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The rapid development of internet technology has heightened interest in identifying teams with high collaborative effectiveness in complex and diverse social networks. Earlier research primarily aimed to minimize team communication costs while ensuring that task-related skill requirements were fulfilled. However, this single-factor approach often leads to uneven workload distribution among team members, which can undermine overall performance. To this end, a multi-constraint optimization method was introduced that considers both communication cost minimization and workload balancing. This method sets an upper limit on the number of skills that can be assigned to any individual within a team. The proposed framework compares two main algorithms: genetic and greedy. Experimental results using the DBLP dataset highlighted the distinct strengths of each. The genetic algorithm (GA) outperformed in reducing communication costs and decreasing team size, whereas the greedy algorithm excelled in lowering the number of disconnected teams and achieving shorter runtime. The inclusion of additional constraints in the multi-constraint optimization framework increased communication costs, extended algorithm runtime and produced larger team sizes compared with the single-constraint model. Nevertheless, the proposed approach provides a flexible solution that can be adapted to different priorities, whether emphasizing strict task requirements or optimizing factors.

Background  Paris polyphylla Sm. is a precious medicinal plant rich in various active ingredients. In addition to the well-known saponins, the flavonoids it contains have unique pharmacological potential in antioxidant, neuroprotective, and metabolic regulation. However, the flavonoids in Paris polyphylla Sm. have not been fully researched and developed yet. In this work, we conducted a comprehensive metabolomics and transcriptomics analysis to reveal the metabolic differences and biosynthetic mechanisms of flavonoids in the leaves, stems, and roots of Paris polyphylla Sm. Results Non-targeted metabolomics analysis detected a total of 332 metabolites in Paris polyphylla Sm., among which flavonoids accounted for 19.49%. The diversity and abundance of flavonoids in leaves are the highest, followed by stems and roots. By comparing the metabolites of the roots, stems, and leaves in Paris polyphylla Sm., it was found that there were 45 differential metabolites (DMs) between the leaves and roots, of which flavonoids accounted for 35%. There are 38 DMs between leaves and stems, of which flavonoids account for 45.45%. And there are 52 DMs in stems and roots, among which flavonoids account for 25.53%. A total of 62,766 genes were detected by transcriptomics, and pairwise comparison showed that there were tens of thousands of differentially expressed genes (DEGs) between each group. Afterwards, we selected 39 flavonoids and related metabolites (e.g., kaempferol-3-O-glucoside, quercetin 3-β-D-glucoside, rutin) for targeted metabolomics validation and performed RT-qPCR validation on 29 key flavonoid synthesis genes (e.g., C4H, CHS, FLS, F3’H) to verify the reliability of non-targeted metabolomics and transcriptomics. Conclusions This work indicated that leaves are the main site for the biosynthesis of flavonoids in Paris polyphylla Sm. Among them, kaempferol-3-O-glucoside, quercetin 3-β-D-glucoside, rutin, and other flavonoids are present in higher contents in leaves ( P  < 0.05). Further research on its biosynthetic mechanism indicates that naringenin chalcone is converted to naringenin by chalcone isomerase (CHI). Among them, CHI may be the rate-limiting enzyme in the biosynthesis of flavonoids in Paris polyphylla Sm. The expression of FLS is higher in leaves ( P  < 0.05) and tends to promote the synthesis of flavonols. This work promotes the utilization of non-medicinal parts of Paris polyphylla Sm. and enhances the sustainable development of this precious traditional Chinese medicine resource.

One of characteristics of large-scale linguistic decision making problems is that decision information with respect to decision making attributes is derived from multi-sources information. In addition, the number of decision makers, alternatives or criteria of decision making problems in the context of big data are increasingly large. Correlation analysis between decision making attributes has become an important issue of large-scale linguistic decision making problems. In the paper, we concentrate on correlation analysis between decision making attributes to deal with large-scale decision making problems with linguistic intuitionistic fuzzy values. Firstly, we proposed a new similarity measure between two linguistic intuitionistic fuzzy sets to formally define correlation between decision making attributes. Then we propose linguistic intuitionistic fuzzy reducible weighted Maclaurin symmetric mean (LIFRWMSM) operator and linguistic intuitionistic fuzzy reducible weighted dual Maclaurin symmetric mean (LIFRWDMSM) operator to aggregate linguistic intuitionistic fuzzy value decision information of correlational decision making attributes, and analyze several important properties of the two operator. Inspired by evaluation based on distance from average solution (EDAS) method, we design a solution scheme and decision steps to deal with large-scale linguistic intuitionistic fuzzy decision making problems. To show the effectiveness and usefulness of the proposed decision method, we employ the choice of buying a house and the selection of travel destination to demonstrate our method and make comparative analysis with others aggregation operators or methods.

