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We present a simple and efficient hypothesis-free machine learning pipeline for risk factor discovery that accounts for non-linearity and interaction in large biomedical databases with minimal variable pre-processing. In this study, mortality models were built using gradient boosting decision trees (GBDT) and important predictors were identified using a Shapley values-based feature attribution method, SHAP values. Cox models controlled for false discovery rate were used for confounder adjustment, interpretability, and further validation. The pipeline was tested using information from 502,506 UK Biobank participants, aged 37–73 years at recruitment and followed over seven years for mortality registrations. From the 11,639 predictors included in GBDT, 193 potential risk factors had SHAP values ≥ 0.05, passed the correlation test, and were selected for further modelling. Of the total variable importance summed up, 60% was directly health related, and baseline characteristics, sociodemographics, and lifestyle factors each contributed about 10%. Cox models adjusted for baseline characteristics, showed evidence for an association with mortality for 166 out of the 193 predictors. These included mostly well-known risk factors (e.g., age, sex, ethnicity, education, material deprivation, smoking, physical activity, self-rated health, BMI, and many disease outcomes). For 19 predictors we saw evidence for an association in the unadjusted but not adjusted analyses, suggesting bias by confounding. Our GBDT-SHAP pipeline was able to identify relevant predictors ‘hidden’ within thousands of variables, providing an efficient and pragmatic solution for the first stage of hypothesis free risk factor identification.

An extremely heavy rainfall event occurred in Zhengzhou, China, on 20 July 2021 and produced an hourly rainfall rate of 201.9 mm, which broke the station record for mainland China. Based on radar observations and a convection-permitting simulation using the WRF-ARW model, this paper investigates the multiscale processes, especially those at the mesoscale, that support the extreme observed hourly rainfall. Results show that the extreme rainfall occurred in an environment characteristic of warm-sector heavy rainfall, with abundant warm moist air transported from the ocean by an abnormally northward-displaced western Pacific subtropical high and Typhoon In-Fa (2021). However, rather than through back building and echo training of convective cells often found in warm-sector heavy rainfall events, this extreme hourly rainfall event was caused by a single, quasi-stationary storm in Zhengzhou. Scale separation analysis reveals that the extreme-rain-producing storm was supported and maintained by the dynamic lifting of low-level converging flows from the north, south, and east of the storm. The low-level northerly flow originated from a mesoscale barrier jet on the eastern slope of the Taihang Mountain due to terrain blocking of large-scale easterly flows, which reached an overall balance with the southerly winds in association with a low-level meso- β -scale vortex located to the west of Zhengzhou. The large-scale easterly inflows that fed the deep convection via transport of thermodynamically unstable air into the storm prevented the eastward propagation of the weak, shallow cold pool. As a result, the convective storm was nearly stationary over Zhengzhou, resulting in record-breaking hourly precipitation.

Widespread surveillance cameras show excellent potential for high spatial and temporal resolution rainfall observation. As the accuracy of surveillance camera-based rain gauges (SRGs) continues to improve, surveillance camera-based rainfall observation networks (SRN) have received increasing attention worldwide. Limited by the availability of surveillance data, there is little investigation into the performance of SRNs. In this study, a simulated SRN construction model is proposed, which employs meteorology and geography research as a priori knowledge and the state-of-the-art achievements of SRG as the basis, bringing the simulated SRNs closer to their practical performance. Regional rainfall observations from SRNs were compared with those from ground gauge network (GRN), real-time and calibrated radars. The experimental results show that (1) SRNs can significantly improve correlation with real-time and calibrated radar observations over GRN; (2) an SRN consisting of 700 SRGs whose estimation accuracy is higher than 80% or 500 SRGs whose estimation is accuracy higher than 90% could achieve a comparable precision performance to that of GRN; (3) as the number of SRGs reaches 900 (the spatial density of SRGs is about 0.47 per km2), the performance of the SRNs tend to be stable. Although increasing the number of cameras helps alleviate the problem of insufficient accuracy of a single SRG during heavy/violent rainfall, excessive cameras may reduce the accuracy due to the inherent measurement errors of SRG. Therefore, developing a robust SRG filtering strategy to find the optimal number of SRGs is essential. Our research provides an important reference for researchers who are holding a sceptical view of the availability of surveillance camera rainfall networks while shedding light on building a new low-cost and high-resolution SRN based on the existing surveillance camera resources.

Accurate nowcasting of severe convective precipitation is critical for early warning yet still remains challenging. Although deep learning methods show promise, most models lack physical constraints, limiting their consistency with atmospheric processes. We introduce FURECast, a deep learning model that not only leverages three‐dimensional structure of polarimetric radar variables (ZH, ZDR, KDP) but also embeds their intrinsic self‐consistency relation as a physical constraint. The model employs an encoder‐translator‐decoder architecture, integrating multi‐level inputs via late fusion and evolving features through cascaded multiscale blocks. Moreover, a novel physical loss term is introduced to enforce microphysical consistency during training. Evaluated on S‐band (GD‐SPOL) and C‐band (NJU‐CPOL) radar data sets, FURECast achieves a 14.1% improvement in 90‐min critical success index (35 dBZ threshold) over the 2D reflectivity‐only baseline, while reducing physical inconsistency by two orders of magnitude. These results underscore the value of 3D polarimetric structure and physics‐guided learning in advancing convective precipitation nowcasting.

