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Inertial and deformability- based particles separations gradually attract more significant attentions. In this work, we present a hybrid chip by combining the advantages of inertial and deformability –based principle. The chip is a triplet parallelizing spiral inertial microfluidic chip interconnected with numerable tilted slits (Spiral-Slits Chip) for continuous separation of circulating tumor cells. Utilizing the inertial lift and viscous drag forces, different sized particles achieve different equilibrium at distinct streamlines of the spiral microchannel. Numerable tilted slits are organized along the flow direction. They frequently transport segregated streamline particles into a paralleled smaller microchannel. These frequent dragging results in the amount of certain sized particles in the original microchannel gradually and dramatically reduced. Inertial separation of distinct sized particles could be achievable. Two arrays of numerable tilted slits function as bridges. This Spiral-Slits Chip could substitute for Red Blood Cells Lysis (RBCL) and is most effective for ultra-high throughput. The overall arrangement of this triplet parallelizing spiral inertial microfluidic reflects stable streamlines distribution in the first main microchannel. Combining with Ellipse filters, robust and reproducible capture of CTCs could be achieved at high flow rates. Optical absorption detection has been tentatively tested, and this could simplify the process.

Transition Metal sulfides (TMSs) are effective sorbents for entrapment of highly polluting thiophiles such as elemental mercury (Hg 0 ). However, the application of these sorbents for mercury removal is stymied by their low accommodation capacities. Among the transition metal sulfides, only CuS has demonstrated industrially relevant accommodation capacity. The rest of the transition metal sulfides have 100-fold lower capacities than CuS. In this work, we overcome these limitations and develop a simple and scalable process to enhance Hg 0 accommodation capacities of TMSs. We achieve this by introducing structural motifs in TMSs by in situ etching. We demonstrate that in situ acid etching produces TMSs with defective surface and pore structure. These structural motifs promote Hg 0 surface adsorption and diffusion across the entire TMSs architecture. The process is highly versatile and the in situ etched transition metal sulfides show over 100-fold enhancement in their Hg 0 accommodation capacities. The generality and the scalability of the process provides a framework to develop TMSs for a broad range of applications. There is an urgent need to develop efficient strategies to remove elemental mercury from industrial flue gases. Here the authors develop a simple, versatile, and scalable strategy to obtain a 100-fold enhancement of the elemental mercury accommodation capacities of a wide range of transition metal sulfides by in situ acid etching.

This paper considers a linear regression model with stochastic restrictions,we propose a new mixed Kibria–Lukman estimator by combining the mixed estimator and the Kibria–Lukman estimator.This new estimator is a general estimation, including OLS estimator, mixed estimator and Kibria–Lukman estimator as special cases. In addition, we discuss the advantages of the new estimator based on MSEM criterion, and illustrate the theoretical results through examples and simulation analysis.

Researchers publish various studies to improve the performance of network intrusion detection systems. However, there is still a high false alarm rate and missing intrusions due to class imbalance in the multi-class dataset. This imbalanced distribution of classes results in low detection accuracy for the minority classes. This paper proposes a Synthetic Multi-minority Oversampling (SMMO) framework by integrating with a collaborative feature selection (CoFS) approach in network intrusion detection systems. Our framework aims to increase the detection accuracy of the extreme minority classes (i.e., user-to-root and remote-to-local attacks) by improving the dataset’s class distribution and selecting relevant features. In our framework, SMMO generates synthetic data and iteratively over-samples multi-minority classes. And the collaboration of correlation-based feature selection with an evolutionary algorithm selects essential features. We evaluate our framework with a random forest, J48, BayesNet, and AdaBoostM1. In a multi-class NSL-KDD dataset, the experimental results show that the proposed framework significantly improves the detection accuracy of the extreme minority classes compared with other approaches.

