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Abstract Detecting micro-terrain is essential for the effective layout and maintenance of transmission lines. To address the issues of detection incompleteness, classification ambiguity, and inefficiency in traditional methods, particularly the challenge of distinguishing between saddle and canyon micro-terrain, this paper optimizes the calculation of micro-terrain features and the strategy of micro-terrain detection, and explores a detection method of micro-terrain around transmission lines based on the GPU parallel random forest. This paper employs the GPU parallel random forest model as the extraction framework, leveraging the computational speed advantage of GPU parallel technology for handling large datasets and the robustness inherent in the ensemble approach of random forests. The DEM data of 49 transmission lines in the study area was used for micro-terrain detection experiments. Most of these 49 routes are situated in mountainous regions with complex terrain and contain diverse micro-terrain categories along their paths, rendering them highly representative. The experimental results demonstrate that the proposed method effectively identifies atypical micro-terrain types and four typical micro-terrain types—saddle, canyon, alpine watershed, and uplift—with a classification accuracy of 97.96% and a Kappa coefficient of 0.974. Compared to the traditional method, which achieves a classification accuracy of 75.19% and a Kappa coefficient of 0.642, the proposed method demonstrates a clear improvement in performance. Moreover, by employing the parallel model, the acceleration ratios for training and classification reach 50.57 and 109.06, respectively, significantly improving the efficiency of micro-terrain detection for large-scale regions. These findings could significantly enhance transmission line maintenance and layout planning by providing more accurate micro-terrain data, enabling better decision-making and resource allocation for infrastructure development and disaster risk mitigation. more...

Background: Fine particulate matter (PM2.5) induces ocular surface toxicity through pyroptosis, oxidative stress, autophagy, and inflammatory responses. However, the precise molecular pathways through which PM2.5 causes corneal damage remain unclear. This study aims to investigate the underlying mechanisms by exposing human corneal epithelial cells (HCECs) to PM2.5. Methods: After the morphology and chemical composition analysis of the PM samples, we conducted both in vivo and in vitro experiments to investigate PM2.5-induced corneal epithelial damage. We assessed corneal barrier function in HCECs using transepithelial electrical resistance (TEER) assays. To explore the molecular mechanisms of PM2.5-induced corneal epithelial damage, we performed whole-transcriptome resequencing, quantitative RT-PCR, and western blotting in vitro. In addition, we analyzed mouse corneas exposed to concentrated ambient PM2.5 through immunofluorescence staining to observe the resulting changes in corneal epithelial protein expression in vivo. Results: Our results showed significant impairment of corneal epithelial barrier function in PM2.5-treated HCECs, as indicated by decreased TEER values. The expression of thrombospondin-1 (THBS1) and claudin-1, both key factors for maintaining corneal epithelial barrier integrity, was markedly reduced at the gene and protein levels in both in vitro and in vivo PM2.5 exposure models. Moreover, the levels of tight junction-associated proteins, including occludin, zonula occludens-1 (ZO-1) and ZO-2, essential components of the corneal epithelial barrier, were significantly diminished in PM2.5-treated HCECs. Conclusion: PM2.5 exposure leads to corneal epithelium damage by disrupting tight junction proteins and THBS1 expression. These findings provide insight into potential pathways for PM2.5-induced ocular toxicity and underscore the need for protective strategies against such environmental pollutants. more...

The integration of modern service and advanced manufacturing industries represents deep industrial reform and profound change in quality concepts, mechanisms, and practices. Integrating these two industries is important for achieving high-quality development and is an area of interest for policymakers and academia. This study analyzes the data of 11 regions in 2021 in the Guangdong–Hong Kong–Macao Greater Bay Area using the fuzzy set qualitative comparative analysis method from the configuration perspective. This study explores the effects of multiple factors, such as economic efficiency, technological innovation, and the policy environment, on improving the coupling coordination level of the modern service and advanced manufacturing industries, and analyzes the path selection of industrial integration development. Overall, economic efficiency, technological innovation, and policy environment factors are not necessary conditions for the integration development of the modern service and advanced manufacturing industries. The multiple impacts of economic efficiency, technological innovation, and policy environment form a multi-restructuring path that drives the integration of the two industries. Owing to the economic development level and resource endowment of different regions, the path dictating the degree of integration between the two industries varies by region. From a holistic perspective, all regions should strive to coordinate the multiple conditions of economic efficiency, technological innovation, and policy environment to break through objective constraints such as the government's conditions and external environment based on their own organizational conditions and resource endowments. Additionally, all regions should strengthen exchanges, cooperation, and integration mechanisms and implement complementary development and distinctive development in core cities in the Greater Bay Area. The results clarify the factors affecting industrial convergence in the Greater Bay Area, enriching existing research methods and laying a foundation for a decision-making theory of industrial integration development. more...

