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Multimodal transport is a challenging, NP-hard problem in combinational optimization and has been solved using evolutionary algorithms, which excel at solving large-scale problems. However, few studies have used evolutionary algorithms, particularly swarm intelligence algorithms, to concurrently handle multiple multimodal transport instances. Ant colony optimization (ACO), which is a population intelligence technique that is adept at identifying the optimal paths in graphs, has been primarily used to address tasks separately rather than concurrently. Therefore, in this study, we introduce a multipopulation-based multitask environment where task-specific populations run in parallel, and ACO serves as the optimizer for each task. A variance-based population diversity measure is then calculated to characterize the distribution differences among individuals. If the population diversity of a specific task falls below a predetermined threshold, the valuable routing traits extracted from other tasks are transferred to the stagnant population. Our method is called population diversity-controlled multitask ACO (PDMTACO). We use multiple benchmark traveling salesman problem (TSP) instances at different scales to validate the efficacy of PDMTACO. Subsequently, we extend PDMTACO to address a series of multimodal transport problems. Our experimental results demonstrate that the use of information transferred by our method significantly reduces its logistics costs and carbon emissions in all multimodal transport tasks.

Cardiovascular disease (CVD) is a prevalent global health issue and one of the leading causes of death. Aging and air pollution are well-established risk factors for CVD. This study aims to investigate the association between air pollutants (sulfur dioxide, carbon monoxide, PM1, PM2.5, nitrogen dioxide, ozone) and the risk of heart disease. Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS) and the China High Air Pollutants (CHAP) database, we employed multivariable-adjusted logistic regression to analyze the relationship between pollutants and heart disease. Additionally, six binary classification machine learning algorithms—AdaBoost, Decision Tree, LightGBM, XGBoost, Random Forest, and GBDT—were used to construct predictive models. The models incorporated air pollutant concentrations (SO₂, CO, PM1, etc.) as core features, along with covariates such as gender, age, and hypertension. The data were split into an 80% training set and a 20% test set, with cross-validation applied to ensure robustness. Multivariable regression analysis revealed that after adjusting for multiple covariates (including BMI, blood glucose, and other pollutants), each 1-unit increase in SO₂ concentration was associated with an odds ratio (OR) of 1.040 for heart disease (95% confidence interval [CI]: 1.027–1.054, p  < 0.00001). Among the machine learning models, Random Forest exhibited the best performance, with an AUC of 0.794 in the training set and 0.726 in the test set. SHAP analysis confirmed that SO₂ was the most impactful pollutant. Subgroup analysis indicated a significant interaction between SO₂ and household registration type ( p  < 0.05). Future research should further explore the mechanisms underlying SO₂-induced cardiac damage and optimize the applicability of predictive models.

Hypoxic-ischemic encephalopathy (HIE) is a leading cause of neonatal brain injury and neurodevelopmental disorders. Pyroptosis, an inflammatory programmed cell death, may offer new therapeutic targets for HIE by modulating cytokine expression and related pathways. This study aims to identify HIE-associated pyroptosis genes and explore potential drugs and molecular mechanisms. The gene microarray data of hypoxic-ischemic brain damage (HIBD) were obtained from the Gene Expression Omnibus (GEO) database. The Limma package was used to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was performed to find significant expression modules. GO and KEGG analyses were carried out for the pathway enrichment of DEGs, as well as protein-protein interaction (PPI) network analysis were subsequently conducted. Cytohubba software was employed to identify hub genes among DEGs. A random forest (RF) model assessed the pyroptosis-related genes, examining their diagnostic performance. Potential therapeutic drugs or compounds targeting the hub genes were screened through DSigDB, and their binding scores and affinities were evaluated by molecular docking. 96 DEGs with HIBD were identified in our result, including 89 up-regulated genes and 7 down-regulated genes. GO and KEGG results indicated that these DEGs were mostly enriched in Cytokine-cytokine receptor interaction, IL-17 signaling pathway and TNF signaling pathway. Using Cytoscape software and WGCNA-related modules, we identified three hub genes- , and -which were further validated in other transcriptomic datasets, all showing significant differential expression. Random forest analysis demonstrated that these three hub genes had AUC values > 0.75, indicating strong diagnostic performance. Immune infiltration analysis revealed that, compared to the control group, the HIBD group exhibited higher levels of innate immune cells (e.g., macrophages, M0 cells, and dendritic cells) and adaptive immune cells (e.g., CD8 naïve T cells, CD4 follicular helper T cells, and Th1 cells). The ssGSEA algorithm results indicated differences in 25 types of immune cells and 10 immune functions. The hub genes were also validated finally. and may be potential hub pyroptosis-related genes for HIBD. The results of this study could improve the understanding of the mechanisms underlying pyroptosis in HIBD.

