Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Analysis of Resistance and Detection of Non-Wild-Type Strains Using Etest Epidemiological Cutoff Values for Amphotericin B and Echinocandins for Bloodstream Candida Infections from a Tertiary Hospital in Qatar

A total of 301 Candida bloodstream isolates collected from 289 patients over 5 years at a tertiary hospital in Qatar were evaluated. Out of all Candida infections, 53% were diagnosed in patients admitted to the intensive care units. ABSTRACT A total of 301 Candida bloodstream isolates collected from 289 patients over 5 years at a tertiary hospital in Qatar were evaluated. Out of all Candida infections, 53% were diagnosed in patients admitted to the intensive care units. Steady increases in non-albicans Candida species were reported from 2009 to 2014 (30.2% for Candida albicans versus 69.8% for the other Candida species). Etest antifungal susceptibility testing was performed on all recovered clinical isolates to determine echinocandin (micafungin and anidulafungin) and amphotericin B susceptibilities and assess non-wild-type (non-WT) strains (strains for which MICs were above the epidemiological cutoff values). DNA sequence analysis was performed on all isolates to assess the presence of FKS mutations, which confer echinocandin resistance in Candida species. A total of 3.9% of isolates (12/301) among strains of C. albicans and C. orthopsilosis contained FKS hot spot mutations, including heterozygous mutations in FKS1. For C. tropicalis, the Etest appeared to overestimate strains non-WT for micafungin, anidulafungin, and amphotericin B, as 14%, 11%, and 35% of strains, respectively, had values above the epidemiological cutoff value. However, no FKS mutations were identified in this species. For all other species, micafungin best reported the echinocandin non-WT strains relative to the FKS genotype, as anidulafungin tended to overestimate non-wild-type strains. Besides C. tropicalis, few strains were classified as non-WT for amphotericin B.
An empirical investigation on psychological characteristics of entrepreneurs

Article history: Received August 16, 2013 Received in revised format 12 September 2013 Accepted 1 November 2013 Available online November 2 2013 This paper presents an empirical investigation on psychological characteristics of entrepreneurs in one of Iranian universities located in city of Zanjan, Iran. The proposed study of this paper uses a standard questionnaire in Likert scale and concentrates on three components of locus of control, risk taking and tolerance of ambiguity. The study chooses a sample of 350 out of 11,000 students who were enrolled in various areas and detected that students maintained higher level of risk taking (t-student = 10.999, P-value = 0.000), higher level of locus of control (t-student = 29.708, P-value = 0.000) and lower level of tolerance of ambiguity (tstudent = -13.584, P-value = 0.000). © 2013 Growing Science Ltd. All rights reserved.
Hierarchical Graph Representation Learning with Differentiable Pooling

Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction. However, current GNN methods are inherently flat and do not learn hierarchical representations of graphs---a limitation that is especially problematic for the task of graph classification, where the goal is to predict the label associated with an entire graph. Here we propose DiffPool, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. DiffPool learns a differentiable soft cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a set of clusters, which then form the coarsened input for the next GNN layer. Our experimental results show that combining existing GNN methods with DiffPool yields an average improvement of 5-10% accuracy on graph classification benchmarks, compared to all existing pooling approaches, achieving a new state-of-the-art on four out of five benchmark datasets.
Diabetes Technology Update: Use of Insulin Pumps and Continuous Glucose Monitoring in the Hospital

The use of continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) systems has gained wide acceptance in diabetes care. These devices have been demonstrated to be clinically valuable, improving glycemic control and reducing risks of hypoglycemia in ambulatory patients with type 1 diabetes and type 2 diabetes. Approximately 30–40% of patients with type 1 diabetes and an increasing number of insulin-requiring patients with type 2 diabetes are using pump and sensor technology. As the popularity of these devices increases, it becomes very likely that hospital health care providers will face the need to manage the inpatient care of patients under insulin pump therapy and CGM. The American Diabetes Association advocates allowing patients who are physically and mentally able to continue to use their pumps when hospitalized. Health care institutions must have clear policies and procedures to allow the patient to continue to receive CSII treatment to maximize safety and to comply with existing regulations related to self-management of medication. Randomized controlled trials are needed to determine whether CSII therapy and CGM systems in the hospital are associated with improved clinical outcomes compared with intermittent monitoring and conventional insulin treatment or with a favorable cost-benefit ratio.
An Adaptive Method for Cross-Recording Speaker Diarization

