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BackgroundCircadian rhythm disruption is typical in Parkinson’s disease (PD) early stage, and it plays an important role in the prognosis of the treatment effect in the advanced stage of PD. There is growing evidence that circadian rhythm genes can influence development of PD. Therefore, this study explored specific regulatory mechanism of circadian genes (C-genes) in PD through bioinformatic approaches.MethodsDifferentially expressed genes (DEGs) between PD and control samples were identified from GSE22491 using differential expression analysis. The key model showing the highest correlation with PD was derived through WGCNA analysis. Then, DEGs, 1,288 C-genes and genes in key module were overlapped for yielding differentially expressed C-genes (DECGs), and they were analyzed for LASSO and SVM-RFE for yielding critical genes. Meanwhile, from GSE22491 and GSE100054, receiver operating characteristic (ROC) was implemented on critical genes to identify biomarkers, and Gene Set Enrichment Analysis (GSEA) was applied for the purpose of exploring pathways involved in biomarkers. Eventually, immune infiltrative analysis was applied for understanding effect of biomarkers on immune microenvironment, and therapeutic drugs which could affect biomarkers expressions were also predicted. Finally, we verified the expression of the genes by q-PCR.ResultsTotally 634 DEGs were yielded between PD and control samples, and MEgreen module had the highest correlation with PD, thus it was defined as key model. Four critical genes (AK3, RTN3, CYP4F2, and LEPR) were identified after performing LASSO and SVM-RFE on 18 DECGs. Through ROC analysis, AK3, RTN3, and LEPR were identified as biomarkers due to their excellent ability to distinguish PD from control samples. Besides, biomarkers were associated with Parkinson’s disease and other functional pathways.ConclusionThrough bioinformatic analysis, the circadian rhythm related biomarkers were identified (AK3, RTN3 and LEPR) in PD, contributing to studies related to PD treatment.

Abstract Background Sweet yellow clover (Melilotus officinalis) is a diploid plant (2n = 16) that is native to Europe. It is an excellent legume forage. It can both fix nitrogen and serve as a medicine. A genome assembly of Melilotus officinalis that was collected from Best corporation in Beijing is available based on Nanopore sequencing. The genome of Melilotus officinalis was sequenced, assembled, and annotated. Results The latest PacBio third generation HiFi assembly and sequencing strategies were used to produce a Melilotus officinalis genome assembly size of 1,066 Mbp, contig N50 = 5 Mbp, scaffold N50 = 130 Mbp, and complete benchmarking universal single-copy orthologs (BUSCOs) = 96.4%. This annotation produced 47,873 high-confidence gene models, which will substantially aid in our research on molecular breeding. A collinear analysis showed that Melilotus officinalis and Medicago truncatula shared conserved synteny. The expansion and contraction of gene families showed that Melilotus officinalis expanded by 565 gene families and shrank by 56 gene families. The contacted gene families were associated with response to stimulus, nucleotide binding, and small molecule binding. Thus, it is related to a family of genes associated with peptidase activity, which could lead to better stress tolerance in plants. Conclusions In this study, the latest PacBio technology was used to assemble and sequence the genome of the Melilotus officinalis and annotate its protein-coding genes. These results will expand the genomic resources available for Melilotus officinalis and should assist in subsequent research on sweet yellow clover plants.

Aiming at the current design and construction of reinforced earth retaining wall in which most of the reinforcement laying methods are of equal length, an inverted trapezoidal reinforcement laying method was proposed. Based on the theory of retaining wall displacement zoning and finite-difference Flac3D numerical simulation, a three-dimensional analytical model of reinforced earth retaining wall was established to explore the effects of the three types of reinforced earth retaining wall on the displacements, horizontal earth pressures, reinforcement tensile stresses, and the potential rupture surfaces under different peak accelerations. The results show that with the increase of peak acceleration, the displacement of the retaining wall gradually increases, and under the same load, changing the layout of reinforcement material the lateral horizontal displacement decreases by 9.3%, and the vertical settlement decreases by 5.3%; The horizontal earth pressure of the three forms of retaining wall does not differ much, and the maximum horizontal earth pressure is located in the middle and lower part of the retaining wall; The tensile stress of the reinforcement material transforms from a single-peak type to a bi-peak type along the height of the wall as peak acceleration increases, with the maximum value located in the middle and lower part of the retaining wall; The shape of rupture zone of potential fracture surface filling area is related to the shape of rupture zone of potential fracture surface filling area. The maximum value is located in the middle and lower part of the retaining wall; The shape of the rupture zone in the filling area of the potential rupture surface is related to the deployment method of the reinforcement material. The inverted trapezoidal reinforcement layout has better seismic effect on reinforced soil retaining wall, which provides reference for reinforcement layout of reinforced soil retaining wall in construction design.

With the development of bio-informatics, visual prostheses have become an effective method for low vision people to restore visual function. To meet the visual needs of visual implant recipients, this study explored the problem of in vitro image processor image recognition and classification. It selected the convolutional neural network framework VGG as the technical core, introduced the fruit fly optimization algorithm for optimizing the VGG recognition model, and constructed a visual prosthesis image recognition model on the ground of improved VGG. The experiment demonstrated that the improved fruit fly search algorithm had an average absolute error and root mean square error values lower than 0.4, which was superior to other intelligent optimization algorithms. The performance of the improved image recognition model has been significantly improved, with a maximum AUC value of 0.942, a recognition accuracy range of 68.29%-97.23%, and a stable fitness curve of around 97.00. The maximum F1 value of the image recognition model designed in the study reached 91.47%, and the loss function curve converged to the minimum value. In the application of visual prostheses, the recognition accuracy and R-squared performance of this model were both the best. Compared with natural human vision, the contrast and functional visual effects matched well, the processing speed was faster, and the delay time did not affect the actual application of visual prostheses, achieving high user satisfaction. This study can enrich and improve the theoretical foundation of visual image analysis and visual prosthetics, and help visually impaired groups improve their lives and quality of life.

