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There is increasing attention on the association of socioeconomic status and individual behaviors (SES/IB) with mental health. However, the impacts of SES/IB on mental disorders are still unclear. To provide evidence for establishing feasible strategies on disease screening and prevention, we implemented Mendelian randomization (MR) design to appraise causality between SES/IB and mental disorders. We conducted a two-sample MR study to assess the causal effects of SES and IB (dietary habits, habitual physical activity, smoking behaviors, drinking behaviors, sleeping behaviors, leisure sedentary behaviors, risky behaviors, and reproductive behaviors) on three mental disorders, including bipolar disorder, major depressive disorder and schizophrenia. A series of filtering steps were taken to select eligible genetic instruments robustly associated with each of the traits. Inverse variance weighted was used for primary analysis, with alternative MR methods including MR-Egger, weighted median, and weighted mode estimate. Complementary methods were further used to detect pleiotropic bias. After Bonferroni correction and rigorous quality control, we identified that SES (educational attainment), smoking behaviors (smoking initiation, number of cigarettes per day), risky behaviors (adventurousness, number of sexual partners, automobile speeding propensity) and reproductive behavior (age at first birth) were causally associated with at least one of the mental disorders. MR study provides robust evidence that SES/IB play broad impacts on mental disorders.

Background Metabolic disturbance has been reported in patients with epilepsy. Still, the evidence about the causal role of metabolites in facilitating or preventing epilepsy is lacking. Systematically investigating the causality between blood metabolites and epilepsy would help provide novel targets for epilepsy screening and prevention. Methods We conducted two-sample Mendelian randomization (MR) analysis. Data for 486 human blood metabolites came from a genome-wide association study (GWAS) comprising 7824 participants. GWAS data for epilepsy were obtained from the International League Against Epilepsy (ILAE) consortium for primary analysis and the FinnGen consortium for replication and meta-analysis. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy. Results 482 out of 486 metabolites were included for MR analysis following rigorous genetic variants selection. After IVW and sensitivity analysis filtration, six metabolites with causal effects on epilepsy were identified from the ILAE consortium. Only four metabolites remained significant associations with epilepsy when combined with the FinnGen consortium [uridine: odds ratio (OR) = 2.34, 95% confidence interval (CI) = 1.48–3.71, P  = 0.0003; 2-hydroxystearate: OR = 1.61, 95% CI = 1.19–2.18, P  = 0.002; decanoylcarnitine: OR = 0.82, 95% CI = 0.72–0.94, P  = 0.004; myo-inositol: OR = 0.77, 95% CI = 0.62–0.96, P  = 0.02]. Conclusion The evidence that the four metabolites mentioned above are associated with epilepsy in a causal way provides a novel insight into the underlying mechanisms of epilepsy by integrating genomics with metabolism, and has an implication for epilepsy screening and prevention.

Before 2000, a large number of mangrove forests in Guangxi were cut down for aqua-cultural purposes. After 2000, with the introduction of national, provincial and municipal policies on mangrove protection, this phenomenon was less frequently seen. By using the object-oriented method, referring to the spectral and texture characteristics of the ground target, and using the Landsat remote sensing images of 2000-2019, this paper extracts and analyzes the area and change of mangrove and aquaculture area in Maowei sea, Guangxi. The results show that the use of object-oriented methods can extract mangroves and aquaculture areas with higher accuracy. The area of mangroves generally shows an upward trend, with the area of aquaculture increasing first and then decreasing.

Background Previous studies have linked gut microbiota with cancer etiology, but the associations for specific gut microbiota are causal or owing to bias remain to be elucidated. Methods We performed a two‐sample Mendelian randomization (MR) analysis to assess the causal effect of gut microbiota on cancer risk. Five common cancers, including breast, endometrial, lung, ovarian, and prostate cancer as well as their subtypes (sample sizes ranging from 27,209 to 228,951) were included as the outcomes. Genetic information for gut microbiota was obtained from a genome‐wide association study (GWAS) comprising 18,340 participants. In univariable MR (UVMR) analysis, the inverse variance weighted (IVW) method was conducted as the primary method, with the robust adjusted profile scores, weighted median, and MR Egger used as supplementary methods for causal inference. Sensitivity analyses including the Cochran Q test, Egger intercept test, and leave‐one‐out analysis were performed to verify the robustness of the MR results. Multivariable MR (MVMR) was performed to evaluate the direct causal effects of gut microbiota on the risk of cancers. Results UVMR detected a higher abundance of genus Sellimonas predicted a higher risk of estrogen receptor‐positive breast cancer (OR = 1.09, 95% CI 1.05–1.14, p = 2.01 × 10−5), and a higher abundance of class Alphaproteobacteria was associated with a lower risk of prostate cancer (OR = 0.84, 95% CI 0.75–0.93, p = 1.11 × 10−3). Sensitivity analysis found little evidence of bias in the current study. MVMR further confirmed that genus Sellimonas exerted a direct effect on breast cancer, while the effect of class Alphaproteobacteria on prostate cancer was driven by the common risk factors of prostate cancer. Conclusion Our study implies the involvement of gut microbiota in cancer development, which provides a novel potential target for cancer screening and prevention, and might have an implication for future functional analysis. We found that sarcopenia and cachexia have different effects on OS for PDAC patients undergoing radical excision by studying the data of 614 PDAC patients.

