Access block,known as exit block or boarding,is defined as a situation in which patients who are admitted or planned for admission remain in the emergency department(ED)as they are unable to be transferred to an inpat...Access block,known as exit block or boarding,is defined as a situation in which patients who are admitted or planned for admission remain in the emergency department(ED)as they are unable to be transferred to an inpatient unit within a reasonable time frame(no longer than 8 hours).[1,2]Access block often occurs due to insufficient hospital capacity and is a major issue in emergency medicine.[3]展开更多
AIM： To evaluate the efficacy of water supplementation treatment in patients with functional dyspepsia or irritable bowe syndrome （IBS） accompanying predominant constipation. METHODS： A total of 3872 patients with...AIM： To evaluate the efficacy of water supplementation treatment in patients with functional dyspepsia or irritable bowe syndrome （IBS） accompanying predominant constipation. METHODS： A total of 3872 patients with functional dyspepsia and 3609 patients with irritable bowel syndrome were enrolled in the study by 18 Italina thermal centres. Patients underwent a first cycle of thermal therapy for 21 d. A year later patients were re-evaluated at the same centre and received another cycle of thermal therapy. A questionnare to collect personal data on social and occupational status, family and pathological case history, life style, clinical records, utilisation of welfare and health structure and devices was administered to each patient at basal time and one year after each thermal treatment. Sixty patients with functional dyspepsia and 20 with IBS and 80 healthy controls received an evaluation of gastric output and oro-cecal transit time by breath test analysis. Breath test was performed at basal time and after water supplementaton therapies. Gastrointestinal symptoms were evaluated at the same time points. Breath samples were analyzed with a mass spectometer and a gascromatograph. Results were expressed as T1/2 and T-lag for octanoic add breath test and as oro-cecal transit time for lactulose breath test. RESULTS： A significant reduction of prevalence of symptoms was observed at the end of the first and second cycles of thermal therapy in dyspeptic and IBS patients, The analysis of variance showed a real and persistant improvement of symptoms in all patients. After water supplementation for 3 wk a reduction of gastric output was observed in 49 （87.5%） of 56 dyspepUc patients. Both T1/2 and T-lag were significantly reduced after the therapy compared to basal values [91 ± 12 （T1/2） and 53± 11 （T-lag）, Tables 1 and 2] with results of octanoic acid breath test similar to healthy subjects. After water supplementation for 3 wk oro-cecal transit time was shorter than that at the beginning of the study. CONCLUSION： Mineral water supplementation treatment for functional dyspepsia or conspipation accompanying IBS can improve gastric add output and intestinal transit time.展开更多
Quantitative precipitation estimation （QPE） plays an important role in meteorological and hydrological applications.Ground-based telemetered rain gauges are widely used to collect precipitation measurements.Spatial ...Quantitative precipitation estimation （QPE） plays an important role in meteorological and hydrological applications.Ground-based telemetered rain gauges are widely used to collect precipitation measurements.Spatial interpolation methods are commonly employed to estimate precipitation fields covering non-observed locations.Kriging is a simple and popular geostatistical interpolation method,but it has two known problems：uncertainty underestimation and violation of assumptions.This paper tackles these problems and seeks an optimal spatial interpolation for QPE in order to enhance spatial interpolation through appropriately assessing prediction uncertainty and fulfilling the required assumptions.To this end,several methods are tested：transformation,detrending,multiple spatial correlation functions,and Bayesian kriging.In particular,we focus on a short-term and time-specific rather than a long-term and event-specific analysis.This paper analyzes a stratiform rain event with an embedded convection linked to the passing monsoon front on the 23 August 2012.Data from a total of 100 automatic weather stations are used,and the rainfall intensities are calculated from the difference of 15 minute accumulated rainfall observed every 1 minute.The one-hour average rainfall intensity is then calculated to minimize the measurement random error.Cross-validation is carried out for evaluating the interpolation methods at regional and local levels.As a result,transformation is found to play an important role in improving spatial interpolation and uncertainty assessment,and Bayesian methods generally outperform traditional ones in terms of the criteria.展开更多
The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It au...The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. （2） An effective sparse hard-cut expec- tation maximization （SHC-EM） learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.展开更多
BACKGROUND Cystic pancreatic lesions consist of a wide variety of lesions that are becoming increasingly diagnosed with the growing use of imaging techniques.Of these,mucinous cysts are especially relevant due to thei...BACKGROUND Cystic pancreatic lesions consist of a wide variety of lesions that are becoming increasingly diagnosed with the growing use of imaging techniques.Of these,mucinous cysts are especially relevant due to their risk of malignancy.However,morphological findings are often suboptimal for their differentiation.Endoscopic ultrasound fine-needle aspiration(EUS-FNA)with molecular analysis has been suggested to improve the diagnosis of pancreatic cysts.AIM To determine the impact of molecular analysis on the detection of mucinous cysts and malignancy.METHODS An 18-month prospective observational study of consecutive patients with pancreatic cystic lesions and an indication for EUS-FNA following European clinical practice guidelines was conducted.These cysts included those>15 mm with unclear diagnosis,and a change in follow-up or with concerning features in which results might change clinical management.EUS-FNA with cytological,biochemical and glucose and molecular analyses with next-generation sequencing were performed in 36 pancreatic cysts.The cysts were classified as mucinous and non-mucinous by the combination of morphological,cytological and biochemical analyses when surgery was not performed.Malignancy was defined as cytology positive for malignancy,high-grade dysplasia or invasive carcinoma on surgical