Miracle-dz/UniFAR-FTUs huggingface dataset:
Sperm Penetration of Cervical Mucus as a Criterion of Male Fertility

Semen samples were examined from 51 fertile men, and from the male partners in 283 infertile marriages. In addition to routine analysis, they were tested for sperm penetration through ovulatory cervical mucus, using the method of Kremer with slight modifications. Comparison of the 2 groups in regard to semen properties showed statistically significant differences between the means for density, percentage of living, motile and abnormal spermatozoa, for motility degree, and for sperm penetration. No significant difference between the means was found for volume, content of fructose and acid phosphatase in the seminal plasma. In the entire series a moderate correlation was found between sperm penetration and percentage of motile spermatozoa, motility degree, density, percentage of living and percentage of abnormal spermatozoa. A low correlation was found between sperm penetration and volume, content of fructose or acid phosphatase in seminal plasma. The variance of sperm penetration due to regression of the other semen properties was only 43%. Sperm penetration had the highest discriminant function coefficient of the tested semen properties. Semen samples from fertile men had high penetration values although other semen properties were sometimes pathological. The sperm penetration test is a most valuable supplement to routine semen analysis for male fertility estimation.
Metabarcoding Insights Into the Trophic Behavior and Identity of Intertidal Benthic Foraminifera

Foraminifera are ubiquitous marine protists with an important role in the benthic carbon cycle. However, morphological observations often fail to resolve their exact taxonomic placement and there is a lack of field studies on their particular trophic preferences. Here, we propose the application of metabarcoding as a tool for the elucidation of the in situ feeding behavior of benthic foraminifera, while also allowing the correct taxonomic assignment of the feeder, using the V9 region of the 18S (small subunit; SSU) rRNA gene. Living foraminiferal specimens were collected from two intertidal mudflats of the Wadden Sea and DNA was extracted from foraminiferal individuals and from the surrounding sediments. Molecular analysis allowed us to confirm that our foraminiferal specimens belong to three genetic types: Ammonia sp. T6, Elphidium sp. S5 and Haynesina sp. S16. Foraminiferal intracellular eukaryote communities reflected to an extent those of the surrounding sediments but at different relative abundances. Unlike sediment eukaryote communities, which were largely determined by the sampling site, foraminiferal intracellular eukaryote communities were driven by foraminiferal species, followed by sediment depth. Our data suggests that Ammonia sp. T6 can predate on metazoan classes, whereas Elphidium sp. S5 and Haynesina sp. S16 are more likely to ingest diatoms. These observations, alongside the use of metabarcoding in similar ecological studies, significantly contribute to our overall understanding of the ecological roles of these protists in intertidal benthic environments and their position and function in the benthic food webs.
Machine Learning Based Classification of Resting-State fMRI Features Exemplified by Metabolic State (Hunger/Satiety)

Objective Resting-state functional magnetic resonance imaging (rs-fMRI) has become an essential measure to investigate the human brain’s spontaneous activity and intrinsic functional connectivity. Several studies including our own previous work have shown that the brain controls the regulation of energy expenditure and food intake behavior. Accordingly, we expected different metabolic states to influence connectivity and activity patterns in neuronal networks. Methods The influence of hunger and satiety on rs-fMRI was investigated using three connectivity models (local connectivity, global connectivity and amplitude rs-fMRI signals). After extracting the connectivity parameters of 90 brain regions for each model, we used sequential forward floating selection strategy in conjunction with a linear support vector machine classifier and permutation tests to reveal which connectivity model differentiates best between metabolic states (hunger vs. satiety). Results We found that the amplitude of rs-fMRI signals is slightly more precise than local and global connectivity models in order to detect resting brain changes during hunger and satiety with a classification accuracy of 81%. Conclusion The amplitude of rs-fMRI signals serves as a suitable basis for machine learning based classification of brain activity. This opens up the possibility to apply this combination of algorithms to similar research questions, such as the characterization of brain states (e.g., sleep stages) or disease conditions (e.g., Alzheimer’s disease, minimal cognitive impairment).
Manipulating Endoplasmic Reticulum-Plasma Membrane Tethering in Plants Through Fluorescent Protein Complementation

