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Tracking the spread of infectious disease during a pandemic has posed a great challenge to the governments and health sectors on a global scale. To facilitate informed public health decision-making, the concerned parties usually rely on short-term daily and weekly projections generated via predictive modeling. Several deterministic and stochastic epidemiological models, including growth and compartmental models, have been proposed in the literature. These models assume that an epidemic would last over a short duration and the observed cases/deaths would attain a single peak. However, some infectious diseases, such as COVID-19, extend over a longer duration than expected. Moreover, time-varying disease transmission rates due to government interventions have made the observed data multi-modal. To address these challenges, this work proposes stochastic epidemiological models under a unified Bayesian framework augmented by a change-point detection mechanism to account for multiple peaks. The Bayesian framework allows us to incorporate prior knowledge, such as dates of influential policy changes, to predict the change-point locations precisely. We develop a trans-dimensional reversible jump Markov chain Monte Carlo algorithm to sample the posterior distributions of epidemiological parameters while estimating the number of change points and the resulting parameters. The proposed method is evaluated and compared to alternative methods in terms of change-point detection, parameter estimation, and long-term forecasting accuracy on both simulated and COVID-19 data of several major states in the United States.

In the last decade, numerous statistical methods have been developed for analyzing microbiome data generated from high-throughput next-generation sequencing technology. Microbiome data are typically characterized by zero inflation, overdispersion, high dimensionality, and sample heterogeneity. Three popular areas of interest in microbiome research requiring statistical methods that can account for the characterizations of microbiome data include detecting differentially abundant taxa across phenotype groups, identifying associations between the microbiome and covariates, and constructing microbiome networks to characterize ecological associations of microbes. These three areas are referred to as differential abundance analysis, integrative analysis, and network analysis, respectively. In this review, we highlight available statistical methods for differential abundance analysis, integrative analysis, and network analysis that have greatly advanced microbiome research. In addition, we discuss each method's motivation, modeling framework, and application.

Additional file 1. Supplementary tables an figures.

Liang Qichao (1873-1929) and Chen Duxiu (1879-1942) were two of the most brilliant writers and influential public intellectuals in late nineteenth-, early twentieth-century China. Born six years apart, both men electrified the country with their publications of New Citizen’s Journal and New Youth, respectively, and heralded the character of a new and modern citizen, befitting a new century and a new China. Central to both men’s concerns is the relationship between the citizen and the state. At the end of his checkered political career, Liang concluded that the uninformed Chinese population would best be governed by enlightened autocracy. Chen, after his expulsion from the Chinese Communist Party and alienation from the Chinese Trotskyists, wavered between the dictatorship of the proletariat and democratic socialism. Did both men seemingly opt for an authoritarian rule for China and reject their ideal of liberty and democracy from their younger days? Our paper aims to test this hypothesis that Chen and Liang both saw the need for a centralized power, albeit in different political frameworks, by using quantitative literature analysis. We examined the similarities and differences between their writings on the pairwise co-occurrence of thirty terms related to the topic of nation-building. We created a network with these thirty terms, where an edge between a pair of terms indicates a significant relationship. The relationship is defined as the proportion of writings where both terms co-occurred. The visualization yields information on some preliminary differences in the writings of both men, to be further examined., Journal of Historical Network Research, Vol. 5 No. 1 (2021): Beyond Guanxi: Chinese Historical Networks

Physical distancing during the COVID-19 pandemic may impact the mental health of older adults, but technology use may buffer this impact. This study aimed to 1) examine changes in older adult technology use during the COVID-19 pandemic and 2) determine if technology use moderates the relationships between decreased in-person communication/activity and the mental health of older adults during the pandemic. Data were taken from the NHATS COVID-19 Round 10 (n= 3,188). Older adults engaged in more technology-based activity (b= .237, p

The growing aging population with disabilities poses challenges to caregiving and health care services but there is little recent data on disability trends. Some studies have shown that disability is decreasing while others have shown it increasing. Understanding these trends among community-dwelling older adults is critical for communities to allocate resources and develop policies. This study updates disability trend data among community-dwelling older adults using nationally representative National Health & Aging Trends Study data. Results revealed that about 30% of Medicare beneficiaries had at least one limitation of the activity of daily living (ADL) from 2011 to 2019. Age 75-79 (IRR=1.55), 80-84 (IRR= to 4.60), 85-89 (IRR=2.99), 90+ (IRR=4.60), female, (IRR=1.18), not a house owner (IRR=1.51), financial strain (IRR=1.71), and receiving Medicaid (IRR=1.84) are associated with a higher likelihood of becoming ADL limited or having more ADL limitations. We will discuss potential policy, intervention, and research implications.