specimen,clinical or morphological progression,metastasis or death related to neoplastic complications during the 6-mo follow-up period.Next-generation sequencing results were compared for cyst type and malignancy.RESULTS Of the 36 lesions included,28(82.4%)were classified as mucinous and 6(17.6%)as non-mucinous.Furthermore,5(13.9%)lesions were classified as malignant.The amount of deoxyribonucleic acid obtained was sufficient for molecular analysis in 25(69.4%)pancreatic cysts.The amount of intracystic deoxyribonucleic acid was not statistically related to the cyst fluid volume obtained from the lesions.Analysis of KRAS and/or GNAS showed 83.33%[95%confidence interval(CI):63.34-100]sensitivity,60%(95%CI:7.06-100)specificity,88.24%(95%CI:69.98-100)positive predictive value and 50%(95%CI:1.66-98.34)negative predictive value(P=0.086)for the diagnosis of mucinous cystic lesions.Mutations in KRAS and GNAS were found in 2/5(40%)of the lesions classified as non-mucinous,thus recategorizing those lesions as mucinous neoplasms,which would have led to a modification of the follow-up plan in 8%of the cysts in which molecular analysis was successfully performed.All 4(100%)malignant cysts in which molecular analysis could be performed had mutations in KRAS and/or GNAS,although they were not related to malignancy(P>0.05).None of the other mutations analyzed could detect mucinous or malignant cysts with statistical significance(P>0.05).CONCLUSION Molecular analysis can improve the classification of pancreatic cysts as mucinous or non-mucinous.Mutations were not able to detect malignant lesions.展开更多
Global spread of infectious disease threatens the well-being of human, domestic, and wildlife health. A proper understanding of global distribution of these diseases is an important part of disease management and poli...Global spread of infectious disease threatens the well-being of human, domestic, and wildlife health. A proper understanding of global distribution of these diseases is an important part of disease management and policy making. However, data are subject to complexities by heterogeneity across host classes. The use of frequentist methods in biostatistics and epidemiology is common and is therefore extensively utilized in answering varied research questions. In this paper, we applied the hierarchical Bayesian approach to study the spatial distribution of tuberculosis in Kenya. The focus was to identify best fitting model for modeling TB relative risk in Kenya. The Markov Chain Monte Carlo (MCMC) method via WinBUGS and R packages was used for simulations. The Deviance Information Criterion (DIC) proposed by [1] was used for models comparison and selection. Among the models considered, unstructured heterogeneity model perfumes better in terms of modeling and mapping TB RR in Kenya. Variation in TB risk is observed among Kenya counties and clustering among counties with high TB Relative Risk (RR). HIV prevalence is identified as the dominant determinant of TB. We find clustering and heterogeneity of risk among high rate counties. Although the approaches are less than ideal, we hope that our formulations provide a useful stepping stone in the development of spatial methodology for the statistical analysis of risk from TB in Kenya.展开更多
Most financial signals show time dependency that,combined with noisy and extreme events,poses serious problems in the parameter estimations of statistical models.Moreover,when addressing asset pricing,portfolio select...Most financial signals show time dependency that,combined with noisy and extreme events,poses serious problems in the parameter estimations of statistical models.Moreover,when addressing asset pricing,portfolio selection,and investment strategies,accurate estimates of the relationship among assets are as necessary as are delicate in a time-dependent context.In this regard,fundamental tools that increasingly attract research interests are precision matrix and graphical models,which are able to obtain insights into the joint evolution of financial quantities.In this paper,we present a robust divergence estimator for a time-varying precision matrix that can manage both the extreme events and time-dependency that affect financial time series.Furthermore,we provide an algorithm to handle parameter estimations that uses the“maximization–minimization”approach.We apply the methodology to synthetic data to test its performances.Then,we consider the cryptocurrency market as a real data application,given its remarkable suitability for the proposed method because of its volatile and unregulated nature.展开更多
Eucommia ulmoides‘Hongye’is a new ornamental variety of E.ulmoides with excellent red or purple foliage.We found that E.ulmoides‘Hongye’exhibited a gradual change from green to red colour under light conditions.Ho...Eucommia ulmoides‘Hongye’is a new ornamental variety of E.ulmoides with excellent red or purple foliage.We found that E.ulmoides‘Hongye’exhibited a gradual change from green to red colour under light conditions.However,the colouring mechanism in the leaves of E.ulmoides‘Hongye’remains unclear.In this study,we compared the pigment content and leaf colour index of E.ulmoides‘Hongye’at five stages with those of E.ulmoides‘Xiaoye’,which was used as the control variety.The transcriptome sequencing data of the first-period(H1,green)and fifth-period(H5,red)leaves were also analysed and compared.The corresponding gene regulation in anthocyanin-related metabolic pathways was then analysed.Physiological results indicated that the contents of flavonoids and anthocyanins in red leaves(H5)were significantly higher than those in green leaves(H1),whereas the chlorophyll content in red leaves(H5)was lower than that in green leaves(H1).Moreover,the carotenoid content did not significantly differ between the two varieties.A transcriptome analysis identified 4240 differentially expressed genes(DEGs),and 20 of these genes were found to be involved in flavonoid and anthocyanin biosynthesis pathways.The results provide a reference for further study of the leaf colouration mechanism in E.ulmoides.展开更多