The bimolecular fluorescence complementation (BiFC) assay has been widely used to examine interactions between integral and peripheral proteins within putative plasma membrane (PM) microdomains. In the course of using BiFC assays to examine the co-localization of plasma membrane (PM) targeted receptor-like kinases (RLKs), such as FLS2, with PM micro-domain proteins such as remorins, we unexpectedly observed heterogeneous distribution patterns of fluorescence on the PM of Nicotiana benthamiana leaf cortical cells. These patterns appeared to co-localize with the endoplasmic reticulum (ER) and with ER-PM contact sites, and closely resembled patterns caused by over-expression of the ER-PM tether protein Synaptotagmin1 (SYT1). Using domain swap experiments with SYT1, we inferred that non-specific dimerization between FLS2-VenusN and VenusC-StRem1.3 could create artificial ER-PM tether proteins analogous to SYT1. The same patterns of ER-PM tethering were produced when a representative set of integral membrane proteins were partnered in BiFC complexes with PM-targeted peripheral membrane proteins, including PtdIns(4)P-binding proteins. We inferred that spontaneous formation of mature fluorescent proteins caused the BiFC complexes to trap the integral membrane proteins in the ER during delivery to the PM, producing a PM-ER tether. This phenomenon could be a useful tool to deliberately manipulate ER-PM tethering or to test protein membrane localization. However, this study also highlights the risk of using the BiFC assay to study membrane protein interactions in plants, due to the possibility of alterations in cellular structures and membrane organization, or misinterpretation of protein-protein interactions. A number of published studies using this approach may therefore need to be revisited.
A critical RCU safety property is... ease of use!

Some might argue that read-copy update (RCU) is too low-level to be targeted by hackers, but the advent of Row Hammer [19] demonstrated the naïveté of such views. After all, if black-hat hackers are ready, willing, and able to exploit hardware bugs such as Row Hammer, they are assuredly ready, willing, and able to exploit bugs in RCU. Nor is it any longer the case that RCU's involvement in exploitable Linux-kernel bugs is strictly theoretical. However, this bug involved not RCU's correctness, but rather its ease of use. Nevertheless, it was a real bug that really needed fixing. This paper describes this bug and the road to its eventual fix.
Do animal models hold value in Autism spectrum disorder (ASD) drug discovery?

ABSTRACT Introduction: Autism spectrum disorder (ASD) defines impairments in a broad range of behaviors in two domains, social communication and repetitive behaviors and/or restricted interests. Drug discovery is ongoing for ASD, but no drugs have been approved for the core behaviors. Animal models are invaluable for drug discovery, but are limited by the face, construct, and predictive validity for ASD. The genetic construct validity of animal models has provided potential targets including biological events early in development which are indeed challenging to treat pharmacologically. Areas covered: The focus of this review is on the current models for ASD being used to test potential therapeutics. Drugs reviewed include sulforaphane, propranolol, oxytocin, vasopressin antagonists, arbaclofen, and bumetanide, that have been evaluated on behaviors with face validity for both the core behaviors of ASD, social and repetitive behaviors, and the modifying behaviors including learning and memory. Expert opinion: Animal models for the core symptoms of ASD have suffered from the same problems hampering research in humans, including lack of a biomarker, heterogeneity of symptom severity, and appropriate endpoints for evaluation. Despite this, the data from animal models has allowed several drugs to move on to clinical testing.
Towards building a high-performance, scale-in key-value storage system

