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Traditional energy-saving optimization of shop scheduling often separates the coupling relationship between a single machine and the shop system, which not only limits the potential of energy-saving but also leads to a large deviation between the optimized result and the actual application. In practice, cutting-tool degradation during operation is inevitable, which will not only lead to the increase in actual machining power but also the resulting tool change operation will disrupt the rhythm of production scheduling. Therefore, to make the energy consumption calculation in scheduling optimization more consistent with the actual machining conditions and reduce the impact of tool degradation on the manufacturing shop, this paper constructs an integrated optimization model including a flexible job shop scheduling problem (FJSP), machining power prediction, tool life prediction and energy-saving strategy. First, an exponential function is formulated using actual cutting experiment data under certain machining conditions to express cutting-tool degradation. Utilizing this function, a reasonable cutting-tool change schedule is obtained. A hybrid energy-saving strategy that combines a cutting-tool change with machine tool turn-on/off schedules to reduce the difference between the simulated and actual machining power while optimizing the energy savings is then proposed. Second, a multi-objective optimization model was established to reduce the makespan, total machine tool load, number of times machine tools are turned on/off and cutting tools are changed, and the total energy consumption of the workshop and the fast and elitist multi-objective genetic algorithm (NSGA-II) is used to solve the model. Finally, combined with the workshop production cost evaluation indicator, a practical FJSP example is presented to demonstrate the proposed optimization model. The prediction accuracy of the machining power is more than 93%. The hybrid energy-saving strategy can further reduce the energy consumption of the workshop by 4.44% and the production cost by 2.44% on the basis of saving 93.5% of non-processing energy consumption by the machine on/off energy-saving strategy.

This paper explores the strategic implications of technological adoption within shipping logistics service supply chains, with a particular focus on blockchain technology (BCT). When integrating new technologies, supply chain stakeholders evaluate associated risks alongside complexity, profitability, and operational challenges, which influence their strategic behaviors. Anchored in the concept of technology trust, this study examines how different risk preferences affect BCT adoption decisions and freight rate strategies. A game-theoretic model is constructed using a mean-variance utility framework to analyze interactions between shipping companies and freight forwarders under three adoption scenarios: no adoption (NN), partial adoption (BN), and full adoption (BB). The results indicate that risk-seeking agents are more likely to adopt BCT early but face greater freight rate volatility in the initial stages. As the technology matures, strategic variability declines and the influence of adaptability on pricing becomes less pronounced. In contrast, risk-neutral and risk-averse participants tend to adopt more conservatively, resulting in slower but more stable pricing dynamics. These findings offer new insights into how technology trust and risk attitudes shape strategic decisions in digitally transforming supply chains. The study also provides practical implications for differentiated pricing strategies, BCT adoption incentives, and collaborative policy design among logistics stakeholders.

To explore atrophy patterns in thalamic nuclei at different phases of amyotrophic lateral sclerosis (ALS) and determine any correlations between thalamic nucleus volume and either cognitive impairments or motor disabilities. We used the King's clinical staging system for ALS to divide 76 consecutive patients with ALS by disease stage. We investigated patterns of thalamic atrophy in the patients and in 94 healthy controls (HCs). Cognitive functions were evaluated with the Mini-Mental State Examination (MMSE), Frontal Assessment Battery, Boston Naming Test, and Auditory Verbal Learning Test. Considering all ALS patients, no significant differences were observed in the volume of any thalamic nuclei between the ALS group and HCs. Thalamic nucleus volumes remained normal in ALS patients at King's Stage 2 and Stage 3. However, atrophy was detected in the bilateral anteroventral nucleus, bilateral pulvinar-limitans, bilateral mediodorsal-paratenial-reuniens, bilateral motor hub, bilateral sensory hub, and bilateral intralaminar nucleus in patients who had reached King's Stage 3. In these patients, the volume of the bilateral motor nuclei was associated with the revised ALS Functional Rating Scale scores, and that of the right pulvinar-limitans independently correlated with MMSE scores. Our study provides a comprehensive profile of thalamic atrophy in ALS patients. The thalamic atrophy patterns in these patients extremely differs at different King's Stages, and we suggest that these alterations might result largely from sequential, regional patterns of TDP-43 pathology in ALS. Furthermore, thalamic atrophy might play important roles in motor disability and global cognitive impairments observed in patients with ALS.

