Browse Articles

Pathways to effective network governance: A fuzzy-set QCA study of tripartite collaboration efficiency with Chinese official Weibo data

In response to the insufficient research on multiagent collaboration mechanisms in existing network governance studies, this paper identifies the key influencing factors of the tripartite collaboration efficiency using the Grey Relational Analysis (GRA) method, and employs Fuzzy-set Qualitative Comparative Analysis (fsQCA) to reveal the collaborative pathways of multiple factors. The 2022 Government Affairs Index Weibo Influence Report issued by the Chinese government is used as the primary data source, selecting five dimensions: platform support, public participation, service level, response capacity, and social influence. Grey Relational Analysis (GRA) is then applied to verify the correlation between these five dimensions and the tripartite collaboration efficiency in government-platform-public interaction. The study finds that: (1) A positive social influence is a necessary condition for achieving high efficiency tripartite collaboration. A lack of interaction with netizens will result in low efficiency in government management of online public opinion. (2) If the government maintains proactive response capabilities, engages in high interaction with stakeholders such as netizens and the media, and improves the service level of both software and hardware in cyberspace, this represents the optimal combination pathway for enhancing tripartite collaboration efficiency. (3) If the government fails to focus on public satisfaction and social influence, even if local governments increase investments in software and hardware and improve service levels, effective management outcomes will not be achieved. This study’s innovation lies in the combined use of GRA and fsQCA to objectively identify the pathways for improving government-platform-public collaboration, providing scientific evidence for enhancing the efficiency of collaborative governance in online public opinion.

Obesity and weight change during eight years in relation to asthma incidence

Abstract Obesity has been associated with increased prevalence of asthma in cross-sectional studies. We aim to examine the relation of obesity and weight change to 8-year asthma incidence in a randomly selected population-based adult cohort. West Sweden asthma study survey was performed in 2008 (18,087 participated) and follow-up in 2016 (12,449 participated). Participants responded to a questionnaire on these two occasions. Obesity was defined as body mass index (BMI) ≥ 30 (kg/m2), and overweight as 25 ≤ BMI < 30 (kg/m2). Asthma was defined with affirmative answer to the question “Have you been diagnosed with asthma by a physician?“. Asthma incidence was reported per 1000 person-year over the 8-year follow-up period. Among participants without asthma (N = 10,769) in 2008, asthma incidence was 1.8 per 1000 person-year. Asthma incidence was higher in 40–60 years old, normal, or overweight participants who gained ≥ 4 BMI (kg/m2), or in those who were obese with a stable BMI. Asthma incidence sharply increased in those who gained ≥ 4 BMI (kg/m2), especially in women. Additionally, the risk of asthma was significantly higher in a dose-dependent manner among those who gained ≥ 0.5 BMI (kg/m2) compared to those with stable, normal weight. The risk of asthma was higher in those who were obese at baseline regardless of their weight change than individuals with stable, normal weight. Our findings highlight the necessity for health care to integrate weight management strategies as a key component of asthma prevention and care, particularly for women in middle age who are overweight or obese.

Utilization of varenicline among Kansas Medicaid enrollees

Background Smoking prevalence among Medicaid beneficiaries is twice that of privately insured adults, yet effective smoking cessation treatments remain underutilized. Understanding varenicline use, the most effective smoking cessation medication, can inform efforts to improve tobacco treatment for Medicaid enrollees. Objectives To describe varenicline utilization among Kansas Medicaid (KanCare) enrollees from 2014–2021, including changes related to the FDA’s December 2016 removal of the black box warning, and explore changes and demographic differences in its use. Research design This retrospective cohort study analyzed pharmacy claims data for varenicline utilization from January 1, 2014, to December 31, 2021. Tobacco use was estimated from the KanCare External Quality Review survey. Subjects The sample included 78,295 new adult enrollees (age 18+) with at least 11 months of continuous coverage. Measures The primary outcome was varenicline utilization, defined as any pharmacy claim for the medication. Secondary outcomes included time to first prescription, completion of the 12-week treatment, and demographic differences in use. Results With an estimated 30% smoking prevalence, only 12.7% of KanCare smokers received varenicline, and just 1.6% completed a full course of treatment. Among 78,295 new enrollees, 2,980 (3.8%) had one or more claims for varenicline, with the mean time from enrollment to first prescription being 1.5 years (SD 1.3). Utilization was higher among males (4.8%) than females (3.4%) and varied by race/ethnicity (Whites: 4.4%, Blacks: 2.7%, American Indians: 2.5%, Hispanics: 1.6%, p<0.0001). Among those who initiated treatment, only 13.5% (N=380) completed the recommended 12-week regimen., Conclusions Despite the high smoking prevalence among KanCare beneficiaries, varenicline is underutilized, with significant demographic disparities. Men and non-Hispanic Whites were more likely to receive varenicline than women and other racial and ethnic groups, despite comparable or higher smoking rates among other subgroups. Medicaid programs must intensify efforts to provide equitable and effective tobacco treatment.

