Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases

Substantial evidence has shown that microRNAs are crucial for biological processes within complex human diseases. Identifying the association of miRNA–disease pairs will contribute to accelerating the discovery of potential biomarkers and pathogenesis. Researchers began to focus on constructing computational models to facilitate the progress of disease pathology and clinical medicine by identifying the potential disease-related miRNAs. However, most existing computational methods are expensive, and their use is limited to unobserved relationships for unknown miRNAs (diseases) without association information. In this manuscript, we proposed a creatively semi-supervised model named bidirectional generative adversarial network for miRNA-disease association prediction (BGANMDA). First, we constructed a microRNA similarity network, a disease similarity network, and Gaussian interaction profile kernel similarity based on the known miRNA–disease association and comprehensive similarity of miRNAs (diseases). Next, an integrated similarity feature network with the full underlying relationships of miRNA–disease pairwise was obtained. Then, the similarity feature network was fed into the BGANMDA model to learn advanced traits in latent space. Finally, we ranked an association score list and predicted the associations between miRNA and disease. In our experiment, a five-fold cross validation was applied to estimate BGANMDA’s performance, and an area under the curve (AUC) of 0.9319 and a standard deviation of 0.00021 were obtained. At the same time, in the global and local leave-one-out cross validation (LOOCV), the AUC value and standard deviation of BGANMDA were 0.9116 ± 0.0025 and 0.8928 ± 0.0022, respectively. Furthermore, BGANMDA was employed in three different case studies to validate its prediction capability and accuracy. The experimental results of the case studies showed that 46, 46, and 48 of the top 50 prediction lists had been identified in previous studies.
Serum Serine and the Risk of All-Cause Mortality: A Nested Case-Control Study From the China Stroke Primary Prevention Trial (CSPPT)

Background Serine plays a key role in numerous cellular processes, the levels and metabolism is therefore of critical importance. However, few data are available to illustrate the association of serine with long-term health effects, especially, the predictive value for long-term mortality. Objective This study was conducted to evaluate the relationship between serum serine levels and all-cause mortality in general hypertensive patients in a longitudinal cohort, and to examine the potential effect modifiers. Methods A nested case-control (NCC) study was conducted utilizing 20702 hypertensive participants from the China Stroke Primary Prevention Trial (CSPPT), a randomized, double-blind, actively controlled trial conducted from May 2008 to August 2013 in China. The current study included 291 cases of all-cause mortality and 291 controls matched on age (≤ 1 year), sex and treatment group. All-cause mortality was the main outcome in this analysis, which included death due to any reason. Results With the increase in serum serine levels, the risk of all-cause mortality first increased before flattening. After adjusting for related variables, the risk of mortality increased significantly with the increase of serum serine levels. Compared with group Q1, the mortality risk of group Q2, Q3 and Q4 were significantly increased [ORs, 95% CI: Q2: 2.32, (1.32–4.07); Q3: 2.59, (1.48–4.54); and Q4: 1.85, (1.07–3.22)]. In the exploratory analysis, we observed three effect modifiers, total homocysteine, 5-Methyltetrahydrofolate, and estimated glomerular filtration rate significantly modified the serum serine and all-cause mortality association. Conclusion Serum serine levels were significantly associated with an increased risk of all-cause mortality in hypertensive patients. Our results and findings, if confirmed further, suggest that serum serine should be considered as a marker for screening risk factors of mortality. Clinical Trial Registration [https://www.clinicaltrials. gov/ct2/show/study/NCT00794885. ], identifier [CSPPT, NCT00794885].
Is Support for Feminism Enough for Change? How Sexism and Gender Stereotypes Might Hinder Gender Justice

Even though formal processes (i.e., gender quotes) are necessary to achieve gender justice, attitudinal changes (i.e., support of egalitarian social norms) are also essential. The endorsement of sexism and gender stereotypes perpetuate inequality on a daily basis, and can be seen as barriers that prevent societies from reaching social justice. Therefore, changing sexist social norms can be understood as a fundamental step in accomplishing gender justice. With the aim of studying Chileans’ sexist norms, we conducted a survey with a representative sample (N = 490) exploring levels of sexism and gender stereotypes, as well as support for the feminist movement. Using Latent Profile Analysis, we identified four groups of citizens: (1) a first group that shows high levels of sexism and low support for the feminist movement (9%); (2) a second group, with low levels of sexism and high support for the feminist movement (20%); (3) a third group with high levels of sexism and high support for the feminist movement (65%); and (4) a fourth group with mid-levels of sexism and support of the feminist movement (6%). We called these groups the Sexist, Feminist, Inconsistent, and Moderate Group, respectively. The four groups showed similar high endorsement of gender stereotypes. These results are twofold. First, they hint that although nowadays gender equality seems to be generally accepted, this coexists with a high prevalence of sexist social norms, represented by the inconsistent group being the most prevalent. Second, gender stereotypes are still deeply rooted in Chilean culture, surprisingly even among feminist citizens.
The Value of HPA Axis Hormones as Biomarkers for Screening and Early Diagnosis of Postpartum Depression: Updated Information About Methodology

