Three-Dimensional Scour at Submarine Pipelines In Unidirectional Steady Current

This paper presents results of an experimental study on 3-dimensional scour at submarine pipelines with uniform sediments under a unidirectional steady current. A dimensional analysis is first conducted to identify all the important non-dimensional parameters. Laboratory experiments are then conducted to study the development of a 3-dimensional pipeline scour hole under different sets of environmental conditions. The results are recorded and the corresponding propagation velocities of the free span calculated. The effects of four parameters on the propagation velocity are studied through the conduct of several groups of experiments; each of which exclusively focuses on one particular parameter. Moreover, the scour pattern under different combinations of environmental conditions is discussed to obtain an improved understanding on the mechanism of scour hole propagation at the span shoulder of pipelines

EEND-SS: Joint End-to-End Neural Speaker Diarization and Speech Separation for Flexible Number of Speakers

In this paper, we present a novel framework that jointly performs speaker diarization, speech separation, and speaker counting. Our proposed method combines end-to-end speaker diarization and speech separation methods, namely, End-to-End Neural Speaker Diarization with Encoder-Decoder-based Attractor calculation (EEND-EDA) and the Convolutional Time-domain Audio Separation Network (ConvTasNet) as multi-tasking joint model. We also propose the multiple 1x1 convolutional layer architecture for estimating the separation masks corresponding to the number of speakers, and a post-processing technique for refining the separated speech signal with speech activity. Experiments using LibriMix dataset show that our proposed method outperforms the baselines in terms of diarization and separation performance for both fixed and flexible numbers of speakers, as well as speaker counting performance for flexible numbers of speakers. All materials will be open-sourced and reproducible in ESPnet toolkit.Comment: submitted to INTERSPEECH 202

GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment

Genome-wide identification and phenotypic characterization of seizure-associated copy number variations in 741,075 individuals

Copy number variants (CNV) are established risk factors for neurodevelopmental disorders with seizures or epilepsy. With the hypothesis that seizure disorders share genetic risk factors, we pooled CNV data from 10,590 individuals with seizure disorders, 16,109 individuals with clinically validated epilepsy, and 492,324 population controls and identified 25 genome-wide significant loci, 22 of which are novel for seizure disorders, such as deletions at 1p36.33, 1q44, 2p21-p16.3, 3q29, 8p23.3-p23.2, 9p24.3, 10q26.3, 15q11.2, 15q12-q13.1, 16p12.2, 17q21.31, duplications at 2q13, 9q34.3, 16p13.3, 17q12, 19p13.3, 20q13.33, and reciprocal CNVs at 16p11.2, and 22q11.21. Using genetic data from additional 248,751 individuals with 23 neuropsychiatric phenotypes, we explored the pleiotropy of these 25 loci. Finally, in a subset of individuals with epilepsy and detailed clinical data available, we performed phenome-wide association analyses between individual CNVs and clinical annotations categorized through the Human Phenotype Ontology (HPO). For six CNVs, we identified 19 significant associations with specific HPO terms and generated, for all CNVs, phenotype signatures across 17 clinical categories relevant for epileptologists. This is the most comprehensive investigation of CNVs in epilepsy and related seizure disorders, with potential implications for clinical practice

Plant microRNA-target interaction identification model based on the integration of prediction tools and support vector machine.

Confident identification of microRNA-target interactions is significant for studying the function of microRNA (miRNA). Although some computational miRNA target prediction methods have been proposed for plants, results of various methods tend to be inconsistent and usually lead to more false positive. To address these issues, we developed an integrated model for identifying plant miRNA-target interactions.Three online miRNA target prediction toolkits and machine learning algorithms were integrated to identify and analyze Arabidopsis thaliana miRNA-target interactions. Principle component analysis (PCA) feature extraction and self-training technology were introduced to improve the performance. Results showed that the proposed model outperformed the previously existing methods. The results were validated by using degradome sequencing supported Arabidopsis thaliana miRNA-target interactions. The proposed model constructed on Arabidopsis thaliana was run over Oryza sativa and Vitis vinifera to demonstrate that our model is effective for other plant species.The integrated model of online predictors and local PCA-SVM classifier gained credible and high quality miRNA-target interactions. The supervised learning algorithm of PCA-SVM classifier was employed in plant miRNA target identification for the first time. Its performance can be substantially improved if more experimentally proved training samples are provided

