Increased Risk of Neonatal Pneumonia in Pregnant Women with Atypical Pre-Labor Rupture of Membrane Assessed at Pregnancy Week 39

Purpose: Neonatal pneumonia is a major newborn disease with a high morbidity rate. We aimed to evaluate whether atypical prelabor rupture of membranes (PROM) is a high-risk factor for causing neonatal pneumonia in a prospective real-world study. Patients and Methods: A total of 250 pregnant women at pregnancy week 39 were non-selectively recruited. All were examined by PROM and neonatal pneumonia related clinical, bedside and lab tests, including body temperature, blood pressure, increased vagina discharge, posterior vault pooling, abdominal tenderness, WBC count, nitrazine test, amniotic fluid index, Leakection (a sICAM-1 based lateral flow immunoassay) and vagina streptococcus examinations. Increased vagina discharge with a Leakection positivity was adopted as a working criterium for identifying atypical PROM. Neonatal pneumonia was diagnosed based on the clinical presentation and lab tests. Results: Twenty cases of neonatal pneumonia (8.0%) were diagnosed after the deliveries of the 250 pregnant women. In these neonatal pneumonia cases, 12 (16.7%) occurred in 72 deliveries with atypical PROM, 2 (16.7%) in 12 deliveries with typical PROM, and 6 (3.6%) in 166 deliveries with non-PROM. Conclusion: In this real-world study, we find that a systematic screening at pregnancy week 39 was very meaningful in revealing atypical PROM. Moreover, atypical PROM is a major risk factor for neonatal pneumonia. Therefore, an early diagnosis and intervention on atypical PROM could potentially reduce the occurrence of neonatal pneumonia.

Effects of urbanization and forest type on species composition and diversity,forest characteristics,biomass carbon sink,and their associations in Changchun,Northeast China:implications for urban carbon stock improvement

Differences in forest attributes and carbon sequestration of each organ and layer between broadleaved and conifer forests of central and outer urban areas are not well-defined,hindering the precise management of urban forests and improvement of function.To clarify the effect of two forest types with different urbanization intensities,we determined differences in vegetation composition and diversity,structural traits,and carbon stocks of 152 plots(20 m×20 m)in urban park forests in Changchun,which had the largest green quantity and carbon density effectiveness.We found that 1.1-fold thicker and healthier trees,and 1.6-to 2.0-fold higher,healthier,denser,and more various shrubs but with sparser trees and herbs occurred in the central urban forests(p<0.05)than in the outer forests.The conifer forests exhibited 30–70%obviously higher tree aboveground carbon sequestration(including stem and leaf)and 20%bigger trees,especially in the outer forests(p<0.05).In contrast,1.1-to 1.5-fold higher branch stocks,healthier and more diverse trees were found in broadleaved forests of both the inner and outer forests(p<0.05).Plant size and dominant species had similarly important roles in carbon stock improvement,especially big-sized woody plants and Pinus tabuliformis.In addition,a higher number of deciduous or needle species positively affected the broadleaved forest of the central urban area and conifer forest of the outer urban area,respectively.These findings can be used to guide precise management and accelerate the improvement of urban carbon function in Northeast China in the future.

Kinematic-mapping-model-guided analysis and optimization of 2-PSS&1-RR circular-rail parallel mechanism for fully steerable phased array antennas

This paper presents a systematic methodology for analyzing and optimizing an innovative antenna mount designed for phased array antennas, implemented through a novel 2-PSS&1-RR circular-rail parallel mechanism. Initially, a comparative motion analysis between the 3D model of the mount and its full-scale prototype is conducted to validate effectiveness. Given the inherent complexity, a kinematic mapping model is established between the mount and the crank-slider linkage, providing a guiding framework for subsequent analysis and optimization. Guided by this model, feasible inverse and forward solutions are derived, enabling precise identification of stiffness singularities. The concept of singularity distance is thus introduced to reflect the structural stiffness of the mount. Subsequently, also guided by the mapping model, a heuristic algorithm incorporating two backtracking procedures is developed to reduce the mount's mass. Additionally, a parametric finite-element model is employed to explore the relation between singularity distance and structural stiffness. The results indicate a significant reduction(about 16%) in the antenna mount's mass through the developed algorithm, while highlighting the singularity distance as an effective stiffness indicator for this type of antenna mount.

