Assessment of circulating proteins in thyroid cancer: Proteome-wide Mendelian randomization and colocalization analysis.

The causality between circulating proteins and thyroid cancer (TC) remains unclear. We employed five large-scale circulating proteomic genome-wide association studies (GWASs) with up to 100,000 participants and a TC meta-GWAS (nCase = 3,418, nControl = 292,703) to conduct proteome-wide Mendelian randomization (MR) and Bayesian colocalization analysis. Protein and gene expressions were validated in thyroid tissue. Through MR analysis, we identified 26 circulating proteins with a putative causal relationship with TCs, among which NANS protein passed multiple corrections (P BH = 3.28e-5, 0.05/1,525). These proteins were involved in amino acids and organic acid synthesis pathways. Colocalization analysis further identified six proteins associated with TCs (VCAM1, LGMN, NPTX1, PLEKHA7, TNFAIP3, and BMP1). Tissue validation confirmed BMP1, LGMN, and PLEKHA7's differential expression between normal and TC tissues. We found limited evidence for linking circulating proteins and the risk of TCs. Our study highlighted the contribution of proteins, particularly those involved in amino acid metabolism, to TCs.

Prevalence and Burden of Multiple Sclerosis in China, 1990-2019: Findings From the Global Burden of Disease Study 2019.

BACKGROUND AND OBJECTIVES: Multiple sclerosis (MS) is the leading cause of neurologic disability in young adults, but the burden caused by MS in China is lacking. We aimed to comprehensively describe the prevalence and health loss due to MS by demographic and geographical variables from 1990 to 2019 across China. METHODS: Data were obtained from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019). We used GBD methodology to systematically analyze the prevalence, disability-adjusted life-years (DALYs), years of life lost (YLLs), and years lived with disability (YLDs) due to MS by age, sex, and location from 1990 to 2019 in mainland China and its provinces. We also compared the MS burden in China with the world and other Group of 20 (G20) countries. RESULTS: In 2019, 42,571 (95% uncertainty interval [UI] 33,001-53,329) individuals in China had MS, which doubled from 1990. The age-standardized prevalence rate of MS was 2.32 per 100,000 (95% UI 1.78-2.91), which increased by 23.31% (95% UI 20.50-25.89) from 1990, with most of the growth occurring after 2010. There was a positive latitudinal gradient with the increasing prevalence from south to north across China. The total DALYs caused by MS were 71,439 (95% UI 58,360-92,254) in 2019, ranking China third among G20 countries. Most of the MS burden in China derived from premature mortality, with the higher fraction of YLLs than that at the global level and most other G20 countries. From 1990 to 2019, the age-standardized DALY and YLL rate had nonsignificant changes; however, the age-standardized YLD rate substantially increased by 23.33% (95% UI 20.50-25.89). The geographic distribution of MS burden varied at the provincial level in China, with a slight downward trend in the age-standardized DALY rates along with increasing Socio-Demographic Index over the study period. DISCUSSION: Although China has a low risk of MS, the substantial and increasing prevalent cases should not be underestimated. The high burden due to premature death and geographic disparity of MS burden reveals insufficient management of MS in China, highlighting the needs for increased awareness and effective intervention.

stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics.

Spatial transcriptomics (ST) has become a powerful tool for exploring the spatial organization of gene expression in tissues. Imaging-based methods, though offering superior spatial resolutions at the single-cell level, are limited in either the number of imaged genes or the sensitivity of gene detection. Existing approaches for enhancing ST rely on the similarity between ST cells and reference single-cell RNA sequencing (scRNA-seq) cells. In contrast, we introduce stDiff, which leverages relationships between gene expression abundance in scRNA-seq data to enhance ST. stDiff employs a conditional diffusion model, capturing gene expression abundance relationships in scRNA-seq data through two Markov processes: one introducing noise to transcriptomics data and the other denoising to recover them. The missing portion of ST is predicted by incorporating the original ST data into the denoising process. In our comprehensive performance evaluation across 16 datasets, utilizing multiple clustering and similarity metrics, stDiff stands out for its exceptional ability to preserve topological structures among cells, positioning itself as a robust solution for cell population identification. Moreover, stDiff's enhancement outcomes closely mirror the actual ST data within the batch space. Across diverse spatial expression patterns, our model accurately reconstructs them, delineating distinct spatial boundaries. This highlights stDiff's capability to unify the observed and predicted segments of ST data for subsequent analysis. We anticipate that stDiff, with its innovative approach, will contribute to advancing ST imputation methodologies.

