487 research outputs found
Recommended mosquito risk assessment criteria for urban environment in London, UK - targeting mosquito-borne diseases
No full textFactors such as climate change and world trade may lead to changes in the range of mosquitoes found in cities. Mosquito-borne diseases are one of the major public health problems worldwide (WHO, 2020). Thus, the risk of mosquitoes appearing in areas where people live is critical. The risk of mosquito have been devised in some countries, but they have mostly focused on assessing water bodies, ignoring the effects of other factors on mosquito survival. This article presents the design of assessment criteria for urban environments based on three basic conditions for mosquito survival, i.e., saccharides, blood, and habitat. The criteria can be used in the assessment of environments in three general directions. The proposed assessment criteria were used in several sites in London to determine the reasonableness of the criteria and continuously improved.<br/
Probiotic consumption influences universal adaptive mutations in indigenous human and mouse gut microbiota
No full textChenchen Ma, Chengcheng Zhang, and Denghui Chen et al. examine how probiotic consumption impacts gut microbiota composition in human and mice through a global, cross-cohort metagenomic analysis. Their results suggest that probiotic consumption may result in widespread variation among the native microbiota in both the human and mouse gut
Research on segmented mirror position error of optical system based on ZEMAX
No full text
We have modeled a segmented mirror optical system by software ZEMAX, and analyzed influence of segmented mirror's position error to the optical system imaging for the first time to author's best knowledge. The primary mirror is composed of 18 segmented mirrors. By adjusting each one's position error of six freedoms, we get six relationship curves between position error and the optical system's image quality. The relationship curves show that some have different wave-front error RMS values when segmented mirrors have same position errors. The middle mirrors are sensitive to the movement along X axis direction, and the outer ones are sensitive to the movement along Y axis direction. The middle ones and outer ones are all sensitive to the tilt error, especially tilt along X, Y axis. (C) 2017 Elsevier GmbH. All rights reserved.</p
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Context-Aware Deep Learning Model for Predicting\\ Non-Mandatory Activity Locations
No full textThe explosion of mobile internet usage has generated vast amounts of data on users' spatiotemporal activities. This data is crucial for studying human movement, enhancing traffic management, and understanding epidemic spread dynamics. Our study aims to tackle the challenge of predicting locations for non-mandatory activities using mobile location data.The thesis introduces the SageGRU model, which combines GraphSage with Attentional Gated Recurrent Units, to predict the next visiting Point of Interest (POI) for non-mandatory activities. SageGRU leverages historical visitation data, categorizes activity types, and considers temporal dimensions and global movement trends using POI-to-POI transition graphs. Validated with the Veraset dataset, SageGRU achieves 10.2\% accuracy at predicting the top location, 20.8\% for the top two locations, and 27.4\% for the top three, significantly outperforming existing models. This highlights the importance of comprehensive spatio-temporal context in predicting non-mandatory activity locations. SageGRU’s capability to reconstruct real-life trajectories and aggregate travel patterns underscores its potential to advance urban mobility and public health planning by offering deeper insights into human movement
ZWINT promotes the proliferation, migration, and invasion of cervical cancer cells by regulating the p53/p21 signaling pathway