Because of the high prevalence of postpartum depression (PPD) and the suffering involved, early diagnosis is urgent; however, current screening tools and diagnosis are inadequate. In addition to conventional methods such as the Edinburgh Postnatal Depression Scale and clinical interviews, several hormones in the hypothalamic–pituitary–adrenal (HPA) axis, such as corticotrophin-releasing hormone, adrenocorticotropic hormone, and cortisol, have been considered because of their critical roles in stress regulation in the mothers. The study designs are complicated, however, and so the effectiveness of these hormones as biomarkers for PPD is still controversial. Such inconsistency may have resulted from the variation in methodology between studies. The methodology problems in the investigation of PPD and HPA axis hormones have not been reported extensively. We therefore sought to summarize the methodological problems of studies published in the past decade, including the strengths and weaknesses of the examinations and the technological difficulties involved. Our findings suggest that (a) suitable samples and appropriate detection methods would reduce heterogeneity among trials; (b) the cutoff value of the scale test should be carefully selected for determining the performance of biomarker tests; (c) evaluation methods and criteria should be chosen with consideration of the tools feasible for use in local hospitals and population; and (d) the cost of diagnosis should be reduced. We hope that these findings provide insight for future investigations of HPA axis hormones as biomarkers for screening and early diagnosis of PPD.
The Impact of the COVID-19 Pandemic on Smoking Consumption: A Systematic Review of Longitudinal Studies

The COVID-19 pandemic has generated a global health crisis that has negatively impacted the mental health and wellbeing of the population. A large amount of scientific literature has emerged since 2019, but none of these studies have focused on assessing the impact of COVID-19 on smoking consumption. We aimed to analyse the changes in smoking consumption during the COVID-19 pandemic through longitudinal studies. This systematic review follows the PRISMA Statement. This study was registered on PROSPERO (CRD42021282235). MEDLINE, ERIC, PsycARTICLES, Scopus, Web of Science and PsycINFO databases were searched from inception to 24 October 2021. We completed an extensive assessment of all prospective cohort studies that aimed to explore the effect of the COVID-19 pandemic on tobacco consumption habits. According to the PICOS's acronym, we included all population (P) types and studies developed before and during the COVID-19 pandemic (I) with a change in nicotine consumption as the outcome (O), as well as prospective cohort studies. The risk of bias was assessed using the Newcastle–Ottawa Scale for observational studies. The results showed that 14 cohorts reported in 11 articles fulfilled the inclusion criteria. A total of 58,052 participants were included in the review. Most of the studies pointed out a reduction in the number of cigarettes and e-cigarettes consumed from baseline (before the pandemic) to follow-up (during the pandemic). Only two studies reported an increase in cigarette or e-cigarette consumption from baseline to follow-up. The majority of studies presented a low risk of bias. In conclusion, the impact of the COVID-19 pandemic on smoking behavior is complex and uncertain. The decrease in smoking consumption during the pandemic could be related to the fear of becoming infected by COVID-19, the advancement of COVID-19, and the reduction in social gatherings. In several cases, the increases in nicotine consumption can be explained by psychological distress. These findings can be used to create strategies to prevent relapses during the post-vaccination phases of the pandemic. Systematic Review Registration: PROSPERO, identifier: CRD42021282235.
Phenotypicand Genotypic Characterization of Clinical Isolates of Intracellular Adherent–Invasive Escherichia coli Among Different Stages, Family History, and Treated Colorectal Cancer Patients in Iran

