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Grazing affects plant community characteristics and the relationship between above-ground biomass and diversity, which has become a hot topic in grassland ecosystem research in recent years. The present study investigated grassland vegetation in the Two Rivers pastoral area based on 60 sampling points using sampling methods. The effects of grazing on the structural characteristics of plant communities were analysed using the α-diversity index, and the relationship between species diversity and the above-ground biomass of plant communities, as well as their changes, was explored. The results showed that grazing disturbance decreased the number of family species in the grassland plant communities and the similarity between communities; grazing activities significantly decreased the above-ground biomass of the grassland plant communities; the height and cover also showed a decreasing trend (p < 0.01), but the density of the grassland plant communities improved (p < 0.01). The Margalef index, the Shannon–Wiener index, and the Simpson index of the grassland plant communities all showed different degrees of decrease after grazing, while the Alatalo index showed an increase; biomass was positively correlated with the Margalef, Shannon–Wiener, and Alatalo indices and negatively correlated with the Simpson index (p < 0.01). Therefore, in the management and maintenance of grasslands, the impact of grazing on the structural characteristics of plant communities should be taken into account, and reasonable response policies should be implemented according to the actual local situation. This study provides a theoretical basis for grassland maintenance and management and the sustainable development of ecosystems in the pastoral areas of the two river sources of the Altay Mountains. [ABSTRACT FROM AUTHOR]

Purpose: This study was designed to investigate influencing factors of out-of-hospital anticoagulation therapy compliance among patients undergoing major orthopaedic surgeries. Methods: A cross-sectional, descriptive study was conducted from July 2022 to February 2023 among outpatients who underwent major orthopedic surgery in our hospital. Patients (n = 200) were surveyed using the General Information Questionnaire, the General Self-Efficacy Scale, the Specificity of Medication-Taking Beliefs Scale, and the Morisky Medication Adherence Scale. Factors that influenced patient compliance were also determined using univariate and multivariate regression analyses. Results: One hundred eighty-three valid questionnaires were returned, the compliance with outpatient anticoagulation therapy among patients undergoing major orthopaedic surgeries was good in 56.3% (103/183) of all cases and poor in 43.7% (80/183). Multivariate logistic regression analysis showed that medication duration, adverse effects, self-efficacy and medication beliefs influenced adherence to out-of-hospital anticoagulation therapy in patients undergoing major orthopedic surgery (P < 0.05). Conclusions: Poor compliance with out-of-hospital anticoagulation therapy in patients undergoing major orthopedic surgery is mainly associated with a long course of medication, adverse reactions, low self-efficacy and low medication beliefs. Healthcare staff should strengthen post-discharge anticoagulation management based on relevant influencing factors to enhance patient compliance. [ABSTRACT FROM AUTHOR]

The improper disposal of hospital waste products containing genetic materials poses a serious safety threat. We present herein an environmentally friendly technology using a graphene-based novel carbon-allotropic surface to remediate such wastes. The used carbon-allotrope is decorated with an enediyne (EDE-1) enriched aromatic pi-conjugated structure to create an efficient and active surface for cleaving DNA strands. Under controlled exposure of ultraviolet (UV) radiation and heat, the developed surface influences genetic degradation without disturbing the bacterial populations present downstream of the water treatment system. The designed material has been extensively characterized using physicochemical and biological tools. Our results indicate that this approach can possibly be introduced in large scale hospital waste disposal streams for remediating genetic hazards and thereby developing a portable self-contained system. [ABSTRACT FROM AUTHOR]

Energy dissipation damping technology is usually used for infrastructure construction in seismic regions. In this study, a lever-type lead viscoelastic node damper (LTLVND), which can capture small rotational displacements of the infrastructure under seismic excitation, is innovatively proposed based on the leverage effect. The characteristics of energy-absorbing capacity of the LTLVND and its mitigation effect on the dynamics of the structure under seismic excitation are studied. Testing and modelling results show that a satisfactory energy dissipation effect can be observed for the innovative lead viscoelastic damper (LTLVND). Finally, a seismic analysis of a concrete frame structure with LTLVNDs is carried out. Pushover analysis and dynamic elastoplastic analysis are included. It is shown that a significant improvement in structural performance under seismic conditions can be achieved with the addition of LTLVNDs at appropriate locations. [ABSTRACT FROM AUTHOR]

Providing effective Through Train Services (TTSs) faces challenges due to complex infrastructure conditions, train performances and passenger demands. To enhance TTSs between two different classes of urban rail transit lines with variations in train speed and capacity, we propose a multi-objective Integer Non-Linear Programming (INLP) model. This model maximizes passenger travel time savings and average train load utilization, and develops an integrated approach to simultaneously optimize the frequencies of through express trains and local trains, as well as the operation zones, stopping patterns and type selection of through trains. Additionally, a Non-Dominated Sorting Genetic Algorithm II is designed to solve the INLP model based on a simple test network and a real-world case from the Nanjing Subway. The unique benefits of our proposed method are demonstrated by a comprehensive compared with the Single Line Operation Mode and the all-stop plans under Through Operation Mode. [ABSTRACT FROM AUTHOR]

The change grassland plant communities demonstrate with elevation has been one of the hot issues in ecological research, and there remain many unsolved problems. In order to further elucidate the rules of grassland plant community change with elevation, this study took the Burqin forest area as a research object, using field survey, redundancy analysis and grey correlation analysis to comprehensively assess the characteristics of change in grassland plant communities with elevation and the relationship of this evolution with environmental factors. The results showed that (1) the numbers of species, community biomass, community cover and community densities of grassland plant communities showed an "M" pattern with the increase in elevation. There were significant changes in the importance values and dominance of plants at different elevations; with increasing elevation, grassland plants became primarily dominated by cold-tolerant and well-adapted perennials. (2) The similarity coefficients of grassland plant communities at different elevations ranged from 0.06 to 0.62, i.e., from very dissimilar to moderately similar. (3) As the elevation increased, the Margalef species richness index, Shannon–Wiener diversity index, Simpson dominance index and Alatalo evenness index all showed an "M" pattern trend. (4) The degrees of correlation between temperature and precipitation and community biomass and species diversity were at a high level, and these were the most important environmental factors affecting the biomass and species diversity of grassland plant communities in the Burqin forest area. The results of this study can provide a theoretical basis for the rational utilization of grassland resources and for the sustainable development of grassland ecosystems in the Burqin forest area. [ABSTRACT FROM AUTHOR]

Two-part model (TPM) is a widely appreciated statistical method for analyzing semi-continuous data. Semi-continuous data can be viewed as arising from two distinct stochastic processes: one governs the occurrence or binary part of data and the other determines the intensity or continuous part. In the regression setting with the semi-continuous outcome as functions of covariates, the binary part is commonly modelled via logistic regression and the continuous component via a log-normal model. The conventional TPM, still imposes assumptions such as log-normal distribution of the continuous part, with no unobserved heterogeneity among the response, and no collinearity among covariates, which are quite often unrealistic in practical applications. In this article, we develop a two-part nonlinear latent variable model (TPNLVM) with mixed multiple semi-continuous and continuous variables. The semi-continuous variables are treated as indicators of the latent factor analysis along with other manifest variables. This reduces the dimensionality of the regression model and alleviates the potential multicollinearity problems. Our TPNLVM can accommodate the nonlinear relationships among latent variables extracted from the factor analysis. To downweight the influence of distribution deviations and extreme observations, we develop a Bayesian semiparametric analysis procedure. The conventional parametric assumptions on the related distributions are relaxed and the Dirichlet process (DP) prior is used to improve model fitting. By taking advantage of the discreteness of DP, our method is effective in capturing the heterogeneity underlying population. Within the Bayesian paradigm, posterior inferences including parameters estimates and model assessment are carried out through Markov Chains Monte Carlo (MCMC) sampling method. To facilitate posterior sampling, we adapt the Polya-Gamma stochastic representation for the logistic model. Using simulation studies, we examine properties and merits of our proposed methods and illustrate our approach by evaluating the effect of treatment on cocaine use and examining whether the treatment effect is moderated by psychiatric problems. [ABSTRACT FROM AUTHOR]

