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Lung adenocarcinoma (LUAD) is a highly aggressive lung cancer with poor prognosis due to lack of reliable biomarkers. Resistance to anoikis drives tumor progression and metastasis. This study aims to develop and validate an anoikis-related prognostic model for LUAD. We employed univariate Cox regression analysis, LASSO regression, and random forest algorithms to identify anoikis-related genes (ARG) from bulk transcriptomic datasets, and establish a 7-gene prognostic signature, validated in two LUAD cohorts from GEO database. We evaluated immune infiltration, molecular functions, and genomic alterations between risk groups and analyzed single-cell RNA sequencing data. IHC and mIF validated TIMP1 expression and its interaction with Treg cells. We developed a 7-gene prognostic model (LDHA, PLK1, TRAF2, ITGB4, SLCO1B3, TIMP1, ZEB2) using machine learning to predict survival in LUAD patients. The model accurately predicted 1-year survival rates (GSE31210: AUC = 0.805; GSE30219: AUC = 0.787), 2-year survival rates (GSE31210: AUC = 0.769; GSE30219: AUC = 0.681), and 3-year survival rates (GSE31210: AUC = 0.695; GSE30219: AUC = 0.735) and correlated with clinical features, immune infiltration, and tumor microenvironment (TME) remodeling. Single-cell sequencing data showed that LUAD patients exhibited an immunosuppressive TME phenotype, which was exacerbated by high TIMP1 expression in epithelial cells, promoting Treg cell activity. The 7-gene ARG prognostic model established in this study shows promising potential as a clinically applicable tool for decision-making.

Single-atom catalysts (SACs) with tunable site density and activity are promising for catalytic processes. However, the relationship between interacting sites and the catalytic mechanism, as well as the effect of the support on this relationship, remains incompletely understood. Here we report a support geometry engineering strategy to control the inter-site distance (d site ) of Cu–N–C (CuNC) SACs via strong interactions between CuNC and a secondary support (ss). This process allows tuning of the binding strength (that is Cu–N bond length) between individual Cu atoms and the N-doped primary supports, concomitantly suppressing defect formation and Cu atom detachment in the CuNC framework. The continuous optimization of the electronic and coordination structure of individual active Cu sites, achieved by reducing the d site to approximately 0.7 nm, enhances their inherent CO 2 -to-methane selectivity and activity. As a result, the ss-engineered CuNC with a moderate d site of 0.68 nm exhibits enhanced methane selectivity of 70% and a partial current density of 303.9 mA cm −2 , over 1.5 times higher than that of unmodified CuNC. The relationship between interacting sites and catalytic mechanisms in single-atom catalysts remains unclear. Here a support engineering strategy is used to tune the binding strength between single-metal atoms and the support, optimizing active Cu sites and enhancing CH 4 formation.

The luminous environment, shaped by daylight and electric light, significantly influences visual performance, physiological responses, and perceptual experiences. While these light sources are often perceived as distinct due to their differing effects on occupants’ cognition and well-being, the underlying mechanisms remain unclear. Nine lighting conditions were evaluated, combining three spectral types—daylight (DL), conventional LED (CLED), and daylight LED (DLED)—with three horizontal illuminance levels (300 lx, 500 lx, and 1000 lx). Twelve healthy subjects completed visual performance tasks (2-back working memory test), physiological measurements (heart rate variability and critical flicker frequency), and subjective evaluations. The results revealed that 500 lx consistently yielded the most favorable outcomes: 2-back task response speed improved by 6.2% over 300 lx and 1000 lx, and the critical flicker frequency difference was smallest, indicating reduced fatigue. DLED lighting achieved cognitive and physiological levels comparable to daylight. Heart rate variability analyzes further confirmed higher alertness levels under 500 lx DLED lighting (LF/HF = 3.31). Subjective ratings corroborated these findings, with perceived alertness and comfort highest under DLED and 500 lx conditions. These results demonstrate that DLED, which offers a balanced spectral composition and improved uniformity, may serve as an effective lighting configuration for supporting both visual and non-visual performance in indoor settings lacking daylight.

Studying extreme ideas in routine choices and discussions is of utmost importance to understand the increasing polarization in society. In this study, we focus on understanding the generation and influence of extreme ideas in routine conversations which we label “eccentric” ideas. The eccentricity of any idea is defined as the deviation of that idea from the norm of the social neighborhood. We collected and analyzed data from two sources of different nature: public social media and online experiments in a controlled environment. We compared the popularity of ideas against their eccentricity to understand individuals’ fascination towards eccentricity. We found that more eccentric ideas have a higher probability of getting a greater number of “likes”. Additionally, we demonstrate that the social neighborhood of an individual conceals eccentricity changes in one’s own opinions and facilitates generation of eccentric ideas at a collective level.

The relationship between size and performance of collaborative human small groups has been studied broadly across management, psychology, economics, sociology, and engineering disciplines. However, empirical research findings on this question remain equivocal. Many of the earlier studies centered on empirical human-subject experiments, which inevitably involved many confounding factors. To obtain more theory-driven mechanistic explanations of the linkage between group size and performance, we developed an agent-based simulation model that describes the complex process of collaborative group decision-making on problem-solving tasks. To find better solutions to a problem with given complexity, these agents repeatedly explore and share solution candidates, evaluate and respond to the solutions proposed by others, and update their understanding of the problem by conducting individual local search and incorporating others’ proposals. Our results showed that under a condition of ineffective information sharing, group size was negatively related to group performance at the beginning of discussion across each level of problem complexity (i.e., low, medium, and high). However, in the long run, larger groups outperformed smaller groups for the problem with medium complexity and equally well for the problem with low complexity because larger groups developed higher solution diversity. For the problem with high complexity, the higher solution diversity led to more disagreements which in turn hindered larger groups’ collaborative problem-solving ability. Our results also suggested that, in small collaborative team settings, effective information sharing can significantly improve group performance for groups of any size, especially for larger groups. This model provides a unified, mechanistic explanation of the conflicting observations reported in the existing empirical literature.

