Miracle-dz/UniFAR-FTUs huggingface dataset:
FOXM1 Expression and Contribution to Genomic Instability and Chemoresistance in High-Grade Serous Ovarian Cancer

Carter J Barger, Ph.D. University of Nebraska Medical Center, 2018 Supervisor: Adam R. Karpf, Ph.D. High-grade serous ovarian cancer (HGSC) is the most common and deadly subtype of epithelial ovarian cancer. Understanding the molecular basis of HGSC will improve diagnosis and treatment approaches. The Cancer Genome Atlas (TCGA) discovered that Forkhead Box M1 (FOXM1) transcription factor activation is the second most frequent molecular alteration in HGSC (84% of cases), second only to mutations of TP53 (100%). We subsequently defined several genetic mechanisms that underlie increased FOXM1 expression in HGSC, including genomic amplifications and RB-E2F deregulation, and showed that FOXM1 promotes cell cycle progression in cell models relevant to HGSC. TCGA analyses revealed that genomic instability, consisting of frequent copy number alterations, as key defining molecular features of HGSC and basal breast, more than any other TCGA cancer type. DNA replication stress results from uncoupling of the replicative helicase and polymerase and is a key mechanism of genomic instability. FOXM1 expression is linked to genomic instability but the underlying mechanism is unclear; induction of DNA replication stress could explain this association. In this context, we revealed novel functions of FOXM1 using fallopian tube epithelial (FTE) cells, an HGSC precursor cell model. We showed that FOXM1 increased DNA fork rate, origin firing, and DNA damage. Furthermore, Cyclin E1 cooperated with FOXM1 to increase its transcriptional activity, which promoted cell cycle progression and genomic instability. In [ Gra b your read er’s atte ntio n with a grea t quot e from the doc ume nt or use this spa ce to emp hasi ze a key
Applicability of subcortical EEG metrics of synaptopathy to older listeners with impaired audiograms

Emerging evidence suggests that cochlear synaptopathy is a common feature of sensorineural hearing loss, but it is not known whether electrophysiological metrics targeting synaptopathy in animals can be applied to a broad range of people, such as those with impaired audiograms. This study investigates the applicability of subcortical electrophysiological measures associated with synaptopathy such as auditory brainstem responses (ABRs) and envelope following responses (EFRs) in older participants with high-frequency sloping audiograms. This is important for the development of reliable and sensitive synaptopathy diagnostics in people with normal or impaired outer-hair-cell function. Broadband click-ABRs at different sound pressure levels and EFRs to amplitude-modulated stimuli were recorded, as well as relative EFR and ABR metrics which reduce individual factors such as head size and noise floor level. Most tested metrics showed significant differences between the groups and did not always follow the trends expected from synaptopathy. Audiometric hearing loss and age-related hearing related deficits interacted to affect the electrophysiological metrics and complicated their interpretation in terms of synaptopathy. This study contributes to a better understanding of how electrophysiological synaptopathy metrics differ in ears with healthy and impaired audiograms, which is an important first step towards unravelling the perceptual consequences of synaptopathy.
Multi-Variate Data Fusion Technique for Reducing Sensor Errors in Intelligent Transportation Systems

Connected vehicles in intelligent transportation system (ITS) scenario rely on environmental data for supporting user-centric applications along the driving time. Sensors equipped in the vehicles are responsible for accumulating data from the environment, followed by the fusion process. Such fusion process provides accurate and stable data required for the applications in a recurrent manner. In order to enhance the data fusion of connected vehicles, this article introduces multi-variate data fusion (MVDF) technique. This technique is competent in handling asynchronous and discrete data from the environment and streamlining them into continuous and delay-less inputs for the applications. The process of data fusion is aided through least square regression learning to determine the errors in different time instances. The indefinite and definite data fusion instances are differentiated using this regression model to identify the errors in fore-hand. Besides, the differentiation relies on the application run-time interval to progress data fusion within the same or extended time instance and data slots. In this manner the differentiation along with the error identification is regular until the application required data is met. The performance of this technique is verified using network simulator experiments for the metrics error, data utilization ratio, and computation time. The results show that this technique improves data utilization under controlled time and fewer errors.
A new emerging zoonotic virus of concern: the 2019 novel Coronavirus (SARS CoV-2)/ Un nuevo virus zoonótico emergente de preocupación: el Coronavirus novel 2019