The analgesic effect of intravenous lidocaine varies with the duration of lidocaine infusion and surgery type. We tested the hypothesis that prolonged lidocaine infusion alleviates postoperative pain in patients recovering from hepatectomy over the first 3 postoperative days. Patients undergoing elective hepatectomy were randomly assigned to receive prolonged i.v. lidocaine treatment or placebo. The primary outcome was incidence of moderate-to-severe movement-evoked pain at 24 h postoperatively. The secondary outcomes included incidence of moderate-to-severe pain during movement and at rest throughout the first 3 postoperative days, postoperative opioid consumption, and pulmonary complications. Plasma lidocaine concentration was also monitored. We enrolled 260 subjects. Intravenous lidocaine lowered the incidence of moderate-to-severe movement-evoked pain at 24 h and 48 h postoperatively (47.7% vs 67.7%, P =0.001; 38.5% vs 58.5%, P =0.001) and reduced movement-evoked pain scores (3.7 [1.7] vs 4.2 [1.6]; mean difference 0.5 [95% confidence interval {CI}: 0.1–0.9]; P =0.018) and morphine equivalent consumption (47.2 [16.7] mg vs 52.6 [19.2] mg; mean difference 5.4 mg [95% CI: 1.0–9.8]; P =0.016) at 24 h postoperatively. Lidocaine also lowered the incidence of postoperative pulmonary complications (23.1% vs 38.5%; P =0.007). Median plasma lidocaine concentrations were 1.5, 1.9, and 1.1 μg ml−1 (inter-quartile ranges: 1.1–2.1, 1.4–2.6, and 0.8–1.6, respectively) after bolus injection, at the end of the surgery, and 24 h postoperatively. Prolonged intravenous lidocaine infusion reduced the incidence of moderate-to-severe movement-evoked pain for 48 h after hepatectomy. However, the reduction in pain scores and opioid consumption by lidocaine was below the minimal clinically important difference. NCT04295330. [ABSTRACT FROM AUTHOR]

A negative Poisson's ratio honeycomb material has the characteristics of anti-conventional deformation behavior and high impact resistance, which is a new lightweight microstructure material with broad application prospects. However, most of the current research is still at the microscopic level and two-dimensional level, and little research has been carried out for three-dimensional structures. Compared with the two-dimensional level, three-dimensional negative Poisson's ratio structural mechanics metamaterials have the advantages of a lighter mass, higher material utilization, and more stable mechanical properties, and they have great potential for development in the fields of aerospace, the defense industry, and vehicles and ships. This paper presents a novel 3D star-shaped negative Poisson's ratio cell and composite structure, inspired by the octagon-shaped 2D negative Poisson's ratio cell. The article carried out a model experimental study with the help of 3D printing technology and compared it with the numerical simulation results. The effects of structural form and material properties on the mechanical characteristics of 3D star-shaped negative Poisson's ratio composite structures were investigated through a parametric analysis system. The results show that the error of the equivalent elastic modulus and the equivalent Poisson's ratio of the 3D negative Poisson's ratio cell and the composite structure is within 5%. The authors found that the size of the cell structure is the main factor affecting the equivalent Poisson's ratio and the equivalent elastic modulus of the star-shaped 3D negative Poisson's ratio composite structure. Furthermore, among the eight real materials tested, rubber exhibited the best negative Poisson's ratio effect, while the copper alloy showed the best effect among the metal materials, with a Poisson's ratio between −0.058 to −0.050. [ABSTRACT FROM AUTHOR]

Nonalcoholic fatty liver disease (NAFLD) is a strong stimulant of cardiovascular diseases, affecting one‐quarter of the world's population. TBC1 domain family member 25 (TBC1D25) regulates the development of myocardial hypertrophy and cerebral ischemia–reperfusion injury; however, its effect on NAFLD/nonalcoholic steatohepatitis (NASH) has not been reported. In this study, we demonstrated that TBC1D25 expression is upregulated in NASH. TBC1D25 deficiency aggravated hepatic steatosis, inflammation, and fibrosis in NASH. In vitro tests revealed that TBC1D25 overexpression restrained NASH responses. Subsequent mechanistic validation experiments demonstrated that TBC1D25 interfered with NASH progression by inhibiting abnormal lipid accumulation and inflammation. TBC1D25 deficiency significantly promoted NASH occurrence and development. Therefore, TBC1D25 may potentially be used as a clinical therapeutic target for NASH treatment. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation (UDA), which can transfer knowledge from labeled source domain to unlabeled target domain, needs to access a large number of labeled source data in the process of generalization. However, the data of two domains may not be accessed at the same time due to data privacy protection. To solve this problem, source-data free domain adaptation (SFDA) began to receive attention. However, too little source information will lead to some performance gaps. To balance the issues between UDA and SFDA, a new setting called Prototype-based domain adaptation (Prototype-DA) is proposed, which further improves the practicability of UDA by using source category prototype instead of source data. At the same time, it can also ensure the privacy of source data like SFDA. Specifically, our training process can be divided into two steps. First, the source data is used to pre-train a source model, and the source category prototypes are obtained after the training of source model. Then, to generalize the source model to the target domain, category maximum mean discrepancy (Category-MMD) is defined so that the target data can be aligned with the source category prototypes. In this way, source category prototypes will transfer knowledge to the target domain together with the source model. Through source category prototypes, Prototype-DA can not only achieve the comparable results than the method using source data, but also protect the privacy of source data to some extent. Furthermore, the target category prototypes are constructed and the consistency between the labels of target category prototypes and the classification results is required. This prototype-label consistency regularization, proposed by us for the first time, helps to extract discriminative features in the target domain. Compared with the previous UDA methods and SFDA methods, extensive experiments on multiple public domain adaptation datasets show that Prototype-DA achieves the state-of-the-art results. At the same time, the traditional UDA theory is expanded to our method setting and makes a theoretical analysis to ensure the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Atherosclerotic cardiovascular disease (ASCVD) remains the leading cause of death worldwide. Increasing evidence suggests that, in addition to traditional metabolic risk factors such as obesity, hypercholesterolemia, hypertension, diabetes mellitus, and insulin resistance (IR), nonalcoholic fatty liver disease (NAFLD) is an emerging driver of ASCVD via multiple mechanisms, mainly by disrupting lipid metabolism. The lack of pharmaceutical treatment has spurred substantial investment in the research and development of NAFLD drugs. However, many reagents with promising therapeutic potential for NAFLD also have considerable impacts on the circulating lipid profile. In this review, we first summarize the mechanisms linking lipid dysregulation in NAFLD to the progression of ASCVD. Importantly, we highlight the potential risks of/benefits to ASCVD conferred by NAFLD pharmaceutical treatments and discuss potential strategies and next-generation drugs for treating NAFLD without the unwanted side effects. [Display omitted] • Nonalcoholic fatty liver disease has emerged as a driver of atherosclerotic cardiovascular disease. • Acetyl-coenzyme A carboxylase inhibitors and farnesoid X receptor agonists disrupt the circulating lipid profile, which may increase the risk of atherosclerotic cardiovascular disease. • A novel molecule, IMA-1, moderately represses acetyl-coenzyme A carboxylase 1 activity and has promising potential in treating nonalcoholic steatohepatitis without causing a significant decrease in polyunsaturated fatty acid levels or hyperlipidemia. [ABSTRACT FROM AUTHOR]