Ethnopharmacological relevance: Two types of traditional Chinese formulas of botanical drugs are prescribed for treating perimenopausal syndrome (PMS), a disorder in middle-aged women during their transition to menopause. One is for treating PMS as kidney deficiency (KD) due to senescence and declining reproductive functions, and the other is for treating it as liver qi stagnation (LQS) in association with stress and anxiety. Despite the time-tested prescriptions, an objective attestation to the effectiveness of the traditional Chinese treatment of PMS is still to be established and the associated molecular mechanism is still to be investigated. Materials and methods: A model for PMS was generated from perimenopausal rats with chronic restraint stress (CRS). The effectiveness of traditional Chinese formulas of botanical drugs and a combination of two of the formulas was evaluated based on 1 H NMR plasma metabolomic, as well as behavioral and physiological, indicators. To investigate whether the formulas contained ligands that could compensate for the declining level of estrogen, the primary cause of PMS, the ligand-based NMR technique of saturation transfer difference (STD) was employed to detect possible interacting molecules to estrogen receptors in the decoction. Results: Each prescription of the classical Chinese formula moderately attenuated the metabolomic state of the disease model. The best treatment strategy however was to combine two traditional Chinese formulas, each for a different etiology, to adjust the metabolomic state of the disease model to that of rats at a much younger age. In addition, this attenuation of the metabolomics of the disease model was by neither upregulating the estrogen level nor supplementing an estrogenic compound. Conclusion: Treatment of PMS with a traditional Chinese formula of botanical drugs targeting one of the two causes separately could ameliorate the disorder moderately. However, the best outcome was to treat the two causes simultaneously with a decoction that combined ingredients from two traditional prescriptions. The data also implicated a new paradigm for phytotherapy of PMS as the prescribed decoctions contained no interacting compound to modulate the activity of estrogen receptors, in contrast to the treatment strategy of hormone replacement therapy.

Effective teamwork in an initially leaderless group requires a high level of collective leadership emerging from dynamic interactions among group members. Leader emergence is a crucial topic in collective leadership, yet it is challenging to investigate as the problem context is typically highly complex and dynamic. Here, we explore leadership emergence and leadership perception by means of computational simulations whose assumptions and parameters were informed by empirical research and human-subject experiments. Our agent-based model describes the process of group planning. Each agent is assigned with three key attributes: talkativeness, intelligence, and credibility. An agent can propose a suggestion to modify the group plan as a speaker or respond and evaluate others’ suggestions and leadership as a listener. Simulation results suggested that agents with high values of talkativeness, intelligence, and credibility tended to be perceived as leaders by their peers. Results also showed that talkativeness may be the most significant and instantaneous predictor for leader emergence of the three investigated attributes: talkativeness, intelligence, and credibility. In terms of group performance, smaller groups may outperform larger groups regarding their problem-solving ability in the beginning, but their performance tends to be of no significant difference in a long run. These results match the empirical literature and offer a mechanistic, operationalized description of the collective leadership processes.

[This corrects the article DOI: 10.1371/journal.pone.0335788.].

[This corrects the article DOI: 10.1371/journal.pone.0335788.].

With the recent advancement of multimedia technologies and machine learning, the types of data that can be used in social sciences and related fields are becoming more and more diverse and multimodal. There are many applications of multimedia data in social sciences, such as analyzing human facial expressions, analyzing human body languages, and human speaking activities. The effective use of multimedia data for social science studies has become a new challenge.Analyzing human behavior is a critical topic in social sciences. There are many resources that could be applied in human behavior analysis, such as a camcorder used to record human behavior. Human facial expressions are the most widely investigated features for human behavior analysis, and also human speaking activities are fundamental to human behavior analysis. However, there are lots of limitations on applying multimedia data for human behavior analysis, such as computational costs and bottlenecks of existing methods.In this dissertation, I present our contributions toward broadening the applications of human facial expression recognition and speech analysis with improvements in feature selection and clustering methods. Our contributions can be summarized in three studies.In the first study, we focused on human-computer interaction applications on light-weight devices, such as facial expression recognition on mobile devices. We calculated the mutual information of the movements of facial landmarks and detected their clusters using hierarchical agglomerative clustering. We also applied a landmark selection method inspired by genetic algorithms to filter out low-utility features from each facial landmark cluster. The selected features were provided to a Support Vector Machine classifier to classify facial expressions. The results showed that our proposed method improved the robustness of performance and reduced computational time by 63.5%.In the second work, we aimed to improve the performance of utterance clustering by processing multichannel (stereo) audio signals. The processed audio signals were generated by combining audio signals from the left and right channels in two ways. Then, d-vectors were extracted, which are audio embedded features. We applied the Gaussian mixture model for supervised utterance clustering. The results showed that our proposed method, which used multichannel audio signals, achieved significantly better performance than a conventional method with mono-audio signals under real-world conditions with utterance overlaps.The third study focused on the multimodal utterance clustering using both audio and visual signals. In this study, we combined visual features with audio features to improve the performance utterance clustering. For utterance clustering, we applied two methods, one is the Gaussian mixture model and the other is the convolutional neural networks. The accuracy rates were greatly improved by combining visual features with audio features compared with using only audio features.