Reconstruction of metabolic pathways by combining probabilistic graphical model-based and knowledge-based methods

Automatic reconstruction of metabolic pathways for an organism from genomics and transcriptomics data has been a challenging and important problem in bioinformatics. Traditionally, known reference pathways can be mapped into an organism-specific ones based on its genome annotation and protein homology. However, this simple knowledge-based mapping method might produce incomplete pathways and generally cannot predict unknown new relations and reactions. In contrast, ab initio metabolic network construction methods can predict novel reactions and interactions, but its accuracy tends to be low leading to a lot of false positives. Here we combine existing pathway knowledge and a new ab initio Bayesian probabilistic graphical model together in a novel fashion to improve automatic reconstruction of metabolic networks. Specifically, we built a knowledge database containing known, individual gene / protein interactions and metabolic reactions extracted from existing reference pathways. Known reactions and interactions were then used as constraints for Bayesian network learning methods to predict metabolic pathways. Using individual reactions and interactions extracted from different pathways of many organisms to guide pathway construction is new and improves both the coverage and accuracy of metabolic pathway construction. We applied this probabilistic knowledge-based approach to construct the metabolic networks from yeast gene expression data and compared its results with 62 known metabolic networks in the KEGG database. The experiment showed that the method improved the coverage of metabolic network construction over the traditional reference pathway mapping method and was more accurate than pure ab initio methods

Messenger RNA profile analysis deciphers new Esrrb responsive genes in prostate cancer cells

Additional file 2: Table S1.  Gene ontology analysis result. Table S2. Esrrb expression with DY131 treatment (control vs. Esrrb + DY131)

Genes targeted by the Hedgehog-signaling pathway can be regulated by Estrogen related receptor β

Additional file 1. Table S1: Known Hh-signaling pathway target genes. Table S2: Result of all pairwise comparisons of differentially expressed genes. Table S3: Hh-signaling differentially responsive genes

Simulation of Fragmentation Characteristics of Projectile Jacket Made of Tungsten Alloy after Penetrating Metal Target Plate using SPH Method

A smooth particle hydrodynamics (SPH) model was used to simulate the fragmentation process of the jacket during penetrator with lateral efficiency (PELE) penetrating the metal target plate to study the fragmentation characteristics of PELE jacket made of tungsten alloy. The validity of the SPH model was verified by experimental results. Then the SPH model was used to simulate the jacket fragmentation under different impact velocity and thickness of target plate. The influence of impact velocity and thickness of target plate on the jacket fragmentation was obtained by analysing the mass distribution and quantity distribution of the fragments formed by the jacket. The results show that the dynamic fragmentation of tungsten alloy can be simulated effectively using the SPH model, Johnson-Cook strength model, maximum tensile stress failure criterion and stochastic failure model. When the thickness of target plate is fixed, the greater the impact velocity, the greater the pressure produced by the projectile impacting the target plate; with the increase of impact velocity, the mass of residual projectile decreases, the number of fragments formed by fragmentation of jacket increases linearly, and the average mass of fragments decreases exponentially. When the impact velocity is constant, the greater the thickness of the target plate, the longer the pressure duration by the projectile impacting the target plate; with the increase of the thickness of target plate, the mass of residual projectile decreases, the number of fragments formed by fragmentation of jacket increases linearly, and the average mass of fragments decreases exponentially. The numerical calculation model and research method adopted in this paper can be used to study the impact fragmentation of solid materials effectively

Fragmentation Behaviour of Radial Layered PELE Impacting Thin Metal Target Plates

