Quasi-Closeness: A Toolkit for Social Network Applications Involving Indirect Connections

We come up with a punishment in the form of exponential decay for the number of vertices that a path passes through, which is able to reconcile the contradictory effects of geodesic length and edge weights. This core thought is the key to handling three typical applications; that is, given an information demander, he may be faced with the following problems: choosing optimal route to contact the single supplier, picking out the best supplier between multiple candidates, and calculating his point centrality, which involves indirect connections. Accordingly, three concrete solutions in one logic thread are proposed. Firstly, by adding a constraint to Dijkstra algorithm, we limit our candidates for optimal route to the sample space of geodesics. Secondly, we come up with a unified standard for the comparison between adjacent and nonadjacent vertices. Through punishment in the form of exponential decay, the attenuation effect caused by the number of vertices that a path passes through has been offset. Then the adjacent vertices and punished nonadjacent vertices can be compared directly. At last, an unprecedented centrality index, quasi-closeness, is ready to come out, with direct and indirect connections being summed up

Evolution of anthropogenic air pollutant emissions in Guangdong Province, China, from 2006 to 2015

Guangdong Province (GD), one of the most prosperous and populous regions in China, still experiences haze events and growing ozone pollution in spite of the substantial air-quality improvement in recent years. Integrated control of fine particulate matter (PM2.5) and ozone in GD calls for a systematic review of historical emissions. In this study, emission trends, spatial variations, source-contribution variations, and reduction potentials of sulfur dioxide (SO2), nitrogen oxides (NO), PM2.5, inhalable particles (PM10), carbon monoxide (CO), ammonia (NH3), and volatile organic compounds (VOCs) in GD from 2006 to 2015 were first examined using a dynamic methodology, taking into account economic development, technology penetration, and emission controls. The relative change rates of anthropogenic emissions in GD during 2006-2015 are -48% for SO2, -0.5% for NO, -16% for PM2.5, -22% for PM10, 13% for CO, 3% for NH3, and 13% for VOCs. The declines of SO2, NO, PM2.5, and PM10 emissions in the whole province mainly resulted from the stringent emission control in the Pearl River delta (PRD) region, where most previous control measures were focused, especially on power plants (SO2 and NO), industrial combustion (SO2, PM2.5, PM10), on-road mobile sources (NO), and dust sources (PM2.5 and PM10). Emissions from other areas (non-PRD, NPRD), nevertheless, remain relatively stable due to the lax control measures and rapidly growing energy consumption. In addition, emission leaks of SO2 and NO from industries are observed from PRD to NPRD in 2010 and 2011. As a result, emissions in NPRD are increasingly important in GD, particularly those from industrial combustion. The contribution of NPRD to the total SO2 emissions in GD, for example, increased from 27% in 2006 to 48% in 2015. On-road mobile sources and solvent use are the two key sources that should receive more effective control measures in GD. Current control-driven emission reductions from on-road mobile sources are neutralized by the substantial growth of the vehicle population, while VOC emissions in GD steadily increase due to the growth of solvent use and the absence of effective control measures. Besides, future work could focus on power plants and industrial combustion in GD and industrial process sources in NPRD, which still have large emission reduction potentials. The historical emission inventory developed in this study not only helps to understand the emission evolution in GD, but also provides robust data to quantify the impact of emission and meteorology variations on air quality and unveil the primary cause of significant air-quality change in GD in the recent decade

Effects of yeast extract supplemented in diet on growth performance, digestibility, intestinal histology, and the antioxidant capacity of the juvenile turbot (Scophthalmus maximus)

An 8-week feeding experiment was conducted on the juvenile turbot (Scophthalmus maximus) to evaluate the influence of yeast extract (YE) supplementation in the diet on growth performance, feed utilization, body composition, nutrient digestibility, intestinal histology, and antioxidant capacity. Four experimental diets were formulated with graded levels of yeast extract 0 (YE0), 1% (YE1), 3% (YE3), and 5% (YE5) and fed to turbots (initial body weight: 4.2 ± 0.1 g) with three replicates per diet and 200 fish in each replicate, respectively. The results showed that turbots fed with diets YE1 and YE3 displayed a significantly higher specific growth rate and protein efficiency rate than those fed with diets YE0 and YE5, while the feed conversion ratios in YE1 and YE3 groups were lower than those in YE0 and YE5. Fish fed with diets YE3 and YE5 showed higher body crude protein contents than those in groups YE0 and YE1. The highest apparent digestibility coefficients for dry matter and crude protein, digestive enzyme activities (trypsin, lipase, and amylase), and the height of the intestinal fold were observed in the YE3 group. YE3 treatment displayed a significantly higher superoxide dismutase (SOD) activity than the YE0 group, while the malondialdehyde (MDA) content in YE1 was significantly lower than those in YE0 and YE5. No significant difference was observed in serum physiological and biochemical parameters among all treatments. Overall, appropriate dietary supplementation of the yeast extract could improve the growth performance, digestibility, and antioxidant capacity of the juvenile turbot, and the recommended yeast extract level in the feed is 2.47%

