Search Results (2,303)

Medulloblastoma is a heterogeneous solid tumor, and its molecular characteristics are the most important prognostic factors for this neoplasm. Unfortunately, the molecular classification of MB-G3 and MB-G-4 medulloblastoma is very complex because of molecular similarity. Therefore, in this work, through unsupervised machine learning-based gene expression profiling, we identified a low molecular profile associated with four molecular groups of medulloblastoma. We performed medulloblastoma expression microarray data mining via the Partek Genomics Suite and Transcriptome Analysis Console (TAC), and we included a total of 25 fresh medulloblastoma tumors that were obtained and hybridized into HG U133 Plus 2.0 Array microarrays. To identify the molecular groups of the 25 patients, we compared them against classified patients, which were obtained from free repositories, and through data mining based on gene expression, compared the expression profiles of our patients. To do so, we performed an analysis via the least squares method via PCA. The molecular groups MB-WNT and MB-SHH were confirmed via immunohistochemistry via β-catenin, YAP1 and GAB1 antibodies in tissue fixed in formalin and embedded in paraffin, and another tissue section was placed on a Visium Spatial slide to perform spatial RNA-seq via Illumina NextSeq 2000 platform sequencers. The data obtained were analyzed with R. We identified the expression profiles associated with the four molecular groups and formed a reference set. Through unsupervised analysis via the least squares method, we assigned the molecular profiles of 25 patients with medulloblastoma, via the integration of bulk and spatial tumor molecular gene expression profiling analysis and with immunohistochemical findings, this strategy was fast and accurate. We observed correlations in three of the trials carried out and, in part, in one study, a patient who presented two tumor strains and two molecular signatures (SHH and G4), which led us to believe that this patient presented mixed phenotypic characteristics. Multigene expression profile analysis of medulloblastoma represents a significant advance in precision medicine; integrating different layers of transcriptomic information allows us to demonstrate underlying molecular changes in the four molecular groups that are essential for personalized therapy. Full article

Microplastics (MPs) are widely recognized as emerging contaminants in freshwater environments. Their identification often relies on extensive sample preparation and chemical treatments, which increase analysis time, reagent use, and overall resource consumption. Consequently, there is a growing need for sustainable analytical approaches enabling reliable MP detection while minimizing sample handling. This study proposes an analytical workflow based on hyperspectral imaging (HSI) as a proof-of-concept approach for direct identification of MPs in freshwater samples. Water samples collected from three different rivers, containing heterogeneous natural materials, were spiked with MPs (250–1000 μm) of three common polymers, namely high-density polyethylene (HDPE), polystyrene (PS), and polypropylene (PP), to simulate realistic contamination scenarios. HSI acquisitions were performed in the short-wave infrared range (SWIR: 1000–2500 nm). Spectral preprocessing and principal component analysis (PCA) were applied for data exploration, while a hierarchical partial least squares-discriminant analysis (Hi-PLS-DA) model was developed to classify five target classes: natural materials, water, HDPE, PS, and PP. Despite sample complexity, the proposed workflow achieved satisfactory classification results, as demonstrated by the predicted class map and the corresponding statistical metrics (sensitivity, specificity, precision, and F1-score: 0.900–0.999). These results highlight the potential of the SWIR-HSI-based approach as a rapid and sustainable method for direct MP identification in freshwater samples and provide methodological insights for rapid MP screening strategies requiring minimal sample preparation. Full article

