Distribution of oxide inclusions in H13 castings under super-gravity field with multi-stage rotation speeds

The 42 kg industrial H13 castings were prepared by different super-gravity fields with multi-rotation speeds,and the distribution of oxide inclusions in the castings was studied.In addition,the inward movement Reynolds number and inward movement time of oxide inclusions as well as the solidification time of molten steel at different positions in the castings were calculated to clarify the removal mechanism of oxide inclusions in super-gravity field.The results show that the large size(i.e.,greater than 10μm)oxide inclusions are mainly concentrated in the inner and outer parts of the super-gravity castings with constant rotation speed(500 r min^(-1))and five-stage rotation speeds(500,600,750,850,and 950 r min^(-1)),respectively,while there are no large oxide inclusions in the super-gravity castings with three-stage rotation speeds(500,600,and 750 r min^(-1)).Although an increase in the particle size of inclusion and the rotation speed in super-gravity field is conducive to the increase in the inward movement Reynolds number of oxide inclusions and reduction in the inward movement time of oxide inclusions,it will reduce the local solidification time of molten steel.In the range of the rotation speed studied,the super-gravity field with three-stage rotation speeds has the best effect on the removal of inclusions in H13 molten steel.

Gene Expression Data Classification Using Consensus Independent Component Analysis

We propose a new method for tumor classification from gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are modeled by independent component analysis （ICA）. Secondly, the most discriminant eigenassays extracted by ICA are selected by the sequential floating forward selection technique. Finally, support vector machine is used to classify the modeling data. To show the validity of the proposed method, we applied it to classify three DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.

HISP： a hybrid intelligent approach for identifying Jirected signaling pathways

Signal transduction plays important roles in biological systems. Unfortunately, our knowledge about signaling pathways is far from complete. Specifically, the direction of signaling flows is less known even though the signaling molecules of some signaling pathways have been determined. In this paper, we propose a novel hybrid intelligent method, namely HISP （Hybrid Intelligent approach for identifying directed Signaling Pathways）, to determine both the topologies of signaling pathways and the direction of signaling flows within a pathway based on integer linear programming and genetic algorithm. By integrating the protein-protein interaction, gene expression, and gene knockout data, our HISP approach is able to determine the optimal topologies of signaling pathways in an accurate way. Benchmark results on yeast MAPK signaling pathways demonstrate the efficiency of our proposed approach. When applied to the EGFR/ErbB signaling pathway in human hepatocytes, HISP unveils a high-resolution signaling path- way, where many signaling interactions were missing by existing computational approaches.

Consistent Alterations of Human Fecal Microbes After Transplantation into Germ-free Mice

Fecal microbiota transplantation(FMT)of human fecal samples into germ-free(GF)mice is useful for establishing causal relationships between the gut microbiota and human phenotypes.However,due to the intrinsic differences between human and mouse intestines and the different diets of the two organisms,it may not be possible to replicate human phenotypes in mice through FMT;similarly,treatments that are effective in mouse models may not be effective in humans.In this study,we aimed to identify human gut microbes that undergo significant and consistent changes(i.e.,in relative abundances)after transplantation into GF mice in multiple experimental settings.We collected 16S rDNA-seq data from four published studies and analyzed the gut microbiota profiles from 1713 human–mouse pairs.Strikingly,on average,we found that only 47%of the human gut microbes could be re-established in mice at the species level,among which more than 1/3 underwent significant changes(referred to as“variable taxa”).Most of the human gut microbes that underwent significant changes were consistent across multiple human–mouse pairs and experimental settings.Consequently,about 1/3 of human samples changed their enterotypes,i.e.,significant changes in their leading species after FMT.Mice fed with a controlled diet showed a lower enterotype change rate(23.5%)than those fed with a noncontrolled diet(49.0%),suggesting a possible solution for rescue.Most of the variable taxa have been reported to be implicated in human diseases,with some recognized as the causative species.Our results highlight the challenges of using a mouse model to replicate human gut microbiota-associated phenotypes,provide useful information for researchers using mice in gut microbiota studies,and call for additional validations after FMT.An online database named FMT-DB is publicly available at http://fmt2mice.humangut.info/#/.

A survey on biomarker identification based on molecular networks

Identifying biomarkers for accurate diagnosis and prognosis of diseases is important for the prevention of disease development. The molecular networks that describe the functional relationships among molecules provide a global view of the complex biological systems. With the molecular networks, the molecular mechanisms underlying diseases can be unveiled, which helps identify biomarkers in a systematic way. Results： In this survey, we report the recent progress on identifying biomarkers based on the topology of molecular networks, and we categorize those biomarkers into three groups, including node biomarkers, edge biomarkers and network biomarkers. These distinct types of biomarkers can be detected under different conditions depending on the data available. Conclusions： The biomarkers identified based on molecular networks can provide more accurate diagnosis and prognosis. The pros and cons of different types of hiomarkers as well as future directions to improve the methods for identifying biomarkers are also discussed.

mTD:A database of microRNAs affecting therapeutic effects of drugs

MicroRNAs（miRNAs）are small noncoding RNAs of 19-24 nt and play important roles in post-transcriptional regulation of gene expression（Baek et al.,2008;Zhou et al.,2011）and various biological processes including growth,

Evaluating oxygen level of Si-deoxidized H13 die steel using ferrous oxide-containing slags at 1873 K

Laboratory-scale experiments were conducted to investigate the oxygen content of Si-deoxidized H13 die steel by ferrous oxide-containing slags at 1873 K.The calculation of thermodynamics and kinetics was performed to evaluate the oxygen level of molten steel through[Si]-[O]equilibrium and[Fe]-[O]equilibrium.The results show that as the FeO content in slag increases,the oxygen content with[Si]-[O]equilibrium(w([O])Si)has almost no change.When both the oxygen content with[Fe]-[O]equilibrium(w([O])Fe)and w([O])Si are less than the initial oxygen content in steel(w(TO)i),the oxygen content in steel(w([O]))depends on the higher value between w([O])Si and w([O])Fe.In the case of w([O])Fe>w(TO)i,the value of w([O])is the difference between the sum of w(TO)i and w([O])Si and the value of w([O])Fe.The reaction rates of[Si]-[O]and[Fe]-[O]are equal,which are controlled by the mass transfer of oxygen in molten steel.The evaluation method is suitable to the whole smelting process of Si-deoxidized H13 die steel.