Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of the Physical Therapist in Reducing Postoperative Complications in Type 2 Diabetics

This article examines current research on the topic of reducing postoperative complications in patients with type 2 diabetes mellitus. Type 2 diabetes is a metabolic condition with rapidly increasing prevalence. Patients with type 2 diabetes are twice as likely to undergo surgery for comorbid conditions compared to individuals without the disease (Zantidis, Iliadis, & Didangelos, 2011). Aside from higher instances of surgical intervention, type 2 diabetic patients experience a higher rate of perioperative complications compared to individuals without the disease (Holt, 2012). Concurrent conditions such as peripheral neuropathy and hyperglycemia put diabetic patients at a higher risk of surgical complications and even mortality (Chuah, Papamargaritis, Pillai, Krishnamoorthy, & le Roux, 2013). Much research has been done exploring the various complications associated with surgical intervention in type 2 diabetics. However, there has yet to be a universally accepted approach to reducing such complications (Chuah et al., 2013). The purpose of this study is to examine current research to create a comprehensive policy that health care professionals should follow when treating type 2 diabetic surgical patients in order to reduce instances of perioperative complications. Two physical therapists were also interviewed to provide qualitative data on what is currently being done in the field to manage type 2 diabetic complications.
Effectiveness And Monitoring Of Antiretroviral Therapy For Hiv-Infected Children In Accra, Ghana

Title: EFFECTIVENESS AND MONITORING OFANTIRETROVIRAL THERAPY FOR HIVINFECTED CHILDREN IN ACCRA, GHANA Authors: Oliver M. Barry, Meghan Prin, Lorna A. Renner, Bamenla Goka, Jonas Kusah, Kwamena W. Sagoe, and Elijah Paintsil. Section of Infectious Diseases, Department of Pediatrics, Yale University, School of Medicine, New Haven, CT. Despite advances in HIV diagnosis and care, many challenges remain in resource-limited settings. As scale-up of antiretroviral therapy (ART) progresses in resource-limited settings, evaluation of available first-line regimens and monitoring techniques is needed to ensure the safety and success of such efforts. This study had two specific aims. First, the study aimed to evaluate the effectiveness of first-line ART regimens at a large HIV program in Ghana where participants are predominantly infected with non-type B HIV virus. Second, the study aimed to investigate the potential value of laboratory-based biomarkers, specifically absolute CD4 cell count, CD4 cell percentage and HIV RNA viral load in predicting failure on ART. This study enrolled a prospective cohort of 87 HIV-infected children receiving care at Korle Bu Teaching Hospital in Accra, Ghana. Blood samples were collected a 4-6 month intervals and participants were followed until treatment failure, defined by WHO criteria, as the primary study outcome. 83% of participants demonstrated effective treatment response to first-line regimens with 0% associated mortality and only 4.7% switched regimens over the study period. Predictors of treatment failure included a shorter time interval between HIV diagnosis and ART initiation (p=0.014) and parents who are both HIV-positive (p=0.05). Lower mean baseline absolute CD4 cell count appears to be a significantly predictor for immunological failure (p=0.041). Absolute CD4 cell count and CD4 cell percentage after ART initiation showed similar trajectories and patterns. The findings demonstrated that available first-line ART regimens were effective, well tolerated and potentially durable in this setting with little evidence of resistance or toxicity. In settings in which CD4 cell percentage is not readily available, absolute CD4 cell count may provide an alternative biomarker for monitoring treatment response. These conclusions hold valuable public health implications in the scale-up of ART access and monitoring.
Efficacy of Diet Therapies in the Treatment of Neurological and Neurodegenerative Diseases

Epilepsy is a prevalent disabling chronic and socially isolating neurological disorder that involves recurrent abno rmal discharges of neurons. Despite seizures afflicting about 10% of people worldwide, antiepileptic drugs (AEDs) are largely unable to m anage s eizures in m any persons with epilepsy. As an alternative to AEDs, diet ary therapies poss ess a broa d therapeu tic potential in both humans and animals models of vari ous neurological and neurodegenerative disease etiologies. My research focus was to identif y the therapeutic effic acy and potential mechanism(s) of action of calorie restriction (CR) and the ketogenic diet (KD) in both t he epileptic EL mouse model and the Mecp2 308/y mouse model of Rett syndrome. My findings indicate that both the KD and CR can reduc e seizure susceptibility in EL mice, a natural model for multifactorial idiopathic generalized epilepsy. CR and c irculating glucose and ketone levels significantly influence the therapeutic efficacy of the KD . A concurrent reduction in circulating plasma glucose lev els and elevation in c irculating plasma β-hydroxybutyrate levels was predicted to associat e with the anticonvulsant effect of these diets in EL mice. For the first time , I was able to show that a KD fed in unrestricted amount is able to reduce seizure thres hold in EL mice. Interestingly, supplementation of c alories in t he form of carbohydrate in the water of calorierestricted EL mice results in a diminish ed anticonvulsant efficacy of the K D. In my effort to elucidate the neuroprotec tive mechanism (s) associated with these changes in metabolite availability, I start ed investigating the complex alterations occurring in multiple integrated neural a nd metabolic processes. Furthermore, I showed that a res tricted KD diet improves aspects of the behavioral abnormalities seen in Rett mice, in particula r with respect to anxiet y. Finally, for the first ti me, I provide a standardized pr otocol for the implementation of diet therapies in the managem ent of an array of neurol ogical and neurodegenerative diseases, which ultimately may help el ucidate the complex neuroprotective mechanism(s) of CR and the KD. This research overall has provided a ne w understanding in the therapeutic efficacy of diets in epilepsy and Rett Syndrome.
A adesão à terapêutica antihipertensora na recorrência de acidentes cerebrovasculares = Compliance with antihypertensive medication in the recurrence of cerebrovascular accidents

