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Numerous observational studies have identified associations between both psoriasis (PsO) and psoriatic arthritis (PsA), and autoimmune diseases (AIDs); however, the causality of these associations remains undetermined. We conducted a bidirectional two-sample Mendelian Randomization study to identify causal associations and directions between both PsO and PsA and AIDs, such as systemic lupus erythematosus (SLE), Crohn's disease (CD), ulcerative colitis (UC), multiple sclerosis (MS), uveitis, bullous pemphigoid (BP), Hashimoto's thyroiditis (HT), rheumatoid arthritis (RA), vitiligo, and ankylosing spondylitis (AS). The causal inferences were drawn by integrating results from four regression models: Inverse Variance Weighting (IVW), MR-Egger, Weighted Median, and Maximum Likelihood. Furthermore, we performed sensitivity analyses to confirm the reliability of our findings. The results showed that CD [IVW odds ratio (OR ), 1.11; 95% confidence interval (CI), 1.06-1.17; = 8.40E-06], vitiligo (OR , 1.16; 95% CI, 1.05-1.28; = 2.45E-03) were risk factors for PsO, while BP may reduce the incidence of PsO (OR , 0.91; 95% CI, 0.87-0.96; = 1.26E-04). CD (OR , 1.07; 95% CI, 1.02-1.12; = 0.01), HT (OR , 1.23; 95% CI, 1.08-1.40; = 1.43E-03), RA (OR , 1.11; 95% CI, 1.02-1.21, = 2.05E-02), AS (OR , 2.18; 95% CI, 1.46-3.27; = 1.55E-04), SLE (OR , 1.04; 95% CI, 1.01-1.08; = 1.07E-02) and vitiligo (OR , 1.27; 95% CI, 1.14-1.42; = 2.67E-05) were risk factors for PsA. Sensitivity analyses had validated the reliability of the results. Our study provides evidence for potential causal relationships between certain AIDs and both PsO and PsA. Specifically, CD and vitiligo may increase the risk of developing PsO, while CD, HT, SLE, RA, AS, and vitiligo may elevate the risk for PsA. Additionally, it is crucial to closely monitor the condition of PsO patients with specific AIDs, as they have a higher likelihood of developing PsA than those without AIDs. Moving forward, greater attention should be paid to PsA and further exploration of other PsO subtypes is warranted.

A solar neutrino detector relying on the charged-current (CC) interaction of ν e on 7 Li is attractive. The total CC interaction cross-section weighted by the solar 8 B electron neutrino spectrum is approximately 60 times that of the neutrino-electron elastic scattering process. The final state effective kinetic energy after the CC interaction on 7 Li directly reflects the neutrino energy, which stands in sharp contrast to the plateau structure of recoil electrons of the elastic scattering. The recent measurement of the optical properties of saturated LiCl water solution, especially the long attenuation length, has once again aroused our interest in LiCl. In this work, with new B(GT) experimental measurements, the CC cross-section on 7 Li is reevaluated to be 3.759 × 10 - 42 cm 2 . Given the high solubility of LiCl of 74.5 g/100 g water at 10  ∘ C and the high natural abundance of 92.41% of 7 Li , a solar neutrino detection proposal is made. The detector with high concentration LiCl water solution has a comparable CC event rate of ν e on 7 Li with that of neutrino-electron elastic scattering. The contained 35 Cl , 6 Li , and H also make a delayed-coincidence detection for ν ¯ e possible. The advantages of studying the upturn effect of solar neutrino oscillation, light sterile neutrinos, and Earth matter effect are investigated in detail. The sensitivities in discovering solar neutrino upturn and light sterile neutrinos are presented.

This study investigated the relationship between dyslipidemia prior to conception and the risk of preeclampsia (PE) in women pregnant by in vitro fertilization and embryo transfer (IVF-ET). The retrospective cohort study consisted of 2994 women who conceived by IVF-ET and delivered live neonates. The study population was divided into two components: a training set for the prediction model development (2288 women) and a test set for validation (706 women). Multivariable logistic regression was used for the development and validation of predictive model for the risk of PE. Among the 2288 women in the training set, 266 women (11.6%) developed PE. Multiple logistic regression analysis identified independent predictors for PE: triglyceride (TG) [adjusted odds ratio (aOR) 1.284; 95% confidence interval (CI) 1.113–1.489, P  < 0.001]; pre-pregnancy BMI; pre- chronic hypertension; twin pregnancy; embryo transfer protocol. These independent predictors for PE were used to form a risk prediction model, and the area under the receiver-operator characteristic (ROC) curve (AUC) in the training and the test set was 0.77 (95% CI 0.73–0.80)and 0.71 AUC (95% CI 0.65–0.77), respectively. In conclusion, higher TG levels before pregnancy were independently associated with the risk for PE in women pregnant by IVF-ET.

