Miracle-dz/UniFAR-FTUs huggingface dataset:
Can Quantitative Balance Measures Discriminate between Functional Ambulation Categories in Chronic Stroke Survivors?

Background: The limited walking ability that follows a stroke restricts a patient’s independent mobility about the home and community, a significant social handicap. This study investigated the relationship between quantitative balance indices and functional ambulation level in stroke patients. The purpose was to explore whether we could provide supportive laboratory evidence for the association between balance and ambulatory level in stroke patients based on computerized dynamic post urography. Study design: Cross-sectional, observational, descriptive study Setting: Physiotherapy department in a tertiary care center in Mumbai Participants: 40 ambulatory stroke patients (mean age=54.07 ± 11.9 years) Main outcome measures: (1) Static and dynamic balance was assessed using computerized force plate system (2) Functional ambulation level was determined using Modified Hoffer functional ambulation classification (FAC). Results: According to FAC, 29.26% of the patients were community walkers. One way ANNOVA showed that static and dynamic balance indices were significantly different with functional ambulation categories. Weight bearing asymmetry during quiet stance (p<0.04); COG sway velocity (p<0.04), weight transfer time (p<0.04) and rising index (p<0.02) during sit to stand can discriminate between household and community ambulators. Conclusion: Balance is a significant factor in the attainment of independent functional ambulation in chronic stroke patients. The functional mobility capability of stroke patients may be quantified analytically using static and dynamic balance indices. The key balance factors identified through this study need to be specifically targeted for training and as outcome measures while monitoring the progress of patients through different functional ambulation categories. Results of this study offer a quantitative method of relating the social disadvantage of stroke patients to the impairments. Citation: Akulwar IS (2019) Can Quantitative Balance Measures Discriminate between Functional Ambulation Categories in Chronic Stroke Survivors? Physiother Rehabil 4: 178.
E46Phase III study of regorafenib versus placebo as maintenance therapy in RAS wild type metastatic colorectal cancer (RAVELLO trial)

TPS789 Background: Treatment of metastatic colorectal cancer (mCRC) has improved due to the introduction of more active chemotherapies (CT) and novel targeted agents that have significantly increased response rate (RR), progression free survival (PFS) and overall survival (OS). Recently, CORRECT and CONCUR trials have demonstrated both activity and efficacy of regorafenib, a small multi-kinase inhibitor, as monotherapy in pretreated mCRC. The wide range of action of regorafenib makes it an ideal candidate for monotherapy in earlier disease treatment lines in which different pathways could be involved in the acquisition of resistance. To improve long term efficacy of first line therapy several therapeutic approaches of maintenance treatment have been explored in mCRC. Methods: RAVELLO is an academic randomized, double-blind, placebo-controlled, multi-center, phase III study designed to evaluate efficacy and safety of regorafenib as maintenance treatment after first line therapy. Eligible patients: patholog.
International Journal of Social Science and Economic Research

The purpose of this study is to assess the role of owner/manager’s personal characteristics in small to medium enterprise brand development. The role of the owner/manager’s personal characteristics in brand development has been overlooked and existing literature has failed to properly conceptualize how brand identity in SMEs is developed. The study utilises a survey research design adopting a quantitative research approach on 500 SMEs operating in three cities in Zimbabwe. Data was collected using a self-administered questionnaire which was administered to the owner/managers of the business utilizing purposive sampling technique. The study excluded SMEs operating in the rural areas of Zimbabwe and only targeted owner/managers; thus, reflecting the supply-side of branding in SME. The findings of the research based on a multiple regression analysis indicate that the owner/manager’s inherent characteristics affect the brand identity conceptualizations in SMEs particularly ethnic identity, extraversion and creativity. On the contrary, age, educational level, agreeableness and neuroticism have no bearing on SME brand identity. The study contributes to knowledge generation in terms of the role of owner/manager’s personal characteristics on SME brand development as extant literature has significantly overlooked the impact of personal characteristics of the owner/manager in the conceptualization of SME branding development. This study contributes to the existing body of knowledge on the conceptualization of brand development in SMEs; any area which has been overlooked by existing literature.
Semantic Information in Working Memory