Simple Summary: Since antimicrobial peptides (AMPs) possess broad-spectrum antimicrobial activity and low drug resistance, their development as antibiotic alternatives are of interest. Previous studies reported that the performance of pigs was improved by AMPs through the protection of the intestine. Nevertheless, direct evidence linking AMPs and skeletal muscle growth is needed. In the current work, Iturin A as a novel antimicrobial peptide was selected to fight engineered STb-Rosetta Escherichia coli (STb-R) to measure pork skeletal muscle growth and further mechanisms. We found the restrictions of body weight gain and skeletal muscle growth caused by STb-R could be rescued after Iturin A gavage, and this was mediated by the hypothalamic-pituitary-Mtorc1 growth axis. The engineered STb-Rosetta Escherichia coli (STb-R) was designed to investigate the effects of Iturin A on the skeletal muscle growth of weaned piglets. A total of 28 piglets were randomly divided into 4 groups (7 piglets per group): the control group (100 mL PBS), the Iturin A group (100 mL 320 mg/kg body weight (BW) Iturin A), the STb-R group (100 mL 1 × 1010 CFU/mL STb-R), and the Iturin A + STb-R group (100 mL 320 mg/kg BW Iturin A + 1 × 1010 CFU/mL STb-R). Compared with the control, STb-R-reduced body weight gain were rescued by Iturin A. The semimembranosus muscle weight recovered to normal level in the Iturin A + STb-R group. The level of relevant genes of the growth axis were elevated by Iturin A, including GHRH in the hypothalamus, GHRHR and GH in the pituitary, and GHR, IGF-1 and IGF-1R in the semimembranosus muscle. Moreover, Iturin A increased the mean fiber area and the number of proliferating cells in the semimembranosus muscle, which were decreased by STb-R. Additionally, the mTORC1 pathway was reactivated by Iturin A to relieve the suppression of STb-R. Collectively, the hypothalamic-pituitary growth axis-mediated Iturin A reactivated the mTORC1 pathway to rescue STb-R-restricted pork skeletal muscle growth. [ABSTRACT FROM AUTHOR]

Domain adaptation for object detection has been extensively studied in recent years. Most existing approaches focus on single-source unsupervised domain adaptive object detection. However, a more practical scenario is that the labeled source data is collected from multiple domains with different feature distributions. The conventional approaches do not work very well since multiple domain gaps exist. We propose a Multi-source domain Knowledge Transfer (MKT) method to handle this situation. First, the low-level features from multiple domains are aligned by learning a shallow feature extraction network. Then, the high-level features from each pair of source and target domains are aligned by the followed multi-branch network. After that, we perform two parts of information fusion: (1) We train a detection network shared by all branches based on the transferability of each source sample feature. The transferability of a source sample feature means the indistinguishable degree to the target domain sample features. (2) For using our model, the target sample features output by the multi-branch network are fused based on the average transferability of each domain. Moreover, we leverage both image-level and instance-level attention to promote positive cross-domain transfer and suppress negative transfer. Our main contributions are the two-stage feature alignments and information fusion. Extensive experimental results on various transfer scenarios show that our method achieves the state-of-the-art performance. • We design a branchy network architecture for MDAD, on which we propose a two-stage feature alignment strategy. • Useful domain information is expected to be fully utilized by a novel information fusion schema. • Two-level attention mechanisms are used to promote positive cross-domain transfer and suppress negative transfer. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation aims to transfer knowledge from labeled source domain to unlabeled target domain. Traditional methods usually achieve domain adaptation by aligning the distributions between two domains once. We propose to align the distributions twice by a disentanglement and reconstruction process, named DTR (Disentanglement Then Reconstruction). Specifically, a feature extraction network shared by both source and target domains is used to obtain the original extracted features, then the domain invariant features and domain specific features are disentangled from the original extracted features. The domain distributions are explicitly aligned when disentangling domain invariant features. Based on the disentangled features, the class prototypes and domain prototypes can be estimated. Then, a reconstructor is trained by the disentangled features. By this reconstructor, we can construct prototypes in the original feature space using the corresponding class prototype and domain prototype similarly. The extracted features are forced to close the corresponding constructed prototypes. In this process, the distribution between two domains is implicitly aligned again. Experiment results on several public datasets confirm the effectiveness of our method. • Our method can use domain invariant features and domain specific features very well. • By learning more compact features, domain distributions will be aligned again. • The experiments on public datasets are conducted and the proposed method works well. • Thorough analysis experiments also illustrates the advantages of our method. [ABSTRACT FROM AUTHOR]

When the training data and the test data do not obey the same distribution, the performances of many object detection methods always decrease greatly. Naturally, domain adaptation methods at feature level are proposed. The basic idea is to adapt the feature extraction network such that the feature distributions of the source and target domains match. We propose a new method that is built directly on the Faster R-CNN model, which not only aligns the source and target data features, but also forces their generated features closer together to further align the source and target domains. Moreover, compared with previous approaches, we construct a more powerful discriminator and a simple generator to solve the domain adaptation problem. The model works like a pair of scissors, so we call it Scissors Networks (SN). We conduct extensive experiments on popular datasets, including Cityscapes, Foggy Cityscapes, SIM10k and KITTI. The experimental results demonstrate that our algorithm is superior to the state-of-the-art deep learning based domain adaptation approaches. [ABSTRACT FROM AUTHOR]

Feature selection is a widely used dimension reduction technique to select feature subsets because of its interpretability. Many methods have been proposed and achieved good results, in which the relationships between adjacent data points are mainly concerned. But the possible associations between data pairs that are not adjacent are always neglected. Different from previous methods, we propose a novel and very simple approach for unsupervised feature selection, named MMFS (Multi-step Markov Probability Relationship for Feature Selection). The idea is using multi-step Markov transition probability to describe the relation between any data pair. Two ways from the positive and negative viewpoints are employed respectively to keep the data structure after feature selection. From the positive viewpoint, the maximum transition probability that can be reached in a certain number of steps is used to describe the relation between two points. Then, the features which can keep the compact data structure are selected. From the viewpoint of negative, the minimum transition probability that can be reached in a certain number of steps is used to describe the relation between two points. On the contrary, the features that least maintain the loose data structure are selected. The two ways can also be combined. Thus three algorithms are proposed. Our main contributions are a novel feature section approach which uses multi-step transition probability to characterize the data structure, and three algorithms proposed from the positive and negative aspects for keeping data structure and select the features to preserve such structure. The performance of our approach is compared with the state-of-the-art methods on eight real-world data sets, and the experimental results show that the proposed MMFS is effective in unsupervised feature selection. [ABSTRACT FROM AUTHOR]