Soil and water conservation measures have good carbon sinking capacity, and the comprehensive management of small watersheds involves plant measures, engineering measures and farming measures, which profoundly affect the capacity of the three major carbon pools of soil, vegetation and water bodies, making them an ideal place to carry out the monitoring and accounting of carbon sinks in soil and water conservation. The purpose of this paper is to monitor and evaluate the carbon sinks of soil and vegetation, to provide techniques and methods for the implementation of dynamic monitoring and evaluation of carbon sinks in soil and water conservation projects, and to provide theoretical and methodological support for the participation of soil and water conservation projects in carbon trading and the study of the formulation of relevant rules. In this study, field sampling and analysis, LiDAR, remote sensing and other related parameters were used to account for the carbon storage of vegetation carbon pools and soil carbon pools in the Luodi River sub-watershed, Changting County, Fujian Province, from 2001 to 2022, and to evaluate the carbon sink capacity of the various soil and water conservation management measures in the sub-watershed. The results show that after 21 years of comprehensive management, various soil and water conservation measures in the Luodi River sub-basin have significantly enhanced the role and capacity of carbon sinks, and the sub-basin’s carbon stock increased by 3.97 × 104 t, with an average annual increase of 1.89 × 103 t/a. From the perspective of the carbon pools, the carbon stocks of soil and vegetation increased by 73.73% and 346.41%, respectively, from 2001 to 2022. The total carbon sunk in the sub-watershed reached 2.90 × 104 t, of which 1.57 × 104 t was in soil carbon sinks and 1.34 × 104 t was in vegetation carbon sinks. There were differences in the ability of various measures to enhance the increment of the carbon sink, among which the Castanea mollissima and the Fertilized Pinus massoniana Forest had the most obvious increase in carbon sunk, followed by the Mixed Needleleaf and Broadleaf Forest, the Nurture and Management Pinus massoniana Forest, and the Horizontal terraces Pinus massoniana Forest, and lastly, the Closed Management Forest and the Morella rubra. Various soil and water conservation measures have obvious effects of carbon retention, carbon sequestration and sink enhancement, while Castanea mollissima and Fertilized Pinus massoniana Forest and other forests that implement land preparation and afforestation with fertilization and nourishment measures have more significant increases in carbon sink capacity, which is an effective measure to improve the benefits of soil and water conservation and increase the amount of carbon sinks.

Summary: The plasma proteome can mediate the association of hospital-treated infections with dementia incidence. We screened up to 37,269 UK Biobank participants aged 50–74 years for the presence of a prevalent hospital-treated infection, subsequently tested as a predictor for ≤1,463 plasma proteins and dementia incidence. Four-way decomposition models decomposed infection-dementia total effect into pure mediation, pure interaction, neither or both through the plasma proteome. Hospital-treated infections increased dementia two-fold. The strongest mediation effect was through the growth differentiation factor 15 (GDF15) protein. Top 17 proteomic mediators explained collectively 5% of the total effect, while pathway analysis of all mediators (k = 221 plasma proteins) revealed top pathways including the immune system, signal transduction, metabolism, disease and metabolism of proteins, with the GDF15 cluster reflecting most strongly the “transmembrane receptor protein tyrosine kinase signaling pathway”. The association of hospital-treated infections with dementia was partially mediated through GDF15 and other plasma proteomic markers.

Objectives: Sleep disturbances are common in amyotrophic lateral sclerosis (ALS). However, previous studies have explored sleep quality at the cross-sectional level and the longitudinal variability characteristics are currently unknown. Our study aimed to longitudinally explore the effect of sleep quality on disease progression in patients with ALS. Methods: All enrolled patients with ALS were first diagnosed and completed the 6- and 12-month follow-ups. Subjective sleep disturbance was assessed using the Pittsburgh Sleep Quality Index (PSQI). Based on the PSQI score at baseline, patients with ALS were classified as poor sleepers (PSQI >5) and good sleepers (PSQI ≤5). Disease progression was assessed using the rate of disease progression, the absolute change from baseline forced vital capacity (ΔFVC) and the percentage change from baseline FVC (ΔFVC%) over the follow-up period. Results: Sixty-three patients were included in the study, 24 (38.1%) were poor sleepers and 39 were good sleepers. The percentage of patients with poor sleep quality was 38.1% at baseline, increasing to 60.3% and 74.6% at 6- and 12-month, respectively. Compared to good sleepers, ΔFVC and ΔFVC% values were greater in poor sleepers (P < 0.001 and P = 0.001, respectively). Poor sleep quality at diagnosis is associated with rapid deterioration of respiratory function during disease progression. Conclusions: Sleep disturbances maybe a potential risk factor for deterioration of respiratory function in patients with ALS. The role of sleep disturbances in disease progression deserves attention, and early assessment and intervention may slow disease progression and improve life quality of patients with ALS.