Mapping and functional characterization of structural variation in 1060 pig genomes

BACKGROUND: Structural variations (SVs) have significant impacts on complex phenotypes by rearranging large amounts of DNA sequence.RESULTS: We present a comprehensive SV catalog based on the whole-genome sequence of 1060 pigs (Sus scrofa) representing 101 breeds, covering 9.6% of the pig genome. This catalog includes 42,487 deletions, 37,913 mobile element insertions, 3308 duplications, 1664 inversions, and 45,184 break ends. Estimates of breed ancestry and hybridization using genotyped SVs align well with those from single nucleotide polymorphisms. Geographically stratified deletions are observed, along with known duplications of the KIT gene, responsible for white coat color in European pigs. Additionally, we identify a recent SINE element insertion in MYO5A transcripts of European pigs, potentially influencing alternative splicing patterns and coat color alterations. Furthermore, a Yorkshire-specific copy number gain within ABCG2 is found, impacting chromatin interactions and gene expression across multiple tissues over a stretch of genomic region of ~200 kb. Preliminary investigations into SV's impact on gene expression and traits using the Pig Genotype-Tissue Expression (PigGTEx) data reveal SV associations with regulatory variants and gene-trait pairs. For instance, a 51-bp deletion is linked to the lead eQTL of the lipid metabolism regulating gene FADS3, whose expression in embryo may affect loin muscle area, as revealed by our transcriptome-wide association studies.CONCLUSIONS: This SV catalog serves as a valuable resource for studying diversity, evolutionary history, and functional shaping of the pig genome by processes like domestication, trait-based breeding, and adaptive evolution.</p

A compendium of genetic regulatory effects across pig tissues

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.</p

Drug Repurposing Applications to Overcome Male Predominance via Targeting G2/M Checkpoint in Human Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is strongly characterized by a male predominance with higher mortality rates and worse responses to treatment in males versus females. Despite the role of sex hormones, other causes that may contribute to sex bias in ESCC remain largely unknown, especially as age increases and the hormone difference begins to diminish between sexes. In this study, we analyzed genomics, transcriptomics, and epigenomics from 663 ESCC patients and found that G2/M checkpoint pathway-related sex bias and age bias were significantly present in multi-omics data. In accordance with gene expression patterns across sexes, ten compounds were identified by applying drug repurposing from three drug sensitivity databases: The Connective Map (CMap), Genomics of Drug Sensitivity in Cancer (GDSC), and The Cancer Therapeutic Response Portal (CTRP). MK1775 and decitabine showed better efficacy in two male ESCC cell lines in vitro and in vivo. The drugs’ relevance to the transition between G2 and M was especially evident in male cell lines. In our study, we first validated the sex bias of the G2/M checkpoint pathway in ESCC and then determined that G2/M targets may be included in combination therapy for male patients to improve the efficacy of ESCC treatment

DL-CRISPR : a deep learning method for off-target activity prediction in CRISPR/Cas9 with data augmentation

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR- associated (Cas) system is a popular and easy to use gene-editing technique, but it has off-target risk. Cutting the off-target sites will harm the cells severely, hence in silico methods are needed to help to avoid this. Most existing in silico approaches mainly relied on a relatively small positive dataset and the data imbalance issue still exists. Besides, some samples used to be considered as negative are later proved to be positive. Hence, it is essential to refresh the dataset and develop more accurate off-target activity prediction programs. In this work, firstly, we extended the current positive dataset and explored the potential differences between positive and negative data based on the new dataset. Then we adopted a new data augmentation method to solve the data imbalance issue, and used the ensemble idea to take more negative data into consideration to make the model close to the real scenario, but at the same time keeping the model balance. Finally, we developed DL-CRISPR, a deep learning framework to predict off-target activity in CRISPR/Cas9. DL-CRISPR is evaluated and compared with other state-of-the-art methods on three kinds of datasets: 5-fold cross validation test datasets, putative off-targets datasets related to specific single guide RNAs (sgRNAs), and putative off-targets datasets related to unseen sgRNAs. DL-CRISPR realizes the best average accuracy, i.e. 98.57%, on 5-fold cross validation datasets and correctly detects more off-targets on datasets related to both seen and unseen sgRNAs.Agency for Science, Technology and Research (A*STAR)Published versionThis work was supported by the A∗STAR-NTU-SUTD AI Partnership under Project RGANS1905