Key-value stores are widely used as storage backends, due to their simple, yet flexible interface for cache, storage, file system, and database systems. However, when used with high performance NVMe devices, their high compute requirements for data management often leave the device bandwidth under-utilized. This leads to a performance mismatch of what the device is capable of delivering and what it actually delivers, and the gains derived from high speed NVMe devices is nullified. In this paper, we introduce KV-SSD (Key-Value SSD) as a key technology in a holistic approach to overcome such performance imbalance. KV-SSD provides better scalability and performance by simplifying the software storage stack and consolidating redundancy, thereby lowering the overall CPU usage and releasing the memory to user applications. We evaluate the performance and scalability of KV-SSDs over state-of-the-art software alternatives built for traditional block SSDs. Our results show that, unlike traditional key-value systems, the overall performance ofKV-SSD scales linearly, and delivers 1.6 to 57x gains depending on the workload characteristics.
tensorflow-tracing: A Performance Tuning Framework for Production

The growing popularity of Deep Neural Networks (DNN) within the mainstream [8] has had a rapid transformative effect on clusters and data centers. DNN training jobs are becoming one of the largest tenants within clusters, and often take hours to weeks to complete; and even a slight performance improvement can save substantial runtime costs. Despite this fact, the DNN specific performance tuning tools are yet to keep up with the needs of the new changes in production environments. On one hand, the existing application-agnostic resource-level tools such as top, Nvidia Nsight (for GPU utilization), IPM (for MPI network monitoring) are too limited to predict or explain the behavior and performance of a job accurately. In DNN applications, there exists a complex relationship among resources. Even though measuring coarse metrics such as bandwidth, latency, and GPU/CPU utilization can draw an overall picture of cluster performance, these metrics are not easily translatable to application-level metrics and do not provide actionable insights on how to handle performance bottlenecks. On the other hand, the short list of application-aware tools, such as MLModelScope [6], TensorBoard [1], and tf.RunOptions [2], while able to provide actionable insights, are mainly designed for the need of application developers and are not intended for production use. Such tools require substantial modification to applications, and early planning as to what, when and how data should be collected. In this article, we introduce tensorflow-tracing to fill the gap between these two classes of performance tun-
Adverse Childhood Experiences and Health Care Utilization in a Low-Income Population

Abstract:Objective. To determine associations of adverse childhood experiences (ACE) with adult health care utilization in an underserved, low-income population. Methods. Questionnaires on ACE were completed by 38,200 adults (mean age 54), two-thirds African American, recruited from community health centers (CHCs) across 12 Southeastern states. Odds ratios (ORs) and accompanying 95% confidence intervals (CIs) were computed. Results. The percentages reporting emergency room visits and doctor's office visits, with high chronic disease index scores, rose monotonically (ptrend<.001) with rising ACE score. Odds ratios (CIs) for those with four or more vs. zero ACEs were 1.37 (95% CI 1.27–1.47) for 1–10 times and 1.80 (95% CI 1.29–2.52) for more than 10 times ER visits, 1.37 (95% CI 1.18–1.59) for over 10 doctor's visits, and 2.29 (95% CI 2.06–2.54) for three or more chronic diseases. Conclusions. High ACE levels were associated with greater chronic disease burden and greater health care utilization in adulthood. Long-lasting effects from ACE on the health care of underserved populations are indicated. There is an urgent need to train health care providers, patients, and their families on ACE effects and treatments for better health care outcomes.
Effectiveness of interventions for the implementation of thromboprophylaxis in hospitalised patients at risk of venous thromboembolism: an updated abridged Cochrane systematic review and meta-analysis of randomised controlled trials