Abstract Objective Expression of glial fibrillary acidic protein (GFAP), a marker of reactive astrocytosis, colocalizes with neuropathology in the brain. Blood levels of GFAP have been associated with cognitive decline and dementia status. However, further examinations at a population‐based level are necessary to broaden generalizability to community settings. Methods Circulating GFAP levels were assayed using a Simoa HD‐1 analyzer in 4338 adults without prevalent dementia from four longitudinal community‐based cohort studies. The associations between GFAP levels with general cognition, total brain volume, and hippocampal volume were evaluated with separate linear regression models in each cohort with adjustment for age, sex, education, race, diabetes, systolic blood pressure, antihypertensive medication, body mass index, apolipoprotein E ε4 status, site, and time between GFAP blood draw and the outcome. Associations with incident all‐cause and Alzheimer's disease dementia were evaluated with adjusted Cox proportional hazard models. Meta‐analysis was performed on the estimates derived from each cohort using random‐effects models. Results Meta‐analyses indicated that higher circulating GFAP associated with lower general cognition (ß = −0.09, [95% confidence interval [CI]: −0.15 to −0.03], p = 0.005), but not with total brain or hippocampal volume (p > 0.05). However, each standard deviation unit increase in log‐transformed GFAP levels was significantly associated with a 2.5‐fold higher risk of incident all‐cause dementia (Hazard Ratio [HR]: 2.47 (95% CI: 1.52–4.01)) and Alzheimer's disease dementia (HR: 2.54 [95% CI: 1.42–4.53]) over up to 15‐years of follow‐up. Interpretation Results support the potential role of circulating GFAP levels for aiding dementia risk prediction and improving clinical trial stratification in community settings.

Age related macular degeneration is a common cause of visual impairment in the elderly, but the etiology is not fully understood. Here, the authors use genetic data, serum proteomics, and AMD phenotypic data from a large Icelandic cohort to discover proteins altered in, causally related to AMD or signifying progression of advanced AMD.

Introduction: Drug-target interaction (DTI) prediction is a key step in drug function discovery and repositioning. The emergence of large-scale heterogeneous biological networks provides an opportunity to identify drug-related target genes, which led to the development of several computational methods for DTI prediction.Methods: Considering the limitations of conventional computational methods, a novel tool named LM-DTI based on integrated information related to lncRNAs and miRNAs was proposed, which adopted the graph embedding (node2vec) and the network path score methods. First, LM-DTI innovatively constructed a heterogeneous information network containing eight networks composed of four types of nodes (drug, target, lncRNA, and miRNA). Next, the node2vec method was used to obtain feature vectors of drug as well as target nodes, and the path score vector of each drug-target pair was calculated using the DASPfind method. Finally, the feature vectors and path score vectors were merged and input into the XGBoost classifier to predict potential drug-target interactions.Results and Discussion: The 10-fold cross validations evaluate the classification accuracies of the LM-DTI. The prediction performance of LM-DTI in AUPR reached 0.96, which showed a significant improvement compared with those of conventional tools. The validity of LM-DTI has also been verified by manually searching literature and various databases. LM-DTI is scalable and computing efficient; thus representing a powerful drug relocation tool that can be accessed for free at http://www.lirmed.com:5038/lm_dti.

Long non-coding RNAs (lncRNAs) play an important regulatory role in gene transcription and post-transcriptional modification, and lncRNA regulatory dysfunction leads to a variety of complex human diseases. Hence, it might be beneficial to detect the underlying biological pathways and functional categories of genes that encode lncRNA. This can be carried out by using gene set enrichment analysis, which is a pervasive bioinformatic technique that has been widely used. However, accurately performing gene set enrichment analysis of lncRNAs remains a challenge. Most conventional enrichment analysis methods have not exhaustively included the rich association information among genes, which usually affects the regulatory functions of genes. Here, we developed a novel tool for lncRNA set enrichment analysis (TLSEA) to improve the accuracy of the gene functional enrichment analysis, which extracted the low-dimensional vectors of lncRNAs in two functional annotation networks with the graph representation learning method. A novel lncRNA–lncRNA association network was constructed by merging lncRNA-related heterogeneous information obtained from multiple sources with the different lncRNA-related similarity networks. In addition, the random walk with restart method was adopted to effectively expand the lncRNAs submitted by users according to the lncRNA–lncRNA association network of TLSEA. In addition, a case study of breast cancer was performed, which demonstrated that TLSEA could detect breast cancer more accurately than conventional tools. The TLSEA can be accessed freely at http://www.lirmed.com:5003/tlsea.

This paper aims to assess the competitiveness of 68 high-growth enterprises listed on China's STAR market and explore the combination of antecedent conditions that led to their listing using fuzzy set qualitative comparative analysis. The interpretive structure model was utilized to identify the factors influencing their competitiveness, and the listing index weight for the STAR market was determined using the analytic hierarchy process. The competitiveness of the listed enterprises was found to be promising, with new energy, next-generation IT, and high-end equipment manufacturing being the most prominent fields. However, energy conservation and environmental protection exhibited relatively weak listed competitiveness. The listing of these enterprises was the result of multiple factors rather than a single factor. The listing paths of high-growth enterprises in China were classified into three types: well-operated with high technical competence and innovation-led, high-profit with low growth and innovation, and large-scale, high-profit, and innovation-led.