A Novel Disturbance Observer Based Fixed-Time Sliding Mode Control for Robotic Manipulators With Global Fast Convergence

This paper proposes a new global fixed-time sliding mode control strategy for the trajectory tracking control of uncertain robotic manipulators.First,a fixed-time disturbance observer(FTDO) is designed to deal with the adverse effects of model uncertainties and external disturbances in the manipulator systems.Then an adaptive scheme is used and the adaptive FTDO(AFTDO) is developed,so that the priori knowledge of the lumped disturbance is not required.Further,a new non-singular fast terminal sliding mode(NFTSM) surface is designed by using an arctan function,which helps to overcome the singularity problem and enhance the robustness of the system.Based on the estimation of the lumped disturbance by the AFTDO,a fixed-time non-singular fast terminal sliding mode controller(FTNFTSMC)is developed to guarantee the trajectory tracking errors converge to zero within a fixed time.The settling time is independent of the initial state of the system.In addition,the stability of the AFTDO and FTNFTSMC is strictly proved by using Lyapunov method.Finally,the fixed-time NFESM(FTNFTSM) algorithm is validated on a 2-link manipulator and comparisons with other existing sliding mode controllers(SMCs) are performed.The comparative results confirm that the FTNFTSMC has superior control performance.

F-doped orthorhombic Nb_(2)O_(5) exposed with 97% (100) facet for fast reversible Liþ-Intercalation

Orthorhombic Nb_(2)O_(5)(T-Nb_(2)O_(5))is attractive for fast-charging Li-ion batteries,but it is still hard to realize rapid charge transfer kinetics for Li-ion storage.Herein,F-doped T-Nb_(2)O_(5) microflowers(F-Nb_(2)O_(5))are rationally synthesized through topotactic conversion.Specifically,F-Nb_(2)O_(5) are assembled by single-crystal nanoflakes with nearly 97%exposed(100)facet,which maximizes the exposure of the feasible Li^(+)transport pathways along loosely packed 4g atomic layers to the electrolytes,thus effectively enhancing the Li^(+)-intercalation performance.Besides,the band gap of F-Nb_(2)O_(5) is reduced to 2.87 eV due to the doping of F atoms,leading to enhanced electrical conductivity.The synergetic effects between tailored exposed crystal facets,F-doping,and ultrathin building blocks,speed up the Li^(+)/electron transfer kinetics and improve the pseudocapacitive properties of F-Nb_(2)O_(5).Therefore,F-Nb_(2)O_(5) exhibit superior rate capability(210.8 and 164.9 mAh g^(-1) at 1 and 10 C,respectively)and good long-term 10 C cycling performance(132.7 mAh g^(-1) after 1500 cycles).

A phase-field model for simulating the propagation behavior of mixed-mode cracks during the hydraulic fracturing process in fractured reservoirs

A novel phase-field model for the propagation of mixed-mode hydraulic fractures,characterized by the formation of mixed-mode fractures due to the interactions between fluids and solids,is proposed.In this model,the driving force for the phase field consists of both tensile and shear components,with the fluid contribution primarily manifesting in the tension driving force.The displacement and pressure are solved simultaneously by an implicit method.The numerical solution's iterative format is established by the finite element discretization and Newton-Raphson(NR)iterative methods.The correctness of the model is verified through the uniaxial compression physical experiments on fluid-pressurized rocks,and the limitations of the hydraulic fracture expansion phase-field model,which only considers mode I fractures,are revealed.In addition,the influence of matrix mode II fracture toughness value,natural fracture mode II toughness value,and fracturing fluid injection rate on the hydraulic fracture propagation in porous media with natural fractures is studied.

Upregulation of α-ENaC induces pancreatic β-cell dysfunction,ER stress,and SIRT2 degradation

Islet beta cells(β-cells)produce insulin in response to high blood glucose levels,which is essential for preserving glucose homeostasis.Voltage-gated ion channels inβ-cells,including Na+,K+,and Ca2+channels,aid in the release of insulin.The epithelial sodium channel alpha subunit(α-ENaC),a voltage-independent sodium ion channel,is also expressed in human pancreatic endocrine cells.However,there is no reported study on the function of ENaC in theβ-cells.In the current study,we found thatα-ENaC was expressed in human pancreatic glandule and pancreatic isletβ-cells.In the pancreas of db/db mice and high-fat diet-induced mice,and in mouse isletβ-cells(MIN6 cells)treated with palmitate,α-ENaC expression was increased.Whenα-ENaC was overexpressed in MIN6 cells,insulin content and glucose-induced insulin secretion were significantly reduced.On the other hand,palmitate injured isletβ-cells and suppressed insulin synthesis and secretion,but increasedα-ENaC expression in MIN6 cells.However,α-ENaC knockout(Scnn1a−/−)in MIN6 cells attenuatedβ-cell disorder induced by palmitate.Furthermore,α-ENaC regulated the ubiquitylation and degradation of sirtuin 2 inβ-cells.α-ENaC also modulatedβ-cell function in correlation with the inositol-requiring enzyme 1 alpha/X-box binding protein 1(IRE1α/XBP1)and protein kinase RNA-like endoplasmic reticulum kinase/C/EBP homologous protein(PERK/CHOP)endoplasmic reticulum stress pathways.These results suggest thatα-ENaC may play a novel role in insulin synthesis and secretion in theβ-cells,and the upregulation ofα-ENaC promotes isletβ-cell dysfunction.In conclusion,α-ENaC may be a key regulator involved in isletβ-cell damage and a potential therapeutic target for type 2 diabetes mellitus.