AbstractBackgroundThe emergency department (ED) triage process serves as a crucial first step for patients seeking acute care, This initial assessment holds crucial implications for patient survival and prognosis. In this study, a systematic review of the existing literature was performed to investigate the performance of machine learning (ML) models in recognizing and predicting the need for intensive care among ED patients. MethodsFour prominent databases (PubMed, Embase, Cochrane Library and Web of Science) were searched for relevant literature published up to April 28, 2023. The Prediction model study Risk of Bias Assessment Tool (PROBAST) was employed to evaluate the risk of bias and feasibility of prediction models. ResultsIn ten studies, the main algorithms used were Gradient Boostin, Logistic Regressio, Neural Network, Support Vector Machines, Random Forest. The performance of each model was as follows: Gradient Boosting had a sensitivity range of 0.3 to 0.96, specificity range of 0.6 to 0.99, accuracy range of 0.37 to 0.99, precision range of 0.3 to 0.96, and AUC value range of 0.68 to 0.93; Logistic Regression had a sensitivity range of 0.46 to 0.97, specificity range of 0.28 to 0.99, accuracy range of 0.66 to 0.97, precision range of 0.27 to 0.63, and AUC value range of 0.72 to 0.97; Neural Networks had a sensitivity range of 0.45 to 0.96, specificity range of 0.58 to 0.99, accuracy range of 0.36 to 0.97, precision range of 0.27 to 0.96, and AUC value range of 0.67 to 0.91; Support Vector Machines had a sensitivity range of 0.49 to 0.83, specificity range of 0.94 to 0.98, accuracy range of 0.33 to 0.97, precision range of 0.53 to 0.94, and AUC values were not reported; Random Forests had a sensitivity range of 0.75 to 0.91, specificity range of 0.77 to 0.94, accuracy range of 0.35 to 0.77, precision range of 0.36 to 0.94, and AUC value of 0.83. ConclusionML models have demonstrated good performance in identifying and predicting critically ill patients in ED triage. However, because of the limited number of studies on each model, further high-quality prospective research is needed to validate these findings.

This study investigated the hepatoprotective effect and underlying mechanisms of D-glucaro-1,4-lactone (1,4-GL), a natural compound found in fruits and vegetables, against acetaminophen (APAP)-induced acute liver injury (ALI) in mice, which had not been previously explored. A stable ALI model was established in male C57BL/6J mice using 300 mg/kg APAP after fasting. Mice were pretreated orally with glutathione (200 mg/kg), or 1,4-GL (100 mg/kg or 200 mg/kg) for five consecutive days before APAP challenge. Serum biochemical markers were measured. Liver histopathology was assessed via H&E staining. Gut microbiota composition was analyzed using 16S rRNA sequencing of fecal samples. Liver metabolites were profiled using HNMR metabolomics. 1,4-GL pretreatment (especially 200 mg/kg) significantly ameliorated APAP-induced liver damage: it reduced serum ALT, AST, TBIL, and MDA levels ( < 0.05), increased GSH and SOD levels ( < 0.05), and attenuated hepatic necrosis and inflammation. 1,4-GL increased the abundance of the beneficial gut bacterium Lactobacillus (significantly reduced by APAP) and elevated hepatic levels of protective metabolites isoleucine, glutamine, and nicotinic acid. Correlation analyses between gut microbiota and liver metabolites revealed that glutamine and nicotinic acid were significantly positively correlated with Firmicutes and Lactobacillus, while showing a significant negative correlation with Lachnoclostridium. Lactobacillus was identified as a key beneficial bacterium, whereas Lachnoclostridium was associated with increased disease severity. 1,4-GL exerts a beneficial regulatory effect on APAP-induced ALI by the Lactobacillus-glutamine/nicotinic acid pathway, highlighting its potential as a therapeutic agent for drug-induced liver injury.

