检索结果-内蒙古大学图书馆

Clinical evaluation of the acuitas® AMR gene panel for rapid detection of bacteria and genotypic antibiotic resistance determinants

引用

DIAGNOSTIC MICROBIOLOGY AND INFECTIOUS DISEASE 2021年第4期100卷 115383-115383页

作者： Riedel, Stefan Halls, Justin Dutta, Sanjucta Toraskar, Nikhil Lemon, Jamie Carter, Kendra Sinclair, Will Lopansri, Bert K. Styer, Amanda M. Wolk, Donna M. Walker, G. Terrance Beth Israel Deaconess Med Ctr Dept Pathol 330 Brookline Ave Boston MA 02215 USA Harvard Med Sch Boston MA 02115 USA OpGen Inc Gaithersburg MD USA Intermt Med Ctr Cent Microbiol Lab Murray UT USA Univ Utah Dept Internal Med Salt Lake City UT 84112 USA Geisinger Hlth Syst Diagnost Med Inst Danville PA USA

Urinary tract infections are leading causes of hospital admissions. Accurate and timely diagnosis is important due to increasing morbidity and mortality from antimicrobial resistance. We evaluated a polymerase chain reaction test (Acuitas AMR Gene Panel with the Acuitas Lighthouse Software) for detection of 5 common uropathogens (Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Pseudomonas aeruginosa, Enterococcus faecalis) and antibiotic resistance genes directly from urine for prediction of phenotypic resistance. Overall percent agreement was 97% for semiquantitative detection of uropathogens versus urine culture using a cutoff of 104 colony forming units per mL urine. Overall accuracy was 91% to 93% for genotypic prediction of common antibiotic resistance harbored by E. coli, K. pneumoniae, and P. mirabilis. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Antimicrobial Resistance Urinary Tract Infection Polymerase Chain Reaction Antimicrobial Susceptibility Testing generalized linear models Syndromic testing Acuitas AMR Gene Panel Acuitas Lighthouse

来源：评论

学校读者我要写书评

暂无评论

Statistical inference with incomplete and high-dimensional data - modeling polytraumatized patients: Inférence statistique avec des données incomplètes et de grandes dimensions - modélisation des polytraumatisés graves

Statistical inference with incomplete and high-dimensional d...

引用

作者： Jiang, Wei Université Paris-Saclay

学位级别：博士

Le problème des données manquantes existe depuis les débuts de l'analyse des données, car les valeurs manquantes sont liées au processus d'obtention et de préparation des donn... 详细信息

Le problème des données manquantes existe depuis les débuts de l'analyse des données, car les valeurs manquantes sont liées au processus d'obtention et de préparation des données. Dans les applications des statistiques modernes et de l'apprentissage machine, où la collecte de données devient de plus en plus complexe et où de multiples sources d'information sont combinées, les grandes bases de données présentent souvent un nombre extraordinairement élevé de valeurs manquantes. Ces données présentent donc d'importants défis méthodologiques et techniques pour l'analyse : de la visualisation à la modélisation, en passant par l'estimation, la sélection des variables, les capacités de prédiction et la mise en oeuvre par des implémentations. De plus, bien que les données en grande dimension avec des valeurs manquantes soient considérées comme des difficultés courantes dans l'analyse statistique aujourd'hui, seules quelques solutions sont disponibles.L'objectif de cette thèse est de développer de nouvelles méthodologies pour effectuer des inférences statistiques avec des données manquantes et en particulier pour des données en grande dimension. La contribution la plus importante est de proposer un cadre complet pour traiter les valeurs manquantes, de l'estimation à la sélection d'un modèle, en se basant sur des approches de vraisemblance. La méthode proposée ne repose pas sur un dispositif spécifique du manque, et permet un bon équilibre entre qualité de l'inférence et implémentations *** contributions de la thèse se composent en trois parties. Dans le chapitre 2, nous nous concentrons sur la régression logistique avec des valeurs manquantes dans un cadre de modélisation jointe, en utilisant une approximation stochastique de l'algorithme EM. Nous étudions l'estimation des paramètres, la sélection des variables et la prédiction pour de nouvelles observations incomplètes. Grâce à des simulations complètes, nous montrons que les estimateurs sont non biaisés et ont de

关键词： Observations manquantes (statistique) Modèles linéaires généralisés Algorithmes EM Analyse de régression Dépendance (statistique) Statistique médicale Missing Observations generalized linear models EM Algorithms Regression Analysis Dependence Medical Statistics

来源：评论

学校读者我要写书评

暂无评论

Statistical Inference for Diverging Number of Parameters beyond linear Regression

Statistical Inference for Diverging Number of Parameters bey...