To evaluate the feasibility and efficacy of diffusion tensor imaging (DTI) for detecting early brain microstructure alterations in germinal matrix-intraventricular hemorrhage (GMH-IVH) rat model. This study used a postnatal day 5 (PND 5) rat model of GMH-IVH. T2-weighted imaging and DTI were performed during acute (6 h and 24 h) and subacute (3d and 7d) phases after GMH-IVH. Four DTI parameters including fractional anisotropy (FA), mean diffusion (MD), axial diffusion (AD) and radial diffusion (RD) were collected in 9 specific brain regions to assess the brain microstructure alterations. Early and long-term neurological function tests were evaluated. Transcriptome sequencing analysis was also performed to investigate possible underlying mechanisms. Regional abnormalities after GMH-IVH were observed in T2-weighted images that showed significant hypointense in striatum region which close to the germinal matrix. DTI parameters also observed changes in striatum region in GMH-IVH. Alterations in other regions of brain including hippocampus, thalamus, external capsule and motor cortex also noted, which were associated with the abnormalities observed in behavioral experiments. Long-term behavioral tests show that compared to sham group, rats in GMH-IVH group caused abnormal motor function. In addition, at 24 h after GMH-IVH, transcriptome analysis results showed that the highly expressed differential genes encode hemoglobin components and down-regulate neurodevelopment-related pathways. DTI imaging allows the early assessment of neurological alteration in GMH-IVH rat pups, and providing great value in evaluating long-term behavioral deficits.

Presently, research concerning alterations in brain structure among individuals with attention deficit hyperactivity disorder (ADHD) predominantly focuses on entire brain volume and cortical thickness. In this study, we extend our examination to the cortical microstructure of male children with ADHD. To achieve this, we employ the gray-white matter tissue contrast (GWC) metric, allowing for an assessment of modifications in gray matter density and white matter microstructure. Furthermore, we explore the potential connection between GWC and the severity of disorder in male children by ADHD. We acquired 3DT1 sequences from the public ADHD-200 database. In this study, we conducted a comparative analysis between 43 male children diagnosed with ADHD and 50 age-matched male controls exhibiting typical development trajectories. Our investigation entailed assessing differences in GWC and cortical thickness. Additionally, we explored the potential correlation between GWC and the severity of ADHD. To delineate the cerebral landscape, each hemisphere was subdivided into 34 cortical regions using freesurfer 7.2.0. For quantification, GWC was computed by evaluating the intensity contrast of non-normalized T1 images above and below the gray-white matter interface. Our findings unveiled elevated GWC within the bilateral lingual, bilateral insular, left transverse temporal, right parahippocampal and right pericalcarine regions in male children with ADHD when contrasted with their healthy counterparts. Moreover, the cortical thickness in the ADHD group no notable distinctions that of control group in all areas. Intriguingly, the GWC of left transverse temporal demonstrated a negative correlation with the extent of inattention experienced by male children with ADHD. Utilizing GWC as a metric facilitates a more comprehensive assessment of microstructural brain changes in children with ADHD. The fluctuations in GWC observed in specific brain regions might serve as a neural biomarker, illuminating structural modifications in male children grappling with ADHD. This perspective enriches our comprehension of white matter microstructure and cortical density in these children. Notably, the inverse correlation between the GWC of the left transverse temporal and inattention severity underscores the potential role of structural and functional anomalies within this region in ADHD progression. Enhancing our insight into ADHD-related brain changes holds significant promise in deciphering potential neuropathological mechanisms.