Abstract Background Helsmoortel–Van der Aa syndrome (HVDAS) is a rare genetic disorder caused by variants in the activity-dependent neuroprotector homeobox (ADNP) gene; hence, it is also called ADNP syndrome. ADNP is a multitasking protein with the function as a transcription factor, playing a critical role in brain development. Furthermore, ADNP variants have been identified as one of the most common single-gene causes of autism spectrum disorder (ASD) and intellectual disability. Methods We assembled a cohort of 15 Chinese pediatric patients, identified 13 variants in the coding region of ADNP gene, and evaluated their clinical phenotypes. Additionally, we constructed the corresponding ADNP variants and performed western blotting and immunofluorescence analysis to examine their protein expression and subcellular localization in human HEK293T and SH-SY5Y cells. Results Our study conducted a thorough characterization of the clinical manifestations in 15 children with ADNP variants, and revealed a broad spectrum of symptoms including global developmental delay, intellectual disability, ASD, facial abnormalities, and other features. In vitro studies were carried out to check the expression of ADNP with identified variants. Two cases presented missense variants, while the remainder exhibited nonsense or frameshift variants, leading to truncated mutants in in vitro overexpression systems. Both overexpressed wildtype ADNP and all the different mutants were found to be confined to the nuclei in HEK293T cells; however, the distinctive pattern of nuclear bodies formed by the wildtype ADNP was either partially or entirely disrupted by the mutant proteins. Moreover, two variants of p.Y719* on the nuclear localization signal (NLS) of ADNP disrupted the nuclear expression pattern, predominantly manifesting in the cytoplasm in SH-SY5Y cells. Limitations Our study was limited by a relatively small sample size and the absence of a longitudinal framework to monitor the progression of patient conditions over time. Additionally, we lacked in vivo evidence to further indicate the causal implications of the identified ADNP variants. Conclusions Our study reported the first cohort of HVDAS patients in the Chinese population and provided systematic clinical presentations and laboratory examinations. Furthermore, we identified multiple genetic variants and validated them in vitro. Our findings offered valuable insights into the diverse genetic variants associated with HVDAS. more...

Under the combined action of the expansion of the chemical industry zone and urban boundaries, urban areas exposed to danger are also increasing. As containers for chemical storage, storage tanks are potential sources of hazards. Conducting target detection for hazard risk analysis is essential. YOLOv5, a single-stage algorithm with good performance, accurately identifies most storage tanks with clear contours and evident positions; however, the identification effect is not good for storage tanks with small targets or unclear boundaries. This study proposes an optimized model based on the YOLOv5 model. First, the Coordinate Attention (CA) mechanism is introduced to make the model focus on the effective information of the target. Second, to improve the detection effect of small targets, the model adds a small target detection head and performs multiscale target detection. Finally, the EIOU loss function is employed instead of the CIOU loss function in the original model to improve the algorithm’s accuracy and speed. Experimental results show that the optimized model significantly improves the detection effect on small targets compared with the original YOLOv5 model. The number of small targets detected by the optimized model is significantly increased compared with the original model, and the size of the smallest targets detected by the optimized model is reduced by about twice compared with the original model. The model’s accuracy, recall rate, and mean average precision (mAP@0.5) are improved, which can be better applied to the detection of storage tanks. more...

