Numerical Simulation of an Offset Jet in Bounded Pool with Deflection Wall

The k-ε turbulent model and VOF methods were used to simulate the three-dimensional turbulence jet. Numerical simulations were carried out for three different kinds of jets in a bounded pool with the deflection wall with angles of 0°, 3°, 6°, and 9°. The numerical simulation agrees well with the experimental data. The studies show that the length of the potential core zone increases with the increase of the deflection angle. The velocity distribution is consistent with the Gaussian distribution and almost not affected by the deflection angle in potential core zone. The decay rates of flow velocity in the transition zone are 1.195, 1.281, 1.439, and 1.532 corresponding to the unilateral deflection angles, 0°, 3°, 6°, and 9°, respectively. The decay rates of velocity in the transition zone are 1.928 and 2.835 corresponding to the bilateral deflection angles 3° and 6°. It is also found that the spread of velocity is stronger in the vertical direction as the deflection angles become smaller. The spread rates of velocity with unilateral deflection wall are higher than those with bilateral deflection walls in the horizontal plane in the pool

Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks

An Undirected Weighted Network (UWN) is commonly found in big data-related applications. Note that such a network's information connected with its nodes, and edges can be expressed as a Symmetric, High-Dimensional and Incomplete (SHDI) matrix. However, existing models fail in either modeling its intrinsic symmetry or low-data density, resulting in low model scalability or representation learning ability. For addressing this issue, a Proximal Symmetric Nonnegative Latent-factor-analysis (PSNL) model is proposed. It incorporates a proximal term into symmetry-aware and data density-oriented objective function for high representation accuracy. Then an adaptive Alternating Direction Method of Multipliers (ADMM)-based learning scheme is implemented through a Tree-structured of Parzen Estimators (TPE) method for high computational efficiency. Empirical studies on four UWNs demonstrate that PSNL achieves higher accuracy gain than state-of-the-art models, as well as highly competitive computational efficiency

A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis

High-Dimensional and Incomplete (HDI) data is commonly encountered in big data-related applications like social network services systems, which are concerning the limited interactions among numerous nodes. Knowledge acquisition from HDI data is a vital issue in the domain of data science due to their embedded rich patterns like node behaviors, where the fundamental task is to perform HDI data representation learning. Nonnegative Latent Factor Analysis (NLFA) models have proven to possess the superiority to address this issue, where a linear bias incorporation (LBI) scheme is important in present the training overshooting and fluctuation, as well as preventing the model from premature convergence. However, existing LBI schemes are all statistic ones where the linear biases are fixed, which significantly restricts the scalability of the resultant NLFA model and results in loss of representation learning ability to HDI data. Motivated by the above discoveries, this paper innovatively presents the dynamic linear bias incorporation (DLBI) scheme. It firstly extends the linear bias vectors into matrices, and then builds a binary weight matrix to switch the active/inactive states of the linear biases. The weight matrix's each entry switches between the binary states dynamically corresponding to the linear bias value variation, thereby establishing the dynamic linear biases for an NLFA model. Empirical studies on three HDI datasets from real applications demonstrate that the proposed DLBI-based NLFA model obtains higher representation accuracy several than state-of-the-art models do, as well as highly-competitive computational efficiency.Comment: arXiv admin note: substantial text overlap with arXiv:2306.03911, arXiv:2302.12122, arXiv:2306.0364

Estimating the Fraction of Non-Coding RNAs in Mammalian Transcriptomes

Recent studies of mammalian transcriptomes have identified numerous RNA transcripts that do not code for proteins; their identity, however, is largely unknown. Here we explore an approach based on sequence randomness patterns to discern different RNA classes. The relative z-score we use helps identify the known ncRNA class from the genome, intergene and intron classes. This leads us to a fractional ncRNA measure of putative ncRNA datasets which we model as a mixture of genuine ncRNAs and other transcripts derived from genomic, intergenic and intronic sequences. We use this model to analyze six representative datasets identified by the FANTOM3 project and two computational approaches based on comparative analysis (RNAz and EvoFold). Our analysis suggests fewer ncRNAs than estimated by DNA sequencing and comparative analysis, but the verity of our approach and its prediction requires more extensive experimental RNA data

CLIPC8: Face liveness detection algorithm based on image-text pairs and contrastive learning