Abstract Ultrasensitive and specific detection of cocaine is of great significance for monitoring cocaine abuse. Herein, a fluorescent aptasensor via coupling CRISPR-Cas12a, with magnetic nanoparticles (MNPs), split-aptamer, and terminal deoxynucleotidyl transferase (TdT), was developed for the detection of cocaine. In short, the complete cocaine aptamer is split into two parts, one is modified on magnetic nanoparticles (MNPs) and the other is free. The presence of cocaine will mediate the binding of these two segments. Then TdT will mediate the extension to form an ultra-long sequence that can bind with multiple CRISPR-Cas12a resulting in the trans-cleavage activity of CRISPR-Cas12a being triggered. Thence, the DNA reporter which is bi-labeled with fluorophore and quencher is cleaved resulting in the generation of a fluorescence signal. The developed fluorescent aptasensor realizes the detection of cocaine with excellent sensitivity and specificity. The detection limit is low down to 33 pM, and the linear range is from 330 to 1.65 × 105 pM. Most importantly, this fluorescent aptasensor can be successfully applied to the determination of cocaine in human plasma samples.

Hesitant fuzzy linguistic decision making is a focus point in linguistic decision making, in which the main method is based on preference ordering. This paper develops a new hesitant fuzzy linguistic TOPSIS method for group multi-criteria linguistic decision making; the method is inspired by the TOPSIS method and the preference degree between two hesitant fuzzy linguistic term sets (HFLTSs). To this end, we first use the preference degree to define a pseudo-distance between two HFLTSs and analyze its properties. Then we present the positive (optimistic) and negative (pessimistic) information of each criterion provided by each decision maker and aggregate these by using weights of decision makers to obtain the hesitant fuzzy linguistic positive and negative ideal solutions. On the basis of the proposed pseudo-distance, we finally obtain the positive (negative) ideal separation matrix and a new relative closeness degree to rank alternatives. We also design an algorithm based on the provided method to carry out hesitant fuzzy linguistic decision making. An illustrative example shows the elaboration of the proposed method and comparison with the symbolic aggregation-based method, the hesitant fuzzy linguistic TOPSIS method and the hesitant fuzzy linguistic VIKOR method; it seems that the proposed method is a useful and alternative decision-making method.

Hesitant fuzzy linguistic term set is defined in hesitant fuzzy linguistic decision frameworks in which all decision makers are agreed on the primary linguistic scale and its membership functions; however, this situation does not always happen, because words mean different things to different people or there are some linguistic term sets with different semantics for different people. Hence, normalization of linguistic information is necessary. Inspired by normalizing linguistic information based on 2-tuple linguistic model, in this paper, the concept of generalized hesitant fuzzy linguistic term set is proposed when decision makers are not agreed on the primary linguistic scale and its membership functions, which is an extension of hesitant fuzzy linguistic term set. Then, t -norms and t -conorms are utilized to define new operations on generalized hesitant fuzzy linguistic term sets and their properties are discussed. Based on these new operations, the likelihood-based comparison relation of generalized hesitant fuzzy linguistic term sets is presented, which is an extension of the likelihood-based comparison relation of hesitant fuzzy linguistic term sets. Accordingly, the generalized hesitant fuzzy linguistic weighted average operator, generalized hesitant fuzzy linguistic weighted geometric operator, generalized hesitant fuzzy linguistic ordered weighted average operator and generalized hesitant fuzzy linguistic ordered weighted geometric operator are provided to fuse generalized hesitant fuzzy linguistic term sets in linguistic decision making. A case study is used to illustrate the practicality of the method based on generalized hesitant fuzzy linguistic term sets and compare with Lee and Chen’s method, Rodriguez’s method and Wei’s method. It seems that the method based on generalized hesitant fuzzy linguistic term sets is flexible and useful method for hesitant fuzzy linguistic decision making.