Background. ATP binding cassette transporter A1 (ABCA1) is a candidate gene within a POAG susceptibility locus by GWAS analysis, and it is involved in IOP modulation via the Cav1/eNOS/NO signaling pathway. We aim to examine the phenotype of ABCA1 deletion in the ABCA1 gene knockout (Abca1−/−) mice. Methods. The anterior segments of Abca1−/− eyes were imaged by slit-lamp microscopy and anterior segment OCT. IOPs were measured by rebound tonometry. By perfusing enucleated eyes at various pressures, the aqueous humor outflow facility was determined. The mRNA expressions of ABCA1, Cav1, and eNOS were measured by RT-qPCR. The protein expressions were analyzed by western blot and immunofluorescence staining. Results. There was no significant difference in the anterior segment morphology of Abca1−/− mice. IOP and aqueous humor outflow facility did not change in Abca1−/− mice compared with wild-type mice. mRNA and protein expressions of ABCA1 were significantly lower in the outflow tissue of Abca1−/− eyes. The expressions of Cav1 and eNOS were both significantly upregulated in the outflow tissue of Abca1−/− eyes. Conclusion. ABCA1 deletion does not affect IOP and aqueous humor outflow function but the Cav1/eNOS/NO pathway is changed in Abca1−/− mice. The function of ABCA1 in aqueous humor outflow still requires further research. more...

Accurate prediction of fine particulate matter (PM2.5) concentration is crucial for improving environmental conditions and effectively controlling air pollution. However, some existing studies could ignore the nonlinearity and spatial correlation of time series data observed from stations, and it is difficult to avoid the redundancy between features during feature selection. To further improve the accuracy, this study proposes a hybrid model based on empirical mode decomposition (EMD), minimal-redundancy-maximal-relevance (mRMR), and geographically weighted neural network (GWNN) for hourly PM2.5 concentration prediction, named EMD-mRMR-GWNN. Firstly, the original PM2.5 concentration sequence with distinct nonlinearity and non-stationarity is decomposed into multiple intrinsic mode functions (IMFs) and a residual component using EMD. IMFs are further classified and reconstructed into high-frequency and low-frequency components using the one-sample t-test. Secondly, the optimal feature subset is selected from high-frequency and low-frequency components with mRMR for the prediction model, thus holding the correlation between features and the target variable and reducing the redundancy among features. Thirdly, the residual component is predicted with the simple moving average (SMA) due to its strong trend and autocorrelation, and GWNN is used to predict the high-frequency and low-frequency components. The final prediction of the PM2.5 concentration value is calculated by an artificial neural network (ANN) composed of the predictive values of each component. PM2.5 concentration prediction experiments in three representational cities, such as Beijing, Wuhan, and Kunming were carried out. The proposed model achieved high accuracy with a coefficient of determination greater than 0.92 in forecasting PM2.5 concentration for the next 1 h. We compared this model with four baseline models in forecasting PM2.5 concentration for the next few hours and found it performed the best in PM2.5 concentration prediction. The experimental results indicated the proposed model can improve prediction accuracy. more...

Objectives: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with genetic and clinical heterogeneity. Owing to the advancement of sequencing technologies, an increasing number of ASD-related genes have been reported. We designed a targeted sequencing panel (TSP) for ASD based on next-generation sequencing (NGS) to provide clinical strategies for genetic testing of ASD and its subgroups.Methods: TSP comprised 568 ASD-related genes and analyzed both single nucleotide variations (SNVs) and copy number variations (CNVs). The Autism Diagnostic Observation Schedule (ADOS) and the Griffiths Mental Development Scales (GMDS) were performed with the consent of ASD parents. Additional medical information of the selected cases was recorded.Results: A total of 160 ASD children were enrolled in the cohort (male to female ratio 3.6:1). The total detection yield was 51.3% for TSP (82/160), among which SNVs and CNVs accounted for 45.6% (73/160) and 8.1% (13/160), respectively, with 4 children having both SNVs and CNV variants (2.5%). The detection rate of disease-associated variants in females (71.4%) was significantly higher than that in males (45.6%, p = 0.007). Pathogenic and likely pathogenic variants were detected in 16.9% (27/160) of the cases. SHANK3, KMT2A, and DLGAP2 were the most frequent variants among these patients. Eleven children had de novo SNVs, 2 of whom had de novo ASXL3 variants with mild global developmental delay (DD) and minor dysmorphic facial features besides autistic symptoms. Seventy-one children completed both ADOS and GMDS, of whom 51 had DD/intellectual disability (ID). In this subgroup of ASD children with DD/ID, we found that children with genetic abnormalities had lower language competence than those without positive genetic findings (p = 0.028). There was no correlation between the severity of ASD and positive genetic findings.Conclusion: Our study revealed the potential of TSP, with lower cost and more efficient genetic diagnosis. We recommended that ASD children with DD or ID, especially those with lower language competence, undergo genetic testing. More precise clinical phenotypes may help in the decision-making of patients with genetic testing. more...