Diffusion Kurtosis Imaging (DKI)-derived metrics are recognized as indicators of maturation in neonates with low-grade germinal matrix and intraventricular hemorrhage (GMH-IVH). However, it is not yet known if these factors are associated with neurodevelopmental outcomes. The objective of this study was to acquire DKI-derived metrics in neonates with low-grade GMH-IVH, and to demonstrate their association with later neurodevelopmental outcomes. In this prospective study, neonates with low-grade GMH-IVH and control neonates were recruited, and DKI were performed between January 2020 and March 2021. These neonates underwent the Bayley Scales of Infant Development test at 18 months of age. Mean kurtosis (MK), radial kurtosis (RK) and gray matter values were measured. Spearman correlation analyses were conducted for the measured values and neurodevelopmental outcome scores. Forty controls (18 males, average gestational age (GA) 30 weeks ± 1.3, corrected GA at MRI scan 38 weeks ± 1) and thirty neonates with low-grade GMH-IVH (13 males, average GA 30 weeks ± 1.5, corrected GA at MRI scan 38 weeks ± 1). Neonates with low-grade GMH-IVH exhibited lower MK and RK values in the PLIC and the thalamus (P < 0.05). The MK value in the thalamus was associated with Mental Development Index (MDI) (r = 0.810, 95% CI 0.695–0.13; P < 0.001) and Psychomotor Development Index (PDI) (r = 0.852, 95% CI 0.722–0.912; P < 0.001) scores. RK value in the caudate nucleus significantly and positively correlated with MDI (r = 0.496, 95% CI 0.657–0.933; P < 0.001) and PDI (r = 0.545, 95% CI 0.712–0.942; P < 0.001) scores. The area under the curve (AUC) were used to assess diagnostic performance of MK and RK in thalamus (AUC = 0.866, 0.787) and caudate nucleus (AUC = 0.833, 0.671) for predicting neurodevelopmental outcomes. As quantitative neuroimaging markers, MK in thalamus and RK in caudate nucleus may help predict neurodevelopmental outcomes in neonates with low-grade GMH-IVH.

Background Autism spectrum disorder (ASD) is a severe neurodevelopmental condition. Its incidence is on the rise worldwide, and in severe cases, it can lead to disability. While emerging evidence implicates cerebrovascular dysfunction in the pathophysiology of neurodevelopmental impairments associated with ASD, systematic characterization of cerebral hemodynamic variations across clinically stratified severity subgroups, particularly among mild–moderate and severe ASD presentations and typically developing (TD) children, remains a critical unmet research need. Methods This cross-sectional neuroimaging study enrolled 121 children aged 2 to 4 years: 16 with severe autism (Childhood Autism Rating Scale (CARS) score > 36), 60 with mild–moderate autism (CARS score between 30 and 36), and 45 TD children. CBF measurements were obtained from nine regions of interest (ROI) in both hemispheres: temporal lobe, parietal lobe, occipital lobe, putamen, thalamus, caudate nucleus, globus pallidus, hippocampus, and amygdala. Intergroup comparisons of CBF values were performed among the three groups. Particular emphasis was placed on analyzing the correlation between thalamic CBF values and serum zinc levels in autistic children. Results Children with severe autism exhibited significantly lower CBF in the temporal lobe, putamen, thalamus, and hippocampus compared to TD children ( p  < 0.05). Within the autism cohort, severe cases demonstrated further CBF reductions in the putamen and thalamus compared to mild–moderate cases ( p  < 0.05). Similarly, children with mild–moderate autism showed reduced CBF in the temporal lobe, putamen, thalamus, and hippocampus compared to TD children ( p  < 0.05). Notably, a significant difference in CBF was observed between the left and right thalamus in both mild–moderate and severe autism groups, with lower blood flow in the left thalamus ( p  < 0.05). A positive correlation was found between thalamic CBF values and serum zinc levels in the autism group. Conclusions Children with severe autism show significantly reduced CBF in critical brain regions. Thus, 3D-pCASL may enable the precise stratification of ASD severity in children and provide an imaging foundation for subsequent therapeutic evaluation.

