Prolonged impacts of COVID-19-associated cystitis:A study on longterm consequences

BACKGROUND The Coronavirus Disease 2019(COVID-19)caused by the severe acute respiratory syndrome coronavirus 2 virus is an international health concern with substantial morbidity and mortality.COVID-associated cystitis(CAC),presents as new onset or exacerbated urinary symptoms,resembling overactive bladder(OAB)symptoms.AIM To examines the long-term outcomes of patients with CAC in the context of Long COVID.METHODS A cohort of 350 patients admitted to Detroit Hospitals with COVID-19 between May and December 2020,displaying CAC symptoms following discharge,was prospectively followed.Initial urologic evaluations occurred at 10-14 wk and were repeated at 21-28 mo postdischarge.Symptoms were managed conservatively,employing behavioral modifications and standard OAB medications.Participants completed surveys assessing urinary symptoms and quality of life(QoL)at both time points.The primary outcome was the Urology Care Foundation Overactive Bladder Assessment Tool.RESULTS 87%of the final cohort(n=310)reported symptom improvement at 21-28 mo post-discharge.Patients with new onset CAC symptoms showed a median decrease of 9-10 points in OAB and QoL scores,while those with existing symptoms experienced a decrease of 6 points.Overall,95.4%of patients with new onset symptoms reported symptom improvement at follow-up,contrasting with 60.7%among those with existing symptoms.CONCLUSION This study presents the first long-term follow-up of adult patients with CAC,revealing a promising prognosis with conservative management measures in the context of Long COVID.These findings provide reassurance to patients regarding symptom resolution and underscore the need for further research into this evolving aspect of COVID-19's impact on urological health.

蛋白质组学技术分析LongEvans大鼠眼优势柱可塑性终止相关蛋白

目的利用二维电泳-质谱技术分析LongEvans大鼠初级视皮层中与眼优势柱终止相关的蛋白质,探索蛋白质组学技术在视觉可塑性研究中的应用。方法取100d龄LongEvans大鼠通过向其初级视皮层中灌注硫酸软骨素酶建立成年眼优势柱移动的动物模型,然后分别提取22d龄大鼠、100d龄大鼠和100d龄硫酸软骨素酶灌注大鼠的初级视皮层灰质总蛋白,利用二维电泳-质谱技术分析与眼优势柱终止相关的蛋白质。结果通过双向电泳确定了与眼优势柱终止相关的4个蛋白质点,质谱分析鉴定了其中2种蛋白质。这2种蛋白为磷脂酰乙醇胺结合蛋白和胶质纤维酸性蛋白。结论二维电泳-质谱技术可以有效地分离和鉴定视皮层中的特殊蛋白质,随着技术的进步和完善,蛋白质组学技术必将在视觉可塑性研究中占有重要地位。

Chromatin accessibility profiling provides insights into larval cuticle color and adult longevity in butterflies

Butterflies are diverse in virtually all aspects of their ontogeny,including morphology,life history,and behavior.However,the developmental regulatory mechanisms underlying the important phenotypic traits of butterflies at different developmental stages remain unknown.Here,we investigated the developmental regulatory profiles of butterflies based on transposase accessible chromatin sequencing(ATAC-seq)at three developmental stages in two representative species(Papilio xuthus and Kallima inachus).

Room-temperature continuous-wave operation of GaN-based blue-violet laser diodes with a lifetime longer than 1000h

GaN-based continuous-wave operated blue-violet laser diodes(LDs) with long lifetime are demonstrated, which are grown on a c-plane GaN substrate by metal organic chemical vapor deposition with a 10 × 600 μm^2 ridge waveguide structure.The electrical and optical characteristics of a blue-violet LD are investigated under direct-current injection at room temperature(25 °C). The stimulated emission wavelength and peak optical power of the LD are around 413 nm and over 600 mW, respectively.In addition, the threshold current density and voltage are as small as 1.46 kA/cm^2 and 4.1 V, respectively. Moreover, the lifetime is longer than 1000 hours under room-temperature continuous-wave operation.

Comprehensive longevity technology of Baosteel No.3 BF

Baosteel No. 3 BF has been running smoothly and stably for more than 18 years and has become China＇ s longest-life blast furnace, producing many advanced economical and technical indexes. Comprehensive longevity technology has been developed in terms of the design, operation, and maintenance of the No. 3 BF during the prolonged period of research and practice. To obtain stable and smooth operation and to prolong the campaign life, the following measures have been taken： appropriate designing of the furnace profile and cooling system; improving raw material quality and optimizing the operating system; improving the cooling intensity and water quality;and adopting various longevity maintenance measures to ensure the safety of the body and the hearth temperature.