Skeleton-based action recognition distinguishes human actions using the trajectories of skeleton joints, which can be a good representation of human behaviors. Conventional methods usually construct classifiers with hand-crafted or the learned features to recognize human actions. Different from constructing a direct action classifier for action recognition task, this paper attempts to identify human actions based on the development trends of behavior sequences. Specifically, we first utilize the memory neural network to construct action predictors for each kind of activity. These action predictors can then output the action trends at the next time step. According to the predictions of these action predictors at each time step and the removal rule, the poor predictors can be eliminated step by step, and the IDentity(ID) number of the last predictor left is considered as the label of the action sequence to be categorized. We compare the proposed action recognition algorithm using sequence prediction learning with other methods on two publicly available datasets. Our experimental results consistently demonstrate the feasibility and effectiveness of the suggested method. It also proves the importance of prediction learning for action recognition. [ABSTRACT FROM AUTHOR]

The flow behavior of particle clusters with thermal convection in confined fluid flow has garnered considerable attention. One of the remaining challenges is to understand the role of a porous structure of particle clusters in the confined thermal fluid flow. In present work, a numerical study based on a double distribution function lattice Boltzmann model has been conducted to unveil the two-sided lid-driven cavity flow containing a hot cluster formed by an array of center-placed solids. First, we investigated the heat transfer by varying the Richardson number (R i , from 0.01 to 1.0) and the solid fraction of the cluster (ϕ , from 0.0143 to 0.6734) with fixed cavity Reynolds number (R e = 1000) and Prandtl number (P r = 0.7). The increase in R i would lead to the formation of non-centrosymmetric streamlines and isotherms and an increase in the total averaged Nusselt number N u ¯ a , in which several vortex structures being identified at high R i and ϕ. Then, we explored the influence of permeability, quantified by the Darcy number (D a), by changing the porous structure of cluster via rearranging the array of solids. It is found that a higher permeability could enhance heat transfer of particle cluster due to the increase in convection, but the smaller surface area of solid particles would limit heat transfer. A correlation of N u ¯ a with respect to R i and D a could be developed based on our two-dimensional simulation results, allowing the evaluation of involved interplay between the porous structure of particle cluster and thermal convection in confined flow via a simple yet effective way. [ABSTRACT FROM AUTHOR]

Muscle regeneration, representing an essential homeostatic process, relies mainly on the myogenic progress of resident satellite cells, and it is modulated by multiple physical and nutritional factors. Here, we investigated how myogenic differentiation-related factors and pathways respond to the first limiting amino acid lysine (Lys) in the fast and slow muscles, and their satellite cells (SCs), of swine. Thirty 28-day-old weaned piglets with similar body weights were subjected to three diet regimens: control group (d 0–28: 1.31% Lys, n = 12), Lys-deficient group (d 0–28: 0.83% Lys, n = 12), and Lys rescue group (d 0–14: 0.83% Lys; d 15–28: 1.31% Lys, n = 6). Pigs on d 15 and 29 were selectively slaughtered for muscular parameters evaluation. Satellite cells isolated from fast (semimembranosus) and slow (semitendinosus) muscles were also selected to investigate differentiation ability variations. We found Lys deficiency significantly hindered muscle development in both fast and slow muscles via the distinct manipulation of myogenic regulatory factors and the Wnt/Ca2+ pathway. In the SC model, Lys deficiency suppressed the Wnt/Ca2+ pathways and myosin heavy chain, myogenin, and myogenic regulatory factor 4 in slow muscle SCs but stimulated them in fast muscle SCs. When sufficient Lys was attained, the fast muscle-derived SCs Wnt/Ca2+ pathway (protein kinase C, calcineurin, calcium/calmodulin-dependent protein kinase II, and nuclear factor of activated T cells 1) was repressed, while the Wnt/Ca2+ pathway of its counterpart was stimulated to further the myogenic differentiation. Lys potentially manipulates the differentiation of porcine slow and fast muscle myofibers via the Wnt/Ca2+ pathway in opposite trends. [ABSTRACT FROM AUTHOR]

Fire spalling prediction of fibre-reinforced concrete containing polypropylene (PP) or steel fibre at elevated temperatures is challenging. It is difficult to use conventional methods, such as finite-element modelling and discrete-element modelling, to solve the problem, because of the complicate coupling mechanism of PP and steel fibres in concrete. To this end, two artificial neural network (ANN) models, one (ANN1) based on a concrete mix study and the other (ANN2) based on a compressive strength study, were introduced to assess the resistance of concrete to explosive spalling. Test data (321 and 318 items, respectively) gathered from the literature were utilised to train the proposed models. Twenty-four concrete mixes (96 groups), that is, seven plain concrete mixes, four high performance concrete mixes reinforced with PP fibre, three ultra-high-performance concrete (UHPC) mixes with reinforced PP fibre and ten UHPC mixes reinforced with PP and steel hybrid fibres were designed and tested to validate the accuracy of the models. The study demonstrates that ANN1 and ANN2 can achieve a predictive accuracy of 89.6% and 84.4% for the explosive spalling, respectively, indicating the feasibility of the proposed models for predicting explosive spalling threat of the hybrid fibre-reinforced concrete. [ABSTRACT FROM AUTHOR]

The lunar orbiter laser altimeter (LOLA) onboard the lunar reconnaissance orbiter has performed high-precision, full-coverage, and high-density laser ranging observations for the entire lunar surface since its launch. Statistics have shown that LOLA has collected 6.94 billion effective altimeter data up to June 2022. Most of the typical orbits in the LOLA dataset have a high quality and exhibit horizontal offsets of almost 7 m and radial offsets of almost 0.5 m. However, there is still a category of orbits in the dataset that will cause apparent noise in the constructed DEM, which is attributed to the orbits with large or anomalous errors. We call such orbits as flawed orbits in this paper. The flawed orbits can be identified and screened by the elevation discrepancy at the crossovers of the orbits. The results show that the flawed orbits are caused by significant along-track errors, which also result in the radial error of up to several kilometers. Moreover, most of the flawed orbits are concentrated in several consecutive time intervals. A correction method is then proposed to correct the flawed orbits in the local region. The position of the flawed orbits is reconstructed using the feature points matching of the DEMs before and after they are removed. Some experimental analyzes show that the apparent terrain artifacts have been eliminated and more identifiable terrain details are reappeared. Identifying and correcting these flawed orbits with significant along-track offsets paves the way for improving the quality of the LOLA data and reconstructing the topography of the Moon. [ABSTRACT FROM AUTHOR]

