Miracle-dz/UniFAR-FTUs huggingface dataset:
Alzheimer,s Disease Risk and Progression: The Role of Nutritional Supplements and their Effect on Drug Therapy Outcome

Alzheimer's disease (AD) is the most common neurodegenerative disease in the elderly population. Despite significant advancements in understanding the genetic and molecular basis of AD, the pathology still lacks treatments that can slow down or reverse the progression of cognitive deterioration. Recently, the relationship between nutrient deficiency and dementia onset has been highlighted. AD is in fact a multifactorial pathology, so that a multi-target approach using combinations of micronutrients and drugs could have beneficial effects on cognitive function in neurodegenerative brain disorders leading to synaptic degeneration. Primarily, this review examines the most recent literature regarding the effects of nutrition on the risk/progression of the disease, focusing attention mostly on antioxidants agents, polyunsaturated fatty acids and metals. Secondly, it aims to figure out if nutritional supplements might have beneficial effects on drug therapy outcome. Even if nutritional supplements showed contrasting evidence of a likely effect of decreasing the risk of AD onset that could be studied more deeply in other clinical trials, no convincing data are present about their usefulness in combination with drug therapies and their effectiveness in slowing down the disease progression.
Tea and sympathy: crafting positive new user experiences on wikipedia

We present the Teahouse, a pilot project for supporting and socializing new Wikipedia editors. Open collaboration systems like Wikipedia must continually recruit and retain new members in order to sustain themselves. Wikipedia's editor decline presents unique exigency for evaluating novel strategies to support newcomers and increase new user retention in such systems, particularly among demographics that are currently underrepresented in the user community. In this paper, we describe the design and deployment of Teahouse, and present preliminary findings. Our findings highlight the importance of intervening early in the editor lifecycle, providing user-friendly tools, creating safe spaces for newcomers, and facilitating positive interactions between newcomers and established community members.
Energy-Momentum in Gauge Gravitation Theory

Building on the first variational formula of the calculus of variations, one can derive the energy-momentum conservation laws from the condition of the Lie derivative of gravitation Lagrangians along vector fields corresponding to generators of general covariant transformations to be equal to zero. The goal is to construct these vector fields. In gauge gravitation theory, the difficulty arises because of fermion fields. General covariant transformations fail to preserve the Dirac spin structure $S_h$ on a world manifold $X$ which is associated with a certain tetrad field $h$. We introduce the universal Dirac spin structure $S\to\Sigma\to X$ such that, given a tetrad field $h:X\to \Sigma$, the restriction of $S$ to $h(X)$ is isomorphic to $S_h$. The canonical lift of vector fields on $X$ onto $S$ is constructed. We discover the corresponding stress-energy-momentum conservation law. The gravitational model in the presence of a background spin structure also is examined.
A Survey on Routing Protocols and Challenge of Holes in Wireless Sensor Networks

Extensive usage of wireless sensor networks is the reason of development of many routing protocols. In this paper, the working of few routing protocols has been discussed, which are energy aware and some of them also provide reliability in data transmission. Performance of various protocols has been presented through simulation results that have been reported by leading researchers for the purpose of their comparison. The challenges faced by wireless sensor networks are also discussed in the paper. These challenges (i.e. coverage holes, routing holes, jamming holes, black/sink holes and worm holes) effect the performance of routing protocols.
Sample Complexity of Real-Coded Evolutionary Algorithms

Researchers studying Evolutionary Algorithms and their applications have always been confronted with the sample complexity problem. The relationship between population size and global convergence is not clearly understood. Population size is usually chosen depending on researcher’s experience. In this paper, we study the population size using Probably Approximately Correct (PAC) learning theory. A ruggedness measure for fitness functions is defined. A sampling theorem that theoretically determines an appropriate population size towards effective convergence is proposed. Preliminary experiments show that the initial population of the proposed size provides good starting point(s) for searching the solution space and thus leads to finding global optima.
REACTION: A naive machine learning approach for sentiment classification

We evaluate a naive machine learning approach to sentiment classification focused on Twitter in the context of the sentiment analysis task of SemEval-2013. We employ a classifier based on the Random Forests algorithm to determine whether a tweet expresses overall positive, negative or neutral sentiment. The classifier was trained only with the provided dataset and uses as main features word vectors and lexicon word counts. Our average F-score for all three classes on the Twitter evaluation dataset was 51.55%. The average F-score of both positive and negative classes was 45.01%. For the optional SMS evaluation dataset our overall average F-score was 58.82%. The average between positive and negative Fscores was 50.11%.
Different Modes of Hydrogen Peroxide Action During Seed Germination

