Two-dimensional horizontal visibility graph analysis of human brain aging on gray matter

Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging.Currently,most structural magnetic resonance imaging literature explores brain aging merely from the perspective of morphological features,which cannot fully utilize the grayscale values containing important intrinsic information about brain structure.In this study,we propose the construction of two-dimensional horizontal visibility graphs based on the pixel intensity values of the gray matter slices directly.Normalized network structure entropy(NNSE)is then introduced to quantify the overall heterogeneities of these graphs.The results demonstrate a decrease in the NNSEs of gray matter with age.Compared with the middle-aged and the elderly,the larger values of the NNSE in the younger group may indicate more homogeneous network structures,smaller differences in importance between nodes and thus a more powerful ability to tolerate intrusion.In addition,the hub nodes of different adult age groups are primarily located in the precuneus,cingulate gyrus,superior temporal gyrus,inferior temporal gyrus,parahippocampal gyrus,insula,precentral gyrus and postcentral gyrus.Our study can provide a new perspective for understanding and exploring the structural mechanism of brain aging.

Cohort study to assess geographical variation in cholangiocarcinoma treatment in England

BACKGROUND Outcomes for cholangiocarcinoma(CCA)are extremely poor owing to the complexities in diagnosing and managing a rare disease with heterogenous sub-types.Beyond curative surgery,which is only an option for a minority of patients diagnosed at an early stage,few systemic therapy options are currently recommended to relieve symptoms and prolong life.Stent insertion to manage disease complications requires highly specialised expertise.Evidence is lacking as to how CCA patients are managed in a real-world setting and whether there is any variation in treatments received by CCA patients.AIM To assess geographic variation in treatments received amongst CCA patients in England.METHODS Data used in this cohort study were drawn from the National Cancer Registration Dataset(NCRD),Hospital Episode Statistics and the Systemic Anti-Cancer Therapy Dataset.A cohort of 8853 CCA patients diagnosed between 2014-2017 in the National Health Service in England was identified from the NCRD.Potentially curative surgery for all patients and systemic therapy and stent insertion for 7751 individuals who did not receive surgery were identified as three end-points of interest.Linear probability models assessed variation in each of the three treatment modalities according to Cancer Alliance of residence at diagnosis,and for socio-demographic and clinical characteristics at diagnosis.RESULTS Of 8853 CCA patients,1102(12.4%)received potentially curative surgery.The mean[95%confidence interval(CI)]percentage-point difference from the population average ranged from-3.96(-6.34 to-1.59)%to 3.77(0.54 to 6.99)%across Cancer Alliances in England after adjustment for patient sociodemographic and clinical characteristics,showing statistically significant variation.Amongst 7751 who did not receive surgery,1542(19.9%)received systemic therapy,with mean[95%CI]percentage-point difference from the population average between-3.84(-8.04 to 0.35)%to 9.28(1.76 to 16.80)%across Cancer Alliances after adjustment,again showing the presence of statistically significant variation for some regions.Stent insertion was received by 2156(27.8%),with mean[95%CI]percentage-point difference from the population average between-10.54(-12.88 to-8.20)%to 13.64(9.22 to 18.06)%across Cancer Alliances after adjustment,showing wide and statistically significant variation from the population average.Half of 8853 patients(n=4468)received no treatment with either surgery,systemic therapy or stent insertion.CONCLUSION Substantial regional variation in treatments received by CCA patients was observed in England.Such variation could be due to differences in case-mix,clinical practice or access to specialist expertise.

Predicting Genotype Information Related to COVID-19 for Molecular Mechanism Based on Computational Methods

Novel coronavirus disease 2019(COVID-19)is an ongoing health emergency.Several studies are related to COVID-19.However,its molecular mechanism remains unclear.The rapid publication of COVID-19 provides a new way to elucidate its mechanism through computational methods.This paper proposes a prediction method for mining genotype information related to COVID-19 from the perspective of molecular mechanisms based on machine learning.The method obtains seed genes based on prior knowledge.Candidate genes are mined from biomedical literature.The candidate genes are scored by machine learning based on the similarities measured between the seed and candidate genes.Furthermore,the results of the scores are used to perform functional enrichment analyses,including KEGG,interaction network,and Gene Ontology,for exploring the molecular mechanism of COVID-19.Experimental results show that the method is promising for mining genotype information to explore the molecular mechanism related to COVID-19.