Nowadays, state-of-the-art speaker diarization systems heavily rely on between-recording variability compensation methods to accurately process large collections of recordings. Variability estimation is performed on consequent training datasets, which must be labeled by speaker. One major problem of such systems is the acoustic mismatch between training and target data that degrades performances. Most of the collections contain lots of speakers speaking in various acoustic conditions. In this paper, we investigate how unlabeled speakers can help improve between-recording variability estimation, to overcome the mismatch issue. We propose a scalable unsupervised adaptation framework for two types of variability compensation. The proposed framework consists in adapting a state-of-the-art diarization and linking system, trained on out-of-domain data, using the data of the collection itself. Experiments in mismatch condition are run on two French Television shows, while the initial training dataset is composed of Radio recordings. Results indicate that the proposed adaptation framework reduces the cross-recording DER of 13% in average for variable collection sizes.
A Risk Model with an Observer in a Markov Environment

We consider a spectrally-negative Markov additive process as a model of a risk process in a random environment. Following recent interest in alternative ruin concepts, we assume that ruin occurs when an independent Poissonian observer sees the process as negative, where the observation rate may depend on the state of the environment. Using an approximation argument and spectral theory, we establish an explicit formula for the resulting survival probabilities in this general setting. We also discuss an efficient evaluation of the involved quantities and provide a numerical illustration.
Effects of Pre-race Apneas on 400-m Freestyle Swimming Performance.

Robertson, C, Lodin-Sundström, A, O'Hara, J, King, R, Wainwright, B, and Barlow, M. Effects of pre-race apneas on 400-m freestyle swimming performance. J Strength Cond Res XX(X): 000-000, 2018-This study aimed to establish whether a series of 3 apneas before a 400-m freestyle time-trial affected swimming performance when compared with and combined with a warm-up. Nine (6 males and 3 females) regional to national standard swimmers completed four 400-m freestyle time-trials in 4 randomized conditions: without warm-up or apneas (CON), warm-up only (WU), apneas only (AP), and warm-up and apneas (WUAP). Time-trial performance was significantly improved after WUAP (275.79 ± 12.88 seconds) compared with CON (278.66 ± 13.31 seconds, p = 0.035) and AP (278.64 ± 4.10 seconds, p = 0.015). However, there were no significant differences between the WU (276.01 ± 13.52 seconds, p > 0.05) and other interventions. Spleen volume compared with baseline was significantly reduced after the apneas by a maximum of ∼45% in the WUAP and by ∼20% in WU. This study showed that the combination of a warm-up with apneas could significantly improve 400-m freestyle swim performance compared with a control and apnea intervention. Further investigation into whether long-term apnea training can enhance this response is justified.
Three Hierarchical Levels of Big-Data Market Model Over Multiple Data Sources for Internet of Things

This paper proposes three hierarchical levels of a competitive big-data market model. We consider that a service provider gathers data from multiple data sources and provides valuable information from refined data as a service to its customers. Under our approach, a service provider determines optimal data procurement from multiple data sources within its budget constraint. The multiple data sources follow the service provider’s action by independently submitting bidding prices to the service provider. Further, customers decide whether to subscribe or not based on the subscription fee, their willingness-to-pay, and the quality of the refined data. We study the economic benefits of such a market model by analyzing the hierarchical decision making procedures as a Stackelberg game. We show the existence and the uniqueness of the Nash equilibrium (NE), and the NE solution is given as a closed form. Finally, we reveal that the obtained unique equilibrium solution maximizes the payoff of all market participants.
Gut‐associated lymphoid tissue (GALT) of the goldfish, Carassius auratus

The head kidney and spleen are major sites of haemopoiesis in fish; a secondary center is found in loose connective tissue of the intestine. In this study we determined the nature of gut‐associated haemopoietic tissue in the goldfish, Carassius auratus, using light and electron microscopy. This tissue is a loose stroma of reticular cells and fibers vascularized by capillaries, venules, and arterioles. The cellular population includes lymphoblasts, small and medium‐sized lymphocytes, plasmocytes, macrophages, and various granulocytes. The most abundant granulocyte is the mast cell, whose large granules stain with Alcian blue and toluidine blue. Heterophils are found in the intestinal connective tissue as well as two other granulocytes: one with ovoid granules having dense parallel lamellae and another with granules containing crystalline inclusions. Immature forms of both granulocytes were also noted. Macrophages containing phagocytosed debris were often located close to the epithelium; they were observed forming clusters with lymphocytes. The epithelium contained a number of migrating leucocytes including lymphocytes and lymphoblasts, macrophages, and heterophils. Although many granulocytes were found in the connective tissue, granulopoiesis does not seem to be a major function. Gut‐associated haemopoietic tissue in goldfish resembles diffuse lymphoid tissue and may be involved in intestinal immune responses.
A Distributed Iterative Algorithm for Multi-Agent MILPs: Finite-Time Feasibility and Performance Characterization

