An Integrated Bioinformatics Approach for the Identification of Melanoma-Associated Biomarker Genes. A Ranking and Stratification Approach as a New Meta-Analysis Methodology for the Detection of Robust Gene Biomarker Signatures of Cancers.

Genome-wide microarray technology has facilitated the systematic discovery of diagnostic biomarkers of cancers and other pathologies. However, meta-analyses of published arrays using melanoma as a test cancer has uncovered significant inconsistences that hinder advances in clinical practice. In this study a computational model for the integrated analysis of microarray datasets is proposed in order to provide a robust ranking of genes in terms of their relative significance; both genome-wide relative significance (GWRS) and genome-wide global significance (GWGS). When applied to five melanoma microarray datasets published between 2000 and 2011, a new 12-gene diagnostic biomarker signature for melanoma was defined (i.e., EGFR, FGFR2, FGFR3, IL8, PTPRF, TNC, CXCL13, COL11A1, CHP2, SHC4, PPP2R2C, and WNT4). Of these, CXCL13, COL11A1, PTPRF and SHC4 are components of the MAPK pathway and were validated by immunocyto- and immunohisto-chemistry. These proteins were found to be overexpressed in metastatic and primary melanoma cells in vitro and in melanoma tissue in situ compared to melanocytes cultured from healthy skin epidermis and normal healthy human skin. One challenge for the integrated analysis of microarray data is that the microarray data are produced using different platforms and bio-samples, e.g. including both cell line- and biopsy-based microarray datasets. In order to address these challenges, the computational model was further enhanced the stratification of datasets into either biopsy or cell line derived datasets, and via the weighting of microarray data based on quality criteria of data. The methods enhancement was applied to 14 microarray datasets of three cancers (breast, prostate, and melanoma) based on classification accuracy and on the capability to identify predictive biomarkers. Four novel measures for evaluating the capability to identify predictive biomarkers are proposed: (1) classifying independent testing data using wrapper feature selection with machine leaning, (2) assessing the number of common genes with the genes retrieved in independent testing data, (3) assessing the number of common genes with the genes retrieved in across multiple training datasets, (4) assessing the number of common genes with the genes validated in the literature. This enhancement of computational approach (i) achieved reliable classification performance across multiple datasets, (ii) recognized more significant genes into the top-ranked genes as compared to the genes detected by the independent test data, and (iii) detected more meaningful genes than were validated in previous melanoma studies in the literature

Three-way Imbalanced Learning based on Fuzzy Twin SVM

Three-way decision (3WD) is a powerful tool for granular computing to deal with uncertain data, commonly used in information systems, decision-making, and medical care. Three-way decision gets much research in traditional rough set models. However, three-way decision is rarely combined with the currently popular field of machine learning to expand its research. In this paper, three-way decision is connected with SVM, a standard binary classification model in machine learning, for solving imbalanced classification problems that SVM needs to improve. A new three-way fuzzy membership function and a new fuzzy twin support vector machine with three-way membership (TWFTSVM) are proposed. The new three-way fuzzy membership function is defined to increase the certainty of uncertain data in both input space and feature space, which assigns higher fuzzy membership to minority samples compared with majority samples. To evaluate the effectiveness of the proposed model, comparative experiments are designed for forty-seven different datasets with varying imbalance ratios. In addition, datasets with different imbalance ratios are derived from the same dataset to further assess the proposed model's performance. The results show that the proposed model significantly outperforms other traditional SVM-based methods

NutriFD: Proving the medicinal value of food nutrition based on food-disease association and treatment networks

There is rising evidence of the health benefit associated with specific dietary interventions. Current food-disease databases focus on associations and treatment relationships but haven't provided a reasonable assessment of the strength of the relationship, and lack of attention on food nutrition. There is an unmet need for a large database that can guide dietary therapy. We fill the gap with NutriFD, a scoring network based on associations and therapeutic relationships between foods and diseases. NutriFD integrates 9 databases including foods, nutrients, diseases, genes, miRNAs, compounds, disease ontology and their relationships. To our best knowledge, this database is the only one that can score the associations and therapeutic relationships of everyday foods and diseases by weighting inference scores of food compounds to diseases. In addition, NutriFD demonstrates the predictive nature of nutrients on the therapeutic relationships between foods and diseases through machine learning models, laying the foundation for a mechanistic understanding of food therapy