The fragmentation mechanism of the penetrator with lateral effect (PELE) after perforating a thin target plate has been summarised and analysed firstly. Then the fragmentation of radial layered PELE was analysed qualitatively and verified by experiment. In the experiment, the target plates were made of 45# steel and 2A12 aluminium respectively. Qualitative analysis and experimental results show that: for normal PELE without layered, after perforating the thin metal target plate, from the bottom to the head of the projectile, the number of fragments formed by the jacket gradually increases, and the mass of the fragment decreases correspondingly. Compared with the normal PELE without layered, the radial layered PELE is less likely to break into fragments, when impacting the thin metal target plate with the same material and thickness under the same impact velocity. However, from the mechanism of the PELE, when the resistance of the target plate is large enough, and the duration of pressure is long enough, the radial layered PELE also can break into fragments with transverse velocity component. The resistance of the target plate plays an important role in the fragmentation of radial layered PELE. The radial layered PELE produced massive fragments with transverse velocity component when impacting the 45# steel plate with5 mm thickness under the impact velocity of 657.2 m/s

Phylogenetic signal in gut microbial community rather than in rodent metabolic traits

This work was supported by the National Natural Science Foundation of China (32090020, 32271575, 32070449, 31872232, and 32270508) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDPB16).Peer reviewedPublisher PD

Variation detection based on next-generation sequencing of type Chinese 1 strains of Toxoplasma gondii with different virulence from China

A: Summary of annotation for SNPs; B: Summary of annotation for indels; C: Summary of annotation for SVs; D: Summary of annotation for CNVs. (DOCX 18 kb

SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes. Its success largely owes to massive labeled training data. However, directly applying SAM to medical image segmentation cannot perform well because SAM lacks medical knowledge -- it does not use medical images for training. To incorporate medical knowledge into SAM, we introduce SA-Med2D-20M, a large-scale segmentation dataset of 2D medical images built upon numerous public and private datasets. It consists of 4.6 million 2D medical images and 19.7 million corresponding masks, covering almost the whole body and showing significant diversity. This paper describes all the datasets collected in SA-Med2D-20M and details how to process these datasets. Furthermore, comprehensive statistics of SA-Med2D-20M are presented to facilitate the better use of our dataset, which can help the researchers build medical vision foundation models or apply their models to downstream medical applications. We hope that the large scale and diversity of SA-Med2D-20M can be leveraged to develop medical artificial intelligence for enhancing diagnosis, medical image analysis, knowledge sharing, and education. The data with the redistribution license is publicly available at https://github.com/OpenGVLab/SAM-Med2D

A targeted next-generation sequencing method for identifying clinically relevant mutation profiles in lung adenocarcinoma

Molecular profiling of lung cancer has become essential for prediction of an individual’s response to targeted therapies. Next-generation sequencing (NGS) is a promising technique for routine diagnostics, but has not been sufficiently evaluated in terms of feasibility, reliability, cost and capacity with routine diagnostic formalin-fixed, paraffin-embedded (FFPE) materials. Here, we report the validation and application of a test based on Ion Proton technology for the rapid characterisation of single nucleotide variations (SNVs), short insertions and deletions (InDels), copy number variations (CNVs), and gene rearrangements in 145 genes with FFPE clinical specimens. The validation study, using 61 previously profiled clinical tumour samples, showed a concordance rate of 100% between results obtained by NGS and conventional test platforms. Analysis of tumour cell lines indicated reliable mutation detection in samples with 5% tumour content. Furthermore, application of the panel to 58 clinical cases, identified at least one actionable mutation in 43 cases, 1.4 times the number of actionable alterations detected by current diagnostic tests. We demonstrated that targeted NGS is a cost-effective and rapid platform to detect multiple mutations simultaneously in various genes with high reproducibility and sensitivity

Proton-air cross section measurement with the ARGO-YBJ cosmic ray experiment

The proton-air cross section in the energy range 1-100 TeV has been measured by the ARGO-YBJ cosmic ray experiment. The analysis is based on the flux attenuation for different atmospheric depths (i.e. zenith angles) and exploits the detector capabilities of selecting the shower development stage by means of hit multiplicity, density and lateral profile measurements at ground. The effects of shower fluctuations, the contribution of heavier primaries and the uncertainties of the hadronic interaction models, have been taken into account. The results have been used to estimate the total proton-proton cross section at center of mass energies between 70 and 500 GeV, where no accelerator data are currently available.Comment: 14 pages, 9 figure