Hydrogen peroxide was initially recognized as a toxic molecule that causes damage at different levels of cell organization and thus losses in cell viability. From the 1990s, the role of hydrogen peroxide as a signaling molecule in plants has also been discussed. The beneficial role of H2O2 as a central hub integrating signaling network in response to biotic and abiotic stress and during developmental processes is now well established. Seed germination is the most pivotal phase of the plant life cycle, affecting plant growth and productivity. The function of hydrogen peroxide in seed germination and seed aging has been illustrated in numerous studies; however, the exact role of this molecule remains unknown. This review evaluates evidence that shows that H2O2 functions as a signaling molecule in seed physiology in accordance with the known biology and biochemistry of H2O2. The importance of crosstalk between hydrogen peroxide and a number of signaling molecules, including plant phytohormones such as abscisic acid, gibberellins, and ethylene, and reactive molecules such as nitric oxide and hydrogen sulfide acting on cell communication and signaling during seed germination, is highlighted. The current study also focuses on the detrimental effects of H2O2 on seed biology, i.e., seed aging that leads to a loss of germination efficiency. The dual nature of hydrogen peroxide as a toxic molecule on one hand and as a signal molecule on the other is made possible through the precise spatial and temporal control of its production and degradation. Levels of hydrogen peroxide in germinating seeds and young seedlings can be modulated via pre-sowing seed priming/conditioning. This rather simple method is shown to be a valuable tool for improving seed quality and for enhancing seed stress tolerance during post-priming germination. In this review, we outline how seed priming/conditioning affects the integrative role of hydrogen peroxide in seed germination and aging.
Chlamydia Regulation of T Cell Immunity against Involvement of LEK1 in Dendritic Cell

We investigated the hypothesis that the enhanced Ag-presenting function of IL-10-deﬁcient dendritic cells (DCs) is related to speciﬁc immunoregulatory cytoskeletal molecules expressed when exposed to Ags. We analyzed the role of a prominent cytoskeletal protein, LEK1, in the immunoregulation of DC functions; speciﬁcally cytokine secretion, costimulatory molecule expression, and T cell activation against Chlamydia . Targeted knockdown of LEK1 expression using speciﬁc antisense oligonucleotides resulted in the rapid maturation of Chlamydia -exposed DCs as measured by FACS analysis of key activation markers (i.e., CD14, CD40, CD54, CD80, CD86, CD197, CD205, and MHC class II). The secretion of mostly Th1 cytokines and chemokines (IL-1a, IL-9, IL-12, MIP-1a, and GM-CSF but not IL-4 and IL-10) was also enhanced by blocking of LEK1. The function of LEK1 in DC regulation involves cytoskeletal changes, since the dynamics of expression of vimentin and actin, key proteins of the cellular cytoskeleton, were altered after exposure of LEK1 knockdown DCs to Chlamydia. Furthermore, targeted inhibition of LEK1 expression resulted in the enhancement of the immunostimulatory capacity of DCs for T cell activation against Chlamydia . Thus, LEK1 knockdown DCs activated immune T cells at least 10-fold over untreated DCs. These results suggest that the effect of IL-10 deﬁciency is mediated through LEK1-related events that lead to rapid maturation of DCs and acquisition
A natural killer cell receptor specific for a major histocompatibility complex class I molecule

Target cell expression of major histocompatibility complex (MHC) class I molecules correlates with resistance to lysis by natural killer (NK) cells. Prior functional studies of the murine NK cell surface molecule, Ly-49, suggested its role in downregulating NK cell cytotoxicity by specifically interacting with target cell H-2Dd molecules. In support of this hypothesis, we now demonstrate a physical interaction between H- 2Dd and Ly-49 in both qualitative and quantitative cell-cell binding assays employing a stable transfected Chinese hamster ovary (CHO) cell line expressing Ly-49 and MHC class I transfected target cells. Binding occurred only when CHO cells expressed Ly-49 at high levels and targets expressed H-2Dd by transfection. Monoclonal antibody blocking experiments confirmed this interaction. These studies indicate that the specificity of natural killing is influenced by NK cell receptors that engage target cell MHC class I molecules.
Asymptotic Orthogonality Analysis of Time-Domain Sparse Massive MIMO Channels

The theoretical analysis of downlink massive MIMO usually assumes the ideal Gaussian channel matrix with asymptotic orthogonality of channel vectors. Meanwhile, recent experiments have shown that massive MIMO channels between a certain user and massive base station antennas appear the spatial common sparsity (SCS) in both the time domain and angle domain. This motivates us to investigate whether realistic sparse massive MIMO channels could provide the favorable propagation condition, and reveal the capacity gap between massive MIMO systems over realistic sparse channels and that under the ideal Gaussian channel matrix assumption. This letter theoretically proves that channel vectors associated with different users in massive MIMO over sparse channels satisfy the asymptotic orthogonality. Moreover, the simulation results confirm the theoretical analysis.
INFERENCE TO THE BEST INDUCTIVE PRACTICES

