An Explorative Study on Document Type Assignment of Review Articles in Web of Science, Scopus and Journals' Website

Accurately assigning the document type of review articles in citation index databases like Web of Science(WoS) and Scopus is important. This study aims to investigate the document type assignation of review articles in web of Science, Scopus and Journals' website in a large scale. 27,616 papers from 160 journals from 10 review journal series indexed in SCI are analyzed. The document types of these papers labeled on journals' website, and assigned by WoS and Scopus are retrieved and compared to determine the assigning accuracy and identify the possible reasons of wrongly assigning. For the document type labeled on the website, we further differentiate them into explicit review and implicit review based on whether the website directly indicating it is review or not. We find that WoS and Scopus performed similarly, with an average precision of about 99% and recall of about 80%. However, there were some differences between WoS and Scopus across different journal series and within the same journal series. The assigning accuracy of WoS and Scopus for implicit reviews dropped significantly. This study provides a reference for the accuracy of document type assigning of review articles in WoS and Scopus, and the identified pattern for assigning implicit reviews may be helpful to better labeling on website, WoS and Scopus

A Hybrid Multi-objective Genetic Algorithm for Bi-objective Time Window Assignment Vehicle Routing Problem

Providing a satisfying delivery service is an important way to maintain the customers’ loyalty and further expand profits for manufacturers and logistics providers. Considering customers’ preferences for time windows, a bi-objective time window assignment vehicle routing problem has been introduced to maximize the total customers’ satisfaction level for assigned time windows and minimize the expected delivery cost. The paper designs a hybrid multi-objective genetic algorithm for the problem that incorporates modified stochastic nearest neighbour and insertion-based local search. Computational results show the positive effect of the hybridization and satisfactory performance of the metaheuristics. Moreover, the impacts of three characteristics are analysed including customer distribution, the number of preferred time windows per customer and customers’ preference type for time windows. Finally, one of its extended problems, the bi-objective time window assignment vehicle routing problem with time-dependent travel times has been primarily studied.</p

Analysis of tumor-related features of non-small cell lung cancer based on TCR repertoire workflow

Objective·To explore the immune-related characteristics of non-small cell lung cancer (NSCLC), discover potential tumor markers in V-J genes, and lay the foundation for establishing a TCR-antigen recognition prediction model.Methods·A total of 704 NSCLC samples were collected to establish a comprehensive T-cell receptor (TCR) repertoire analysis workflow. The upstream analysis included steps such as raw data processing, quality control, filtering, TCR sequence identification, and extraction. The downstream analysis included repertoire clone distribution, clone typing, V-J gene sharing, CDR3 distribution characteristics, and clone tracking. The sample clone distribution was analyzed by using indices such as Shannon-Weiner index and Chao1 index. Clone typing was performed based on the number of clone amplifications to explore differences among different types. The degree of V-J gene segment sharing was analyzed, and the sharing of low-frequency clone types was determined through clone amplification weight analysis of V-J genes by using two samples of papillary thyroid carcinoma. Finally, analysis of the distribution characteristics of V genes and high-frequency clone type CDR3, and clone tracking analysis were conducted to monitor changes in tumor immune clone frequencies before and after analysis, aiming to identify potential tumor markers.Results·① Significant differences were observed in clone distribution and clone typing among different NSCLC tissues, as well as among different ages and genders. ② Specific highly-shared V-J genes were identified in the analysis of V-J gene sharing, and non-normal distribution of high-clone V genes and amino acid high-frequency clone types were found in the CDR3 distribution analysis. ③ In the analysis of high-frequency clone type clone tracking, highly expressed or newly expressed high-frequency clone types were observed in NSCLC, suggesting that these clone types could serve as potential tumor-associated antigens or bind with CDR3 reference sequences of new antigens. ④ It was found that the expression frequency of TRBJ2-5 gene, originally low-expressed, significantly increased, indicating its potential role as a key low-frequency gene in tumor immune response.Conclusion·The TRAV21 and TRBV6.5 genes show high clone amplification in NSCLC and could serve as potential tumor biomarkers

