Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion Textures: Modeling, Classification, and Segmentation Using Mixed-State Markov Random Fields

A motion texture is an instantaneous motion map extracted from a dynamic texture. We observe that such motion maps exhibit values of two types: a discrete component at zero (absence of motion) and continuous motion values. We thus develop a mixed-state Markov random field model to represent motion textures. The core of our approach is to show that motion information is powerful enough to classify and segment dynamic textures if it is properly modeled regarding its specific nature and the local interactions involved. A parsimonious set of 11 parameters constitutes the descriptive feature of a motion texture. The motivation of the proposed formulation runs toward the analysis of dynamic video contents, and we tackle two related problems. First, we present a method for recognition and classification of motion textures, by means of the Kullback--Leibler distance between mixed-state statistical models. Second, we define a two-frame motion texture maximum a posteriori (MAP)-based segmentation method applicable .
Sparse Tomography

A wavelet-based sparsity-promoting reconstruction method is studied in the context of tomography with severely limited projection data. Such imaging problems are ill-posed inverse problems, or very sensitive to measurement and modeling errors. The reconstruction method is based on minimizing a sum of a data discrepancy term based on an 2-norm and another term containing an 1-norm of a wavelet coefficient vector. Depending on the viewpoint, the method can be considered (i) as finding the Bayesian maximum a posteriori (MAP) estimate using a Besov-space B1 11(T 2) prior, or (ii) as deterministic regularization with a Besov-norm penalty. The minimization is performed using a tailored primal-dual path following interior-point method, which is applicable to problems larger in scale than commercially available general-purpose optimization package algorithms. The choice of “regularization parameter” is done by a novel technique called the S-curve method, which can be used to incorporate a priori information on the sparsity of the unknown target to the reconstruction process. Numerical results are presented, focusing on uniformly sampled sparse-angle data. Both simulated and measured data are considered, and noise-robust and edge-preserving multiresolution reconstructions are achieved. In sparse-angle cases with simulated data the proposed method offers a significant improvement in reconstruction quality (measured in relative square norm error) over filtered back-projection (FBP) and Tikhonov regularization.
Optimal Solvers for Fourth-Order PDEs Discretized on Unstructured Grids

This paper provides a class of optimal algorithms for the linear algebraic systems arising from direct finite element discretization of the fourth-order equation with different boundary conditions on any polygonal domains that are partitioned by unstructured grids. Several preconditioners are presented, and the preconditioned conjugate gradient methods applied with these preconditioners are proved to converge uniformly with respect to the size of the linear systems. As a result, for the first time in the literature, it is rigorously proved in this paper that a discretized finite element system for fourth-order PDEs on an unstructured grid of size $N$ can be solved within ${\mathcal O}(N\log N)$ operations. One main ingredient in constructing these optimal preconditioners is a mixed-form discretization of the fourth-order problem. Such a mixed-form discretization often leads to an approximation of the original solution that is either suboptimal or divergent, but it nevertheless provides optimal preconditio.
Two-Level QTT-Tucker Format for Optimized Tensor Calculus

We propose a combined tensor format, which encapsulates the benefits of Tucker, tensor train (TT), and quantized TT (QTT) formats. The structure is composed of subtensors in TT representations, so the approximation problem is proven to be stable. We describe all important algebraic and optimization operations, which are recast to the TT routines. Several examples on explicit function and operator representations are provided. The asymptotic storage complexity is at most cubic in the rank parameter, that is larger than for the global QTT approximation, but the numerical examples manifest that the ranks in the two-level format usually increase more slowly with the approximation accuracy than the QTT ones. In particular, we observe that high rank peaks, which usually occur in the TT/QTT representations, are significantly relaxed. Thus the reduced costs can be achieved.
Sickness and Stress in Operational Flying

To the medical branch of an air force, the social or environmental conditions of pressing importance are the psychological stresses inevitable in air warfare. Only in war is it possible to observe the effects of such factors as acute hazard and intensive operational effort, with their resultant anxiety and fatigue, on the performance and well-being of such a highly selected population as the air crews of the Royal Air Force. Both the immediate effects of these stresses on efficiency and the long-term results in health were, therefore, the urgent concern of those whose duty it was to minimize these effects by every means open to the medical branch of a combatant force. The central issue was one of limitation of spells of duty? either short term, where the effects of hours of prolonged attention were to be forestalled, or long term, where the limits of an operational tour had to be set at a level high enough to ensure an adequate operational return for the training investment made yet not so long as to endanger health and morale. The problems of peacetime practice in social and industrial medicine are hardly as dramatic,, yet the essential mechanism of the adjustment of men to the less hazardous but frequently harassing conditions of post-war life are identical. This account of some typical studies of sickness in relation to measurable environmental and personal factors is given in the hope oT displaying the potentialities of the methods used in the study of problems of sickness and morale in an industrial population. Research on duty limitation in the R.A. F. during the war had a tripartite approach by clinical, laboratory, and field surveys. The clinical studies have been described by Symonds (1943), the laboratory work by Russell Davis (1948); in the present paper is set out an application of statistical methods to data collected in the course of active operations in the field.
Innovation more than an artefact? Conceptualizing the effects of drawing medicine into management