Survey concerning internal medicine physicians and prolonged QT interval:Knowledge and treatment practices

Prolongation of the QT interval is associated with adverse cardiac events specifically Torsades de pointes(TdP).There are multiple mediations that have a known,possible,or conditional risk for prolonged QT interval,but general practitioners’knowledge of these medications is unknown.We conducted a survey to assess internal medicine(IM)providers’knowledge of risk factors and medications associated with prolonged QT as well as provider experience and comfort when treating patients with prolonged QT.A 17-question,anonymous survey was constructed in 2019 and distributed to IM providers and residents at a tertiary care center.Questions included demographic information,6 Likert-scale questions gauging provider experience with prolonged QT,and 10 multiple choice clinical vignettes to assess clinical knowledge.Data was analyzed descriptively.Knowledge was assessed via clinical vignettes and compared by level of training.Forty-one responses were received out of a total of 87 possible respondents(47.1%response rate).About 70%of respondents see patients with acquired prolonged QT once monthly or more.95%rarely see congenital prolonged QT.When presented with QTc drug issues,73%of providers seldom or sometimes consulted pharmacy,but about half used online resources.The average correct score on the clinical vignettes was 5.59/10,with the highest scores seen in attending physicians in their first five years of practice(6.96/10).Our survey suggests that IM providers commonly encounter QT prolonging drugs.Educational efforts to improve knowledge of drug and patient risk factors for TdP may be needed.

Dose Adjustment for Normal Eating: Longer Term Perspectives of Adults with Type 1 Diabetes

Aim:?To explore the longer term blood glucose self-monitoring outcomes and frequency of monitoring for outpatients with diabetes type 1 after completion of the Dose Adjustment for Normal Eating (DAFNE) course. The hypothesis was that DAFNE outcomes would differ according to frequency of glucose monitoring.Methods:?A?sequential data-triangulation design using existing?baseline (T0) and 12-months (T12)?DAFNE course data and interview data from 12 randomly selected participants who had completed the course two years ago.?Results: Age range was 23 to 70 years with HbA1c 6.1% to 12.6% at T0?and 6.1% to 11.4% at T12. Comparisons of HbA1c, PAID, HAD subscales?anxiety?and?depression,?and covariate data between T0?and T12indicatedsignificant reductions in the mean depression and PAID scores (both?P?< 0.001) for the whole group. For the two groups who recorded their blood glucose less than three times or three or more times per day, changes were not significantly different. For both groups, the trend between T0?and T12?was downwards for change in mean blood glucose level and all survey scales. The proportion of all participants with T12?HbA1c at or below their T0?value was greater than 50% (Proportion = 69%, 95% CI: 56% - 79%) but only the highest HbA1c tertile group showed a significant difference (P?= 0.003). There was an average decrease in the incidence of hypoglycaemic events of 0.6 overall: The greatest change was for the high HbA1c tertile with a mean decrease of 0.8. The interview data suggested that DAFNE graduates experimented more with food, exercise, and insulin;gained knowledge;learnt personal body needs;increased awareness of blood glucose level;gained confidence and improved their quality of life. Conclusions: There was insufficient evidence to conclude that frequency of blood glucose monitoring influenced metabolic control. However, people with type 1 diabetes who undertake the less restricted DAFNE approach to diabetes self-management can improve their quality of life and glycaemic control.

A Research on the Relationship between Intestinal Flora and Human Longevity

The exploration of human life and health is advancing with the changes of the times.With the growth of age,the occurrence of chronic diseases of human immunity and organ system is frequent,which has a serious impact on human health.Genes,environment and other random factors determine the outcome of longevity,and intestinal flora is considered to be a decisive factor affecting human health and longevity,mainly because of its huge impact on human immunity,growth and development.The study of the relationship between intestinal flora and longevity is beneficial to improve the health status of the elderly and improve the overall life level of human beings,which has great scientific research value.This review will review the role of intestinal flora in longevity.

Optimizing health-span:advances in stem cell medicine and longevity research

Introduction The pursuit of optimal health and longevity is a long-standing human aspiration[1,2].Advances in public health policies and population medicine have significantly extended life expectancy,which has tripled from 20–30 years a century and a half ago.However,this has also led to an increase in age-related diseases.As we age,stem cells,the foundation of every organ and tissue in our body,lose their regenerative capacity,leading to tissue decline and age-related diseases.Environmental conditions and disease treatments can also negatively impact the quality of life and health span[3].