No full textCervical cancer leads to 300,000 deaths annually and the mechanism of cervical carcinogenesis remains unclear. Zeste White 10-interacting kinetochore protein (ZWINT) is uniquely elevated in malignancies, promoting proliferation, migration, and colony formation of cancer cells. To investigate the role of ZWINT in proliferation, migration, invasion of cervical cancer, and evaluate the potential ability of ZWINT as a therapeutic target. First, ZWINT expression in cervical cancer was analyzed using the bioinformatic methods and assessed in several cervical cancer cell lines. The cell viability and colony formation assays were used to evaluate cell proliferation. Then, transwell assay was performed to investigate cell migration and invasion. Moreover, western blot was used to measure the expression level of ZWINT, matrix metalloproteinase 9 (MMP-9), N-cadherin, E-cadherin, p53 and p21 in CaSki and HeLa cells with ZWINT overexpression or knockdown. The bioinformatic analysis and western blot assay revealed the expression of ZWINT was significantly increased in cervical cancer. The cell viability and colony formation analysis illustrated that cell proliferation could be promoted by ZWINT overexpression and suppressed by ZWINT knockdown. Moreover, ZWINT promoted migration and invasion of CaSki and HeLa cells, through regulating the expression of MMP-9, N-cadherin, and E-cadherin. Furthermore, ZWINT attenuated the expression of p53 and p21 in cervical cancer cells. In summary, ZWINT functions in promoting cell proliferation, migration, and invasion of cervical cancer cells by suppressing p53/p21 signaling pathway, which indicated ZWINT is a potential therapeutic target for cervical cancer treatment
Statistical Learning for Latent Attribute Models
No full textLatent variable models are popularly used in unsupervised learning to uncover the latent structures underlying observed data and have seen great successes in representation learning in many applications and scientific disciplines. Latent attribute models, also known as cognitive diagnosis models or diagnostic classification models, are a special family of discrete latent variable models that have been widely applied in modern psychological and biomedical research with diagnostic purposes. Despite the wide usage in various fields, the models' discrete nature and complex restricted structures pose many new challenges for efficient learning and statistical inference. Moreover, with the large-scale item and subject pools emerging in modern educational and psychological measurements, efficient algorithms for uncovering latent structures of both items and subjects are desired. This dissertation studies four important problems that arise in this context.
(I) The first part develops novel methodologies and efficient algorithms to learn the latent and hierarchical structures in latent attribute models. Specifically, researchers in many applications are interested in hierarchical structures among the latent attributes, such as prerequisite relationships among target skills in educational settings. However, in most cognitive diagnosis applications, the number of latent attributes, the attribute-attribute hierarchical structures, the item-attribute dependence structures, as well as the item-level diagnostic models, need to be fully or partially pre-specified, which may be subjective and misspecified as noted by many recent studies. In this part, we consider the problem of jointly learning these latent quantities and hierarchical structures from observed data with minimal model assumptions. A penalized likelihood approach is proposed for joint learning, an Expectation-Maximization (EM) algorithm is developed for efficient computation, and statistical consistency theory is established under mild conditions.
(II) The second part generalizes the methodologies in part I to simultaneously infer the subgroup structures of both subjects and items. We consider the model-based co-clustering algorithms and aim to automatically select numbers of clusters and uncover latent block structures. Specifically, based on latent block models, we propose a penalized co-clustering approach that is capable of learning the numbers of clusters and inner block structures simultaneously. Efficient EM algorithms have been developed and comprehensive simulation studies demonstrate their superiority.
(III) The third part concerns the important yet unaddressed problem of testing the latent hierarchical structures in latent attribute models. Testing the hierarchical structures is shown to be equivalent to testing the sparsity structure of the proportion parameter vector. However, due to the irregularity of the problem, the asymptotic distribution of the popular likelihood ratio test becomes nonstandard and tends to provide unsatisfactory finite sample performance under practical conditions. To tackle these challenges, we discuss the conditions of testability issues, provide statistical understandings of the failures, and propose a practical resampling-based procedure.
