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Objective The colonization ability and efficiency of two Escherichia coli (E. coli)-engineered strains, Nissle1917 and BW25113, in the intestine were evaluated in mice, we aimed to screen out strains for subsequent research on clustered regularly interspersed short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) system-engineered bacteria to eliminate drug-resistant bacteria via high intestinal colonization efficiency. Methods Seventy ICR mice (18–20 g), half male and half female, were randomly divided into 7 treatment groups by gender, with 10 mice in each group (6 for experiment and 4 for control). The experimental group was gavaged with 2×1010 of the engineered strains at a final volume of 200 μL, and the control group was gavaged with an equal volume of PBS. At 1, 3, 6, 12, 24, 48, and 72 hours after gavage, the mesenteric lymph nodes, stomach, ileocecal and colonic tissues, and intestinal contents of the mice were removed. The two E. coli strains were detected using plate inoculation, fluorescence microscopy, and PCR amplification to compare their in vivo colonization ability and efficiency. Results At 1, 3, 6, 12, 24, and 48 hours after gavage, both E. coli strains had colonized in the stomach, ileocecal and colonic tissues as detected using the three methods, and no leakage of E. coli fluid from the lymph nodes was observed; at 72 hours, only Nissle1917 colonized in the ileocecal and colonic tissues, comparing the colonization efficiency of the two E. coli strains, that of Nissle1917 was 100% and that of BW25113 was 0 at 72 hours. Conclusion Nissle1917 has a higher colonization efficiency than BW25113 and can colonize in the mucosal surface of ileocecal and colonic tissues for a long time, suggesting that it can be used as a carrier for the CRISPR system to prevent and control drug resistance gene transmission.

Abstract Background Bowen's disease is a cutaneous squamous cell carcinoma (CSCC) in situ. If left untreated, BD may progress to invasive CSCC. CSCC is one of the most common cutaneous carcinoma in the elderly and the advanced, metastasis CSCC usually have a poor outcomes. However, the mechanisms of invasion and metastasis from Bowen’s disease to CSCC is complicated and still unclear. Objectives The aim of this study was to explore the biomarkers and molecular alterations in Bowen’s disease development process via analyzing the proteomics changes in tissues of CSCC, Bowen disease and healthy skin. Methods A total of 7 individuals with CSCC (5 for proteomics study and 2 for validation), 7 individuals with Bowen disease (5 for proteomics study and 2 for validation) and 7 healthy controls (5 for proteomics study and 2 for validation) presented to the Department of Dermatology, Yijishan Hospital, the First Affiliated Hospital of Wannan Medical College between January 2021 and December 2021 were enrolled. The proteomics analysis was performed to screen differentially expressed proteins/gens (DEPs/DEGs) in the lesions of CSCC, Bowen disease and healthy skin tissues. The transcriptomic data (GSE32628) of CSCC was selected and downloaded from the GEO database. The common DEGs in our proteomics results and GSE32628 between CSCC and healthy skin tissues were selected. And then, the common DEGs which significantly up or down-regulated between CSCC and Bowen disease in our proteomics results were further screened to identify using Western blot methods in the validation group. CSCC A431 cells were transfected with SERPINB1 small interfering RNA (si-SERPINB1) or small interfering RNA negative control (si-NC). To explore the effect of SERPINB1 silencing on migration and invasion ability of A431 cells. Results A total of 501 proteins were differentially expressed between the CSCC and healthy skin tissues, with 332 up-regulated and 169 down-regulated at least 1.5-fold with a P value

Background: The increasing incidence of endometrial cancer (EC) has highlighted the need for improved early detection methods. This study aimed to develop and validate a novel DNA methylation classifier, EMPap, for EC detection using cervical scrapings. Methods: EMPap incorporated the methylation status of BHLHE22 and CDO1, along with age and body mass index (BMI), into a logistic regression model to calculate the endometrial cancer methylation (EM) score for identifying EC in cervical scrapings. We enrolled 1297 patients with highly suspected EC, including 196 confirmed EC cases, and assessed the EMPap performance in detecting EC. Results: EMPap demonstrated robust diagnostic accuracy, with an area under the curve of 0.93, sensitivity of 90.3%, and specificity of 89.3%. It effectively detected EC across various disease stages, grades, and histological subtypes, and consistently performed well across patient demographics and symptoms. EMPap correctly identified 87.5% of the type II ECs and 53.8% of premalignant lesions. Notably, compared with transvaginal ultrasonography (TVS) in patients with postmenopausal bleeding, EMPap exhibited superior sensitivity (100% vs. 82.0%) and specificity (85.2% vs. 38.5%). In asymptomatic postmenopausal women, EMPap maintained high sensitivity (89.5%) and negative predictive value (NPV) (98.3%). Conclusions: This study demonstrated the potential of EMPap as an effective tool for EC detection. Despite the limited sample size, EMPap showed promise for identifying type II EC and detecting over 50% of premalignant lesions. As a DNA methylation classifier, EMPap can reduce unnecessary uterine interventions and improve diagnosis and outcomes. [ABSTRACT FROM AUTHOR]