Applications of land surface model to economic and environmental-friendly optimization of nitrogen fertilization and irrigation.

Land surface models (LSMs) have prominent advantages for exploring the best agricultural practices in terms of both economic and environmental benefits with regard to different climate scenarios. However, their applications to optimizing fertilization and irrigation have not been well discussed because of their relatively underdeveloped crop modules. We used a CLM5-Crop LSM to optimize fertilization and irrigation schedules that follow actual agricultural practices for the cultivation of maize and wheat, as well as to explore the most economic and environmental-friendly inputs of nitrogen fertilizer and irrigation (FI), in the North China Plain (NCP), which is a typical intensive farming area. The model used the indicators of crop yield, farm gross margin (FGM), nitrogen use efficiency (NUE), water use efficiency (WUE), and soil nitrogen leaching. The results showed that the total optimal FI inputs of FGM were the highest (230 ± 75.8 kg N ha-1 and 20 ± 44.7 mm for maize; 137.5 ± 25 kg N ha-1 and 362.5 ± 47.9 mm for wheat), followed by the FIs of yield, NUE, WUE, and soil nitrogen leaching. After multi-objective optimization, the optimal FIs were 230 ± 75.8 kg N ha-1 and 20 ± 44.7 mm for maize, and 137.5 ± 25 kg N ha-1 and 387.5 ± 85.4 mm for wheat. By comparing our model-based diagnostic results with the actual inputs of FIs in the NCP, we found excessive usage of nitrogen fertilizer and irrigation during the current cultivation period of maize and wheat. The scientific collocation of fertilizer and water resources should be seriously considered for economic and environmental benefits. Overall, the optimized inputs of the FIs were in reasonable ranges, as postulated by previous studies. This result hints at the potential applications of LSMs for guiding sustainable agricultural development.

Health assessment of an electro-hydraulic servo pump control system for servomotor based on LGA deep neural network.

Due to its advantages of having a high power-to-weight ratio and being energy-efficient, the electro-hydraulic servo pump control system (abbreviated as EHSPCS) is frequently employed in the industrial field, such as the electro-hydraulic servo pump control (EHSPC) servomotor for steam turbine valve regulation control. However, the EHSPCS has strong nonlinearity and time-varying features, and the factors that cause system performance degradation are complex. Once a system failure occurs, it may lead to serious accidents, causing serious casualties and economic losses. To address the above issues, a system health assessment method based on LSTM-GRNN-ANN (LGA) deep neural network is proposed in this paper. Firstly, with oil volume gas content, servo motor air-gap flux density, and system leakage coefficient as the health assessment performance indicators, a health assessment performance index system for the EHSPCS is built, Furthermore, the system performance index threshold is set. Secondly, an LGA deep neural network is constructed by combining LSTM, GRNN and ANN, and a deep neural network based on the LGA is used to create an EHSPCS health assessment model. Subsequently, system feature parameter extraction, algorithm design, and parameter debugging are carried out. Finally, an EHSPCS experimental platform is established, typical system failure simulation experiments are designed, and comparative experimental analysis is conducted. The experimental findings demonstrate that the average accuracy of the system health assessment model based on the LGA deep neural network suggested in this paper is 96.37%, compared to 89.84%, 87.99% for LSTM and GRNN, which validates the accuracy of the system health assessment model based on the LGA deep neural network.

Large-Scale Wearable Textile-Based Sweat Sensor with High Sensitivity, Rapid Response, and Stable Electrochemical Performance.

Textile-based sweat sensors display great potential to enhance wearable comfort and health monitoring; however, their widespread application is severely hindered by the intricate manufacturing process and electrochemical characteristics. To address this challenge, we combined both impregnation coating technology and conjugated electrospinning technology to develop an electro-assisted impregnation core-spinning technology (EAICST), which enables us to simply construct a sheath-core electrochemical sensing yarn (TPFV/CPP yarn) via coating PEDOT:PSS-coated carbon fibers (CPP) with polyurethane (TPU)/polyacrylonitrile (PAN)/poloxamer (F127)/valinomycin as shell. The TPFV/CPP yarn was sewn into the fabric and integrated with a sensor to achieve a detachable feature and efficiently monitor K+ levels in sweat. By introducing EAICST, a speed of 10 m/h can be realized in the continuous preparation of the TPFV/CPP yarn, while the interconnected pores in the yarn sheath enable it to quickly capture and diffuse sweat. Besides, the sensor exhibited excellent sensitivity (54.26 mV/decade), fast response (1.7 s), anti-interference, and long-term stability (5000 s or more). Especially, it also possesses favorable washability and wear resistance properties. Taken together, this study provides a crucial technical foundation for the development of advanced wearable devices designed for sweat analysis.