Cooperative perception in the field of connected autonomous vehicles (CAVs) aims to overcome the inherent limitations of single-vehicle perception systems, including long-range occlusion, low resolution, and susceptibility to weather interference. In this regard, we propose a high-precision 3D object detection V2V cooperative perception algorithm. The algorithm utilizes a voxel grid-based statistical filter to effectively denoise point cloud data to obtain clean and reliable data. In addition, we design a feature extraction network based on the fusion of voxels and PointPillars and encode it to generate BEV features, which solves the spatial feature interaction problem lacking in the PointPillars approach and enhances the semantic information of the extracted features. A maximum pooling technique is used to reduce the dimensionality and generate pseudoimages, thereby skipping complex 3D convolutional computation. To facilitate effective feature fusion, we design a feature level-based crossvehicle feature fusion module. Experimental validation is conducted using the OPV2V dataset to assess vehicle coperception performance and compare it with existing mainstream coperception algorithms. Ablation experiments are also carried out to confirm the contributions of this approach. Experimental results show that our architecture achieves lightweighting with a higher average precision (AP) than other existing models.

Current understanding of soil carbon dynamics suggests that plant litter quality and soil mineralogy control the formation of mineral-associated soil organic carbon (SOC). Due to more efficient microbial anabolism, high-quality litter may produce more microbial residues for stabilisation on mineral surfaces. To test these fundamental concepts, we manipulate soil mineralogy using pristine minerals, characterise microbial communities and use stable isotopes to measure decomposition of low- and high-quality litter and mineral stabilisation of litter-C. We find that high-quality litter leads to less (not more) efficient formation of mineral-associated SOC due to soil microbial community shifts which lower carbon use efficiency. Low-quality litter enhances loss of pre-existing SOC resulting in no effect of litter quality on total mineral-associated SOC. However, mineral-associated SOC formation is primarily controlled by soil mineralogy. These findings refute the hypothesis that high-quality plant litters form mineral-associated SOC most efficiently and advance our understanding of how mineralogy and litter-microbial interactions regulate SOC formation. This study challenges the hypothesis that high-quality plant litters form stable, mineral-associated soil organic carbon most efficiently, providing evidence that litter-microbial interactions and soil mineralogy regulate soil organic carbon formation.

Gut microbiota and its metabolites have regulatory effects on PCOS related ovarian dysfunction and insulin resistance. (EcN) is a genetically controlled probiotic with an excellent human safety record for improving gut microbiome metabolic disorders and immune system disorders. Here we focused to explore the application and effect of probiotic EcN on the gut microbiota-metabolism-IL-22-mitochondrial damage axis in PCOS. PCOS mice were constructed with dehydroepiandrosterone (DHEA) and treated with EcN, FMT or IL-22 inhibitors. Clinically control and PCOS subjects were included for further analysis. Serum and follicular fluid supernatant levels of sex hormones, insulin, glucose, cholesterol, and inflammatory factors were detected by ELISA and biochemical reagents. The pathological changes of ovarian tissues were observed by HE staining. The JC-1 level and COX4 gene expression in granulosa cells was detected by ELISA and RT-qPCR. The expressions of progesterone receptor A (PR-A), LC3II/I, Beclin1, p62 and CytC were detected by western blot. The number of autophagosomes in granulosa cells was observed by electron microscopy. 16S rRNA and LC-MS/MS were used to analyze the changes of gut microbiota and metabolism. EcN promoted the recovery of sex hormone levels and ovarian tissue morphology, promoted the expression of IL-22, COX4 and PR-A in granulosa cells, and inhibited mitophagy in PCOS mice. EcN decreased the number of gut microbiota, and significantly increased the abundance of , , and in PCOS mice. EcN improved metabolic disorders in PCOS mice by improving Amino sugar and nucleotide sugar metabolism pathways. IL-22 was positively associated with , and Progesterone, negatively associated with , Luteinizing hormone, Testosterone, N-Acetylglucosamin, L-Fucose and N-Acetylmannosamin. FMT reconfirmed that EcN ameliorated mitochondrial damage in granulosa cells of PCOS mice by gut microbiota, but this process was blocked by IL-22 inhibitor. Clinical trials have further demonstrated reduced IL-22 levels and mitochondrial damage in granulosa cells in PCOS patients. EcN improved IL-22 level and mitochondrial damage of granulosa cells in PCOS mice by promoting the recovery of sex hormone levels and ovarian tissue morphology, inhibiting the amount of gut microbiota, and promoting amino sugar and nucleotide sugar metabolism.