Additional file 1. Supplement 1. Compositions and dosages of TCM granules.

Supplement 1

With the advanced imaging technology, digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis. This process produces massive imaging data that capture histological details in high resolution. Recent developments in deep-learning methods have enabled us to automatically detect and characterize the tumor regions in pathology images at large scale. From each identified tumor region, we extracted 30 well-defined descriptors that quantify its shape, geometry, and topology. We demonstrated how those descriptor features were associated with patient survival outcome in lung adenocarcinoma patients from the National Lung Screening Trial (n=143). Besides, a descriptor-based prognostic model was developed and validated in an independent patient cohort from The Cancer Genome Atlas Program program (n=318). This study proposes new insights into the relationship between tumor shape, geometrical, and topological features and patient prognosis. We provide software in the form of R code on GitHub: https://github.com/estfernandez/Slide_Image_Segmentation_and_Extraction.

Power grid message has become the basic carrier of information transmission in smart substation. In the face of the ever-occurring threat of power network information security, the security of power network message is becoming increasingly important. In view of the fact that traditional message recognition methods cannot adapt to complex and changing grid messages, this paper proposes a rule-based self-learning method for grid message security identification. Firstly, this paper studies the information network of intelligent substation based on IEC61850 and the frame structure and flow characteristics of grid message. Then, it analyses the rule self-learning method of grid message recognition, and further introduces the specific implementation process of this method. Finally, a test platform is constructed to verify the effectiveness of the rule-based self-learning method for grid message security identification.

Medical imaging is a form of technology that has revolutionized the medical field in the past century. In addition to radiology imaging of tumor tissues, digital pathology imaging, which captures histological details in high spatial resolution, is fast becoming a routine clinical procedure for cancer diagnosis support and treatment planning. Recent developments in deep-learning methods facilitate the segmentation of tumor regions at almost the cellular level from digital pathology images. The traditional shape features that were developed for characterizing tumor boundary roughness in radiology are not applicable. Reliable statistical approaches to modeling tumor shape in pathology images are in urgent need. In this paper, we consider the problem of modeling a tumor boundary with a closed polygonal chain. A Bayesian landmark-based shape analysis (BayesLASA) model is proposed to partition the polygonal chain into mutually exclusive segments to quantify the boundary roughness piecewise. Our fully Bayesian inference framework provides uncertainty estimates of both the number and locations of landmarks. The BayesLASA outperforms a recently developed landmark detection model for planar elastic curves in terms of accuracy and efficiency. We demonstrate how this model-based analysis can lead to sharper inferences than ordinary approaches through a case study on the 246 pathology images from 143 non-small cell lung cancer patients. The case study shows that the heterogeneity of tumor boundary roughness predicts patient prognosis (p-value < 0.001). This statistical methodology not only presents a new model for characterizing a digitized object's shape features by using its landmarks, but also provides a new perspective for understanding the role of tumor surface in cancer progression.

Tumor shape is a key factor that affects tumor growth and metastasis. This paper proposes a topological feature computed by persistent homology to characterize tumor progression from digital pathology and radiology images and examines its effect on the time-to-event data. The proposed topological features are invariant to scale-preserving transformation and can summarize various tumor shape patterns. The topological features are represented in functional space and used as functional predictors in a functional Cox proportional hazards model. The proposed model enables interpretable inference about the association between topological shape features and survival risks. Two case studies are conducted using consecutive 133 lung cancer and 77 brain tumor patients. The results of both studies show that the topological features predict survival prognosis after adjusting clinical variables, and the predicted high-risk groups have worse survival outcomes than the low-risk groups. Also, the topological shape features found to be positively associated with survival hazards are irregular and heterogeneous shape patterns, which are known to be related to tumor progression.