引用

作者： Xia, Lu University of Michigan

学位级别：博士

In the big data era, regression models with a large number of covariates have emerged as a common tool to tackle problems arising from business, engineering, genomics, neuroimaging, and epidemiological studies. Drawing statistical inference for these models has sparked much interest over the past few years. Albeit successful for high dimensional linear models, high dimensional inference approaches beyond linear regression are limited and present unsatisfactory performance, theoretically or numerically. In this dissertation, we focus on de-biased lasso, which has been one of the most popular methods for high dimensional inferences. We propose procedures that provide better bias correction and confidence interval coverage, and draw reliable inference for regression parameters in the "large n, diverging p" scenario. In general, we caution against applying de-biased lasso and its variants to models beyond linear regression when parameters outnumber the sample size. Following an overview outlined in Chapter I, we focus on the generalized linear models (GLMs) in Chapter II. Extensive numerical simulations indicate that de-biased lasso may not adequately remove biases for high dimensional GLMs, and thus yield unreliable confidence intervals. We have further found that several key assumptions, especially the sparsity condition on the inverse Hessian matrix, may not hold for GLMs. In a "large n, diverging p" scenario, we consider an alternative de-biased lasso approach that inverts the Hessian matrix of the concerned model without requiring matrix sparsity, and establish the asymptotic distributions of linear combinations of the estimates. Simulations evidence that our proposed de-biased estimator performs better in bias correction and confidence interval coverage for a wide range of p/n ratios. We apply our method to the Boston Lung Cancer Study, an epidemiology study on the mechanisms underlying lung cancer, and investigate the joint effects of genetic variants on overal

关键词： De-biased lasso Diverging dimension Statistical inference Confidence intervals generalized linear models Cox proportional hazards model Statistics and Numeric Data Science

来源：评论

学校读者我要写书评

暂无评论

Inference Procedures on the generalized Poisson Distribution from Multiple Samples: Comparisons with Nonparametric models for Analysis of Covariance (ANCOVA) of Count Data

引用

Open Journal of Statistics 2021年第3期11卷 420-436页

作者： Maha Al-Eid Mohamed M. Shoukri Department of Biostatistics Epidemiology and Scientific Computing King Faisal Specialist Hospital and Research Center Riyadh Saudi Arabia Department of Epidemiology and Biostatistics Schulich School of Medicine and Dentistry University of Western Ontario London Ontario Canada

Count data that exhibit over dispersion (variance of counts is larger than its mean) are commonly analyzed using discrete distributions such as negative binomial, Poisson inverse Gaussian and other models. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial and the Poisson inverse Gaussian have variance larger than the mean and therefore are more appropriate to model over-dispersed count data. As an alternative to these two models, we shall use the generalized Poisson distribution for group comparisons in the presence of multiple covariates. This problem is known as the ANCOVA and is solved for continuous data. Our objectives were to develop ANCOVA using the generalized Poisson distribution, and compare its goodness of fit to that of the nonparametric generalized Additive models. We used real life data to show that the model performs quite satisfactorily when compared to the nonparametric generalized Additive models.

关键词： Count Regression Over Dispersion generalized linear models Analysis of Covariance generalized Additive models

来源：评论

学校读者我要写书评

暂无评论

Online stochastic gradient descent on non-convex losses from high-dimensional inference

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2021年第1期22卷 4788-4838页

作者： Gerard Ben Arous Reza Gheissari Aukosh Jagannath Courant Institute of Mathematical Sciences New York University New York NY Departments of Statistics and EECS University of California Berkeley CA Departments of Statistics and Actuarial Science and Applied Mathematics University of Waterloo Waterloo ON Canada

Stochastic gradient descent (SGD) is a popular algorithm for optimization problems arising in high-dimensional inference tasks. Here one produces an estimator of an unknown parameter from independent samples of data by iteratively optimizing a loss function. This loss function is random and often non-convex. We study the performance of the simplest version of SGD, namely online SGD, from a random start in the setting where the parameter space is *** develop nearly sharp thresholds for the number of samples needed for consistent estimation as one varies the dimension. Our thresholds depend only on an intrinsic property of the population loss which we call the information exponent. In particular, our results do not assume uniform control on the loss itself, such as convexity or uniform derivative bounds. The thresholds we obtain are polynomial in the dimension and the precise exponent depends explicitly on the information exponent. As a consequence of our results, we find that except for the simplest tasks, almost all of the data is used simply in the initial search phase to obtain non-trivial correlation with the ground truth. Upon attaining nontrivial correlation, the descent is rapid and exhibits law of large numbers type *** illustrate our approach by applying it to a wide set of inference tasks such as phase retrieval, and parameter estimation for generalized linear models, online PCA, and spiked tensor models, as well as to supervised learning for single-layer networks with general activation functions.