Abstract We review here the origin, outbreak characteristics and main epidemiological features of the novel Coronavirus (2019nCoV) responsible of a new coronavirus disease (COVID-19). Rapid global health authorities' responses are now in course and international scientific collaboration is urgently need. Previous outbreaks experiences with similar viral agents have increased the capacity to containment and control of these recurrent health menaces.
Implicit semantic prediction error can account for N400 effects on articles that do not differ in meaning: A neural network model

N400 effects on indefinite articles (a/an) compatible or incompatible with expected nouns have been initially taken as strong evidence for probabilistic pre-activation of phonological word forms, and recently been intensely debated because they have been difficult to replicate. Here, we simulate these effects using a neural network model of sentence comprehension that we previously used to simulate a broad range of empirical N400 effects. The model produces the effects when the cue validity of the articles concerning upcoming noun meaning is high, but fails to produce the effects when the cue validity of the articles is low due to adjectives presented between articles and nouns during training, providing a possible explanation for the small size of the effects in empirical studies. The model accounts for article induced N400 effects without assuming pre-activation of word forms, and instead simulates these effects as the stimulus-induced change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error.
Global Analysis of C Concurrency in High-Level Synthesis

When mapping C programs to hardware, high-level synthesis (HLS) tools reorder independent instructions, aiming to obtain a schedule that requires as few clock cycles as possible. However, when synthesizing multithreaded C programs, reordering opportunities are limited by the presence of atomic operations (“atomics”), the fundamental concurrency primitives in C. Existing HLS tools analyze and schedule each thread in isolation. In this article, we argue that thread-local analysis is conservative, especially since HLS compilers have access to the entire program. Hence, we propose a global analysis that exploits information about memory accesses by all threads when scheduling each thread. Implemented in the LegUp HLS tool, our analysis is sensitive to sequentially consistent (SC) and weak atomics and supports loop pipelining. Since the semantics of C atomics is complicated, we formally verify that our analysis correctly implements the C memory model using the Alloy model checker. Compared with thread-local analysis, our global analysis achieves a $2.3\times $ average speedup on a set of lock-free data structures and data-flow patterns. We also apply our analysis to a larger application: a lock-free, streamed, and load-balanced implementation of Google’s PageRank, where we see a $1.3\times $ average speedup compared with the thread-local analysis.
Joint Bi-Static Radar and Communications Designs for Intelligent Transportation

The cooperation of radar and communications becomes important in vehicular environments due to the demand for radar-assisted communications or communications-assisted radar. In this paper, the tradeoff between bi-static radar and communications in a joint radar-communications setting is studied. We propose three schemes by using time division, superposition or their mixture. For each scheme, three optimization problems are formulated to maximize either the probability of detection for radar subject to a minimum communications rate, the communications rate subject to a minimum probability of detection for radar, or a combined measure of tradeoff. Specifically, given a fixed amount of total time or power for both communications and radar, the optimal power allocation and/or time allocation between radar and communications are derived. Numerical results show that the superposition scheme outperforms the time division scheme and the mixture scheme with considerable performance gains. They also show that the surveillance channel in radar and the communications channel are more important than the direct channel in radar.
Deep visual unsupervised domain adaptation for classification tasks: a survey

: Learning methods are challenged when there is not enough labeled data. It gets worse when the existing learning data have different distributions in different domains. To deal with such situations, deep unsupervised domain adaptation techniques have newly been widely used. This paper surveys such domain adaptation methods that have been used for classiﬁcation tasks in computer vision. The survey includes the very recent papers on this topic that have not been included in the previous surveys and introduces a taxonomy by grouping methods published on unsupervised domain adaptation into ﬁve groups of: discrepancy, adversarial-, reconstruction, representation, and attention-based methods.
Triagem da sarcopenia e fragilidade em pacientes com úlceras venosas crônicas: um estudo transversal