Under the aegis of Lisbon Treaty, individual member states of EU （European Union） are ＂encouraged to implement evidence-based policies in order to improve their provision of sporting facilities and opportunities＂....Under the aegis of Lisbon Treaty, individual member states of EU （European Union） are ＂encouraged to implement evidence-based policies in order to improve their provision of sporting facilities and opportunities＂. In this framework, a survey （Eurobarometer 72.3） was commissioned by the European Commission, which helps us to understand the behaviour of European societies in the field of participation in sport and in physical activities. In the paper, we measure the levels of involvement of EU citizens in active life styles. We do not analyse issued results, but use the individual record file, reprocessing data in different ways. We try to reconstruct the COMPASS （Co-Ordinated Monitoring of Participation in Sports） general model with the available information. The patterns of participation are studied in relation to the socio-demographic variables. The main result is the individualisation of a ＂six groups＂ （six typologies） solution for a cluster analysis of the participants to the survey. The clusters may be so labelled： occasional sport engagement; active participation; intensive open air activities; fitness world; traditional sport world; non active people. We find strong differences in the levels of participation among EU countries, whose determinants are both motivational and socio-demographic, and are linked to the national sport policies.展开更多
Background: World?wide grassland birds are in decline due to habitat loss and degradation resulting from inten?sive agricultural practices. Understanding how key grassland habitat attributes determine grassland bird d...Background: World?wide grassland birds are in decline due to habitat loss and degradation resulting from inten?sive agricultural practices. Understanding how key grassland habitat attributes determine grassland bird densities is required to make appropriate conservation decisions. We examine drivers of bird densities in a South African grass?land area that has been managed for biodiversity conservation with reduced grazing pressure.Methods: We estimated the density of the eight most common grassland bird species encountered in our area to evaluate the effects of recent grassland management changes on the avifauna. We collected data on birds and habitat from the austral summers of 2006/2007, 2007/2008 and 2010/2011. We used hierarchical distance sampling methods to estimate density of birds relative to two main habitat variables, i.e., grass cover and height. In addition, we used regression splines within these distance sampling models as a more flexible description of suitable ranges of grass height and cover for each species.Results: For most species, density is related to grass height and cover as expected. The African Quailfinch(Ortygospiza atricollis) and Common Quail(Coturnix coturnix) preferred relatively short and open grass. The Yellow?breasted Pipit(Anthus chloris), African Pipit(Anthus cinnamomeus) and Red?capped Lark(Calandrella cinerea) preferred short and relatively dense grass, while the Wing?snapping Cisticola(Cisticola ayresii) preferred grass of intermediate height and cover. The Cape Longclaw(Macronyx capensis) and Zitting Cisticola(Cisticola juncidis) preferred tall and dense grass. Our results agree with previous studies that grass height combined with grass cover are the most important habitat features that managers should manipulate in order to increase the density of target species. The regression splines show that the effect of these two habitat variables on density is well described by linear relationships for most species.Conclusions: This study supports previous studies suggesting that grazing and fire are important tools for manage?ment to use in order to create a mosaic of grass height and cover that would support high densities of desired spe?cies. We suggest that conservation managers of these grasslands combine fire and grazing as management tools to create suitable habitats for grassland birds in general.展开更多
This study utilizes the enzyme-substrate complex theory to predict the clinical efficacy of COVID-19 treatments at the biological systems level, using molecular docking stability indicators. Experimental data from the...This study utilizes the enzyme-substrate complex theory to predict the clinical efficacy of COVID-19 treatments at the biological systems level, using molecular docking stability indicators. Experimental data from the Protein Data Bank and molecular structures generated by AlphaFold 3 were used to create macromolecular complex templates. Six templates were developed, including the holo nsp7-nsp8-nsp12 (RNA-dependent RNA polymerase) complex with dsRNA primers (holo-RdRp-RNA). The study evaluated several ligands—Favipiravir-RTP, Remdesivir, Abacavir, Ribavirin, and Oseltamivir—as potential viral RNA polymerase inhibitors. Notably, the first four of these ligands have been clinically employed in the treatment of COVID-19, allowing for comparative analysis. Molecular docking simulations were performed using AutoDock 4, and statistical differences were assessed through t-tests and Mann-Whitney U tests. A review of the literature on COVID-19 treatment outcomes and inhibitors targeting RNA polymerase enzymes was conducted, and the inhibitors were ranked according to their clinical efficacy: Remdesivir > Favipiravir-RTP > Oseltamivir. Docking results obtained from the second and third templates aligned with clinical observations. Furthermore, Abacavir demonstrated a predicted efficacy comparable to Favipiravir-RTP, while Ribavirin exhibited a predicted efficacy similar to that of Remdesivir. This research, focused on inhibitors of SARS-CoV-2 RNA-dependent RNA polymerase, establishes a framework for screening AI-generated drug templates based on clinical outcomes. Additionally, it develops a drug screening platform based on molecular docking binding energy, enabling the evaluation of novel or repurposed drugs and potentially accelerating the drug development process.展开更多
We revisit a comparison of two discriminant analysis procedures, namely the linear combination classifier of Chung and Han (2000) and the maximum likelihood estimation substitution classifier for the problem of classi...We revisit a comparison of two discriminant analysis procedures, namely the linear combination classifier of Chung and Han (2000) and the maximum likelihood estimation substitution classifier for the problem of classifying unlabeled multivariate normal observations with equal covariance matrices into one of two classes. Both classes have matching block monotone missing training data. Here, we demonstrate that for intra-class covariance structures with at least small correlation among the variables with missing data and the variables without block missing data, the maximum likelihood estimation substitution classifier outperforms the Chung and Han (2000) classifier regardless of the percent of missing observations. Specifically, we examine the differences in the estimated expected error rates for these classifiers using a Monte Carlo simulation, and we compare the two classifiers using two real data sets with monotone missing data via parametric bootstrap simulations. Our results contradict the conclusions of Chung and Han (2000) that their linear combination classifier is superior to the MLE classifier for block monotone missing multivariate normal data.展开更多