Much of our cognitive abilities rely on maintaining and processing the meaning of different objects, words or actions. This meaning is known as semantics however, the use of semantics in working memory have been mostly ignored and instead attributed to long-term semantic memory. Using Baddeley and Hitch’s model of working memory this study aims to attribute semantic maintenance to a component within the model, examine differences between types of semantic material to episodic material and how these were affected by serial position effects. Related semantics, unrelated semantics and episodic material were included in the study which were all represented by word-pairs. 42 participants, using a convenience sampling method, were randomly assigned to one of three experimental conditions designed to alter the amount of material memorized. The first experimental condition, by using immediate recall, demonstrated that semantic materials had a significantly superior recall rate than episodic materials. The second experimental condition showed that the effect articulatory suppression didn’t significantly reduce the amount memorized. The third condition demonstrated that delayed recall significantly reduced the amount of unrelated semantics. These findings showed that semantic materials had superior recall but related and unrelated semantic material were found to be maintained by different systems as unrelated material was subject to decay or interference. The results showed the importance using simple and precise language to provide a more effective way to memorize and learn.
Moral Philosophy and Politics

Policymakers are responsible for key decisions about political governance. Usually, they are selected or elected based on experience and then supported in their decision-making by the additional counsel of subject experts. Those satisfied with this system believe these individuals – generally speaking – will have the right intuitions about the best types of action. This is important because political decisions have ethical implications; they affect how we all live in society. Nevertheless, there is a wealth of research that cautions against trusting human judgment as it can be severely flawed. This paper will look at the root causes of the most common errors of human judgment before arguing – contra the instincts of many – that future AI systems could take a range of political decisions more reliably. I will argue that, if/when engineers establish ethically robust systems, governments will have a moral obligation to refer to them as a part of decision-making.
RANLP 2019 Multilingual Headline Generation Task Overview

The objective of the 2019 RANLP Multilingual Headline Generation (HG) Task is to explore some of the challenges highlighted by current state of the art approaches on creating informative headlines to news articles: non-descriptive headlines, out-of-domain training data, generating headlines from long documents which are not well represented by the head heuristic, and dealing with multilingual domain. This tasks makes available a large set of training data for headline generation and provides an evaluation methods for the task. Our data sets are drawn from Wikinews as well as Wikipedia. Participants were required to generate headlines for at least 3 languages, which were evaluated via automatic methods. A key aspect of the task is multilinguality. The task measures the performance of multilingual headline generation systems using the Wikipedia and Wikinews articles in multiple languages. The objective is to assess the performance of automatic headline generation techniques on text documents covering a diverse range of languages and topics outside the news domain.
C-terminal binding proteins 1 and 2 in traumatic brain injury-induced inflammation and their inhibition as an approach for anti-inflammatory treatment

Traumatic brain injury (TBI) induces an acute inflammatory response in the central nervous system that involves both resident and peripheral immune cells. The ensuing chronic neuroinflammation causes cell death and tissue damage and may contribute to neurodegeneration. The molecular mechanisms involved in the maintenance of this chronic inflammation state remain underexplored. C-terminal binding protein (CtBP) 1 and 2 are transcriptional coregulators that repress diverse cellular processes. Unexpectedly, we find that the CtBPs can transactivate a common set of proinflammatory genes both in lipopolysaccharide-activated microglia, astrocytes and macrophages, and in a mouse model of the mild form of TBI. We also find that the expression of these genes is markedly enhanced by a single mild injury in both brain and peripheral blood leukocytes in a severity- and time-dependent manner. Moreover, we were able to demonstrate that specific inhibitors of the CtBPs effectively suppress the expression of the CtBP target genes and thus improve neurological outcome in mice receiving single and repeated mild TBIs. This discovery suggests new avenues for therapeutic modulation of the inflammatory response to brain injury.
Critical Analysis of Brain Magnetic Resonance Images Tumor Detection and Classification Techniques

The image segmentation, tumor detection and extraction of tumor area from brain MR images are the main concern but time-consuming and tedious task performed by clinical experts or radiologist, while the accuracy relies on their experiences only. So, to overcome these limitations, the usage of computer-aided design (CAD) technology has become very important. Magnetic resonance imaging (MRI) and Computed Tomography (CT) are the two major imaging modalities that are used for brain tumor detection. In this paper, we have carried out a critical review of different image processing techniques of brain MR images and critically evaluate these different image processing techniques in tumor detection from brain MR images to identify the gaps and limitations of those techniques. Therefore, to obtain precise and better results, the gaps can be filled and limitations of various techniques can be improved. We have observed that most of the researchers have employed these stages such as Pre-processing, Feature extraction, Feature reduction, and Classification of MR images to find benign and malignant images. We have made an effort in this area to open new dimensions for the readers to explore the concerned field of research.
An overlapping mesh based splitting method for the incompressible Navier-Stokes equations