利用长短期记忆网络LSTM对赤道太平洋海表面温度短期预报

海表面温度作为海洋中一个最重要的变量,对全球气候、海洋生态等有很大的影响,因此十分有必要对海表面温度(SST)进行预报。深度学习具备高效的数据处理能力,但目前利用深度学习对整个赤道太平洋的SST短期预报及预报技巧的研究仍较少。本文基于最优插值海表面温度(OISST)的日平均SST数据,利用长短期记忆(LSTM)网络构建了未来10天赤道太平洋(10°S~10°N,120°E~80°W)SST的逐日预报模型。LSTM预报模型利用1982~2010年的观测数据进行训练,2011~2020年的观测数据作为初值进行预报和检验评估。结果表明:赤道太平洋东部地区预报均方根误差(RMSE)大于中、西部,东部预报第1天RMSE为0.6℃左右,而中、西部均小于0.3℃。在不同的年际变化位相,预报RMSE在拉尼娜出现时期最大,正常年份次之,厄尔尼诺时期最小,RMSE在拉尼娜时期比在厄尔尼诺时期可达20%。预报偏差整体表现为东正、西负。相关预报技巧上,中部最好,可预报天数基本为10天以上,赤道冷舌附近可预报天数为4~7天,赤道西边部分地区可预报天数为3天。预报模型在赤道太平洋东部地区各月份预报技巧普遍低于西部地区,相比较而言各区域10、11月份预报技巧最低。总的来说,基于LSTM构建的SST预报模型能很好地捕捉到SST在时序上的演变特征,在不同案例中预报表现良好。同时该预报模型依靠数据驱动,能迅速且较好地预报未来10天以内的日平均SST的短期变化。

Prevalence and risk factors associated with long COVID symptoms in children and adolescents in a southern province of Vietnam

Objective:To investigate the prevalence and risk factors associated with long COVID symptoms among children and adolescents who have recovered from COVID-19.Methods:This study applied a cross-sectional approach within community settings in a southern province of Vietnam.A structured questionnaire featuring socio-demographic information and common long COVID symptoms was employed.Phi correlation coefficients assessed associations among pairs of long COVID symptoms.Additionally,multivariable logistic regression models were performed to investigate the risk factors of long COVID in recovered COVID-19 children and adolescents.Results:Among 422 participants,39.3%reported long COVID symptoms,with a prevalence of 45.2%(SD=0.5)in children and 22.2%(SD=0.4)in adolescents.Common symptoms reported were cough 34.6%(SD=0.5),fatigue 20.6%(SD=0.4),shortness of breath 10.9%(SD=0.3),and lack of appetite 6.6%(SD=0.3).Concerning risk factors of long COVID,a higher risk was observed among demographic groups,including girls(OR 1.25,95%CI 1.15-1.37;P<0.001,reference:boys),children compared to adolescents(OR 1.24,95%CI 1.12-1.37;P<0.001),overweight individuals(OR 1.14,95%CI 1.02-1.27;P=0.018,reference:healthy weight),and participants without any COVID-19 vaccination(OR 1.36,95%CI 1.20-1.54;P<0.001),or have received only one single dose(OR 1.35,95%CI 1.10-1.64;P=0.004)compared to those who have received two doses.Besides,patients with a COVID-19 treatment duration exceeding two weeks also had a higher risk of long COVID(OR 1.32,95%CI 1.09-1.60;P=0.003)than those who recovered less than seven days.Conclusions:The insights from this study provide crucial guidance for predicting the factors associated with the occurrence of long COVID in pediatric patients,contributing to strategic interventions aimed at mitigating the long COVID risks among children and adolescents in Vietnam.

Numerical Simulation of Rainfall-induced Xianchi Reservoir Landslide in Yunyang,Chongqing,China

A calamitous landslide happened at 22:00 on September 1,2014 in the Yunyang area of Chongqing City,southwest China,enforcing the evacuation of 508 people and damaging 23 buildings.The landslide volume comprised 1.44 million m^(3) of material in the source area and 0.4 million m^(3) of shoveled material.The debris flow runout extended 400 m vertically and 1600 m horizontally.The Xianchi reservoir landslide event has been investigated as follows:(1)samples collected from the main body of landslide were carried out using GCTS ring shear apparatus;(2)the parameters of shear and pore water pressure have been measured;and(3)the post-failure characteristics of landslide have been analyzed using the numerical simulation method.The excess pore-water pressure and erosion in the motion path are considered to be the key reasons for the long-runout motion and the scale-up of landslides,such as that at Xianchi,were caused by the heavy rainfall.The aim of this paper is to acquired numerical parameters and the basic resistance model,which is beneficial to improve simulation accuracy for hazard assessment for similar to potentially dangerous hillslopes in China and elsewhere.