Rice is an important food crop but is susceptible to diseases. However, currently available spot segmentation models have high computational overhead and are difficult to deploy in field environments. To address these limitations, a lightweight rice leaf spot segmentation model (MV3L-MSDE-PGFF-CA-DeepLabv3+, MMPC-DeepLabv3+) was developed for three common rice leaf diseases: rice blast, brown spot and bacterial leaf blight. First, the lightweight feature extraction network MobileNetV3_Large (MV3L) was adopted as the backbone of the model. Second, based on Haar wavelet downsampling, a multi-scale detail enhancement (MSDE) module was proposed to improve decision-making ability of the model in transitional regions such as spot gaps, and to improve the sticking and blurring problems at the boundary of spot segmentation. Meanwhile, the PagFm-Ghostconv Feature Fusion (PGFF) module was proposed to significantly reduce the computational overhead of the model. Furthermore, coordinate attention (CA) mechanism was incorporated before the PGFF module to improve robustness of the model in complex environments. A hybrid loss function integrating Focal Loss and Dice Loss was ultimately proposed to mitigate class imbalance between disease and background pixels in rice disease imagery. Validated on rice disease images captured under natural illumination conditions, the MMCP-DeepLabv3+ model achieved a mean intersection over union (MIoU) of 81.23% and mean pixel accuracy (MPA) of 89.79%, with floating-point operations (Flops) and the number of model parameters (Params) reduced to 9.695 G and 3.556 M, respectively. Compared to the baseline DeepLabv3+, this represents a 1.89% improvement in MIoU, a 0.83% increase in MPA, alongside 93.1% and 91.6% reductions in Flops and Params. The MMPC-DeepLabv3+ model demonstrated superior performance over DeepLabv3+, U-Net, PSPNet, HRNetV2, and SegFormer, achieving an optimal balance between recognition accuracy and computational efficiency, which establishes a novel paradigm for rice lesion segmentation in precision agriculture.

Stomach adenocarcinoma (STAD) is known for its high prevalence and poor prognosis, which underscores the need for novel therapeutic targets. Peroxidasin (PXDN), an enzyme with peroxidase activity, has been linked to cancer development in previous studies. However, its specific role in STAD is not well understood. In our study, we used public databases and clinical specimens to determine that PXDN expression is significantly elevated in STAD tissues and serves as an independent prognostic marker for patient outcomes. Our in vitro assays demonstrated that silencing PXDN significantly reduced STAD cell proliferation, invasion, and migration. Mechanistically, we found that PXDN promotes epithelial‒mesenchymal transition and angiogenesis in STAD cells and may be regulated by the PI3K/AKT pathway. Further analysis revealed that PXDN levels affect the sensitivity of STAD cells to various chemotherapeutic and small molecule drugs. Additionally, we observed a significant association between PXDN levels and the abundances of various immune cell types in patients with STAD. Our study highlighted a strong link between PXDN levels and the tumor immune microenvironment (TIM), suggesting that PXDN is a useful metric for evaluating the response to immune checkpoint inhibitors. Moreover, we found that PXDN is significantly associated with multiple immune checkpoints. In summary, our findings indicate that PXDN plays a critical role in STAD and that its level could serve as a potential prognostic biomarker. Thus, targeting PXDN may represent an effective treatment strategy for STAD.

Muon tomography (MT), based on atmospheric cosmic rays, is a promising technique suitable for nondestructive imaging of the internal structures of mountains. This method uses the measured flux distribution after attenuation, combined with the known muon angular and energy distributions and a 3D satellite map, to perform tomographic imaging of the density distribution inside a probed volume. A muon tomography station (MTS) requires direction-sensitive detectors with a high resolution for optimal tracking of incident cosmic-ray muons. The spherical liquid scintillator detector is one of the best candidates for this application due to its uniform detection efficiency for the whole 4π solid angle and its excellent ability to distinguish muon signals from the radioactive background via the difference in the energy deposit. This type of detector, with a 1.3 m diameter, was used in the Jinping Neutrino Experiment (JNE). Its angular resolution is 4.9 degrees. Following the application of imaging for structures of Jinping Mountain with JNE published results based on the detector, we apply it to geological prospecting. For mountains below 1 km in height and 2.8 g/cm3 in the reference rock, we demonstrate that this kind of detector can image internal regions with densities of ≤2.1 g/cm3 or ≥3.5 g/cm3 and hundreds of meters in size.

The soil water content (SWC) is a critical factor in agricultural production. To achieve real-time and nondestructive monitoring of the SWC, an experiment was conducted to measure the hyperspectral reflectance of soil samples with varying levels of water content. The soil samples were divided into two parts, SWC higher than field capacity (super-θf) and SWC lower than field capacity (sub-θf), and the outliers were detected by Monte Carlo cross-validation (MCCV). The raw spectra were processed using Savitzky–Golay (SG) smoothing and then the spectral feature variable of SWC was extracted by using a combination of competitive adaptive reweighted sampling (CARS) and random frog (Rfrog). Based on the extracted feature variables, an extreme learning machine (ELM), a back-propagation artificial neural network (BPANN), and a support vector machine (SVM) were used to establish the prediction model. The results showed that the accuracy of retrieving the SWC using the same model was poor, under two conditions, i.e., SWC above and below θf, mainly due to the influence of the lower accuracy of the super-θf part. The number of feature variables extracted by the sub-θf and super-θf datasets were 25 and 18, respectively, accounting for 1.85% and 1.33% of the raw spectra, and the variables were widely distributed in the NIR range. Among the models, the best results were achieved by the BPANN model for both the sub-θf and the super-θf datasets; the R2p, RMSEp, and RRMSE of the sub-θf samples were 0.941, 1.570%, and 6.685%, respectively. The R2p, RMSEp, and RRMSE of the super-θf samples were 0.764, 1.479%, and 4.205%, respectively. This study demonstrates that the CARS–Rfrog–BPANN method was reliable for the prediction of SWC.