We present an overlapping mesh based splitting method for solving the incompressible Navier-Stokes equations on unfitted mesh settings. The developed method is based on Nitsche’s method for weakly imposing the interface condition between the overlapping meshes, and it uses a ghost penalty approach for stabilizing the cut elements produced by the mesh intersection. The developed method is tested on the Taylor-Green vortex problem for fluid flows in both the high and low regime of the Reynolds number. The numerical results show optimal order of convergence or higher for the considered space-time error norms in the low Reynolds regime. For the high Reynolds regime, the results are promising but inconclusive for concluding any final convergence rate in time. Suggestions for further studies are to consider a more physically realistic flow such as the benchmark test case of a channel flow around a cylinder, as well as adding some stabilization for convection dominated flows. Master thesis: 30 ECTS credits Supervisor: André Massing (andre.massing@umu.se) Examiner: Krister Wiklund (krister.wiklund@umu.se) Karl-Johan Lundström 24th November 2019
Silkworm sHSP19.9 and sHSP20.8 with ATP on the thermal induced aggregation of Bovine Liver Catalase (BLC)

Adenosine Tri Phosphate (ATP) plays a significant role in the function of molecular chaperones of the large heat shock protein families. However, its role in the functions of chaperones of the small heat shock protein (sHSP) families is not understood very well. This paper reports the findings of a study on the role of ATP on the structure and function of the two important small heat shock proteins (sHSPs); sHSP19.9 and sHSP20.8. The study revealed that ATP at high concentration (10 mM) helps sHSP19.9 to show effective CLA against bovine liver catalase (BLC) aggregation at 60oC rather than at low (3 mM) concentration. ATP induces the association of sHSPs with substrate proteins and stabilizes the sHSPs structure. But sHSP20.8 shows opposite; the potential performance was observed at low concentration of AT (below 3 mM), which messaged that the association process is low affinity in nature. So the ATP-induced association of the both sHSPs with substrate enhanced its aggregation prevention ability and also enhanced the refolding yield of BLC from the unfolded state.
LGraph: A Unified Data Model and API for Productive Open-Source Hardware Design

We present LGraph, a unified data model and API for productive open-source hardware design. It is inspired by live incremental synthesis and aims to provide a fast infrastructure for a productive hardware development flow. In this paper, LGraph is described both from the perspective of a hardware designer as well as an EDA software developer. Key features of LGraph include a unified data model and API, a fast memory mapped library design, integration with third-party tools and hierarchical design traversal for third-party tools such as OpenTimer, Mockturtle and ABC.
Development of a technique for inductive measurement of laser-induced magnetization dynamics in thin films

We present the development of a technique for studying laser-induced magnetization dynamics, based on inductive measurement. The technique could provide a simple tool for studying laser-induced demagnetization in thin films and associated processes, such as Gilbert damping and magnetization precession. It was successfully tested using a nanosecond laser and NiZn ferrite samples and – after further development – it is expected to be useful for observation of ultra-fast demagnetization. The combination of optical excitation and inductive measurement enables to study laser-induced magnetization dynamics in both thin and several micrometre thick films and might be the key to a new principle of ultrafast broadband UV–IR pulse detection.
A multiscale method for large-scale inverse modeling: single-phase transient flow

High-resolution geologic models that incorporate observed state data are expected to effectively enhance the reliability of performance prediction. One of the major challenges faced is how to solve the large-scale inverse modeling problem, i.e., to infer high-resolution models from the given observed state data that are related to the model parameters according to the known physical rules, e.g., the flow and transport partial differential equations. There are basically two problems, one is the high-dimensional problem and the other is the inverse problem. A multiscale method is presented in this work to attack these problems with the aid of a gradientbased optimization algorithm. Within this method, the model responses (i.e., the simulated state data) are very efficiently computed from the high-resolution model using the multiscale finite-volume method. The mismatch between the observations and the simulations is then defined as the objective function and the fine-scale sensitivity coefficient (i.e., the derivative of the objective function with respect to each node’s attribute) is computed by a multiscale adjoint method for subsequent optimization. The difficult high-dimensional optimization problem is reduced to a one-dimension using the gradient-based gradual deformation method. A synthetic single-phase transient flow example problem is employed to ∗Corresponding author. E-mail: fu jianlin ac@yahoo.com †E-mail: jcaers@stanford.edu ‡E-mail: tchelepi@stanford.edu
Distribution of Medically Relevant Antibiotic Resistance Genes and Mobile Genetic Elements in Soils of Temperate Forests and Grasslands Varying in Land Use