Customer churn poses a significant challenge for the banking and finance industry in the United States, directly affecting profitability and market share. This study conducts a comprehensive comparative analysis of ma...Customer churn poses a significant challenge for the banking and finance industry in the United States, directly affecting profitability and market share. This study conducts a comprehensive comparative analysis of machine learning models for customer churn prediction, focusing on the U.S. context. The research evaluates the performance of logistic regression, random forest, and neural networks using industry-specific datasets, considering the economic impact and practical implications of the findings. The exploratory data analysis reveals unique patterns and trends in the U.S. banking and finance industry, such as the age distribution of customers and the prevalence of dormant accounts. The study incorporates macroeconomic factors to capture the potential influence of external conditions on customer churn behavior. The findings highlight the importance of leveraging advanced machine learning techniques and comprehensive customer data to develop effective churn prevention strategies in the U.S. context. By accurately predicting customer churn, financial institutions can proactively identify at-risk customers, implement targeted retention strategies, and optimize resource allocation. The study discusses the limitations and potential future improvements, serving as a roadmap for researchers and practitioners to further advance the field of customer churn prediction in the evolving landscape of the U.S. banking and finance industry.展开更多
In statistical decision theory, the risk function quantifies the average performance of a decision over the sample space. The risk function, which depends on the parameter of the model, is often summarized by the Baye...In statistical decision theory, the risk function quantifies the average performance of a decision over the sample space. The risk function, which depends on the parameter of the model, is often summarized by the Bayes risk, that is its expected value with respect to a design prior distribution assigned to the parameter. However, since expectation may not be an adequate synthesis of the random risk, we propose to examine the whole distribution of the risk function. Specifically, we consider point and interval estimation for the two parameters of the Pareto model. Using conjugate priors, we derive closed-form expressions for both the expected value and the density functions of the risk of each parameter under suitable losses. Finally, an application to wealth distribution is illustrated.展开更多
Currently, no clinically approved therapeutic drugs specifically target dengue virus infections. This study aims to evaluate the potential of antiviral drugs originally developed for other purposes as viable candidate...Currently, no clinically approved therapeutic drugs specifically target dengue virus infections. This study aims to evaluate the potential of antiviral drugs originally developed for other purposes as viable candidates for combating dengue virus. The RNA-elongating NS5-NS3 complex is a critical molecular structure responsible for dengue virus replication. Using the cryo-electron microscopy (Cryo-EM) structures available in the Protein Data Bank and AlphaFold 3 predictions, this study simulated the replication complexes of dengue virus serotypes 1, 2, 3, and 4. The RNA-dependent RNA polymerase (RdRp) domain of the NS5 protein within the NS5-NS3 complex was selected as the molecular docking template. Molecular docking simulations were conducted using AutoDock4. Seven small molecules—AT-9010, RK-0404678, Oseltamivir, Remdesivir, Favipiravir-RTP, Abacavir, and Ribavirin—were assessed for binding affinity by calculating their binding energies, where lower values indicate stronger molecular interactions. Based on published data, antiviral replication assays were conducted for the four dengue virus serotypes. AT-9010 and RK-0404678 were used as benchmarks for antiviral replication efficacy, while Oseltamivir served as the control group. The Mann-Whitney U test was employed to classify the clinical antiviral candidates—Remdesivir, Favipiravir-RTP, Abacavir, and Ribavirin. Results demonstrated that among the four small molecules, Favipiravir-RTP exhibited the highest binding affinity with the RdRp domain of the NS5-NS3 complex across all four dengue virus serotypes. Statistical classification revealed that in five simulated scenarios—including the four virus serotypes and Cryo-EM structural data—Favipiravir-RTP shared three classifications with the benchmark molecule AT-9010. Based on these findings, Favipiravir-RTP, a broad-spectrum antiviral agent, shows potential as a therapeutic option for inhibiting dengue virus replication. However, further clinical trials are necessary to validate their efficacy in humans.展开更多
In popular Baba-Engle-Kraft-Kroner(BEKK)and dynamic conditional correlation(DCC)multivariate generalized autoregressive conditional heteroskedasticity models,the large number of parameters and the requirement of posit...In popular Baba-Engle-Kraft-Kroner(BEKK)and dynamic conditional correlation(DCC)multivariate generalized autoregressive conditional heteroskedasticity models,the large number of parameters and the requirement of positive definiteness of the covariance and correlation matrices pose some difficulties during the estimation process.To avoid these issues,we propose two modifications to the BEKK and DCC models that employ two spherical parameterizations applied to the Cholesky decompositions of the covariance and correlation matrices.In their full specifications,the introduced Cholesky-BEKK and Cholesky-DCC models allow for a reduction in the number of parameters compared with their traditional counterparts.Moreover,the application of spherical transformation does not require the imposition of inequality constraints on the parameters during the estimation.An application to two crude oils,WTI and Brent,and the main exchange rate prices demonstrates that the Cholesky-BEKK and Cholesky-DCC models can capture the dynamics of covariances and correlations.In addition,the Kupiec test on different portfolio compositions confirms the satisfactory performance of the proposed models.展开更多
AIM To assess the accuracy of shear wave elastography(SWE)alone and in combination with aminotransferase platelet ratio index(APRI)score in the staging of liver fibrosis.METHODS A multicenter prospective study was con...AIM To assess the accuracy of shear wave elastography(SWE)alone and in combination with aminotransferase platelet ratio index(APRI)score in the staging of liver fibrosis.METHODS A multicenter prospective study was conducted to assess the accuracy of SWE(medians)and APRI to predict biopsy results.The analysis focused on distinguishing the different stages of liver disease,namely,F0 from F1-4,F0-1 from F2-4,F0-2 from F3-4 and F0-3 from F4;F0-F1 from F2-F4 being of primary interest.The area under the receiver operating characteristic(AUROC)curve was computed using logistic regression model.The role of age,gender and steatosis was also assessed.RESULTS SWE alone accurately distinguished F0-1 from F2-4 with a high probability.The AUROC using SWE alone was 0.91 compared to 0.78 for using the APRI score alone.The APRI score,when used in conjunction with SWE,did not make a significant contribution to the AUROC.SWE and steatosis were the only significant predictors that differentiated F0-1 from F2-4 with an AUROC of 0.944.CONCLUSION Our study validates the use of SWE in the diagnosis and staging of liver fibrosis.Furthermore,the probability of a correct diagnosis is significantly enhanced with the addition of steatosis as a prognostic factor.展开更多
基金supported by the Project of Science and Technology Commission of Jiading,Shanghai(JDKW-2016-W03)the Scientific Research Projects of the Shanghai Municipal Health Commission for Youths(20204Y0016)+1 种基金the National Natural Science Foundation of China(72174041)。
文摘Access block,known as exit block or boarding,is defined as a situation in which patients who are admitted or planned for admission remain in the emergency department(ED)as they are unable to be transferred to an inpatient unit within a reasonable time frame(no longer than 8 hours).[1,2]Access block often occurs due to insufficient hospital capacity and is a major issue in emergency medicine.[3]
文摘AIM： To evaluate the efficacy of water supplementation treatment in patients with functional dyspepsia or irritable bowe syndrome （IBS） accompanying predominant constipation. METHODS： A total of 3872 patients with functional dyspepsia and 3609 patients with irritable bowel syndrome were enrolled in the study by 18 Italina thermal centres. Patients underwent a first cycle of thermal therapy for 21 d. A year later patients were re-evaluated at the same centre and received another cycle of thermal therapy. A questionnare to collect personal data on social and occupational status, family and pathological case history, life style, clinical records, utilisation of welfare and health structure and devices was administered to each patient at basal time and one year after each thermal treatment. Sixty patients with functional dyspepsia and 20 with IBS and 80 healthy controls received an evaluation of gastric output and oro-cecal transit time by breath test analysis. Breath test was performed at basal time and after water supplementaton therapies. Gastrointestinal symptoms were evaluated at the same time points. Breath samples were analyzed with a mass spectometer and a gascromatograph. Results were expressed as T1/2 and T-lag for octanoic add breath test and as oro-cecal transit time for lactulose breath test. RESULTS： A significant reduction of prevalence of symptoms was observed at the end of the first and second cycles of thermal therapy in dyspeptic and IBS patients, The analysis of variance showed a real and persistant improvement of symptoms in all patients. After water supplementation for 3 wk a reduction of gastric output was observed in 49 （87.5%） of 56 dyspepUc patients. Both T1/2 and T-lag were significantly reduced after the therapy compared to basal values [91 ± 12 （T1/2） and 53± 11 （T-lag）, Tables 1 and 2] with results of octanoic acid breath test similar to healthy subjects. After water supplementation for 3 wk oro-cecal transit time was shorter than that at the beginning of the study. CONCLUSION： Mineral water supplementation treatment for functional dyspepsia or conspipation accompanying IBS can improve gastric add output and intestinal transit time.
基金funded by the Korea Meteorological Administration Research and Development Program (Grant No. CATER 2013-2040)supported by the Brain Pool program of the Korean Federation of Science and Technology Societies (KOFST) (Grant No. 122S-1-3-0422)
文摘Quantitative precipitation estimation （QPE） plays an important role in meteorological and hydrological applications.Ground-based telemetered rain gauges are widely used to collect precipitation measurements.Spatial interpolation methods are commonly employed to estimate precipitation fields covering non-observed locations.Kriging is a simple and popular geostatistical interpolation method,but it has two known problems：uncertainty underestimation and violation of assumptions.This paper tackles these problems and seeks an optimal spatial interpolation for QPE in order to enhance spatial interpolation through appropriately assessing prediction uncertainty and fulfilling the required assumptions.To this end,several methods are tested：transformation,detrending,multiple spatial correlation functions,and Bayesian kriging.In particular,we focus on a short-term and time-specific rather than a long-term and event-specific analysis.This paper analyzes a stratiform rain event with an embedded convection linked to the passing monsoon front on the 23 August 2012.Data from a total of 100 automatic weather stations are used,and the rainfall intensities are calculated from the difference of 15 minute accumulated rainfall observed every 1 minute.The one-hour average rainfall intensity is then calculated to minimize the measurement random error.Cross-validation is carried out for evaluating the interpolation methods at regional and local levels.As a result,transformation is found to play an important role in improving spatial interpolation and uncertainty assessment,and Bayesian methods generally outperform traditional ones in terms of the criteria.
基金Supported by the National Natural Science Foundation of China under Grant No 60972106the China Postdoctoral Science Foundation under Grant No 2014M561053+1 种基金the Humanity and Social Science Foundation of Ministry of Education of China under Grant No 15YJA630108the Hebei Province Natural Science Foundation under Grant No E2016202341
文摘The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. （2） An effective sparse hard-cut expec- tation maximization （SHC-EM） learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.
基金FIB Hospital Universitario de La Princesa,No.G-83727081.