Sex and age disparities in multi-metal mixture exposure and cognitive impairment in urban elderly individuals: The mediation effect and biological function of metabolites.

Studies on the relationships between metal mixtures exposure and cognitive impairment in elderly individuals are limited, particularly the mechanism with metabolite. Few studies are available on the potential sex and age specific associations between metal exposure, metabolites and cognitive impairment. We examined plasma metal and blood metabolite concentrations among 1068 urban elderly participants. Statistical analysis included a battery of variable selection approaches, logistic regression for metal/metabolite associations, and Bayesian kernel machine regression (BKMR) to identify mixed effects of metals/metabolites on cognitive impairment risk. Our results showed that As was positively associated with cognitive impairment in the female (OR 95 % CI = 2.21 (1.36, 3.57)) and 60- to 70-year-old (OR 95 % CI = 2.60 (1.54, 4.41)) groups, Cr was positively associated with cognitive impairment in the male (OR 95 % CI = 2.15 (1.27, 3.63)) and 60- to 70-year-old (OR 95 % CI = 2.10 (1.24, 3.57)) groups, and Zn was negatively associated with cognitive impairment, especially in the female (OR 95 % CI = 0.46 (0.25, 0.84)), 60- to 70-year-old (OR 95 % CI =0.24 (0.12, 0.45)) and ≥ 80-year-old (OR 95 % CI = 0.19 (0.04, 0.86)) groups. Positive associations were observed between combined metals (Cr, Cu and As) and cognitive impairment, but Zn alleviated this tendency, especially in elderly individuals aged ≥80 years. Negative associations were observed between metabolites and cognitive impairment, especially in male, female and 60-70 years old groups. The mediation effects of metabolites on the association between metal exposure and cognitive impairment were observed, and the percentages of these effects were 15.60 % (Glu-Cr), 23.00 % (C5:1-Cu) and 16.36 % (Glu-Zn). Cr, Cu, and Zn could increase cognitive impairment risk through the "Malate-Aspartate Shuttle", "Glucose-Alanine Cycle", etc., pathways. Overall, we hypothesize that metabolites have mediation effects on the relationship between multi-metal exposure and cognitive impairment and that there are sex and age differences.

Social class, schadenfreude, and children's prosocial behavior in moral contexts.

Previous research has shown mixed results regarding the relationship between social class and children's prosocial behavior. The current study aims to further our understanding of these findings by exploring the relationship between social class and children's prosocial behavior in a moral context. Study 1 (N = 833) found that when a target child pursued a morally negative goal and subsequently experienced misfortune, children from higher social class, compared to those from lower social class, experienced greater schadenfreude and exhibited less prosocial behavior. The relation between social class and prosocial behavior was mediated by schadenfreude. Study 2 (N = 389) investigated whether the greater schadenfreude experienced by children from higher social class was due to a weaker empathic response to misfortune or a stronger sense of deservingness. The results revealed a sequential mediation effect of social class on prosocial behavior through deservingness and schadenfreude. These findings provide insight into the impact of social class on the development of children's moral judgment, emotions, and behavior. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A scoping review of fair machine learning techniques when using real-world data.

OBJECTIVE: The integration of artificial intelligence (AI) and machine learning (ML) in health care to aid clinical decisions is widespread. However, as AI and ML take important roles in health care, there are concerns about AI and ML associated fairness and bias. That is, an AI tool may have a disparate impact, with its benefits and drawbacks unevenly distributed across societal strata and subpopulations, potentially exacerbating existing health inequities. Thus, the objectives of this scoping review were to summarize existing literature and identify gaps in the topic of tackling algorithmic bias and optimizing fairness in AI/ML models using real-world data (RWD) in health care domains. METHODS: We conducted a thorough review of techniques for assessing and optimizing AI/ML model fairness in health care when using RWD in health care domains. The focus lies on appraising different quantification metrics for accessing fairness, publicly accessible datasets for ML fairness research, and bias mitigation approaches. RESULTS: We identified 11 papers that are focused on optimizing model fairness in health care applications. The current research on mitigating bias issues in RWD is limited, both in terms of disease variety and health care applications, as well as the accessibility of public datasets for ML fairness research. Existing studies often indicate positive outcomes when using pre-processing techniques to address algorithmic bias. There remain unresolved questions within the field that require further research, which includes pinpointing the root causes of bias in ML models, broadening fairness research in AI/ML with the use of RWD and exploring its implications in healthcare settings, and evaluating and addressing bias in multi-modal data. CONCLUSION: This paper provides useful reference material and insights to researchers regarding AI/ML fairness in real-world health care data and reveals the gaps in the field. Fair AI/ML in health care is a burgeoning field that requires a heightened research focus to cover diverse applications and different types of RWD.