We present a case study highlighting prenatal ultrasound findings in monozygotic twins with chromosome 17q12 deletion syndrome. Fetus A exhibited bilateral fetal pyelectasis and talipes equinovarus, while fetus B showed hyperechogenic kidneys. Despite sharing the same de novo variant, the twins displayed distinct clinical phenotypes, suggesting the presence of non‐genetic factors influencing the phenotypic variability of this syndrome. This case represents the first documented instance of prenatally identified identical twins affected by 17q12 deletion syndrome. [ABSTRACT FROM AUTHOR]

In order to improve the walking stability of a biped robot in multiple scenarios and reduce the complexity of the Central Pattern Generator (CPG) model, a new CPG walking controller based on multivariate linear mapping was proposed. At first, in order to establish a dynamics model, the lower limb mechanical structure of the biped robot was designed. According to the Lagrange and angular momentum conservation method, the hybrid dynamic model of the biped robot was established. The initial value of the robot's passive walking was found by means of Poincaré mapping and cell mapping methods. Then, a multivariate linear mapping model was established to form a new lightweight CPG model based on a Hopf oscillator. According to the parameter distribution of the new CPG model, a preliminary parameter-tuning idea was proposed. At last, the joint simulation of MATLAB and V-REP shows that the biped robot based on the new CPG control has a stable periodic gait in flat and uphill scenes. The proposed method could improve the stability and versatility of bipedal walking in various environments and can provide general CPG generation and a tuning method reference for robotics scholars. [ABSTRACT FROM AUTHOR]

In the domain of bionic walking control for biped robots, optimizing the parameters of the central pattern generator (CPG) presents a formidable challenge due to its high-dimensional and nonlinear characteristics. The traditional particle swarm optimization (PSO) algorithm often converges to local optima, particularly when addressing CPG parameter optimization issues. To address these challenges, one improved particle swarm optimization algorithm aimed at enhancing the stability of the walking control of biped robots was proposed in this paper. The improved PSO algorithm incorporates a spiral function to generate better particles, alongside optimized inertia weight factors and learning factors. Evaluation results between the proposed algorithm and comparative PSO algorithms were provided, focusing on fitness, computational dimensions, convergence rates, and other metrics. The biped robot walking validation simulations, based on CPG control, were implemented through the integration of the V-REP (V4.1.0) and MATLAB (R2022b) platforms. Results demonstrate that compared with the traditional PSO algorithm and chaotic PSO algorithms, the performance of the proposed algorithm is improved by about 45% (two-dimensional model) and 54% (four-dimensional model), particularly excelling in high-dimensional computations. The novel algorithm exhibits a reduced complexity and improved optimization efficiency, thereby offering an effective strategy to enhance the walking stability of biped robots. [ABSTRACT FROM AUTHOR]

Accurate prediction of epidemics is pivotal for making well-informed decisions for the control of infectious diseases, but addressing heterogeneity in the system poses a challenge. In this study, we propose a novel modeling framework integrating the spatio-temporal heterogeneity of susceptible individuals into homogeneous models, by introducing a continuous recruitment process for the susceptibles. A neural network approximates the recruitment rate to develop a Universal Differential Equations (UDE) model. Simultaneously, we pre-set a specific form for the recruitment rate and develop a mechanistic model. Data from a COVID Omicron variant outbreak in Shanghai are used to train the UDE model using deep learning methods and to calibrate the mechanistic model using MCMC methods. Subsequently, we project the attack rate and peak of new infections for the first Omicron wave in China after the adjustment of the dynamic zero-COVID policy. Our projections indicate an attack rate and a peak of new infections of 80.06% and 3.17% of the population, respectively, compared with the homogeneous model's projections of 99.97% and 32.78%, thus providing an 18.6% improvement in the prediction accuracy based on the actual data. Our simulations demonstrate that heterogeneity in the susceptibles decreases herd immunity for ~37.36% of the population and prolongs the outbreak period from ~30 days to ~70 days, also aligning with the real case. We consider that this study lays the groundwork for the development of a new class of models and new insights for modelling heterogeneity. Author summary: This study developed a novel modeling framework that explicitly integrates spatial heterogeneity into traditionally homogeneous compartmental models for precise and efficient prediction of epidemics of infectious diseases. We employed the deep learning method to learn the recruitment rate of susceptible individuals from data given its inherent complexity and limited information. Moreover, we predefined the mechanistic form of the recruitment rate to facilitate interpretation and scenario analysis, where we fitted the recruitment rate of mechanistic model to those obtained from deep learning-based approach. Compared to the homogeneous model, our mechanistic model predicts significantly lower peak and final sizes of the epidemic, as well as reduced levels of herd immunity. Using real data as a benchmark, our model's predictive accuracy has markedly improved. The framework of our mechanistic model employs a straightforward approach to integrate heterogeneity into compartment models, thereby facilitating the extension of the modelling framework to encompass various aspects of infectious disease transmission with spatio-temporal heterogeneity among the susceptible population. [ABSTRACT FROM AUTHOR]