Soil water content is one of the critical indicators in agricultural systems. Visible/near-infrared hyperspectral remote sensing is an effective method for soil water estimation. However, noise removal from massive spectral datasets and effective feature extraction are challenges for achieving accurate soil water estimation using this technology. This study proposes a method for hyperspectral remote sensing soil water content estimation based on a combination of continuous wavelet transform (CWT) and competitive adaptive reweighted sampling (CARS). Hyperspectral data were collected from soil samples with different water contents prepared in the laboratory. CWT, with two wavelet basis functions (mexh and gaus2), was used to pre-process the hyperspectral reflectance to eliminate noise interference. The correlation analysis was conducted between soil water content and wavelet coefficients at ten scales. The feature variables were extracted from these wavelet coefficients using the CARS method and used as input variables to build linear and non-linear models, specifically partial least squares (PLSR) and extreme learning machine (ELM), to estimate soil water content. The results showed that the correlation between wavelet coefficients and soil water content decreased as the decomposition scale increased. The corresponding bands of the extracted wavelet coefficients were mainly distributed in the near-infrared region. The non-linear model (ELM) was superior to the linear method (PLSR). ELM demonstrated satisfactory accuracy based on the feature wavelet coefficients of CWT with the mexh wavelet basis function at a decomposition scale of 1 (CWT(mexh_1)), with R , RMSE, and RPD values of 0.946, 1.408%, and 3.759 in the validation dataset, respectively. Overall, the CWT(mexh_1)-CARS-ELM systematic modeling method was feasible and reliable for estimating the water content of sandy clay loam.

To evaluate the effectiveness of advanced practice nurse–guided home-based rehabilitation exercise program (HREPro) among patients with lower limb spasticity post-stroke. This randomized controlled study recruited 121 patients with lower limb spasticity post-stroke. Intervention (n = 59) and control (n = 62) groups underwent 12-month HREPro and conventional rehabilitation, respectively, after discharge. The Fugl–Meyer assessment of spasticity measurement, modified Ashworth scale of motor function, 10-Meter Walk Test of walking ability, and Barthel index of activities of daily living (ADL) were evaluated at 0, 3, 6, and 12 months after discharge. Significant differences were found in spasticity degree, motor function, walking ability, and ADL at 6 and 12 months after discharge between the control and intervention groups. Lower limb spasticity and ADL in the intervention group were significantly improved. HREPro is effective for rehabilitation of patients with lower limb spasticity post-stroke and has favorable home application.

Unconventional water resources (e.g., saline water, etc.) for irrigation as a promising supplementary water source can alleviate the freshwater shortage in the agriculture of red soil areas in Southern China. It should be noted that the presence of soluble salt in this water source may have detrimental influences on soil water infiltration and crop growth. Understanding the effect of unconventional water irrigation (UWI) on water infiltration in red soil is important. Previous studies have shown that the salinity of UWI can alter soil hydraulic properties to change soil water movement in saline soils. However, the underlying mechanism and factors of water infiltration in red soil under UWI with different salinity levels remain limited. Therefore, a laboratory experiment (one-dimensional vertical infiltration experiment and centrifuge method) was conducted to evaluate the effect of UWI with different salinity levels [0 (the distilled water, CK), 1 (S1), 2 (S2), 3 (S3), 5 (S5), and 10 (S10) g L−1] on the soil water infiltration process, soil water characteristic curve (SWCC), soil water constants estimated using the SWCC, saturated and unsaturated hydraulic conductivity (KS and K) as well as the soil chemistry of soil profile [pH, electrical conductivity (EC), and Na+ and Cl− contents]. The primary factors of soil water infiltration were identified using stepwise regression and path analysis methods. The results showed that UWI salinity decreased water infiltration by 1.53–7.99% at the end of infiltration in red soil, following the order of CK > S1 > S5 > S2 > S3 > S10. Moreover, UWI could enhance soil water availability with an increase of 8.55–12.68% in available water capacity. In contrast, lower KS and K were observed in S1–S10, and there was a negative linear relationship between irrigation salinity and KS. UWI also produced the EC, Na+, and Cl− accumulations in the soil profile. As the salinity level of UWI increased, the accumulations aggravated. Soil acidification was found in S1–S5, while soil alkalization was observed in S10. Additionally, α, PWP, and KS were the primary factors influencing the water infiltration of red soil. This study can help optimize the soil infiltration model under UWI and establish a foundation for unconventional water management in the red soil regions of Southern China and other similar regions. In addition, the undisturbed red soil under agricultural unconventional water irrigation and the long-term effect of unconventional water application should be considered.