Preoperative chemotherapy has shown benefits for locally advanced and borderline resectable pancreatic cancer. Neoadjuvant chemotherapy (NAC) has also been attempted in resectable pancreatic cancer (RPC); however, its role remains controversial. This study aimed to compare the clinical difference between NAC and upfront resection (UR) in RPC. Electronic databases including PubMed, Embase, Medline, Web of Science, ClinicalTrials.gov , and Cochrane Central Register of Controlled Trials were searched for relevant articles from inception to February 2019 that addressed the overall survival in patients with RPC treated with or without NAC to identify eligible studies. Eleven studies were included in the final meta-analysis. The quality assessment of the included studies was based on the Newcastle–Ottawa quality scale. Data of the unresectable rate, R0 resection rate, and positive lymph node rate were also extracted in each study for further analysis. Pooled hazard ratio (HR), odds ratio (OR), and 95% confidence intervals (CIs) were calculated to assess the strength of the association. A total of eleven studies (eight cohort studies and three randomized controlled trials) involving 9773 patients were included. Ten of the eleven studies followed the "intention-to-treat" principle. NAC was found to be significantly associated with a higher R0 resection rate (P < 0.0001; OR = 2.62, 95% CI 1.70–4.03) and increased negative lymph node rate (P < 0.00001; OR = 0.34, 95% CI 0.31–0.37). However, compared with the UR group, NAC was related to a lower surgical resection rate (P = 0.0004; OR = 2.18, 95% CI 1.41–3.37). Overall, the NAC group exhibited no benefits in terms of overall survival compared with that in the UR group (P = 0.10; HR = 0.86, 95% CI 0.73–1.03). In the subgroup analysis, however, patients who received gemcitabine-based regimen as the NAC strategy had more favorable overall survival than that in the UR group (P = 0.04; HR = 0.75, 95% CI 0.57–0.99). NAC may be associated with a lower resection rate; however, it is associated with an increased R0 resection rate and lymph node negative rate. Although overall survival was similar in patients with or without NAC, gemcitabine-based NAC might provide longer overall survival. Further large-volume, randomized controlled trials are needed to validate the improved prognosis of patients undergoing NAC. [ABSTRACT FROM AUTHOR]

By utilizing the radio-frequency metal-oxide-semiconductor field-effect transistor switch, the multi-stage stacked printed circuit board Blumlein lines, and the induction voltage adder, a voltage generator with MHz repetition rates and nanosecond duration was designed and fabricated. The shunting current of the multi-stage stacked Blumlein lines, which consists of the coupling current at the load ends and the leakage current at the switch ends, is clarified. The circuit simulation and experiment of the three-stage stacked Blumlein lines are carried out; the result shows that the experiment and simulation agree well. The five-stage IVA prototype is designed for generating voltage pulses of ∼10 kV in amplitude and 8–11 ns in duration with MHz burst repetition rates. The voltage generator with MHz repetition rates and nanosecond duration demonstrated an important application prospect to reduce the electron beam initial emittance of the high energy accelerator. [ABSTRACT FROM AUTHOR]

Ferroelectric heterostructures with bi-stable state of polarization are appealing for data storage as well as tunable functionalities such as memristor behavior. While an increasing number of experimental and theoretical studies suggest that polarization persists in ultrathin epitaxial heterostructures approaching just a couple of unit cells, the switching of such polar order is much less well understood, and whether polarization can be reversed in ultrathin ferroelectric heterostructures remains to be answered. Here we fabricate high-quality 7-unit cell thick BaTiO 3 (BTO) films on Nb-doped single crystalline SrTiO 3 (NSTO) substrate, and demonstrate their apparent yet unambiguously false polarization reversal due to charge injection using comprehensive piezoresponse force microscopy (PFM) studies. The presence of weak polar order consistent with linear piezoelectricity is confirmed at the atomic scale by high resolution integrated differential phase contrast (IDPC) of scanning transmission electron microscopy (STEM) as well as macroscopic second harmonic generation (SHG), while the lack of polarization reversal under the voltage applied is supported by density functional theory calculation showing the persistence of dead layer on the surface. Nevertheless, poling-induced electric conduction differing by two orders of magnitude is observed, demonstrating resistive switching in ferroelectric heterostructure in the absence of polarization reversal, even with weak polar order. Our finding has technological implications on emerging memristor applications with potentially more accessible states than bi-stable polarization modulated mechanism, and raises technical challenges to unambiguously demonstrate polarization switching in ultrathin films at their critical size limit. Image, graphical abstract [ABSTRACT FROM AUTHOR]

Modern trams have been widely used around the world, especially in China. This paper explores the main influencing factors of modern trams' passenger flow at the early operational stage. The system dynamics model is adopted for dealing with the problem on hand. Tram Line 1 in Huai'an, Jiangsu Province, China is selected as the case study. Data are collected using the RP and SP survey. The sensitivity test and extreme condition test are performed. The simulation results demonstrate that four variables (i.e., land development intensity, fares, service level, and transfer efficiency) significantly affect passenger flow. Land development intensity is the most significant factor, and the effect of service level on passenger flow is higher than that of the fares. The departure interval of 10 minutes is the maximum psychological limit that passengers can bear, and 2 RMB is a reasonable price. Such conclusions can provide guidance for the planning and design of modern trams and address the problem of shortage of passengers at an early stage. [ABSTRACT FROM AUTHOR]

A deep convolutional neural network (CNN) becomes a widely used tool for object detection. Many previous works have achieved excellent performance on object detection benchmarks. However, these works present generic detectors whose performance will drop rapidly when they are applied to a surveillance scene. In this paper, we propose an efficient method to construct a scene-specific regression model based on a generic CNN-based classifier. Our regression model is an adaptive deep CNN (ADCNN), which can predict object locations in the surveillance scene. First, we transfer the generic CNN-based classifier to the surveillance scene by selecting useful kernels. Second, we learn the context information of the surveillance scene in our regression model for accurate location prediction. Our main contributions are: 1) a transfer learning method that selects useful kernels in the generic CNN-based classifier; 2) a special architecture that can effectively learn the local and global context information in the surveillance scene; and 3) a new objective function to effectively train parameters in ADCNN. Compared with some state-of-the-art models, ADCNN achieves the best performance on three surveillance data sets for pedestrian detection and one surveillance data set for vehicle detection. [ABSTRACT FROM AUTHOR]

Semicontinuous variable analysis is a widely appreciated statistical method in such disciplines as social science, medicines, and economics. In detecting underlying structure and representing possible interrelationships, statistical analysis using a two-part model is appropriated. In this paper, we present a general extension of two-part model to the situation where the unobserved factors are included in the two parts to interpret external variability in semicontinuous variable. Auxiliary information on these factors is manifested by continuous responses via measurement model, while the interrelationships among factors are exploited through structural equation model. Moreover, under longitudinal setting, dynamic characteristics of responses between any two occasions are represented by transition model. Procedures for model fitting, parameter estimation, model selection and prediction are developed within the Bayesian paradigm. Markov Chains Monte Carlo method is used to implement posterior analysis. Empirical results including a simulation and a real example are used to illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]

Objectives. Stroke is a common condition after a transient ischemic attack (TIA) or minor ischemic stroke (IS). Adding clopidogrel to aspirin may yield more beneficial outcomes than aspirin mono-therapy; meanwhile, the risk of bleeding in the acute phase remains poorly understood. Therefore, there is increasing emphasis on the risks and benefits of clopidogrel with aspirin compared with aspirin mono-therapy in an effort to treat TIA/IS. Design. We searched several electronic databases, including PubMed, Cochrane, and Embase, to identify eligible randomized controlled trials (RCTs) based on the index words comparing dual-antiplatelet therapy to aspirin mono-therapy for secondary stroke prevention updated to December, 2018. Results. A total of 11 RCTs met our inclusion criteria. The pooled analysis showed that clopidogrel plus aspirin was associated with a trend toward a reduction in recurrent IS (RR = 0.72, 95%CI = 0.65-0.81, p < .001), but not the recurrent stroke rate (RR = 0.81, 95% CI = 0.63-1.03, p = .09) than aspirin mono-therapy. There were differences in bleeding episodes (RR = 1.81, 95%CI = 1.65-1.99, p < .001), moderate-severe major bleeding (RR = 1.64, 95% CI = 1.24-2.16, p = .0005), or mild bleeding (RR = 2.25, 95%CI = 1.54-3.31, p < .001) between the study groups. Meanwhile, no benefit of reducing the risk of intracranial hemorrhage with dual-antiplatelet therapy was found in TIA/IS patients (RR = 1.44, 95% CI = 0.95-2.19, p = .09). Conclusions. The addition of clopidogrel to aspirin for patients with TIA or IS appeared to significantly reduce the risk of IS recurrence with a possible increase in the risk of bleeding compared with aspirin alone. [ABSTRACT FROM AUTHOR]