文摘BACKGROUND Cystic pancreatic lesions consist of a wide variety of lesions that are becoming increasingly diagnosed with the growing use of imaging techniques.Of these,mucinous cysts are especially relevant due to their risk of malignancy.However,morphological findings are often suboptimal for their differentiation.Endoscopic ultrasound fine-needle aspiration(EUS-FNA)with molecular analysis has been suggested to improve the diagnosis of pancreatic cysts.AIM To determine the impact of molecular analysis on the detection of mucinous cysts and malignancy.METHODS An 18-month prospective observational study of consecutive patients with pancreatic cystic lesions and an indication for EUS-FNA following European clinical practice guidelines was conducted.These cysts included those>15 mm with unclear diagnosis,and a change in follow-up or with concerning features in which results might change clinical management.EUS-FNA with cytological,biochemical and glucose and molecular analyses with next-generation sequencing were performed in 36 pancreatic cysts.The cysts were classified as mucinous and non-mucinous by the combination of morphological,cytological and biochemical analyses when surgery was not performed.Malignancy was defined as cytology positive for malignancy,high-grade dysplasia or invasive carcinoma on surgical specimen,clinical or morphological progression,metastasis or death related to neoplastic complications during the 6-mo follow-up period.Next-generation sequencing results were compared for cyst type and malignancy.RESULTS Of the 36 lesions included,28(82.4%)were classified as mucinous and 6(17.6%)as non-mucinous.Furthermore,5(13.9%)lesions were classified as malignant.The amount of deoxyribonucleic acid obtained was sufficient for molecular analysis in 25(69.4%)pancreatic cysts.The amount of intracystic deoxyribonucleic acid was not statistically related to the cyst fluid volume obtained from the lesions.Analysis of KRAS and/or GNAS showed 83.33%[95%confidence interval(CI):63.34-100]sensitivity,60%(95%CI:7.06-100)specificity,88.24%(95%CI:69.98-100)positive predictive value and 50%(95%CI:1.66-98.34)negative predictive value(P=0.086)for the diagnosis of mucinous cystic lesions.Mutations in KRAS and GNAS were found in 2/5(40%)of the lesions classified as non-mucinous,thus recategorizing those lesions as mucinous neoplasms,which would have led to a modification of the follow-up plan in 8%of the cysts in which molecular analysis was successfully performed.All 4(100%)malignant cysts in which molecular analysis could be performed had mutations in KRAS and/or GNAS,although they were not related to malignancy(P>0.05).None of the other mutations analyzed could detect mucinous or malignant cysts with statistical significance(P>0.05).CONCLUSION Molecular analysis can improve the classification of pancreatic cysts as mucinous or non-mucinous.Mutations were not able to detect malignant lesions.
文摘Global spread of infectious disease threatens the well-being of human, domestic, and wildlife health. A proper understanding of global distribution of these diseases is an important part of disease management and policy making. However, data are subject to complexities by heterogeneity across host classes. The use of frequentist methods in biostatistics and epidemiology is common and is therefore extensively utilized in answering varied research questions. In this paper, we applied the hierarchical Bayesian approach to study the spatial distribution of tuberculosis in Kenya. The focus was to identify best fitting model for modeling TB relative risk in Kenya. The Markov Chain Monte Carlo (MCMC) method via WinBUGS and R packages was used for simulations. The Deviance Information Criterion (DIC) proposed by [1] was used for models comparison and selection. Among the models considered, unstructured heterogeneity model perfumes better in terms of modeling and mapping TB RR in Kenya. Variation in TB risk is observed among Kenya counties and clustering among counties with high TB Relative Risk (RR). HIV prevalence is identified as the dominant determinant of TB. We find clustering and heterogeneity of risk among high rate counties. Although the approaches are less than ideal, we hope that our formulations provide a useful stepping stone in the development of spatial methodology for the statistical analysis of risk from TB in Kenya.
文摘Most financial signals show time dependency that,combined with noisy and extreme events,poses serious problems in the parameter estimations of statistical models.Moreover,when addressing asset pricing,portfolio selection,and investment strategies,accurate estimates of the relationship among assets are as necessary as are delicate in a time-dependent context.In this regard,fundamental tools that increasingly attract research interests are precision matrix and graphical models,which are able to obtain insights into the joint evolution of financial quantities.In this paper,we present a robust divergence estimator for a time-varying precision matrix that can manage both the extreme events and time-dependency that affect financial time series.Furthermore,we provide an algorithm to handle parameter estimations that uses the“maximization–minimization”approach.We apply the methodology to synthetic data to test its performances.Then,we consider the cryptocurrency market as a real data application,given its remarkable suitability for the proposed method because of its volatile and unregulated nature.
基金Natural Science Foundation of Henan Province of China(202300410554)Key R&D and Promotion Project of Henan Province(Science and Technology Research)(192102110169,202102110229)].
文摘Eucommia ulmoides‘Hongye’is a new ornamental variety of E.ulmoides with excellent red or purple foliage.We found that E.ulmoides‘Hongye’exhibited a gradual change from green to red colour under light conditions.However,the colouring mechanism in the leaves of E.ulmoides‘Hongye’remains unclear.In this study,we compared the pigment content and leaf colour index of E.ulmoides‘Hongye’at five stages with those of E.ulmoides‘Xiaoye’,which was used as the control variety.The transcriptome sequencing data of the first-period(H1,green)and fifth-period(H5,red)leaves were also analysed and compared.The corresponding gene regulation in anthocyanin-related metabolic pathways was then analysed.Physiological results indicated that the contents of flavonoids and anthocyanins in red leaves(H5)were significantly higher than those in green leaves(H1),whereas the chlorophyll content in red leaves(H5)was lower than that in green leaves(H1).Moreover,the carotenoid content did not significantly differ between the two varieties.A transcriptome analysis identified 4240 differentially expressed genes(DEGs),and 20 of these genes were found to be involved in flavonoid and anthocyanin biosynthesis pathways.The results provide a reference for further study of the leaf colouration mechanism in E.ulmoides.