The traditional methods for classifying elemental concentrations such as the cumulative frequency method, the logarithmic interval method, and the mean–standard deviation method all have the limitation of depending on a specific dataset. An objective “ruler” that can measure the elemental concentration level regardless of the amount of data (even for a single sample) and enables comparisons among different elements and regions is highly necessary. Recently, the 19-level fixed-value method was proposed as a “ruler” to measure the elemental concentrations of Sn, Li, Mo, and Ni objectively and to facilitate comparisons across elements and regions. However, the method for Au has not been proposed until now. In this paper, we propose the “ruler” for Au, which objectively divides Au concentrations into 19 levels with 18 fixed values from the detection limit to the cut-off grade with easily understood numbers. The “ruler” for Au along with those for Mo and Sn was applied to geochemical survey data at 1:200,000 and 1:50,000 scales, respectively, in the Zhongchuan area of Western Qinling, China, to classify elemental concentrations and draw geochemical maps. The results show that elemental concentrations can be measured using the “ruler” to assess the background, anomaly, and mineralization levels objectively, and the levels can be compared across different elements, regions, and even different scales. Geochemical maps show that in the study area, known gold deposits are all associated with high anomalies or mineralization levels of Au, while the Mo and Sn concentrations are predominantly at background levels. These results are consistent with the known mineral resources in this area. When superimposing geochemical maps of larger scales onto those of smaller scales, the variation in the elemental concentration levels with different survey scales indicates valuable geochemical meanings for mineral exploration.

This study investigates the specific circulation anomalies that have sustained the low Antarctic sea ice state since 2016. Firstly, we find a significant strengthening and southward shift in the Ferrel Cell (FC) during 2016–2022, resulting in a marked increase in southward transport of heat and moisture. Secondly, this enhanced FC is closely associated with a stronger mid‐latitude wave pattern. This pattern is zonally asymmetric and greatly amplifies the poleward advections of heat and moisture, leading to the increased downward longwave radiation, more liquid precipitation and sea ice retreat in specific regions, including the western Pacific and Indian Ocean sectors, Ross and northern Weddell Seas. The mechanism deduced from the short‐term period is further supported by the results of 40 ensemble members of simulations. The southward expansion of the FC and sea ice decline are closely linked to La Niña‐like conditions but may also be driven by anthropogenic global warming. Plain Language Summary Following the sudden decline in 2016, the Antarctic sea ice extent has persisted at historically low levels. In 2023, it reached unprecedented record lows. However, the specific atmospheric circulation anomalies that have sustained the Antarctic sea ice at low levels are still unknown. It is well‐established that the Ferrel Cell, a mid‐latitude atmospheric meridional circulation, plays a pivotal role in the energy exchange between the high‐ and mid‐latitudes. Our findings indicate that the enhanced Ferrel Cell zonally intensified southward transport of heat and moisture over the sea ice regions, which sustains the overall low Antarctic sea ice state. Additionally, in the horizontal plane, the enhanced mid‐latitude wave pattern is responsible for the regional sea ice retreat over the western Pacific sector, Ross Sea, Indian Ocean sector, and northern Weddell Sea, and is also closely associated with the enhanced Ferrel Cell. The effects of the enhanced Ferrel Cell on Antarctic sea ice decline are further supported by the results of large ensemble simulations. Therefore, this study suggests that concurrent with the southward shifting of the Ferrel Cell, the stronger warm and moist air intrusions, and the increased liquid precipitation, restrict the Antarctic sea ice expansion following its sudden decline. Key Points Since 2016, the low Antarctic sea ice extent has persisted, consistent with heat and moisture accumulation over the sea ice edges The Ferrel Cell was enhanced and shifted southward, leading to the increased southward heat/moisture advection, and liquid precipitation The effects of the enhanced Ferrel Cell on Antarctic sea ice decline are further supported by the results of large ensemble simulations

