Characterization of pig farms in Hung Yen, Hai Duong and Bac Ninh provinces

peer reviewedIn order to characterization of pig farms in the Red River Delta, a study was conducted on 90 pig farms in Hung Yen, Hai Duong and Bac Ninh provinces from June to December 2006. Results show that most of the pig farms had been built for five years with a small size (0.5 hectare per farm). The invested capital was about 300-400 millions VND per farm. Four main sow groups used in the farms included crossbred exotic sows (51.1%), crossbred sow between local and exotic breeds (14.4%), purebred Landrace and Yorkshire breeds (15.6 and 18.9%, respectively). The boars were various (Duroc 30%, Yorkshire 21%, Landrace 13%, PiÐtrain × Duroc 36% and others). The pigs farms were faced with several difficulties such as limited land, lack of invested capital, uncontrolled quality of breeding pigs, high costs of feed, poor hygiene condition and diseases

A Survey on Some Parameters of Beef and Buffalo Meat Quality

A survey was carried out on 13 Vietnamese Yellow cattle, 14 LaiSind cattle and 18 buffalos in Hanoi to estimate the quality of longissimus dorsi in terms of pH, color, drip loss, cooking loss and tenderness at 6 different postmortem intervals. It was found that the pH value of longissimus dorsi was not significantly different among the 3 breeds (P>0.05), being reduced rapidly during the first 36 hours postmortem, and then stayed stable. The value was in the range that was considered to be normal. Conversely, the color values L*, a* and b* tended to increase and also stable at 36 hours postmortem, except that for LaiSind cattle at 48 hours. According to L* scale, the meat of Yellow and LaiSind cattle met the normal quality but the buffalo meat was considered to be dark cutters. The tenderness of longissimus dorsi was significantly different among the breeds (P<0.05). The value was highest at 48 hours and then decreased for LaiSind and buffalo, but for Yellow cattle the value decreased continuously after slaughtering In terms of tenderness buffalo meat and Yellow cattle meat were classified as “intermediate”, while LaiSind meat was out of this interval and classified as “tough”. Drip loss ratio was increased with the time of preservation (P<0.05). The cooking loss ratio was lowest at 12 hours and higher at the next period, but there was no significant difference among the periods after 36 hours postmotem.Peer reviewe

Novel machine learning approach toward classification model of HIV-1 integrase inhibitors

HIV-1 (human immunodeficiency virus-1) has been causing severe pandemics by attacking the immune system of its host. Left untreated, it can lead to AIDS (acquired immunodeficiency syndrome), where death is inevitable due to opportunistic diseases. Therefore, discovering new antiviral drugs against HIV-1 is crucial. This study aimed to explore a novel machine learning approach to classify compounds that inhibit HIV-1 integrase and screen the dataset of repurposing compounds. The present study had two main stages: selecting the best type of fingerprint or molecular descriptor using the Wilcoxon signed-rank test and building a computational model based on machine learning. In the first stage, we calculated 16 different types of fingerprint or molecular descriptors from the dataset and used each of them as input features for 10 machine-learning models, which were evaluated through cross-validation. Then, a meta-analysis was performed with the Wilcoxon signed-rank test to select the optimal fingerprint or molecular descriptor types. In the second stage, we constructed a model based on the optimal fingerprint or molecular descriptor type. This data followed the machine learning procedure, including data preprocessing, outlier handling, normalization, feature selection, model selection, external validation, and model optimization. In the end, an XGBoost model and RDK7 fingerprint were identified as the most suitable. The model achieved promising results, with an average precision of 0.928 ± 0.027 and an F1-score of 0.848 ± 0.041 in cross-validation. The model achieved an average precision of 0.921 and an F1-score of 0.889 in external validation. Molecular docking was performed and validated by redocking for docking power and retrospective control for screening power, with the AUC metrics being 0.876 and the threshold being identified at −9.71 kcal mol−1. Finally, 44 compounds from DrugBank repurposing data were selected from the QSAR model, then three candidates were identified as potential compounds from molecular docking, and PSI-697 was detected as the most promising molecule, with in vitro experiment being not performed (docking score: −17.14 kcal mol−1, HIV integrase inhibitory probability: 69.81%)</p

KGG:Knowledge-Guided Graph Self-Supervised Learning to Enhance Molecular Property Predictions

Molecular property prediction has become essential in accelerating advancements in drug discovery and materials science. Graph Neural Networks have recently demonstrated remarkable success in molecular representation learning; however, their broader adoption is impeded by two significant challenges: (1) data scarcity and constrained model generalization due to the expensive and time-consuming task of acquiring labeled data and (2) inadequate initial node and edge features that fail to incorporate comprehensive chemical domain knowledge, notably orbital information. To address these limitations, we introduce a Knowledge-Guided Graph (KGG) framework employing self-supervised learning to pretrain models using orbital-level features in order to mitigate reliance on extensive labeled data sets. In addition, we propose novel representations for atomic hybridization and bond types that explicitly consider orbital engagement. Our pretraining strategy is cost efficient, utilizing approximately 250,000 molecules from the ZINC15 data set, in contrast to contemporary approaches that typically require between two and ten million molecules, consequently reducing the risk of potential data contamination. Extensive evaluations on diverse downstream molecular property data sets demonstrate that our method significantly outperforms state-of-the-art baselines. Complementary analyses, including t-SNE visualizations and comparisons with traditional molecular fingerprints, further validate the effectiveness and robustness of our proposed KGG approach. The key advantages of KGG are its data efficiency and architectural versatility, driven by orbital-informed representations. By distilling essential chemical knowledge from modest corpora, it avoids extensive pretraining and excels in low-data fine-tuning, providing a robust and chemically meaningful foundation for diverse GNN architectures.</p

The Bulk Metric in the Theory with Two Extra Dimensions

The metric of a warped space-time with two extra dimensions is established by means of the Einstein equations in six dimensions and the compactification of two extra dimensions on a square . It is shown that at every among two fixed points our manifold reduces to the five-dimensional Randall - Sundrum space - time and the hierarchy problem could be solved

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin