Large-scale Sample Research Articles

Aridity-Driven Change in Microbial Carbon Use Efficiency and Its Linkage to Soil Carbon Storage.

Global warming is generally predicted to increase aridity in drylands, while the effects of aridity changes on microbial carbon use efficiency (CUE) and its linkage to soil organic carbon (SOC) storage remain unresolved, limiting the accuracy of soil carbon dynamic predictions under changing climates. Here, by employing large-scale soil sampling from 50 sites along an ~6000 km aridity gradient in northern China, we report a significant decreasing trend in microbial CUE (ranging from approximately 0.07 to 0.59 across the aridity gradient) with increasing aridity. The negative effect of aridity on microbial CUE was further verified by an independent moisture manipulation experiment, which revealed that CUE was lower under lower moisture levels than under higher moisture levels. Aridity-induced increases in physicochemical protection or decreases in microbial diversity primarily mediated the decrease in CUE with increasing aridity. Moreover, we found a highly positive microbial CUE-SOC relationship, and incorporating CUE improved the explanatory power of SOC variations along the aridity gradient. Our findings provide empirical evidence for aridity-induced reductions in microbial CUE over a broad geographic scale and highlight that increasing aridity may be a crucial mechanism underlying SOC loss by suppressing the ability of soil microorganisms to sequester carbon.

Simple and robust high-throughput serum proteomics workflow with low-microflow LC-MS/MS.

Clinical proteomics has substantially advanced in identifying and quantifying proteins from biofluids, such as blood, contributing to the discovery of biomarkers. The throughput and reproducibility of serum proteomics for large-scale clinical sample analyses require improvements. High-throughput analysis typically relies on automated equipment, which can be costly and has limited accessibility. In this study, we present a rapid, high-throughput workflow low-microflow LC-MS/MS method without automation. This workflow was optimized to minimize the preparation time and costs by omitting the depletion and desalting steps. The developed method was applied to data-independent acquisition (DIA) analysis of 235 samples, and it consistently yielded approximately 6000 peptides and 600 protein groups, including 33 FDA-approved biomarkers. Our results demonstrate that an 18-min DIA high-throughput workflow, assessed through intermittently collected quality control samples, ensures reproducibility and stability even with 2 µL of serum. It was successfully used to analyze serum samples from patients with diabetes having chronic kidney disease (CKD), and could identify five dysregulated proteins across various CKD stages.

Urine-based SERS and multivariate statistical analysis for identification of non-muscle-invasive bladder cancer and muscle-invasive bladder cancer.

Bladder cancer (BC) is an epidemiological urologic malignancy that continues to increase each year. Early diagnosis and prognosis monitoring is always significant in clinical practice, especially in distinguishing non-muscle-invasive bladder cancer (NMIBC) from muscle-invasive bladder cancer (MIBC), due to the various depths of tumor invasion related to different therapeutic schedules and recurrence rates. Common diagnostic approaches are too invasive or generally inefficient in accuracy and specificity. In this work, a totally non-invasive and cost-effective method is established by investigating urine samples using surface-enhanced Raman spectroscopy (SERS) and multivariate statistical analysis. The comparison of urine SERS spectra shows the intensities of characteristic peaks for DNA/RNA, hypoxanthine, albumin, D-( +)-galactosamine, fatty acids, and some amino acids are distinguishable in BC occurrence and invasion progression. A PLS-LDA-based two-step binary classification scheme is performed on urine SERS spectra and the diagnostic accuracies were 97.7% and 96.3% for healthy individuals versus BC patients and NMIBC versus MIBC patients, respectively. Moreover, the impact of urine SERS spectral lengths in reaching high-precision recognition of BC is investigated. The results show that the Raman peaks at 803, 893, 1139, 1375, and 1466cm-1 play an essential role in correctly categorizing healthy control, NMIBC, and MIBC patients, and SERS spectra ranges from 400 to 1600cm-1 are enough for this identification task. These findings provide a sensitive, label-free, rapid, and totally non-invasive way for assessment of invasion depth of BC to its early diagnosis and prognosis monitoring, as well as valuable insights for selecting reasonable spectral range to enhance the measurement efficiency especially in large-scale sample datasets.

Reference Values for Handgrip Strength, Five Times Sit-to-stand, and Gait Speed in Patients on Hemodialysis.