BDLR:lncRNA identification using ensemble learning

Long non-coding RNAs(lncRNAs)play an important role in many life activities such as epigenetic material regulation,cell cycle regulation,dosage compensation and cell differentiation regulation,and are associated with many human diseases.There are many limitations in identifying and annotating lncRNAs using traditional biological experimental methods.With the development of high-throughput sequencing technology,it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning method.Based on the Bagging method and Decision Tree algorithm in ensemble learning,this paper proposes a method of lncRNAs gene sequence identification called BDLR.The identification results of this classification method are compared with the identification results of several models including Byes,Support Vector Machine,Logical Regression,Decision Tree and Random Forest.The experimental results show that the lncRNAs identification method named BDLR proposed in this paper has an accuracy of 86.61%in the human test set and 90.34%in the mouse for lncRNAs,which is more than the identification results of the other methods.Moreover,the proposed method offers a reference for researchers to identify lncRNAs using the ensemble learning.

Inhibition of ALDH2 by disulfiram leads to synthetic lethality via ROS strikes twice in ARID1A-deficient cholangiocarcinoma

Synthetic lethality describes an interaction whereby the co-occurrence of two mutations leads to cell death but one mutation alone does not,which can be exploited for cancer therapeutics.1 Due to lacking effective nonsurgical treatment and early clinical diagnosis markers,patients have high mortality and low overall survival rates in cholangiocarcinoma(CCA).

The lifestyle transition of Arthrobotrys oligospora is mediated by microRNA-like RNAs

The lifestyle transition of fungi,defined as switching from taking organic material as nutrients to pathogens,is a fundamental phenomenon in nature.However,the mechanisms of such transition remain largely unknown.Here we show microRNA-like RNAs(milRNAs)play a key role in fungal lifestyle transition for the first time.We identified milRNAs by small RNA sequencing in Arthrobotrys oligospora,a known nematode-trapping fungus.Among them,7 highly expressed milRNAs were confirmed by northern-blot analysis.Knocking out two milRNAs significantly decreased A.oligospora’s ability to switch lifestyles.We further identified that two of these milRNAs were associated with argonaute protein QDE-2 by RNA-immunoprecipitation(RIP)analysis.Three of the predicted target genes of milRNAs were found in immunoprecipitation(IP)products of QDE-2.Disruption of argonaute gene qde-2 also led to serious defects in lifestyle transition.Interestingly,knocking out individual milRNAs or qde-2 lead to diverse responses under different conditions,and qde-2 itself may be targeted by the milRNAs.Collectively,it indicates the lifestyle transition of fungi is mediated by milRNAs through RNA interference(RNAi)machinery,revealing the wide existence of miRNAs in fungi kingdom and providing new insights into understanding the adaptation of fungi from scavengers to predators and the mechanisms underlying fungal infections.

Brain Emotional Oscillatory Activity for Anger Revealed by Event-Related Spectral Perturbation

In order to find out the special cognitive emotional information to reach good accuracy in the recognition of anger emotions, the brain emotional oscillatory activity induced by relaxation and anger affective pictures is investigated in the amplitude measurement. A visual evoked affective experiment is designed and carried out to collect the electroencephalogram(EEG)data labeled with anger and relaxation emotion states. Twenty-one healthy college students(female 9, male 12) are employed to stimulate emotion by different affective pictures. Event-Related Spectral Perturbation(ERSP) is used to discover the pronounced features of anger tendency prediction in the time-frequency domain. ERSP maps exhibit that there is a difference between the female and male group, which is statistically significant within the 150-250 ms and 350-450 ms time range of alpha band. The male group is more susceptible to anger than female group, while the female group is faster in emotional regulation than the male group.These feature values could be used to identify the tendency of angry emotion, which can provide certain reference basis for further research on the predict the tendency of aggressive behavior.

Disclosing incoherent sparse and low-rank patterns inside homologous GPCR tasks for better modelling of ligand bioactivities

There are many new and potential drug targets in G protein-coupled receptors(GPCRs)without sufficient ligand associations,and accurately predicting and interpreting ligand bioactivities is vital for screening and optimizing hit compounds targeting these GPCRs.To efficiently address the lack of labeled training samples,we proposed a multi-task regression learning with incoherent sparse and low-rank patterns(MTR-ISLR)to model ligand bioactivities and identify their key substructures associated with these GPCRs targets.That is,MTR-ISLR intends to enhance the performance and interpretability of models under a small size of available training data by introducing homologous GPCR tasks.Meanwhile,the low-rank constraint term encourages to catch the underlying relationship among homologous GPCR tasks for greater model generalization,and the entry-wise sparse regularization term ensures to recognize essential discriminative substructures from each task for explanative modeling.We examined MTR-ISLR on a set of 31 important human GPCRs datasets from 9 subfamilies,each with less than 400 ligand associations.The results show that MTR-ISLR reaches better performance when compared with traditional single-task learning,deep multi-task learning and multi-task learning with joint feature learning-based models on most cases,where MTR-ISLR obtains an average improvement of 7%in correlation coefficient(r2)and 12%in root mean square error(RMSE)against the runner-up predictors.The MTR-ISLR web server appends freely all source codes and data for academic usages.^(1))