Redox-active organic molecules that store positive charge in aqueous redox flow cells (catholyte redoxmers) frequently exhibit poor chemical stability for reasons that are not entirely understood. While for some catholyte molecules, deprotonation in their charged state is resposible for shortening the lifetime, for well designed molecules that avoid this common fate, it is seldom known what causes their eventual decomposition as it appears to be energetically prohibitive. Here, a highly soluble (1.6 M) phenoxazine molecule with a redox potential of 0.48 V vs. Ag/AgCl has been examined in flow cells. While this molecule has highly reversible redox chemistry, during cycling the capacity fades in a matter of hours. Our analyses suggest a cooperative decomposition pathway involving disproportionation of two charged molecules followed by anion substitution and deprotonation. This example suggests that cooperative reactions can be responsible for unexpectedly low chemical instability in the catholyte redoxmers and that researchers need to be keenly aware of such reactions and methods for their mitigation.

MicroRNAs (miRNAs) are closely associated with the occurrences and developments of many complex human diseases. Increasing studies have shown that miRNAs emerge as new therapeutic targets of small molecule (SM) drugs. Since traditional experiment methods are expensive and time consuming, it is particularly crucial to find efficient computational approaches to predict potential small molecule-miRNA (SM-miRNA) associations. Considering that integrating multi-source heterogeneous information related with SM-miRNA association prediction would provide a comprehensive insight into the features of both SMs and miRNAs, we proposed a novel model of Small Molecule-MiRNA Association prediction based on Heterogeneous Network Representation Learning (SMMA-HNRL) for more precisely predicting the potential SM-miRNA associations. In SMMA-HNRL, a novel heterogeneous information network was constructed with SM nodes, miRNA nodes and disease nodes. To access and utilize of the topological information of the heterogeneous information network, feature vectors of SM and miRNA nodes were obtained by two different heterogeneous network representation learning algorithms (HeGAN and HIN2Vec) respectively and merged with connect operation. Finally, LightGBM was chosen as the classifier of SMMA-HNRL for predicting potential SM-miRNA associations. The 10-fold cross validations were conducted to evaluate the prediction performance of SMMA-HNRL, it achieved an area under of ROC curve of 0.9875, which was superior to other three state-of-the-art models. With two independent validation datasets, the test experiment results revealed the robustness of our model. Moreover, three case studies were performed. As a result, 35, 37, and 22 miRNAs among the top 50 predicting miRNAs associated with 5-FU, cisplatin, and imatinib were validated by experimental literature works respectively, which confirmed the effectiveness of SMMA-HNRL. The source code and experimental data of SMMA-HNRL are available at https://github.com/SMMA-HNRL/SMMA-HNRL.

Abstract Background Infiltrating immune and stromal cells are important components of the endometrial cancer (EC) microenvironment, which has a significant effect on the biological behavior of EC, suggesting that unique immune-related genes may be associated with the prognosis of EC. However, the association of immune-related genes with the prognosis of EC has not been elucidated. We attempted to identify immune-related genes with potentially prognostic value in EC using The Cancer Genome Atlas database and the relationship between immune microenvironment and EC. Methods We analyzed 578 EC samples from TCGA database and used weighted gene co-expression network analysis to screen out immune-related genes. We constructed a protein–protein interaction network and analyzed it using STRING and Cytoscape. Immune-related genes were analyzed through conjoint Cox regression and random forest algorithm analysis were to identify a multi-gene prediction model and stratify low-risk and high-risk groups of EC patients. Based on these data, we constructed a nomogram prediction model to improve prognosis assessment. Evaluation of Immunological, gene mutations and gene enrichment analysis were applied on these groups to quantify additional differences. Results Using conjoint Cox regression and random forest algorithm, we found that TRBC2, TRAC, LPXN, and ARHGAP30 were associated with the prognosis of EC and constructed four gene risk models for overall survival and a consistent nomogram. The time-dependent receiver operating characteristic curve analysis revealed that the area under the curve for 1-, 3-, and 5-y overall survival was 0.687, 0.699, and 0.76, respectively. These results were validated using a validation cohort. Immune-related pathways were mostly enriched in the low-risk group, which had higher levels of immune infiltration and immune status. Conclusion Our study provides new insights for novel biomarkers and immunotherapy targets in EC.

Abstract Background Fluoropyrimidine plus platinum chemotherapy remains the standard first line treatment for gastric cancer (GC). Guidelines exist for the clinical interpretation of four DPYD genotypes related to severe fluoropyrimidine toxicity within European populations. However, the frequency of these single nucleotide polymorphisms (SNPs) in the Latin American population is low (

ObjectivesBased on incentive theory of motivation, this paper aims to estimate China's basic pension insurance's annual efficiency and inter-period efficiency changes from 2015 to 2019 and further examine the relationship between official tenure and basic pension insurance governance performance.MethodsThe DEA—BCC model was used to evaluate the operating efficiency of basic pension insurance in 31 provinces of China. And four panel Tobit models were constructed to examine the heterogeneous linkages between officials' tenure and governance efficiency in different regions of China.ResultsThe results showed that there was an inverted U-shaped relationship between the official tenure and the governance efficiency of basic pension insurance. The younger an official was in his current position, the more apparent the inverted U-shaped relationship between the tenure of an official and the governance efficiency of basic pension insurance. We also found that localized government officials showed better governance efficiency of basic pension insurance. However, as the term of office of officials increased, the governance efficiency of non-localized officials showed a steeper negative effect.ConclusionThis study firstly reveals a significant relationship between the characteristics of officials and the operation of China's basic pension insurance system, which is a complement to the study of China's basic pension systems.

