Effect of Bacillus subtilis,Enterococcus faecium,and Enterococcus faecalis supernatants on serotonin transporter expression in cells and tissues

BACKGROUND Bacillus subtilis(B.subtilis),Enterococcus faecium(E.faecium),and Enterococcus faecalis(E.faecalis)are probiotics that are widely used in the clinical treatment of irritable bowel syndrome(IBS).Whether the supernatants of these three probiotics can improve gastrointestinal sensation and movement by regulating the serotonin transporter(SERT)expression needs to be clarified.AIM To investigate whether B.subtilis,E.faecium,and E.faecalis supernatants can upregulate SERT expression in vitro and in vivo.METHODS Caco-2 and HT-29 cells were stimulated with probiotic culture supernatants for 12 and 24 h,respectively.A male Sprague-Dawley rat model of post-infectious irritable bowel syndrome(PI-IBS)was established and the rats were treated with phosphate-buffered saline(group A)and three probiotics culture supernatants(groups B,C,and D)for 4 wk.The levels of SERT were detected by quantitative PCR and western blotting.RESULTS The levels of SERT at post-treatment 12 and 24 h were significantly elevated in Caco-2 cells treated with B.subtilis supernatant compared with those in the control group(aP<0.05).Those levels were markedly upregulated in Caco-2 cells stimulated with E.faecium and E.faecalis supernatants at 24 h(aP<0.05).In addition,SERT expression in groups B,C,and D was significantly higher than that in group A in the 2nd wk(aP<0.05).Increased SERT expression was only found in group D in the 3rd wk(aP<0.05).However,there was no significant difference in SERT expression between the groups in the last week(P>0.05).CONCLUSION The supernatants of B.subtilis,E.faecium,and E.faecalis can upregulate SERT expression in intestinal epithelial cells and the intestinal tissues in the rat model of PI-IBS.

An improved YOLOv7 for the state identification of sliding chairs in railway turnout

The sliding chairs are important components that support the switch rail conversion in the railway turnout.Due to the harsh environmental erosion and the attack from the wheel vibration,the failure rate of the sliding chairs accounts for up to 10%of the total failure number in turnout.However,there is little research carried out in the existing literature to diagnose the deterioration states of the sliding chairs.To fill out this gap,by utilizing the images containing the sliding chairs,we propose an improved You Only Look Once version 7(YOLOv7)to identify the state of the sliding chairs.Specifically,to meet the challenge brought by the small inter-class differences among the sliding chair states,we first integrate the Convolutional Block Attention Module(CBAM)into the YOLOv7 backbone to screen the information conducive to state identification.Then,an extra detector for a small object is customized into the YOLOv7 network in order to detect the small-scale sliding chairs in images.Meanwhile,we revise the localization loss in the objective function as the Efficient Intersection over Union(EIoU)to optimize the design of the aspect ratio,which helps the localization of the sliding chairs.Next,to address the issue caused by the varying scales of the sliding chairs,we employ K-means++to optimize the priori selection of the initial anchor boxes.Finally,based on the images collected from real-world turnouts,the proposed method is verified and the results show that our method outperforms the basic YOLOv7 in the state identification of the sliding chairs with 4%improvements in terms of both mean Average Precision@0.5(mAP@0.5)and F1-score.

肠道菌群与脂肪性肝病研究新进展

目前,肠道菌群已成为一个当今研究的热点问题之一.脂肪性肝病是指由于各种原因引起的肝细胞内脂肪堆积过多的病变,引起肝的正常结构、生理及生化功能受损,最终出现临床症状的一类疾病总称,一般包括非酒精性脂肪性肝病和酒精性肝病两大类.肠道菌群和肠道通透性的变化可以通过肠-肝轴进一步影响脂肪性肝病的发展.同样,在脂肪性肝病的发生发展过程中,肠道菌群的也发生不同程度的改变.本文主要介绍肠-肝轴与脂肪性肝病的关系、脂肪性肝病进展过程中肠道菌群的变化及益生菌在脂肪性肝病治疗中的应用新进展.