Toxoplasma gondii infection induces anxiety in hosts during the chronic stage; however, its role in pre-anxiety-like behaviors during the acute stage remains poorly understood. This study investigates the role of Bradyzoite Formation Deficient 2 (BFD2), a transcription factor essential for tachyzoite-to-bradyzoite differentiation, in inflammation, apoptosis, and behavioral changes during acute T. gondii infection. Using CRISPR/Cas9-mediated gene editing, we generated a Bfd2 knockout strain (ME49∆ bfd2 ) and observed reduced parasite proliferation and plaque formation, indicating BFD2’s role in promoting T. gondii survival. RNA sequencing analysis of infected BV2 cells revealed that Bfd2 deletion significantly downregulated inflammatory responses, with reduced expression of key inflammatory markers (interleukin 1 beta ((IL-1β), interferon gamma (IFN-γ), and tumor necrosis factor alpha (TNF-α)) during acute infection. Next, we used western blotting, real-time quantitative PCR (qPCR) and enzyme-linked immunosorbent assays (ELISAs) to verify that BFD2 improves the inflammation induced by acute stage T. gondii infection. In vivo studies confirmed that BFD2 exacerbates brain inflammation and neuronal apoptosis specifically during the acute stage, with no significant effects during the chronic stage. Behavior was assessed using the elevated plus maze test and open field test. Compared with the uninfected group and ME49∆ bfd2 group, the ME49 group mice showed an increased percentage of distance in the open arms and time in the open arm. The results showed that the total distance traveled, distance in the center, and time in the center were significantly decreased in the ME49 group, and the total distance traveled (mm) had no significant changes in the ME49∆ bfd2 . These demonstrated that BFD2 contributes to pre-anxiety-like behaviors in mice during acute stage T. gondii infection. These findings highlight BFD2 as a critical regulator of acute-stage inflammation, neuronal damage, and behavioral alterations, providing insights to develop targeted interventions against T. gondii infection.

RNA-binding motif protein 10 (RBM10), one of the members of the RNA-binding protein (RBP) family, has a tumor suppressor role in multiple cancers. However, the functional role of RBM10 in lung adenocarcinoma (LUAD) and the underlying molecular mechanism remains unclear. In this study, we observed that RBM10 is significantly downregulated in LUAD tissues compared with normal tissues. Low RBM10 expression is significantly associated with poor outcome of LUAD patients. and experiments show that RBM10 inhibits cell proliferation, metastasis and EMT progression in LUAD. Mechanistically, we demonstrate that RBM10 interacts with β-catenin interacting protein 1 (CTNNBIP1) and positively regulates its expression, disrupting the binding of β-catenin to the transcription factor TCF/LEF, thereby inactivating the Wnt/β-catenin pathway. In conclusion, this is the first study reporting the role of RBM10 in suppressing LUAD progression at least via partly inactivating the Wnt/β-catenin pathway, which provides new insights into the tumorigenesis and metastasis of LUAD. Thus, RBM10 may be a promising new therapeutic target or clinical biomarker for LUAD therapy in the future.

Accurate localization of microseismic sources is essential in fields such as oil and gas extraction and underground energy storage. Current seismic source localization methods based on full waveform inversion exhibit a high degree of nonlinearity and involve complex gradient calculations for the objective function. However, data-driven neural network microseismic source localization methods lack physical constraints, which can compromise geological validity. To address these challenges, this paper proposes a microseismic source localization method that integrates full waveform inversion with a recurrent neural network. First, the seismic wavefield propagation operator is designed using convolutional kernels to achieve networked microseismic forward modeling. Next, chain differentiation of the neural network is employed to calculate the gradient for full waveform inversion in reverse, improving computational efficiency. Finally, by minimizing the error between the observed and forward-modeled data, the spatial components of the seismic source are optimized, and non-maximum suppression is applied to obtain the spatial location of the seismic source. The experimental results reveal that the proposed method achieves high localization accuracy, high computational efficiency, and resistance to noise.

In recent decades, the melting of the Greenland Ice Sheet (GrIS) has become one of the major causes of global sea-level rise. Supraglacial lakes (SGLs) are typical hydrological features produced on the surface of the GrIS during the melt seasons. The existence and evolution of SGLs play an important role in the melting process of the ice sheet surface. To understand the distribution and recent changes of SGLs in Greenland, this study developed a random forest (RF) algorithm incorporating the texture and morphological features to automatically identify SGLs based on the Google Earth Engine (GEE) platform. Sentinel-2 imagery was used to map the SGLs inventory in Greenland during the 2016–2018 melt seasons and to explore the spatial and temporal variability characteristics of SGLs. Our results show changes in SGLs from 2016 to 2018, with the total area decreasing by ~1152.22 km2 and the number increasing by 1134; SGLs are mainly distributed in western Greenland (SW, CW, NW) and northeastern Greenland (NE), where the NE region has the largest number of observed SGLs and the largest SGL was with the surface area of 16.60 km2 (2016). SGLs were found to be most active in the area with the elevation of 800–1600 m and the slope of 0–5°, and showed a phenomenon of retreating to lower elevation areas and developing to steeper slope areas. Our work provided a method for rapid inventory of SGLs. This study will help monitor the mass balance of the GrIS and predict future rapid ice loss from Greenland.