Antibiotic-resistant pathogens claim the lives of thousands of people each year and are currently considered as one of the most serious threats to public health. Apart from clinical environments, soil ecosystems also represent a major source of antibiotic resistance determinants, which can potentially disseminate across distinct microbial habitats and be acquired by human pathogens via horizontal gene transfer. Therefore, it is of global importance to retrieve comprehensive information on environmental factors, contributing to an accumulation of antibiotic resistance genes and mobile genetic elements in these ecosystems. Here, medically relevant antibiotic resistance genes, class 1 integrons and IncP-1 plasmids were quantified via real time quantitative PCR in soils derived from temperate grasslands and forests, varying in land use over a large spatial scale. The generated dataset allowed an analysis, decoupled from regional influences, and enabled the identification of land use practices and soil characteristics elevating the abundance of antibiotic resistance genes and mobile genetic elements. In grassland soils, the abundance of the macrolide resistance gene mefA as well as the sulfonamide resistance gene sul2 was positively correlated with organic fertilization and the abundance of aac(6′)-lb, conferring resistance to different aminoglycosides, increased with mowing frequency. With respect to forest soils, the beta-lactam resistance gene blaIMP-12 was significantly correlated with fungal diversity which might be due to the fact that different fungal species can produce beta-lactams. Furthermore, except blaIMP-5 and blaIMP-12, the analyzed antibiotic resistance genes as well as IncP-1 plasmids and class-1 integrons were detected less frequently in forest soils than in soils derived from grassland that are commonly in closer proximity to human activities.
Macroeconomic Determinants of Private Investment in Sub-Saharan Africa

Using panel data covering the period 2000–2017 for 35 Sub Saharan African (SSA) countries, this study applied the pooled regression, fixed effects and random effects models as well as the Panel Corrected Standard Error (PCSE) technique in an attempt to analyze the macroeconomic determinants of private investment. Critical diagnostic tests were carried out: unit root tests indicated that the employed data was stationary. Further diagnostic tests also indicated that the fixed effects model is suffering from heteroskedasticity, autocorrelation and cross-sectional dependence. The PCSE technique was applied to counter the detected statistical problems. Main study findings indicated that, in the SSA region, private investment was determined by GDP, real interest rates, public investment and inflation. The research then proposed four policy recommendations for consideration by the SSA countries in order to materialize the much awaited private sector growth.
[Re] Unsupervised Scalable Representation Learning for Multivariate Time Series

The paper Unsupervised Scalable Representation Learning for Multivariate Time Series by [1] presents an unsupervised approach to learning representations for time series that can be used for subsequent classification. An encoder architecture with dilated causal convolutional blocks is trained to minimize a triplet loss. The triplet loss is based on the idea that a subseries, xpos, of a time series, xref , should be closer to xref in representation space than the representation of a randomly sampled time series from the dataset, xneg. This type of loss was first introduced by [2]. To evaluate the strength of the learned representations, an SVM classifier is trained using the labels corresponding to these representations. The evaluation is performed on 159 datasets, together constituting themain benchmarking datasets for time series. The first group of datasets, the UCR Archive [3], contains univariate, short (between 15 and 2844 time steps) series; the second group, theUEAdataset [4], containsmultivariate time series of up to 1345 dimensions and varying sample lengths. The last dataset, the Individual Household Electric Power Consumption (IHEPC) dataset from the UCI Machine Learning Repository [5], is a single seven-dimensional time series with more than two million time steps. The authorsʼ intention with using this variety of datasets is to show the universality of their method, which is one of their main claims. We have re-implemented the authorsʼ method from scratch as best as we could by using only the paper as instruction. It should be noted that a code repository for the article was made public by the authors, but this was not employed for the purpose of investigating the replicability of the work starting from scratch. This means that we adhere to the Replication track of the Reproducibility challenge for NeurIPS 2019. Our implementation, as well as the authors ,̓ was made using the Pytorch [6] library and can be found at https://github.com/lilfelix/reproducibility_NeurIPS19. The main motivation to do a full replication study, avoiding the use of readily provided code, is to better be able to detect if there are parts of the implementation that are crucial for the results, but not presented as such in the original paper. This risk exists generally in machine learning [7], and has in particular been brought to light for deep learning (e.g. [8], [9]), due to the latter s̓ typically vast number of hyperparameters to tune.