Sprouted potatoes are not allowed for healthy diet. A good knowledge of the sprouting stage of potatoes can help manage the storage conditions and guide market distribution, thus enabling the quality assurance of potatoes on table. This article presented an intelligent method for precautionary analysis of potato eyes based on hyperspectral imaging technique. Potential potato eyes were classified into two categories according to the time gap to the sprouting date, i.e. by-sprouting and pre-sprouting potato eyes, representing eyes about to sprout and eyes that will take a while to sprout. Features used for classification were extracted by two methods, including successive projections algorithm (SPA) and a newly-developed sine fit algorithm (SFA). Then classifiers of fisher discriminant analysis (FDA) and least square support vector machine (LSSVM) were utilized for classification of potential sprouting potato eyes. Results showed that FDA was more effective than LSSVM in classifying pre-sprouting and by-sprouting potato eyes, and SFA performed well in FDA classifier with the recognition accuracy of 95.3% for prediction set. It is concluded that hyperspectral imaging has the potential for predicting the sprouting stages of potato eyes. Keywords: potato tuber, potato eyes, sprouting stage, hyperspectral imaging, sine fit algorithm(SFA), quality and safety, prediction DOI: 10.25165/j.ijabe.20181102.2748 Citation: Gao Y W, Li Q W, Rao X Q, Ying Y B. Precautionary analysis of sprouting potato eyes using hyperspectral imaging technology. Int J Agric & Biol Eng, 2018; 11(2): 153–157.

Interventions to reduce disability are crucial for older adults with disabilities to avert unnecessary hospitalizations or nursing home placements and improve daily life. Developed and tested at one research site, multiple health systems and community based organizations have since implemented CAPABLE. All published or peer reviewed tests of CAPABLE were reviewed (six studies, 11 sites) with a total of 1087 low-income community-dwelling older adults with disabilities. Participants were an average age of 74-79, cognitively intact, and self-reported difficulty with one or more activities of daily living (ADL). These trials were reviewed by extracting the participants’ scores on main outcomes, ADLs and IADLs, and when available, fall efficacy, depression, pain and cost savings. All studies yielded improvements in ADL and IADL limitations, with small to strong effect sizes. Studies with the complete dose of CAPABLE showed more improvement in ADLs and cost savings than the studies that implemented a decreased dose.

This study tested associations between income decline and financial difficulty with mental health (lack of feeling anxious/depressed, recurring thoughts/nightmares, avoiding activities/thoughts, feeling jumpy/on guard) and sleep quality during COVID-19 among a national sample of 3,188 older adults. Approximately 8% of US older adults reported income decline and 6% reported financial difficulty. Although income decline and financial difficulty rates were both statistically significantly higher among those financially strained before COVID-19 (19% and 34%, respectively), income decline was more common among those with incomes ≥200% of the poverty threshold (9%) whereas financial difficulty was more common among those with incomes

The damage effect characteristics of GaAs pseudomorphic high electron mobility transistor (pHEMT) under the irradiation of C band high-power microwave (HPM) is investigated in this paper. Based on the theoretical analysis, the thermoelectric coupling model is established, and the key damage parameters of the device under typical pulse conditions are predicted, including the damage location, damage power, etc. By the injection effect test and device microanatomy analysis through using scanning electron microscope (SEM) and energy dispersive spectrometer (EDS), it is concluded that the gate metal in the first stage of the device is the vulnerable to HPM damage, especially the side below the gate near the source. The damage power in the injection test is about 40 dBm and in good agreement with the simulation result. This work has a certain reference value for microwave damage assessment of pHEMT.

The advances of next-generation sequencing technology have accelerated study of the microbiome and stimulated the high throughput profiling of metagenomes. The large volume of sequenced data has encouraged the rise of various studies for detecting differentially abundant taxonomic features across healthy and diseased populations, with the ultimate goal of deciphering the relationship between the microbiome diversity and health conditions. As the microbiome data are high-dimensional, typically featuring by uneven sampling depth, overdispersion and a huge amount of zeros, these data characteristics often hamper the downstream analysis. Moreover, the taxonomic features are implicitly imposed by the phylogenetic tree structure and often ignored. To overcome these challenges, we propose a Bayesian hierarchical modeling framework for the analysis of microbiome count data for differential abundance analysis. Under this framework, we introduce a bi-level Bayesian hierarchical model that allows a flexible choice of the count generating process, and hyperpriors in the feature selection scheme. We particularly focus on employing a zero-inflated negative binomial model with a Bayesian nonparametric prior model on the bottom level, and applying Gaussian mixture models for differentially abundant taxa detection on the top level. Our method allows for the simultaneous modeling of sample heterogeneity and detecting differentially abundant taxa. We conducted comprehensive simulations and summarized the improved statistical performances of the proposed model. We applied the model in two real microbiome study datasets and successfully identified biologically validated differentially abundant taxa. We hope that the proposed framework and model can facilitate further microbiome studies and elucidate disease etiology., 58 pages including the main text and the supplementary material