鼠李糖乳杆菌治疗肠易激综合征机制新进展

肠易激综合征(irritable bowel syndrome,IBS)是临床上常见的功能性胃肠病,其会影响患者的生活质量.因此探寻IBS的有效治疗方法具有重要意义.益生菌可改善IBS患者的症状并提高其生活质量,其中备受关注的是鼠李糖乳杆菌(Lactobacillus rhamnosus GG,LGG).LGG是目前被研究最多的益生菌之一.LGG已经在临床上被用来治疗IBS,且近年来LGG治疗IBS机制不断有新的进展.LGG可通过调节肠道菌群失调、保护肠道屏障功能、抗炎、调节肠道免疫、改善内脏高敏感和抑菌来发挥作用从而改善IBS症状.本文就LGG治疗IBS的可能机制作一综述.

Rail fastener detection of heavy railway based on deep learning

Image detection based on machine learning and deep learning currently has a good application prospect for railway fault diagnosis,with good performance in feature extraction and the accuracy of image localization and good classification results.To improve the speed of locating small target objects of fasteners,the YOLOv5 framework model with faster algorithm speed is selected.To improve the classification accuracy of fasteners,YOLOv5-based heavy-duty railway rail fastener detection is proposed.The anchor size is modified on the original basis to improve the attention to small targets of fasteners.The CBAM(Convolutional Block Attention Module)module and TPH(Transformer Prediction Head)module are introduced to improve the speed and accuracy issues.The rail fasteners are divided into 6 categories.Experiment comparisons show that before the improvement,the MAP@0.5 value of all categories are close to the peak of 0.989 after the epoch of 150,and the F1 score approaches 1 with confidence in the interval(0.2,0.95).The improved mAP@0.5 value approached the highest value of 0.991 after the epoch of 75,and the F1 score approached 1 with confidence in the interval(0.01,0.95).The experiment results indicate that the improved YOLOv5 model proposed in this paper is more suitable for the task of detecting rail fasteners.

A reasoning diagram based method for fault diagnosis of railway point system

Railway Point System(RPS)is an important infrastructure in railway industry and its faults may have significant impacts on the safety and efficiency of train operations.For the fault diagnosis of RPS,most existing methods assume that sufficient samples of each failure mode are available,which may be unrealistic,especially for those modes of low occurrence frequency but with high risk.To address this issue,this work proposes a novel fault diagnosis method that only requires the power signals generated under normal RPS operations in the training stage.Specifically,the failure modes of RPS are distinguished through constructing a reasoning diagram,whose nodes are either binary logic problems or those that can be decomposed into the problems of the binary logic.Then,an unsupervised method for the signal segmentation and a fault detection method are combined to make decisions for each binary logic problem.Based on the results of decisions,the diagnostic rules are established to identify the failure modes.Finally,the data collected from multiple real-world RPSs are used for validation and the results demonstrate that the proposed method outperforms the benchmark in identifying the faults of RPSs.

ZmMS39 encodes a callose synthase essential for male fertility in maize(Zea mays L.)

Callose contributes to many biological processes of higher plants including pollen development,cell plate and vascular tissue formation,as well as regulating the transport function of plasmodesmata.The functions of callose synthase genes in maize have been little studied.We describe a maize male-sterile mutant 39(ms39)characterized by reduced plant height.In this study,we confirmed using CRISPR/Cas9 technology that a mutation in Zm00001d043909(ZmCals12),encoding a callose synthase,is responsible for the male sterility of the ms39 mutant.Compared with male-fertile plants,callose deposition around the dyads and tetrads in ms39 anthers was significantly reduced.Increased cell autophagy observed in ms39 anthers may have been due to the premature programmed cell death of tapetal cells,leading to collapse of the anther wall structure.Disordered glucose metabolism in ms39 may have intensified autophagy in anthers.Evaluation of the ms39 gene on maize heterosis by paired-crossed experiment with 11 maize inbred lines indicated that ms39 can be used for maize hybrid seed production.

Distributed optimization for discrete-time multiagent systems with nonconvex control input constraints and switching topologies

This paper addresses the distributed optimization problem of discrete-time multiagent systems with nonconvex control input constraints and switching topologies.We introduce a novel distributed optimization algorithm with a switching mechanism to guarantee that all agents eventually converge to an optimal solution point,while their control inputs are constrained in their own nonconvex region.It is worth noting that the mechanism is performed to tackle the coexistence of the nonconvex constraint operator and the optimization gradient term.Based on the dynamic transformation technique,the original nonlinear dynamic system is transformed into an equivalent one with a nonlinear error term.By utilizing the nonnegative matrix theory,it is shown that the optimization problem can be solved when the union of switching communication graphs is jointly strongly connected.Finally,a numerical simulation example is used to demonstrate the acquired theoretical results.