Hydrogeochemical Disparities and Constraints of Water Produced from Various Coal Seams in the Baode Block, Ordos Basin, China.

The hydrogeochemical characteristics of coalbed water play a crucial role in assessing the production level of coalbed methane (CBM) due to its involvement in the entire process of CBM generation, migration, accumulation, and extraction. To investigate variations in hydrochemical characteristics and controlling factors among different coal seams, a representative CBM field (Baode block) within the Ordos basin in China was chosen as a target. We have systematically collected produced water samples from coal seams of the Permian Shanxi Formation (P1s) and Taiyuan Formation (P1t). Tests and analyses were conducted on conventional cation and anions, trace elements, pH value, total dissolved solids (TDS), stable isotopes of hydrogen and oxygen in water, and inorganic carbon (δD, δ18O, and δ13CDIC). The findings indicate that the P1s coal seam primarily contains HCO3-Na type water, while the P1t coal seam consists of Cl-Na and HCO3-Na types of water. The disparity in water types between P1s and P1t can be attributed to interactions between water and rocks. The isotopic compositions of δD, δ18O, and δ13CDIC suggest that the sampled coalbed waters originate from atmospheric precipitation, with subsequent microbial activity. It is suggested that TDS content along with bicarbonate concentration can serve as effective indicators for determining high productivity due to weaker hydraulic conditions and a more enclosed water environment in P1t coal seams; threshold values being >1000 mg/L for TDS and >10 mequiv/L for bicarbonate concentration. Additionally, microbial activity is found to be more widespread in P1t compared to P1s. Principal component analysis reveals a significantly higher contribution of conventional ions toward TDS content observed within the P1t coal seam compared to that of P1s coal seam, accompanied by alterations in pH control parameters. The water produced from the P1s coalbed is primarily controlled by evaporite and silicate weathering/dissolution coupled with substantial cation exchange. Conversely, the water in the P1t coalbed is mainly influenced by silicate weathering/dissolution as well as evaporative concentration, with a limited occurrence of cation exchange. Moreover, there are distinct disparities in ion sources between P1s and P1t. These research findings provide a scientific foundation for assessing the development potential of CBM and optimizing extraction systems within similar CBM areas.

Clay minerals/sodium alginate/polyethylene hydrogel adsorbents control the selective adsorption and reduction of uranium: Experimental optimization and Monte Carlo simulation study.

Clay minerals formations are potential geological barrier (host rocks) for the long-rerm storage of uranium tailing in deep geological repositories. However, there are still obstacles to the efficient retardation of uranium because of the competition between negatively charged regions at the clay minerals end face, surface and between layers, as well as low mineralization capacity. Herein, employing a simple method, we used sodium alginate (SA), an inexpensive natural polymer material, polyethylene (PE), and the natural clay minerals montmorillonite (Mt), nontronite (Nt), and beidellite (Bd) to prepare three hydrogel adsorbents, (denoted as Mt/PE-@SA, Nt/PE-@SA, and Bd/PE-@SA), respectively. The application of obtained hydrogel adsorbents further extends to uranium(VI) removal from aqueous. Due to the synergistic action of SA group and PE group, hydrogel adsorbents showed select adsorption and mineralization effect on uranium(VI), among which the maximum uranium(VI) adsorption capacity of Nt/PE-@SA was 133.3 mg·g-1 and Mt/PE-@SA exhibited strong selectivity for uranium(VI) in the presence of coexisting metal ions. Cyclic voltammetry studies indicated the mitigation and immobilization of uranium species onto adsorbents by both reduction and mineralization. Besides, the synergistic adsorption of SA and PE on clay minerals was hypothesized, and the idea was supported by structure optimizations results from Monte Carlo dynamics simulation (MCD). Three obtained hydrogel adsorbents structural model was constructed based on its physicochemical characterization, the low energy adsorption sites and adsorption energies are investigated using MCD simulation. The simulation results show that obtained hydrogel adsorbents have a strong interaction with uranium(VI), which ensures the high adsorption capacity of those materials. Most importantly, this work demonstrates a new strategy for preparing mineral-based hydrogel adsorbents with enough stability and provides a new perspective for uranium(VI) removal in complex environment.

