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Intervertebral disk degeneration (IDD) is a significant cause of low back pain, characterized by excessive senescence and apoptosis of nucleus pulposus cells (NPCs). However, the precise mechanisms behind this senescence and apoptosis remain unclear. This study aimed to investigate the role of T-box transcription factor T (Tbxt) in IDD both in vitro and in vivo, using a hydrogen peroxide (H2O2)-induced NPCs senescence and apoptosis model, as well as a rat acupuncture IDD model. First, the expression of p16 and cleaved-caspase 3 significantly increased in degenerated human NPCs, accompanied by a decrease in Tbxt expression. Knockdown of Tbxt exacerbated senescence and apoptosis in the H2O2-induced NPCs degeneration model. Conversely, upregulation of Tbxt alleviated these effects induced by H2O2. Mechanistically, bioinformatic analysis revealed that the direct downstream target genes of Tbxt were highly enriched in autophagy-related pathways, and overexpression of Tbxt significantly activated autophagy in NPCs. Moreover, the administration of the autophagy inhibitor, 3‐methyladenine, impeded the impact of Tbxt on the processes of senescence and apoptosis in NPCs. Further investigation revealed that Tbxt enhances autophagy by facilitating the transcription of ATG7 through its interaction with a specific motif within the promoter region. In conclusion, this study suggests that Tbxt mitigates H2O2-induced senescence and apoptosis of NPCs by activating ATG7-mediated autophagy. NEW & NOTEWORTHY: This study investigates the role of Tbxt in IDD. The results demonstrate that knockdown of Tbxt exacerbates H2O2-induced senescence and apoptosis in NPCs and IDD, whereas upregulation of Tbxt significantly protects against IDD both in vivo and in vitro. Mechanistically, in the nucleus, Tbxt enhances the transcription of ATG7, leading to increased expression of ATG7 protein levels. This, in turn, promotes elevated autophagy levels, ultimately alleviating IDD. [ABSTRACT FROM AUTHOR]

This paper studies the multi-trip vehicle routing problem with multiple time windows, which extends the multi-trip vehicle routing problem by deciding not only the sequence of customers that each vehicle serves but also the service time window of each customer. It also requires that the delivery service time is within the selected time windows and that the total demand of the customers served by the vehicle on each trip does not exceed the maximum carrying capacity. For solving the studied problem, we develop a mixed integer linear programming model with the objective of minimizing the total travel distance of vehicles and design a tailored iterative local search heuristic. Within the framework of the iterative local search, an improved Solomon greedy insertion algorithm suitable for multiple time windows and multi-trip scenarios is designed to generate the initial solution, and local search operators such as Or-opt and Relocate, as well as Random Exchange perturbation operations, are also developed. The experiment results demonstrate the effectiveness of the proposed model and algorithm and confirm that by providing customers with multiple time windows option, carriers can flexibly plan vehicle routes and select appropriate service time windows, thereby reducing the number of vehicles used and the total distance travelled and improve delivery success. [ABSTRACT FROM AUTHOR]

Relevant studies have indicated the association of HCG18 with tumour occurrence and progression. In this study, we observed that PM2.5 can enhance the growth of lung adenocarcinoma cells by modulating the expression of HCG18. Further investigations, including overexpression and knockout experiments, elucidated that HCG18 suppresses miR‐195, which in turn upregulates the expression of ATG14, resulting in the upregulation of autophagy. Consequently, exposure to PM2.5 leads to elevated HCG18 expression in lung tissues, which in turn increases Atg14 expression and activates autophagy pathways through inhibition of miR‐195, thereby contributing to oncogenesis. [ABSTRACT FROM AUTHOR]

Metabolic dysfunction of the extracellular matrix (ECM) is one of the primary causes of intervertebral disc degeneration (IVDD). Previous studies have demonstrated that the transcription factor Brachyury (Bry) has the potential to promote the synthesis of collagen II and aggrecan, while the specific mechanism is still unknown. In this study, we used a lipopolysaccharide (LPS)-induced model of nucleus pulposus cell (NPC) degeneration and a rat acupuncture IVDD model to elucidate the precise mechanism through which Bry affects collagen II and aggrecan synthesis in vitro and in vivo. First, we confirmed Bry expression decreased in degenerated human nucleus pulposus (NP) cells (NPCs). Knockdown of Bry exacerbated the decrease in collagen II and aggrecan expression in the lipopolysaccharide (LPS)-induced NPCs degeneration in vitro model. Bioinformatic analysis indicated that Smad3 may participate in the regulatory pathway of ECM synthesis regulated by Bry. Chromatin immunoprecipitation followed by quantitative polymerase chain reaction (ChIP-qPCR) and luciferase reporter gene assays demonstrated that Bry enhances the transcription of Smad3 by interacting with a specific motif on the promoter region. In addition, Western blot and reverse transcription-qPCR assays demonstrated that Smad3 positively regulates the expression of aggrecan and collagen II in NPCs. The following rescue experiments revealed that Bry-mediated regulation of ECM synthesis is partially dependent on Smad3 phosphorylation. Finally, the findings from the in vivo rat acupuncture-induced IVDD model were consistent with those obtained from in vitro assays. In conclusion, this study reveals that Bry positively regulates the synthesis of collagen II and aggrecan in NP through transcriptional activation of Smad3. NEW & NOTEWORTHY: Mechanically, in the nucleus, Bry enhances the transcription of Smad3, leading to increased expression of Smad3 protein levels; in the cytoplasm, elevated substrate levels further lead to an increase in the phosphorylation of Smad3, thereby regulating collagen II and aggrecan expression. Further in vivo experiments provide additional evidence that Bry can alleviate IVDD through this mechanism. [ABSTRACT FROM AUTHOR]