Face recognition technology is widely used in the financial field, and various types of liveness attack behaviors need to be addressed. Existing liveness detection algorithms are trained on specific training datasets and tested on testing datasets, but their performance and robustness in transferring to unseen datasets are relatively poor. To tackle this issue, we propose a face liveness detection method based on image-text pairs and contrastive learning, dividing liveness attack problems in the financial field into eight categories and using text information to describe the images of these eight types of attacks. The text encoder and image encoder are used to extract feature vector representations for the classification description text and face images, respectively. By maximizing the similarity of positive samples and minimizing the similarity of negative samples, the model learns shared representations between images and texts. The proposed method is capable of effectively detecting specific liveness attack behaviors in certain scenarios, such as those occurring in dark environments or involving the tampering of ID card photos. Additionally, it is also effective in detecting traditional liveness attack methods, such as printing photo attacks and screen remake attacks. The zero-shot capabilities of face liveness detection on five public datasets, including NUAA, CASIA-FASD, Replay-Attack, OULU-NPU and MSU-MFSD also reaches the level of commercial algorithms. The detection capability of proposed algorithm was verified on 5 types of testing datasets, and the results show that the method outperformed commercial algorithms, and the detection rates reached 100% on multiple datasets. Demonstrating the effectiveness and robustness of introducing image-text pairs and contrastive learning into liveness detection tasks as proposed in this paper

Fecal Metabolomics and Potential Biomarkers for Systemic Lupus Erythematosus

The role of metabolomics in autoimmune diseases has been a rapidly expanding area in researches over the last decade, while its pathophysiologic impact on systemic lupus erythematosus (SLE) remains poorly elucidated. In this study, we analyzed the metabolic profiling of fecal samples from SLE patients and healthy controls based on ultra-high-performance liquid chromatography equipped with mass spectrometry for exploring the potential biomarkers of SLE. The results showed that 23 differential metabolites and 5 perturbed pathways were identified between the two groups, including aminoacyl-tRNA biosynthesis, thiamine metabolism, nitrogen metabolism, tryptophan metabolism, and cyanoamino acid metabolism. In addition, logistic regression and ROC analysis were used to establish a diagnostic model for distinguishing SLE patients from healthy controls. The combined model of fecal PG 27:2 and proline achieved an area under the ROC curve of 0.846, and had a good diagnostic efficacy. In the present study, we analyzed the correlations between fecal metabolic perturbations and SLE pathogenesis. In summary, we firstly illustrate the comprehensive metabolic profiles of feces in SLE patients, suggesting that the fecal metabolites could be used as the potential non-invasive biomarkers for SLE

STCA-SNN: self-attention-based temporal-channel joint attention for spiking neural networks

Spiking Neural Networks (SNNs) have shown great promise in processing spatio-temporal information compared to Artificial Neural Networks (ANNs). However, there remains a performance gap between SNNs and ANNs, which impedes the practical application of SNNs. With intrinsic event-triggered property and temporal dynamics, SNNs have the potential to effectively extract spatio-temporal features from event streams. To leverage the temporal potential of SNNs, we propose a self-attention-based temporal-channel joint attention SNN (STCA-SNN) with end-to-end training, which infers attention weights along both temporal and channel dimensions concurrently. It models global temporal and channel information correlations with self-attention, enabling the network to learn ‘what’ and ‘when’ to attend simultaneously. Our experimental results show that STCA-SNNs achieve better performance on N-MNIST (99.67%), CIFAR10-DVS (81.6%), and N-Caltech 101 (80.88%) compared with the state-of-the-art SNNs. Meanwhile, our ablation study demonstrates that STCA-SNNs improve the accuracy of event stream classification tasks