Combining fecal immunochemical testing and questionnaire-based risk assessment in selecting participants for colonoscopy screening in the Chinese National Colorectal Cancer Screening Programs: A population-based cohort study.

BACKGROUND: Screening reduces colorectal cancer (CRC) burden by allowing early resection of precancerous and cancerous lesions. An adequate selection of high-risk individuals and a high uptake rate for colonoscopy screening are critical to identifying people more likely to benefit from screening and allocating healthcare resources properly. We evaluated whether combining a questionnaire-based interview for risk factors with fecal immunochemical test (FIT) outcomes for high-risk assessment is more efficient and economical than a questionnaire-based interview-only strategy. METHODS AND FINDINGS: In this multicenter, population-based, prospective cohort study, we enrolled community residents aged 40 to 74 years in 29 provinces across China. From 2016 to 2020, a total of 1,526,824 eligible participants were consecutively enrolled in the Cancer Screening Program in Urban China (CanSPUC) cohort, and 940,605 were enrolled in the Whole Life Cycle of Cancer Screening Program (WHOLE) cohort, with follow-up to December 31, 2022. The mean ages were 56.89 and 58.61 years in CanSPUC and WHOLE, respectively. In the WHOLE cohort, high-risk individuals were identified by combining questionnaire-based interviews to collect data on risk factors (demographics, diet history, family history of CRC, etc.) with FIT outcomes (RF-FIT strategy), whereas in the CanSPUC cohort, high-risk individuals were identified using only interview-based data on risk factors (RF strategy). The primary outcomes were participation rate and yield (detection rate of advanced neoplasm, early-stage detection rate of CRCs [stage I/II], screening yield per 10,000 invitees), which were reported for the entire population and for different gender and age groups. The secondary outcome was the cost per case detected. In total, 71,967 (7.65%) and 281,985 (18.47%) individuals were identified as high-risk and were invited to undergo colonoscopy in the RF-FIT group and RF group, respectively. The colonoscopy participation rate in the RF-FIT group was 26.50% (19,071 of 71,967) and in the RF group was 19.54% (55,106 of 281,985; chi-squared test, p < 0.001). A total of 102 (0.53%) CRCs and 2,074 (10.88%) advanced adenomas were detected by the RF-FIT, versus 90 (0.16%) and 3,593 (6.52%) by the RF strategy (chi-squared test, both p < 0.001). The early-stage detection rate using the RF-FIT strategy was significantly higher than that by the RF strategy (67.05% versus 47.95%, Fisher's exact test, p = 0.016). The cost per CRC detected was $24,849 by the RF-FIT strategy versus $55,846 by the RF strategy. A limitation of the study was lack of balance between groups with regard to family history of CRC (3.5% versus 0.7%). CONCLUSIONS: Colonoscopy participation and screening yield were better with the RF-FIT strategy. The association with CRC incidence and mortality reduction should be evaluated after long-term follow-up.

Ecological Risk and Pollution Assessment of Heavy Metals in Farmland Soil Profile with Consideration of Atmosphere Deposition in Central China.