Objective To assess the effectiveness of system-wide interventions designed to increase the implementation of thromboprophylaxis and decrease the incidence of venous thromboembolism (VTE) in hospitalised medical and surgical patients at risk of VTE. Design Systematic review and meta-analysis of randomised controlled trials (RCTs). Data sources Medline, PubMed, Embase, BIOSIS, CINAHL, Web of Science, CENTRAL, DARE, EED, LILACS and clinicaltrials.gov without language restrictions from inception to 7 January 2017, as well as the reference lists of relevant review articles. Eligibility criteria for selecting studies RCTs that evaluated the effectiveness of system-wide interventions such as alerts, multifaceted, education, and preprinted orders when compared with no intervention, existing policy or another intervention. Results We included 13 RCTs involving 35 997 participants. Eleven RCTs had data available for meta-analysis. Compared with control, we found absolute increase in the prescription of prophylaxis associated with alerts (21% increase, 95% CI [15% to 275%]) and multifaceted interventions (4% increase, 95% CI [3% to 11%]), absolute increase in the prescription of appropriate prophylaxis associated with alerts (16% increase, 95% CI [12% to 20%]) and relative risk reductions (risk ratio 64%, 95% CI [47% to 86%]) in the incidence of symptomatic VTE associated with alerts. Computer alerts were found to be more effective than human alerts, and multifaceted interventions with an alert component appeared to be more effective than multifaceted interventions without, although comparative pooled analyses were not feasible. The quality of evidence for improvement in outcomes was judged to be low to moderate certainty. Conclusions Alerts increased the proportion of patients who received prophylaxis and appropriate prophylaxis, and decreased the incidence of symptomatic VTE. Multifaceted interventions increased the proportion of patients who received prophylaxis but were found to be less effective than alerts interventions. Trial registration number CD008201.
Predicting Severe Infection and Effects of Hypogammaglobulinemia During Therapy With Rituximab in Rheumatic and Musculoskeletal Diseases

To evaluate predictors of serious infection events (SIEs) during rituximab (RTX) therapy and effects of hypogammaglobulinemia on SIE rates, and humoral response and its persistence after discontinuation of RTX in the treatment of rheumatic and musculoskeletal diseases (RMDs).
Classificação de espécies de peixe utilizando redes neurais convolucional

Data classification is present in different real problems, such as recognizing patterns in images, differentiating defective parts in a production line, classifying benign and malignant tumors, among many others. Many of these problems have data patterns that are hard to identify, which requires more advanced techniques for resolution. Recently, several works addressing different artificial neural network architectures have been applied to solve classification problems. When the classification problem must be obtained through images, currently, the standard methodology is the use of convolutional neural networks. Thus, in this report convolutional neural networks are used to classify fish species. Classificacao de dados esta presente em diversos problemas reais, tais como: reconhecer padroes em imagens, diferenciar pecas defeituosas em uma linha de producao, classificar tumores benignos e malignos, dentre diversas outras. Muitos desses problemas possuem padroes de dados dificeis de serem identificados, o que requer, consequentemente, tecnicas mais avancadas para sua resolucao. Recentemente, diversos trabalhos abordando diferentes arquiteturas de redes neurais artificiais vem sendo aplicados para solucionar problemas de classificacao. Quando a classificacao do problema deve ser obtida por meio de imagens, atualmente a metodologia padrao e uso de redes neurais convolucionais. Sendo assim, neste trabalho sao utilizadas redes neurais convolucionais para classificacao de especies de peixes.
Molecular Modelling of Potential Candidates for the Treatment of Depression

A lot of research initiatives in the last decades have been focused on the search of new strategies to treat depression. However, despite the availability of various antidepressants, current treatment is still far from ideal. Unwanted side effects, modest response rates and the slow onset of action are the main shortcomings. As a strategy to improve symptomatic relief and response rates, the dual modulation of the serotonin transporter and the histamine H3 receptor by a single chemical entity has been proposed in the literature. Accordingly, this work aims to elucidate key structural features responsible for the dual inhibitory activity of the hexahydro‐pyrrolo‐isoquinoline derivatives. For this purpose, two approaches were employed, four‐dimensional quantitative structure‐activity relationship (4D‐QSAR) and molecular docking. The 4D‐QSAR models for both receptors allowed the identification of the pharmacophore groups critical for the modelled biological activity, whereas the binding mode of this class of compounds to the human serotonin transporter was assessed by molecular docking. The findings can be applicable to design new antidepressants.
Interplay Between LOX Enzymes and Integrins in the Tumor Microenvironment