We deal with decision making in a large-scale multi-agent system, where each agent aims at minimizing a local cost function subject to local constraints, and the local decision variables of all agents are coupled through a global constraint. We consider a cooperative framework where the multi-agent decision problem is formulated as a constrained optimization program with the sum of the local costs as global cost to be minimized with respect to the local decision variables of all agents, subject to both local and global constraints. We focus on a non-convex linear set-up where all costs and constraints are linear but local decision variables are discrete or include a discrete component, and propose a distributed iterative scheme based on dual decomposition and consensus to solve the resulting mixed integer linear program. Our approach extends recent results in the literature to a distributed set-up with a time-varying communication network and allows to: reduce the computational and communication effort, achieve resilience to communication failures, and also preserve privacy of local information. The approach is demonstrated on a numerical example of optimal charging of plug-in electric vehicles.
A temporal neural network model for object recognition using a biologically plausible decision making layer

Brain can recognize different objects as ones that it has experienced before. The recognition accuracy and its processing time depend on task properties such as viewing condition, level of noise and etc. Recognition accuracy can be well explained by different models. However, less attention has been paid to the processing time and the ones that do, are not biologically plausible. By extracting features temporally as well as utilizing an accumulation to bound decision making model, an object recognition model accounting for both recognition time and accuracy is proposed. To temporally extract informative features in support of possible classes of stimuli, a hierarchical spiking neural network, called spiking HMAX is modified. In the decision making part of the model the extracted information accumulates over time using accumulator units. The input category is determined as soon as any of the accumulators reaches a threshold, called decision bound. Results show that not only does the model follow human accuracy in a psychophysics task better than the classic spiking HMAX model, but also it predicts human response time in each choice. Results provide enough evidence that temporal representation of features are informative since they can improve the accuracy of a biological plausible decision maker over time. This is also in line with the well-known idea of speed accuracy trade-off in decision making studies.
Analyzing memory accesses for performance and correctness of parallel programs

The demand for large compute capabilities in scientific computing led to wide use and acceptance of highly-parallel computer architectures during the last decade. This trend is manifested in the TOP500, listing the fastest supercomputer of the world, in which about 40 % of the performance share results from accelerator-based systems. Programming for these architectures in the past often required a timeconsuming rewrite of the compute-intensive application parts, until more productive approaches like Open Accelerators (OpenACC) or the target offloading features of Open Multi-Processing (OpenMP) came to existence. However, parallel programming for heterogeneous architectures is still a complex and error-prone task, posing several challenges to the programmer who wants to achieve high application performance. One key factor for the understanding of the performance and the correctness of a parallel program is reflected in the analysis of the memory accesses. This work takes a holistic view on the hardware properties, the programming paradigm, its particular implementation and the interfaces for an adequate tool support with respect to both aspects. The improvement of the performance and the validation of an application requires a deep comprehension of the dynamic runtime behavior. Here, the appropriate data and thread placement is essential for the performance, and the order of the memory accesses is essential for the deterministic behavior or rather the correctness of the application. Therefore, this work will first present a systematic methodology for the assessment of OpenMP for target devices, patterns for the efficient usage of task-based programming on Non-Uniform Memory Access (NUMA) architectures, and the improvement of standard-compliant tool support. Based on the gathered insights, an OpenMP epoch model for correctness checking is defined, which respects the OpenMP semantics including the runtime and memory model. The evaluation of the developed concepts is shown by application to realworld performance analysis and correctness checking tools.
A Context-Aware Edge-Based VANET Communication Scheme for ITS

We propose a context-aware edge-based packet forwarding scheme for vehicular networks. The proposed scheme employs a fuzzy logic-based edge node selection protocol to find the best edge nodes in a decentralized manner, which can achieve an efficient use of wireless resources by conducting packet forwarding through edges. A reinforcement learning algorithm is used to optimize the last two-hop communications in order to improve the adaptiveness of the communication routes. The proposed scheme selects different edge nodes for different types of communications with different context information such as connection-dependency (connection-dependent or connection-independent), communication type (unicast or broadcast), and packet payload size. We launch extensive simulations to evaluate the proposed scheme by comparing with existing broadcast protocols and unicast protocols for various network conditions and traffic patterns.
A Hierarchical Deep Learning Natural Language Parser for Fashion