The emerging contamination of pathogenic bacteria in the soil has caused a serious threat to public health and environmental security. Therefore, effective methods to inactivate pathogenic bacteria and decrease the environmental risks are urgently required. As a century-old technique, bacteriophage (phage) therapy has a high efficiency in targeting and inactivating pathogenic bacteria in different environmental systems. This review provides an update on the status of bacteriophage therapy for the inactivation of pathogenic bacteria in the soil environment. Specifically, the applications of phage therapy in soil-plant and soil-groundwater systems are summarized. In addition, the impact of phage therapy on soil functioning is described, including soil function gene transmission, soil microbial community stability, and soil nutrient cycling. Soil factors, such as soil temperature, pH, clay mineral, water content, and nutrient components, influence the survival and activity of phages in the soil. Finally, the future research prospects of phage therapy in soil environments are described. Unlabelled Image • Phage therapy has extensive application prospect in soil environment. • Phage therapy inactivated pathogenic bacteria in groundwater-soil-plant system. • Phage therapy significantly impeded ARB migration in vertical soil column. • The influence of phage therapy on soil functioning deserves further attention. [ABSTRACT FROM AUTHOR]

To increase the system spatial resolution, this paper presents an eight-channel analog front-end (AFE) with wide bandwidth, low noise for frequency modulation continuous wave (FMCW) LiDAR application. The eight channels are independent of each other. For each channel, the fully differential shunt feedback transimpedance amplifier is adopted to improve the signal noise ratio, and the adaptive dc current cancellation (ADCC) is proposed to prevent the saturation with dc current. The cascaded differential amplifier is employed to achieve gain boosting, bandwidth compensation, and better driving capability. The proposed AFE is fabricated in 0.18 μm CMOS technology. Experimental results indicate the largest transimpedance gain, bandwidth, input referred noise current and dc cancellation are 100 dB, 190 MHz, 37.7 nArms and 280 μA, respectively. The power consumption of each channel is 70 mW with a 3.3 V power supply. The total area of the proposed AFE is approximately equal to 1.2 mm × 2.4 mm, including IO pads. [ABSTRACT FROM AUTHOR]

This paper presents a high-energy-efficiency and high-area-efficiency 12-bit column-parallel successive-approximation-register/single-slope analog-to-digital converter (SAR/SS ADC) for high-speed infrared focal plane readout circuits. The proposed SAR/SS ADC divides the quantization process into upper 8-bit coarse conversion by the SAR ADC and lower 4-bit fine conversion by the SS ADC. The SAR ADC uses the scaled reference-voltage technique to achieve 8-bit conversion using only a 4-bit capacitor DAC. In addition, the SS ADC is designed with an additional 1-bit redundant bit to reduce settling error mismatch between coarse and fine conversion by the proposed error correction method. The proposed SAR/SS ADC is designed in a 0. 18 μ m CMOS process, and each column occupies an area of 30 μ m × 487 μ m. The post-layout simulation results show that the SAR/SS consumes 73.15 μ W for each column and has a DNL of −0.5/+0.6 LSB and an INL of −0.6/+0.4 LSB. The FoM is only 35.72 fJ/step with 2 μ s conversion time, which achieves an appropriate trade-off between conversion speed, power consumption, and area. [ABSTRACT FROM AUTHOR]

[Display omitted] • Screen out the most suitable protease and the optimal hydrolysis conditions. • Epitopes were reduced to 4 after alkaline protease and Lactobacillus helveticus. • Obtain the seven residual allergenic peptides from treated products. • αs 1 -casein (AA84-90) can be used as an immune tolerance peptide for further study. This paper has investigated the residual allergenicity of cow's milk treated by enzymatic hydrolysis combined with Lactobacillus fermentation (Lb. Plantarum and Lb. helveticus). The treated products were comprehensively evaluated by SDS-PAGE, RP-HPLC, ELISA, and Caco-2 models. And the allergenic changes of residual allergenic peptides were explored by DC-T co-culture. The results showed that alkaline protease was the most suitable protease that targeted to destroy epitopes of milk major allergen than trypsin, pepsin, and papain by prediction. And the residual epitopes were reduced to four which was treated by alkaline protease combined with Lb. helveticus. The transport absorption capacity of treated products was almost twice than milk. Meanwhile, the seven residual allergenic peptides were obtained from treated products. Among them, αs 1 -casein (AA84-90) can be used as an immune tolerance peptide for further study. Lb. helveticus combined with alkaline protease treatment may be considered promising strategy of protect from cow's milk allergy. [ABSTRACT FROM AUTHOR]

• Theoretical measurement and optimization of drug solubility. • Predicting the solubility of Pantoprazole sodium sesquihydrate via ML. • The models were validated with experimental data. • Gradient boosting was selected as the main model of this work. Theoretical/experimental measurement and optimization of solubility is considered as an active field of research in different scientific areas such as pharmaceutics. Novel orally administered therapeutic medicines have recently suffered from low solubility and bioavailability. Therefore, finding better methods to improve the drug solubility and bioavailability are of prime importance. Application of CO 2 supercritical fluid (CO 2 SCF) as a well-known procedure has attracted numerous attentions in pharmaceutical industry to increase the drug solubility and bioavailability due to its low flammability, reasonable cost, and negligible detrimental impacts for ecosystem. Pantoprazole sodium sesquihydrate is an extensively-used drug, which is indicated for the alleviation of the undesirable signs of esophagitis caused by stomach acid due to gastroesophageal reflux disease (GERD). The purpose of this research is to analyze and optimize the solubility of Pantoprazole sodium sesquihydrate using four different tree-based models where we have temperature and pressure as input features. These models include the regression tree, the boosting model based on it (gradient boosting), the bagging model based on it (random forest), and finally a more recently developed boosting model (extreme gradient boosting). All of these models, after optimization with their hyper-parameters, show an R2-score of more than 0.9. Finally, extreme gradient boosting was selected as the main model of this work, which has a 1.07 × 10-2 maximum error and an RMSE error rate of 5.85 × 10-3. [ABSTRACT FROM AUTHOR]

Although antibiotic resistance represents a significant and growing public health concern, the contribution of bacteriophages (phages) to the mobilization of antibiotic resistance genes (ARGs) in the environment has not been extensively studied. Recent studies, however, suggest that phages play an important role in the acquisition, maintenance, and spread of ARGs than previously expected. This Opinion article offers an update on the contribution of phages to environmental antibiotic resistance. A better understanding of the mechanisms and factors that promote antibiotic resistance may significantly contribute to the implementation of control strategies. Given the growing global concerns about antibiotic resistance, it becomes essential to explore the mechanisms and pathways by which this phenomenon evolves and spreads. Phages, viruses that infect bacteria, have emerged as prime suspects in bacterial adaptation and evolution by facilitating the exchange of genetic material. Recent studies based on high-throughput sequencing approaches suggest that environmental phages play a more important role in the acquisition, maintenance, and spread of antibiotic resistance genes than previously expected. Despite recent advances, further studies are needed to establish the extent to which phages contribute to the mobilization of ARGs in environmental settings. [ABSTRACT FROM AUTHOR]