Low levels of muscle strength and function predict adverse clinical outcomes in patients on hemodialysis; however, reference values remain lacking. We described reference values for handgrip strength, five times sit-to-stand (STS-5), and 4-m gait speed in a large-scale sample in patients on hemodialysis. Baseline data from the SARCopenia trajectories and associations with adverse clinical outcomes in patients on HemoDialysis (SARC-HD) study were analyzed. Muscle strength was evaluated using handgrip strength and the STS-5, whereas physical performance was evaluated using usual 4-m gait speed before a midweek dialysis session. Sex- and age-specific smoothed reference curves for each test at the 3rd, 15th, 50th, 85th, and 97th percentiles were constructed using generalized additive models for location shape and scale. Comparisons between sex and age were also performed. Data from 1004 patients (39% female; 19 to 96 years; 49% ≥ 60 years) were analyzed. Declines in muscle strength and physical performance were observed with advancing age in both sexes. However, among males, muscle strength and performance were similar between 18 and 49 years of age. Males exhibited substantially greater performance in handgrip strength (10.3 kg, 95% confidence interval (CI) 9.1 to 11.4) and 4-m gait speed (-0.10 seconds, 95% CI: -0.14 to -0.05) compared to females. Older patients, independent of sex, exhibited poorer performance on most tests. Lower handgrip strength in the arm with arteriovenous fistula was observed in both sexes (males -2.3 kg, 95% CI: -2.8 to -1.7; and females -2.1 kg, 95% CI: -2.6 to -1.6). Reference values obtained in this study may be used in clinical and research settings to identify patients on hemodialysis with low physical function according to sex and age. Future studies should test these reference values as potential predictors of adverse clinical outcomes.

A magnetic SERS-imprinted sensor for the determination of cardiac troponin I based on proteolytic peptide technology

Acute myocardial infarction is a sudden and high-mortality disease that can be accurately diagnosed by measuring the level of cardiac troponin I in the blood. Currently, cTnI commonly used clinical detection methods usually have excellent sensitivity and are suitable for large-scale sample detection analysis. However, most of these methods are operated through multiple steps of fixation, incubation, reaction and separation, and most of them require professionals to operate complex instruments, which greatly limits their applicability in real-time rapid detection. Therefore, a method that requires low professional skills and can perform rapid detection is necessary. We presents an alternative strategy to quantify cTnI in clinical serum samples using a combination of SERS and magnetic molecularly imprinted polymers (MMIP). MMIPs was synthesized under Polyvinyl Pyrrolidone (PVP) conditions without vinyl modification using characteristic peptide as template, MAA and DMAm as functional monomers. The internal Raman probe 4-MBA was connected through Ag-SH bonds in MMIPs to solve the problem that the target object had no Raman characteristic peak. MMIPs with high magnetic and adsorptive properties showed characteristic absorption peaks at the Raman shift of 1582 cm−1 after specific capture of the target templates. The Raman signals of the 4-MBA were reduced due to shielding effects and the detection range of this method was 0.001–100 ng mL−1. The recoveries and relative standard deviations (RSDs) of the spiked experiments were 103.1%–106.3 % and 3.54 %–7.38 %, respectively. In summary, this work had appropriate sensitivity and specificity, and there was no significant difference in detection results after ELISA verification. It provided a flexible and practical analysis method for detecting cTnI based on SERS technology. In addition, the imprinting materials of other disease markers can be prepared by changing the template molecules, which provides a new idea for the detection of other biomarkers.

Enrichment of low-abundance osteopontin in bovine milk via reciprocating free-flow isoelectric focusing.

Osteopontin (OPN) in milk plays an important role in intestinal and brain development in early infancy, and great attention has been focused on OPN isolation to add extra OPN in infant formula. However, large-scale OPN isolation is limited by the low efficiency of sample pretreatment. Herein, we utilized preparative reciprocating free-flow isoelectric focusing (RFFIEF) to showcase the enrichment of low-abundance OPN in bovine milk, which contained an extremely high concentration of unwanted proteins. The reciprocating IEF format and the design of the multi-channel collector allowed us to enrich OPN in 1 L milk within 6h. We removed 97.5% of unwanted proteins and obtained an enrichment factor of 11. Thus, our RFFIEF method can be applied to the preparative pretreatment of the large-scale milk sample and potentially improve the efficiency of downstream OPN purification.