Resumo Contexto Pacientes com úlceras venosas reportam múltiplas comorbidades e são mais propensos a ser fisicamente inativos. A sarcopenia e a fragilidade aumentam a vulnerabilidade de um indivíduo para maior dependência e/ou morte. Objetivos Verificar presença da sarcopenia e fragilidade em pacientes portadores de úlceras venosas crônicas. Métodos Estudo observacional e transversal, realizado com 9 pacientes com idade média de 67,4 ± 8,42 anos e portadores de úlcera venosa nos membros inferiores classificadas no Consenso Internacional de Doenças Venosas Crônicas (CEAP) em estágio 6. Para identificação e classificação da sarcopenia, foi avaliada a força (dinamometria manual), a velocidade da marcha (teste de caminhada de 10 metros) e a massa muscular (circunferência da panturrilha). Para triagem de fragilidade, foram utilizados os critérios de Fried: perda de peso não intencional, fadiga, redução da força e da velocidade da caminhada e baixa atividade física. Resultados O fenótipo de fragilidade foi mais frequente (n = 9; 100%) em relação à sarcopenia (n = 1; 11,1%). Entre os critérios de Fried, os mais frequentes foram a exaustão (n = 9; 100%), seguida pela baixa atividade física (n = 8; 88,8%) e fraqueza muscular (n = 5; 55%). Por fim, o critério menos frequente foi a diminuição da velocidade da marcha (n = 2; 22,2%). No diagnóstico de sarcopenia, foi observada redução da força associada à redução da massa muscular (n = 1; 11,1%). Conclusões Pacientes com úlceras venosas crônicas apresentam condição de fragilidade ou pré-fragilidade, enquanto a condição de sarcopenia foi pouco frequente.
Improving Spellchecking for Children: Correction and Design

Children commonly use software applications such as search engines and word processors in the classroom environment. However, a major barrier to using these programs successfully is the ability of children to type and spell effectively. While many programs make use of spellcheckers to provide spelling corrections to their users, they are designed for more traditional users (i.e., adults) and have proven inadequate for children. The aim of this work is twofold: first, to address the types of spelling errors children make by researching, developing, and evaluating algorithms to generate and rank candidate spelling suggestions; and second, to evaluate the impact interactive elements have on children’s spellchecking behaviors seeking to improve their user experience. Motivated by children’s phonological strategies to spell, a phonetic encoding strategy is used to map words and misspellings to phonetic keys to effectively and efficiently provide spelling correction candidates. Machine learning methods, including Learning to Rank, are used to rank candidates effectively and reveal the importance of phonetic features. Experimental results show this method is able to more accurately provide and rank spelling corrections when handling misspellings generated by children in both essay writing and web search settings when compared to state-of-the-art baselines. The design of an interactive spellchecker reveals children’s propensity towards visual and audio cues. A study on visual and audio cues show an influence on children’s selection habits and a positive impact on assisting children in selecting correct spelling suggestions. v
Microbiological and Sensory Characteristics of Mould-Ripened Salami under Different Packaging Conditions

SUMMARY The preservation of mould-ripened salami was investigated during 48 days at 19-20 °C under different packaging conditions: (i) high barrier film filled with air, 100% N2 or under vacuum, (ii) biaxially oriented polypropylene film, (iii) microperforated polyethylene film and (iv) unpackaged. Sensory, texture profile, physicochemical and microbiological analyses were performed. Fungal quantification revealed two data groups. In group 1 (consisting of salami in microperforated polyethylene film, biaxially oriented polypropylene film and unpackaged) the conidium viability was relatively constant. In group 2 (salami preserved in high barrier film filled with air, 100% N2 or under vacuum) the conidium viability decreased due to the absence of oxygen and the high carbon dioxide volume fraction. SEM micrographs showed micromorphological changes in fungal structure; microperforated polyethylene film, biaxially oriented polypropylene film and unpackaged conditions preserved the conidial morphology, while high barrier film filled with air, 100% N2 or vacuum conditions collapsed the hyphae and most of the conidia. Salami packed in microperforated polyethylene film and biaxially oriented polypropylene film showed the most acceptable organoleptic characteristics and lower hardness and chewiness values after packaging.
Distributed Cooperative MPC for Autonomous Driving in Different Traffic Scenarios