Abstract Background Lung adenocarcinoma (LUAD) remains one of the world’s most known aggressive malignancies with a high mortality rate. Molecular biological analysis and bioinformatics are of great importance as they have recently occupied a large area in the studies related to the identification of various biomarkers to predict survival for LUAD patients. In our study, we attempted to identify a new prognostic model by developing a new algorithm to calculate the allele frequency deviation (AFD), which in turn may assist in the early diagnosis and prediction of clinical outcomes in LUAD. Method First, a new algorithm was developed to calculate AFD using the whole-exome sequencing (WES) dataset. Then, AFD was measured for 102 patients, and the predictive power of AFD was assessed using Kaplan–Meier analysis, receiver operating characteristic (ROC) curves, and area under the curve (AUC). Finally, multivariable cox regression analyses were conducted to evaluate the independence of AFD as an independent prognostic tool. Result The Kaplan–Meier analysis showed that AFD effectively segregated patients with LUAD into high-AFD-value and low-AFD-value risk groups (hazard ratio HR = 1.125, 95% confidence interval CI 1.001–1.26, p = 0.04) in the training group. Moreover, the overall survival (OS) of patients who belong to the high-AFD-value group was significantly shorter than that of patients who belong to the low-AFD-value group with 42.8% higher risk and 10% lower risk of death for both groups respectively (HR for death = 1.10; 95% CI 1.01–1.2, p = 0.03) in the training group. Similar results were obtained in the validation group (HR = 4.62, 95% CI 1.22–17.4, p = 0.02) with 41.6%, and 5.5% risk of death for patients who belong to the high and low-AFD-value groups respectively. Univariate and multivariable cox regression analyses demonstrated that AFD is an independent prognostic model for patients with LUAD. The AUC for 5-year survival were 0.712 and 0.86 in the training and validation groups, respectively. Conclusion AFD was identified as a new independent prognostic model that could provide a prognostic tool for physicians and contribute to treatment decisions.

Abstract Background Lung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients. Methods Raw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature. Results A prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041–39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779–3.276]; P

Abstract Background Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. Results In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5–100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. Conclusion These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

Abstract Background Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing. Results All panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5–20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden. Conclusion This comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.

Electrospinning provides a versatile, efficient and low-cost method for the preparation of continuous nanofibres from various polymers. In this study, the polyhedral oligomeric silsesquioxanes (POSS) block copolymer was synthesized via atom transfer radical polymerization. The smooth fiber, porous fiber or hierarchically porous microspheres were prepared by electrospinning from POSS block copolymer, poly(vinylidene fluoride) (PVDF) and aluminium oxide (Al2O3). The influence of copolymer concentration, the ratio of the solvents, the diameter and concentration of the Al2O3 on the surface morphology were investigated. Porous fibers and porous microspheres were prepared by regulating the ratio of the solvents from the phase separation and breath figure methods. The dynamic behavior of the water droplet with the constant volume impacting on the electrospinning films were reported. The morphology evolution, restitution coefficient, the change of energy of the water droplets are examined. The droplet bounces several times on the superhydrophobic surface, while the droplet remains pinned and does not rebound when the contact angles was lower than 150°. On the other hand, the water droplets were splashed on the Al2O3 based electrospinning films. Finally, the mechanical properties of the electrospinning films were investigated.

Soil erosion is a major environmental problem in China. Planning for soil erosion control requires accurate soil erosion rate and spatial distribution information. The aim of this article is to present the methods and results of the national soil erosion survey of China completed in 2011. A multi-stage, unequal probability, systematic area sampling method was employed. A total of 32,948 sample units, which were either 0.2–3 km2 small catchments or 1 km2 grids, were investigated on site. Soil erosion rates were calculated with the Chinese Soil Loss Equation in 10 m by 10 m grids for each sample unit, along with the area of soil loss exceeding the soil loss tolerance and the proportion of area in excess of soil loss tolerance relative to the total land area of the sample units. Maps were created by using a spatial interpolation method at national, river basin, and provincial scales. Results showed that the calculated average soil erosion rate was 5 t ha−1 yr−1 in China, and was 18.2 t ha−1 yr−1 for sloped, cultivated cropland. Intensive soil erosion occurred on cropland, overgrazing grassland, and sparsely forested land. The proportions of soil loss tolerance exceedance areas of sample units were interpolated through the country in 250 m grids. The national average ratio was 13.5%, which represents the area of land in China that requires the implementation of soil conservation practices. These survey results and the maps provide the basic information for national conservation planning and policymaking.

With the rapid popularity of smart terminals, the device-to-device(D2D) communication is recognized as one of the most promising techniques in the fifth-generation(5G) communications networks due to its capabilities of substantially improving the spectral efficiency, relieving the traffic burden in the base stations(BSs), and reducing the terminals' power consumptions, etc. By properly activating some D2D links(DLs), the sum data rate of the D2D-aided cellular networks(CNs) can be substantially improved. However, constrained by the severe interference imposed on the conventional cellular users (CUs) by the activating D2D users (DUs), the sum data rate of the D2D-aided CNs cannot be unlimitedly improved in a crude way of simply increasing the number/density of DLs. In other words, there must exist a maximum/optimal number of activating DLs in terms of the sum data rate, as revealed in this paper. By identifying the maximum/optimal number of activating DLs, the closed-form expressions for sum data rates of both cellular links(CLs) and DLs can be obtained. Numerical results show that the optimum number of activating DLs, as a function of several critical parameters such as DU's transmit power, signal-to-interference-plus-noise ratio threshold and outage probability of CLs/DLs, etc, can be determined by implementing the proposed algorithms.