Cascaded capsule twin attentional dilated convolutional network for malicious URL detection

Malware is one of the most popular cyber-attacks, and it is becoming more common on the network every day. In contrast to benign transmission, which typically exhibits symmetrical patterns, malware communication often shows asymmetrical behaviours, making detection a complex challenge. Fortunately, malware can be distinguished and identified for actual activities utilizing a variety of artificial intelligence methods. However, insufficient work has been allocated to the problem of handling high-dimensional and huge data. This paper proposes a novel deep learning-based approach to identify malicious Uniform Resource Locators (URLs) specifically designed to handle the challenges posed by large-scale and complex data. Initially, input data is sourced from a comprehensive Kaggle dataset, which includes diverse and large-scale URL samples. The URLs are then transformed into vector representations using a Vector Embedding Module, which employs a character-level word embedding technique to capture intricate patterns within the URLs. To further refine the data, the Chaotic Kookaburra Efficient-Bo Network (CKEBO-Net) is applied to extract the most significant features from these vectors, effectively reducing the dimensionality and computational burden. Subsequently, the Cascaded Capsule Twin Attentional Dilated Convolutional Network (C2TA_DiCN) model is introduced to classify and identify malicious URLs with high precision. This model leverages the unique strengths of capsule networks and attentional mechanisms, enhancing its capability to capture subtle dependencies within the data. Furthermore, the Lyrebird Meta-heuristic Optimization (LMO) algorithm is used to fine-tune the model parameters appropriately, ensuring that the training process is efficient and robust. The proposed approach is implemented using Python and rigorously evaluated on the Kaggle dataset. Simulation results demonstrate that the proposed method significantly outperforms existing models, achieving a malicious URL detection accuracy of 99.7%.

Relationships between Frailty and the Risk of Glaucoma in Middle-aged and Older Adults

PurposeIncreased frailty in older individuals increases health risks, but its relationship with glaucoma, the leading cause of irreversible blindness in middle-aged and older adults, is unclear. We investigated the association between frailty and glaucoma in a large-scale representative sample and explored possible causal relationships. DesignCombined cross-sectional and Mendelian randomization (MR) study. ParticipantsIn the cross-sectional analysis, we included 5744 participants of the US National Health and Nutrition Examination Surveys 2005–2008 aged ≥40. For the MR analysis, frailty genome-wide association study (GWAS) data were sourced from a UK Biobank and TwinGen meta-analysis, and GWAS data on glaucoma subtypes were derived from FinnGen. MethodsAccording to the 49-item frailty index, we classified participants into nonfrail (≤0.10), prefrail (0.10−0.21), and frail (>0.21) groups. Using survey-weighted logistic regression models adjusted for multiple covariates, we explored the association between frailty and glaucoma. We further assessed causation using MR. Main Outcome MeasuresThe associations between different levels of frailty (nonfrail, prefrail, and frail) and glaucoma, as well as causal relationships between genetically predicted frailty and various subtypes of glaucoma (primary open-angle glaucoma, primary angle-closure glaucoma, normotensive glaucoma, exfoliation glaucoma, and suspected glaucoma). ResultsAfter adjusting for covariates, higher frailty levels were significantly associated with glaucoma in frail individuals (odds ratio [OR]=1.83, 95% confidence interval [CI]=1.05–3.19, P=0.036) but not prefrail (OR=1.90, 95% CI=0.99–3.64, P=0.052). The association was significantly stronger among male participants (P interaction=0.042). The variation in the association between frailty and glaucoma did not reach statistical significance across age groups (P interaction=0.575) or race groups (P interaction=0.092). MR revealed that genetically predicted frailty was linked to greater risks for primary open-angle glaucoma (OR=1.67, 95% CI=1.24–2.25, P=0.001), primary angle-closure glaucoma (OR=2.78, 95% CI=1.48–5.20, P=0.001), exfoliation glaucoma (OR=1.70, 95% CI=1.18–2.43, P=0.004), and suspected glaucoma (OR=1.74, 95% CI=1.30–2.34, P<0.001), but not for normotensive glaucoma (OR=1.01, 95% CI=0.61–1.68, P=0.956). ConclusionsOur study revealed an association between frailty and increased glaucoma risk and emphasized the significance of glaucoma screening in frail individuals. Targeted healthcare strategies can help prevent or delay irreversible blindness among middle-aged and older adults.