关键词： stochastic gradient descent parameter estimation non-convex optimization supervised learning generalized linear models tensor PCA

来源：评论

学校读者我要写书评

暂无评论

Risk assessment of toxic hydrogen sulfide concentrations on swine farms

引用

JOURNAL OF CLEANER PRODUCTION 2021年 312卷 127746-127746页

作者： Brglez, Spela Klemes, Jiri Jaromir Graz Univ Technol Inst Theoret Comp Sci Inffeldgasse 16b-1 A-8010 Graz Austria

Hydrogen sulfide is a highly toxic gas, causing many lethal accidents in farming environment. Until now, the understanding of the interactive impact of different variables on hydrogen sulfide concentrations on swine farms has been limited. The risk of high hydrogen sulfide concentrations has not yet been assessed comprehensively. The main goal of this study is to identify and investigate the variables, which significantly influence hydrogen sulfide concentrations, and to assess the risk of exposure to toxic hydrogen sulfide concentrations on swine farms. A generalized linear model was built to show the intertwining impact of different variables, and, on the basis of this model, sensitivity analysis, stress tests and Monte Carlo simulations were conducted to thoroughly assess the risk of exposure to dangerous hydrogen sulfide concentrations. Use of generalized linear models and risk assessment tools is a novelty in this context. The model results show that the most significant variables are the location of sample origin, manure disturbance, pig age, weight and diet. If hydrogen sulfide is measured outside a barn, the concentration is up to 47-times lower than inside a barn. Hydrogen sulfide concentration increases by a factor of 7 if manure is pumped during the observation period. Operations with growing pigs have the highest expected hydrogen sulfide concentrations in comparison to other pig age groups. Risk assessment analysis additionally reveals that certain combinations of variable values result in lethal hydrogen sulfide concentrations, and that the likelihood of such scenarios can reach 0.69%.

关键词： Hydrogen sulfide Swine farm generalized linear models Monte Carlo Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

Effects of multiple stressors on amphibian oviposition: Landscape and local determinants in central Japan

引用

ECOLOGICAL INDICATORS 2021年 128卷 107824-107824页

作者： Zheng, Xiaojun Natuhara, Yosihiro Li, Jian Li, Guanlin Du, Yizhou Jia, Hui Dai, Zhicong Du, Daolin Zhong, Shan Qin, Dan Jiangsu Univ Sch Environm & Safety Engn Inst Environm & Ecol Zhenjiang 212013 Jiangsu Peoples R China Nagoya Univ Grad Sch Environm Studies Ecol Grp Nagoya Aichi 4648601 Japan Univ Sydney Fac Engn Sch Comp Sci J12-1 Cleveland St Darlington NSW 2008 Australia Jiangsu Univ Sch Energy & Power Engn Zhenjiang 212013 Jiangsu Peoples R China

Amphibian declines are caused by multiple stressors at the landscape and local levels, resulting in habitat loss, fragmentation, splits, and degradation. However, the combined effects of different landscape scales on the amphibian declines are poorly understood. We examined the breeding distribution of three frog species, Rana japonica, Rana ornativentris, and Bufo japonicus formosus, along an ecological gradient from urban to mountain areas. The number of egg masses and data on landscape and local variables were collected at 124 sites in Toyota, Okazaki, and Shitara, central Japan. The best model with the lowest value of the Akaike Information Criteria (AIC) was obtained using landscape variables at a 500-m-radius buffer for all three species. The number of egg masses increased with forest area for all frog species. Yet, other variables differently affected the number of egg masses of three species at each site. The egg-mass number of R. japonica was positively affected by the residential region with much vegetation area & elevation difference, and negatively affected by elevation. That of R. ornativentris was positively influenced by the abandoned paddy-field, and negatively influenced by the total road density. Regarding B. japonicus formosus, its egg-mass number was positively affected by the residential region with much vegetation area, and negatively affected by elevation difference. At the local scale, the water area positively affected the number of egg masses of all three species, while the percentage of concrete revetment has negative effects. This study not only assesses how the assemblage of frogs respond to different habitats at multiple spatial scales along the land use gradient (urban-mountain), but also emphasizes the importance of strategic management actions in agricultural landscapes, allowing the maintenance of habitat diversity for amphibians.

