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[Display omitted] Biological networks are known to be highly modular, and the dysfunction of network modules may cause diseases. Defining the key modules from the omics data and establishing the classification model is helpful in promoting the research of disease diagnosis and prognosis. However, for applying modules in downstream analysis such as disease states discrimination, most methods only utilize the node information, and ignore the node interactions or topological information, which may lead to false positives and limit the model performance. In this study, we propose an omics data analysis method based on feature linear relationship and graph convolutional network (LCNet). In LCNet, we adopt a way of applying the difference of feature linear relationships during disease development to characterize physiological and pathological changes and construct the differential linear relation network, which is simple and interpretable from the perspective of feature linear relationship. A greedy strategy is developed for searching the highly interactive modules with a strong discrimination ability. To fully utilize the information of the detected modules, the personalized sub-graphs for each sample based on the modules are defined, and the graph convolutional network (GCN) classifiers are trained to predict the sample labels. The experimental results on public datasets show the superiority of LCNet in classification performance. For Breast Cancer metabolic data, the identified metabolites by LCNet involve important pathways. Thus, LCNet can identify the module biomarkers by feature linear relationship and a greedy strategy, and label samples by personalized sub-graphs and GCN. It provides a new manner of utilizing node (molecule) information and topological information in the defined modules for better disease classification. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the impact of microwave-vacuum drying (MVD) on moisture uniformity in mushroom slices and whole mushrooms (Agaricus bisporus) by near infrared (NIR) hyperspectral imaging in combination with multivariate analysis. The partial least squares regression (PLSR) models based on standard normal variate (SNV) preprocessing spectra obtained good results for predicting moisture contents (MC) with the coefficient of determination in prediction ( R p 2 ) of 0.985 for mushroom slices and 0.963 for whole mushrooms. The MC of caps was 5–11 g/100 g higher than that of stipes for the first 12 min of drying and after 18 min of drying the MC of stipes was 6–22 g/100 g higher than that of caps. The MC of peripheral caps of whole mushrooms was 2–12 g/100 g higher than that of centers for the first 30 min of drying, but the MC of peripheral caps was 4–5 g/100 g lower than that of centers in the last 20 min of drying. In addition, the difference of MC between caps and stipes (6–22 g/100 g) in mushroom slices was more significant than that between the center and periphery (4–5 g/100 g) in whole mushrooms during the middle and later periods of drying procedure. • Rates of shrinkages, color changes and evaporation vary during drying. • Caps of mushroom slices tend to lose moisture faster than stipes. • Stipes under caps of whole mushrooms can cause uneven moisture distribution. • Moisture of sliced and whole mushrooms shows different patterns during drying. [ABSTRACT FROM AUTHOR]

The C2H2 type zinc-finger transcription factor Nerfin-1 expresses dominantly in Drosophila nervous system and plays an important role in early axon guidance decisions and preventing neurons dedifferentiation. Recently, increasing reports indicated that INSM1 (homologue to nerfin-1 in mammals) is a useful marker for prognosis of neuroendocrine tumors. The dynamic expression of Nerfin-1 is regulated post-transcriptionally by multiple microRNAs; however, its post-translational regulation is still unclear. Here we showed that the protein turnover of Nerfin-1 is regulated by Slimb, the substrate adaptor of SCF Slimb ubiquitin ligase complex. Mechanistically, Slimb associates with Nerfin-1 and promotes it ubiquitination and degradation in Drosophila S2R + cells. Furthermore, we determined that the C-terminal half of Nerfin-1 (Nerfin-1 CT ) is required for its binding to Slimb. Genetic epistasis assays showed that Slimb misexpression antagonizes, while knock-down enhances the activity of Nerfin-1 CT in Drosophila eyes. Our data revealed a new link to understand the underlying mechanism for Nerfin-1 turnover in post-translational level, and provided useful insights in animal development and disease treatment by manipulating the activity of Slimb and Nerfin-1. [ABSTRACT FROM AUTHOR]