Public open spaces (POS) in old communities are important settings for daily neighborhood life, yet many renovated POS remain underused after physical upgrading. Existing evaluations often rely on subjective perceptions, providing limited evidence on how facilities are associated with vitality. This study analyzes the associations between facility provision and POS vitality in 63 renovated POS across 11 old communities in Jiulongpo District, Chongqing, China. POS vitality is operationalized through two behavioral dimensions, use frequency and stay duration, derived from video detection and tracking using YOLOv8 and ByteTrack. Facility provision was then classified by facility type and examined in relation to the vitality indicators through descriptive analysis and Generalized Estimating Equations models. Descriptive evidence indicates substantial heterogeneity in both facility provision and POS vitality. Resting amenities and landscape elements are more commonly provided, whereas children’s facilities show the lowest provision and greater spatial selectivity. Higher use frequency and longer stay duration are concentrated in some POS. The Generalized Estimating Equations analysis further indicates that facilities are not associated with vitality in a uniform way. Children’s facilities show the strongest positive associations with both use frequency and stay duration despite their limited provision, supporting their key role in POS vitality. Landscape elements and lighting facilities are more closely associated with stay duration, highlighting the role of environmental support in sustaining longer use. In contrast, the negative associations for fitness facilities, together with the non-significant results for resting and sanitation amenities, suggest that not all facility provision translates into stronger vitality. Taken together, renovation performance should be judged not by the quantity of upgraded facilities alone, but by whether facilities support the behavioral dimensions of vitality that a POS is expected to achieve. Full article

Background: Advances in monitoring systems featuring wearable sensors, computer vision, and artificial intelligence (AI) have been increasingly used in sports science and rehabilitation practices as a means of movement pattern analysis, injury prevention, and training optimization. These technologies are becoming essential components of athlete-performance analysis and rehabilitation-monitoring systems designed to support biomechanical assessment, athlete development, and movement-quality evaluation. Athlete-performance analysis and rehabilitation monitoring increasingly rely on intelligent multimodal sensing systems capable of continuously evaluating movement quality, biomechanical patterns, training execution, and recovery progress. Human activity recognition (HAR) serves as a key enabling technology for these applications by providing automated assessment of human movement using wearable and vision-based sensing modalities. Therefore, the purpose of this study was to develop and evaluate an attention-based multimodal framework that integrates wearable inertial sensing and RGB video analysis for robust athlete-performance assessment and rehabilitation monitoring through accurate recognition of human movement patterns. Methods: Athlete-performance analysis and rehabilitation monitoring combining inertial sensor data and RGB-based visual information was introduced. Inertial signals were segmented with adaptive windowing, whereas silhouette refinement was performed to analyze motion structures from visual inputs in support of athlete-performance analysis and rehabilitation monitoring. Temporal, spatial, and motion features such as trajectory, orientation, and skeleton-based space-time representations were calculated from multimodal inputs. The proposed framework was designed to capture complex movement dynamics associated with rehabilitation exercises and sports-related motion patterns across heterogeneous sensing environments. Extracted features were then combined and optimized with a multimodal feature fusion approach, while the Ranger optimization algorithm was utilized during the process. An attention-based deep learning classifier was implemented to classify movement activities. Results: The results showed that the proposed framework reached accuracy scores of 88.40% and 87.96% on the VIDIMU dataset and the UTD-MHAD dataset respectively. Recognition performance across both inertial and vision-based modalities provided greater robustness than single-modality solutions. The integration of wearable sensing and computer vision modalities further improved the ability of the framework to analyze complex movement behaviors under varying execution conditions and environmental variations. Conclusion: The proposed multimodal framework provides a foundation for intelligent athlete-performance and rehabilitation-monitoring systems by integrating wearable sensing, computer vision, and attention-based artificial intelligence for robust movement analysis. The findings highlight its potential to support biomechanical assessment, movement-quality evaluation, training-performance monitoring, rehabilitation tracking, and injury-risk management in modern sports and healthcare environments. Full article

