-assembled genomes (MAGs). The assembled MAGs include Pseudomonas sp. strain NID84 and Acinetobacter sp. strain N2D84 from D84 and Enterococcus sp. strain N4D85, Enterococcus sp. strain N5D85, Lactobacillus sp. strain N6D85, and Leuconostoc sp. strain N7D85 from D85. Acinetobacter sp. strain N2D84 was identified as a human pathogen with a probability of 92%. ABSTRACT This study reports two feces metagenomes (D84 and D85) and six metagenome-assembled genomes (MAGs). The assembled MAGs include Pseudomonas sp. strain NID84 and Acinetobacter sp. strain N2D84 from D84 and Enterococcus sp. strain N4D85, Enterococcus sp. strain N5D85, Lactobacillus sp. strain N6D85, and Leuconostoc sp. strain N7D85 from D85. Acinetobacter sp. N2D84 was identified as a human pathogen with a probability of 92%.
Quasi-Three-Dimensional Diffusion of Li ions in Li3FePO4CO3: First-Principles Calculations for Cathode Materials of Li-Ion Batteries

Crystal structure, electronic structure, and diffusion mechanism of Li ions in a new cathode material, Li3FePO4CO3, for Li-ion batteries were investigated based on first-principles calculations by using the GGA+U method. Eight possible elementary diffusion processes and three possible diffusion pathways of polaron–Li vacancy complex were revealed. The quasi-three-dimensional characteristic of the Li diffusion was explored. In the [010] and [001] directions, Li ions can diffuse with the barriers of 0.37 and 0.33 eV, respectively. Along the [100] direction, Li ions can hop through carbon layers with a barrier of 0.5 eV, but might be blocked by iron layers.
Complete Genome Sequence of a Japanese Clinical Isolate of Haemophilus influenzae Type a Strain TAMBA230

Haemophilus influenzae causes severe infections such as pneumonia and meningitis. Here, we report the complete genome of H. influenzae type a strain TAMBA230, which was isolated in 2019 from a patient exhibiting bacteremia. This represents the first case in Japan of a H. influenzae type a strain associated with invasive infection. ABSTRACT Haemophilus influenzae causes severe infections such as pneumonia and meningitis. Here, we report the complete genome of H. influenzae type a strain TAMBA230, which was isolated in 2019 from a patient exhibiting bacteremia. This represents the first case in Japan of an H. influenzae type a strain associated with invasive infection.
SucupiraBot: An Interactive Question-Answering System for the Sucupira Platform

Conversational user interfaces have been an increasingly popular way to obtain quick information in natural language, especially with advances in the area of natural language processing (NLP) fueled by the surge of deep neural networks. Real-world cases include IBM Watson and Azure Microsoft chatbots, among others. Such examples, however, are most present in the commercial spectrum, with few being actively used by the scientific community. In this work, we develop an Interactive Question Answering System (IQA) for the Sucupira platform, Brazil's biggest open platform of postgraduate content, deployed as conversational interfaces for two popular messaging platforms: Telegram and Discord. We also propose the use of multilingual embeddings for ordering answers by a language-independent measuer of semantic similarity. Our IQA was evaluated with an study with 16 participants. The results were mostly positive, indicating that the use of conversational user interfaces, along with NLP techniques, may be a valid alternative for retrieving scientific information from the Sucupira plataform.
MSL-DE: A Lexical Substitution Method based on Dictionaries and Embeddings

Lexical Substitution has a noticeable evolution in the literature, mainly in the data sources used for the generation of substitutes that feed the process. Of course, dictionaries and thesauri are widely used for grouping synonyms in their structure, but the polysemy of words prevents a direct exchange of terms without analyzing the context. Vector space models, such as embeddings, are used to represent substitutes and also contexts. However, the representation of words considering only contextual factors, in many cases, may incur the approximation of terms that are not exactly synonyms. The characteristics mentioned above suggest that the simultaneous use of dictionaries and embeddings is a promising alternative for the process. Thus, we present a method using information contained in merged dictionaries, in addition to their linguistic relations structured in taxonomies. The method measures the preservation of the meaning of the sentence with the potential synonym by observing its frequency of application in small contexts. In addition, we also consider a complete context to generate input from vector operations highlighting the best synonyms in a previously selected set. The results show the efficiency of the method, surpassing many methods consolidated in the literature in the prediction of the best substitute for words contained in instances of a known benchmark.
Etude des nanofils de silicium et de leur intégration dans des systèmes de récupération d'énergie photovoltaïque