关键词： Habitat suitability Amphibians' oviposition generalized linear models Ecological gradient Species protection

来源：评论

学校读者我要写书评

暂无评论

The Socioeconomic Determinants of Terrorism: A Bayesian Model Averaging Approach

引用

DEFENCE AND PEACE ECONOMICS 2020年第3期31卷 269-288页

作者： Sanso-Navarro, Marcos Vera-Cabello, Maria Univ Zaragoza Dept Anal Econ Zaragoza Spain Ctr Univ Def Zaragoza Zaragoza Spain

This paper introduces model uncertainty into the empirical study of the determinants of terrorism at country level. This is done by adopting a Bayesian model averaging approach and accounting for the over-dispersed count data nature of terrorist attacks. Both a broad measure of terrorism and incidents per capita have been analyzed. Our results suggest that, among the set of regressors considered, those reflecting labor market conditions and economic prospects tend to receive high posterior inclusion probabilities. These findings are robust to changes in the model specification and sample composition and are not meaningfully affected by the generalized linear regression model applied. Evidence of a geographically heterogeneous relationship between terrorism and its determinants is also provided.

关键词： Terrorism determinants model uncertainty Bayesian model averaging generalized linear models

来源：评论

学校读者我要写书评

暂无评论

The Population Map of Changes in the Spatiotemporal Sensitivity of Visual Neurons Across Saccadic Eye Movements 54

The Population Map of Changes in the Spatiotemporal Sensitiv...

引用

54th Asilomar Conference on Signals, Systems and Computers

作者： Roy, Manish Akbarian, Amir Noudoost, Behrad Nategh, Neda Univ Utah Dept Elect & Comp Engn Salt Lake City UT 84112 USA Univ Utah Dept Ophthalmol & Visual Sci Salt Lake City UT USA

ISBN: (纸本)9780738131269

Numerous physiological studies have reported changes in neurophysiological responses to visual stimuli around the time of saccadic eye movements in various brain areas. We have recently developed a statistical model framework capable of capturing fast dynamic changes in neuronal sensitivity across saccadic eye movements from the neural activity in extrastriate areas of nonhuman primates. Having obtained precise spatiotemporal processing kernels of neurons, representing the neurons' spatiotemporal sensitivity, estimated using these models, we observed tremendous diversity in these changes in extrastriate cortex. Building upon this kernel-based description of perisaccadic sensitivity changes, we characterize and track the spatial and temporal dynamics of neurons across saccadic eye movements, and for a large population of extrastriate neurons. Such a network-level description is crucial for understanding the sources of these dynamics and interpreting their functional significance.

关键词： generalized linear models spatiotemporal kernels receptive field modulation saccade visual cortex

来源：评论

学校读者我要写书评

暂无评论

Effect of changing in weather conditions on Eastern Mediterranean coastal lagoon fishery

引用

REGIONAL STUDIES IN MARINE SCIENCE 2021年 48卷

作者： Cerim, Hasan Ozdemir, Nedim Cremona, Fabien Oglu, Burak Mugla Sitki Kocman Univ Fac Fisheries TR-48000 Mugla Turkey Estonian Univ Life Sci Chair Hydrobiol & Fishery Inst Agr & Environm Sci EE-51006 Tartu Estonia

Climate change shows itself in many different ways on marine life. The fishery is also a part of marine life and affected by climate change-driven weather conditions directly or indirectly. In the present study, relationships between commercial species (grey mullet -approximate to 90% of total capture-and gilthead seabream) that were captured from lagoon traps in Koycegiz lagoon (Turkey) and local weather conditions were analysed. The machine learning method Random Forests (RF) was used to pre-select the model predictors. RF results showed that while temperature-related parameters, cloudy days, and wind speed were the most effective parameters, precipitation-parameters were the least important parameters for these two species catch. generalized linear models (GLMs) were applied to each fish species with the best pre-selected parameters, with the resulting equation being used for future prediction of the two fish species. Future prediction of predictors was calculated by monthly autoregressive integrated moving average (ARIMA) and 20th/80th percentile intervals were used as the scenarios. Simulations showed that an increase in some weather parameters (wind speed, seawater temperature, maximum air temperature, cloudy days) lead to an increase in grey mullet and (wind speed) gilthead seabream catch. models proved that the impact of the weather parameters differs for those two targeted fish species although they live in the same environment. We recommend that individual fish species (and/or catch) should be used in the models, not the whole fish yield. Moreover, the model can also be used for non-commercial species in ecosystem-based studies. Changes in weather parameters due to climate change should be monitored to make proper decision on fishery management. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Climate change generalized linear models Turkish lagoon fishery Meteorology Eastern Mediterranean

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：