Background/Objectives: Artificial intelligence (AI) offers a promising approach for detecting and classifying symptom severity in patients with Parkinson’s disease (PD). The objective was to provide an overview of AI methods performance used for this classification through a systematic review and meta-analysis conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Methods: The Google Scholar, IEEE Xplore, PubMed/MedLine, and ScienceDirect databases were searched for the period 2015–2025. The studies included were original, peer-reviewed studies written in English that addressed an AI method based on machine learning (ML) or deep learning (DL) for the classification of PD patients. The dataset used had to be “Gait in Parkinson’s Disease,” in which the severity of disease symptoms was assessed using the Hoehn and Yahr (H&Y) scale. Studies had to report at least one of the five performance metrics: accuracy, sensitivity, specificity, precision, and F1 score. Two reviewers independently selected articles, assessed the risk of bias using PROBAST (Prediction Model Study Risk of Bias Assessment Tool), and extracted data. The logit-transformed values were pooled separately by performance metrics and by severity level using a random-effects model. Cochran’s Q test, the I² statistic, and inter-study variability (τ²), computed using the generalized inverse variance method with the restricted maximum likelihood model, were used to assess heterogeneity. Forest plots with 95% confidence intervals were used to present the results. Possible causes of heterogeneity were explored using a subgroup analysis (ML vs. DL) and a sensitivity analysis. Finally, publication bias (Egger’s test) and the certainty of the evidence (using GRADE—Grading of Recommendations Assessment, Development, and Evaluation) were assessed to verify the generalizability of the results. Results: Among the 257 unique records, 12 studies were included. The methods demonstrated very high overall performance (>92%): accuracy (96.4%, 95% CI: 95.9–96.9%), specificity (97.7%, 95% CI: 97.3–98.1%), sensitivity (94.0%, 95% CI: 92.7–95.2%), precision (93.4%, 95% CI: 92.0–94.6%), F1 score (92.1%, 95% CI: 90.6–93.4%). Accuracy, specificity, and precision were high for all H&Y levels. However, the more advanced the symptoms, the lower the sensitivity (97.3% for H&Y0 vs. 92.1% for H&Y3). ML models achieved the best results for classifying healthy patients (H&Y0: 95.7% to 98.2%), while DL approaches performed better for classifying higher severity levels (>92%). Heterogeneity and inter-study variability were moderate (I²: 40–50% and τ²: 0.3–0.4) for precision and F1 score, and high (I² > 90% and τ² > 0.6) for accuracy, specificity, and sensitivity. The GRADE analysis revealed low-quality evidence for precision and F1 score and very-low quality for accuracy, specificity, and sensitivity. Conclusions: Thus, AI-based wearable gait assessment devices show great promise in terms of aiding clinical decision-making and treatment personalization. However, further research using a rigorous methodology (PROBAST) is needed to ensure the generalizability of the results and the clinical viability of the proposed solutions. Full article

Background/Objectives: Absent septum pellucidum (ASP) is a rare fetal midline brain finding that may occur in isolation or alongside broader central nervous system (CNS) malformations, genetic disorders, or septo-optic dysplasia (SOD). Accurate prenatal diagnosis and counseling remain challenging because apparently isolated ASP may be reclassified following fetal magnetic resonance imaging (MRI), postnatal neuroimaging, or specialist assessment. This structured narrative review aimed to synthesize current evidence on prenatal imaging findings, associated anomalies, genetic evaluation, and postnatal outcomes in fetuses with ASP. Methods: This structured narrative review used PRISMA-informed reporting. PubMed and Google Scholar were searched for full-text English-language studies published from 2014 through the updated search date (8 June 2026). Data on gestational age at diagnosis, imaging classification, associated anomalies, genetic testing, postnatal assessment, and neurodevelopmental, ophthalmological, and endocrine outcomes were extracted. Study methodological quality was appraised using Joanna Briggs Institute tools. Results: Seven studies comprising 342 fetal ASP cases were included. Of these, 94 cases (27.5%) were classified as isolated ASP prenatally, but only 57 remained isolated postnatally when follow-up data were available. SOD was confirmed after birth in 11 of 94 (11.7%) fetuses with prenatally isolated ASP. As definitions, imaging protocols, genetic testing strategies, and follow-up duration differed substantially across studies, these pooled values are descriptive observations rather than formal quantitative estimates. Conclusions: ASP is a heterogeneous prenatal finding. The prognosis is most favorable when ASP remains isolated following a detailed prenatal and postnatal evaluation. Multidisciplinary follow-up involving fetal medicine, neuroradiology, genetics, ophthalmology, endocrinology, and neurology is essential for risk stratification and counseling. Full article