The use of recycled concrete aggregates (RCA) instead of natural aggregates in hot-mix asphalt mixtures is one of the ways to achieve energy savings and reduce carbon emissions in road engineering. However, the cement mortar on the surface of RCA adversely affects the adhesion properties between asphalt and aggregates, leading to a reduction in the performance characteristics of asphalt mixtures. In this study, a silane coupling agent (SCA) was employed to improve the adhesion properties of the RCA/asphalt interface. The enhancement mechanism of SCA on the RCA/asphalt interface was investigated from multiple perspectives, including macroscopic properties, interfacial microstructure, and nanoscale interfacial interactions. Firstly, the adhesion behavior and tensile strength of the interface between RCA and asphalt were determined through a boiling water test and direct tensile test, both before and after SCA modification. Secondly, scanning electron microscopy (SEM) was employed to observe the surface microstructure of RCA and the microstructure of the RCA/asphalt interface. Finally, the main component of mortar, calcium silicate hydrate (C-S-H), was taken as the research subject of investigation to examine the hydrogen bonding, interaction energy, and interface transition zone of the C-S-H/asphalt interface system using the molecular dynamics methodology. The results demonstrate a two-level enhancement in the adhesion performance of the interface at the macroscopic scale following SCA modification. The interface tensile strength increased by 72.2% and 119.7% under dry and wet conditions, respectively. At the microscopic scale, it was observed that the surface pores of RCA were repaired after SCA modification, resulting in a more tightly bonded interface between the RCA and asphalt. At the nanoscale, SCA modification reduces the hydrophilicity of the C-S-H surface, increases the interaction energy and water resistance of the C-S-H/asphalt interface, and enhances the weak interface transition zone between C-S-H and asphalt. This study provides a theoretical basis for using SCA to enhance the bond strength of the RCA/asphalt interface and lays the foundation for the application of RCA asphalt mixtures on highways.

The aim of this study was to investigate the possible interaction of mitochondrial dysfunction and inflammatory cytokines in the risk of anxiety and depression. We utilized the UK Biobank for the sample of this study. A mitochondria-wide association(MiWAS) and interaction analysis was performed to investigate the interaction effects of mitochondrial DNA (mtDNA)×C-reactive protein (CRP) on the risks of self-reported anxiety (N = 72,476), general anxiety disorder (GAD-7) scores (N = 80,853), self-reported depression (N = 80,778), Patient Health Questionnaire (PHQ-9) scores (N = 80,520) in total samples, females and males, respectively, adjusting for sex, age, Townsend deprivation index (TDI), education score, alcohol intake, smoking and 10 principal components. In all, 25 mtSNPs and 10 mtSNPs showed significant level of association with self-reported anxiety and GAD-7 score respectively. A total of seven significant mtDNA × CRP interactions were found for anxiety, such as m.3915G>A(MT-ND1) for self-reported anxiety in total subjects (P = 6.59 × 10−3), m.4561T>C(MT-ND2) (P = 3.04 × 10−3) for GAD-7 score in total subjects. For depression, MiWAS identified 17 significant mtSNPs for self-reported depression and 14 significant mtSNPs for PHQ-9 scores. 17 significant mtDNA associations (2 for self-reported depression and 15 for PHQ-9 score) was identified, such as m.14869G>A(MT-CYB; P = 2.22 × 10−3) associated with self-reported depression and m.4561T>C (MT-ND2; P value = 3.02 × 10−8) associated with PHQ-9 score in all subjects. In addition, 5 common mtDNA shared with anxiety and depression were found in MiWAS, and 4 common mtDNA variants were detected to interact with CRP for anxiety and depression, such as m.9899T>C(MT-CO3). Our study suggests the important interaction effects of mitochondrial function and CRP on the risks of anxiety and depression.

In Moso bamboo, the mechanism of long terminal repeat (LTR) retrotransposon-derived long non-coding RNA (TElncRNA) in response to cold stress remains unclear. In this study, several Pe-TElncRNAs were identified from Moso bamboo transcriptome data. qRT-PCR analysis showed that the expression of a novel Pe-TElncRNA2 in Moso bamboo seedlings reached its highest level at 8 hours of cold treatment at 4 °C and was significantly higher in the stems compared to the leaves, roots, and buds. Furthermore, cellular localization analysis revealed that Pe-TElncRNA2 expression was significantly higher in the cytoplasm than in the nucleus. Pe-TElncRNA2 overexpression in Moso bamboo protoplasts showed that Pe-TElncRNA2 positively regulated the expression of FZR2 , NOT3 , ABCG44 and AGD6 genes. Further validation of this lncRNA in Arabidopsis thaliana enhanced antioxidant activities, as evidenced by increased superoxide dismutase (SOD) activity and proline content, as well as maximum photochemical efficiency PS II in dark-adapted leaves ( F v / F m ), in the transgenic plants compared to the wild-type controls. Conversely, malondialdehyde (MDA) content, a lipid peroxidation marker (a marker of oxidative stress), was significantly reduced in the transgenic plants. Notably, the expression levels of both Pe-TElncRNA2 and the genes that were regulated by this lncRNA were upregulated in the transgenic plants after two days of cold stress treatment. These findings elucidate the critical role of LTR retrotransposon-derived lncRNAs in mediating cold stress responses in Moso bamboo.