IntroductionFragile X syndrome (FXS) is a X-linked neurodevelopmental disorder (NDD). This study aims to investigate the incidence of FXS in Chinese children and analyze the comprehensive clinical characteristics of these FXS children.MethodsChildren diagnosed with idiopathic NDD were recruited between 2016 and 2021 from the department of Child Health Care, Children's Hospital of Fudan University. We combined tetraplet-primed PCR-capillary electrophoresis and whole exome sequencing (WES)/panel or array-based comparative genomic hybridization (array-CGH) to identify the size of the CGG repeats and the mutations or copy number variations (CNVs) in the genome and in FMR1. The clinical features of FXS children were analyzed according to pediatricians' recording, parental questionnaires, the results of examinations and follow-up.ResultsThe incidence of FXS in Chinese children with idiopathic NDD was 2.4% (42/1753) and in those with FXS, 2.38% had a deletion (1/42). Here, we present the clinical characteristics of 36 children with FXS. Overweight was observed in two boys. The average intelligence quotient (IQ)/development quotient (DQ) of all FXS patients was 48. The average ages of meaningful words and walking alone were 2 years and 10 months and 1 year and 7 months, respectively. The most frequent repetitive behavior was stimulated by hyperarousal to sensory stimulation. On social aspects, social withdrawal, social anxiety, and shyness accounted for 75%, 58%, and 56% of the total number of children, respectively. Approximately 60% of FXS children in this cohort were emotionally labile and prone to temper tantrums. Self-injury and aggression toward others could also be observed, at 19% and 28%, respectively. The most frequent behavioral problem was attention-deficit hyperactivity disorder (ADHD) seen in 64% and the most common facial features were a narrow and elongated face and large or prominent ears in 92% of patients.DiscussionScreening of FMR1 full mutation provides the possibility for patients' further medical supports and the clinical features of FXS children obtained in this study will increase the understanding and diagnosis of FXS. more...

Storm surge disasters result in severe casualties and economic losses. Accurate prediction of storm surge water level is crucial for disaster assessment, early warning, and effective disaster management. Machine learning methods are relatively more efficient and straightforward compared to numerical simulation approaches. However, most of the current research on storm surge water level prediction based on machine learning methods is primarily focused on point predictions. In this study, we explore the feasibility of spatial water level prediction using the ConvLSTM model. We focus on the coastal area of Guangdong Province and employ MIKE21(2019) software to simulate historical typhoons that have made landfall in the region from 1991 to 2018. We construct two datasets: one for direct water level prediction and the other for indirect water level prediction based on water level changes. Utilizing the ConvLSTM network, we employ it to forecast storm surges on both datasets, effectively capturing both temporal and spatial characteristics and thus ensuring the production of dependable results. When directly predicting water levels, we achieve an MAE (mean absolute error) of 0.026 m and an MSE (mean squared error) of 0.0038 m2. In contrast, the indirect prediction approach yields even more promising results, with an MAE of 0.014 m and an MSE of 0.0007 m2. Compared to traditional numerical simulation methods, the ConvLSTM-based approach is simpler, faster, and able to predict water levels accurately without boundary conditions or topographies. Furthermore, we consider worst-case scenarios by predicting the maximum water increase value using the random forest model. Our results indicate that the random forest model can serve as a valuable reference for forecasting the maximum water increase value of typhoon storm surges, supporting effective emergency responses to disasters. more...