Prefabricated mechanical, electrical, and plumbing (MEP) systems have been increasingly adopted in energy station projects; however, systematic evaluation frameworks capable of integrating construction performance, cost constraints, and uncertain multi-indicator assessments remain limited. To address this gap, this study constructs an Analytic Hierarchy Process (AHP)–Entropy–Fuzzy evaluation framework to assess the comprehensive benefits of BIM-enabled prefabricated MEP construction in energy stations. A hierarchical evaluation system was established based on five dimensions: schedule, quality, cost, safety, and environmental performance, and ten secondary indicators were defined. The Analytic Hierarchy Process was used to determine expert-based subjective weights, the entropy method was applied to capture objective data variability, and multiplicative normalization was employed to obtain combined weights. A fuzzy comprehensive evaluation model was then introduced to transform heterogeneous construction records into comparable benefit levels and scores. The prefabricated method scored 87.80 and was classified as “high”, whereas the conventional method scored 60.85 and was classified as “low”. A Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-based sensitivity analysis further showed that, under 10%, 20%, and 50% criterion-weight perturbations, the prefabricated group consistently achieved higher closeness coefficients than the conventional group. The smallest margin occurred when the schedule weight was reduced by 50%, but the prefabricated group retained a positive advantage. The results demonstrate that Building Information Modeling (BIM)-enabled prefabricated MEP construction can achieve superior overall project performance through the coordinated optimization of schedule, cost, safety, quality, and environmental objectives, offering a practical evaluation framework and decision-support tool for the industrialized delivery of future energy infrastructure projects. Full article

Objective: Minimally invasive surgery in Hirschsprung disease (HSCR) management was introduced in the mid-1990s. Despite decades of clinical application of various laparoscopic approaches, there remains a paucity of high-powered prospective studies and comprehensive systematic reviews in the literature. This study aimed to systematically review and summarize published techniques and outcomes of laparoscopic- and robotic-assisted surgery in HSCR. Methods: A systematic literature review was conducted using PubMed and the Cochrane Library. Studies reporting technical and outcome data of laparoscopic- or robotic-assisted surgery for HSCR were included. Data extraction and analysis were performed in accordance with the PRISMA 2020 guidelines. Parameters of interest included surgical technique, age at primary pull-through (PT), operative time, and functional outcomes. Outcomes of laparoscopic- versus robotic-assisted Yancey–Soave PT were compared. Results: A total of 700 publications were screened, of which seven studies met the inclusion criteria. Data from 556 patients were analyzed. A total of 338 underwent laparoscopic-assisted, and 218 underwent robotic-assisted pull-through. Large variability of the reported transanal resection technique (modified Yancey–Soave PT) was reported. Four studies reported functional outcomes in patients aged over four years. Three studies directly compared laparoscopic- and robotic-assisted PT; two reported no difference in the incidence of postoperative Hirschsprung-associated enterocolitis (HAEC). Functional outcomes were assessed using the Krickenbeck classification in three studies and the bowel function score in one study, with no significant differences reported in patients aged >4 years. Conclusions: Laparoscopic- and robotic-assisted Yancey–Soave PT appears to be safe for HSCR. Large variability in the applied surgical technique—despite being commonly classified as modified Yancey–Soave PT—as well as heterogeneity in the bowel function assessment, limit direct comparability between studies. To date, no single minimally invasive approach has demonstrated clear superiority over others. Prospective, randomized controlled studies are required to enable robust comparative evaluation of techniques, overall costs, and outcomes. Full article