Pre-clinical training, technical adjustment and human case experience of transoral robotic surgery using Versius System

ObjectiveThis study describes the training experience with the Versius Robotic System (CMR Surgical) for Transoral Robotic Surgery (TORS), the setup of the robot and its applicability in a human case. MethodsA model mannequin with anatomical characteristics simulating the TORS working space was used for training purposes. Optimal docking, positioning of robotic arms and placement of instruments were defined using the mannequin. A human patient was selected based on the inclusion criteria, and a standardized protocol for robotic techniques was developed. The surgical procedure was conducted in accordance with ethical guidelines. ResultsThe synthetic polymer mannequin mimicking human tissues and anatomy was used to define the robot set up for TORS. The patient was indicated for bilateral tonsillectomy due to recurrent episodes of tonsillitis. Bipolar Maryland and monopolar scissors were used. The procedure was uneventful. The patient remained hospitalized for 1 day and returned to work activities within 6-days, with no complications. ConclusionThe specialized mannequin proved to be effective in the simulation, training proposal and set up standardization. Furthermore, the Versius robot was found to be safe and achieved excellent results during surgery. Despite being very promising, the Versius system is still in its developmental phase and requires larger-scale samples for a more comprehensive evaluation. Level of evidence4.

284 (PB272): Enhancing Biobank Quality Control: An NGS-QC-PANEL as a Superior Alternative to STR Assays for Large-Scale Sample Authentication

284 (PB272): Enhancing Biobank Quality Control: An NGS-QC-PANEL as a Superior Alternative to STR Assays for Large-Scale Sample Authentication

Reproducible Sex Differences in Personalized Functional Network Topography in Youth.

A key step towards understanding psychiatric disorders that disproportionately impact female mental health is delineating the emergence of sex-specific patterns of brain organization at the critical transition from childhood to adolescence. Prior work suggests that individual differences in the spatial organization of functional brain networks across the cortex are associated with psychopathology and differ systematically by sex. We aimed to evaluate the impact of sex on the spatial organization of person-specific functional brain networks. We leveraged person-specific atlases of functional brain networks defined using nonnegative matrix factorization in a sample of n = 6437 youths from the Adolescent Brain Cognitive Development Study. Across independent discovery and replication samples, we used generalized additive models to uncover associations between sex and the spatial layout ("topography") of personalized functional networks (PFNs). Next, we trained support vector machines to classify participants' sex from multivariate patterns of PFN topography. Finally, we leveraged transcriptomic data from the Allen Human Brain Atlas to evaluate spatial correlations between sex differences in PFN topography and gene expression. Sex differences in PFN topography were greatest in association networks including the fronto-parietal, ventral attention, and default mode networks. Machine learning models trained on participants' PFNs were able to classify participant sex with high accuracy. Brain regions with the greatest sex differences in PFN topography were enriched in expression of X-linked genes as well as genes expressed in astrocytes and excitatory neurons. Sex differences in PFN topography are robust, replicate across large-scale samples of youth, and are associated with expression patterns of X-linked genes. These results suggest a potential contributor to the female-biased risk in depressive and anxiety disorders that emerge at the transition from childhood to adolescence.

Highly resilient aerogel/sponge nested structures assisted multimodal tactile sensory system for robotic embodied perception

Large language models (LLMs) exhibit outstanding reasoning abilities when robots perform tasks, but are limited to the passively receiving information and lack multi-feature perception of realistic objects. Humans obtain complementary and coupled information from interactions with objects through the skin. Here, a multimodal tactile sensory system is reported to assist the LLM-equipped robots in actively perceiving and understanding multiple features of objects including material, compliance and thermal insulation. A multi-parametric tactile sensing array, where each sensor in the array comprises of a triboelectric unit, a decoupling pressure-temperature dual-mode sensing unit and a heating unit, is constructed to acquire the multiparametric tactile signals. The pressure-temperature sensing unit is fabricated from the developed aerogel/sponge nested composite structures, which exhibit stable compression resilience under the compression strain of over 90 %, impressive fatigue resistance with stable responses after 10,000 compression cycles, and linear thermoelectric responses over wide temperature gradients. The multi-parametric tactile signals are analyzed using Mamba time series network model, resulting in the high-precision clustering of multiple features (up to 98.3 %). Supported by a multimodal interactive perception framework, the LLM realizes multi-feature understanding of objects. With large-scale sample datasets and multi-task training, the LLM-equipped robots powered by the multimodal tactile sensory system are expected to realize embodied intelligence.