Enhanced thermal emission from metal-free,fully epitaxial structures with epsilon-near-zero InAs layers

We introduce a novel method to create mid-infrared(MIR)thermal emitters using fully epitaxial,metal-free structures.Through the strategic use of epsilon-near-zero(ENZ)thin films in InAs layers,we achieve a narrow-band,wide-angle,and p-polarized thermal emission spectra.This approach,employing molecular beam epitaxy,circumvents the complexities associated with current layered structures and yields temperature-resistant emission wavelengths.Our findings contribute a promising route towards simpler,more efficient MIR optoelectronic devices.

A three-dimensional co-continuous network structure polymer electrolyte with efficient ion transport channels enabling ultralong-life all solid-state lithium metal batteries

Solid polymer electrolytes(SPEs)have emerged as one of the most promising candidates for the construction of solid-state lithium batteries due to their excellent flexibility,scalability,and interface compatibility with electrodes.Herein,a novel all-solid polymer electrolyte(PPLCE)was fabricated by the copolymer network of liquid crystalline monomers and poly(ethylene glycol)dimethacrylate(PEGDMA)acts as a structural frame,combined with poly(ethylene glycol)diglycidyl ether short chain interspersed serving as mobile ion transport entities.The preparaed PPLCEs exhibit excellent mechanical property and out-standing electrochemical performances,which is attributed to their unique three-dimensional cocontinuous structure,characterized by a cross-linked semi-interpenetrating network and an ionic liquid phase,resulting in a distinctive nanostructure with short-range order and long-range disorder.Remarkably,the addition of PEGDMA is proved to be critical to the comprehensive performance of the PPLCEs,which effectively modulates the microscopic morphology of polymer networks and improves the mechanical properties as well as cycling stability of the solid electrolyte.When used in a lithiumion symmetrical battery configuration,the 6 wt%-PPLCE exhibites super stability,sustaining operation for over 2000 h at 30 C,with minimal and consistent overpotential of 50 mV.The resulting Li|PPLCE|LFP solid-state battery demonstrates high discharge specific capacities of 160.9 and 120.1 mA h g^(-1)at current densities of 0.2 and 1 C,respectively.Even after more than 300 cycles at a current density of 0.2 C,it retaines an impressive 73.5%capacity.Moreover,it displayes stable cycling for over 180 cycles at a high current density of 0.5C.The super cycle stability may promote the application for ultralong-life all solid-state lithium metal batteries.

Estimation of free core nutation parameters and availability of computing options

The Earth’s Free Core Nutation(FCN) causes Earth tides and forced nutation with frequencies close to the FCN that exhibit resonance effects.High-precision superconducting gravimeter(SG) and very long baseline interferometry(VLBI) provide good observation techniques for detecting the FCN parameters.However,some choices in data processing and solution procedures increase the uncertainty of the FCN parameters.In this study,we analyzed the differences and the effectiveness of weight function and ocean tide corrections in the FCN parameter detection using synthetic data,SG data from thirty-one stations,and the 10 celestial pole offset(CPO) series.The results show that significant discrepancies are caused by different computing options for a single SG station.The stacking method,which results in a variation of0.24-5 sidereal days(SDs) in the FCN period(T) and 10^(3)-10^(4) in the quality factor(Q) due to the selection of the weighting function and the ocean tide model(OTM),can effectively suppress this influence.The statistical analysis results of synthetic data shows that although different weight choices,while adjusting the proportion of diurnal tidal waves involved,do not significantly improve the accuracy of fitted FCN parameters from gravity observations.The study evaluated a series of OTMs using the loading correction efficiency.The fitting of FCN parameters can be improved by selecting the mean of appropriate OTMs based on the evaluation results.Through the estimation of the FCN parameters based on the forced nutation,it was found that the weight function P_(1) is more suitable than others,and different CPO series(after 2009) resulted in a difference of 0.4 SDs in the T and of 103 in the Q.We estimated the FCN parameters for SG(T=430.4±1.5 SDs and Q=1.52×10^(4)±2.5×10^(3)) and for VLBI(T=429.8±0.7 SDs,Q=1.88×10^(4)±2.1×10^(3)).

Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.

Credit Card Fraud Detection Using Improved Deep Learning Models

Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.

An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.

A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet.Regrettably,this development has expanded the potential targets that hackers might exploit.Without adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or alteration.The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks.This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)units.The proposed model can identify various types of cyberattacks,including conventional and distinctive forms.Recurrent networks,a specific kind of feedforward neural networks,possess an intrinsic memory component.Recurrent Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended periods.Metrics such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual cyberattacks.RNNs are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection Model.This model utilises Recurrent Neural Networks,specifically exploiting LSTM techniques.The proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.