Analysis of the spatiotemporal distribution of online public opinion topics can help understand the hotspots of public concern. The topic model is employed widely in public opinion topic clustering for social media data. In order to handle topic-clustering of low-quality geospatial social media data, such as microblog data, with short text and timeliness characteristics, this study proposed a Dirichlet multinomial mixture over time (DMMOT) model to cluster microblog topic for public opinion analysis. The DMMOT model assumes that a single document belongs to a single topic, in line with the characteristics of a short text, and it introduces the probability distribution of “topic-time” in the process of topic generation. The model parameter inference process was presented in detail by exploring the Gibbs sampling method. Results generated using the DMMOT model in case study show that the “topic-word” distribution is semantically aggregated within various topics, and “topic-time” distribution clustered within a time window under each topic. Furthermore, the characteristics of the trend of each topic over time are basically consistent with the corresponding trend of topic in reality in terms of content. These indicate that the DMMOT model improves topic clustering for short text to some extent. Furthermore, the DMMOT model performed well in both temporal and spatial analysis of public opinion topics based on microblog data. more...

Rett syndrome (RTT) is a neurodevelopmental disorder caused by loss-of-function variants in the MECP2 gene, currently with no cure. Neuroimaging is an important tool for obtaining non-invasive structural and functional information about the in vivo brain. Multiple approaches to magnetic resonance imaging (MRI) scans have been utilized effectively in RTT patients to understand the possible pathological basis. This study combined developmental evaluations with clinical severity, T1-weighted imaging, and diffusion tensor imaging, aiming to explore the structural alterations in cohorts of young girls with RTT, idiopathic autism spectrum disorder (ASD), or typical development. Voxel-based morphometry (VBM) was used to determine the voxel-wised volumetric characteristics of gray matter, while tract-based spatial statistics (SPSS) was used to obtain voxel-wised properties of white matter. Finally, a correlation analysis between the brain structural alterations and the clinical evaluations was performed. In the RTT group, VBM revealed decreased gray matter volume in the insula, frontal cortex, calcarine, and limbic/paralimbic regions; TBSS demonstrated decreased fractional anisotropy (FA) and increased mean diffusivity (MD) mainly in the corpus callosum and other projection and association fibers such as superior longitudinal fasciculus and corona radiata. The social impairment quotient and clinical severity were associated with these morphometric alterations. This monogenic study with an early stage of RTT may provide some valuable guidance for understanding the disease pathogenesis. At the same time, the pediatric-adjusted analytic pipelines for VBM and TBSS were introduced for significant improvement over classical approaches for MRI scans in children. more...

The analysis and modeling of spatial and temporal changes in land use can reveal changing urban spatial patterns and trends. In this paper, we introduce a linear transformation optimization Markov (LTOM) model that can be exploited to estimate the state transition probability matrix of land use, building a loosely coupled ANN-CA-LTOM model for simulating and predicting land use changes. The advantages of this model are that it is flexible and high expansibility; it can maintain semantic coupling between the Artificial Neural Networks (ANN), Cellular Automata (CA), and LTOM model and enhance their functions; and it can break the limitation of requiring two periods of land use data when calculating the transition probability matrix. We also construct a suitability atlas of land use as the transition rules into the CA-LTOM model, taking into account the regional natural and socioeconomic driver factors, by exploiting the ANN model. The ANN-CA-LTOM model is employed to simulate the distribution of the three major types of land use, i.e., construction land, agricultural land, and unused land, in the Nansha District, China, in 2018 and 2020. The results show that the model performs well and the overall accuracy of the land use simulation was 97.72%, with a kappa coefficient of 0.962761. Furthermore, the simulated and predicted results of land use changes from 2021 to 2023 in Nansha District show changing trends in construction, agricultural, and unused land use. This study provides an approach for estimating a Markov transition probability matrix and a coupled mode of the models for simulating and predicting land use changes. more...