Sensorineural hearing loss (SNHL) can arise from a diverse range of congenital and acquired factors. Detecting it early is pivotal for nurturing speech, language, and cognitive development in children with SNHL. In our study, we utilized synthetic magnetic resonance imaging (SyMRI) to assess alterations in both gray and white matter within the brains of children affected by SNHL. The study encompassed both children diagnosed with SNHL and a control group of children with normal hearing {1.5-month-olds ( = 52) and 3-month-olds ( = 78)}. Participants were categorized based on their auditory brainstem response (ABR) threshold, delineated into normal, mild, moderate, and severe subgroups.Clinical parameters were included and assessed the correlation with SNHL. Quantitative analysis of brain morphology was conducted using SyMRI scans, yielding data on brain segmentation and relaxation time.Through both univariate and multivariate analyses, independent factors predictive of SNHL were identified. The efficacy of the prediction model was evaluated using receiver operating characteristic (ROC) curves, with visualization facilitated through the utilization of a nomogram. It's important to note that due to the constraints of our research, we worked with a relatively small sample size. Neonatal hyperbilirubinemia (NH) and children with inner ear malformation (IEM) were associated with the onset of SNHL both at 1.5 and 3-month groups. At 3-month group, the moderate and severe subgroups exhibited elevated quantitative T1 values in the inferior colliculus (IC), lateral lemniscus (LL), and middle cerebellar peduncle (MCP) compared to the normal group. Additionally, WMV, WMF, MYF, and MYV were significantly reduced relative to the normal group. Additionally, SNHL-children with IEM had high T1 values in IC, and LL and reduced WMV, WMF, MYV and MYF values as compared with SNHL-children without IEM at 3-month group. LL-T1 and WMF were independent risk factors associated with SNHL. Consequently, a prediction model was devised based on LL-T1 and WMF. ROC for training set, validation set and external set were 0.865, 0.806, and 0.736, respectively. The integration of T1 quantitative values and brain volume segmentation offers a valuable tool for tracking brain development in children affected by SNHL and assessing the progression of the condition's severity.

In the era of big data, the industry environment is complex and volatile, and enterprises need to innovate their business models by creating innovative value networks and reconstructing transaction systems to avoid being eroded in the process of global economic transformation. The traditional closed innovation processes are no longer deemed suitable for advancing firm innovation. Instead, open innovation has become a pivotal strategic choice in the quest to foster innovative developments. This study builds upon the paradox of open innovation (OI) in the age of big data, and introduces the innovation expropriability theory. Utilizing survey data from high-tech industrial park firms in the Yangtze River Delta region of China, it empirically examines the influence of open innovation on firm innovation performance (TIP). Furthermore, it explores the independent and joint moderating effects of rival absorptive capacity (RAC) and appropriability regimes (ARs). The findings reveal that OI significantly improves FIP. The effect of OI on FIP is positively moderated by ARs, while the independent moderating effect of RAC is not significant. However, a joint moderating effect is observed between RAC and ARs. Further investigation reveals that the moderating role of RAC depends on the presence of ARs, indicating a matching relationship between RAC and ARs. This research holds significant implications for firms in implementing open innovation strategies and ensuring effective intellectual property protection to enhance innovation performance in the era of big data.

Urban haze pollution is becoming increasingly serious, which is considered very harmful for humans by World Health Organization (WHO). Haze forecasts can be used to protect human health. In this paper, a Selective ENsemble based on an Extreme Learning Machine (ELM) and Improved Discrete Artificial Fish swarm algorithm (IDAFSEN) is proposed, which overcomes the drawback that a single ELM is unstable in terms of its classification. First, the initial pool of base ELMs is generated by using bootstrap sampling, which is then pre-pruned by calculating the pair-wise diversity measure of each base ELM. Second, partial-based ELMs among the initial pool after pre-pruning with higher precision and with greater diversity are selected by using an Improved Discrete Artificial Fish Swarm Algorithm (IDAFSA). Finally, the selected base ELMs are integrated through majority voting. The Experimental results on 16 datasets from the UCI Machine Learning Repository demonstrate that IDAFSEN can achieve better classification accuracy than other previously reported methods. After a performance evaluation of the proposed approach, this paper looks at how this can be used in haze forecasting in China to protect human health.