Retain in the City, Return Flow, or Blind Direction: A Study on the Differentiation Mechanism of Migrant Workers’ Migration Willingness under the Background of China’s Strategy for Integrated Urban–Rural Development

Against the backdrop of urban–rural integration development in China, the government has further strengthened its support for rural migrant workers’ urban employment and entrepreneurship, as well as their urbanization. Nevertheless, influenced by the current urban–rural development environment and public policies, a portion of rural migrant workers have evolved into two distinct groups: those with clear intentions to settle in cities or return to their hometowns and those trapped in a “neither-nor” situation, who are unable to fully integrate into urban life or seamlessly return to the countryside. This study, based on field investigations conducted in 21 cities across seven major geographical regions in China, reveals a ternary differentiation in migration intentions among rural migrant workers: “Retain in the cities”, “Return flow”, and “Blind direction”. In advancing the urban–rural integration strategy, the “clearly-oriented” groups, choosing to stay or return, can serve as dynamic forces in reconciling resources between urban and rural areas, thereby fostering closer urban–rural ties. Conversely, the “aimless wandering” group, characterized by stagnation, confusion, frequent mobility, and recurrent unemployment, poses numerous negative impacts on urban–rural interaction and coordination, hindering the in-depth integration of urban and rural areas to some extent. Drawing upon the internationally recognized Push-Pull Theory and Social Integration Theory within the field of population migration, this study conducts an empirical analysis of large-scale sample data to explore the characteristic factors and formation mechanisms underlying the “staying”, “returning”, and “blind direction” intentions, with a particular focus on the internal dynamics shaping and evolving the “aimless wandering” group. Finally, grounded in the empirical findings, this study advocates a collaborative approach between the Chinese government and various sectors of society to address the issue by promoting employment guidance, enhancing security measures, and other initiatives that encourage rural migrant workers to either stay in cities or return to their hometowns. It aims to provide policy recommendations for a definitive resolution of the rural migrant worker issue during the crucial period of urban–rural integration development.

A hierarchical reinforcement learning model explains individual differences in attentional set shifting

Attentional set shifting refers to the ease with which the focus of attention is directed and switched. Cognitive tasks, such as the widely used CANTAB IED, reveal great variation in set shifting ability in the general population, with notable impairments in those with psychiatric diagnoses. The attentional and learning processes underlying this cognitive ability and how they lead to the observed variation remain unknown. To directly test this, we used a modelling approach on two independent large-scale online general-population samples performing CANTAB IED, with one including additional psychiatric symptom assessment. We found a hierarchical model that learnt both feature values and dimension attention best explained the data and that compulsive symptoms were associated with slower learning and higher attentional bias to the first relevant stimulus dimension. These data showcase a new methodology to analyse data from the CANTAB IED task, as well as suggest a possible mechanistic explanation for the variation in set shifting performance, and its relationship to compulsive symptoms.

Building a framework to drive government systems' adoption of cloud computing through IT knowledge

abstractMany governments in developing countries are finding it challenging to integrate cloud computing into their e-government systems because of the limited effective identification and evaluation of important factors. Cloud computing usage in government organizations is still restricted due to concerns over security, regulatory challenges, cultural resistance to new technologies, infrastructure limitations, and other factors (Abied et al. in Sustainability 14:15590, 2022). This research suggests a model that analyses the factors influencing the adoption of cloud computing in e-government systems within Jordanian government organizations by decision-makers. The survey was distributed online to a purposive sample consisting of 373 IT professionals and top-level managers in government organizations in Jordan. The study demonstrates that the technological factors which include complexity, compatibility, relative advantage, and security, the organisational factors which consist of top management support and cost, and the environmental factors which include industry competition and government rules and regulations, had a significant and positive impact on the adoption of cloud-based services in government systems. Furthermore, IT knowledge held by decision makers was recognized as a key factor in the adoption of cloud computing for e-government systems. This research is anticipated to provide significant insights for any organizations making investment decisions about the use of cloud-based services. This research considers the assessment of multiple aspects to encourage top management to analyse the consequences of implementing cloud services for e-government systems. This case study might not be applied broadly to all types of organizations in Jordan. Future research should employ large-scale samples to ensure the study's representativeness and could also broaden the scope by including samples from similar countries to validate the model or its implementation across different cultures, IT infrastructure and geographical settings. In addition, it is recommended that upcoming research consider using the level of experience and education as moderators.