SH3 and Multiple Ankyrin Repeat Domains 3 (SHANK3)-caused autism spectrum disorder (ASD) may present a unique opportunity to clarify the heterogeneous neuropathological mechanisms of ASD. However, the specificity and commonality of disrupted large-scale brain organization in SHANK3-deficient children remain largely unknown. The present study combined genetic tests, neurobehavioral evaluations, and magnetic resonance imaging, aiming to explore the disruptions of both local and networked cortical structural organization in ASD children with and without SHANK3 deficiency. Multiple surface morphological parameters such as cortical thickness (CT) and sulcus depth were estimated, and the graph theory was adopted to characterize the topological properties of structural covariance networks (SCNs). Finally, a correlation analysis between the alterations in brain morphological features and the neurobehavioral evaluations was performed. Compared with typically developed children, increased CT and reduced nodal degree were found in both ASD children with and without SHANK3 defects mainly in the lateral temporal cortex, prefrontal cortex (PFC), temporo-parietal junction (TPJ), superior temporal gyrus (STG), and limbic/paralimbic regions. Besides commonality, our findings showed some distinct abnormalities in ASD children with SHANK3 defects compared to those without. Locally, more changes in the STG and orbitofrontal cortex were exhibited in ASD children with SHANK3 defects, while more changes in the TPJ and inferior parietal lobe (IPL) in those without SHANK3 defects were observed. For the SCNs, a trend toward regular network topology was observed in ASD children with SHANK3 defects, but not in those without. In addition, ASD children with SHANK3 defects showed more alterations of nodal degrees in the anterior and posterior cingulate cortices and right insular, while there were more disruptions in the sensorimotor areas and the left insular and dorsomedial PFC in ASD without SHANK3 defects. Our findings indicate dissociable disruptions of local and networked brain morphological features in ASD children with and without SHANK3 deficiency. Moreover, this monogenic study may provide a valuable path for parsing the heterogeneity of brain disturbances in ASD. more...

In response to pathological stimulation, methylation status conversion of the genome drives changes of cell feature and is able to promote disease development. Yet the role of methylation in the development of thyroid-associated ophthalmopathy (TAO) remains to be evaluated. Overexpansion of orbital tissue is the key feature of TAO. In this study, the methylation profile of orbital adipose/connective tissue from TAO patients and normal individuals were compared. After screening 3,739 differentially methylated probes, the distribution and properties of these probes were analyzed. Furthermore, enriched biological functions of these genes associated with differential methylation and the relationship between their methylation status and expression profile were also identified, including PTPRU and VCAM-1. According to our results, methylation was involved in disregulated immune response and inflammation in TAO and might contribute to activation of fibroblast and adipogenesis, leading to the expansion of orbital tissue. Neuropathy and neurobehavioral symptoms were also potentially associated with methylation. These results may help to extend the understanding of methylation in TAO and provide more insights into diagnosis and treatment of patients. more...

Mutations of the SHANK3 gene are found in some autism spectrum disorder (ASD) patients, and animal models harboring SHANK3 mutations exhibit a variety of ASD-like behaviors, presenting a unique opportunity to explore the underlying neuropathological mechanisms and potential pharmacological treatments. The histone deacetylase (HDAC) valproic acid (VPA) has demonstrated neuroprotective and neuroregenerative properties, suggesting possible therapeutic utility for ASD. Therefore, SHANK3-associated ASD-like symptoms present a convenient model to evaluate the potential benefits, therapeutic window, and optimal dose of VPA. We constructed a novel shank3-deficient (shank3ab–/–) zebrafish model through CRISPR/Cas9 editing and conducted comprehensive morphological and neurobehavioral evaluations, including of core ASD-like behaviors, as well as molecular analyses of synaptic proteins expression levels. Furthermore, different VPA doses and treatment durations were examined for effects on ASD-like phenotypes. Compared to wild types (WTs), shank3ab–/– zebrafish exhibited greater developmental mortality, more frequent abnormal tail bending, pervasive developmental delay, impaired social preference, repetitive swimming behaviors, and generally reduced locomotor activity. The expression levels of synaptic proteins were also dramatically reduced in shank3ab–/– zebrafish. These ASD-like behaviors were attenuated by low-dose (5 μM) VPA administered from 4 to 8 days post-fertilization (dpf), and the effects persisted to adulthood. In addition, the observed underexpression of grm5, encoding glutamate metabotropic receptor 5, was significantly improved in VPA-treated shank3ab–/– zebrafish. We report for the first time that low-dose VPA administered after neural tube closure has lasting beneficial effects on the social deficits and repetitive behavioral patterns in shank3-deficient ASD model zebrafish. These findings provide a promising strategy for ASD clinical drug development. more...