A cooperative control approach for autonomous vehicles is developed in order to perform different complex traffic maneuvers, e.g., double lane-switching or intersection situations. The problem is formulated as a distributed optimal control problem for a system of multiple autonomous vehicles, and then solved using a nonlinear Model Predictive Control (MPC) technique, where the distributed approach is used to make the problem computationally feasible in real-time. To provide safety, a collision avoidance constraint is introduced, also in a distributed way. In the proposed method, each vehicle computes its own control inputs using estimated states of neighboring vehicles. In addition, a compatibility constraint is defined that takes collision avoidance into account but also ensures that each vehicle does not deviate significantly from what is expected by neighboring vehicles. The method allows us to construct a cost function for several different traffic scenarios. The asymptotic convergence of the system to the desired destination is proven, in the absence of uncertainty, and disturbances, for a sufficiently small MPC control horizon. Simulation results show that the distributed algorithm scales well with increasing number of vehicles.
Rozpoznawanie i monitorowanie przebiegu reumatoidalnego zapalenia stawów – przewodnik dla lekarzy rodzinnych

Reumatoidalne zapalenie stawów (RZS) jest ogólno­ ustrojową, wielonarządową chorobą tkanki łącznej cechującą się przewlekłym przebiegiem o zmien­ nym obrazie klinicznym. Postępujący charakter schorzenia wiąże się, w przypadku nieskutecznego leczenia, z ograniczeniem sprawności funkcjonal­ nej, a współwystępujące powikłania systemowe nierzadko prowadzą do przedwczesnej śmierci. Spośród zapalnych chorób stawów RZS wyróżnia się najwyższą chorobowością, szacowaną w Polsce na 0,9% [1]. W ostatnich latach podkreśla się zna­ czenie wczesnego włączenia leków modyfikujących przebieg choroby oraz wpływ chorób współwystę­ pujących z RZS na efektywność leczenia choroby zapalnej stawów. Wielodyscyplinarna opieka nad pacjentem, w tym wsparcie lekarza rodzinnego, jest niezbędna do zapewnienia kompleksowej kontroli przebiegu choroby.
Enhancing Matrix Multiplication With a Monolithic 3-D-Based Scratchpad Memory

Convolutional neural networks (CNNs) are one of the most popular machine learning algorithms. The convolutional layers, which account for the most execution time of CNNs, are implemented with matrix multiplication because the convolution operation performs dot products between filters and local regions of the input. On the other hand, GPUs with thousands of cores were proven to significantly accelerate matrix multiplication, compared to CPUs with a limited number of cores, especially for large matrices. However, the current memory architecture allows only one row access at a time so that multiple accesses are necessary to read the column data of the second matrix, thus slowing down matrix multiplication. In this study, we adopt the monolithic 3-D integration for the GPU scratchpad memory, called monolithic 3-D integration (M3D) scratchpad memory (SPM), to enhance matrix multiplication. The M3D SPM allows one access to read the column data of the second matrix, similar to the case of the first matrix. The simulation results show that our M3D SPM improves the system performance by 46.3% for the $32\times 32$ matrix multiplication, over the conventional 2-D SPM where the column data of the second matrix are read sequentially.
Citicoline in the Treatment of Cognitive Impairment

Vascular cognitive impairment is a process which is more frequent in patients with cardiovascular risk factors. The etiology of this kind of impairment could be related to different types of cerebrovascular disorders, given that silent cerebral infarctions or microinfarcts, correlated with small vessel disease, are one of the principal etiologies. Microinfarcts, associated with small vessel diseases, should be considered one of the possible causes of clinical suspicion in patients with cardiovascular risk factors who are asking for help for cognitive complaints. Among the proposed treatments for cognitive impairment there is citicoline. This is an updated review of the possible use of citicoline in the treatment of cognitive impairment.
Economic modelling to evaluate Smart Specialisation: an analysis of research and innovation targets in Southern Europe