Heavy metals (HMs) in agricultural land have caused serious environmental problems, resulting in severe contamination of the food chain and posing potential health threats. This study aims to investigate the pollution levels and potential ecological risks of HMs in farmland soils in central China, taking into account atmospheric deposition. Several indices were used to assess the status of HMs and compare surface soil with deeper soil. Descriptive statistics, Pearson correlation, and UMAP clustering methods were utilized to identify the characteristics of HMs. Additionally, stepwise linear regression models were employed to quantify the contributions of different variables to the potential ecological risks of HMs. The results showed that the average content of Zn in surface soil (289.41 ± 87.72 mg/kg) was higher than in the deeper soil (207.62 ± 37.81 mg/kg), and similar differences were observed in the mean values of related Igeo (1.622 ± 0.453 in surface soil and 1.183 ± 0.259 in deeper soil) and PEI (0.965 ± 0.292 in surface soil and 0.692 ± 0.126 in deeper soil) indices. This indicates that surface soil is more heavily polluted. The UMAP results confirmed the high variability of HMs in the surface soil, while PCA results suggested the importance of pollution and ecological risk indices. The stepwise linear model revealed that different variable structures contribute differently to the risk. In conclusion, Cr and Zn were found to be the major contaminants in the local farmland soil, with higher concentrations in the surface soil. The geoaccumulation and total potential ecological risk were classified as low risk. High variability of HMs was observed in the surface soil. Therefore, HM-related pollution indices and ecological risk indices are important for assessing the contamination status of local HMs. The local potential ecological risk can be attributed to specific heavy metals, each of which can have different effects on the local ecological risk.

Risk of papillary thyroid carcinoma and nodular goiter associated with exposure to semi-volatile organic compounds: A multi-pollutant assessment based on machine learning algorithms.

BACKGROUND: Exposure to semi-volatile organic compounds (SVOCs) may link to thyroid nodule risk, but studies of mixed-SVOCs exposure effects are lacking. Traditional analytical methods are inadequate for dealing with mixed exposures, while machine learning (ML) seems to be a good way to fill the gaps in the field of environmental epidemiology research. OBJECTIVES: Different ML algorithms were used to explore the relationship between mixed-SVOCs exposure and thyroid nodule. METHODS: A 1:1:1 age- and gender-matched case-control study was conducted in which 96 serum SVOCs were measured in 50 papillary thyroid carcinoma (PTC), 50 nodular goiters (NG), and 50 controls. Different ML techniques such as Random Forest, AdaBoost were selected based on their predictive power, and variables were selected based on their weights in the models. Weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) were used to assess the mixed effects of the SVOCs exposure on thyroid nodule. RESULTS: Forty-three of 96 SVOCs with detection rate >80 % were included in the analysis. ML algorithms showed a consistent selection of SVOCs associated with thyroid nodule. Fluazifop-butyl and fenpropathrin are positively associated with PTC and NG in single compound models (all P < 0.05). WQS model shows that exposure to mixed-SVOCs was associated with an increased risk of PTC and NG, with the mixture dominated by fenpropathrin, followed by fluazifop-butyl and propham. In the BKMR model, mixtures showed a significant positive association with thyroid nodule risk at high exposure levels, and fluazifop-butyl showed positive effects associated with PTC and NG. CONCLUSION: This study confirms the feasibility of ML methods for variable selection in high-dimensional complex data and showed that mixed exposure to SVOCs was associated with increased risk of PTC and NG. The observed association was primarily driven by fluazifop-butyl and fenpropathrin. The findings warranted further investigation.

Assessment of visual function in blind mice and monkeys with subretinally implanted nanowire arrays as artificial photoreceptors.

Retinal prostheses could restore image-forming vision in conditions of photoreceptor degeneration. However, contrast sensitivity and visual acuity are often insufficient. Here we report the performance, in mice and monkeys with induced photoreceptor degeneration, of subretinally implanted gold-nanoparticle-coated titania nanowire arrays providing a spatial resolution of 77.5 µm and a temporal resolution of 3.92 Hz in ex vivo retinas (as determined by patch-clamp recording of retinal ganglion cells). In blind mice, the arrays allowed for the detection of drifting gratings and flashing objects at light-intensity thresholds of 15.70-18.09 µW mm-2, and offered visual acuities of 0.3-0.4 cycles per degree, as determined by recordings of visually evoked potentials and optomotor-response tests. In monkeys, the arrays were stable for 54 weeks, allowed for the detection of a 10-µW mm-2 beam of light (0.5° in beam angle) in visually guided saccade experiments, and induced plastic changes in the primary visual cortex, as indicated by long-term in vivo calcium imaging. Nanomaterials as artificial photoreceptors may ameliorate visual deficits in patients with photoreceptor degeneration.

Development of a prediction model for postoperative complications and economic burden analysis in older patients with hip fractures.