MethylomeDB: a database of DNA methylation profiles of the brain

MethylomeDB (http://epigenomics.columbia.edu/methylomedb/index.html) is a new database containing genome-wide brain DNA methylation profiles. DNA methylation is an important epigenetic mark in the mammalian brain. In human studies, aberrant DNA methylation alterations have been associated with various neurodevelopmental and neuropsychiatric disorders such as schizophrenia, and depression. In this database, we present methylation profiles of carefully selected non-psychiatric control, schizophrenia, and depression samples. We also include data on one mouse forebrain sample specimen to allow for cross-species comparisons. In addition to our DNA methylation data generated in-house, we have and will continue to include published DNA methylation data from other research groups with the focus on brain development and function. Users can view the methylation data at single-CpG resolution with the option of wiggle and microarray formats. They can also download methylation data for individual samples. MethylomeDB offers an important resource for research into brain function and behavior. It provides the first source of comprehensive brain methylome data, encompassing whole-genome DNA methylation profiles of human and mouse brain specimens that facilitate cross-species comparative epigenomic investigations, as well as investigations of schizophrenia and depression methylomes

Microbial assemblies associated with temperature sensitivity of soil respiration along an altitudinal gradient

10 páginas.. 4 figuras.- referencias.- Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2022.153257Identifying the drivers of the response of soil microbial respiration to warming is integral to accurately forecasting the carbon-climate feedbacks in terrestrial ecosystems. Microorganisms are the fundamental drivers of soil microbial respiration and its response to warming; however, the specific microbial communities and properties involved in the process remain largely undetermined. Here, we identified the associations between microbial community and temperature sensitivity (Q10) of soil microbial respiration in alpine forests along an altitudinal gradient (from 2974 to 3558 m) from the climate-sensitive Tibetan Plateau. Our results showed that changes in microbial community composition accounted for more variations of Q10 values than a wide range of other factors, including soil pH, moisture, substrate quantity and quality, microbial biomass, diversity and enzyme activities. Specifically, co-occurring microbial assemblies (i.e., ecological clusters or modules) targeting labile carbon consumption were negatively correlated with Q10 of soil microbial respiration, whereas microbial assemblies associated with recalcitrant carbon decomposition were positively correlated with Q10 of soil microbial respiration. Furthermore, there were progressive shifts of microbial assemblies from labile to recalcitrant carbon consumption along the altitudinal gradient, supporting relatively high Q10 values in high-altitude regions. Our results provide new insights into the link between changes in major microbial assemblies with different trophic strategies and Q10 of soil microbial respiration along an altitudinal gradient, highlighting that warming could have stronger effects on microbially-mediated soil organic matter decomposition in high-altitude regions than previously thought.This research was supported by the National Natural Science Foundation of China (32071595 and 41830756). We also thank the Fundamental Research Funds for the Central Universities (Program no. 2662019PY010 and 2662019QD055), Natural Science Fund of Hubei Province (2019CFA094), and the Strategic Priority Research Program (A) of the Chinese Academy of Sciences (Grant No. XDA20040502). We thank Hailong Li for his assistance in field sampling, and Jinhuang Lin for mapping sample locations. M.D-B. is supported by a Ramón y Cajal grant from the Spanish Government (agreement no. RYC2018-025483-I). ReferencesPeer reviewe

Genome-Wide Divergence of DNA Methylation Marks in Cerebral and Cerebellar Cortices

Emerging evidence suggests that DNA methylation plays an expansive role in the central nervous system (CNS). Large-scale whole genome DNA methylation profiling of the normal human brain offers tremendous potential in understanding the role of DNA methylation in brain development and function.Using methylation-sensitive SNP chip analysis (MSNP), we performed whole genome DNA methylation profiling of the prefrontal, occipital, and temporal regions of cerebral cortex, as well as cerebellum. These data provide an unbiased representation of CpG sites comprising 377,509 CpG dinucleotides within both the genic and intergenic euchromatic region of the genome. Our large-scale genome DNA methylation profiling reveals that the prefrontal, occipital, and temporal regions of the cerebral cortex compared to cerebellum have markedly different DNA methylation signatures, with the cerebral cortex being hypermethylated and cerebellum being hypomethylated. Such differences were observed in distinct genomic regions, including genes involved in CNS function. The MSNP data were validated for a subset of these genes, by performing bisulfite cloning and sequencing and confirming that prefrontal, occipital, and temporal cortices are significantly more methylated as compared to the cerebellum.These findings are consistent with known developmental differences in nucleosome repeat lengths in cerebral and cerebellar cortices, with cerebrum exhibiting shorter repeat lengths than cerebellum. Our observed differences in DNA methylation profiles in these regions underscores the potential role of DNA methylation in chromatin structure and organization in CNS, reflecting functional specialization within cortical regions