ABSTRACT We make the case for a technology-enabled approach to Smart Specialisation policymaking aimed at increasing its effectiveness by assessing the general equilibrium effects of the European Cohesion Policy objectives contained in the regional Operational Programmes prepared by the regional policymakers. We simulate the impact of achieving the research and development personnel targets on gross domestic product and its components in a set of Southern European regions. We discuss the implications and challenges of the proposed methodology for future assessments of Smart Specialisation and for its implementation in the regions of the European Union.
Influence of grain type and oil concentration of dried corn distillers’ grain with solubles on ruminal fermentation and in vitro gas production in cattle fed high-concentrate diets

Abstract: To determine the effects of grain type (rolled-corn vs. rolled-barley) and dried corn distillers’ grains with solubles (DDGS) oil concentration (low = 4.5% vs. moderate = 7.9%) on ruminal pH, ammonia (NH3) and volatile fatty acid (VFA) concentrations, in vitro enteric methane (CH4) and carbon dioxide (CO2) production, and digestive enzyme activity, eight ruminally cannulated Holstein steers (715 ± 61.4 kg) were used in a 4 × 8 Latin rectangle with a 2 × 2 factorial arrangement of treatments. Diets were offered for ad libitum intake. Experimental periods were 24 d with 7 d of diet adaptation, 7 d of sample collection, and a 10 d transition between periods. No differences were observed for ruminal NH3, total VFA concentration, pH, or in vitro concentration of CH4 or CO2 after 24 h of incubation among treatments. The lag time for in vitro gas production and degradation was greater (P = 0.03) from steers fed diets containing rolled-corn than rolled-barley. There were grain type × DDGS interactions (P ≤ 0.02) observed for α-amylase and trypsin activity (U L−1 ruminal fluid). Maltase activity (U L−1 ruminal fluid) was greater (P ≤ 0.01) in steers fed diets containing rolled-corn than rolled-barley and in steers fed diets containing low- compared with moderate-oil DDGS. These results indicate that, although ruminal enzyme activity was influenced, feeding different grain types (rolled-corn vs. rolled-barley) or DDGS with differing oil concentration (moderate vs. low) did not negatively influence ruminal fermentation or in vitro CH4 production.
RNA Polymerase II Independent Recruitment of SPT6 at Transcription Start Sites in Arabidopsis

SPT6 is a conserved transcription regulator that is generally viewed as an elongation factor. However, emerging evidence show its potential role in the control of transcription initiation at genic and intragenic promoters. Here we first present the genome-wide occupancy of Arabidopsis SPT6-like (SPT6L) and demonstrate its conserved role in facilitating RNA Polymerase II (RNAPII) occupancy across transcribed genes. Further, we show that SPT6L enrichment is shifted, unexpectedly, from gene body to the transcription starting site (TSS) when its association with RNAPII is disrupted. Finally, we demonstrate that recruitment of SPT6L starts at TSS, and then spreads to the gene body during transcription. These findings refine the mechanisms underlying SPT6L recruitment in transcription and shed light on the role of SPT6L in transcription initiation.
Genetic variation and QTLs related to root development in upland New Rice for Africa (NERICA) varieties

To understand variation in the root development traits (total root length (TRL), maximum root length (MRL) and root number) of 18 New Rice for Africa (NERICA) varieties, seedlings were hydroponically grown under deficient and sufficient concentrations of two forms of nitrogen, NH4+ and NO3−. The donor African rice variety, ‘CG14’ (Oryza glaberrima Steud.), showed greater TRL and MRL than three background Asian rice varieties (Oryza sativa L.). Wide distribution was observed in all traits of the 18 NERICAs. The 18 NERICAs and parental varieties were classified into three cluster groups by cluster analysis. Cluster Ia included only ‘CG14’. Comparative analysis characterized cluster Ib (including ‘NERICA7’) as an active root elongation group, and cluster II (including ‘WAB56-104’) as an active primordia development group. QTL analysis of F2 plants developed from a cross between ‘WAB56-104’ and ‘NERICA7’ detected two putative quantitative trait loci (QTLs) for root elongation on chromosome 1. Of these, a major QTL, designated as qRL1.4-NERICA7, was an NH4+-responsive QTL, which was narrowed down to a 0.7-Mbp region through progeny testing using F7 lines. qRL1.4-NERICA7 should help us understand genetic control in NERICAs, and improve root elongation in rice breeding programs.