Squeeze and Excitation Convolution with Shortcut for Complex Plasma Image Recognition

Complex plasma widely exists in thin film deposition,material surface modification,and waste gas treatment in industrial plasma processes.During complex plasma discharge,the configuration,distribution,and size of particles,as well as the discharge glow,strongly depend on discharge parameters.However,traditional manual diagnosis methods for recognizing discharge parameters from discharge images are complicated to operate with low accuracy,time-consuming and high requirement of instruments.To solve these problems,by combining the two mechanisms of attention mechanism(strengthening the extraction of the channel feature)and shortcut connection(enabling the input information to be directly transmitted to deep networks and avoiding the disappearance or explosion of gradients),the network of squeeze and excitation convolution with shortcut(SECS)for complex plasma image recognition is proposed to effectively improve the model performance.The results show that the accuracy,precision,recall and F1-Score of our model are superior to other models in complex plasma image recognition,and the recognition accuracy reaches 97.38%.Moreover,the recognition accuracy for the Flowers and Chest X-ray publicly available data sets reaches 97.85%and 98.65%,respectively,and our model has robustness.This study shows that the proposed model provides a new method for the diagnosis of complex plasma images and also provides technical support for the application of plasma in industrial production.

Study on Influencing Factors and Control Points of Design Estimate of Landscaping Engineering

In recent years,China’s landscaping projects have developed vigorously,and the growth rate of urban garden green space areas has been maintained at about 5%.Overall,with the development of the national economy and the support of macro policies,people’s demand for close to nature and beautify the environment is gradually increasing,which has brought new growth momentum for the development of the landscaping industry.Simultaneously,from the perspective of future economic development and urban development,the landscaping industry still has a lot of room for development.However,with the rapid development of landscape engineering,the problem of cost control of landscape engineering is becoming more prominent,the phenomenon of budget overestimation is common,and there are many factors affecting the cost of landscape engineering,which brings difficulties and challenges to the analysis of its influencing factors and cost management.How to scientifically analyze the influencing factors and control the cost has become an important link in the landscaping project.To solve the above problems,this paper takes the design stage of landscaping engineering as the background,takes the design estimate of landscaping engineering as the research object,through literature research and data collection,fully excavates the main influencing factors of the design estimate stage of landscaping engineering,analyzes the key points of cost control,and provides reference ideas and directions for the later cost management and control.

Research on Intelligent Cost Estimation of Engineering Foundation Projects Based on CSIs Theory

Against the backdrop of rapid development in China’s construction and infrastructure sectors,discrepancies between project budgets and actual costs have become pronounced,manifesting in project overruns and suspensions,posing significant challenges.To address inaccuracies in investment targets and operational complexities,this study focuses on a beam-bridge construction project in a district of Shijiazhuang city as a case study.Drawing upon historical analogs,the project employs a Work Breakdown Structure(WBS)to decompose the engineering works.Building on theories of Cost Significant(CS)and Whole Life Costing(WLC),the study constructs Cost Significant Items(CSIs)and develops a CNN-BiLSTM-Attention neural network for nonlinear prediction.By identifying significant cost drivers in engineering projects,this paper presents a streamlined cost estimation method that significantly reduces computational burdens,simplifies data collection processes,and optimizes data analysis and forecasting,thereby enhancing prediction accuracy.Finally,validation with real-world cost fluctuation data demonstrates minor errors,meeting predictive requirements across project execution phases.