Exploring the influence of cement and cement hydration products on strength and interfacial adhesion in emulsified cold recycled mixture: A molecular dynamics and experimental investigation

This study employed experimental methods and molecular dynamics simulations to investigate how cement and its hydration products affect the mechanical strength and interfacial properties of emulsified cold recycled mixtures (ECRM). The effects of cement hydration product characteristics, such as content, number of nucleation sites, calcium-silicon (Ca/Si) ratio, degree of hydration, and types of hydration products, on the adhesion and water resistance of the cement-emulsified asphalt mortar (CEAM)-aggregate interface, were studied. The findings revealed that increasing the cement content by up to 2% improved the ECRM\u27s mechanical strength. However, the mechanical strength decreased beyond a cement content of 2%. Molecular dynamics simulation demonstrated that augmented cement hydration product content fostered greater adhesion at the interface between CEAM and the aggregate. The presence of an adequate number of nucleation sites was crucial for cement hydration products\u27 effectiveness in strengthening the interface adhesion. Moreover, hydration products with higher degrees of hydration or Ca/Si ratios exhibited a more pronounced impact on enhancing interface adhesion. Furthermore, cement hydration products enhanced the interfacial water resistance of the CEAM-aggregate interface. Nonetheless, the extent of this improvement depended on the interfacial water content. When the interfacial water content was high, the water resistance decreased, and the influence of cement hydration products became negligible. These results highlight the important role of cement and its hydration products in determining the mechanical properties and interfacial characteristics of ECRM and provide insights for optimizing the design and application of such materials in engineering practice

Multi-dimensional patterns of variation in root traits among coexisting herbaceous species in temperate steppes

Characterizing patterns of variation in plant traits across species and environmental gradients is critical for understanding performance of species in ecosystems. One-dimensional pattern of variation has been demonstrated in leaf traits, which is known as the leaf economic spectrum. However, it is unclear whether such a spectrum exists for root traits. For roots of 15 species from temperate grasslands, we determined respiration rate, relative growth rate, life span and 10 morphological, chemical and anatomical root traits. We further evaluated pairwise and multiple-trait relationships by Pearson's correlation and principle component analysis including phylogenetic contrasts. We found that root functions were related to three clusters of variation. Root respiration rate and relative growth rate were positively correlated with average root diameter (AD), but they were negatively correlated with specific root length (SRL). In contrast, root life span was not correlated with AD, but it was positively correlated with SRL. These results are inconsistent with the presumption of the root economic spectrum. The principle components analysis revealed a multi-dimensional pattern of variation in root traits among the 15 coexisting herbaceous species. Moreover, species within the same phylogenetic clades tended to have similar root trait syndromes. Most of the root traits exhibited a significant phylogenetic signal. Synthesis. Our results do not support a one-dimensional root economic spectrum in the coexisting herbaceous species of temperate grasslands. In contrast, the pattern of variation in root traits was multi-dimensional. We further demonstrated that species in different phylogenetic clades possess diverse root trait syndromes for efficient resource acquisition. Our findings provide a next step in understanding root functions and plant strategies in temperate grasslands

A Hybrid Prediction Method for Plant lncRNA-Protein Interaction

Long non-protein-coding RNAs (lncRNAs) identification and analysis are pervasive in transcriptome studies due to their roles in biological processes. In particular, lncRNA-protein interaction has plausible relevance to gene expression regulation and in cellular processes such as pathogen resistance in plants. While lncRNA-protein interaction has been studied in animals, there has yet to be extensive research in plants. In this paper, we propose a novel plant lncRNA-protein interaction prediction method, namely PLRPIM, which combines deep learning and shallow machine learning methods. The selection of an optimal feature subset and subsequent efficient compression are significant challenges for deep learning models. The proposed method adopts k-mer and extracts high-level abstraction sequence-based features using stacked sparse autoencoder. Based on the extracted features, the fusion of random forest (RF) and light gradient boosting machine (LGBM) is used to build the prediction model. The performances are evaluated on Arabidopsis thaliana and Zea mays datasets. Results from experiments demonstrate PLRPIM’s superiority compared with other prediction tools on the two datasets. Based on 5-fold cross-validation, we obtain 89.98% and 93.44% accuracy, 0.954 and 0.982 AUC for Arabidopsis thaliana and Zea mays, respectively. PLRPIM predicts potential lncRNA-protein interaction pairs effectively, which can facilitate lncRNA related research including function prediction