With the increasing uncertainty and complexity of industrial production, dynamic events are inevitably happened during the production process. This paper deals with the transportation time constrained dynamic flexible job shop scheduling problem with machine breakdown, new jobs arrival, jobs cancellation, and processing time change of operations. Considering the actual production situation, a makespan rescheduling model based on transportation time is established. A Monte‐Carlo tree search based algorithm is proposed to solve the dynamic flexible job shop scheduling problem such that the makespan is minimized. In order to improve the accuracy of the developed algorithm and the quality of the obtained scheduling schemes, subtree keeping policy, multi‐branching simulation, reinforcement learning, ε−$$ \varepsilon - $$greedy strategy, and expectation‐best evaluation method are employed. Moreover, a subtree pruning policy is embedded to improve the search efficiency of the developed algorithm. Simulation experiments are conducted on a series of instances of different sizes to compare the developed approach with the GA‐based scheduling approach and the Monte‐Carlo tree search based approach. The simulation results indicate that the developed approach is superior to the existing scheduling approaches in terms of the qualities of the obtained solutions. [ABSTRACT FROM AUTHOR]

Wetland ecosystems store large amounts of carbon, and CO2 and CH4 fluxes from this ecosystem receive the double impact of climate change and human activities. Nonetheless, research on how multi-gradient warming and nitrogen and phosphorus additions affect these wetland greenhouse gas emissions is still limited, particularly in alpine wetland ecosystems. Therefore, we conducted a field experiment on the Tibetan Plateau wetlands, investigating the effects of warming and nitrogen and phosphorus additions on the CO2 and CH4 fluxes in alpine wetlands. Results indicated that warming enhanced the CO2 absorption and CH4 emission in the alpine meadow ecosystem, possibly related to changes in plant growth and microbial activity induced by warming, while we noticed that the promotion of CO2 uptake weakened with the increase in the magnitude of warming, suggesting that there may be a temperature threshold beyond which the ecosystem's capacity for carbon sequestration may be reduced. Nitrogen addition increased CH4 emission, with the effect on CO2 absorption shifting from inhibition to enhancement as the amount of applied nitrogen or phosphorus increased. The interaction between warming and nitrogen and phosphorus additions further influenced CH4 emission, exhibiting a synergistic enhancement effect. This study deepens our understanding of the greenhouse gas responses of alpine wetland ecosystems to warming and nitrogen and phosphorus additions, which is significant for predicting and managing ecosystem carbon balance under global change. [ABSTRACT FROM AUTHOR]

Biomass-derived carbon materials are being increasingly utilized in various fields. This paper reports a facile synthesis method for O/N co-doped hierarchical porous carbon, utilizing the inherent pore structure and high heteroatom content of sesame meal. The O/N co-doped hierarchical porous carbon (SC-120) exhibits a hierarchical pore structure and abundant surface functional groups, the specific surface area reached 1743 m2 g−1, and the O and N element contents were measured at 10.87% and 1.95% respectively. In the three-electrode system, the specific capacitance of SC-120 reached 358.6 F g−1 at a current density of 0.5 A g−1, and a specific capacitance of 262.5 F g−1 is maintained even at a high current density of 30 A g−1. Constructed using SC-120 as the electrode, the symmetrical supercapacitor exhibits a high energy density of 9.23 W h kg−1 at a power density of 100 W kg−1 and excellent cyclic stability of 93.58% capacitance retention rate after 7500 charge–discharge testing. Additionally, SC-120 demonstrates potential for electromagnetic interference shielding, attaining an efficiency of 33.18 dB and a shielding effectiveness of 99.59% within the X-band frequency range of 8.2–12.4 GHz. [ABSTRACT FROM AUTHOR]

Gravity measurements have important applications in geophysics, resource exploration, geodesy, and inertial navigation. The range of classical gravimetry includes laser interferometer (LI)-based absolute gravimeters, spring relative gravimeters, superconducting gravimeters, airborne/marine gravimeters, micro-electromechanical-system (MEMS) gravimeters, as well as gravity satellites and satellite altimetry. Atomic gravimetry is a new absolute gravity measurement technology based on atom interferometers (AIs) and features zero drift, long-term stability, long-term continuous measurements, and high precision. Atomic gravimetry has been used to measure static, marine, and airborne gravity; gravity gradient; as well as acceleration to test the weak equivalence principle at the China Space Station. In this paper, classical gravimetry is introduced, and the research progress on static and airborne/marine atomic gravimeters, space AIs, and atomic gravity gradiometers is reviewed. In addition, classical and atomic gravimetry are compared. Future atomic gravimetry development trends are also discussed with the aim of jointly promoting the further development of gravity measurement technologies alongside classical gravimetry. [ABSTRACT FROM AUTHOR]