3D point cloud lithology identification based on stratigraphically constrained continuous clustering

Abstract Three-dimensional laser scanning provides high-precision spatial data for automated lithology identification in geological outcrops. However, existing methods exhibit limited performance in transition zones with blurred boundaries and demonstrate reduced classification accuracy under complex stratigraphic conditions. This study proposes a Stratigraphically Constrained Continuous Clustering (SCCC) framework to address these limitations. The framework incorporates sedimentological principles of lateral continuity through a dynamic density-threshold hierarchical clustering algorithm that optimizes lithological unit boundaries using adjacency-based cluster merging criteria. A patch-level feature aggregation module, integrated within the proposed SCCC framework, constructs a multimodal feature space by aggregating geometric covariance matrices and spectral distribution entropy into compact patch-level feature vectors. Random forest classifier subsequently performs lithology discrimination. Experimental validation using the Qingshuihe Formation outcrop dataset demonstrates that SCCC achieves overall accuracy of 94.64%, F1-score of 94.58%, and mean intersection over union of 90.87%. These results surpass traditional machine learning (SVM, XGBoost) and deep learning methods (PointNet) by 26.22–68.36%, indicating substantial improvements in classification accuracy and boundary delineation within transition zones. SCCC particularly enhances recognition capabilities for sandstone-mudstone thin interbeds and conglomerate-sandstone transitional zones. Ablation experiments confirm that stratigraphic constraints effectively suppress noise while improving computational efficiency, reducing memory usage by 83.3% and processing time by 85.7%. This method provides a high-precision, interpretable technical pathway for intelligent geological exploration through deep integration of geological principles with computational models.

Oral microbiome dysbiosis in acute ischemic stroke and transient ischemic attack patients

Oral microbiome (bacterial community) may influence systemic inflammation and vascular health, which both are critical factors in a pathogenesis of ischemic stroke. This study aimed to evaluate differences in the saliva microbiome of acute ischemic stroke (AIS) and transient ischemic attack (TIA) patients compared with matched healthy controls, hypothesizing that AIS and TIA patients are associated with oral microbiome shift. A prospective case-control study was conducted in Naresuan University Hospital, Thailand, to compare the saliva microbiome of AIS and TIA stroke patients of Thai ethnic with matched healthy controls. Microbial profiles were analyzed by metagenomics combined 16S rRNA gene sequencing to assess microbial alpha diversity, taxonomic composition, beta diversity, and microbial functional pathways.Forty-one patients (31 AIS and 10 TIA) and 20 age- and sex-matched stroke-free healthy controls were included in this study. Baseline characteristics were comparable between groups, apart from higher rates of hypertension, diabetes, and smoking in the patient group. Patients exhibited significantly higher alpha-diversity genus richness by OTUs and Chao1 index than controls (p < 0.001), highlighting an altered microbial community structure. Phylum-level analysis revealed an increased abundance of Bacillota (p = 0.0285) in the patient group, with a statistically decreasing trend for Bacteroidota, Actinomycetota and Pseudomonadota (p < 0.05). At the genus level, Streptococcus was more significantly abundant in the patients (p = 0.0171), while Prevotella was reduced. The patient and control groups were statistically separated in beta-diversity analysis (PERMANOVA, p < 0.001), with species biomarker analysis by LEfSe (Linear discriminant analysis effect size) could suggest species markers for each group. Functional pathway analysis showed the patient group the significantly higher in functional categories of, for examples, xenobiotics biodegradation and metabolism, cardiovascular diseases, signal transduction, and membrane transport (Welch’s t-test, p < 0.05). In conclusion, this study demonstrated the statistical alterations in the saliva microbiome of AIS and TIA patients, characterized by increased genus richness diversity and relatively distinct microbial shifts that may be associated with stroke-related inflammation. The findings suggest the saliva microbiome analysis as potential as a non-invasive biomarker for stroke risk and its role in stroke pathophysiology.