A novel variation in DEPDC5 causing familial focal epilepsy with variable foci

BackgroundDisheveled, EGL-10, and pleckstrin (DEP) domain-containing protein 5 (DEPDC5) is a component of GTPase-activating protein (GAP) activity toward the RAG complex 1 (GATOR1) protein, which is an inhibitor of the amino acid-sensing branch of the mammalian target of rapamycin complex 1 (mTORC1) pathway. GATOR1 complex variations were reported to correlate with familial focal epilepsy with variable foci (FFEVF). With the wide application of whole exome sequencing (WES), more and more variations in DEPDC5 were uncovered in FFEVF families.MethodsA family with a proband diagnosed with familial focal epilepsy with variable foci (FFEVF) was involved in this study. Whole exome sequencing (WES) was performed in the proband, and Sanger sequencing was used to confirm the variation carrying status of the family members. Mini-gene splicing assay was performed to validate the effect on the alternative splicing of the variation.ResultsA novel variant, c.1217 + 2T&gt;A, in DEPDC5 was identified by WES in the proband. This splicing variant that occurred at the 5′ end of intron 17 was confirmed by mini-gene splicing assays, which impacted alternative splicing and led to the inclusion of an intron fragment. The analysis of the transcribed mRNA sequence indicates that the translation of the protein is terminated prematurely, which is very likely to result in the loss of function of the protein and lead to the occurrence of FFEVF.ConclusionThe results suggest that c.1217 + 2T&gt;A variations in DEPDC5 might be the genetic etiology for FFEVF in this pedigree. This finding expands the genotype spectrum of FFEVF and provides new etiological information for FFEVF

Decoding the spermatogonial stem cell niche under physiological and recovery conditions in adult mice and humans

The intricate interaction between spermatogonial stem cell (SSC) and testicular niche is essential for maintaining SSC homeostasis; however, this interaction remains largely uncharacterized. In this study, to characterize the underlying signaling pathways and related paracrine factors, we delineated the intercellular interactions between SSC and niche cell in both adult mice and humans under physiological conditions and dissected the niche-derived regulation of SSC maintenance under recovery conditions, thus uncovering the essential role of C-C motif chemokine ligand 24 and insulin-like growth factor binding protein 7 in SSC maintenance. We also established the clinical relevance of specific paracrine factors in human fertility. Collectively, our work on decoding the adult SSC niche serves as a valuable reference for future studies on the aetiology, diagnosis, and treatment of male infertility.</p

Reinforcement Learning for Autonomous Underwater Vehicles via Data-Informed Domain Randomization

Autonomous Underwater Vehicles (AUVs) or underwater vehicle-manipulator systems often have large model uncertainties from degenerated or damaged thrusters, varying payloads, disturbances from currents, etc. Other constraints, such as input dead zones and saturations, make the feedback controllers difficult to tune online. Model-free Reinforcement Learning (RL) has been applied to control AUVs, but most results were validated through numerical simulations. The trained controllers often perform unsatisfactorily on real AUVs; this is because the distributions of the AUV dynamics in numerical simulations and those of real AUVs are mismatched. This paper presents a model-free RL via Data-informed Domain Randomization (DDR) for controlling AUVs, where the mismatches between the trajectory data from numerical simulations and the real AUV were minimized by adjusting the parameters in the simulated AUVs. The DDR strategy extends the existing adaptive domain randomization technique by aggregating an input network to learn mappings between control signals across domains, enabling the controller to adapt to sudden changes in dynamics. The proposed RL via DDR was tested on the problems of AUV pose regulation through extensive numerical simulations and experiments in a lab tank with an underwater positioning system. These results have demonstrated the effectiveness of RL-DDR for transferring trained controllers to AUVs with different dynamics

Day-to-Day Traffic Assignment Model considering Information Fusion and Dynamic Route Adjustment Ratio

A new day-to-day traffic assignment model is proposed to describe travelers’ day-to-day behavioral changes with advanced traffic information system. In the model, travelers’ perception is updated by a double exponential-smoothing learning process combining experience and traffic information that is explicitly modelled. Route adjustment ratio is dynamically determined by the difference between perceived and expected utilities. Through theoretical analyses, we investigate the existence of its fixed point and the influence factors of uniqueness of the fixed point. An iterative-based algorithm that can solve the fixed point is also given. Numerical experiments are then conducted to investigate effects of several main parameters on its convergence, which provides insights for traffic management. In addition, we compare the system efficiencies under the static route adjustment ratio and dynamic route adjustment ratio and show the application of the model