The occurrence and development of diseases are related to the dysfunction of biomolecules (genes, metabolites, etc.) and the changes of molecule interactions. Identifying the key molecules related to the physiological and pathological changes of organisms from omics data is of great significance for disease diagnosis, early warning and drug-target prediction, etc. A novel feature selection algorithm based on the feature individual distinguishing ability and feature influence in the biological network (FS-DANI) is proposed for defining important biomolecules (features) to discriminate different disease conditions. The feature individual distinguishing ability is evaluated based on the overlapping area of the feature effective ranges in different classes. FS-DANI measures the feature network influence based on the module importance in the correlation network and the feature centrality in the modules. The feature comprehensive weight is obtained by combining the feature individual distinguishing ability and feature influence in the network. Then crucial feature subset is determined by the sequential forward search (SFS) on the feature list sorted according to the comprehensive weights of features. FS-DANI is compared with the six efficient feature selection methods on ten public omics datasets. The ablation experiment is also conducted. Experimental results show that FS-DANI is better than the compared algorithms in accuracy, sensitivity and specificity on the whole. On analyzing the gastric cancer miRNA expression data, FS-DANI identified two miRNAs (hsa-miR-18a* and hsa-miR-381), whose AUCs for distinguishing gastric cancer samples and normal samples are 0.959 and 0.879 in the discovery set and an independent validation set, respectively. Hence, evaluating biomolecules from the molecular level and network level is helpful for identifying the potential disease biomarkers of high performance. [ABSTRACT FROM AUTHOR]

In biological data, feature relationships are complex and diverse, they could reflect physiological and pathological changes. Defining simple and efficient classification rules based on feature relationships is helpful for discriminating different conditions and studying disease mechanism. The popular data analysis method, k top scoring pairs (k-TSP), explores the feature relationship by focusing on the difference of the relative level of two features in different groups and classifies samples based on the exploration. To define more efficient classification rules, we propose a new data analysis method based on the linear combination of k > 0 top scoring pairs (LC-k-TSP). LC-k-TSP applies support vector machine (SVM) to define the best linear relationship of each feature pair, scores feature pairs by the discriminative abilities of the corresponding linear combinations and selects k disjoint top scoring pairs to construct an ensemble classifier. Experiments on twelve public datasets showed the superiority of LC-k-TSP over k-TSP which evaluates the relationship of every two features in the same way. The experiment also illustrated that LC-k-TSP performed similarly to SVM and random forest (RF) in accuracy rate. LC-k-TSP studies the own unique linear combination for each feature pair and defines simple classification rules, it is easy to explore the biomedical explanation. Finally, we applied LC-k-TSP to analyze the hepatocellular carcinoma (HCC) metabolomics data and define the simple classification rules for discrimination of different liver diseases. It obtained accuracy rates of 89.76% and 89.13% in distinguishing between small HCC and hepatic cirrhosis (CIR) groups as well as between HCC and CIR groups, superior to 87.99% and 80.35% by k-TSP. Hence, defining classification rules based on feature relationships is an effective way to analyze biological data. LC-k-TSP which checks different feature pairs by their corresponding unique best linear relationship has the superiority over k-TSP which checks each pair by the same linear relationship. Availability and implementation: http://www.402.dicp.ac.cn/download_ok_4.htm. [ABSTRACT FROM AUTHOR]

Timely accurate and cost-efficient detection of colorectal cancer (CRC) is of great clinical importance. This study aims to establish prediction models for detecting CRC using plasma cell-free DNA (cfDNA) fragmentomic features. Whole-genome sequencing (WGS) was performed on cfDNA from 620 participants, including healthy individuals, patients with benign colorectal diseases and CRC patients. Using WGS data, three machine learning methods were compared to build prediction models for the stratification of CRC patients. The optimal model to discriminate CRC patients of all stages from healthy individuals achieved a sensitivity of 92.31% and a specificity of 91.14%, while the model to separate early-stage CRC patients (stage 0-II) from healthy individuals achieved a sensitivity of 88.8% and a specificity of 96.2%. Additionally, the cfDNA fragmentation profiles reflected disease-specific genomic alterations in CRC. Overall, this study suggests that cfDNA fragmentation profiles may potentially become a noninvasive approach for the detection and stratification of CRC. • This study has identified clearly different profiles of cfDNA fragmentation profile and end motifs of the CRC patients compared to the healthy individuals or patients with colorectal benign disease. • cfDNA fragmentation profiles exhibit a better diagnostic value for the detection of early-stage CRC. • The genome-wide cfDNA fragmentation profiles reflected disease-specific genomic alterations in CRC. • cfDNA fragmentation profiles may potentially become a noninvasive assay for the detection and stratification of CRC. [ABSTRACT FROM AUTHOR]