This work presents a hierarchical deep learning natural language parser for fashion. Our proposal intends not only to recognize fashion-domain entities but also to expose syntactic and morphologic insights. We leverage the usage of an architecture of specialist models, each one for a different task (from parsing to entity recognition). Such architecture renders a hierarchical model able to capture the nuances of the fashion language. The natural language parser is able to deal with textual ambiguities which are left unresolved by our currently existing solution. Our empirical results establish a robust baseline, which justifies the use of hierarchical architectures of deep learning models while opening new research avenues to explore.
Anatomy of Online Hate: Developing a Taxonomy and Machine Learning Models for Identifying and Classifying Hate in Online News Media

Online social media platforms generally attempt to mitigate hateful expressions, as these comments can be detrimental to the health of the community. However, automatically identifying hateful comments can be challenging. We manually label 5,143 hateful expressions posted to YouTube and Facebook videos among a dataset of 137,098 comments from an online news media. We then create a granular taxonomy of different types and targets of online hate and train machine learning models to automatically detect and classify the hateful comments in the full dataset. Our contribution is twofold: 1) creating a granular taxonomy for hateful online comments that includes both types and targets of hateful comments, and 2) experimenting with machine learning, including Logistic Regression, Decision Tree, Random Forest, Adaboost, and Linear SVM, to generate a multiclass, multilabel classification model that automatically detects and categorizes hateful comments in the context of online news media. We find that the best performing model is Linear SVM, with an average F1 score of 0.79 using TF-IDF features. We validate the model by testing its predictive ability, and, relatedly, provide insights on distinct types of hate speech taking place on social media.
Comparing Prescriptive and Descriptive Gender Stereotypes About Children, Adults, and the Elderly

Gender stereotypes have descriptive components, or beliefs about how males and females typically act, as well as prescriptive components, or beliefs about how males and females should act. For example, women are supposed to be nurturing and avoid dominance, and men are supposed to be agentic and avoid weakness. However, it is not clear whether people hold prescriptive gender stereotypes about children of different age groups. In addition, research has not addressed prescriptive gender stereotypes for the elderly. The current research measured prescriptive gender stereotypes for children, adults, and elderly men and women in 3 studies to (a) compare how prescriptive gender stereotypes change across age groups and (b) address whether stereotypes of males are more restrictive than stereotypes of females. Students (Studies 1 and 2) and community members (Study 3), which were all U.S. and majority White samples, rated how desirable it was for different target groups to possess a list of characteristics from 1 (very undesirable) to 9 (very desirable). The target age groups included toddlers, elementary-aged, adolescent, young adult, adult, and elderly males and females. The list of 21 characteristics was created to encompass traits and behaviors relevant across a wide age range. In a meta-analysis across studies, prescriptive stereotypes were defined as characteristics displaying a sex difference of d > 0.40 and an average rating as desirable for positive prescriptive stereotypes (PPS) or undesirable for negative proscriptive stereotypes (NPS) for male or females of each age group. Results replicated previous research on prescriptive stereotypes for adults: Women should be communal and avoid being dominant. Men should be agentic, independent, masculine in appearance, and interested in science and technology, but avoid being weak, emotional, shy, and feminine in appearance. Stereotypes of boys and girls from elementary-aged to young adults still included these components, but stereotypes of toddlers involved mainly physical appearance and play behaviors. Prescriptive stereotypes of elderly men and women were weaker. Overall, boys and men had more restrictive prescriptive stereotypes than girls and women in terms of strength and number. These findings demonstrate the applicability of prescriptive stereotypes to different age groups.
Skin Ceramide Profile in Children With Atopic Dermatitis

Background Atopic dermatitis (AD) is a common disease, which involves a disruption of the skin barrier function. Skin ceramide (CER) composition, which plays crucial roles in maintaining the barrier function of the stratum corneum, is changed in patients with AD. Objective The aim of this study was to identify and quantify skin CER subclasses in association with disease severity in pediatric patients with AD. Methods Two hundred thirteen patients were entered into the observational study. We compared their CER profiles using normal-phase high-performance liquid chromatography coupled with dynamic multiple reaction monitoring mass spectrometry. Results In total, 12 subclasses of CERs were identified. We found that 2 subclasses, that is, CER[AS] and CER[NS], were elevated (P = 0.007 and 0.012, respectively) and correlated with Severity Scoring of Atopic Dermatitis (P = 0.004 and 0.004, respectively). Conclusions Skin CER abundances are changed in children with AD compared with control subjects.