Background Obesity and depression are both highly prevalent public health disorders and evidence on their relationship is inconsistent. This study examined whether depressive symptoms are associated with current obesity, and further, whether obesity in turn is associated with an increased odds of depressive symptoms five years later after accounting for potential lifestyle confounders and depressive symptoms at baseline. Methods Data were obtained from the 1958 British birth cohort ( N  = 9217 for cross-sectional and 7340 for prospective analysis). Clinical Interview Schedule-Revised and Mental Health Inventory-5 were used for screening depressive symptoms at ages 45 and 50 years, respectively. General and central obesity were defined using measurements of body mass index (BMI) and waist circumference (WC) at 45 years, respectively. Results There was a cross-sectional association between depressive symptoms and obesity: participants with ≥2 depressive symptoms had 31% (95%CI 11% to 55%) higher odds of general and 26% higher odds of central obesity (95%CI 8% to 47%). In prospective analyses, both general and central obesity were associated with higher odds of depressive symptoms five years later among women but not in men (P interaction  < 0.01). After adjustment for depressive symptoms at baseline, sociodemographic and lifestyle factors, women with general obesity had 38% (95% CI 7% to 77%) and women with central obesity 34% (95%CI 9% to 65%) higher odds of depression compared to others. Conclusions Depressive symptoms are associated with concurrent obesity and related lifestyle factors among women and men in mid-life. Our study suggests that obesity in turn affects long-term risk of depressive symptoms in women but not in men, independently of concurrent associations, providing an important target group for the implementation of preventative strategies.

Objectives: Dendrobium officinale polysaccharide (DOP) is the main active ingredient in a valuable traditional Chinese medicine, which exerts several pharmacological activities including hepatoprotection and hypoglycemic effects. However, the effects of DOP on obesity-associated insulin resistance (IR) and lipid metabolism remain unknown. This study aimed to investigate the role of DOP in IR and abnormal lipid metabolism in obese mice. Methods: IR models were established using 3T3-L1 adipocytes, C2C12 myocytes, and primary cultured hepatocytes exposed to palmitate acid. After treatment with DOP, insulin-stimulated glucose uptake, glucose release, and AKT phosphorylation was detected. Fasting blood glucose, fasting serum insulin, the glucose tolerance test (GTT), and the insulin tolerance test (ITT) were measured to evaluate IR of obese mice. Lipid analysis was conducted to evaluate the effects of DOP on lipid metabolism in obese mice. Results: In vitro , DOP treatment ameliorated palmitic acid-induced IR in adipocytes, myocytes, and hepatocytes. DOP regulated cellular insulin sensitivity via the peroxisome proliferator-activated receptor-γ (PPAR-γ). Furthermore, administration of DOP significantly reduced the IR and visceral adipose tissue (VAT) inflammation of diet-induced obese (DIO) and the genetically-induced obesity mice (ob/ob) mouse models. In addition, DOP treatment attenuated the high-fat diet (HFD)-induced liver lipid accumulation by reducing liver triglycerides (TG), plasma free fatty acid (FFA), serum cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) levels, while increasing HDL-C levels. Conclusion: DOP could improve obesity-associated IR and abnormal lipid metabolism through its activities on PPAR-γ, and may serve as a potential therapeutic agent for obesity-associated insulin resistance and lipid metabolism disorder.

Hailstorms rank among the most destructive extreme weather events globally, causing substantial property damage. While limited case studies suggest that cities may exacerbate hailstorms, the underlying mechanisms remain uncertain because of the complex physical processes. Here, we examine a hailstorm formation pathway associated with convective merging process using long-term observational data and high-resolution numerical simulations. This pathway helps explain the rising frequency of hailstorms across two distinct climate regimes, North America and East Asia. We find that merger hailstorms (MHs) occur approximately twice as often and tend to be more intense than non-merging normal hailstorms (NHs), which have been traditionally considered as the primary hailstorm formation mode. Favorable environmental conditions support the initiation of multiple convective cells and their subsequent merging, a tendency that may be enhanced by anthropogenic heat in large cities. Projections from a machine-learning model indicate an increase in the MH frequency and a decrease in NH frequency in North America. Together, these findings highlight an underexplored hailstorm formation pathway and suggest that climate change and human activities may play a role in shaping future hailstorm characteristics and the associated risks. This study reveals a merger-type hailstorm formation pathway that is twice as frequent and more intense than traditional hailstorms and is projected to increase in North American cities, potentially amplified by anthropogenic influence.