Organochlorinated pesticides expedite the enzymatic degradation of DNA

Extracellular DNA in the environment may play important roles in genetic diversity and biological evolution. However, the influence of environmental persistent organic contaminants such as organochlorinated pesticides (e.g., hexachlorocyclohexanes [HCHs]) on the enzymatic degradation of extracellular DNA has not been elucidated. In this study, we observed expedited enzymatic degradation of extracellular DNA in the presence of α-HCH, β-HCH and γ-HCH. The HCH-expedited DNA degradation was not due to increased deoxyribonuclease I (DNase I) activity. Our spectroscopic and computational results indicate that HCHs bound to DNA bases (most likely guanine) via Van der Waals forces and halogen bonds. This binding increased the helicity and accumulation of DNA base pairs, leading to a more compact DNA structure that exposed more sites susceptible to DNase I and thus expedited DNA degradation. This study provided insight into the genotoxicity and ecotoxicity of pesticides and improved our understanding of DNA persistence in contaminated environments

Deep reinforcement learning for real-time economic energy management of microgrid system considering uncertainties

The electric power grid is changing from a traditional power system to a modern, smart, and integrated power system. Microgrids (MGs) play a vital role in combining distributed renewable energy resources (RESs) with traditional electric power systems. Intermittency, randomness, and volatility constitute the disadvantages of distributed RESs. MGs with high penetrations of renewable energy and random load demand cannot ignore these uncertainties, making it difficult to operate them effectively and economically. To realize the optimal scheduling of MGs, a real-time economic energy management strategy based on deep reinforcement learning (DRL) is proposed in this paper. Different from traditional model-based approaches, this strategy is learning based, and it has no requirements for an explicit model of uncertainty. Taking into account the uncertainties in RESs, load demand, and electricity prices, we formulate a Markov decision process for the real-time economic energy management problem of MGs. The objective is to minimize the daily operating cost of the system by scheduling controllable distributed generators and energy storage systems. In this paper, a deep deterministic policy gradient (DDPG) is introduced as a method for resolving the Markov decision process. The DDPG is a novel policy-based DRL approach with continuous state and action spaces. The DDPG is trained to learn the characteristics of uncertainties of the load, RES output, and electricity price using historical data from real power systems. The effectiveness of the proposed approach is validated through the designed simulation experiments. In the second experiment of our designed simulation, the proposed DRL method is compared to DQN, SAC, PPO, and MPC methods, and it is able to reduce the operating costs by 29.59%, 17.39%, 6.36%, and 9.55% on the June test set and 30.96%, 18.34%, 5.73%, and 10.16% on the November test set, respectively. The numerical results validate the practical value of the proposed DRL algorithm in addressing economic operation issues in MGs, as it demonstrates the algorithm’s ability to effectively leverage the energy storage system to reduce the operating costs across a range of scenarios

Multi-index fuzzy comprehensive evaluation model with information entropy of alfalfa salt tolerance based on LiDAR data and hyperspectral image data

Rapid, non-destructive and automated salt tolerance evaluation is particularly important for screening salt-tolerant germplasm of alfalfa. Traditional evaluation of salt tolerance is mostly based on phenotypic traits obtained by some broken ways, which is time-consuming and difficult to meet the needs of large-scale breeding screening. Therefore, this paper proposed a non-contact and non-destructive multi-index fuzzy comprehensive evaluation model for evaluating the salt tolerance of alfalfa from Light Detection and Ranging data (LiDAR) and HyperSpectral Image data (HSI). Firstly, the structural traits related to growth status were extracted from the LiDAR data of alfalfa, and the spectral traits representing the physical and chemical characteristics were extracted from HSI data. In this paper, these phenotypic traits obtained automatically by computation were called Computing Phenotypic Traits (CPT). Subsequently, the multi-index fuzzy evaluation system of alfalfa salt tolerance was constructed by CPT, and according to the fuzzy mathematics theory, a multi-index Fuzzy Comprehensive Evaluation model with information Entropy of alfalfa salt tolerance (FCE-E) was proposed, which comprehensively evaluated the salt tolerance of alfalfa from the aspects of growth structure, physiology and biochemistry. Finally, comparative experiments showed that: (1) The multi-index FCE-E model based on the CPT was proposed in this paper, which could find more salt-sensitive information than the evaluation method based on the measured Typical Phenotypic Traits (TPT) such as fresh weight, dry weight, water content and chlorophyll. The two evaluation results had 66.67% consistent results, indicating that the multi-index FCE-E model integrates more information about alfalfa and more comprehensive evaluation. (2) On the basis of the CPT, the results of the multi-index FCE-E method were basically consistent with those of Principal Component Analysis (PCA), indicating that the multi-index FCE-E model could accurately evaluate the salt tolerance of alfalfa. Three highly salt-tolerant alfalfa varieties and two highly salt-susceptible alfalfa varieties were screened by the multi-index FCE-E method. The multi-index FCE-E method provides a new method for non-contact non-destructive evaluation of salt tolerance of alfalfa

miR‐155 promotes macrophage pyroptosis induced by Porphyromonas gingivalis through regulating the NLRP3 inflammasome