YOD1 (OTUD2) is a pivotal deubiquitinating enzyme (DUB) of the Otubain family. It plays an essential role in protein degradation and the regulation of cell signal transduction, influencing various biological processes. The activity of YOD1 is mediated through its three structural domains: the Ubiquitin Regulatory X (UBX) domain, the Zinc Finger (ZNF) domain, and the Ovarian Tumor (OTU) domain. Specifically, the UBX domain regulates protein interactions, mitochondrial quality control, and immune responses. The ZNF domain facilitates protein localization, degradation, and cancer progression. The OTU domain drives deubiquitination, ensuring protein stability and regulating key signaling pathways. In recent years, increasing attention has been paid to the role of YOD1 in various diseases. Studies have demonstrated that YOD1 influences critical cellular activities such as proliferation, apoptosis, migration, and invasion by modulating key signaling pathways, including Hippo and TGF-β. Abnormal YOD1 expression is closely linked to the development of several cancers, including breast, liver, and lung cancer. Moreover, in non-malignant conditions such as inflammation and vascular diseases, YOD1 plays a key role in maintaining tissue homeostasis and repairing damaged tissues. Given its multifaceted roles in both tumorigenic and non-tumorigenic contexts, YOD1 is considered a promising target for therapeutic strategies. This review systematically analyzes the key signaling pathways regulated by YOD1, with a focus on elucidating its potential therapeutic value in cancer and various non-tumor diseases. Additionally, it provides a comprehensive review of the recent research progress in the field of YOD1 inhibitors. Our aim is to underscore the significance of YOD1 as a potential therapeutic target, to provide a theoretical basis for the development of innovative therapeutic strategies, and to offer new perspectives and insights for future related research.

Cumulative evidence shows that gut microbiome can influence brain function and behavior via the inflammatory processes. However, the role of interaction between gut dysbiosis and C-reactive protein (CRP) in the development of anxiety and depression remains to be elucidated. In this study, a total of 3321 independent single nucleotide polymorphism (SNP) loci associated with gut microbiome were driven from genome-wide association study (GWAS). Using individual level genotype data from UK Biobank, we then calculated the polygenetic risk scoring (PRS) of 114 gut microbiome related traits. Moreover, regression analysis was conducted to evaluate the possible effect of interaction between gut microbiome and CRP on the risks of Patient Health Questionnaire-9 (PHQ-9) (N = 113,693) and Generalized Anxiety Disorder-7 (GAD-7) (N = 114,219). At last, 11 candidate CRP × gut microbiome interaction with suggestive significance was detected for PHQ-9 score, such as F_Ruminococcaceae (β = − 0.009, P  = 2.2 × 10 –3 ), G_Akkermansia (β = − 0.008, P  = 7.60 × 10 –3 ), F_Acidaminococcaceae (β = 0.008, P  = 1.22 × 10 –2 ), G_Holdemanella (β = − 0.007, P  = 1.39 × 10 –2 ) and O_Lactobacillales (β = 0.006, P  = 1.79× 10 –2 ). 16 candidate CRP × gut microbiome interaction with suggestive significance was detected for GAD-7 score, such as O_Bacteroidales (β = 0.010, P  = 4.00×  10 –4 ), O_Selenomonadales (β = − 0.010, P  = 1.20 × 10 –3 ), O_Clostridiales (β = 0.009, P  = 2.70 × 10 –3 ) and G_Holdemanella (β = − 0.008, P  = 4.20 × 10 –3 ). Our results support the significant effect of interaction between CRP and gut microbiome on the risks of anxiety and depression, and identified several candidate gut microbiomes for them.