Microglia, the main immune cells in the brain, participate in the innate immune response in the central nervous system (CNS). Studies have shown that microglia can be polarized into pro-inflammatory M1 and anti-inflammatory M2 phenotypes. Accumulated evidence suggests that over-activated M1 microglia release pro-inflammatory mediators that damage neurons and lead to Parkinson’s disease (PD). In contrast, M2 microglia release neuroprotective factors and exert the effects of neuroprotection. Camptothecin (CPT), an extract of the plant Camptotheca acuminate , has been reported to have anti-inflammation and antitumor effects. However, the effect of CPT on microglia polarization and microglia-mediated inflammation responses has not been reported. In our study we found that CPT improved motor performance of mice and reduced the loss of neurons in the substantia nigra (SN) of the midbrain in LPS-injected mice. In the mechanism study, we found that CPT inhibited M1 polarization of microglia and promotes M2 polarization via the AKT/Nrf2/HO-1 and NF-κB signals. Furthermore, CPT protected the neuroblastoma cell line SH-SY5Y and dopaminergic neuron cell line MN9D from damage mediated by microglia activation. In conclusion, our results demonstrate that CPT regulates the microglia polarization phenotype via activating AKT/Nrf2/HO-1 and inhibiting NF-κB pathways, inhibits neuro-inflammatory responses, and exerts neuroprotective effects in vivo and in vitro .

BackgroundHigh milk intake has been associated with cardio-metabolic risk. We conducted a Mendelian Randomization (MR) study to obtain evidence for the causal relationship between milk consumption and cardio-metabolic traits using the lactase persistence (LCT-13910 C > T, rs4988235) variant as an instrumental variable.MethodsWe tested the association of LCT genotype with milk consumption (for validation) and with cardio-metabolic traits (for a possible causal association) in a meta-analysis of the data from three large-scale population-based studies (1958 British Birth Cohort, Health and Retirement study, and UK Biobank) with up to 417,236 participants and using summary statistics from consortia meta-analyses on intermediate traits (N = 123,665–697,307) and extended to cover disease endpoints (N = 86,995–149,821).ResultsIn the UK Biobank, carriers of ‘T’ allele of LCT variant were more likely to consume milk (P = 7.02 × 10−14). In meta-analysis including UK Biobank, the 1958BC, the HRS, and consortia-based studies, under an additive model, ‘T’ allele was associated with higher body mass index (BMI) (Pmeta-analysis = 4.68 × 10−12) and lower total cholesterol (TC) (P = 2.40 × 10−36), low-density lipoprotein cholesterol (LDL-C) (P = 2.08 × 10−26) and high-density lipoprotein cholesterol (HDL-C) (P = 9.40 × 10−13). In consortia meta-analyses, ‘T’ allele was associated with a lower risk of coronary artery disease (OR:0.86, 95% CI:0.75–0.99) but not with type 2 diabetes (OR:1.06, 95% CI:0.97–1.16). Furthermore, the two-sample MR analysis showed a causal association between genetically instrumented milk intake and higher BMI (P = 3.60 × 10−5) and body fat (total body fat, leg fat, arm fat and trunk fat; P < 1.37 × 10−6) and lower LDL-C (P = 3.60 × 10−6), TC (P = 1.90 × 10−6) and HDL-C (P = 3.00 × 10−5).ConclusionsOur large-scale MR study provides genetic evidence for the association of milk consumption with higher BMI but lower serum cholesterol levels. These data suggest no need to limit milk intakes with respect to cardiovascular disease risk, with the suggested benefits requiring confirmation in further studies.

Epicardial adipose tissue (EAT) significantly contributes to the progression of cardiovascular diseases (CVDs). However, manually quantifying EAT volume is labor-intensive and susceptible to human error. Although there have been some deep learning-based methods for automatic quantification of EAT, they are mostly uninterpretable and fail to harness the complete anatomical characteristics. In this study, we proposed an enhanced deep learning method designed for EAT quantification on coronary computed tomography angiography (CCTA) scan, which integrated both data-driven method and specific morphological information. A total of 108 patients who underwent routine CCTA examinations were included in this study. They were randomly assigned to training set ( n  = 60), validation set ( n  = 8), and test set ( n  = 40). We quantified and calculated the EAT volume based on the CT attenuation values within the predicted pericardium. The automatic method demonstrated strong agreement with expert manual quantification, yielding a median Dice score coefficients (DSC) of 0.916 (Interquartile Range (IQR): 0.846–0.948) for 2D slices. Meanwhile, the median DSC for the 3D volume was 0.896 (IQR: 0.874–0.908) between these two measures, with an excellent correlation of 0.980 ( p  < 0.001) for EAT volumes. Additionally, our model’s Bland-Altman analysis revealed a low bias of -2.39 cm³. The incorporation of pericardial anatomical structures into deep learning methods can effectively enhance the automatic quantification of EAT. The promising results demonstrate its potential for clinical application.