Soil cadmium pollution facilitated the invasion of alligator weed through enhanced herbivore resistance and competitive ability over a congeneric species.

A number of invasive plant species, such as Alternanthera philoxeroides, have been documented to be able to accumulate trace metal elements in their tissues. Since metal accumulation in plants can serve as a defence against herbivores, we hypothesized that metal pollution will increase herbivore resistance of metal-accumulating invasive plant species and such a benefit will grant them a competitive advantage over local co-occurring plants. In this study, we compared the differences in plant growth and herbivore feeding preference between A. philoxeroides and its native congener Alternanthera sessilis in single and mixed cultures with and without soil cadmium (Cd) pollution. The results showed that A. philoxeroides plants were more tolerant to Cd stress and accumulated more Cd in the leaves than A. sessilis. Cd exposure increased the resistance of A. philoxeroides against a specialist and a generalist herbivore compared with A. sessilis. Competition experiments indicated that Cd stress largely increased the competitive advantage of A. philoxeroides over A. sessilis with or without herbivore pressures. The differences in herbivore resistance between the two plant species under soil Cd stress are most likely due to the deterring effect of Cd accumulation and Cd-enhanced mechanical defences rather than changes in leaf specialized metabolites.

Preparation of strong, tough and conductive soy protein isolate/poly(vinyl alcohol)-based hydrogel via the synergy of biomineralization and salting out.

Conductive hydrogels have shown a great potential in the field of flexible electronic devices. However, conductive hydrogels prepare by traditional methods are difficult to combine high strength and toughness, which limits their application in various fields. In this study, a strategy for preparing conductive hydrogels with high strength and toughness by using the synergistic effect of biomineralization and salting-out was pioneered. In simple terms, by immersing the CaCl2 doped soy protein isolate/poly(vinyl alcohol)/dimethyl sulfoxide (SPI/PVA/DMSO) hydrogel in Na2CO3 and Na3Cit complex solution, the biomineralization aroused by Ca2+ and CO32-, and the salting-out effect of both NaCl and Na3Cit would enhance the mechanical properties of SPI/PVA/DMSO hydrogel. Meanwhile, the ionic conductivity of the hydrogel would also increase due the introduction of cation and anion. The mechanical and electrical properties of SPI/PVA/DMSO/CaCO3/Na3Cit hydrogels were significantly enhanced by the synergistic effect of biomineralization and salting-out. The optimum tensile strength, toughness, Young's modulus and ionic conductivity of the hydrogel were 1.4 ± 0.08 MPa, 0.51 ± 0.04 MPa and 1.46 ± 0.01 S/m, respectively. The SPI/PVA/DMSO/CaCO3/Na3Cit hydrogel was assembled into a strain sensor. The strain sensor had good sensitivity (GF = 3.18, strain in 20 %-500 %) and could be used to accurately detect various human movements.

Interpretable machine learning model based on the systemic inflammation response index and ultrasound features can predict central lymph node metastasis in cN0T1-T2 papillary thyroid carcinoma.

Background: It is arguable whether individuals with T1-T2 papillary thyroid cancer (PTC) who have a clinically negative (cN0) diagnosis should undergo prophylactic central lymph node dissection (pCLND) on a routine basis. Many inflammatory indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and systemic immune-inflammatory index (SII), have been reported in PTC. However, the associations between the systemic inflammation response index (SIRI) and the risk of central lymph node metastasis (CLNM) remain unclear. Methods: Retrospective research involving 1,394 individuals with cN0T1-T2 PTC was carried out, and the included patients were randomly allocated into training (70%) and testing (30%) subgroups. The preoperative inflammatory indices and ultrasound (US) features were used to train the models. To assess the forecasting factors as well as drawing nomograms, the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were utilized. Then eight interpretable models based on machine learning (ML) algorithms were constructed, including decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). The performance of the models was evaluated by incorporating the area under the precision-recall curve (auPR) and the area under the receiver operating characteristic curve (auROC), as well as other conventional metrics. The interpretability of the optimum model was illustrated via the shapley additive explanations (SHAP) approach. Results: Younger age, larger tumor size, capsular invasion, location (lower and isthmus), unclear margin, microcalcifications, color Doppler flow imaging (CDFI) blood flow, and higher SIRI (≥0.77) were independent positive predictors of CLNM, whereas female sex and Hashimoto thyroiditis were independent negative predictors, and nomograms were subsequently constructed. Taking into account both the auROC and auPR, the RF algorithm showed the best performance, and superiority to XGBoost, CatBoost and ANN. In addition, the role of key variables was visualized in the SHAP plot. Conclusions: An interpretable ML model based on the SIRI and US features can be used to predict CLNM in individuals with cN0T1-T2 PTC.