Introduction and objective: Stroke is the second most prevalent cause of death throughout the world, and in Portugal its mortality is considerably high. Hence, the optimization of its treatment and the investment in its secondary prevention are of particular importance. Our aim, in this study, was to identify predictive factors which could be associated with a greater probability of recurrence of stroke. Methods: We retrospectively studied 84 patients, who were selected from a population of patients admitted for cerebrovascular events. We defined some variables, with information regarding the patient (group Dt), and another ones, with information regarding the disease itself (group Dç). This population was divided in two groups: the first, containing the ones who had recurrence of the disease, and the second containing those who didn’t have it. These groups were characterized and compared, using the variables previously referred, and appropriate statistical tests and multivariate analysis were used to identify independent predictors of stroke recurrence. Results: The analysis showed no significant differences between the populations who had vs who hadn’t stroke recurrence, either concerning the variables from group Dt or group Dç. Therefore, it was not possible to identify independent predictors of stroke recurrence. Conclusion: Contrary to what was first expected, it was not possible to establish a predictive value of any of the variables considered, either regarding the patient or the disease itself.
Optimization of care strategies in hemophilia

1. Transmural support by specialized nurses leads to improvement of perceived support and communication with the hemophilia treatment center. (This thesis) 2. Especially in moderate severe and mild hemophilia patients, self-ee cacy is an important measure as when to contact the hemophilia team is of more importance than knowledge of appropriate dosing. (This thesis) 3. Group Medical Appointments play an important role in hemophilia patient care as they lead to enhanced information transfer, increased social support, and increased patient insight into disease, lifestyle adjustments and treatment. (This thesis) 4. Underdosing and overdosing of Factor 8/9 concentrates in the perioperative setting can be overcome by iterative pharmacokinetic-guided dosing based on population pharmacoki-netic models of heterogeneous patient groups. (This thesis) 5. Daily prophylactic dosing of factor concentrates is acceptable for patients, parents and hemophilia professionals when risk of bleeding and costs of treatment for society decrease signii cantly. (This thesis) 6. As milder disease leads to non-adherence to treatment, monitoring of adherence becomes paradoxically more important when disease symptoms improve. (Osterberg et al. 2005 N Engl J Med) 7. A simple low cost unit specializing in care of sick neonates in a resource pour setting and run by local health workers following adequate training, can reduce the neonatal mortality signii cantly. (Turner et al. 2013 Plos One) 8. Health care should be dee ned by what patients value as related to the cost of achieving those outcomes. (Porter et al. 2013 Harvard Business Review) 9. The underrepresentation of research by women in high impact journals, combined with an increased number of female physicians is a great threat for the quality of future medical research. (Adapted from Filardo, 2016 BMJ) 10. Addressing religious, spiritual, cultural, and familial issues are important components of chronic disease management. (Adapted from Dam et al. 2013 JAMA Int Med) 11. Twinning is an indicator of higher maternal capacity and identii es those women whose enhanced phenotypic quality with a higher life-expectancy which allows them to bear the elevated burden.
Ab Initio Nuclear Thermodynamics.