We propose a novel radiation-enhanced dual-inverted bowlers antenna to pursue a maximal radiated electric field (E-field). Based on increasing the stored energy and the high-frequency component of the excitation pulse, the new structure significantly improves the radiation performance without increasing the generator output voltage or antenna size. Computer simulations show that the radiated E-field increases by a factor of 2.7 relative to the same sized conventional biconical antenna. Under a charge voltage of 300 kV, the experimental far-field voltage is 110 kV (22 kV/m at 5 m) and the voltage gain is 0.37. This voltage gain is an improvement of at least 23% over typical biconical antennas. This work brings new opportunities to improve the radiation performance of high-power mesoband systems.

Current superabsorbent hydrogels commercially applied in the disposable diapers have disadvantages such as weak mechanical strength, poor biocompatibility, and lack of antimicrobial activity, which may induce skin allergy of body. To overcome these hassles, we have developed novel cellulose based hydrogels via simple chemical cross-linking of quaternized cellulose (QC) and native cellulose in NaOH/urea aqueous solution. The prepared hydrogel showed superabsorbent property, high mechanical strength, good biocompatibility, and excellent antimicrobial efficacy against Saccharomyces cerevisiae. The presence of QC in the hydrogel networks not only improved their swelling ratio via electrostatic repulsion of quaternary ammonium groups, but also endowed their antimicrobial activity by attraction of sections of anionic microbial membrane into internal pores of poly cationic hydrogel leading to the disruption of microbial membrane. Moreover, the swelling properties, mechanical strength, and antibacterial activity of hydrogels strongly depended on the contents of quaternary ammonium groups in hydrogel networks. The obtained data encouraged the use of these hydrogels for hygienic application such as disposable diapers.

Microbiome `omics approaches can reveal intriguing relationships between the human microbiome and certain disease states. Along with the identification of specific bacteria taxa associated with diseases, recent scientific advancements provide mounting evidence that metabolism, genetics and environmental factors can all modulate these microbial effects. However, the current methods for integrating microbiome data and other covariates are severely lacking. Hence, we present an integrative Bayesian zero-inflated negative binomial regression model that can both distinguish differentially abundant taxa with distinct phenotypes and quantify covariate-taxa effects. Our model demonstrates good performance using simulated data. Furthermore, we successfully integrated microbiome taxonomies and metabolomics in two real microbiome datasets to provide biologically interpretable findings. In all, we proposed a novel integrative Bayesian regression model that features bacterial differential abundance analysis and microbiome-covariate effects quantifications, which makes it suitable for general microbiome studies.

In the low voltage apparatus manufacturing industry, the welding quality of electrical contact directly determines the switching capacity, lifespan and reliability of electrical equipment. Currently, only a little research on online monitoring of large-size electrical contact high frequency induction brazing has been carried out. In this research, CJ400 electrical contact was used as samples to be welded in the way of pulse induction brazing. And the electrical parameters during the whole welding process were collected and integrated with the dual 32-bit ARM (Advanced RISC Machines) controller. The relationship between welding quality and integral value of electrical parameter was analyzed. The results showed that the deviation between the ideal integral value of electrical parameters and the actual integral value of electrical parameters could be identified an indication for welding quality.

The quality of heavy-duty machine tool foundations can drastically affect the operating life and working precision of the tool, and the high cost of manufacture has drawn a lot of attention. This article summarized the research status of the relevant literature on the characteristics, vibration isolation, foundation optimization, and quality inspection of heavy-duty machine tool-foundation system, induced the influencing laws of the influencing factors of the system, reviewed the highlights and achievements in the research of heavy machine tool-foundation system at present, and put forward some problems and development directions existing in the research of heavy machine tool-foundation system. It lays a foundation for realizing the judgment of the concrete foundation quality and improving the processing precision and the maintenance of the heavy machine tool.