(IV) The fourth part introduces a unified estimation framework to bridge the gap between parametric and nonparametric methods in cognitive diagnosis to better understand their relationship. In particular, a number of parametric and nonparametric methods for estimating latent attribute models have been developed and applied in a wide range of contexts. However, in the literature, a wide chasm exists between these two families of methods, and their relationship to each other is not well understood. Driven by this divide, we propose a unified framework and provide both theoretical analysis and practical recommendations under various cognitive diagnosis settings.PhDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/174627/1/chenchma_1.pd
Afterthought of the Old Summer Palace Restoration - Study on Protection and Development of the Chinese Landscape Cultural Heritage Based on Ontology
No full textTuning the Hydrogen Bond Network Inside the Helmholtz Plane for Industrial Hydrogen Evolution
No full textAbstract The role of the hydrogen bond network (HBN) within the Helmholtz plane (HP) in regulating the hydrogen evolution kinetics for catalyst development remains ambiguous owing to the lack of fundamental understanding. Herein, leveraging ab initio molecular dynamics simulations, it is discovered that introducing weak metal bonds in Ru/RuO 2 remarkably reshapes the HBN. Subsequently, Ru/RuO 2 nanosheets loaded with single Ga atoms (Ga SA ‐Ru/RuO 2 ) are successfully synthesized using a one‐step annealing strategy. In situ characterizations and theoretical calculations demonstrate that the atomic electric field generated by the weak Ru─Ga bonds can further improve the proportion of 4‐coordinated hydrogen‐bonded water and free water, thus ensuring the sufficient supply of reactants under high current density. Especially, the Ga SA ‐Ru/RuO 2 ‐based anion exchange membrane water electrolyzers (AEMWEs) require only 1.69 and 1.84 V to reach an industrial current density of 1,000 mA cm⁻ 2 in alkaline water and seawater conditions, respectively, and operate stably for 200 h. This study offers an atomic‐level perspective for designing highly efficient catalysts for alkaline hydrogen production.Fundamental Research Funds for the Central Universities https://doi.org/10.13039/501100012226National Natural Science Foundation of China https://doi.org/10.13039/501100001809National Research Foundation Singapore https://doi.org/10.13039/501100001381National Research Foundation https://doi.org/10.13039/50110000132
Minimum Sample Size Estimate for Classifying Invasive Lung Adenocarcinoma
No full textStatistical Learning Theory (SLT) plays an important role in prediction estimation and machine learning when only limited samples are available. At present, determining how many samples are necessary under given circumstances for prediction accuracy is still an unknown. In this paper, the medical diagnosis on lung cancer is taken as an example to solve the problem. Invasive adenocarcinoma (IA) is a main type of lung cancer, often presented as ground glass nodules (GGNs) in patient’s CT images. Accurately discriminating IA from non-IA based on GGNs has important implications for taking the right approach to treatment and cure. Support Vector Machine (SVM) is an SLT application and is used to classify GGNs, wherein the interrelation between the generalization and the lower bound of necessary sampling numbers can be effectively recovered. In this research, to validate the interrelation, 436 GGNs were collected and labeled using surgical pathology. Then, a feature vector was constructed for each GGN sample through the fully connected layer of AlexNet. A 10-dimensional feature subset was then selected with the p-value calculated using Analysis of Variance (ANOVA). Finally, four sets with different sample sizes were used to construct an SVM classifier. Experiments show that a theoretical estimate of minimum sample size is consistent with actual values, and the lower bound on sample size can be solved under various generalization requirements
Detecting differentially expressed genes for syndromes by considering change in mean and dispersion simultaneously
No full textAbstract Background Using next-generation sequencing technology to measure gene expression, an empirically intriguing question concerns the identification of differentially expressed genes across treatment groups. Existing methods aim to identify genes whose mean expressions differ among treatment groups by assuming equal dispersion across all groups. For syndromes, however, various combinations of gene expression alterations can result in the same disease, leading to greater heteroscedasticity in the biological replicates in the disease group compared to the normal group. Traditional methods that only consider changes in the mean will fail to fully analyze gene expression in such a scenario. In addition, sequencing technology is relatively expensive; most labs can only afford a few replicates per treatment group, which poses further challenges to reliably estimating the mean and dispersion under each treatment condition. Results We designed an empirical Bayes method and a pooled permutation test to simultaneously consider the change in mean and dispersion across treatment groups. We further computed confidence intervals based on Bayes estimates to identify differentially expressed genes that are unique to each disease sample as well as those that are common across all disease samples. We illustrated our method by applying it to gene expression data from a large offspring syndrome experiment, which motivated this study. We compared our method to competing approaches through simulation studies that mimicked the real datasets to demonstrate the effectiveness of our proposed method. Conclusions We will show that, compared to popular methods that only aim to find the difference in the mean, our method can capture greater variation in the disease group to effectively identify differentially expressed genes for syndromes
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