Factors associated with anxiety and depression in patients with erectile dysfunction: a cross-sectional study.

BACKGROUND: Few studies have investigated factors associated with anxiety and depression among patients with erectile dysfunction (ED). This study aimed to investigate associated factors and the prevalence of anxiety and depression in this special group in China. METHODS: Data from 511 patients with ED aged 18-60 years were collected between July 2021 and April 2022. The 5-item International Index of Erectile Function (IIEF-5) questionnaire, self-rating anxiety scale (SAS) and self-rating depression scale (SDS) were used to evaluate erectile function, anxiety and depression, respectively. Univariate analysis and multivariate linear regression analyses were used to explore the associated factors of depression and anxiety. RESULTS: The prevalence of anxiety and depression among ED patients was 38.16% and 64.97%, respectively. The mean anxiety index score was 47.37 ± 6.69 points, and the mean depression index was 54.72 ± 9.10 points. Multiple linear regression analysis showed that worse ED, low education level, and smoking were positively associated with increased risk of anxiety and depression. In addition, younger age, longer onset time, and irregular sleep were positively associated with high risk of anxiety, and irregular exercise was associated with severe depression. CONCLUSIONS: The prevalence of depression and anxiety in ED patients is high, and the severity of ED, age, education level, smoking, onset time, regular sleep, and exercise were associated with anxiety or depression. Reversible risk factors should be avoided and individualized psychological support services are necessary for ED patients.

Performance of the three-dimensional laser scanning method to monitor the moisture content of similar material models.

In mining safety and other fields, similar material simulation is the main research method to study the movement and deformation of rock formation and ground surface. However, the inaccurate subsidence laws could be obtained because the strength of the composition materials like gypsum and lime is easily affected by moisture. Therefore, it is crucial to monitor the moisture content when carrying simulation experiments. This paper discussed the feasibility of indirectly measuring the moisture content of similar material models using the three-dimensional (3D) laser scanning reflection intensity through three experiments on similar material specimens. The results showed that the laser reflection intensity was sensitive to the moisture content, incidence angle, and distance with three different relationships and the influence of the two factors could be weakened through the established correction models. However, it was recommended restricting the incidence angle to less than 20° and setting the distance from 4 to 10 m to reduce the complexity of correction. The accuracy of this method reached 1.1% under the monitoring condition of 4 m and the normal incidence, which could meet the requirements for monitoring the moisture content of similar material models. The research results of the paper provide a new method to monitor the moisture content in similar material models.

Big Data Analytics with Datalog Queries on Spark.

There is great interest in exploiting the opportunity provided by cloud computing platforms for large-scale analytics. Among these platforms, Apache Spark is growing in popularity for machine learning and graph analytics. Developing efficient complex analytics in Spark requires deep understanding of both the algorithm at hand and the Spark API or subsystem APIs (e.g., Spark SQL, GraphX). Our BigDatalog system addresses the problem by providing concise declarative specification of complex queries amenable to efficient evaluation. Towards this goal, we propose compilation and optimization techniques that tackle the important problem of efficiently supporting recursion in Spark. We perform an experimental comparison with other state-of-the-art large-scale Datalog systems and verify the efficacy of our techniques and effectiveness of Spark in supporting Datalog-based analytics.