Lactobacillus from fermented bamboo shoots prevents inflammation in DSS-induced colitis mice via modulating gut microbiome and serum metabolites

Fermented bamboo shoots(FBS)is a region-specific food widely consumed in Southwestern China,with Lactobacillus as the predominant fermenting bacteria.However,the probiotic potential of Lactobacillus derived from FBS reminds largely unexplored,especially for diseases with a low prevalence in areas consuming FBS,namely,inflammatory bowel disease.In this study,Lactiplantibacillus pentosus YQ001 and Lentilactobacillus senioris YQ005 were screening by in vitro probiotic tests to further investigate the probioticlike bioactivity in dextran sulfate sodium(DSS)-induced ulcerative colitis(UC)mouse.They exhibited more positive probiotic effects than Lactobacillus rhamnosus GG in preventing intestinal inflammatory response.The results revealed that both strains improved the abundance of deficient intestinal microbiota in UC mice,including Muribaculaceae and Akkermansia.In the serum metabolome,they modulated the DSS-disturbed levels of metabolites,with significant increment of cinnamic acid.Meanwhile,they reduced the expression levels of interleukin-1β(IL-1β),interleukin-6(IL-6)inflammatory factors and increased zonula occludens-1(ZO-1),Occludin,and cathelicidin-related antimicrobial peptide(CRAMP)in the colon.Consequently,these results demonstrated that Lactobacillus spp.isolates derived from FBS showed promising probiotic activity based on the gut microbiome homeostasis modulation,anti-inflammation and intestinal barrier protection in UC mice.

Deep learning model based on primary tumor to predict lymph node status in clinical stage IA lung adenocarcinoma:a multicenter study

Objective:To develop a deep learning model to predict lymph node(LN)status in clinical stage IA lung adeno-carcinoma patients.Methods:This diagnostic study included 1,009 patients with pathologically confirmed clinical stage T1N0M0 lung adenocarcinoma from two independent datasets(699 from Cancer Hospital of Chinese Academy of Medical Sciences and 310 from PLA General Hospital)between January 2005 and December 2019.The Cancer Hospital dataset was randomly split into a training cohort(559 patients)and a validation cohort(140 patients)to train and tune a deep learning model based on a deep residual network(ResNet).The PLA Hospital dataset was used as a testing cohort to evaluate the generalization ability of the model.Thoracic radiologists manually segmented tumors and interpreted high-resolution computed tomography(HRCT)features for the model.The predictive performance was assessed by area under the curves(AUCs),accuracy,precision,recall,and F1 score.Subgroup analysis was performed to evaluate the potential bias of the study population.Results:A total of 1,009 patients were included in this study;409(40.5%)were male and 600(59.5%)were female.The median age was 57.0 years(inter-quartile range,IQR:50.0-64.0).The deep learning model achieved AUCs of 0.906(95%CI:0.873-0.938)and 0.893(95%CI:0.857-0.930)for predicting pN0 disease in the testing cohort and a non-pure ground glass nodule(non-pGGN)testing cohort,respectively.No significant difference was detected between the testing cohort and the non-pGGN testing cohort(P=0.622).The precisions of this model for predicting pN0 disease were 0.979(95%CI:0.963-0.995)and 0.983(95%CI:0.967-0.998)in the testing cohort and the non-pGGN testing cohort,respectively.The deep learning model achieved AUCs of 0.848(95%CI:0.798-0.898)and 0.831(95%CI:0.776-0.887)for predicting pN2 disease in the testing cohort and the non-pGGN testing cohort,respectively.No significant difference was detected between the testing cohort and the non-pGGN testing cohort(P=0.657).The recalls of this model for predicting pN2 disease were 0.903(95%CI:0.870-0.936)and 0.931(95%CI:0.901-0.961)in the testing cohort and the non-pGGN testing cohort,respectively.Conclusions:The superior performance of the deep learning model will help to target the extension of lymph node dissection and reduce the ineffective lymph node dissection in early-stage lung adenocarcinoma patients.

Multiscale Modeling of Magnetic Distribution of Ribbon Magnetic Cores

The multiscale finite element method(MsFEM)combined with conventional finite element method(CFEM)is proposed to solve static magnetic field in the ribbon magnetic core with non-periodical corners considered.Firstly,a simple 2-dimensional electrostatic problem is used to introduce the MsFEM implementation process.The results are compared to analytical method,as well as conventional FEM.Then,an exam-ple of magneto-static problem is considered for a ribbon magnetic core built sheet by sheet as well as corners taken into considera-tion.Conventional FEM and MsFEM are used to compute the magneto-static field by adopting scalar magnetic potential.Both magnetic potential and magnetic flux density on a certain path are compared.It is shown that the results obtained by MsFEM agree well with the one from conventional FEM.Moreover,MsFEM combined with FEM is potentially a general strategy for mul-tiscale modeling of ribbon magnetic cores with complex and non-periodical structures considered,like corners and T-joints,which can effectively reduce the computational cost.