We propose a new Monte Carlo method called the pinhole trace algorithm for ab initio calculations of the thermodynamics of nuclear systems. For typical simulations of interest, the computational speedup relative to conventional grand-canonical ensemble calculations can be as large as a factor of one thousand. Using a leading-order effective interaction that reproduces the properties of many atomic nuclei and neutron matter to a few percent accuracy, we determine the location of the critical point and the liquid-vapor coexistence line for symmetric nuclear matter with equal numbers of protons and neutrons. We also present the first ab initio study of the density and temperature dependence of nuclear clustering.
Multi-Dimension Modulation for Image Restoration with Dynamic Controllable Residual Learning

Based on the great success of deterministic learning, to interactively control the output effects has attracted increasingly attention in the image restoration field. The goal is to generate continuous restored images by adjusting a controlling coefficient. Existing methods are restricted in realizing smooth transition between two objectives, while the real input images may contain different kinds of degradations. To make a step forward, we present a new problem called multi-dimension (MD) modulation, which aims at modulating output effects across multiple degradation types and levels. Compared with the previous single-dimension (SD) modulation, the MD task has three distinct properties, namely joint modulation, zero starting point and unbalanced learning. These obstacles motivate us to propose the first MD modulation framework -- CResMD with newly introduced controllable residual connections. Specifically, we add a controlling variable on the conventional residual connection to allow a weighted summation of input and residual. The exact values of these weights are generated by a condition network. We further propose a new data sampling strategy based on beta distribution to balance different degradation types and levels. With the corrupted image and the degradation information as inputs, the network could output the corresponding restored image. By tweaking the condition vector, users are free to control the output effects in MD space at test time. Extensive experiments demonstrate that the proposed CResMD could achieve excellent performance on both SD and MD modulation tasks.
Array languages make neural networks fast

Most implementations of machine learning algorithms are based on special-purpose frameworks such as TensorFlow or PyTorch. While these frameworks are convenient to use, they introduce multi-million lines of code dependency that one has to trust, understand and potentially modify. As an alternative, this paper investigates a direct implementation of a state of the art Convolutional Neural Network (CNN) in an array language. While our implementation requires 150 lines of code to define the special-purpose operators needed for CNNs, which are readily provided through frameworks such as TensorFlow and PyTorch, our implementation outperforms these frameworks by factors 2 and 3 on a fixed set of hardware — a 64-core GPU-accelerated machine; for a simple example network. The resulting specification is written in a rank-polymorphic data-parallel style, and it can be immediately leveraged by optimising compilers. Indeed, array languages make neural networks fast.
RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation

Object detection and instance segmentation are two fundamental computer vision tasks. They are closely correlated but their relations have not yet been fully explored in most previous work. This paper presents RDSNet, a novel deep architecture for reciprocal object detection and instance segmentation. To reciprocate these two tasks, we design a two-stream structure to learn features on both the object level (i.e., bounding boxes) and the pixel level (i.e., instance masks) jointly. Within this structure, information from the two streams is fused alternately, namely information on the object level introduces the awareness of instance and translation variance to the pixel level, and information on the pixel level refines the localization accuracy of objects on the object level in return. Specifically, a correlation module and a cropping module are proposed to yield instance masks, as well as a mask based boundary refinement module for more accurate bounding boxes. Extensive experimental analyses and comparisons on the COCO dataset demonstrate the effectiveness and efficiency of RDSNet. The source code is available at https://github.com/wangsr126/RDSNet.
An Efficient Approach for Using Expectation Maximization Algorithm in Capsule Networks

Capsule Networks (CapsNets) are brand-new architectures that have shown ground-breaking results in certain areas of Computer Vision (CV). In 2017, Hinton and his team introduced CapsNets with routing-by-agreement in "Sabour et al" and in a more recent paper "Matrix Capsules with EM Routing" they proposed a more complete architecture with Expectation-Maximization (EM) algorithm. Unlike the traditional convolutional neural networks (CNNs), this architecture is able to preserve the pose of the objects in the picture. Due to this characteristic, it has been able to beat the previous state-of-the-art results on the smallNORB dataset, which includes images with various view points. Also, this new architecture is more robust to white box adversarial attacks. However, CapsNets have two major drawbacks. They can’t perform as well as CNNs on complex datasets and, they need a huge amount of time for training. We try to mitigate these shortcomings by finding optimum settings of EM routing iterations for training CapsNets. Unlike the past studies, we use un-equal numbers of EM routing iterations for different stages of the CapsNet. We manage to achieve higher accuracies than the original CapsNet while training the network up to three times faster. For our research, we use three datasets: Yale face dataset, Belgium Traffic Sign dataset, and Fashion-MNIST dataset.
Get real: realism metrics for robust limit order book market simulations

Market simulation is an increasingly important method for evaluating and training trading strategies and testing "what if" scenarios. The extent to which results from these simulations can be trusted depends on how realistic the environment is for the strategies being tested. As a step towards providing benchmarks for realistic simulated markets, we enumerate measurable stylized facts of limit order book (LOB) markets across multiple asset classes from the literature. We apply these metrics to data from real markets and compare the results to data originating from simulated markets. We illustrate their use in five different simulated market configurations: The first (market replay) is frequently used in practice to evaluate trading strategies; the other four are interactive agent based simulation (IABS) configurations which combine zero intelligence agents, and agents with limited strategic behavior. These simulated agents rely on an internal "oracle" that provides a fundamental value for the asset. In traditional IABS methods the fundamental originates from a mean reverting random walk. We show that markets exhibit more realistic behavior when the fundamental arises from historical market data. We further experimentally illustrate the effectiveness of IABS techniques as opposed to market replay.
Geometric derived Hall algebra