Background: Low-flow, low-gradient aortic stenosis with reduced left ventricular ejection fraction is a heterogeneous condition with challenging severity assessment. Aortic valve calcification reflects fibro-calcific remodeling, while oxidative stress plays a key role in its pathogenesis. Serum uric acid, a marker of oxidative stress, may be associated with valvular calcification. This study investigated the relationship between serum uric acid levels and aortic valve calcification in this population. Methods: This retrospective study included 85 patients. Aortic valve calcification was quantified using computed tomography with the Agatston method, and patients were categorized as true severe or pseudo-severe according to sex-specific calcium thresholds. Of the patients, 57 were classified as true severe and 28 as pseudo-severe aortic stenosis. Results: Patients with higher calcification burden had significantly elevated serum uric acid levels (6.77 ± 1.57 vs. 5.08 ± 1.10 mg/dL, p < 0.001). Serum uric acid showed a modest correlation with aortic valve calcium score (ρ = 0.339, p = 0.002) and remained independently associated with CT-defined true severe low-flow, low-gradient aortic stenosis in multivariable analysis. ROC analysis yielded an area under the curve of 0.823 and identified a serum uric acid threshold of 5.45 mg/dL associated with a greater likelihood of CT-defined true severe low-flow, low-gradient aortic stenosis. Conclusions: Serum uric acid is associated with CT-derived aortic valve calcification and may provide insight into underlying fibro-calcific remodeling in this population. Full article

Background/Objectives: Long-term real-world data on dupilumab effectiveness and response trajectories in paediatric atopic dermatitis (AD) remain limited. This study evaluated long-term effectiveness, safety, response durability, and treatment trajectories in paediatric patients with moderate-to-severe AD treated with dupilumab. Methods: This retrospective single-centre cohort study included 55 paediatric patients with moderate-to-severe AD treated with dupilumab. Clinical outcomes, including Eczema Area and Severity Index (EASI), body surface area (BSA), and Children’s Dermatology Life Quality Index (CDLQI), were assessed longitudinally throughout treatment. The primary endpoint was the achievement of EASI-75 over the course of treatment, including timing of response onset. Secondary endpoints included EASI-90 achievement, longitudinal changes in disease severity and quality of life, treatment durability, safety, and response trajectory analyses. Patients were additionally classified into mutually exclusive trajectory groups based on timing and durability of EASI-75 achievement. Results: Dupilumab treatment was associated with rapid and sustained clinical improvement. Some patients achieved EASI-75 as early as week 4, highlighting interindividual variability in response kinetics. At week 16, EASI-75 was achieved in 85.5% of patients and EASI-90 in 47.3%. Clinical effectiveness further improved during long-term follow-up, with EASI-75 achieved in 96.2% and EASI-90 in 86.5% of patients at the last available follow-up. Median CDLQI decreased from 22.0 at baseline to 3.0 during follow-up, indicating marked improvement in quality of life. Most patients (83.6%) were classified as early responders, while 10.9% demonstrated delayed but clinically meaningful improvement during continued treatment. Loss of response after initial EASI-75 achievement was uncommon (3.7%). Adverse events were predominantly mild, with eosinophilia and conjunctivitis representing the most frequently observed findings. Conclusions: Overall, dupilumab demonstrated high long-term effectiveness and favourable tolerability in paediatric patients with moderate-to-severe AD. Trajectory analyses revealed clinically meaningful heterogeneity in response kinetics despite favourable long-term outcomes, highlighting that delayed responders may still achieve substantial benefit during continued therapy. Full article