There is increasing evidence showing that microbial dysbiosis impacts the health and cancer risk of the host. An association between adherent–invasive Escherichia coli (AIEC) and colorectal cancer (CRC) has been revealed. Cyclomodulins (CMs) have been receiving increasing attention for carcinogenic changes. In this study, the incidence and features of intracellular AIEC and cyclomodulin-encoding genes were investigated and the phylogenetic grouping and genetic relatedness were evaluated. E. coli strains were isolated from the colorectal biopsies. Adhesion and invasion assays and intramacrophage cell survival test were performed to separate the AIEC isolates. Virulence genotyping for the genes htrA, dsbA, chuA, and lpfA and the cyclomodulin toxins was also conducted. In addition, phylogenetic grouping of the isolates was determined. Subsequently, repetitive element sequence-based PCR (rep-PCR) fingerprinting was performed. A total of 24 AIEC pathovars were isolated from 150 patients. The prevalence rates of htr, dsbA, and lpfA were 70.83% and that of chuA was 91.66%. The frequencies of the cyclomodulin toxins were as follows: cnf1, 29.2%; cnf2, 25%; colibactin, 29.2%; and cdt, 4.2%; cif was not found. Among the AIEC isolates, 4.2%, 4.2%, 54.2%, 29.2%, and 8.3% with phylotypes A or C, B1, B2, D, and E were identified, respectively. Left-sided colon carcinoma and adenocarcinoma T≥1 stage (CRC2) were colonized by B2 phylogroup AIEC-producing CMs more often than the samples from the other groups. Close genetic relatedness was observed in AIEC isolates with rep-PCR.
Practical Performance of Random Projections in Linear Programming

The use of random projections in mathematical programming allows standard solution algorithms to solve instances of much larger sizes, at least approximately. Approximation results have been derived in the relevant literature for many specific problems, as well as for several mathematical programming subclasses. Despite the theoretical developments, it is not always clear that random projections are actually useful in solving mathematical programs in practice. In this paper we provide a computational assessment of the application of random projections to linear programming. 2012 ACM Subject Classification Mathematics of computing → Mathematical optimization
Efficient Exact Learning Algorithms for Road Networks and Other Graphs with Bounded Clustering Degrees

The completeness of road network data is significant in the quality of various routing services and applications. We introduce an efficient randomized algorithm for exact learning of road networks using simple distance queries, which can find missing roads and improve the quality of routing services. The efficiency of our algorithm depends on a cluster degree parameter, d max , which is an upper bound on the degrees of vertex clusters defined during our algorithm. Unfortunately, we leave open the problem of theoretically bounding d max , although we conjecture that d max is small for road networks and other similar types of graphs. We support this conjecture by experimentally evaluating our algorithm on road network data for the U.S. and 5 European countries of various sizes. This analysis provides experimental evidence that our algorithm issues a quasilinear number of queries in expectation for road networks and similar graphs.
A Fast Data Structure for Dynamic Graphs Based on Hash-Indexed Adjacency Blocks

Several dynamic graph data structures have been proposed in literature. Yet, these data structures either offer limited support for arbitrary graph algorithms or they are designed as part of specific frameworks (e.g., for GPUs or specialized hardware). Such frameworks are difficult to adopt to arbitrary graph computations and lead practitioners to fall back to less sophisticated solutions when dealing with dynamic graphs. In this work, we propose a new “dynamic hashed blocks” (DHB) data structure for sparse dynamic graphs and matrices on general-purpose CPU architectures. DHB combines an efficient block-based memory layout to store incident edges with an additional per-vertex hash index for high degree vertices. This hash index allows us to quickly insert edges without introducing duplicates, while the block-based memory layout retains advantageous cache locality properties of traditional adjacency arrays. Experiments show that DHB outperforms competing dynamic graph structures for edge insertions, updates, deletions, and traversal operations. Compared to static CSR layouts, DHB exhibits only a small overhead in traversal performance. DHB’s interface is similar to general-purpose abstract graph data types and can be easily used as a drop-in replacement for traditional adjacency arrays. To demonstrate that, we modify the well-known NetworKit framework to use DHB instead of its own dynamic graph representation. Experiments show that this modification only slightly penalizes the performance of graph algorithms while considerably boosting update rates.
Routing in Multimodal Transportation Networks with Non-scheduled Lines

Over the last decades, new mobility offers have emerged to enlarge the coverage and the accessibility of public transportation systems. In many areas, public transit now incorporates on-demand transport lines, that can be activated at user need. In this paper, we propose to integrate lines without predefined schedules but with predefined stop sequences into a state-of-the-art trip planning algorithm for public transit, the Trip-Based Public Transit Routing algorithm [33]. We extend this algorithm to non-scheduled lines and explain how to model other modes of transportation, such as bike sharing, with this approach. The resulting algorithm is exact and optimizes two criteria: the earliest arrival time and the minimal number of transfers. Experiments on two large datasets show the interest of the proposed method over a baseline modelling.
Synthetic Oligopeptides Derived from γ -Core Regions of Tomato Cysteine-Rich Peptides Possess Antimicrobial Activity against Human and Plant Pathogens