We give a geometric formulation of Toen's derived Hall algebra by constructing Grothendieck's six operations for the derived category of lisse-etale constructible sheaves on the derived stacks of complexes. Our formulation is based on an variant of Laszlo and Olsson's theory of derived categories and six operations for algebraic stacks. We also give an $\infty$-theoretic explanation of the theory of derived stacks, which was originally constructed by Toen and Vezzosi in terms of model theoretical language.
Fine-grained Classification of Rowing teams

Fine-grained classification tasks such as identifying different breeds of dog are quite challenging as visual differences between categories is quite small and can be easily overwhelmed by external factors such as object pose, lighting, etc. This work focuses on the specific case of classifying rowing teams from various associations. Currently, the photos are taken at rowing competitions and are manually classified by a small set of members, in what is a painstaking process. To alleviate this, Deep learning models can be utilised as a faster method to classify the images. Recent studies show that localising the manually defined parts, and modelling based on these parts, improves on vanilla convolution models, so this work also investigates the detection of clothing attributes. The networks were trained and tested on a partially labelled data set mainly consisting of rowers from multiple associations. This paper resulted in the classification of up to ten rowing associations by using deep learning networks the smaller VGG network achieved 90.1\% accuracy whereas ResNet was limited to 87.20\%. Adding attention to the ResNet resulted into a drop of performance as only 78.10\% was achieved.
Semiclassical work and quantum work identities in Weyl representation

We derive a semiclassical nonequilibrium work identity by applying the Wigner–Weyl quantization scheme to the Jarzynski identity for a classical Hamiltonian. This allows us to extend the concept of work to the leading order in ℏ. We propose a geometric interpretation of this semiclassical Jarzynski relation in terms of trajectories in a complex phase space and illustrate it with the exactly solvable case of the quantum harmonic oscillator.
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data

Neural networks have recently been established as a viable classification method for imaging mass spectrometry data for tumor typing. For multi-laboratory scenarios however, certain confounding factors may strongly impede their performance. In this work, we introduce Deep Relevance Regularization, a method of restricting what the neural network can focus on during classification, in order to improve the classification performance. We demonstrate how Deep Relevance Regularization robustifies neural networks against confounding factors on a challenging inter-lab dataset consisting of breast and ovarian carcinoma. We further show that this makes the relevance map -- a way of visualizing the discriminative parts of the mass spectrum -- sparser, thereby making the classifier easier to interpret
DeepMeshFlow: Content Adaptive Mesh Deformation for Robust Image Registration

Image alignment by mesh warps, such as meshflow, is a fundamental task which has been widely applied in various vision applications(e.g., multi-frame HDR/denoising, video stabilization). Traditional mesh warp methods detect and match image features, where the quality of alignment highly depends on the quality of image features. However, the image features are not robust in occurrence of low-texture and low-light scenes. Deep homography methods, on the other hand, are free from such problem by learning deep features for robust performance. However, a homography is limited to plane motions. In this work, we present a deep meshflow motion model, which takes two images as input and output a sparse motion field with motions located at mesh vertexes. The deep meshflow enjoys the merics of meshflow that can describe nonlinear motions while also shares advantage of deep homography that is robust against challenging textureless scenarios. In particular, a new unsupervised network structure is presented with content-adaptive capability. On one hand, the image content that cannot be aligned under mesh representation are rejected by our learned mask, similar to the RANSAC procedure. On the other hand, we learn multiple mesh resolutions, combining to a non-uniform mesh division. Moreover, a comprehensive dataset is presented, covering various scenes for training and testing. The comparison between both traditional mesh warp methods and deep based methods show the effectiveness of our deep meshflow motion model.
Value-of-Information based Arbitration between Model-based and Model-free Control

There have been numerous attempts in explaining the general learning behaviours using model-based and model-free methods. While the model-based control is flexible yet computationally expensive in planning, the model-free control is quick but inflexible. The model-based control is therefore immune from reward devaluation and contingency degradation. Multiple arbitration schemes have been suggested to achieve the data efficiency and computational efficiency of model-based and model-free control respectively. In this context, we propose a quantitative 'value of information' based arbitration between both the controllers in order to establish a general computational framework for skill learning. The interacting model-based and model-free reinforcement learning processes are arbitrated using an uncertainty-based value of information. We further show that our algorithm performs better than Q-learning as well as Q-learning with experience replay.