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From early life to adulthood, the microbiota play a crucial role in the health of the infant. The microbiota in early life are not only a key regulator of infant health but also associated with long-term health. Pregnancy to early life is the golden time for the establishment of the infant microbiota, which is affected by both environmental and genetic factors. Recently, there is an explosion of the studies on the role of microbiota in human diseases, but the application to disease or health is relatively limited because many aspects of human microbiota remain controversial, especially about the infant microbiota. Therefore, a critical and conclusive review is necessary to understand fully the relationship between the microbiota and the health of infant. In this article, we introduce in detail the role of microbiota in the infant from pregnancy to early life to long-term health. The main contents of this article include the relationship between the maternal microbiota and adverse pregnancy outcomes, the establishment of the neonatal microbiota during perinatal period and early life, the composition of the infant gut microbiota, the prediction of the microbiota for long-term health, and the future study directions of microbiota.

Formation Control of Multi-Agent Systems: A Graph Rigidity Approach Marcio de Queiroz, Louisiana State University, USA Xiaoyu Cai, FARO Technologies, USA Matthew Feemster, U.S. Naval Academy, USA A comprehensive guide to formation control of multi-agent systems using rigid graph theory This book is the first to provide a comprehensive and unified treatment of the subject of graph rigidity-based formation control of multi-agent systems. Such systems are relevant to a variety of emerging engineering applications, including unmanned robotic vehicles and mobile sensor networks. Graph theory, and rigid graphs in particular, provides a natural tool for describing the multi-agent formation shape as well as the inter-agent sensing, communication, and control topology. Beginning with an introduction to rigid graph theory, the contents of the book are organized by the agent dynamic model (single integrator, double integrator, and mechanical dynamics) and by the type of formation problem (formation acquisition, formation manoeuvring, and target interception). The book presents the material in ascending level of difficulty and in a self-contained manner; thus, facilitating reader understanding. Key features: Uses the concept of graph rigidity as the basis for describing the multi-agent formation geometry and solving formation control problems. Considers different agent models and formation control problems. Control designs throughout the book progressively build upon each other. Provides a primer on rigid graph theory. Combines theory, computer simulations, and experimental results. Formation Control of Multi-Agent Systems: A Graph Rigidity Approach is targeted at researchers and graduate students in the areas of control systems and robotics. Prerequisite knowledge includes linear algebra, matrix theory, control systems, and nonlinear systems.

Osteoimmunity is involved in regulating the balance of bone remodeling and resorption, and is essential for maintaining normal bone morphology. The interaction between immune cells and osteoclasts in the bone marrow or joint cavity is the basis of osteoimmunity, in which the macrophage-osteoclast axis plays a vital role. Monocytes or tissue-specific macrophages (macrophages resident in tissues) are an important origin of osteoclasts in inflammatory and immune environment. Although there are many reports on macrophages and osteoclasts, there is still a lack of systematic reviews on the macrophage-osteoclast axis in osteoimmunity. Elucidating the role of the macrophage-osteoclast axis in osteoimmunity is of great significance for the research or treatment of bone damage caused by inflammation and immune diseases. In this article, we introduced in detail the concept of osteoimmunity and the mechanism and regulators of the differentiation of macrophages into osteoclasts. Furthermore, we described the role of the macrophage-osteoclast axis in typical bone damage caused by inflammation and immune diseases. These provide a clear knowledge framework for studying macrophages and osteoclasts in inflammatory and immune environments. And targeting the macrophage-osteoclast axis may be an effective strategy to treat bone damage caused by inflammation and immune diseases.

In the context of cultural tourism integration, tourists’ spatial perception intention is an important carrier of spatial evaluation. In historic cultural districts represented by Jinan Mingfu City, tourists’ perceptual depth remains underexplored, leading to a misalignment between cultural tourism development and spatial quality needs. Taking Jinan Mingfu City as a representative case of a historic cultural district, while the living heritage model has revitalized local economies, the absence of a tourist perspective has resulted in misalignment between cultural tourism development and spatial quality requirements. This study establishes a technical framework encompassing “data crawling-factor aggregation-human-machine collaborative optimization”. It integrates Python web crawlers, SnowNLP sentiment analysis, and TF-IDF text mining technologies to extract physical elements; constructs a three-dimensional evaluation framework of “visual perception-spatial comfort-cultural experience” through SPSS principal component analysis; and quantifies physical element indicators such as green vision rate and signboard clutter index through street view semantic segmentation (OneFormer framework). A synergistic mechanism of machine scoring and manual double-blind scoring is adopted for correlation analysis to determine the impact degree of indicators and optimization strategies. This study identified that indicators such as green vision rate, shading facility coverage, and street enclosure ratio significantly influence tourist evaluations, with a severe deficiency in cultural spaces. Accordingly, it proposes targeted strategies, including visual landscape optimization, facility layout adjustment, and cultural scenario implementation. By breaking away from traditional qualitative evaluation paradigms, this study provides data-based support for the spatial quality enhancement of historic districts, thereby enabling the transformation of these areas from experience-oriented protection to data-driven intelligent renewal and promoting the sustainable development of cultural tourism.