Mathematics learning disability(MLD) is an important area of learning disability.Now there emerging a leading paradigm for carrying out research on MLD from the viewpoint of online information processing,and more and more studies focused on discussing the cognitive processing mechanism of MLD.The definition and screening methods of the MLD are still in dispute,therefore it needs further research to explore the different information processing features of different kinds of mathematics.Based on the three-factor model put forward by Baddeley and Hitch(1974),this study designed the experiment tasks of central executive system,visuo-spatial sketchpad and phonological loop,exploring the differences of the three components of working memory between the 55 MLD students and 56 students who are good at math(with 48 male students and 63 female students,average age was 11.97-year-old).Then,according to the criteria of national math curriculum,math learning was further divided into two parts,that is,(a) counting and algebra,(b) space and geometry.The cognitive processing mechanism of various kinds of mathematics study was investigated.In the first study,a serial cognitive-behavior computerized tasks were composed to test the three components of working memory,such as,the stop signal task and Flanker's tasks testifying the central executive function,N-back and Spatial Figure Position tasks testifying the visuo-spatial sketchpad,and the digit span and sentence span tasks which tested the phonological loop.The cognitive characteristics of the MLD students in some specific math leaning area were analyzed.In the second study,the author chose the specific math knowledge,Axial Symmetry and Centro Symmetry tasks from the space and geometry,which was learned by grade seven students,to discover the cognitive feature between the MLD group and the group who were good at math.The results showed that:(1) the MLD group performed poorer than the normal group in central executive system,visuo-spatial sketchpad and phonological loop,indicating the WM deficits among the MLD students was domain-general.(2) the working memory deficit in the MLD students was domain general,but various kinds of math learning belonged to different cognitive processing mechanism.The tasks of counting and algebra were influenced by the combined role played by the central executive system,visuo-spatial sketchpad and phonological loop;the tasks of space and geometry were influenced by the central executive system,visuo-spatial sketchpad instead of phonological loop.(3) visuo-spatial sketchpad predicted the performance of Axial Symmetry and Centro symmetry tasks,then followed by central executive system,and phonological loop had little effect to this task.The second stud testified the specificity of phonological loop,and the impact to geometry exerted by visuo-spatial sketchpad was obvious.This indicated that during math learning,working memory was not only domain specific but also domain general.The central executive system,visuo-spatial sketchpad were characterized by domain generality and phonological loop was characterized by domain specificity.

Nonlinear autoregressive models are very useful for modeling many natural processes, however, the size of the class of these models is large. Functional-coefficient autoregressive models (FCAR) are useful structures for reducing the size of the class of these models. Although this structure reduces the class of nonlinear models, it is broad enough to include some common time series models as specific cases. A recent development in estimating nonlinear time series data is the spline backfitted kernel (SBK) method. This method combines the computational speed of splines with the asymptotic properties of kernel smoothing. To estimate a component function in the model, all other component functions are pre-estimated with splines and then the difference is taken of the observed time series and the pre-estimates. This difference is then used as pseudo-responses for which kernel smoothing is used to estimate the function of interest. By constructing the estimates in this way, the method does not suffer from the curse of dimensionality. In this paper, we adapt the SBK method to FCAR models., Comment: arXiv admin note: substantial text overlap with arXiv:1502.03486 by other authors

With the ever growing concern on the speed at which fossil fuel reserves are being used up and the damage that burning them does to the environment, the development of sustainable fuels has become an increasingly attractive topic (Wyman & Hinman, 1990; Lynd & Wang, 2004; Herrera, 2004; Tanaka, 2006; Chandel et al., 2007; Dien et al., 2006; Marelne Cot, et al., 2007). The interest partially caused by environment concern, especially global warming due to emission of Greenhouse Gas (GHG). Other factors include the rise of oil prices due to its unrenewability, interest in diversifying the energy matrix, security of energy supply and, in some cases, rural development (Walter et al., 2008). The bioethanol such as sugarcane ethanol is an important part of energy substitutes (Wheals et al., 1999). This chapter was focused on the development and trends of the sugarcane ethanol in China. Based on the analysis of the challenge and the chance during the development of the sugarcane ethanol in China, it introduced a novel process which is suitable for China, and mainly talked about simultaneous production of sugar and ethanol from sugarcane, the development of sugarcane varieties ,ethanol production technology, and prospect aspects. We hope it will provide references for evaluation the feasibility of sugarcane ethanol in China, and will be helpful to the fuel ethanol development in China.