L'objectif de cette these porte sur la fabrication et la caracterisation de cellules solaires a jonction radiale a base d'assemblee de nanofils de silicium cristallin. Une etude sur la croissance des nanofils a partir de deux catalyseurs metalliques (cuivre et aluminium) dans une machine de depot chimique en phase vapeur (CVD) a pression reduite est presentee. L'influence des conditions de croissance sur la morphologie, le dopage et la contamination des nanofils par le catalyseur est analysee par des mesures electriques, chimiques (SIMS, Auger) et structurales (SEM, TEM, Raman). Le cuivre est utilise pour la fabrication d'une cellule solaire avec des nanofils de type p et une jonction radiale creee avec du silicium amorphe de type n. Les performances photovoltaiques de la cellule solaire sont ensuite mesurees et interpretees. Un rendement de conversion de 5% est mesure sur une cellule avec des nanofils de hauteur 1,5µm.
Collaborative Filtering Strategy for Product Recommendation Using Personality Characteristics of Customers

Research indicates that people can receive more useful product recommendations if the filtering process considers their personality. In this paper, we propose a hybrid strategy for Recommender Systems (using matrix factorization and personality-based neighborhood) to recommend the best products calculated for a particular customer (user). The proposed user profile used in the definition of the neighborhood involves these three personality models: Big Five (or OCEAN, or Five-Factor Model), Needs, and Values. We experimented with data from more than 10,000 Amazon customers. We inferred their personality characteristics from the analysis of reviews via IBM Watson Personality Insights. The results indicated that the proposed strategy's performance was better than that of the state-of-the-art algorithms analyzed. Besides, there was no statistical difference between using only the Big Five model or using it together with the Needs and Values models.
Tenascin-C: From Discovery to Structure-Function Relationships

Forty years ago, the tremendous complexity of extracellular matrix (ECM) was still largely an uncharted area, mainly because many of its components could only be solubilized by denaturing agents. Known were just five types of collagens, elastin, a couple of proteoglycans, and a few ECM glycoproteins, among them fibronectin, thrombospondin-1, and laminin-111 (1). The best studied was fibronectin (2), which became notorious for promoting specific cell adhesion to collagens. In parallel, the search for yet undetected large ECM glycoproteins continued. In 1981-82, Carter (3) observed several novel glycoproteins in human fibroblast ECM extracts. Among them, "GP250" was shown to be distinct from fibronectin but resisted isolation. However, between 1983-85 several research groups independently discovered and characterized a similar ECM glycoprotein that later became known as tenascin-C (see below). Its subunits were comparable in size to fibronectin but instead of dimers formed large (>10 kDa) disulfide-linked oligomers. In the following paragraphs, the history and context of the individual discoveries of tenascin-C is briefly recounted. I then describe how a combination of methods available at the time lead to a detailed structural model of tenascin-C. This was the basis for trying to assign functions to different parts of the molecule. For reasons outlined below, this turned out to be more difficult than for fibronectin.
Mammals Make Use of Cashew Plantations in a Mixed Forest–Cashew Landscape

Heterogeneous landscapes harboring mosaics of natural habitat and agriculture are increasingly gaining conservation focus in the tropics. While research on cash crops such as coffee, rubber, and oil palm has led to an understanding of their role as supplementary habitats, such has remained limited in cashew plantations despite it being a cash crop of global significance. We conducted a study to understand the occurrence of terrestrial mammal species in a mixed forest–cashew landscape in the northern Western Ghats, India. During January to April 2016, we used trail cameras to sample a total area of 25 km2 divided into 100 grid cells of 0.25 km2 each. We deployed six trail cameras for a 24 h period in each grid cell and obtained photo-captures of 11 species in forests, of which nine were detected in cashew plantations, but most species showed low capture rates. For three of these species—Indian crested porcupine, sambar, and wild pig—we modeled habitat use as a function of site-specific attributes. Wild pig showed a higher probability of use of cashew than forest, while porcupine and sambar did not show any pronounced differences between the two land uses. The probability of habitat use by sambar and porcupine was positively influenced by undergrowth but not for the wild pig. Wild pig habitat use was positively related to increased proximity of human settlements and increasing distance to forests, but the pattern was unreliable for sambar and porcupine. Our preliminary study demonstrates that a subset of terrestrial mammals in the forest makes use of cashew plantations and highlights the need for further research in forest–cashew landscapes to assess conservation opportunities.