Intrusion detection systems get more attention to secure the computers and network systems. Researchers propose different network intrusion detection systems using machine learning techniques. However, the massive amount of data that contain irrelevant and redundant features is still challenging the intrusion detection systems. The redundancy and irrelevance of features may slow the processing time and decrease prediction performance. This paper proposes a Heterogeneous Ensemble Feature Selection (HEFS) method to select the relevant features while achieving better attack detection performance. The proposed method fuses the output feature subsets of five filter feature selection methods, using a union combination method, to obtain an ensemble features subset. HEFS method uses merit-based evaluation to avoid the internal redundancy of the obtained ensemble features subset and acquire the final optimal features. We evaluate the HEFS method with random forest, J48, random tree, and REP tree. In a multi-class NSL-KDD dataset, the experimental results show that the proposed method achieves better prediction performance than the specific feature selection methods and other frameworks.

RNA interference (RNAi) has been widely used in functional genomics of insects and received intensive attention in the development of RNAi-based plants for insect control. Ecdysone receptor (EcR) and ultraspiracle protein (USP) play important roles in molting, metamorphosis, and reproduction of insects. EcR and USP orthologs and their function in grain aphid (Sitobion avenae F.) have not been documented yet. Here, RT-PCR, qRT-PCR, dsRNA feeding assay and aphid bioassay were employed to isolate EcR and USP orthologs in grain aphid, investigate their expression patterns, and evaluate the effect of RNAi on aphid survival and fecundity, and its persistence. The results indicated that SaEcR and SaUSP exhibited similar expression profiles at different developmental stages. Oral administration of dsRNAs of SaEcR and dsSaUSP significantly decreased the survival of aphids due to the down-regulation of these two genes, respectively. The silencing effect was persistent and transgenerational, as demonstrated by the reduced survival and fecundity due to knock-down of SaEcR and SaUSP in both the surviving aphids and their offspring, even after switching to aphid-susceptible wheat plants. Taken together, our results demonstrate that SaEcR and SaUSP are essential genes in aphid growth and development, and could be used as RNAi targets for wheat aphid control.

Numerous studies have identified risk factors for physical restraint (PR) use in older adults in long-term care facilities. Nevertheless, there is a lack of predictive tools to identify high-risk individuals. We aimed to develop machine learning (ML)-based models to predict the risk of PR in older adults. This study conducted a cross-sectional secondary data analysis based on 1026 older adults from 6 long-term care facilities in Chongqing, China, from July 2019 to November 2019. The primary outcome was the use of PR (yes or no), identified by 2 collectors' direct observation. A total of 15 candidate predictors (older adults' demographic and clinical factors) that could be commonly and easily collected from clinical practice were used to build 9 independent ML models: Gaussian Naïve Bayesian (GNB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), and light gradient boosting machine (Lightgbm), as well as stacking ensemble ML. Performance was evaluated using accuracy, precision, recall, an F score, a comprehensive evaluation indicator (CEI) weighed by the above indicators, and the area under the receiver operating characteristic curve (AUC). A net benefit approach using the decision curve analysis (DCA) was performed to evaluate the clinical utility of the best model. Models were tested via 10-fold cross-validation. Feature importance was interpreted using Shapley Additive Explanations (SHAP). A total of 1026 older adults (mean 83.5, SD 7.6 years; n=586, 57.1% male older adults) and 265 restrained older adults were included in the study. All ML models performed well, with an AUC above 0.905 and an F score above 0.900. The 2 best independent models are RF (AUC 0.938, 95% CI 0.914-0.947) and SVM (AUC 0.949, 95% CI 0.911-0.953). The DCA demonstrated that the RF model displayed better clinical utility than other models. The stacking model combined with SVM, RF, and MLP performed best with AUC (0.950) and CEI (0.943) values, as well as the DCA curve indicated the best clinical utility. The SHAP plots demonstrated that the significant contributors to model performance were related to cognitive impairment, care dependency, mobility decline, physical agitation, and an indwelling tube. The RF and stacking models had high performance and clinical utility. ML prediction models for predicting the probability of PR in older adults could offer clinical screening and decision support, which could help medical staff in the early identification and PR management of older adults.