Present-day land subsidence risk in the metropolitan cities of Italy

Abstract Land subsidence affects many world metropolises, impacting their infrastructure and population. This work showcases an innovative methodology for exposure-vulnerability rating, hazard quantification and risk assessment that integrates remotely sensed information on ground displacement, land cover and settlement characteristics. Land subsidence-induced deformation and structural stress are quantified within the 15 metropolitan cities of Italy, along with the amount of residential/non-residential infrastructure and population exposed. A total of 1.44 out of 2665 km2 urbanised land within the 15 cities is at high risk due to significant angular distortions (and, sometimes, additive threat from horizontal strain) affecting very high exposure-vulnerability infrastructure; for more than 2700 buildings there is high likelihood of already occurred/incipient structural damage. This reference knowledge-base on present-day subsidence-induced risk can inform land and risk management at national scale, and provides a baseline for future assessments to build upon with a look to the next decades and sustainable urban development.

Comparison between random and convenience samples in a multicenter survey to evaluate medical students’ quality of life

Evaluating medical students’ mental and physical health is challenged by difficulties in obtaining randomized samples and the limitations of convenience samples. We conducted a multicenter study to assess the educational environment, quality of life, and emotional competence of medical students. Between 2011 and 2012, a total of 1,350 randomly selected students from 22 schools and 1,201 volunteer students from 50 schools across Brazil completed all questionnaires (WHOQOL-BREF, VERAS-Q, IRI, RS-14, BDI, PSQI, ESS, IDATE, MBI, and DREEM). Monitoring, support for local researchers, and personalized feedback strategies were applied to ensure the participation of randomized students, achieving a response rate of 81.8%. The platform was also available to volunteers. The statistical analysis examined the effect of these two recruitment strategies using general linear models controlling for sex, age, body mass, course year, physical activity and metabolic equivalents, school type, city population, and location. A significance level of 5% and effect sizes estimated by Cohen’s eta-squared were applied to the variables of interest, both using the Bonferroni correction. The volunteer group had more women, fewer students from the final course years, and a larger number of students from private schools and larger cities. These variables largely explained the statistically significant differences and effect sizes observed between randomized and volunteer groups. In conclusion, although some valuable lessons and motivational strategies were identified, the considerable effort required to achieve high adherence through active outreach may not be justified, as results between random and volunteer samples showed minimal differences in questionnaire responses. Our findings suggest that future studies should consider lighter motivational strategies to reduce the burden on research teams. Data and R scripts to replicate the statistical analysis are available in Harvard Dataverse at https://doi.org/10.7910/DVN/YECV8E.

Concentrations of ciprofloxacin in food defined as safe alter the gut microbiome and ciprofloxacin susceptibility in humans: an interventional clinical study

Air transportation carbon dioxide emission forecasting: An improved back propagation neural network

To address the challenges of increasing carbon dioxide (CO2) emissions and climate change caused by the growth of air traffic, accurate prediction of CO2 emissions in civil aviation has become crucial. This study proposes a CO2 emission prediction method based on an improved back propagation (BP) neural network, where the Improved Sparrow Search Algorithm (ISSA) is employed to optimize the hyperparameters of the BP neural network, thereby enhancing the prediction capability for CO2 emissions in civil aviation. To overcome the limitations of the traditional SSA, such as the tendency to fall into local optima during population initialization and the search process, this paper introduces Tent mapping for population initialization and incorporates adaptive t-distribution-based perturbation for individual position updates during the mutation operation, aiming to improve the algorithm’s global search ability and convergence performance. Subsequently, the ISSA algorithm is applied to optimize the weights and biases of the BP neural network, further constructing an ISSA-BP neural network-based prediction model for civil aviation CO2 emissions. Experimental results demonstrate that the improved BP neural network outperforms other comparative models in terms of prediction accuracy and error control, enabling accurate prediction of civil aviation CO2 emissions. This research provides a solid theoretical foundation for formulating precise energy-saving and emission-reduction strategies in civil aviation.