Effects of Tillage Depth on Nutrients and Microbial Communities in Tobacco-Planting Soil

The implementation of appropriate tillage practices is of great significance for agricultural production. However, the effects of different tillage depths on soil nutrients content and microbial communities in tobacco-planting soils are still lacking systematic research. In this study, three different tillage depths of 15 cm (T1), 20 cm (T2), and 30 cm (T3) were set up for field experiments in Liupanshui, Guizhou Province, to explore the effects of tillage depth on tobacco-planting soil nutrients and bacterial and fungal communities based on 16S rRNA and ITS sequencing and figure out the key factors affecting soil microbial communities. The results showed that T2 and T3 increased the contents of organic matter, total nitrogen, total phosphorus, available phosphorus, and available potassium in tobacco-planting soil, and increased the diversity of bacterial communities compared with T1. There was no significant difference in the structure of bacterial and fungal communities in different tillage depth treatments, but some dominant genera were significantly enriched in T2 and T3. Desulfobacter, Setophoma, Humicola, and Acremonium were significantly enriched in T2. Chthonomonas and Fusarium were significantly enriched in T3. These genera favor the decomposition of organic matter and the cycling of nutrients, and control soil pests and diseases. Redundancy analysis indicated that TP and AK were the key factors influencing the dominant genera of bacteria and fungi. This study provides a scientific basis for the selection of soil tillage depth for tobacco production in this region.

Fuzzy Controller-Based Design and Simulation of an Automatic Parking System

With the development of automatic driving and fuzzy theory, people pay more and more attention to the application of fuzzy logic in engineering technology. The automatic parking module in the automatic driving system has always been the focus of research. Automatic parking modules can greatly assist drivers in parking operations, greatly reduce parking difficulties and make people more convenient and fast parking. In this paper, an automatic parking system based on the fuzzy controller is proposed. The fuzzy controller of automatic parking system is constructed by using fuzzy theory, and the robustness of the whole system is examined by fuzzy logic. Firstly, the vehicle motion rules and trajectory changes are analyzed in detail, and the real parking lot model is simulated. Then, the input and output variables of the whole system are analyzed by fuzzy theory and the membership function is constructed. Based on the experience of human experts, the parking rules are tested and summarized, and a reasonable and practical rule base is established. Finally, MATLAB is used to code, build the visual interface of parking lot and vehicles, and draw the cyclic iterative function to detect the vehicle position and direction angle, so as to act as a sensor. The results show that using a fuzzy controller to construct an automatic parking system can effectively improve the parking level.

Breast Cancer Prediction Based on Machine Learning

Breast cancer is a significant health concern, necessitating accurate prediction models for early detection and improved patient outcomes. This study presents a comparative analysis of three machine learning models, namely, Logistic Regression, Decision Tree, and Random Forest, for breast cancer prediction using the Wisconsin breast cancer diagnostic dataset. The dataset comprises features computed from fine needle aspirate images of breast masses, with 357 benign and 212 malignant cases. The research findings highlight that the Random Forest model, leveraging the top 5 predictors—“concave points_mean”, “area_mean”, “radius_mean”, “perimeter_mean”, and “concavity_mean”, achieves the highest predictive accuracy of approximately 95% and a cross-validation score of approximately 93% for the test dataset. These results demonstrate the potential of machine learning approaches in breast cancer prediction, underscoring their importance in aiding early detection and diagnosis.

Flexible Polydimethylsiloxane Composite with Multi-Scale Conductive Network for Ultra-Strong Electromagnetic Interference Protection

Highly conductive polymer composites(CPCs) with excellent mechanical flexibility are ideal materials for designing excellent electromagnetic interference(EMI) shielding materials,which can be used for the electromagnetic interference protection of flexible electronic devices.It is extremely urgent to fabricate ultra-strong EMI shielding CPCs with efficient conductive networks.In this paper,a novel silver-plated polylactide short fiber(Ag@PL ASF,AAF) was fabricated and was integrated with carbon nanotubes(CNT) to construct a multi-scale conductive network in polydimethylsiloxane(PDMS) matrix.The multi-scale conductive network endowed the flexible PDMS/AAF/CNT composite with excellent electrical conductivity of 440 S m-1and ultra-strong EMI shielding effectiveness(EMI SE) of up to 113 dB,containing only 5.0 vol% of AAF and 3.0 vol% of CNT(11.1wt% conductive filler content).Due to its excellent flexibility,the composite still showed 94% and 90% retention rates of EMI SE even after subjected to a simulated aging strategy(60℃ for 7 days) and 10,000 bending-releasing cycles.This strategy provides an important guidance for designing excellent EMI shielding materials to protect the workspace,environment and sensitive circuits against radiation for flexible electronic devices.