Background: Pericardial involvement is the most common cardiac manifestation of systemic lupus erythematosus (SLE), ranging from mild effusion to recurrent pericarditis and cardiac tamponade. The influence of antiphospholipid syndrome (APS) on lupus-related pericardial disease remains unclear. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines and registered in PROSPERO. PubMed, Web of Science, Scopus, and the Cochrane Library were searched from inception to January 2026 for observational studies evaluating pericardial manifestations in adult SLE patients. APS/aPL status was considered a potential modifying factor when reported. Results: Seven observational studies were included. Pericardial involvement ranged from acute and recurrent pericarditis to large effusions and cardiac tamponade. Across studies, it was consistently associated with higher disease activity and markers of immune activation. Recurrent pericarditis emerged as a clinically relevant phenotype linked to more severe disease and worse outcomes. Cardiac tamponade, although rare, was associated with significant morbidity and mortality. APS/aPL-related data were heterogeneous and inconsistently reported across studies. No consistent APS-specific association with pericardial disease could be established, although APS or aPL-related findings were occasionally reported in selected severe or clinically complex presentations. Conclusions: Pericardial involvement in SLE reflects systemic inflammatory burden and spans a broad clinical spectrum. Current evidence regarding APS remains limited and heterogeneous, although APS may contribute to disease complexity in selected severe presentations. Importantly, isolated aPL positivity should not be interpreted as equivalent to formally classified APS. Prospective studies with standardized definitions and systematic assessment of APS are needed. Full article

Background/Objectives: Severe brain injury (SBI) in very preterm infants includes heterogeneous lesions with distinct timing, burden and outcomes. We used cranial ultrasound (CUS) to describe SBI entity, documented timing, three-domain burden, deaths following documented withdrawal, withholding or non-escalation of life-sustaining treatment for poor neurological prognosis (neuro-WWLST), and survivor outcomes. Methods: Retrospective single-center cohort (1991–2020) of 2841 very preterm infants (<32 weeks’ gestation and/or birth weight ≤ 1500 g) with complete CUS within 48 h after birth. CUS was summarized by four windows, three domains—parenchymal lesion, intraventricular hemorrhage (IVH) and ventriculomegaly—and three mutually exclusive entities: periventricular hemorrhagic infarction (PVHI), cystic periventricular leukomalacia (cPVL and grade 3 IVH without PVHI/cPVL (IVH3 entity). Cross-outcome analyses used common maximal-burden CUS. Results: SBI occurred in 286/2841 infants (10.1%) and neuro-WWLST death in 45/2841 infants (1.6%); 43/45 occurred within SBI, and 43/89 SBI deaths (48.3%) followed documented neuro-WWLST. Using common maximal-burden CUS, severe three-domain involvement was more frequent among neuro-WWLST deaths than survivors (37.2% vs. 8.6%). Among SBI survivors with follow-up, cerebral palsy (CP) occurred in 87/176 (49.4%) and clinically classified school-age cognitive sequelae in 50/155 (32.3%). Outcomes varied by entity, with mainly ambulatory unilateral CP after PVHI, more frequent non-ambulatory bilateral CP after cPVL, and a heterogeneous IVH3 profile. Severe three-domain involvement identified a small subgroup with higher outcome burden, but outcomes were not deterministic. Conclusions: A structured, descriptive CUS approach separating lesion entity, documented timing and multidomain burden may support transparent cohort-level description of SBI trajectories, documented neuro-WWLST deaths and survivor outcomes. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor control, balance, and gait impairments that significantly elevate fall risk. Traditional gait analysis focuses on spatiotemporal parameters, while gait variability, asymmetry, and balance measures offer more sensitive indicators of PD-related motor deficits. Machine learning studies using wearable gait data frequently report high classification accuracy but lack biomechanical interpretability and methodological rigor. Using the PhysioNet Gait in Parkinson’s Disease database, 93 individuals with PD and 72 healthy controls were analyzed during level-ground walking. Key biomechanical differences were identified: stride time coefficient of variation was significantly higher in PD bilaterally (left p = 0.001; right p = 0.003); swing-phase time was significantly reduced in both limbs (left p = 0.003; right p = 0.001); anterior–posterior center of pressure (COP) variability was significantly lower in PD for both limbs (p < 0.001); and COP path symmetry index was the most prominent asymmetry marker, significantly elevated in PD relative to controls (p = 0.003). A machine-learning analysis pipeline identified HistGradientBoosting as the best-performing classifier (AUC = 0.992; accuracy = 97.6%), but leave-one-study-out evaluation exposed substantial cross-protocol heterogeneity (AUC: 0.500–1.000), indicating that the model relied partly on dataset-specific patterns and may not generalize to independent acquisition protocols. Shapley Additive Explanations (SHAP) analysis showed classification was driven by a multimodal combination of clinical severity measures and biomechanical gait features rather than wearable metrics alone. A pre-specified gait-only sensitivity analysis that excluded clinical severity variables (UPDRS, UPDRSM, Hoehn and Yahr) confirmed that biomechanical features alone retained moderate, but substantially reduced, discriminative ability (gait-only holdout AUC = 0.844), supporting the interpretation that the headline performance reflects multimodal clinical separation rather than a stand-alone wearable-gait biomarker. These findings indicate that Parkinsonian gait impairment is characterized by timing instability and constrained forward COP progression. The combination of biomechanical analysis with interpretable predictive modeling represents a structured analysis pipeline for gait-based PD assessment; however, external validation in independent cohorts and prospective testing across acquisition protocols are required before such a pipeline can be deployed as a clinically generalizable digital biomarker. Full article