Long non-coding RNAs (lncRNAs) do not encode proteins, yet they have been well established to be involved in complex regulatory functions, and lncRNA regulatory dysfunction can lead to a variety of human complex diseases. LncRNAs mostly exert their functions by regulating the expressions of target genes, and accurate prediction of potential lncRNA target genes would be helpful to further understanding the functional annotations of lncRNAs. Considering the limitations in traditional computational methods for predicting lncRNA target genes, a novel model which was named Weighted Average Fusion Network Representation learning for predicting LncRNA Target Genes (WAFNRLTG) was proposed. First, a novel heterogeneous network was constructed by integrating lncRNA sequence similarity network, mRNA sequence similarity network, lncRNA-mRNA interaction network, lncRNA-miRNA interaction network and mRNA-miRNA interaction network. Next, four popular network representation learning methods were utilized to gain the representation vectors of lncRNA and mRNA nodes. Then, the representations of lncRNAs and target genes in the heterogeneous network were obtained with the weighted average fusion network representation learning method. Finally, we merged the representations of lncRNAs and related target genes to form lncRNA-gene pairs, trained the XGBoost classifier and predicted potential lncRNA target genes. In five-cross validations on the training and independent datasets, the experimental results demonstrated that WAFNRLTG obtained better AUC scores (0.9410, 0.9350) and AUPR scores (0.9391, 0.9350). Moreover, case studies of three common lncRNAs were performed for predicting their potential lncRNA target genes and the results confirmed the effectiveness of WAFNRLTG. The source codes and all data of WAFNRLTG can be freely downloaded at https://github.com/HGDYZW/WAFNRLTG.

To investigate the biaxial mechanical characteristics of reactive powder concrete (RPC), RPC plate specimens and bone-shaped specimens were tested under compression-compression and compression-tension loadings, respectively. The strengths and strains of the specimens were recorded, and the crack patterns and failure modes in various stress states were examined. Based on the test data, the characteristics of biaxial strength were analyzed, and a biaxial failure criterion was established. The characteristics of major stress-strain curves and failure modes in different biaxial stress states were determined. The results show that the ratio between the biaxial compression strength and the uniaxial compression strength was 1.44–1.58 for RPC. When the stress ratio under compression-tension was −0.05, the tensile strength decreased by 48%. Under compression-compression, the proportional limit of RPC was about 95%, and its peak strain was high. Under compression-tension, as the compressive stress increased, the elastic modulus decreased, and the peak strain in the tensile direction increased. When the RPC specimens were under compression-compression, the failure mode of RPC was splitting failure. Under compression-tension, the failure mode changed from single-crack tensile failure to multicrack compressive failure with increasing confining stress.

Objective To investigate the determinants of willingness and practice of physicians’ online medical services (OMS) uptake based on social ecosystem theory, so as to formulate OMS development strategies.Design Cross-sectional survey.Setting Research was conducted in two comprehensive hospitals and two community hospitals in Jiangsu, China, and the data were gathered from 1 June to 31 June 2020.Participants With multistage sampling, 707 physicians were enrolled in this study.Outcome measure Descriptive statistics were reported for the basic characteristics. χ2 test, Mann-Whitney U test and Spearman’s correlation analysis were used to perform univariate analysis. Linear regression and logistic regression were employed to examine the determinants of physicians’ OMS uptake willingness and actual uptake, respectively.Results The mean score of the physicians’ OMS uptake willingness was 17.33 (range 5–25), with an SD of 4.39, and 53.3% of them reported having conducted OMS. In the micro system, factors positively associated with willingness included holding administrative positions (b=1.03, p

Abstract Introduction Targeted therapies are based on specific gene alterations. Various specimen types have been used to determine gene alterations, however, no systemic comparisons have yet been made. Herein, we assessed alterations in selected cancer‐associated genes across varying sample sites in lung cancer patients. Materials and Methods Targeted deep sequencing for 48 tumor‐related genes was applied to 153 samples from 55 lung cancer patients obtained from six sources: Formalin‐fixed paraffin‐embedded (FFPE) tumor tissues, pleural effusion supernatant (PES) and pleural effusion cell sediments (PEC), white blood cells (WBCs), oral epithelial cells (OECs), and plasma. Results Mutations were detected in 96% (53/55) of the patients and in 83% (40/48) of the selected genes. Each sample type exhibited a characteristic mutational pattern. As anticipated, TP53 was the most affected sequence (54.5% patients), however this was followed by NOTCH1 (36%, across all sample types). EGFR was altered in patient samples at a frequency of 32.7% and KRAS 10.9%. This high EGFR/ low KRAS frequency is in accordance with other TCGA cohorts of Asian origin but differs from the Caucasian population where KRAS is the more dominant mutation. Additionally, 66% (31/47) of PEC samples had copy number variants (CNVs) in at least one gene. Unlike the concurrent loss and gain in most genes, herein NOTCH1 loss was identified in 21% patients, with no gain observed. Based on the relative prevalence of mutations and CNVs, we divided lung cancer patients into SNV‐dominated, CNV‐dominated, and codominated groups. Conclusions Our results confirm previous reports that EGFR mutations are more prevalent than KRAS in Chinese lung cancer patients. NOTCH1 gene alterations are more common than previously reported and reveals a role of NOTCH1 modifications in tumor metastasis. Furthermore, genetic material from malignant pleural effusion cell sediments may be a noninvasive manner to identify CNV and participate in treatment decisions.