Realizing multimodal tactile perception for robots is highly desirable for achieving more intelligent human–machine interaction. A real‐time intelligent glove system that can capture and monitor hand tactile information, including pressure, bending, and temperature is proposed. This system processes the collected data and visualizes the tactile information in a 2D image. The intelligent glove system integrates a palm‐shaped sensor unit, a multi‐channel data acquisition board, and a data analysis module. The glove consists of 34 flexible sensors, including 28 pressure sensors, five bending sensors, and one temperature sensor, which convert biological information into electrical signals. By enhancing the performance of the system, the intelligent glove achieves high sensitivity and good fault tolerance, allowing it to analyze the multimodal tactile information of the entire hand with fast responsivity. By analyzing the acquired multi‐channel electrical signals, the system can analyze object shapes and various gripping states. This work paves the way for wearable devices used in multimodal tactile perception and promotes the emergence of new modes of human–machine interaction. [ABSTRACT FROM AUTHOR]

The flexibility and responsiveness of seru production have caught the attention of manufacturing and electronics industries. However, multi-skilled worker assignment poses a crucial and challenging decision-making problem for seru production systems. The existing literature on this problem for pure seru production systems primarily focuses on improving efficiency indexes, which often leads to an unbalanced workload among workers. To address this issue, this article investigates multi-skilled worker assignment for a hybrid assembly line-seru production system that comprises divisional serus and a short assembly line. To balance workload and optimize production efficiency, a bi-objective integer nonlinear programming model is developed. This model jointly optimizes worker-to-seru, worker-to-line, batch-to-seru, task-to-worker, and the processing sequence of each batch. A meta-heuristic method, combining Non-Dominated Sorting Genetic Algorithm II (NSGA-II) with Multi-Objective Simulated Annealing (MOSA), NSGA-II-MOSA, is designed to solve the model. The results of numerical experiments demonstrate that the proposed model and solving method can greatly reduce workload imbalance while maintaining production efficiency. Moreover, NSGA-II-MOSA provides better Pareto solutions than three well-known multi-objective optimization approaches. [ABSTRACT FROM AUTHOR]

Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods. [ABSTRACT FROM AUTHOR]

Background: Child eating behaviors (CEBs) and parental feeding practices (PFPs) play critical roles in childhood obesity. However, the bidirectional relationships between CEBs and PFPs remain equivocal. This longitudinal study aimed to explore their bidirectional relationships. Methods: A convenience sample of 870 parents with preschoolers was recruited in this longitudinal study (Shanghai, China). Three non-responsive feeding practices (NFPs), three responsive feeding practices (RFPs), five CEBs, and covariates were collected using validated questionnaires at baseline and the 6-month follow-up. Cross-lagged analyses using structural equation modeling (SEM) were performed to examine their bidirectional relationships. Results: Eight hundred and fifty-three parents completed questionnaires, with a response rate of 98%. The mean age of their children at baseline was 4.39 years (standard deviation = 0.72 years). Eighteen out of sixty longitudinal cross-lagged paths were statistically significant. Parental encouragement of healthy eating and content-restricted feeding were found to be bidirectionally associated with child food fussiness. Four parent-driven associations and one child-driven association were identified between RFPs and CEBs. For example, monitoring was negatively associated with children's unhealthy eating habits (β = −0.066, standard error (SE) = 0.025, p < 0.01). Eight child-driven associations and one parent-driven association were observed between NFPs and CEBs. For example, higher child satiety responsiveness predicted a higher pressure to eat (β = 0.057, SE = 0.029, p < 0.01) and the use of food as a reward (β = 0.083, SE = 0.031, p < 0.01). Conclusions: There were bidirectional, parent-driven, and child-driven associations. Parents should be encouraged to adopt RFPs to shape CEBs. Increasing parents' understanding of CEBs and providing them with reasonable coping strategies would help optimize PFPs. [ABSTRACT FROM AUTHOR]