Rapid urbanization and industrialization have profoundly affected aquatic ecosystems in urban rivers, with phytoplankton taxa and functional group composition being particularly sensitive to environmental changes. Field surveys were conducted in the Nanming River, Guiyang, in October 2018 and July 2019, with 33 sampling sites evenly distributed across the upstream, midstream, and downstream reaches. The results revealed that: (1) The phytoplankton community comprised 6 phyla, 53 genera, and 61 species, dominated by Bacillariophyta, Chlorophyta, and Cyanobacteria. The community was classified into 20 functional groups, among which B, D, MP, P, and S1 were dominant and exhibited clear spatial heterogeneity along the longitudinal gradient. (2) Analysis of variance indicated that physicochemical parameters were the dominant factors explaining the variation in phytoplankton taxonomic and functional groups, with their independent contribution significantly higher than that of anthropogenic disturbance indicators and geographical factors. Redundancy analysis further identified NH₄-N, TP, and TN as key environmental factors. Spearman’s correlation analysis further indicated that human activities alter ambient environmental conditions, which are significantly correlated with dissolved oxygen and chlorophyll a levels, thereby driving the differentiation of phytoplankton niches. (3) Functional group succession followed a distinct spatial pattern: upstream areas were dominated by groups P, SN, and Y, reflecting agricultural non-point source inputs; midstream areas were dominated by groups W1, H1, and S1, characteristic of urban complex pollution; and downstream areas were dominated by groups C and X1, indicating cumulative nutrient loading. Collectively, this study elucidates the driving mechanisms of phytoplankton dynamics in karst urban rivers and provides a scientific foundation for water quality monitoring, eutrophication risk pre-warning, and aquatic ecological restoration. Full article

DoS and DDoS attacks remain a major threat to service availability in modern IP and IoT networks, yet many learning-based detectors depend on dataset-specific flow exports, feature tables, or preprocessing conventions. This article presents a unified sequence-level detection pipeline designed to process heterogeneous public datasets through the same representation. Raw PCAP/PCAPNG traces from CIC-IDS-2017, CIC-DDoS-2019, and CICIoT2023 are converted into one-second aggregates per destination host using header-only features derived from IP, TCP, UDP, and ICMP metadata, source diversity, and packet timing. Dataset-specific annotations are used only to assign binary DoS/DDoS labels to this common representation. The resulting time-ordered aggregates are grouped into fixed-length temporal windows and classified by a compact transformer encoder, TemporalDosTransformer, which produces a window-level attack probability. The study focuses on whether a clean PCAP-based aggregation and labelling flow can support consistent DoS/DDoS detection across multiple datasets without payload inspection, flow-exporter dependence, or dataset-specific feature engineering. Full article