Purpose: The high rates of disability and mortality due to postoperative complications of hip fractures in the elderly, especially the oldest-old individuals, have become an increasingly serious global public health concern. This study aimed to establish a nomogram prediction model and analyze the economic burden to guide clinical decision-making and improve patient prognosis. Methods: Data of 514 patients aged over 80 years with hip fractures who received surgical treatment were retrospectively collected, and the patients were divided into training and validation cohorts. Independent risk factors for postoperative complications were identified based on logistic regression analysis, and a nomogram was constructed. The model was evaluated for its discrimination and consistency using receiver operating characteristic (ROC) curves and calibration curves, and for its clinical benefit using decision curve analysis (DCA). The economic burden was analyzed using propensity score matching (PSM). Results: The American Society of Anesthesiologists (ASA) classification ≥Ⅲ, anemia, male sex, diabetes mellitus, and the number of comorbidities were found to be independent risk factors for postoperative complications in oldest-old patients with hip fracture (all P < 0.05). The areas under the curve (AUC) of the nomogram prediction model for the training and validation cohorts were 0.743 and 0.767, respectively, indicating reliable discrimination. The calibration curves and DCA showed that the model has good consistency and high benefits. The direct economic burden of postoperative complications for the patients was US$1045.10. Conclusions: The nomogram model can accurately quantify the risk of postoperative complications among oldest-old patients with hip fractures and guide clinical professionals to implement early and targeted preventive treatment for high-risk patients.

Patterns of Social Determinants of Health and Child Mental Health, Cognition, and Physical Health.

Importance: Social determinants of health (SDOH) influence child health. However, most previous studies have used individual, small-set, or cherry-picked SDOH variables without examining unbiased computed SDOH patterns from high-dimensional SDOH factors to investigate associations with child mental health, cognition, and physical health. Objective: To identify SDOH patterns and estimate their associations with children's mental, cognitive, and physical developmental outcomes. Design, Setting, and Participants: This population-based cohort study included children aged 9 to 10 years at baseline and their caregivers enrolled in the Adolescent Brain Cognitive Development (ABCD) Study between 2016 and 2021. The ABCD Study includes 21 sites across 17 states. Exposures: Eighty-four neighborhood-level, geocoded variables spanning 7 domains of SDOH, including bias, education, physical and health infrastructure, natural environment, socioeconomic status, social context, and crime and drugs, were studied. Hierarchical agglomerative clustering was used to identify SDOH patterns. Main Outcomes and Measures: Associations of SDOH and child mental health (internalizing and externalizing behaviors) and suicidal behaviors, cognitive function (performance, reading skills), and physical health (body mass index, exercise, sleep disorder) were estimated using mixed-effects linear and logistic regression models. Results: Among 10 504 children (baseline median [SD] age, 9.9 [0.6] years; 5510 boys [52.5%] and 4994 girls [47.5%]; 229 Asian [2.2%], 1468 Black [14.0%], 2128 Hispanic [20.3%], 5565 White [53.0%], and 1108 multiracial [10.5%]), 4 SDOH patterns were identified: pattern 1, affluence (4078 children [38.8%]); pattern 2, high-stigma environment (2661 children [25.3%]); pattern 3, high socioeconomic deprivation (2653 children [25.3%]); and pattern 4, high crime and drug sales, low education, and high population density (1112 children [10.6%]). The SDOH patterns were distinctly associated with child health outcomes. Children exposed to socioeconomic deprivation (SDOH pattern 3) showed the worst health profiles, manifesting more internalizing (ß = 0.75; 95% CI, 0.14-1.37) and externalizing (ß = 1.43; 95% CI, 0.83-2.02) mental health problems, lower cognitive performance, and adverse physical health. Conclusions: This study shows that an unbiased quantitative analysis of multidimensional SDOH can permit the determination of how SDOH patterns are associated with child developmental outcomes. Children exposed to socioeconomic deprivation showed the worst outcomes relative to other SDOH categories. These findings suggest the need to determine whether improvement in socioeconomic conditions can enhance child developmental outcomes.

Identifying and optimizing ecological spatial patterns based on the bird distribution in the Yellow River Basin, China.