Privacy Preserving Data Mining (PPDM) addresses the problem of developing accurate models about aggregated data without access to precise information in individual data record. A widely studied \emph{perturbation-based PPDM} approach introduces random perturbation to individual values to preserve privacy before data is published. Previous solutions of this approach are limited in their tacit assumption of single-level trust on data miners. In this work, we relax this assumption and expand the scope of perturbation-based PPDM to Multi-Level Trust (MLT-PPDM). In our setting, the more trusted a data miner is, the less perturbed copy of the data it can access. Under this setting, a malicious data miner may have access to differently perturbed copies of the same data through various means, and may combine these diverse copies to jointly infer additional information about the original data that the data owner does not intend to release. Preventing such \emph{diversity attacks} is the key challenge of providing MLT-PPDM services. We address this challenge by properly correlating perturbation across copies at different trust levels. We prove that our solution is robust against diversity attacks with respect to our privacy goal. That is, for data miners who have access to an arbitrary collection of the perturbed copies, our solution prevent them from jointly reconstructing the original data more accurately than the best effort using any individual copy in the collection. Our solution allows a data owner to generate perturbed copies of its data for arbitrary trust levels on-demand. This feature offers data owners maximum flexibility., Comment: 20 pages, 5 figures. Accepted for publication in IEEE Transactions on Knowledge and Data Engineering

Agronomic traits and quality indices are closely related to patterns of nutrient uptake among different sugarcane varieties. Knowing the optimal nutrient needs of specific varieties could help growers provide adequate fertilizer while avoiding over-fertilization. Field trials on the variations of growth, uptake of nitrogen (N), phosphorus (P), potassium (K) and related quality indices among different new varieties of sugarcane were compared to the widely planted variety ROC 22, which was conducted in Zhanjiang, Guangdong, in southern China. The results showed that the highest germination rate was observed in YT 55, and both the highest tillering rate and both the formative rate of stalk and millable canes were observed in BC 2–32, respectively. YT 55 had the highest cane yield (154.1 t/ha) and sugar yield (20.4 t/ha). The accumulation of N, P and K in YT 55 reached 290.5, 67.9 and 447.5 kg/ha, respectively, which was 1.6, 1.6 and 1.4-fold higher than observed in ROC 22. The nutrient use efficiency of nitrogen/phosphorus/potassium fertilizer (NPK) to produce cane and sugar in ROC 22 and YT 60 was superior among the tested varieties. The differences in cane and sugar yield, and nutrient utilization efficiencies among the investigated varieties point out the need for variety specific nutrient recommendations. This study also confirmed that distinct differences existed in the yield of both cane and sugar produced in different sugarcane varieties if more fertilizers are supplied to sugarcane during the tillering and elongation stage.

Commercial depth sensors usually generate noisy and missing depths, especially on specular and transparent objects, which poses critical issues to downstream depth or point cloud-based tasks. To mitigate this problem, we propose a powerful RGBD fusion network, SwinDRNet, for depth restoration. We further propose Domain Randomization-Enhanced Depth Simulation (DREDS) approach to simulate an active stereo depth system using physically based rendering and generate a large-scale synthetic dataset that contains 130K photorealistic RGB images along with their simulated depths carrying realistic sensor noises. To evaluate depth restoration methods, we also curate a real-world dataset, namely STD, that captures 30 cluttered scenes composed of 50 objects with different materials from specular, transparent, to diffuse. Experiments demonstrate that the proposed DREDS dataset bridges the sim-to-real domain gap such that, trained on DREDS, our SwinDRNet can seamlessly generalize to other real depth datasets, e.g. ClearGrasp, and outperform the competing methods on depth restoration with a real-time speed. We further show that our depth restoration effectively boosts the performance of downstream tasks, including category-level pose estimation and grasping tasks. Our data and code are available at https://github.com/PKU-EPIC/DREDS, Comment: ECCV 2022