Cryo-PRO facilitates whole blood cryopreservation for single-cell RNA sequencing of immune cells from clinical samples.

Single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) has enhanced our understanding of host immune mechanisms in small cohorts, particularly in diseases with complex and heterogeneous immune responses such as sepsis. However, PBMC isolation from blood requires technical expertise, training, and approximately two hours of onsite processing using Ficoll density gradient separation ('Ficoll') for scRNA-seq compatibility, precluding large-scale sample collection at most clinical sites. To minimize onsite processing, we developed Cryo-PRO (Cryopreservation with PBMC Recovery Offsite), a method of PBMC isolation from cryopreserved whole blood that allows immediate onsite sample cryopreservation and subsequent PBMC isolation in a central laboratory prior to sequencing. We compared scRNA-seq results from samples processed using Cryo-PRO versus standard onsite Ficoll separation in 23 patients with sepsis. Critical scRNA-seq outputs including cell substate fractions and marker genes were similar for each method across multiple cell types and substates, including an important monocyte substate enriched in patients with sepsis (Pearson correlation 0.78, p<0.001; 70% of top marker genes shared). Cryo-PRO reduced onsite sample processing time from >2 hours to <15 minutes and was reproducible across two enrollment sites, thus demonstrating potential for expanding scRNA-seq in multicenter studies of sepsis and other diseases.

Large-Scale Sampling Reveals the Strain-Level Diversity of Burkholderia Symbionts in Riptortus pedestris and R. linearis (Hemiptera: Alydidae).

Burkholderia (sensu lato) is a diverse group of β-Proteobacteria that exists worldwide in various environments. The SBE clade of this group was thought to be mutualistic with stinkbugs. Riptortus-Burkholderia was suggested as an ideal model system for studying insect-microbe symbiosis. To explore the strain-level diversity of Burkholderia at the individual and population levels of Riptortus stinkbugs (Hemiptera: Alydidae), and to uncover the factors affecting the Burkholderia community, large-scale sampling of two Riptortus species and deep sequencing data (16S amplicon) were used in the present study. Our results showed that: (1) the proportions of facultative symbiotic bacteria Burkholderia were very high, with an average proportion of 87.1% in the samples; (2) only six out of 1373 Burkholderia amplicon sequence variants (ASVs) did not belong to the SBE clade, accounting for only 0.03% of Burkholderia; (3) a relatively small number of Burkholderia ASVs had a large number of sequences, with 22, 54, and 107 ASVs accounting for more than 1.0%, 0.1%, and 0.01% of the total Burkholderia sequences, respectively; (4) multiple Burkholderia ASVs were present in most Riptortus individuals, but there was one dominant or two codominant ASVs, and codominance was more likely to occur when the genetic distance between the two codominant ASVs was small; and (5) the beta diversity of Burkholderia was significantly different between the two host species (PerMANOVA: both Jaccard and Bray-Curtis, p < 0.001) and among localities (PerMANOVA: both Jaccard and Bray-Curtis, p < 0.001). Two-way PerMANOVA also indicated that both the host (Bray-Curtis, p = 0.020; Jaccard, p = 0.001) and geographical location (Bray-Curtis, p = 0.041; Jaccard, p = 0.045) influence Burkholderia communities; furthermore, Mantel tests showed that the Burkholderia communities were significantly correlated with the geographical distance of sample locations (R = 0.056, p = 0.001). Together, our findings demonstrate the fine-scale diversity of Burkholderia symbionts and suggest a region- and host-dependent pattern of Burkholderia in Riptortus stinkbugs.