Cancer-associated fibrocytes (CAFs), a key component of the tumour microenvironment, are marked by their heterogeneity. They also exhibit a high degree of plasticity. In the last two decades there has been a strong association established between CAFs and colorectal carcinoma (CRC). However, there are no comprehensive statistics on CRC or CAFs, and the potential directions for research. The study performed a literature review spanning from January 1, 2004, to March 27, 2025, within the Web of Science Core Collection Database. VOSviewer and CiteSpace software were used to perform bibliometric analysis and visualization. Microsoft Excel and R was also utilized. The analysis included 1145 articles. The articles in question were published across 359 different journals, and included 4032 keywords. The number of publications increased significantly between 2010 and 2025. China was the leading contributor to the total number of publications, and the United States led the global list of citations. Sun Yat-sen University and Shanghai Jiao Tong University are renowned research institutions. Notable researchers such as De Wever, Olivier and Bracke, Marc from Ghent University Hospital, and Pena, Cristina from Puerta de Hierro Majadahonda University Hospital are among the most productive and highly cited authors. CANCERS has the most publications, and the highest citation rate. CAFs are a major focus of research in CRC. This includes the effect of CAFs, such as on cell proliferation and angiogenesis. This study uses bibliometric analyses to present a comprehensive view of research in CAFs, CRCs from 2004 until March 27, 2025. The study highlights important research areas, anticipates future directions and offers valuable insights to future efforts in the field.

Face anti-spoofing is a critical component of face recognition technology. However, it suffers from poor generalizability for cross-scenario target domains due to the simultaneous presence of unseen domains and unknown attack types. In this paper, we first propose a challenging but practical problem for face anti-spoofing, open-set single-domain generalization-based face anti-spoofing, aiming to learn face anti-spoofing models that generalize well to unseen target domains with known and unknown attack types based on a single source domain. To address this problem, we propose a novel unknown-aware causal generalized representation learning framework. Specifically, the proposed network consists of two modules: (1) causality-inspired intervention domain augmentation, which generates out-of-distribution images to eliminate spurious correlations between spoof-irrelevant variant factors and category labels for generalized causal feature learning; and (2) unknown-aware probability calibration, which performs known and unknown attack detection based on the original and generated images to further improve the generalizability for unknown attack types. The results of extensive qualitative and quantitative experiments demonstrate that the proposed method learns well-generalized features for both domain shift and unknown attack types based on a single source domain. Our method achieves state-of-the-art cross-scenario generalizability for both live faces and known attack types and unknown attack types.

The functionalization of VHHs enables their application in almost every aspect of biomedical inquiry. Amino modification remains a common strategy for protein functionalization, though is considered to be inferior to site-specific methods and cause protein property changes. In this paper, four anti-β2M VHHs were selected and modified on the amino group by NHS-Fluo. The impacts of amino modification on these VHHs were drastically different, and among all th examples, the modified NB-1 maintained the original stability, bioactivity and homogeneity of unmodified NB-1. Specific recognition of VHHs targeting β2M detected by fluorescence imaging explored the possible applications of VHHs. Via this study, we successfully functionalized the anti-β2M VHHs through amino modification and the results are able to instruct the simple and fast functionalization of VHHs in biomedical researches.

Adult diffuse gliomas with FGFR3‐TACC3 (F3T3) fusion are rare and highly heterogeneous central nervous system (CNS) tumors. Current research on the biological behavior of these tumors is limited, especially regarding whether histopathologically low‐grade tumors are indolent or represent early‐stage high‐grade tumors, thereby posing challenges for grading. In this single‐center study of 17 patients with adult F3T3 fusion diffuse gliomas (F3T3 gliomas), both low‐ and high‐grade F3T3 gliomas presented distinctive recurrent histopathological features, such as oligodendrocyte‐like cells, branched vessels and frequent calcifications. Molecularly, TERT promoter mutations and 7+/10− chromosomal alterations were common; one patient with recurrent glioma with histopathological features of polymorphous low‐grade neuroepithelial tumor of the young (PLNTY) had additional CDK4 and MDM2 amplifications. Methylation profiling of 3 samples revealed varied results. A patient whose tumor had histopathological features consistent with PLNTY and a methylation subtype classified as the mesenchymal subtype of glioblastoma (GBM) experienced tumor recurrence 8 months after surgery. After 5–62 months of follow‐up, seven patients relapsed, and six died; the primary tumors of the three patients with recurrence presented histopathological characteristics of low‐grade glioma (LGG). GBM patients had worse overall survival than LGG patients ( p  = 0.035) but similar progression‐free survival ( p  = 0.47), indicating that LGG patients may experience recurrence. For adults with tumors with histopathological features of PLNTY, further molecular and methylation analyses are needed for grading. If TERT promoter mutations are present, even with a PLNTY methylation profile, these tumors can still exhibit the biological behavior of high‐grade gliomas.