Abstract Background With the widespread use of multiple amplicon-sequencing (MAS) in genetic variation detection, an efficient tool is required to remove primer sequences from short reads to ensure the reliability of downstream analysis. Although some tools are currently available, their efficiency and accuracy require improvement in trimming large scale of primers in high throughput target genome sequencing. This issue is becoming more urgent considering the potential clinical implementation of MAS for processing patient samples. We here developed pTrimmer that could handle thousands of primers simultaneously with greatly improved accuracy and performance. Result pTrimmer combines the two algorithms of k-mers and Needleman-Wunsch algorithm, which ensures its accuracy even with the presence of sequencing errors. pTrimmer has an improvement of 28.59% sensitivity and 11.87% accuracy compared to the similar tools. The simulation showed pTrimmer has an ultra-high sensitivity rate of 99.96% and accuracy of 97.38% compared to cutPrimers (70.85% sensitivity rate and 58.73% accuracy). And the performance of pTrimmer is notably higher. It is about 370 times faster than cutPrimers and even 17,000 times faster than cutadapt per threads. Trimming 2158 pairs of primers from 11 million reads (Illumina PE 150 bp) takes only 37 s and no more than 100 MB of memory consumption. Conclusions pTrimmer is designed to trim primer sequence from multiplex amplicon sequencing and target sequencing. It is highly sensitive and specific compared to other three similar tools, which could help users to get more reliable mutational information for downstream analysis.

Background: Differential gene expression patterns are commonly used as biomarkers to predict treatment responses among heterogeneous tumors. However, the link between response biomarkers and treatment-targeting biological processes remain poorly understood. Here, we develop a prognosis-guided approach to establish the determinants of treatment response. Methods: The prognoses of biological processes were evaluated by integrating the transcriptomes and clinical outcomes of ~26,000 cases across 39 malignancies. Gene-prognosis scores of 39 malignancies (GEO datasets) were used for examining the prognoses, and TCGA datasets were selected for validation. The Oncomine and GEO datasets were used to establish and validate transcriptional signatures for treatment responses. Findings: The prognostic landscape of biological processes was established across 39 malignancies. Notably, the prognoses of biological processes varied among cancer types, and transcriptional features underlying these prognostic patterns distinguished response to treatment targeting specific biological process. Applying this metric, we found that low tumor proliferation rates predicted favorable prognosis, whereas elevated cellular stress response signatures signified resistance to anti-proliferation treatment. Moreover, while high immune activities were associated with favorable prognosis, enhanced lipid metabolism signatures distinguished immunotherapy resistant patients. Interpretation: These findings between prognosis and treatment response provide further insights into patient stratification for precision treatments, providing opportunities for further experimental and clinical validations. Fund: National Natural Science Foundation, Innovative Research Team in University of Ministry of Education of China, National Key Research and Development Program, Natural Science Foundation of Guangdong, Science and Technology Planning Project of Guangzhou, MRC, CRUK, Breast Cancer Now, Imperial ECMC, NIHR Imperial BRC and NIH. Keywords: Prognosis, Biological processes, Treatment response, Cell-proliferation, Immune processes

Non-small cell lung cancer (NSCLC) is the most deadly cancer in the world due to its often delayed diagnosis. Identification of biomarkers with high sensitivity, specificity, and accessibility for early detection, such as circulating microRNAs, is therefore of utmost importance. In the present study, we identified a significantly higher expression of miR-146a-5p in the serum and tissue samples of NSCLC patients than that of the healthy controls. In parallel, miR-146a-5p was also highly expressed in three human NSCLC adenocarcinoma-cell lines (A549, H1299, and H1975) compared to the human bronchial epithelium cell line (HBE). By dual-luciferase reporter assay and manipulation of the expressions of miR-146a-5p and its target gene, tumor necrosis factor receptor-associated factor 6 (TRAF6), we showed that the functional effects of miR-146a-5p on NSCLC cell survival and migration were mediated by direct binding to and suppression of TRAF6. Overexpression of TRAF6 sufficiently reversed miR-146a-5p-induced cancer cell proliferation, migration, and apoptosis resistance. Our data implied that miR-146a-5p/TRAF6/NF-κB-p65 axis could be a promising diagnostic marker and a therapeutic target for NSCLC.

Abstract Background Breast cancer stem cells (BCSCs) are considered responsible for cancer relapse and drug resistance. Understanding the identity of BCSCs may open new avenues in breast cancer therapy. Although several discoveries have been made on BCSC characterization, the factors critical to the origination of BCSCs are largely unclear. This study aimed to determine whether genomic mutations contribute to the acquisition of cancer stem-like phenotype and to investigate the genetic and transcriptional features of BCSCs. Methods We detected potential BCSC phenotype-associated mutation hotspot regions by using whole-genome sequencing on parental cancer cells and derived serial-generation spheres in increasing order of BCSC frequency, and then performed target deep DNA sequencing at bulk-cell and single-cell levels. To identify the transcriptional program associated with BCSCs, bulk-cell and single-cell RNA sequencing was performed. Results By using whole-genome sequencing of bulk cells, potential BCSC phenotype-associated mutation hotspot regions were detected. Validation by target deep DNA sequencing, at both bulk-cell and single-cell levels, revealed no genetic changes specifically associated with BCSC phenotype. Moreover, single-cell RNA sequencing showed profound transcriptomic variability in cancer cells at the single-cell level that predicted BCSC features. Notably, this transcriptomic variability was enriched during the transcription of 74 genes, revealed as BCSC markers. Breast cancer patients with a high risk of relapse exhibited higher expression levels of these BCSC markers than those with a low risk of relapse, thereby highlighting the clinical significance of predicting breast cancer prognosis with these BCSC markers. Conclusions Transcriptomic variability, not genetic mutations, distinguishes BCSCs from non-BCSCs. The identified 74 BCSC markers have the potential of becoming novel targets for breast cancer therapy.