Metro train rescheduling by adding backup trains under disrupted scenarios

In large cities with heavily congested metro lines, unexpected disturbances often occur, which may cause severe delay of multiple trains, blockage of partial lines, and reduction of passenger service. Metro dispatchers have taken a practical strategy of rescheduling the timetable and adding several backup trains in storage tracks to alleviate waiting passengers from crowding the platforms and recover from such disruptions. In this study,we first develop a mixed integer programming model to determine the optimal train rescheduling plan with considerations of in-service and backup trains. The aim of train rescheduling is to frequently dispatch trains to evacuate delayed passengers after the disruption. Given the nonlinearity of the model, several linearization techniques are adapted to reformulate the model into an equivalent linear model that can be easily handled by the optimization software. Numerical experiments are implemented to verify the effectiveness of the proposed train rescheduling approach.

A Vertex-Centered and Positivity-Preserving Finite Volume Scheme for Two-Dimensional Three-Temperature Radiation Diffusion Equations on General Polygonal Meshes

Two-dimensional three-temperature(2-D 3-T)radiation diffusion equa-tions are widely used to approximately describe the evolution of radiation energy within a multimaterial system and explain the exchange of energy among electrons,ions and photons.In this paper,we suggest a new positivity-preserving finite volume scheme for 2-D 3-T radiation diffusion equations on general polygonal meshes.The vertex unknowns are treated as primary ones for which the finite volume equations are constructed.The edgemidpoint and cell-centered unknowns are used as auxiliary ones and interpolated by the primary unknowns,which makes the final scheme a pure vertex-centered one.By comparison,most existing positivity-preserving finite volume schemes are cell-centered and based on the convex decomposition of the co-normal.Here,the conormal decomposition is not convex in general,leading to a fixed stencil of the flux approximation and avoiding a certain search algo-rithm on complex grids.Moreover,the new scheme effectively alleviates the nu-merical heat-barrier issue suffered by most existing cell-centered or hybrid schemes in solving strongly nonlinear radiation diffusion equations.Numerical experiments demonstrate the second-order accuracy and the positivity of the solution on various distorted grids.For the problem without analytic solution,the contours of the nu-merical solutions obtained by our scheme on distorted meshes accord with those on smooth quadrilateral meshes.

Nodal Discontinuous Galerkin Method for Time-Domain Lorentz Model Equations in Meta-Materials

In this paper,Nodal discontinuous Galerkin method is presented to approxi-mate Time-domain Lorentz model equations in meta-materials.The upwind flux is cho-sen in spatial discrete scheme.Low-storage five-stage fourth-order explicit Runge-Kutta method is employed in time discrete scheme.An error estimate of accuracy O(τ^(4)+h^(n))is proved under the L^(2)-norm,specially O(τ^(4)+h^(n+1))can be obtained.Numerical exper-iments for transverse electric(TE)case and transverse magnetic(TM)case are demon-strated to verify the stability and the efficiency of the method in low and higher wave frequency.

Condition monitoring and fault diagnosis strategy of railway point machines using vibration signals

Condition monitoring of railway point machines is important for train operation safety and effectiveness.Referring to the fields of mechanical equipment fault detection,this paper proposes a fault detection and identification strategy of railway point machines via vibration signals.A comprehensive feature distilling approach by combining variational mode decomposition(VMD)energy entropy and time-and frequency-domain statistical features is presented,which is more effective than single type of feature.The optimal set of features was selected with ReliefF,which helps improve the diagnosis accuracy.Support vector machine(SVM),which is suitable for a small sample,is adopted to realize diagnosis.The diagnosis accuracy of the proposed method reaches 100%,and its effectiveness is verified by experiment comparisons.In this paper,vibration signals are creatively adopted for fault diagnosis of railway point machines.The presented method can help guide field maintenance staff and also provide reference for fault diagnosis of other equipment.