从信号到知识——基于人工智能的医学影像裸数据诊断价值初探

Encouraging and astonishing developments have recently been achieved in image-based diagnostic technology.Modern medical care and imaging technology are becoming increasingly inseparable.However,the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction(from signal to image).Artificial intelligence(AI)technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows.In this prospective study,for the first time,we develop an AI-based signal-toknowledge diagnostic scheme for lung nodule classification directly from the computed tomography(CT)raw data(the signal).We find that the raw data achieves almost comparable performance with CT,indicating that it is possible to diagnose diseases without reconstructing images.Moreover,the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts(with a gain ranging from 0.01 to 0.12),demonstrating that raw data contains diagnostic information that CT does not possess.Our results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis.

掺氢对Cummins ISM370柴油机CO、CO_2、HC排放特性的影响

为降低柴油机的排放,近年来,研究加入氢气对重型柴油机排放所产生的影响逐步引起了研究者的关注。在Cummins ISM370柴油机中添加不同比例氢气(最高氢气占进气管氢气空气容积比达7%)与柴油组成混合燃料,进行CO、CO2、HC等排放特性研究。研究结果表明:在各种负荷下,氢气和柴油的混合燃料有助于降低CO、CO2比排放,但总体对THC影响不大;随掺氢量增加,CO、CO2和THC的各自比排放随着负荷的变化规律不尽相同;应该根据负荷选择恰当的加氢比例,最大限度发挥加氢对减少排放的积极作用。

2004 Mack MD11柴油机燃料加氢后NO_x与微粒排放特性

为了降低柴油机的排放,氢作为柴油机燃料的研究正在引起研究者的关注。该文进行了在2004 Mack MD11柴油机中添加不同比例氢气(最高氢气比例达7%)与柴油形成混合燃料的NOx、微粒(PM,particulate matter)排放特性研究。研究表明:负荷工况不同,添加氢气对NOx排放特性的影响不同;随着添加氢的增加,有NO转化为NO2现象;NOx排放很大程度还与发动机可变截面涡轮增压系统(VGT,variable-geometry gas turbine)和废气再循环系统(EGR,exhaust gas recirculation)工作状况有关;添加氢气后即使在大、全负荷下,NOx排放量也没有明显增加。这主要归因于2004 Mack MD11采取了EGR,并且随着负荷增加,EGR率也在增加。在各种负荷工况下,添加氢气对降低PM排放量的作用明显,PM排放量减小率一般达50%以上,最高达75%。

Novel sensor selection strategy for LPI based on an improved IMMPF tracking method

Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent performance of the low probability of intercept（LPI）. In order to reduce the emission times of the radar, a novel sensor selection strategy based on an improved interacting multiple model particle filter（IMMPF） tracking method is presented. Firstly the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Then a sensor selection approach for LPI takes use of both the target＇s maneuverability and the state＇s uncertainty to decide the radar＇s radiation time. The radar will work only when the target＇s maneuverability and the state＇s uncertainty exceed the control capability of the passive sensors. Tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations.

Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.

Derivation of a Representative Engine Duty Cycle from On-Road Heavy-Duty Vehicle Driving Data

The heavy-duty vehicle fleet involved in delivering water and sand makes noticeable issues of exhaust emissions and fuel consumption in the process of shale gas development. To examine the possibility of converting these heavy-duty diesel engines to run on natural gas-diesel dual-fuel, a transient engine duty cycle representing the real-world engine working conditions is necessary. In this paper, a methodology is proposed, and a target engine duty cycle comprising of 2231 seconds is developed from on-road data collected from 11 on-road sand and water hauling trucks. The similarity of inherent characteristics of the developed cycle and the base trip observed is evidenced by the 2.05% error of standard deviation and average values for normalized engine speed and engine torque. Our results show that the proposed approach is expected to produce a representative cycle of in-use heavy-duty engine behavior.

A Novel Rolling and Fractional-ordered Grey System Model and Its Application for Predicting Industrial Electricity Consumption

Accurate and reasonable prediction of industrial electricity consumption is of great significance for promoting regional green transformation and optimizing the energy structure.However,the regional power system is complicated and uncertain,affected by multiple factors including climate,population and economy.This paper incorporates structure expansion,parameter optimization and rolling mechanism into a system forecasting framework,and designs a novel rolling and fractional-ordered grey system model to forecast the industrial electricity consumption,improving the accuracy of the traditional grey models.The optimal fractional order is obtained by using the particle swarm optimization algorithm,which enhances the model adaptability.Then,the proposed model is employed to forecast and analyze the changing trend of industrial electricity consumption in Fujian province.Experimental results show that industrial electricity consumption in Fujian will maintain an upward growth and it is expected to 186.312 billion kWh in 2026.Compared with other seven benchmark prediction models,the proposed grey system model performs best in terms of both simulation and prediction performance metrics,providing scientific reference for regional energy planning and electricity market operation.