The 1975 discovery of long-lost letters between Albert Einstein and the astronomer Willem de Sitter

Rapid reagent free COVID19 detection using MEMS based FTIR spectroscopy and machine learning in NIR and MIR regions

Abstract This study presents rapid, reagent-free detection of COVID-19 using miniaturized MEMS-based Fourier-transform infrared (FTIR) spectrometers integrated with machine learning models. Two portable spectrometers analyze 363 nasopharyngeal swab samples stored in viral transport medium (VTM). The first spectrometer covers the near-infrared (NIR) region (1.3–2.6 μm) and second spectrometer extends from the near infrared to the mid infrared (MIR) region (1.75–4.0 μm). The NIR system uses a transmission configuration, while the one extended to the MIR performs attenuated total reflectance (ATR) measurements on both wet and dried samples. Spectral data undergo preprocessing and analysis using interval partial least squares discriminant analysis (iPLS-DA), with model training and evaluation conducted through Monte Carlo cross-validation. The MIR wet sample model achieves a diagnostic performance with 79% accuracy, a 98% sensitivity, and an area under the curve (AUC) of 0.8. The MIR dry sample model achieves an 80% accuracy and an AUC of 0.79, while the NIR model reaches 66% accuracy and an AUC of 0.64. Spectral features appear in the Amide A and B regions in the MIR range, and in the C–H overtone bands in the NIR range. The full measurement process, including sample handling, completes in under six minutes, supporting its suitability for real-time, point-of-care (POC) testing.

Dysglycemia and the airway microbiome in cystic fibrosis

Background Cystic fibrosis-related diabetes (CFRD) is one of the most common non-pulmonary complications in people living with cystic fibrosis (pwCF), seen in up to 50% of adults. Even when correcting for severity of CFTR mutations, those with CFRD have more pulmonary exacerbations, lower lung function, and increased mortality than those with normal glucose tolerance (NGT). Methods Expectorated sputum samples were collected from 63 pwCF during routine outpatient visits (29 with CFRD, 12 with IGT and 22 with NGT). Oral glucose tolerance test results, A1c levels, and pulmonary function tests closest to the time of sputum collection were obtained from the medical record. Samples underwent metagenomics sequencing and raw reads were processed through the bioBakery workflow for taxonomic profiling at the species level as well as predicted functional profiling and antibiotic resistance profiling. Viral profiling was performed with Marker-MAGu. Differences in alpha diversity, beta diversity, and differential abundance were assessed. Microbiome and phage signatures of CFRD were generated using sparse partial least squares models which were subsequently used as a primary predictor of lung function using multivariate linear regression. Results In linear models, CFRD status compared to NGT was associated with a lower alpha diversity (reciprocal Simpson −1.98 [−3.80,-0.16], p = 0.033) and differences in microbial community composition (Bray Curtis dissimilarity PERMANOVA R2 0.17, p = 0.011). Pseudomonas aeruginosa and Streptococcus gordonii had higher relative abundance in CRFD vs NGT participants (2.43 [0.027, 4.82], unadjusted p = 0.056 and 1.11 [0.58, 1.64] unadjusted p= < .001 respectively). There were global differences between CFRD vs NGT in both functional pathways and antibiotic resistance genes. In multivariate models adjusting for age, sex, antibiotic use, and modulator therapies, virome but not microbiome signatures of CFRD were associated with lower FEV1 percent predicted (−6.4 [95% CI −10.2, −2.6]%, p = 0.001 for each 10% increase in virome score). Conclusion Differences in the airway microbiome in those with dysglycemia in CF are associated with poorer lung function.