Urban parks can effectively reduce surface temperatures, which is an important strategic approach to reducing the urban heat island effect. Quantifying the cooling effect of urban parks and identifying their main internal influencing factors is important for improving the urban thermal environment, achieving maximum cooling benefits, and improving urban sustainability. In this study, we extracted data frobut this is often unrealisticm 28 urban parks in Zhengzhou, China. We combined multivariate data, such as Landsat 8 data, to retrieve the land surface temperature (LST), extract the park interior landscape, and quantify the cooling effect using three cooling indices: park cooling distance ( L ∆max ), temperature difference magnitude (∆ T max ), and temperature gradient ( G temp ). Furthermore, the relationship between the internal landscape characteristics of the park and the average LST and cooling indices of the park was analyzed. The results showed that different buffer ranges affect the LST-distance fitting results of urban parks, and a 300-m buffer zone is the optimal fitting interval. However, specific parks should be analyzed to select the optimal buffer range and reduce the cooling index calculation errors. Additionally, the mean values of LST, ∆ T max , L ∆max , and G temp for the 28 parks in Zhengzhou were 34.11, 3.22 °C, 194.02 m, and 1.78 °C/hm, respectively. Park perimeter (PP), park area, internal green area (GA), and landscape shape index (LSI) were both significantly correlated with ∆ T max and the main factors associated with maintaining a low LST in parks. L ∆ max was mainly affected by the GA, LSI, and perimeter-area ratio, whereas G temp was positively correlated with PP. Finally, the threshold value of efficiency for parks in Zhengzhou was 0.83 ha, and comprehensive parks showed optimal cooling in every aspect.

Precise control of stem cell (SC) proliferation ensures tissue homeostasis. In the Drosophila intestine, injury-induced regeneration involves initial activation of intestinal SC (ISC) proliferation and subsequent return to quiescence. These two phases of the regenerative response are controlled by differential availability of the BMP type I receptor Thickveins (Tkv), yet how its expression is dynamically regulated remains unclear. Here we show that during homeostasis, the E3 ubiquitin ligase Highwire and the ubiquitin-proteasome system maintain low Tkv protein expression. After ISC activation, Tkv is stabilized by proteasome inhibition and undergoes endocytosis due to the induction of the nucleoside diphosphate kinase Abnormal Wing Disc (AWD). Tkv internalization is required for the activation of the Smad protein Mad, and for the return to quiescence after a regenerative episode. Our data provide insight into the mechanisms ensuring tissue homeostasis by dynamic control of somatic stem cell activity. Regeneration after injury in the Drosophila intestine involves early activation of intestinal stem cells (ISCs) and subsequent return to quiescence. Here the authors show that return to quiescence by ISCs involves BMP Type I receptor Tkv protein stabilization along with AWD mediated internalization into endocytic vesicles.

To enhance the performance of Ti6Al4V titanium alloy joints, ultrasonic frequency pulsed TIG welding was employed. The microstructure and mechanical properties of the joints were systematically investigated. Results show that the weld microstructure is predominantly composed of acicular α phase, lath α phase, and a minor amount of β phase. Compared with conventional TIG welding, the application of ultrasonic frequency pulse current effectively refined the grains, achieving an average grain size of 0.54 μm. Concurrently, the proportion of high-angle grain boundaries increased from 96.1% to 97.6%. The average hardness of the fusion zone exceeded that of the base metal and was significantly increased by the ultrasonic frequency pulse current, reaching 350 HV compared to 330 HV for conventional welds. Furthermore, the ultrasonic frequency pulsed TIG joints exhibited higher yield strength and elongation than their conventional welds. These findings demonstrate that introducing ultrasonic frequency current during TIG welding effectively improves the properties of Ti6Al4V welded joints.