ObjectiveThe aim of this study is to detect pyroptosis in macrophages stimulated with Porphyromonas gingivalis and elucidate the mechanism by which P. gingivalis induces pyroptosis in macrophages.MethodsThe immortalized human monocyte cell line U937 was stimulated with P. gingivalis W83. Flow cytometry was carried out to detect pyroptosis in macrophages. The expression of miR‐155 was detected by real‐time PCR and inhibited using RNAi. Suppressor of cytokine signaling (SOCS) 1, cleaved GSDMD, caspase (CAS)‐1, caspase‐11, apoptosis‐associated speck‐like protein (ASC), and NOD‐like receptor protein 3 (NLRP3) were detected by Western blotting, and IL‐1β and IL‐18 were detected by ELISA.ResultsThe rate of pyroptosis in macrophages and the expression of miR‐155 increased upon stimulation with P. gingivalis and pyroptosis rate decreased when miR‐155 was silenced. GSDMD‐NT, CAS‐11, CAS‐1, ASC, NLRP3, IL‐1β, and IL‐18 levels increased, but SOCS1 decreased in U937 cells after stimulated with P. gingivalis. These changes were weakened in P. gingivalis‐stimulated U937 macrophages transfected with lentiviruses carrying miR‐155 shRNA compared to those transfected with non‐targeting control sequence. However, there was no significant difference in ASC expression between P. gingivalis‐stimulated shCont and shMiR‐155 cells.ConclusionsPorphyromonas gingivalis promotes pyroptosis in macrophages during early infection. miR‐155 is involved in this process through regulating the NLRP3 inflammasome.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152887/1/odi13198_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152887/2/odi13198.pd

The effects of ethylene on the HCl-extractability of trace elements during soybean seed germination

Background: Ethylene is capable of promoting seed germination in some plant species. Mobilization of metals such as Fe, Cu, Mn, and Zn in mature seeds takes place when seeds are germinating. However, whether ethylene is involved in the regulation of soybean seed germination and metal element mobilization during early seed germination stage remains unknown. In the present study, seeds were treated with ethylene synthesis inhibitor aminoethoxyvinylglycine (AVG) and ethylene precursor 1-aminocyclopropane-1-carboxylic acid (ACC), and double distilled H2O (ddH20) treatment was used as control. Ethylene emission, ACC synthase (ACS) expression, ACS enzyme activity and Ca, Zn, Mn, Cu and Fe content in hypocotyls were qualified to analyze the relationship between ethylene and mobilization of these elements. Results: The results showed that ACS expression, ACS enzyme activity and ethylene emission peaked at 1 and 7 d after sowing. AVG inhibited ethylene production, promoted the hypocotyls length, ACS expression and its activity, concentrations of total and HCl-extractable Zn, and HCl-extractable Fe in hypocotyls, while ACC caused opposite effects. AVG and ACC treatment had no significantly effects on total and HCl-extractable Ca, Cu and HCl-extractable Mn. Total Mn concentration was promoted by AVG at 1, 3, and 5 d significantly, while ACC treatment tended to have no significantly effects on Mn concentration. Conclusion: These findings suggested that ethylene is at least partly involved in the regulation of soybean seed germination. Remobilization of Zn and Fe may be negatively regulated by ethylene

Chinese language teachers’ dichotomous identities when teaching ingroup and outgroup students

Research into second language teacher identity has experienced a shift in recent years from a cognitive perspective to social constructionist orientation. The existing research in Chinese language literature in relation to Foreign Language (CFL) teachers’ identity shift is principally in relation to the change of social, cultural, and institutional contexts. Built on the current literature, this research asks: “How might teachers’ self-images or self-conceptualizations be renegotiated when they are located within their own mainstream cultural and educational system, yet comprised of students from various cultural backgrounds?” The data were collected from a group of CFL teachers in a South China university. The research found that students’ backgrounds largely impacted on, and led to, the teachers’ dichotomous relational identities, but did not dramatically change the teachers’ perception on what or how much subject knowledge to be possessed to make an ideal CFL teacher. This attribute of their identity was sustained even though the teaching content was modified at a practical level in response to groups’ differences. Further, the CFL teachers’ pedagogical identity remained stable with only minor modifications when teaching “ingroups” and “outgroups” of students