Abstract Innovation is increasingly viewed as a key behaviour for high-performing healthcare organizations of the future. The growing significance of chronic diseases including multi-morbidity, the emerging health workforce crisis, the persistent quality and safety problems as well as the accelerating costs of hospital care underline the urgency of finding better ways to deliver health services. Against this background, policy makers assume that involving doctors in management roles will not only enhance financial control and effective governance but also serve the innovation agenda. However, this relationship is not well understood as there is not sufficient research on the subject. In this positioning paper, we draw on a review of the existing literature on innovation and professions in general. Our objective is to explore the impact of medical participation in management on innovation, and provide a framework that conceptualises this relationship.
A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis

Changes in human microbiome are associated with many human diseases. Next generation sequencing technologies make it possible to quantify the microbial composition without the need for laboratory cultivation. One important problem of microbiome data analysis is to identify the environmental/biological covariates that are associated with different bacterial taxa. Taxa count data in microbiome studies are often over‐dispersed and include many zeros. To account for such an over‐dispersion, we propose to use an additive logistic normal multinomial regression model to associate the covariates to bacterial composition. The model can naturally account for sampling variabilities and zero observations and also allow for a flexible covariance structure among the bacterial taxa. In order to select the relevant covariates and to estimate the corresponding regression coefficients, we propose a group ℓ1 penalized likelihood estimation method for variable selection and estimation. We develop a Monte Carlo expectation‐maximization algorithm to implement the penalized likelihood estimation. Our simulation results show that the proposed method outperforms the group ℓ1 penalized multinomial logistic regression and the Dirichlet multinomial regression models in variable selection. We demonstrate the methods using a data set that links human gut microbiome to micro‐nutrients in order to identify the nutrients that are associated with the human gut microbiome enterotype.
Bayesian Spatial Transformation Models with Applications in Neuroimaging Data

The aim of this article is to develop a class of spatial transformation models (STM) to spatially model the varying association between imaging measures in a three‐dimensional (3D) volume (or 2D surface) and a set of covariates. The proposed STM include a varying Box–Cox transformation model for dealing with the issue of non‐Gaussian distributed imaging data and a Gaussian Markov random field model for incorporating spatial smoothness of the imaging data. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations and real data analysis demonstrate that the STM significantly outperforms the voxel‐wise linear model with Gaussian noise in recovering meaningful geometric patterns. Our STM is able to reveal important brain regions with morphological changes in children with attention deficit hyperactivity disorder.
Program and System Level Interventions to Address Tobacco Amongst Individuals with Schizophrenia

ABSTRACT Most individuals with schizophrenia are tobacco smokers, and smoking is associated with increased morbidity and mortality, changes in psychiatric medication metabolism, increased psychiatric hospitalizations, and increased risk for other substance use. Since individuals with schizophrenia receive most of their clinical care within psychiatric settings, there is a need to integrate tobacco assessment and smoking cessation treatment within the mental health system. Better addressing of tobacco will require program-level changes as well as broader system-level changes. Organizational change strategies can be effective in addressing the barriers to integrated care. Broader system-level changes include initiatives by national organizations to enhance training and create consensus on implementing change, establishing networks with existing tobacco control efforts, and increasing funding for treatment and research for this population. The purpose of this article is to address the barriers to change and share strategies to better address tobacco and integrate treatment into routine practice.
Multiple kernel learning with random effects for predicting longitudinal outcomes and data integration

Predicting disease risk and progression is one of the main goals in many clinical research studies. Cohort studies on the natural history and etiology of chronic diseases span years and data are collected at multiple visits. Although, kernel‐based statistical learning methods are proven to be powerful for a wide range of disease prediction problems, these methods are only well studied for independent data, but not for longitudinal data. It is thus important to develop time‐sensitive prediction rules that make use of the longitudinal nature of the data. In this paper, we develop a novel statistical learning method for longitudinal data by introducing subject‐specific short‐term and long‐term latent effects through a designed kernel to account for within‐subject correlation of longitudinal measurements. Since the presence of multiple sources of data is increasingly common, we embed our method in a multiple kernel learning framework and propose a regularized multiple kernel statistical learning with random effects to construct effective nonparametric prediction rules. Our method allows easy integration of various heterogeneous data sources and takes advantage of correlation among longitudinal measures to increase prediction power. We use different kernels for each data source taking advantage of the distinctive feature of each data modality, and then optimally combine data across modalities. We apply the developed methods to two large epidemiological studies, one on Huntington's disease and the other on Alzheimer's Disease (Alzheimer's Disease Neuroimaging Initiative, ADNI) where we explore a unique opportunity to combine imaging and genetic data to study prediction of mild cognitive impairment, and show a substantial gain in performance while accounting for the longitudinal aspect of the data.
A Modeling Exploration of How Synaptic Feedback to Descending Projection Neurons Shapes the Activity of an Oscillatory Network