Although studies have demonstrated that fine particulate matter (PM2.5) induces ocular surface damage, PM2.5 exposure causes cornea toxicity is not entirely clear. The aim of this study is to investigate the role of the nod-like receptor family pyrin domain containing three (NLRP3) inflammasome-mediated pyroptosis in PM2.5-related corneal toxicity. Human corneal epithelial cells (HCECs) were exposed to different concentrations of PM2.5, and the cell viability, expressions of NLRP3 inflammasome mediated pyroptosis axis molecules and intracellular reactive oxygen species (ROS) formation were measured in HCECs. Animal experiments were undertaken to topically apply PM2.5 suspension to mouse eyes for three months and the pyroptosis related molecules in the mouse corneas were measured. Results: Our results showed a dose-dependent decrease of HCEC viability in the PM2.5-treated cells. NLRP3 inflammasome-mediated pyroptosis axis (NLRP3, ASC, GSDMD, caspase-1, IL-1β, and IL-18) were activated in the PM2.5-treated HCECs, accompanied by increased ROS formation. Further in vivo study confirmed the activation of this pathway in the mouse corneas exposed to PM2.5. In conclusion, this study provids novel evidence that PM2.5 induces corneal toxicity by triggering cell pyroptosis. more...

The efficient discovery of significant group patterns from large-scale spatiotemporal trajectory data is a primary challenge, particularly in the context of urban traffic management. Existing studies on group pattern discovery mainly focus on the spatial gathering and moving continuity of vehicles or animals; these studies either set too many limitations in the shape of the cluster and time continuity or only focus on the characteristic of the gathering. Meanwhile, little attention has been paid to the equidirectional movement of the aggregated objects and their loose coherence moving. In this study, we propose the concept of loosely moving congestion patterns that represent a group of moving objects together with similar movement tendency and loose coherence moving, which exhibit a potential congestion characteristic. Meanwhile, we also develop an accelerated algorithm called parallel equidirectional cluster-recombinant (PDCLUR) that runs on graphics processing units (GPUs) to detect congestion patterns from large-scale raw taxi-trajectory data. The case study results demonstrate the performance of our approach and its applicability to large trajectory dataset, and we can discover some significant loosely moving congesting patterns and when and where the most congested road segments are observed. The developed algorithm PDCLUR performs satisfactorily, affording an acceleration ratio of over 65 relative to the traditional sequential algorithms. more...

Online public opinion reflects social conditions and public attitudes regarding special social events. Therefore, analyzing the temporal and spatial distributions of online public opinion topics can contribute to understanding issues of public concern, grasping and guiding the developing trend of public opinion. However, how to evaluate the validity of classification of online public opinion remains a challenging task in the topic mining field. By combining a Bidirectional Encoder Representations from Transformers (BERT) pre-training model with the Latent Dirichlet Allocation (LDA) topic model, we propose an evaluation method to determine the optimal classification number of topics from the perspective of semantic similarity. The effectiveness of the proposed method was verified based on the standard Chinese corpus THUCNews. Taking Coronavirus Disease 2019 (COVID-19)-related geotagged posts on Weibo in Wuhan city as an example, we used the proposed method to generate five categories of public opinion topics. Combining spatial and temporal information with the classification results, we analyze the spatial and temporal distribution patterns of the five optimal public opinion topics, which are found to be consistent with the epidemic development, demonstrating the feasibility of our method when applied to practical cases. more...

Nighttime light (NTL) remote sensing data have been widely used to derive socioeconomic indicators at the national and regional scales to study regional economic development. However, most previous studies only chose a single measurement indicator (such as GDP) and adopted simple regression methods to investigate the economic development of a certain area based on DMSP-OLS or NPP-VIIRS stable NTL data. The status quo shows the problems of using a single evaluation index—it has a low evaluation precision. The LJ1-01 satellite is the first dedicated NTL remote sensing satellite in the world, launched in July 2018. The data provided by LJ1-01 have a higher spatial resolution and fewer blooming phenomena. In this paper, we compared the accuracy of the LJ1-01 data and NPP-VIIRS data in detecting county-level multidimensional economic development. In three provinces in China, namely, Hubei, Hunan and Jiangxi, 20 socioeconomic parameters were selected from the following five perspectives: economic conditions, people’s livelihood, social development, public resources and natural vulnerability. Then, a County-level Economic Index (CEI) was constructed to evaluate the level of multidimensional economic development, with the spatial pattern of the multidimensional economic development also identified across the study area. The present study adopted the random forest (RF) and linear regression (LR) algorithms to establish the regression model individually, and the results were evaluated by cross-validation. The results show that the RF algorithm greatly improves the accuracy of the model compared with the LR algorithm, and thus is suitable for the study of NTL data. In addition, a better determinate coefficient (R2) based on the LJ1-01 data (0.8168) was obtained than that from the NPP-VIIRS data (0.7245) in the RF model, which reflects that the LJ1-01 data offer better potential in the evaluation of socioeconomic parameters and can be used to identify, both accurately and efficiently, multidimensional economic development at the county level. more...