: Plant cysteine-rich peptides (CRPs) represent a diverse group of molecules involved in different aspects of plant physiology. Antimicrobial peptides, which directly suppress the growth of pathogens, are regarded as promising templates for the development of next-generation pharmaceuticals and ecologically friendly plant disease control agents. Their oligopeptide fragments are even more promising because of their low production costs. The goal of this work was to explore the antimicrobial activity of nine short peptides derived from the γ -core-containing regions of tomato CRPs against important plant and human pathogens. We discovered antimicrobial activity in peptides derived from the defensin-like peptides, snakins, and MEG, which demonstrates the direct involvement of these CRPs in defense reactions in tomato. The CRP-derived short peptides appeared particularly active against the gram-positive bacterium Clavibacter michiganensis, which causes bacterial wilt—opening up new possibilities for their use in agriculture to control this dangerous disease. Furthermore, high inhibitory potency of short oligopeptides was demonstrated against the yeast Cryptococcus neoformans , which causes serious diseases in humans, making these peptide molecules promising candidates for the development of next-generation pharmaceuticals. Studies of the mode of action of the two most active peptides indicate fungal membrane permeabilization as a mechanism of antimicrobial action.
Reliability for Nanosafety Research – Considerations on the Basis of a Comprehensive Literature Review

After 15 years of intense research on the risks of nanomaterials, the evaluation of specific nanomaterials is still difficult. The more research is performed, the more studies which demonstrate critical effects of nanomaterials are published. In this regard, it should be noted that most published studies do not show usable toxicological results, although these are well-cited as such and appear as headlines in the daily press. There is a lack of reliability of the scientific data, especially for nanosafety research. Here, this statement is illustrated by examples and solutions are shown.
TCFHs, IIB warped AdS backgrounds and hidden symmetries

We present the twisted covariant form hierarchies (TCFHs) on the internal spaces of all type IIB warped AdS backgrounds. As a result we demonstrate that the form bilinears on the internal spaces satisfy a generalisation of the conformal Killing-Yano equation. We also explore some of the properties of the TCFHs, like for example the holonomy of the TCFH connections. In addition, we present examples where the form bilinears generate hidden symmetries for particle probes propagating on the internal spaces of some AdS backgrounds. These include the maximally supersymmetric AdS 5 solution as well as some of the near horizon geometries of intersecting IIB branes.
Mobile multirobot manipulation by image recognition

— In this paper we considered the problem of mobile multirobot manipulation, with information extracted from a fixed position camera for command inputs that is analyzed by a real time image recognition algorithm. We used 1320 images for training 11 gesture commands, for decision making we used deep learning through a CNN. The proposed method is evaluated using Kappa’s agreement analysis, with overall score ( K ), of 0.9708, meaning almost perfect agreement between the prediction of the CNN and the result expected. This approach allows multiple robotic agents to perform collaborative tasks in real time using hand gestures.
Meat quality from four genetic groups of bulls slaughtered at 14 months old - doi: 10.4025/actascianimsci.v34i4.14728

Carcass characteristics and chemical composition of Longissimus muscle of 32 young bulls from four genetic groups slaughtered at 14 months old were evaluated. Experimental design was completely randomized and distributed into four genetics groups: 1. Caracu (CAR); 2. Canchin (CAN); 3. Charolais vs. Caracu (CHC) and 4. Aberdeen Angus vs. Canchin (AAC). Carcass dressing and carcass conformation were lower for CAR than CAN, CHC and AAC genetic groups. No difference has been reported with regard to moisture, ash, crude protein, total lipids and total cholesterol among four genetics groups. The genetic groups influence fatty acid profile on Longissimus muscle in bulls. Cattle with zebu genes had the highest rates in unsaturated fatty acids, saturated fatty acids represented approximately 50% of total fatty acid composition on Longissimus muscle. The genetic groups presented differences in PUFA, SFA, n-6 and n-3 percentages and n-6 and n-3 ratios.
A Parallel Decision Tree Based Algorithm on MPI for Multi-label Classification Learning

Multi-label classification is an important area of data mining, in where decision tree is one of the effective means to solve the problem. It faced a huge challenge of performance caused by large size of data. First, we translate the multi-label classification to several binary classifications. Then we analyzed the potential parallelism of decision tree based multi-label classification algorithm from four parts and overall applied them in the training and predicting phases. The parallel algorithm was implemented with MPI and the performance of parallel decision tree based multi-label classification algorithm is analyzed and compared program designations and experiments, which demonstrate that our parallel algorithm could improve the computing efficiency and still has some extensibilities. Keywords-multi-label classification; decision tree; parallelization; MPI