Daily briefing: Physics Nobel for quantum tunnelling on a macroscopic scale

A short photoperiod alters brain metabolism and cold resistance in Drosophila melanogaster

Abstract To survive, animals need to prepare for winter in advance, and this process begins in the brain in response to the shortening of the photoperiod in fall. Here, we demonstrate that exposing adult flies for just 14 days to a short photoperiod at a constant temperature of 20 °C increases their cold resistance and dramatically alters brain metabolism. Such flies have significantly lower levels of monosaccharides, and a lower ATP/AMP ratio in their brains than flies exposed to a long photoperiod, despite being less active and eating more. The levels of storage and structural lipids (triacylglycerols and phospholipids) as well as the number of lipid droplets in the brain increase, suggesting the utilization of glucose for the synthesis of lipids via the citrate shuttle. In addition, during short days, the ratio between the reduced and oxidized forms of glutathione increase, as do detoxification processes and autophagy. This suggests that the brain of short-term flies is less sensitive to oxidative stress and neurodegeneration, which is essential for survival throughout the winter. Overall, our results show that exposure to a short photoperiod has significant metabolic and physiological consequences in the fly brain that serve to prepare for the coming winter.

A novel TRPV5/6-like channel from a scleractinian coral

The calcium regulation mechanisms that underlie skeleton formation in stony corals are poorly understood. In epithelial tissues from vertebrates, transient receptor potential vanilloids 5 and 6 (TRPV5 and TRPV6), members of the TRP channel superfamily, play a significant role in transepithelial Ca 2+ transport. Particularly, TRPV5 is a constitutively active channel with a primary function in the Ca 2+ reabsorption mechanism of renal epithelium. It is characterized by a marked inward rectification and a high Ca 2+ permeability at physiological resting membrane potentials. Here, we report the cloning and characterization of a gene that encodes a protein homologous to the inward-rectifier cation channel TRPV5 in the reef-building coral Pocillopora damicornis . We assessed its biophysical properties and found that this channel displays inwardly rectifying Na + currents in the absence of divalent cations and can permeate Ca 2+ , similar to the human TRPV5 channel. When compared to the human TRPV5, the specific blocker of this channel, miconazole, decreased the currents in a dose-dependent manner but did not affect the coral TRPV5/6-like-mediated currents. Interestingly, a monoterpene that has been shown to produce bleaching in corals, is also a blocker of the TRPV5/6-like channel. Altogether, our findings identify for the first time a novel TRPV5/6-like channel in scleractinian corals, whose potential physiological functions may include Ca 2+ transport to support the calcification mechanism.

Hybrid deep learning for smart paddy disease diagnosis using self supervised hierarchical reconstruction and attention based temporal analysis

Abstract Accurate and early disease detection in paddy crops is essential for maximizing crop yield which ensures food security. Traditional methods are often labor-intensive, time-consuming, and domain-specific expertise. Feed-forward deep-learning models will perform accurate disease detection through the identification of spatial patterns. However, they cannot predict the diseases at the early stages due to the lack of temporal information. Temporal observations will help perform continuous monitoring and detect minute changes in the crops at the early times. To tackle this problem, we proposed Self-Supervised Deep Hierarchical Reconstruction (SSDHR), and Long Short-Term Memory (LSTM) which perform early disease detection based on the spatial and temporal data respectively. The SSDHR network uses multi-branch convolution kernels to extract distinct discriminative characteristics rather than conventional leaf-based indicators. It incorporates spatial, and temporal-based attention mechanism Symmetric Fusion Attention (SFA) to improve feature selection and XGBoost (XGB) classifier for better stability. According to experimental findings, the suggested framework achieves a 99.25% accuracy rate in identifying and classifying 13 paddy classes, including normal, blast, hispa, tungro, white stem borer, brown spot, leaf roller, downy mildew, yellow stem borer, bacterial leaf blight, bacterial leaf streak, black stem borer, and bacterial panicle blight.

Multi-step ahead streamflow and uncertainty forecasting using a HyMoLAP rainfall-runoff model-based framework integrated with Bayesian neural networks in the Ouémé river basin, Benin

Multi-step forecasting is crucial for capturing future streamflow variations and managing water resources but remains challenging due to limited accuracy of upstream flow forecasts and meteorological predictions over lead times. While data-driven methods are commonly used, this study extends the Hydrological Model based on the Least Action Principle (HyMoLAP) from daily rainfall-runoff simulation to multi-day-ahead streamflow predictions. Additionally, it integrates Bayesian Long Short-Term Memory (Bayesian LSTM), primarily to enable uncertainty quantification (UQ). Applied to the Bonou and Savè sub-catchments of the Ouémé River Basin, Benin, the HyMoLAP-based framework yields NSE values ranging from 0.997 to 0.921 at Bonou and from 0.970 to 0.799 at Savè, showing slightly higher performance than the LSTM model overall, except at Savè from the 3-day lead time onward where it becomes slightly lower, with a more pronounced difference at the 7-day horizon. Our UQ approach provides reliable prediction intervals, with a coverage probability around 90%, as nearly 90% of the observed data fall within the 90% credible intervals in both sub-catchments.