Abstract Heterogeneous longitudinal data have become prevalent in medical, biological, and social studies. This paper proposes a finite mixture of varying coefficient models for handling heterogeneous populations. Each component of the mixture is modeled by a varying coefficient mixed-effect model that characterizes the longitudinal relations among variables. The identifiability of the mixture model is studied. Regression splines with equally spaced knots are used to approximate the varying coefficient functions, and a nested expectation maximization algorithm is developed to obtain the maximum likelihood estimation. We propose a penalized likelihood method based on the smoothly clipped absolute deviation (SCAD) penalty for the component selection of finite mixture of varying coefficient model. A modified BIC-based criterion based on the SCAD penalty, the BIC SCAD , is proposed for selecting the penalty parameter and spline space simultaneously. The asymptotic properties of parameter estimation and component selection consistency are studied under mild conditions. Simulation studies are conducted to illustrate the component selection, parameter estimation, and inference of the proposed method. The model is then applied to a heterogeneous longitudinal data set from a study of the treatment effect on the use of heroin in the California Civil Addict Program. [ABSTRACT FROM AUTHOR]

Generally the performance of a pedestrian detector will decrease rapidly, when it is trained on a fixed training set but applied to specific scenes. The reason is that in the training set only a few samples are useful for the specific scenes while other samples may disturb the accurate detections. Traditional methods solve this problem by transfer learning which suffer the problem of keeping source samples or artificially labeling a few samples in the detection phase. In this paper, we propose a new method to bypass these defects by predicting pedestrian classifier for each sample in the detection phase. A classifier regression model is trained in the source domain in which each sample has a proprietary classifier. In the detection phase, a pedestrian classifier is predicted for each candidate window in an image. Thus, for the samples in the target domain, the pedestrian classifiers are different. Our main contributions are: (1) a new adaptive detector without keeping source samples or labeling a few new target samples; (2) a new dimensionality reduction method for classifier vector which simultaneously ensures the performance of both reconstruction and classification; (3) a two-stage regression neural model which can handle the high-dimensional regression problem effectively. Experiments prove that our method can achieve the state-of-the-art results on two pedestrian datasets. [ABSTRACT FROM AUTHOR]

Abstract Antibiotic resistant pathogenic bacteria (ARPB) residual in soil-plant system has caused serious threat against public health and environmental safety. Being capable of targeted lysing host bacteria, phage therapy has been proposed as promising method to control the ARPB contamination in environments. In this study, microcosm trials were performed to investigate the impact of various phage treatments on the dissipation of tetracycline resistant Escherichia coli K-12 and chloramphenicol resistant Pseudomonas aeruginosa PAO1 in soil-carrot system. After 70 days of incubation, all the four phage treatments significantly decreased the abundance of the pathogenic bacteria and the corresponding antibiotic resistance genes (tetW and cmlA) in the soil-carrot system (p < 0.05), following the order of the cocktail phage treatment (phages ΦYSZ1 + ΦYSZ2) > the polyvalent phage (ΦYSZ3 phage with broad host range) treatment > host-specific phage (ΦYSZ2 and ΦYSZ1) treatments > the control. In addition, the polyvalent phage treatment also exerted positive impact on the diversity and stability of the bacterial community in the system, suggesting that this is an environmentally friendly technique with broad applications in the biocontrol of ARPB/ARGs in soil-plant system. Graphical abstract Unlabelled Image Highlights • Phage therapy targeted inactivation of ARPB in soil-plant system effectively. • Cocktail treated phage therapy decreased ARPB and ARGs most efficiently. • Polyvalent phage treatment maintained the diversity of bacterial community. • Polyvalent phage therapy is an environment-friendly biocontrol technology. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Ag/Bi 4 O 7 /g-C 3 N 4 ternary metal/non-metal Z-scheme heterojunction was prepared. • A photodeposition experiment confirmed Z-scheme heterojunction in Ag/Bi 4 O 7 /g-C 3 N 4. • Ag adhered to g-C 3 N 4 nanosheets, constructing plasmonic metal/semiconductor system. • The visible-light photoreduction of aqueous Cr(VI) is enhanced. • The ternary Ag/Bi 4 O 7 /g-C 3 N 4 nanosheets have favorable cycling stability. Abstract In this paper, we successfully constructed a ternary metal/non-metal nanomaterial which can synergize Z-scheme heterojunction and plasmonic metal/semiconductor for enhancing visible light photoreduction of aqueous Cr(VI). The as-formed ternary metal/non-metal nanomaterial was composed of g-C 3 N 4 nanosheets mutual modified by Ag nanoparticles and Bi 4 O 7. In the ternary nanomaterial, g-C 3 N 4 nanosheets and Bi 4 O 7 fabricated Z-scheme heterojunction, g-C 3 N 4 nanosheets and Ag nanoparticles formed plasmonic metal/semiconductor system, synchronously. The photodeposition experiments were designed to indirect demonstrate the Z-scheme heterojunction via photocatalytic oxidation and reduction deposition reactions, AgNO 3 was used as the electron acceptor and Pb(NO 3) 2 as the electron donor, photocatalytic reduction product and oxidation product were selectively deposited on g-C 3 N 4 nanosheets and Bi 4 O 7 , respectively, revealing the Z-scheme heterojunction was definite formed. In addition, X-ray photoelectron spectroscopy (XPS), photoluminescence (PL), UV–vis absorption, and Fourier-transform infrared spectroscopy (FTIR) were studied to indicate that the plasmonic metal/semiconductor system was existent in the ternary nanomaterial. The visible light photoreduction of ternary Ag/Bi 4 O 7 /g-C 3 N 4 nanosheets was evaluated by photoreducing aqueous Cr(VI). The photoreduction results show that the ternary Ag/Bi 4 O 7 /g-C 3 N 4 nanosheets is much higher than the pristine bulk g-C 3 N 4 and g-C 3 N 4 nanosheets. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Biochar impeded the vertical migration of ARPB from top soil to lower soil. • Polyvalent phage targeted inactivation of ARPB along different soil layers. • Biochar amendment with phage therapy synergistically eliminated ARG risk. • Combined treatment maintained the diversity of the bacteria community. Abstract The vertical migration of antibiotic resistance pathogenic bacteria (ARPB) and antibiotic resistance genes (ARGs) in the surface soil-vadose soil system has become a new threat to ecological safety and public health; there is an imperative need to develop an efficient technique for targeted control and inactivation of ARPB in these systems. In this work, undisturbed soil columns (0 ∼ −5 m) were constructed to investigate the impact of biochar amendment or/and polyvalent bacteriophage (ΦYSZ-KK) therapy on the vertical control and inactivation of tetracycline-resistant Escherichia coli K-12 and chloramphenicol-resistant Klebsiella pneumonia K-6. The simultaneous application of polyvalent phage and biochar impeded the vertical migration of ARPB from the top soil to lower soil layers and stimulated the ARPB dissipation in the soil column. After 60-day incubation, levels of ARPB and ARGs decreased significantly in the soil column by magnitudes of 2-6. Additionally, high throughput sequencing indicated that the simultaneous application of biochar and phage clearly maintained the structure and diversity of the soil microbial communities (p < 0.05). This work therefore demonstrates that the application of a biochar/phage combination is an environmentally friendly, efficacious measure for the control and inactivation of ARPB/ARGs in vertical soil column systems. [ABSTRACT FROM AUTHOR]