In the Yellow River Basin (YRB), there exists a rich biodiversity of species that has been shaped by its unique geography, climate, and human activities. However, the high speed of economic development has resulted in the fragmentation and loss of habitats that are crucial for the survival of these species. To address this problem, constructing ecological networks has emerged as a promising approach for biodiversity preservation. In the study, we centered on the YRB and employed bird communities as an indicator species to identify ecological sources by combining bioclimatic variables and land use data with the Maximum Entropy (MaxEnt) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models. We generated a resistance surface using various data such as Digital Elevation Model (DEM), the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), nighttime light, road density, railway density, and waterway density. So, we then simulated ecological corridors applying the Minimum Cumulative Resistance (MCR) model and constructed a bird diversity protection network. The results we found suggested that bird hotspots were predominantly clustered upstream and downstream in the YRB. We identified 475 sources covering a total area of 65,088 km2, 681 corridors with a total length of 11,495.05 km. This network served as a critical ecological facility to sustain and protect biodiversity. The bird ecological corridors in the YRB showed that a dense east-west pattern in the central area, with a short length in the west and east and a long length in the central area. Although the central region lacked ecological sources, the east and west were still connected as a tight whole. Two scenarios showed adding ecological stepping stones had a better optimization effect than enhancing ecological connectivity.

Non-Intrusive Speech Quality Assessment Based on Deep Neural Networks for Speech Communication.

Traditionally, speech quality evaluation relies on subjective assessments or intrusive methods that require reference signals or additional equipment. However, over recent years, non-intrusive speech quality assessment has emerged as a promising alternative, capturing much attention from researchers and industry professionals. This article presents a deep learning-based method that exploits large-scale intrusive simulated data to improve the accuracy and generalization of non-intrusive methods. The major contributions of this article are as follows. First, it presents a data simulation method, which generates degraded speech signals and labels their speech quality with the perceptual objective listening quality assessment (POLQA). The generated data is proven to be useful for pretraining the deep learning models. Second, it proposes to apply an adversarial speaker classifier to reduce the impact of speaker-dependent information on speech quality evaluation. Third, an autoencoder-based deep learning scheme is proposed following the principle of representation learning and adversarial training (AT) methods, which is able to transfer the knowledge learned from a large amount of simulated speech data labeled by POLQA. With the help of discriminative representations extracted from the autoencoder, the prediction model can be trained well on a relatively small amount of speech data labeled through subjective listening tests. Fourth, an end-to-end speech quality evaluation neural network is developed, which takes magnitude and phase spectral features as its inputs. This phase-aware model is more accurate than the model using only the magnitude spectral features. A large number of experiments are carried out with three datasets: one simulated with labels obtained using POLQA and two recorded with labels obtained using subjective listening tests. The results show that the presented phase-aware method improves the performance of the baseline model and the proposed model with latent representations extracted from the adversarial autoencoder (AAE) outperforms the state-of-the-art objective quality assessment methods, reducing the root mean square error (RMSE) by 10.5% and 12.2% on the Beijing Institute of Technology (BIT) dataset and Tencent Corpus, respectively. The code and supplementary materials are available at https://github.com/liushenme/AAE-SQA.

Geographic disparities in trends of thyroid cancer incidence and mortality from 1990 to 2019 and a projection to 2030 across income-classified countries and territories.

Background: The rising incidence of thyroid cancer (TC) has generated growing concern globally; yet there are no studies examining whether this incidence was followed by a rise in related mortality. We aimed to comprehensively quantify current trends and future projections of TC incidence and mortality, and to explore the association between the TC burden and socioeconomic inequality in different income strata. Methods: We obtained incidence and mortality data on TC and population from the 2019 Global Burden of Disease (GBD) study and the United Nations' World Population Prospects 2022. We applied an age-period-cohort (APC) model to estimate the overall annual percentage change (net drift) and age, period, and cohort effects from 1990 to 2019, and also constructed a Bayesian APC model to predict the TC burden through 2030. Results: Over a third of global TC cases belonged to the high-income group. From 1990 to 2019, net drifts of TC incidence were >0 in all income groups, while a modest reduction (net drift <0) in mortality was observed in most income groups, except for the lower-middle-income group. Unfavourable age, period, and cohort effects were most notable in Vietnam, China, and Korea. The age-standardised incidence rate (ASIR) is predicted to increase whereas the age-standardized mortality rate (ASMR) is expected to decrease globally between 2020 and 2030, with geographic heterogeneity being detected across income groups. We observed a positive correlation between ASIR and universal health coverage index and health worker density, but a negative one between ASMR and the two indicators, primarily in upper-middle-income and high-income countries. Conclusions: Opposite patterns in incidence and mortality of TC raise concerns about overdiagnosis, particularly in upper-middle-income and high-income countries. Discrepancies in the distribution of health service accessibility, including diagnostic techniques and therapeutic care, should be addressed by narrowing health inequalities in the TC burden across countries.