高熵合金金属烯用于酸性条件下全水分解

质子交换膜水电解槽(PEMWE)因其在低温下的高效率和高功率密度,成为新一代电解槽的发展方向.在水的电解过程中,设计高效稳定的析氢反应(HER)和析氧反应(OER)催化剂是进一步提高电解槽应用的前提.根据HER和OER“火山型”分布曲线,贵金属(Ir,Ru等)依然是主要的基准电催化剂.对于酸性条件下全水分解,Ir基和Ru基双功能催化剂仍然是最常见的选择.然而,与Ir基催化剂相比,Ru基催化剂在酸性条件下的高溶解速率易导致催化剂快速失活,大大降低了其实际应用价值.目前,酸性条件下全水分解的Ir基催化剂也取得了一些成果,如合金(如PdCu/Ir,Au@AuIr_(2),IrTe纳米棒和IrNi合金纳米花)、钙钛矿(如AIrO_(3))、硒化物(如Li-IrSe_(2))和团簇(如Ir纳米团簇,IrNi纳米团簇)等.然而,Ir基材料在高电流密度下仍然面临质量活性低和稳定性有限的挑战(100 mA cm^(-2)时的过电位超过420 mV,酸性整体水分解在高电流密度下的长期稳定性差).上述问题使得电催化剂无法满足PEMWE的应用要求.因此,开发具有优异电化学活性和稳定性的新型Ir基电催化剂成为当务之急.近年来,至少由五种元素组成的高熵合金(HEAs)材料具有在极端条件下的硬度、韧性、腐蚀性和热稳定性等的强耐受性,因而引起了研究者的广泛兴趣.同时,不同组分的可控性也赋予了HEAs固有的晶格畸变和可调电子结构的协同效应,在催化领域也发挥着重要作用.目前,尚未见使用HEAs催化剂进行酸性条件下全水分解的相关报道.从形貌上看,金属烯具有表面金属原子体积比大、表面金属缺陷丰富、稳定性较好等优点,从而表现出较好的催化性能.因此,结合HEAs协同效应和金属烯结构优点的电催化剂,有望解决酸性条件下全水分解电催化剂的设计问题.本文成功制备了具有丰富的无定形和晶体结构的IrPdRhMoW HEA金属烯作为一种高效的双功能电催化剂,用于酸性条件下全水分解.在0.5 mol L^(-1)H_(2)SO_(4)溶液中,IrPdRhMoW/C在10 mA cm^(-2)下HER和OER的过电位分别为15和188 mV,远强于商业催化剂(HER:Pt/C,47 mV和OER:RuO_(2),305 mV)和大部分已报道的贵金属基催化剂.使用IrPdRhMoW/C进行全水分解,只需1.48 V的电池电压即可获得10 mA cm^(-2)的电流密度,而实现100 mA cm^(-2)的电流密度所需的电池电压为1.59 V.IrPdRhMoW/C在100 mA cm^(-2)水分解100 h后仍保持较好的电活性和结构稳定性,这也是在高电流密度下实现酸性条件下全水分解的长期稳定性.密度泛函理论计算结果表明,由于晶体和非晶结构的共存,HEA中电子结构的具有自平衡效应.强轨道耦合不仅最大限度地提高了对HER和OER的电活性,而且稳定了金属位点的价态,以实现持久的电催化全水分解.综上,本工作不仅成功制备了HEA金属烯,而且将其应用于酸性条件下全水分解.此外,电荷自平衡效应可以解决酸性条件下全水分解过程中活性与稳定性之间的问题,且有望进一步应用到其他电催化剂的设计和合成中.

Facile synthesis of the Mn_(3)O_(4)polyhedron grown on N-doped honeycomb carbon as high-performance negative material for lithium-ion batteries

Because of their large volume variation and inferior electrical conductivity,Mn_(3)O_(4)-based oxide anode materials have short cyclic lives and poor rate capability,which obstructs their development.In this study,we successfully prepared a Mn_(3)O_(4)/N-doped honeycomb carbon composite using a smart and facile synthetic method.The Mn_(3)O_(4)nanopolyhedra are grown on N-doped honeycomb carbon,which evidently mitigates the volume change in the charging and discharging processes but also improves the electrochemical reaction kinetics.More importantly,the Mn-O-C bond in the Mn_(3)O_(4)/N-doped honeycomb carbon composite benefits electrochemical reversibility.These features of the Mn_(3)O_(4)/N-doped honeycomb carbon(NHC)composite are responsible for its superior electrochemical performance.When used for Li-ion batteries,the Mn_(3)O_(4)/N-doped honeycomb carbon anode exhibits a high reversible capacity of 598 mAh·g^(−1)after 350 cycles at 1 A·g^(−1).Even at 2 A·g^(−1),the Mn_(3)O_(4)/NHC anode still delivers a high capacity of 472 mAh·g^(−1).This work provides a new prospect for synthesizing and developing manganese-based oxide materials for energy storage.