文摘Under the aegis of Lisbon Treaty, individual member states of EU （European Union） are ＂encouraged to implement evidence-based policies in order to improve their provision of sporting facilities and opportunities＂. In this framework, a survey （Eurobarometer 72.3） was commissioned by the European Commission, which helps us to understand the behaviour of European societies in the field of participation in sport and in physical activities. In the paper, we measure the levels of involvement of EU citizens in active life styles. We do not analyse issued results, but use the individual record file, reprocessing data in different ways. We try to reconstruct the COMPASS （Co-Ordinated Monitoring of Participation in Sports） general model with the available information. The patterns of participation are studied in relation to the socio-demographic variables. The main result is the individualisation of a ＂six groups＂ （six typologies） solution for a cluster analysis of the participants to the survey. The clusters may be so labelled： occasional sport engagement; active participation; intensive open air activities; fitness world; traditional sport world; non active people. We find strong differences in the levels of participation among EU countries, whose determinants are both motivational and socio-demographic, and are linked to the national sport policies.
基金supported in the position of Bird Life South Africa Ingula Project Manager with funding by Eskom through The Ingula PartnershipFund supported the first author with a vehicle for the duration of the project,while employed by Bird Life South Africasupported by the National Research Foundation of South Africa(Grant 85802)
文摘Background: World?wide grassland birds are in decline due to habitat loss and degradation resulting from inten?sive agricultural practices. Understanding how key grassland habitat attributes determine grassland bird densities is required to make appropriate conservation decisions. We examine drivers of bird densities in a South African grass?land area that has been managed for biodiversity conservation with reduced grazing pressure.Methods: We estimated the density of the eight most common grassland bird species encountered in our area to evaluate the effects of recent grassland management changes on the avifauna. We collected data on birds and habitat from the austral summers of 2006/2007, 2007/2008 and 2010/2011. We used hierarchical distance sampling methods to estimate density of birds relative to two main habitat variables, i.e., grass cover and height. In addition, we used regression splines within these distance sampling models as a more flexible description of suitable ranges of grass height and cover for each species.Results: For most species, density is related to grass height and cover as expected. The African Quailfinch(Ortygospiza atricollis) and Common Quail(Coturnix coturnix) preferred relatively short and open grass. The Yellow?breasted Pipit(Anthus chloris), African Pipit(Anthus cinnamomeus) and Red?capped Lark(Calandrella cinerea) preferred short and relatively dense grass, while the Wing?snapping Cisticola(Cisticola ayresii) preferred grass of intermediate height and cover. The Cape Longclaw(Macronyx capensis) and Zitting Cisticola(Cisticola juncidis) preferred tall and dense grass. Our results agree with previous studies that grass height combined with grass cover are the most important habitat features that managers should manipulate in order to increase the density of target species. The regression splines show that the effect of these two habitat variables on density is well described by linear relationships for most species.Conclusions: This study supports previous studies suggesting that grazing and fire are important tools for manage?ment to use in order to create a mosaic of grass height and cover that would support high densities of desired spe?cies. We suggest that conservation managers of these grasslands combine fire and grazing as management tools to create suitable habitats for grassland birds in general.
文摘This study utilizes the enzyme-substrate complex theory to predict the clinical efficacy of COVID-19 treatments at the biological systems level, using molecular docking stability indicators. Experimental data from the Protein Data Bank and molecular structures generated by AlphaFold 3 were used to create macromolecular complex templates. Six templates were developed, including the holo nsp7-nsp8-nsp12 (RNA-dependent RNA polymerase) complex with dsRNA primers (holo-RdRp-RNA). The study evaluated several ligands—Favipiravir-RTP, Remdesivir, Abacavir, Ribavirin, and Oseltamivir—as potential viral RNA polymerase inhibitors. Notably, the first four of these ligands have been clinically employed in the treatment of COVID-19, allowing for comparative analysis. Molecular docking simulations were performed using AutoDock 4, and statistical differences were assessed through t-tests and Mann-Whitney U tests. A review of the literature on COVID-19 treatment outcomes and inhibitors targeting RNA polymerase enzymes was conducted, and the inhibitors were ranked according to their clinical efficacy: Remdesivir > Favipiravir-RTP > Oseltamivir. Docking results obtained from the second and third templates aligned with clinical observations. Furthermore, Abacavir demonstrated a predicted efficacy comparable to Favipiravir-RTP, while Ribavirin exhibited a predicted efficacy similar to that of Remdesivir. This research, focused on inhibitors of SARS-CoV-2 RNA-dependent RNA polymerase, establishes a framework for screening AI-generated drug templates based on clinical outcomes. Additionally, it develops a drug screening platform based on molecular docking binding energy, enabling the evaluation of novel or repurposed drugs and potentially accelerating the drug development process.
文摘We revisit a comparison of two discriminant analysis procedures, namely the linear combination classifier of Chung and Han (2000) and the maximum likelihood estimation substitution classifier for the problem of classifying unlabeled multivariate normal observations with equal covariance matrices into one of two classes. Both classes have matching block monotone missing training data. Here, we demonstrate that for intra-class covariance structures with at least small correlation among the variables with missing data and the variables without block missing data, the maximum likelihood estimation substitution classifier outperforms the Chung and Han (2000) classifier regardless of the percent of missing observations. Specifically, we examine the differences in the estimated expected error rates for these classifiers using a Monte Carlo simulation, and we compare the two classifiers using two real data sets with monotone missing data via parametric bootstrap simulations. Our results contradict the conclusions of Chung and Han (2000) that their linear combination classifier is superior to the MLE classifier for block monotone missing multivariate normal data.