Currently, research on road traffic safety is mostly focused on traffic safety evaluations based on statistical indices for accidents. There is still a need for in-depth investigation on preaccident identification of safety risks. In this study, the correlations between high-incidence locations for aberrant driving behaviors and locations of road traffic accidents are analyzed based on vehicle OBD data. A road traffic safety risk estimation index system with road traffic safety entropy (RTSE) as the primary index and rapid acceleration frequency, rapid deceleration frequency, rapid turning frequency, speeding frequency, and high-speed neutral coasting frequency as secondary indices is established. A calculation method of RTSE is proposed based on an improved entropy weight method. This method involves three aspects, namely, optimization of the base of the logarithm, processing of zero-value secondary indices, and piecewise calculation of the weight of each index. Additionally, a safety risk level determination method based on two-step clustering (density and k-means clustering) is also proposed, which prevents isolated data points from affecting safety risk classification. A risk classification threshold calculation method is formulated based on k-mean clustering. The results show that high-incidence locations for aberrant driving behaviors are consistent with the locations of traffic accidents. The proposed methods are validated through a case study on four roads in Chongqing with a total length of approximately 38 km. The results show that the road traffic safety trends characterized by road safety entropy and traffic accidents are consistent.

Narrow gap TIG welding is a high efficiency and low-cost welding technique for heavy structures building. Due to the narrow groove’s constriction, the TIG arc characteristics are different from butt welding. Understanding the unique arc characteristics of narrow gap TIG welding is the foundation for investigating the heat and mass transfer, metallurgic process, as well as process design. This research conducted numerical simulation on the TIG arc plasma in the narrow groove. The effects of welding current and arc length on the arc characteristics are investigated. Results show that, with the welding current increasing, the global velocity magnitude of plasma rises. The evolution of axial velocity and radial velocity has different responses to the current changes. The arc pressure increases drastically, and the global temperature of arc plasma also goes up. With the arc length increasing, global axial velocity rises, but the axial velocity and its gradients decrease near the anode surface. Centripetal radial velocity near the cathode increases, while centrifugal radial velocity rises at the outside of arc plasma and drops near the central axis. The maximum arc pressure on the anode surface decreases. At the lower part of the arc, arc temperature decreases near the central axis and increases at the outside of the arc.

Background Tuberculosis spondylitis (TS), commonly known as Pott’s disease, is a severe type of skeletal tuberculosis that typically requires surgical treatment. However, this treatment option has led to an increase in healthcare costs due to prolonged hospital stays (PLOS). Therefore, identifying risk factors associated with extended PLOS is necessary. In this research, we intended to develop an interpretable machine learning model that could predict extended PLOS, which can provide valuable insights for treatments and a web-based application was implemented. Methods We obtained patient data from the spine surgery department at our hospital. Extended postoperative length of stay (PLOS) refers to a hospitalization duration equal to or exceeding the 75th percentile following spine surgery. To identify relevant variables, we employed several approaches, such as the least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) based on support vector machine classification (SVC), correlation analysis, and permutation importance value. Several models using implemented and some of them are ensembled using soft voting techniques. Models were constructed using grid search with nested cross-validation. The performance of each algorithm was assessed through various metrics, including the AUC value (area under the curve of receiver operating characteristics) and the Brier Score. Model interpretation involved utilizing methods such as Shapley additive explanations (SHAP), the Gini Impurity Index, permutation importance, and local interpretable model-agnostic explanations (LIME). Furthermore, to facilitate the practical application of the model, a web-based interface was developed and deployed. Results The study included a cohort of 580 patients and 11 features include (CRP, transfusions, infusion volume, blood loss, X-ray bone bridge, X-ray osteophyte, CT-vertebral destruction, CT-paravertebral abscess, MRI-paravertebral abscess, MRI-epidural abscess, postoperative drainage) were selected. Most of the classifiers showed better performance, where the XGBoost model has a higher AUC value (0.86) and lower Brier Score (0.126). The XGBoost model was chosen as the optimal model. The results obtained from the calibration and decision curve analysis (DCA) plots demonstrate that XGBoost has achieved promising performance. After conducting tenfold cross-validation, the XGBoost model demonstrated a mean AUC of 0.85 ± 0.09. SHAP and LIME were used to display the variables’ contributions to the predicted value. The stacked bar plots indicated that infusion volume was the primary contributor, as determined by Gini, permutation importance (PFI), and the LIME algorithm. Conclusions Our methods not only effectively predicted extended PLOS but also identified risk factors that can be utilized for future treatments. The XGBoost model developed in this study is easily accessible through the deployed web application and can aid in clinical research.