Neurogenesis as a potential strategy to improve the consequences of intracerebral hemorrhage (ICH). The current study investigates the effects of withaferin A (WFA) in combination with leptin (LEP) on ICH and neurogenesis mechanisms. LEP levels were dramatically reduced on days 7 and 14 following ICH insults in mice, but continuous WFA therapy significantly improved the potency of intrinsic LEP on day 14 after ICH. Furthermore, WFA combined with LEP enhances intrinsic neurogenesis and lessen motor deficits and long-term cognitive outcomes after ICH. In parallel, leptin deficiency in ob / ob mice limits enhancement of neurogenesis following ICH in response to WFA combined with LEP treatment. Importantly, the functional recovery conferred by WFA combined with LEP after ICH was inhibited by neurogenesis suppression. Mechanistically, this study unveiled that the signal transducer and activator of transcription-3 (STAT3) / suppressor of cytokine signaling-3 (SOCS3) pathway is a critical signaling pathway through which WFA combined with LEP treatment promotes intrinsic neurogenesis after ICH. Collectively, the results of this study elucidate the neuroprotective effects of WFA and LEP in ICH, and highlight a potential approach for ICH cell therapy. • WFA combination with leptin (LEP) shows prominent neuroprotection against ICH during its late phase. • WFA combination with LEP enhances neurogenesis and reduces neurological disorder after ICH. • WFA combination with LEP promote neurogenesis mediated by activating STAT3/SOCS3 signaling. [ABSTRACT FROM AUTHOR]

The LIM-homeodomain (LIM-HD) family member Lmx1a has been successfully used to induce dopaminergic neurons from other cell types, thus showing significant implications in replacement therapies of Parkinson's disease, but the underlying mechanism remains elusive. In this study, we used Drosophila eye as a model system to investigate how forced expression of dLmx1a , the fly homolog of human Lmx1a , alters cell identify. We found that ectopic expression of dLmx1a suppresses the formation of Drosophila eye tissue and identified the LIM and HD as two essential domains. dLmx1a requires and physically binds to Chip, a well-known cofactor of LIM-HD proteins. Chip connects two dLmx1a proteins to form a functional tetrameric complex. In addition, we provide evidence showing that dLmx1a expression results in the suppression of two retina determination gene eyes absent ( eya ) and string ( stg ). Taken together, our findings identified Chip as a novel partner of dLmx1a to alter cell differentiation in Drosophila eye through repressing eya and stg expression, and provide an animal model for further understanding the molecular mechanism whereby Lmx1a determines cell fate. [ABSTRACT FROM AUTHOR]

The Hippo pathway has been implicated in controlling organ size and tumorigenesis and the underlying molecular mechanisms have attracted intensive attentions. In this work, we identified dSmurf as a new regulator of Wts, a core component of the Hippo pathway, in Drosophila . Our data revealed that Wts and dSmurf colocalize to cytoplasm and physically form an immunoprecipitated complex in S2 cells. Sufficient knock-down of dSmurf increases the protein abundance of Wts and thus increases phosphorylation level at S168 of Yki, the key downstream target of Wts in the Hippo pathway. Genetic epistasis assays showed that halving dosage of dSmurf dominantly enhances the phenotype caused by overexpression of Wts and restrains Yki activity in Drosophila eyes. Our works defines a novel role of dSmurf in animal development through modulating Wts turnover and thereby Hippo signal transduction, implying that targeting dSmurf may be a promising therapeutic strategy to manipulate the Hippo pathway in pathological conditions. [ABSTRACT FROM AUTHOR]