文摘Customer churn poses a significant challenge for the banking and finance industry in the United States, directly affecting profitability and market share. This study conducts a comprehensive comparative analysis of machine learning models for customer churn prediction, focusing on the U.S. context. The research evaluates the performance of logistic regression, random forest, and neural networks using industry-specific datasets, considering the economic impact and practical implications of the findings. The exploratory data analysis reveals unique patterns and trends in the U.S. banking and finance industry, such as the age distribution of customers and the prevalence of dormant accounts. The study incorporates macroeconomic factors to capture the potential influence of external conditions on customer churn behavior. The findings highlight the importance of leveraging advanced machine learning techniques and comprehensive customer data to develop effective churn prevention strategies in the U.S. context. By accurately predicting customer churn, financial institutions can proactively identify at-risk customers, implement targeted retention strategies, and optimize resource allocation. The study discusses the limitations and potential future improvements, serving as a roadmap for researchers and practitioners to further advance the field of customer churn prediction in the evolving landscape of the U.S. banking and finance industry.
文摘In statistical decision theory, the risk function quantifies the average performance of a decision over the sample space. The risk function, which depends on the parameter of the model, is often summarized by the Bayes risk, that is its expected value with respect to a design prior distribution assigned to the parameter. However, since expectation may not be an adequate synthesis of the random risk, we propose to examine the whole distribution of the risk function. Specifically, we consider point and interval estimation for the two parameters of the Pareto model. Using conjugate priors, we derive closed-form expressions for both the expected value and the density functions of the risk of each parameter under suitable losses. Finally, an application to wealth distribution is illustrated.
文摘Currently, no clinically approved therapeutic drugs specifically target dengue virus infections. This study aims to evaluate the potential of antiviral drugs originally developed for other purposes as viable candidates for combating dengue virus. The RNA-elongating NS5-NS3 complex is a critical molecular structure responsible for dengue virus replication. Using the cryo-electron microscopy (Cryo-EM) structures available in the Protein Data Bank and AlphaFold 3 predictions, this study simulated the replication complexes of dengue virus serotypes 1, 2, 3, and 4. The RNA-dependent RNA polymerase (RdRp) domain of the NS5 protein within the NS5-NS3 complex was selected as the molecular docking template. Molecular docking simulations were conducted using AutoDock4. Seven small molecules—AT-9010, RK-0404678, Oseltamivir, Remdesivir, Favipiravir-RTP, Abacavir, and Ribavirin—were assessed for binding affinity by calculating their binding energies, where lower values indicate stronger molecular interactions. Based on published data, antiviral replication assays were conducted for the four dengue virus serotypes. AT-9010 and RK-0404678 were used as benchmarks for antiviral replication efficacy, while Oseltamivir served as the control group. The Mann-Whitney U test was employed to classify the clinical antiviral candidates—Remdesivir, Favipiravir-RTP, Abacavir, and Ribavirin. Results demonstrated that among the four small molecules, Favipiravir-RTP exhibited the highest binding affinity with the RdRp domain of the NS5-NS3 complex across all four dengue virus serotypes. Statistical classification revealed that in five simulated scenarios—including the four virus serotypes and Cryo-EM structural data—Favipiravir-RTP shared three classifications with the benchmark molecule AT-9010. Based on these findings, Favipiravir-RTP, a broad-spectrum antiviral agent, shows potential as a therapeutic option for inhibiting dengue virus replication. However, further clinical trials are necessary to validate their efficacy in humans.
文摘In popular Baba-Engle-Kraft-Kroner(BEKK)and dynamic conditional correlation(DCC)multivariate generalized autoregressive conditional heteroskedasticity models,the large number of parameters and the requirement of positive definiteness of the covariance and correlation matrices pose some difficulties during the estimation process.To avoid these issues,we propose two modifications to the BEKK and DCC models that employ two spherical parameterizations applied to the Cholesky decompositions of the covariance and correlation matrices.In their full specifications,the introduced Cholesky-BEKK and Cholesky-DCC models allow for a reduction in the number of parameters compared with their traditional counterparts.Moreover,the application of spherical transformation does not require the imposition of inequality constraints on the parameters during the estimation.An application to two crude oils,WTI and Brent,and the main exchange rate prices demonstrates that the Cholesky-BEKK and Cholesky-DCC models can capture the dynamics of covariances and correlations.In addition,the Kupiec test on different portfolio compositions confirms the satisfactory performance of the proposed models.
基金the Aga Khan United States Research funding body and research support team for funding this study
文摘AIM To assess the accuracy of shear wave elastography(SWE)alone and in combination with aminotransferase platelet ratio index(APRI)score in the staging of liver fibrosis.METHODS A multicenter prospective study was conducted to assess the accuracy of SWE(medians)and APRI to predict biopsy results.The analysis focused on distinguishing the different stages of liver disease,namely,F0 from F1-4,F0-1 from F2-4,F0-2 from F3-4 and F0-3 from F4;F0-F1 from F2-F4 being of primary interest.The area under the receiver operating characteristic(AUROC)curve was computed using logistic regression model.The role of age,gender and steatosis was also assessed.RESULTS SWE alone accurately distinguished F0-1 from F2-4 with a high probability.The AUROC using SWE alone was 0.91 compared to 0.78 for using the APRI score alone.The APRI score,when used in conjunction with SWE,did not make a significant contribution to the AUROC.SWE and steatosis were the only significant predictors that differentiated F0-1 from F2-4 with an AUROC of 0.944.CONCLUSION Our study validates the use of SWE in the diagnosis and staging of liver fibrosis.Furthermore,the probability of a correct diagnosis is significantly enhanced with the addition of steatosis as a prognostic factor.