Emerging on-line reservation services and special car services have greatly affected the development of the taxi industry. Surprisingly, taking a taxi is still a significant problem in many large cities. In this paper, we present an effective solution based on the Hidden Markov Model to predict the upcoming services of vacant taxis that appear at some fixed locations and at specific times. The model introduces a weighted confusion matrix and a modified Viterbi algorithm, combining the factors of time of day and traffic conditions. In our framework, the hotspot or hidden states extraction is implemented through kernel density estimation (KDE) and fuzzy partitioning of traffic zones is done via a Fuzzy C Means (FCM) algorithm. We implement the proposed model on a large-scale dataset of taxi trajectories in Beijing. In this use case, tests demonstrate the high accuracy of the modeling framework in predicting the upcoming services of vacant taxis. We further analyze the factors affecting the predictive accuracy via a prediction accuracy analysis and prediction location evaluation. The findings of this paper can provide intelligence for the improvement of taxi services, to increase the passenger capacity of taxis and also to improve the probability of passengers finding taxis. more...

Abstract The role of cancer-associated fibroblasts (CAFs) in modulating the tumor microenvironment (TME) is gaining attention, yet their impact on prognosis and therapeutic response in colon cancer remains unclear. Here, we identified genes associated with CAF infiltration via weighted gene co-expression network analysis (WGCNA) utilizing data from The Cancer Genome Atlas (TCGA) and GSE39582 cohorts. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to construct CAF molecular signatures (CAFscore). Patients were categorized into high and low CAFscore groups to analyze clinicopathological traits, somatic mutations, immune evasion, and treatment responses. In this study, a total of 244 genes were correlated with CAF infiltration, with 11 linked to overall survival. Notably, FSTL3, CRIP2, and SLC2A3 were selected for the CAFscore. A higher CAFscore was associated with poorer prognoses, increased malignancy, and therapeutic resistance, particularly among patients with high tumor mutation burden and microsatellite instability. Furthermore, elevated FSTL3 expression was associated with reduced CD8+ T cell infiltration, indicating a suppressive TME. Mechanistically, CAFs may promote immune evasion via NAMPT ligand-receptor interactions based on single-cell RNA sequencing data. Thus, the CAFscore is crucial for personalizing treatment strategies and identifying patients who require more aggressive management. more...

The Chinese scientist Chunchun Hu analyzes the conflict between Russia and Ukraine in the article "Europe's historical responsibility in the Ukraine war". He emphasizes that this conflict is one of the most serious geopolitical confrontations since the end of the Cold War and that further escalation cannot be ruled out. Hu states that Russia will not give up its goals in the war and that NATO and the EU have imposed comprehensive sanctions against Russia. He also points out that Europe has learned little from history and that it is time for Europeans to reconsider their logic and put an end to madness. The author appeals to Europe not to see the conflict as a final battle between freedom and unfreedom, but as a continuation of Europe's historical logic. He emphasizes that the conflict is primarily a European problem and that many countries abstained from the UN vote on the situation in Ukraine. The author poses the question of whether Europe would accept Chinese assistance and whether Europe would see Chinese mediation as a compromise peace. He argues that the future of Europe must be considered in conjunction with Russia and that a European security system that excludes Russia cannot serve peace. The author emphasizes Europe's historical responsibility for world peace and warns of a bleak future if Europe chooses confrontation. [Extracted from the article] more...