Rhythmic activity which underlies motor output is often initiated and controlled by descending modulatory projection pathways onto central pattern generator (CPG) networks. In turn, these descending pathways receive synaptic feedback from their target CPG network, which can influence the CPG output. However, the mechanisms underlying such bi-directional synaptic interactions are mostly unexplored. We develop a reduced mathematical model, including both feed-forward and feedback circuitry, to examine how the synaptic interactions involving two projection neurons, MCN1 and CPN2, can produce and shape the activity of the gastric mill CPG in the crab stomatogastric nervous system. We use simplifying assumptions that are based on the behavior of the biological system to reduce this model down to 2 dimensions, which allows for phase plane analysis of the model output. The model shows a distinct activity for the gastric mill rhythm that is elicited when MCN1 and CPN2 are co-active compared to the rhythm elicited by MCN1 activity alone. Furthermore, the presence of feedback to the projection neuron CPN2 provides a distinct locus of pattern generation in the model which does not require reciprocally inhibitory interactions between the gastric mill CPG neurons, but is instead based on a half-center oscillator that occurs through a tri-synaptic pathway that includes CPN2. Our modeling results show that feedback to projection pathways may provide additional mechanisms for the generation of motor activity. These mechanisms can have distinct dependence on network parameters and may therefore provide additional flexibility for the rhythmic motor output.
Marginal mean models for zero‐inflated count data

Zero‐inflated regression models have emerged as a popular tool within the parametric framework to characterize count data with excess zeros. Despite their increasing popularity, much of the literature on real applications of these models has centered around the latent class formulation where the mean response of the so‐called at‐risk or susceptible population and the susceptibility probability are both related to covariates. While this formulation in some instances provides an interesting representation of the data, it often fails to produce easily interpretable covariate effects on the overall mean response. In this article, we propose two approaches that circumvent this limitation. The first approach consists of estimating the effect of covariates on the overall mean from the assumed latent class models, while the second approach formulates a model that directly relates the overall mean to covariates. Our results are illustrated by extensive numerical simulations and an application to an oral health study on low income African‐American children, where the overall mean model is used to evaluate the effect of sugar consumption on caries indices.
Parallel Random Coordinate Descent Method for Composite Minimization: Convergence Analysis and Error Bounds

In this paper we employ a parallel version of a randomized (block) coordinate descent method for minimizing the sum of a partially separable smooth convex function and a fully separable nonsmooth convex function. Under the assumption of Lipschitz continuity of the gradient of the smooth function, this method has a sublinear convergence rate. Linear convergence rate of the method is obtained for the newly introduced class of generalized error bound functions. We prove that the new class of generalized error bound functions encompasses both global/local error bound functions and smooth strongly convex functions. We also show that the theoretical estimates on the convergence rate depend on the number of blocks chosen randomly and a natural measure of separability of the smooth component of the objective function.
Cold preservation of the small intestine with the new Celsior‐solution First experimental results

Abstract The aim of the present study was to evaluate the potential of Celsior, a recently developed cardioplegia and heart storage solution, to protect the small bowel during ischemic storage. Small bowel segments were isolated from rats, flushed with either UW or Celsior solution, and cold‐stored for 18 h at 4°C in the respective solution. After ischemic storage, some preparations were freeze‐clamped for analysis of tissue metabolites while other preparations were tested for structural and functional integrity by isolated perfusion in vitro using a previously validated model. After 18 h of ischemic storage no significant differences were seen between Celsior and UW with regard to the development of edema, energy charge, or creatine phosphate, but lactate accumulation was significantly reduced in the Celsior group, although glucose catabolism was not inhibited. Histological evaluation of the cold‐stored organs showed no differences with regard to structural integrity between the two groups. Total vascular resistance upon reperfusion was significantly lower in the Celsior group (666 ± 126 vs 827 ± 88 MPa s m‐3*), as was the intestinal release of LDH (9.7 ± 4.4 vs 18.2 ± 4.6 U/l *). Carbohydrate absorption from the intestinal lumen amounted to venous effluent concentrations of 0.58 ± 0.24 vs 0.18 ± 0.15 mg%* of galactose in the Celsior and UW groups, respectively. Within the limits of this in vitro pilot study, Celsior provided better postischemic recovery of the small bowel than UW in terms of vascular perfusion characteristics, enzyme release, and carbohydrate absorption and may, thus, be considered a suitable alternative for intestinal organ preservation.
Tendon and Muscle Imaging in Sports