Elucidating the Role of Mass Transfer in Electrochemical Redox Reactions on Electrospun Fibers

Mass transfer can tune the surface concentration of reactants and products and subsequently infl uence the catalytic perfor-mance.The morphology of nanomaterials plays an important role in the mass transfer of reaction microdomains,but related studies are lacking.Herein,a facile electrospinning technique utilizing cellulose was employed to fabricate a series of carbon nanofi bers with diff erent diameters,which exhibited excellent electrochemical nitrate reduction reaction and oxygen evolu-tion reaction activities.Furthermore,the microstructure of electrocatalysts could infl uence the gas-liquid-solid interfacial mass transfer,resulting in diff erent electrochemical performances.

Target tracking method of Siamese networks based on the broad learning system

Target tracking has a wide range of applications in intelligent transportation,real‐time monitoring,human‐computer interaction and other aspects.However,in the tracking process,the target is prone to deformation,occlusion,loss,scale variation,background clutter,illumination variation,etc.,which bring great challenges to realize accurate and real‐time tracking.Tracking based on Siamese networks promotes the application of deep learning in the field of target tracking,ensuring both accuracy and real‐time performance.However,due to its offline training,it is difficult to deal with the fast motion,serious occlusion,loss and deformation of the target during tracking.Therefore,it is very helpful to improve the performance of the Siamese networks by learning new features of the target quickly and updating the target position in time online.The broad learning system(BLS)has a simple network structure,high learning efficiency,and strong feature learning ability.Aiming at the problems of Siamese networks and the characteristics of BLS,a target tracking method based on BLS is proposed.The method combines offline training with fast online learning of new features,which not only adopts the powerful feature representation ability of deep learning,but also skillfully uses the BLS for re‐learning and re‐detection.The broad re‐learning information is used for re‐detection when the target tracking appears serious occlusion and so on,so as to change the selection of the Siamese networks search area,solve the problem that the search range cannot meet the fast motion of the target,and improve the adaptability.Experimental results show that the proposed method achieves good results on three challenging datasets and improves the performance of the basic algorithm in difficult scenarios.

Acute flare of systemic lupus erythematosus with extensive gastrointestinal involvement: A case report and review of literature

BACKGROUND Lupus mesenteric vasculitis(LMV)is a serious condition that may occur as an acute manifestation of gastrointestinal(GI)involvement and is not easily diagnosed by physicians.Delayed diagnosis and treatment of LMV may lead to rapid disease progression and can be life threatening.CASE SUMMARY A previously healthy 27-year-old woman presented with abdominal pain following a history of fatigue and consumption of cold water.Laboratory invest-igations,physical examinations,and enhanced abdominal computed tomography(CT)suggested systemic lupus erythematosus complicated by LMV.She received treatments,such as GI decompression,somatostatin,glucocorticoids,and immu-nosuppressants,and was evaluated using color ultrasonography.Twenty days later,the patient reported no stomach discomfort and was able to consume semi-liquid food.Laboratory investigations showed that inflammatory factors decreased to normal levels and complement levels increased slightly.One year after discharged,she recovered with methylprednisolone being tapered to 4 mg per day,mycophenolate mofetil to 0.75 g bid,and hydroxychloroquine to 0.2 g bid;however,only C3 complement level was slightly below the normal level.CONCLUSION Early diagnosis of LMV is essential for successful treatment;this depends on a combination of clinical manifestations,laboratory investigations,and imaging findings.Enhanced CT is preferred,but ultrasonography can be used for prompt screening and follow-up.

Analysis of Spatial and Temporal Variation and Forecast Model of Sandstorm Weather in Ulanqab City

Based on the data of sandstorm at 11 stations in Ulanqab City from 1990 to 2021,the spatial and temporal variation characteristics of sand-storm weather were analyzed firstly,and then the conceptual models of cold front and Mongolian cyclone sandstorm were obtained by analyzing sandstorm cases.Finally,the forecast points of the two types of sandstorm weather were given to provide some scientific basis and reference for the prediction of local sandstorm weather in the future.