Members of the lysyl oxidase (LOX) family are secreted copper-dependent amine oxidases that catalyze the covalent crosslinking of collagens and elastin in the extracellular matrix (ECM), an essential process for the structural integrity of all tissues. LOX enzymes can also remodel the tumor microenvironment and have been implicated in all stages of tumor initiation and progression of many cancer types. Changes in the ECM can influence several cancer cell phenotypes. Integrin adhesion complexes (IACs) physically connect cells with their microenvironment. This review article summarizes the main findings on the role of LOX proteins in modulating the tumor microenvironment, with a particular focus on how ECM changes are integrated by IACs to modulate cells behavior. Finally, we discuss how the development of selective LOX inhibitors may lead to novel and effective therapies in cancer treatment.
Codd's World: Topics and their Evolution in the Database Community Publication Graph

Scholarly network analysis is the study of a scientific research network aiming to discover meaningful insights and making data-driven research decisions. Analyzing such networks has become increasingly challenging, due to the amount of scientific research that is added every day. Furthermore, online resources often include information from other online sources (e.g., academic social platforms), enabling to study networks on a larger and more complex scope. In this paper, we present a study on a specific research network: The (relational) database community publication graph, that we call Codd’s World ; a transitive closure over citations from the foundational work of E.F. Codd. We specifically analyze the topics of the published papers, the relevance of authors and papers, and how this relates to raw publication counts. Among our findings, we show that topic modeling can be a useful entry point for scholarly network analysis.
Information retrieval by metabrowsing

This article investigates a new, effective browsing approach called metabrowsing. It is an alternative for current information retrieval systems, which still face six prominent difficulties. We identify and classify the difficulties and show that the metabrowsing approach alleviates the difficulties associated with query formulation and missing domain knowledge. Metabrowsing is a high-level way of browsing through information: instead of browsing through document contents or document surrogates, the user browses through a graphical representation of the documents and their relations to the domain. The approach requires other cognitive skills from the user than what is currently required. Yet, a user evaluation in which the metabrowsing system was compared with an ordinary query-oriented system showed only some small indicatory differences in effectiveness, efficiency, and user satisfaction. We expect that more experience with metabrowsing will result in a significantly better performance difference. Hence, our conclusion is that the development of new cognitive skills requires some time before the technologies are ready to be used.
Morphological changes during replicative senescence in bovine ovarian granulosa cells

ABSTRACT The objective of this study was to evaluate replicative senescence of bovine granulosa cells (bGCs) during in vitro long-term culture. WST-1 assay analysis showed that bGCs proliferation was reduced from primary culture to 14th passage. The several bGCs from the 3rd passage and 7th passage exposed the weak activity of beta-galactosidase, while a strongly positive staining of beta-galactosidase was observed in bGCs from 14th passage. Flow cytometry analysis showed that bGCs were induced to cell cycle arrest at G0/G1 phase through in vitro expansion. TERT transcript expression of bGCs was downregulated from primary culture to 14th passage. The cell and nuclear area of bGCs were dramatically increased from 14th passage to 25th passage. The nucleocytoplasmic ratio of bGCs was dramatically reduced in 22th passage (4.32%) and 25th passage (2.45%), comparing to previous passages: primary culture (10.67%), 7th passage (9.21%), or 14th passage (10.33%). The number of microfilament bundle of bGCs was increased in 22nd passage (67.42 ± 17.76) and 25th passage (56.31 ± 22.45). The diameter of microfilament bundle of bGCs in 25th passage was dramatically increased to 1.88 ± 0.32 µm comparing to the primary culture (1.15 ± 0.03 µm). In this study, we also assessed the nuclear form factor which illustrates the level of nuclear circular form. A reduction of nuclear form factor was observed in bGCs during long-term in vitro expansion. The changes of nuclear form factor were correlated to other senescent characteristics, especially the nucleocytoplasmic ratio.