The relationship between CpG content and DNA methylation has attracted considerable interest in recent years. Direct or indirect methods have been developed to investigate their regulatory functions based on various hypotheses, large cohort studies, and meta-analyses. However, all of these analyses were performed at units of CpG blocks and, thus, the influence of finer genome structure has been neglected. Herein, we present a novel algorithm of base-pair resolution to systematically investigate the relationship between CpG contents and DNA methylation. By introducing the concept of ‘complementary index’ we examined the methylomes of 34 adult and 7 embryonic tissues and successfully fitted the relationship of DNA methylation and CpG density into a nonlinear mathematical model. A further algorithm was developed to locate the regions where CpG density does not match expectations from the model, termed ‘conflict of gap’ (COG) regions. Interestingly, COGs are highly concordant in human and mouse and their distributions display a tissue-specific pattern. Based on COG methylation patterns we correctly classified tissues according to their function or origin. We demonstrate that COGs based on our method can reveal more and deeper information than traditional differential methylation region (DMR) approaches. We also found that when COGs are located near to transcription start site (TSS), these regions can determine which promoters will be utilized for initiating gene transcription. Furthermore, COGs located far from the TSS perform as enhancers in terms of histone modification, sequence conservation, transcription factor binding, and DNase I-hypersensitivity.

The aggregation behavior of three tadpole-shaped Polyhedral oligomeric silsesquioxane (POSS) based block copolymers using different blocks poly(methyl methacrylate) (PMMA) and poly(trifluoroethyl methacrylate) (PTFEMA) with different block sequence and ratio (POSS-PTFEMA161-b-PMMA236, POSS-PMMA277-b-PTFEMA130, and POSS-PMMA466-b-PTFEMA172) was investigated on the air-water interface. The interfacial rheology of three block copolymers was studied by surface pressure isotherm, compression modulus measurements, and compression and expansion hysteresis analysis on the Langmuir trough. The block sequence and ratio play a great role in self-assembly behavior at the interface. Based on surface pressure isotherm analysis, a thin film with low elasticity was achieved for the POSS-PTFEMA161-b-PMMA236. Moreover, for the block copolymer with same segment sequence (POSS-PMMA2-b-PTFEMA), the thin film compression capability is increased with increasing the PMMA ratio. The morphology of the deposited LB thin film was illustrated by atomic force microscopy (AFM) and X-ray photoelectron spectroscopy (XPS). We observed that a thin film was composed by crater-shaped quasi-2D micelles for POSS-PTFEMA-b-PMMA, while it was proved that only flaky texture was observed for both POSS-PMMA277-b-PTFEMA130 and POSS-PMMA466-b-PTFEMA172. The thickness and area of flaky aggregates were greatly related to PMMA ratio. The different interface self-assembly structure evolution was proposed based on the interfacial rheology and thin film morphology studies.

The porous structure films of PS containing silica nanoparticles of different hydrophobicity were obtained from breath figure method. For the porous films, the pores at the peripheral region and the center region were compared. It was found that the pores at the peripheral region appeared more uniform in size and showed higher degree of ordering. In addition, the patterns of the porous film became more disordered with the enhancing concentration of nanoparticles, and the pore size of the pore was increased. With the increase of the content of SiOH, that is, decrease in particle hydrophobicity, the Si elements were aggregated at the surface of the film and the interior of the pores. A hierarchical porous structure had been obtained at appropriate humidity and particle concentration. Finally, a possible mechanism of the hierarchical structure was proposed.

MicroRNAs (miRNAs) are small non-coding RNAs that regulate a variety of biological processes. The latest version of the miRBase database (Release 18) includes 1,157 mouse and 680 rat mature miRNAs. Only one new rat mature miRNA was added to the rat miRNA database from version 16 to version 18 of miRBase, suggesting that many rat miRNAs remain to be discovered. Given the importance of rat as a model organism, discovery of the completed set of rat miRNAs is necessary for understanding rat miRNA regulation. In this study, next generation sequencing (NGS), microarray analysis and bioinformatics technologies were applied to discover novel miRNAs in rat kidneys. MiRanalyzer was utilized to analyze the sequences of the small RNAs generated from NGS analysis of rat kidney samples. Hundreds of novel miRNA candidates were examined according to the mappings of their reads to the rat genome, presence of sequences that can form a miRNA hairpin structure around the mapped locations, Dicer cleavage patterns, and the levels of their expression determined by both NGS and microarray analyses. Nine novel rat hairpin precursor miRNAs (pre-miRNA) were discovered with high confidence. Five of the novel pre-miRNAs are also reported in other species while four of them are rat specific. In summary, 9 novel pre-miRNAs (14 novel mature miRNAs) were identified via combination of NGS, microarray and bioinformatics high-throughput technologies.