Moderating effect for illness uncertainty on the relationship of depressive and anxiety symptoms among patients with type 1 diabetes in Taif region, Saudi Arabia.

Illness uncertainty was found to be associated with the development of depressive and anxiety symptoms among patients with type 1 diabetes and chronic illness in general. However, the moderating effect of illness uncertainty was not examined in sufficient depth. The current study evaluates how the path from diabetes distress to depression and anxiety is mediated by illness uncertainty, ambiguity, symptoms and course predictability, and illness complexity. Descriptive cross-sectional survey of a large-scale sample of patients living with type one diabetes in Saudi Arabia. We utilized structural equation modelling mediation analysis to examine the effect of illness uncertainty and its subcategories (illness-related ambiguity, symptoms and course predictability, and illness complexity) on depressive and anxiety symptoms. The current survey analyzed data pertaining to (n = 536) type one diabetes patients. Mean Mishel Uncertainty of Illness Scale score was 80.8 points (Cronbach's α = 0.91) signifying moderate uncertainty among our patients. Diabetes-related uncertainty was associated with marriage (t = 3.337, P = 0.0009937), diabetes complications (t = 5.257, P < 0.00001), pain (r = 0.2247, P < 0.00001), and children count (correlation coefficient r = 0.195, P < 0.00001). The prevalence of depression was (n = 367, 68.5%) and for anxiety was (n = 173, 30.3%). Illness uncertainty correlated with depressive (r = 0.2484, P < 0.00001) and anxiety (r = 0.2548, P < 0.00001) symptoms' scores. Illness uncertainty exerted a partial moderating effect on both anxiety (β = 0.060, P < 0.001) and depressive symptoms (β =0.056, P < 0.001). We observed a partial moderating effect for diabetes-related ambiguity and diabetes-related symptom unpredictability in terms of depressive and anxiety symptoms. However, for diabetes-related course unpredictability, the moderating effect was significant only for anxiety. Diabetes-related complexity did not exert a significant moderating effect on either depressive or anxiety symptoms. We confirmed high levels of depression and anxiety among patients with type one diabetes in Saudi Arabia. Our findings suggest that illness uncertainty affects both diabetes-related distress and depression constructs and is likely to be affected by them.

Determinants of rural credit in India: evidence from a large-scale sample survey

Determinants of rural credit in India: evidence from a large-scale sample survey

Time-series China urban land use mapping (2016–2022): An approach for achieving spatial-consistency and semantic-transition rationality in temporal domain

The global urbanization trend is geographically manifested through city expansion and the renewal of internal urban structures and functions. Time-series urban land use (ULU) maps are vital for capturing dynamic land changes in the urbanization process, giving valuable insights into urban development and its environmental consequences. Recent studies have mapped ULU in some cities with a unified model, but ignored the regional differences among cities; and they generated ULU maps year by year, but ignored temporal correlations between years; thus, they could be weak in large-scale and long time-series ULU monitoring. Accordingly, we introduce an temporal-spatial-semantic collaborative (TSS) mapping framework to generating accurate ULU maps with considering regional differences and temporal correlations. Firstly, to support model training, a large-scale ULU sample dataset based on OpenStreetMap (OSM) and Sentinel-2 imagery is automatically constructed, providing a total number of 56,412 samples with a size of 512 × 512 which are divided into six sub-regions in China and used for training different classification models. Then, an urban land use mapping network (ULUNet) is proposed to recognize ULU. This model utilizes a primary and an auxiliary encoder to process noisy OSM samples and can enhance the model's robustness under noisy labels. Finally, taking the temporal correlations of ULU into consideration, the recognized ULU are optimized, whose boundaries are unified by a time-series co-segmentation, and whose categories are modified by a knowledge-data driven method. To verify the effectiveness of the proposed method, we consider all urban areas in China (254,566 km2), and produce a time-series China urban land use dataset (CULU) at a 10-m resolution, spanning from 2016 to 2022, with an overall accuracy of CULU is 82.42%. Through comparison, it can be found that CULU outperforms existing datasets such as EULUC-China and UFZ-31cities in data accuracies, spatial boundaries consistencies and land use transitions logicality. The results indicate that the proposed method and generated dataset can play important roles in land use change monitoring, ecological-environmental evolution analysis, and also sustainable city development.