A new kind of nonlinear energy sink (NES) is proposed to control the vibration of a flexible structure with simply supported boundaries in the present work. The new kind of absorber is assembled at the end of structures and absorbs energy through the rotation angle at the end of the structure. It is easy to design and attached to the support of flexible structures. The structure and the absorber are coupled just with a nonlinear restoring moment and the damper in the absorber acts on the structure indirectly. In this way, all the linear characters of the flexible structure will not be changed. The system is investigated by a special perturbation method and verified by simulation. Parameters of the absorber are fully discussed to optimize the efficiency of it. For the resonance, the maximum motion is restrained up to 90% by the optimized absorber. For the impulse, the vibration of the structure could attenuate rapidly. In addition to the high efficiency, energy transmits to the absorber uniaxially. For the high efficiency, convenience of installation and the immutability of linear characters, the new kind of rotating absorber provides a very good strategy for the vibration control. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Outline of the anticancer properties and associated molecular mechanism mediated by lycorine. • Comprehensive analysis of the structure activity relationship associated with anticancer activity of lycorine. • Summary of the pharmacological aspects and implications for future directions with this compound. Abstract Nature is the most abundant source for novel drug discovery. Lycorine is a natural alkaloid with immense therapeutic potential. Lycorine is active in a very low concentration and with high specificity against a number of cancers both in vivo and in vitro and against various drug-resistant cancer cells. This review summarized the therapeutic effect and the anticancer mechanisms of lycorine. At the same time, we have discussed the pharmacology and comparative structure-activity relationship for the anticancer activity of this compound. The researches outlined in this paper serve as a foundation to explain lycorine as an important lead compound for new generation anticancer drug design and provide the principle for the development of biological strategies to utilize lycorine in the treatment of cancers. [ABSTRACT FROM AUTHOR]

We present a practical and inexpensive method to reconstruct 3D scenes that include transparent and mirror objects. Our work is motivated by the need for automatically generating 3D models of interior scenes, which commonly include glass. These large structures are often invisible to cameras or even to our human visual system. Existing 3D reconstruction methods for transparent objects are usually not applicable in such a room-sized reconstruction setting. Our simple hardware setup augments a regular depth camera (e.g., the Microsoft Kinect camera) with a single ultrasonic sensor, which is able to measure the distance to any object, including transparent surfaces. The key technical challenge is the sparse sampling rate from the acoustic sensor, which only takes one point measurement per frame. To address this challenge, we take advantage of the fact that the large scale glass structures in indoor environments are usually either piece-wise planar or a simple parametric surface. Based on these assumptions, we have developed a novel sensor fusion algorithm that first segments the (hybrid) depth map into different categories such as opaque/transparent/infinity (e.g., too far to measure) and then updates the depth map based on the segmentation outcome. We validated our algorithms with a number of challenging cases, including multiple panes of glass, mirrors, and even a curved glass cabinet. [ABSTRACT FROM AUTHOR]

Understanding the interaction between a cold particle and confined fluid flow is of practical importance in many industrial applications. In this work, the motion of a two-dimensional circular neutrally buoyant particle in thermal flows in the two-sided lid-driven cavity has been numerically investigated by the lattice Boltzmann model with immersed boundary method. We examined the effects of Grashof number (Gr) and Reynolds number (Re) of cavity on the motion of particle in the range of 104 ≤ Gr ≤ 107 and 500 ≤ Re ≤ 3000. It is found that the motion of the cold particle in the cavity flow manifests four different modes, driven by the competition between buoyancy-driven natural convection and lid-driven forced convection. With the increase in Gr or the decrease in Re, the motion of the cold particle would evolve from modes I to IV. We further obtained the diagram of motion modes of the cold particle with regard to Gr and Re. And a power law correlation that relates the critical Gr to Re of the cavity is proposed and capable of well predicting the transition of particle motion modes. [ABSTRACT FROM AUTHOR]

The emerging mixed contamination of antibiotics and microplastics in greenhouse soil has made the control of antibiotic resistant gene (ARG) transmission a novel challenge. In this work, surfactant sophorolipid was applied to enhance the dissipation of tetracycline (TC) and tet genes in the presence of microplastics in greenhouse soil. During 49 days of incubation, soil bacteria and phages were both found to be the crucial reservoirs of ARGs. Meanwhile, microplastic’s presence significantly inhibited the dissipation of TC and ARGs in the soil. However, sophorolipid application was proved to outweigh the negative impact caused by microplastic existence, and lead to the highest dissipation of soil TC and ARGs. Significant positive correlation was detected between the dissipation rate of water-soluble and exchangeable TC content and bacteria/phage co-mediated ARG levels. This also held true between the two fractions of soil TC and the ratio of ARG level in the bacteria to that in the phages (B ARGs /P ARGs ). The opposite impacts of microplastic presence and sophorolipid amendment on the TC/ARG dissipation found in this work provides new information for understanding ARG transmission between bacteria and phages in the mixed contaminated greenhouse soil. [ABSTRACT FROM AUTHOR]

The binary nanocomposite of silver nanoparticles/graphitic carbon nitride nanosheets (AgNPs/g-C 3 N 4 nanosheets) has been prepared through polymerization-photodeposition method. The UV–vis absorption spectra show that the composite material has a significant absorption in the visible region, and indirectly demonstrates the surface plasmon resonance (SPR) of Ag nanoparticles in composites. The as-prepared AgNPs/g-C 3 N 4 nanosheets photocatalysts exhibit distinctly enhanced photocatalytic activities toward the degradation of Methyl orange (more than 95% after 60 min) under visible light irradiation, which is about 20 and 3 times higher than that of bulk g-C 3 N 4 and g-C 3 N 4 nanosheets. The cyclic stability test proves that hybrid photocatalyst has a strong stability in the degradation of organic dyes. The improved visible-light-driven photocatalytic activities of AgNPs/g-C 3 N 4 nanosheets can be attributed to the SPR of Ag nanoparticles as well as generation, separation and transportation of the photogenerated carriers, and the higher specific surface area for improved visible-light utilization efficiency. [ABSTRACT FROM AUTHOR]

The nonlinear response of a flexible structure, subjected to generally supported conditions with nonlinearities, is investigated for the first time. An analytical procedure is proposed first. Moreover, a simulation technique usually employed in static analysis is developed for confirmation. Generally, ordinary perturbation methods could analyze dynamics of flexible structures with linear boundary conditions. As nonlinear boundaries are taken into account, they are out of operation for the modal shape that is hardly to be obtained, which is the key to the analysis. In order to overcome this, nonlinear boundary conditions are rescaled and the technique of modal revision is employed. Consequently, each governing equation with different time-scales could be analyzed exactly according to corresponding rescaled boundary conditions. The total response of any point at the flexible structure will be composed by harmonic responses yielded by the analytical method. Furthermore, the differential quadrature element method (DQEM), a numerical simulation technique could satisfy boundary conditions strictly, is introduced to certify analytical results. The comparison shows a reasonable agreement between these two methods. In fact, the accuracy of the analytical method for nonlinear boundaries could be explained in theory. Based on the certification, boundary nonlinearities are discussed in detail analytically and found to play an important role in responses. Because of the important role played by the nonlinear factors in the vibration and control of the flexible structure, this paper will open the vibration analysis and numerical study of the flexible structure with nonlinear constraints. [ABSTRACT FROM AUTHOR]