Late Cenozoic river reorganization related to tectonic extrusion formed the modern drainage system in southeastern Tibet

The formation and organization of southeastern Tibetan river systems reflect feedbacks between tectonics, climate, and landscape evolution. However, our understanding of these interactions remains unclear. We approach this issue from the view that the river incision histories and their bedload provenance provide complementary evidence for reconstructing the evolution of the ancient river system. Zircon and apatite (U-Th)/He data from the middle Jinsha (upper Yangtze) River gorge reveal two episodes of accelerated river incision at 18-17 Ma and 8-7 Ma. The onset of the former incision event, coeval with other incision events along the upper-middle Jinsha and Lancang Rivers, implies their hydrological connection and formation of a south-flowing river system. Additionally, detrital geochronology and thermochronology data of late Oligocene–Miocene fluvial-alluvial sediments along the Red River valley provide constraints on their ages and provenance, suggesting emergence of a throughgoing paleo-Red River in the early-middle Miocene. This ancient Red River was disconnected from the upper Jinsha River but connected with Yalong River (upper Yangtze tributary), forming another south-flowing river system. Landscape evolution modelling supports the view that these south-flowing rivers were successively captured by the westward-propagating Yangtze River in the middle-late Miocene, giving birth to the modern drainages of southeastern Tibet. The resultant integration of the upper-middle and lower Jinsha River is manifested by the progressively upstream-propagating acceleration in river incision from 13-9 Ma to 6-5 Ma along the gorges. Our findings from southeastern Tibet suggest that orogen-scale topographic change, river reorganization, and a coeval increase in biodiversity were driven by extrusion tectonics.

Immersive teaching model for traditional Chinese opera costume design based on virtual reality: digital cultural heritage, inheritance, and innovation

Physiological responses to full and segmented duet routines in elite artistic swimmers

Artistic swimming combines prolonged breath-hold periods with high-intensity movements, resulting in unique physiological demands. Direct measurement of key variables such as oxygen uptake (VO₂) during routines is limited by frequent immersion. However, VO₂ monitoring is essential for understanding the balance between aerobic and anaerobic energy contributions, guiding training strategies and reducing injury risk. This study aimed to analyze the acute physiological responses, VO₂, blood lactate concentration, and heart rate, during free duet routines in elite artistic swimmers, using a segmented protocol that emphasized the two longest apneas. Sixteen elite artistic swimmers performed both complete and segmented versions of the routine. VO₂ was estimated using retro-extrapolation, while lactate was measured after each phase, and heart rate was continuously monitored. The protocol included six measurement points: pre-routine, pre- and post-apnea 1 and 2, and post-routine. VO₂ increased rapidly, reaching nearly 90% of VO₂ peak within 67 seconds (mean: 61.8 ± 15.1 mL·min ⁻ ¹·kg ⁻ ¹). Blood lactate concentration rose progressively, peaking at 5.93 ± 1.41 mmol·L ⁻ ¹. Heart rate exhibited large fluctuations, with a maximum of 203.8 ± 5.0 beats·min ⁻ ¹ and a minimum of 71.9 ± 16.6 beats·min ⁻ ¹, reflecting a bradycardic response during apneas. No significant changes were observed in VO₂ or lactate between pre- and post-apnea values, as measured around the two longest apneas within the routine. These findings suggest that, under the specific conditions of this study, short-duration apneas (< 20 s) may be insufficient on their own to elicit distinct physiological shifts. However, the progressive increases observed in blood lactate and heart rate throughout the full routine suggest that the overall physiological load may be influenced more by sustained exercise intensity and the cumulative effect of repeated apneas than by isolated breath-hold events.