Summary Tendon and muscle lesions appear in manifold forms clinically as well as on magnetic resonance or ultrasound images. Tendinous abnormalities may be classified and staged with respect to their biomechanical role in a kinetic chain by using the concept of the tendon overuse syndrome (TOS). In the first phase, painful functional impairment of movements occurs without any morphological changes. In the second stage, abnormalities of the gliding tissue in the form of bursitis, tendovaginitis or peritendinitis are observed. In the third stage, such lesions are followed by degenerative changes of the tendon itself. Often, they present more clearly than during the early forms of TOS and three types of tendon degeneration can be differentiated: tendinosis at distinct points along the course of the tendon, fibroostosis at the tendon insertion and compression or impingement syndromes. Rupture of fibres following tendinosis may be considered as the last or fourth stage of TOS.
Accelerated failure time models for semi‐competing risks data in the presence of complex censoring

Statistical analyses that investigate risk factors for Alzheimer's disease (AD) are often subject to a number of challenges. Some of these challenges arise due to practical considerations regarding data collection such that the observation of AD events is subject to complex censoring including left‐truncation and either interval or right‐censoring. Additional challenges arise due to the fact that study participants under investigation are often subject to competing forces, most notably death, that may not be independent of AD. Towards resolving the latter, researchers may choose to embed the study of AD within the “semi‐competing risks” framework for which the recent statistical literature has seen a number of advances including for the so‐called illness‐death model. To the best of our knowledge, however, the semi‐competing risks literature has not fully considered analyses in contexts with complex censoring, as in studies of AD. This is particularly the case when interest lies with the accelerated failure time (AFT) model, an alternative to the traditional multiplicative Cox model that places emphasis away from the hazard function. In this article, we outline a new Bayesian framework for estimation/inference of an AFT illness‐death model for semi‐competing risks data subject to complex censoring. An efficient computational algorithm that gives researchers the flexibility to adopt either a fully parametric or a semi‐parametric model specification is developed and implemented. The proposed methods are motivated by and illustrated with an analysis of data from the Adult Changes in Thought study, an on‐going community‐based prospective study of incident AD in western Washington State.
Pointwise influence matrices for functional‐response regression

We extend the notion of an influence or hat matrix to regression with functional responses and scalar predictors. For responses depending linearly on a set of predictors, our definition is shown to reduce to the conventional influence matrix for linear models. The pointwise degrees of freedom, the trace of the pointwise influence matrix, are shown to have an adaptivity property that motivates a two‐step bivariate smoother for modeling nonlinear dependence on a single predictor. This procedure adapts to varying complexity of the nonlinear model at different locations along the function, and thereby achieves better performance than competing tensor product smoothers in an analysis of the development of white matter microstructure in the brain.
Antimicrobial Susceptibility Patterns Of Salmonella Species In Contemporary Medical Practice: Challenges And Prospects In Treatment Of Enteric Fevers

Treatment of enteric fever is increasingly becoming very challenging due to the increasing wave of antibiotic resistance. This study is a review of the contemporary antimicrobial susceptibility pattern of Salmonella species. The antimicrobial susceptibility pattern of Salmonella species to a wide range of antimicrobial agents was compiled for a period of 10 years (1997-2006). Data was obtained from published articles on both in vitro and in vivo susceptibility patterns of the organism to various drugs during the time interval. Data obtained were analysed using simple descriptive methods. Of the 23,504 isolates of Salmonella species reviewed, they were found to be 98% susceptible to cefepime and carbapenem, 91% to azithromycin, 82.1% to cefixime and 73% to quinolones. Also susceptibility to chloramphenicol, erythromycin, streptomycin, ampicillin, gentamicin, co-trimoxazole, augmentin and amikacin was found to be 11.9%, 10.9%, 13.0%, 16.8%, 46.3%, 38.0%, 54.3%, 6.9% and 63.0% respectively. This review has demonstrated increasing resistance of Salmonella species to antibiotics. Empirical treatment for enteric fevers should, therefore, be discouraged while quinolones, cefepime, carbapenem, azithromycin and third generation cephalosporins be given preference. KEY WORDS: Susceptibility, Antimicrobial, Salmonella species, Enteric fever