Harman and Kulkarni (2007) provide a rigorous and informative discussion of reliable reasoning, drawing philosophical conclusions from the elegant formal results of statistical learning theory. They have presented a strong case that statistical learning theory is highly relevant to issues in philosophy and psychology concerning inductive inferences. Although I agree with their general thrust, I want to take issue with some of the philosophical and psychological conclusions they reach. I will first discuss the general problem of assessing norms and propose a metanormative decision procedure that can then be applied to the assessment of inductive methods. In particular, I will apply it to the assessment of the method of inference that Harman and Kulkarni call transduction. METANORMATIVITY Philosophy is inherently normative in that it is concerned not just with how people do think and act, but also with how they ought to think and act. Normative issues arise in general epistemological deliberations about how people should make inferences and in general ethical deliberations about the morality of kinds of actions. But normative issues also abound in much more specific practices, for example concerning how scientists should use theories and experiments to investigate the world and how doctors should decide what medical treatments are best. By a norm I mean a practice that ought to be followed in a particular domain. In this sense, Harman and Kulkarni can be viewed as working to help establish norms for inductive inference. By metanormativity I mean the meta-level consideration of how norms ought to be established (Hardy-Valee and Thagard, 2008). In contemporary philosophy, the most popular approach to metanormativity is a method that John Rawls (1971) called reflective equilibrium, which involves an ongoing process modifying general principles and particular judgments to better accord with each
Classifier Grouping to Enhance Data Locality for a Multi-threaded Object Detection Algorithm

Object detection has become an enabling function for modern smart embedded devices to perform intelligent applications and interact with the environment appropriately and promptly. However, the limited computation resource of embedded devices has become a barrier to execute the computation intensive object detection algorithm. Leveraging the multi-threading scheme on embedded multi-core systems provides an opportunity to boost the performance. However, the memory bottleneck limits the performance scalability. Improving data locality of applications and maximizing the data reuse for on-chip caches have therefore become critical design concerns. This paper comprehensively analyzes the memory behavior and data locality of a multi-threaded object detection algorithm. A novel Classifier-Grouping scheme is proposed to significantly enhance the data reuse for on-chip caches of embedded multicore systems. By executing a multi-threaded object detection algorithm on a cycle-accurate multi-core simulator, the proposed approach can achieve up to 62% better performance when compared with the original parallel program.
Mapping mixed-criticality applications on multi-core architectures

A common trend in real-time embedded systems is to integrate multiple applications on a single platform. Such systems are known as mixed-criticality (MC) systems when the applications are characterized by different criticality levels. Nowadays, multicore platforms are promoted due to cost and performance benefits. However, certification of multicore MC systems is challenging as concurrently executed applications of different criticalities may block each other when accessing shared platform resources. Most of the existing research on multicore MC scheduling ignores the effects of resource sharing on the response times of applications. Recently, a MC scheduling strategy was proposed, which explicitly accounts for these effects. This paper discusses how to combine this policy with an optimization method for the partitioning of tasks to cores as well as the static mapping of memory blocks, i.e., task data and communication buffers, to the banks of a shared memory architecture. Optimization is performed at design time targeting at minimizing the worst-case response times of tasks and achieving efficient resource utilization. The proposed optimization method is evaluated using an industrial application.
Query-performance prediction and cluster ranking: two sides of the same coin

We show that two tasks which were independently addressed in the information retrieval literature actually amount to the exact same task. The first is query performance prediction; i.e., estimating the effectiveness of a search performed in response to a query in the absence of relevance judgments. The second task is cluster ranking, that is, ranking clusters of similar documents by their presumed effectiveness (i.e., relevance) with respect to the query. Furthermore, we show that several state-of-the-art methods that were independently devised for each of the two tasks are based on the same principles. Finally, we empirically demonstrate that using insights gained in work on query-performance prediction can help, in many cases, to improve the performance of a previously proposed cluster ranking method.
Total least squares for anomalous change detection

A family of subtraction-based anomalous change detection algorithms is derived from a total least squares (TLSQ) framework. This provides an alternative to the well-known chronochrome algorithm, which is derived from ordinary least squares. In both cases, the most anomalous changes are identified with the pixels that exhibit the largest residuals with respect to the regression of the two images against each other. The family of TLSQbased anomalous change detectors is shown to be equivalent to the subspace RX formulation for straight anomaly detection, but applied to the stacked space. However, this family is not invariant to linear coordinate transforms. On the other hand, whitened TLSQ is coordinate invariant, and special cases of it are equivalent to canonical correlation analysis and optimized covariance equalization. What whitened TLSQ offers is a generalization of these algorithms with the potential for better performance.
Detection of Toxoplasma gondii in Diabetic Patients Using the Nested PCR Assay via RE and B1 Genes

Background Toxoplasma gondii is an obligate intracellular protozoan parasite that exists worldwide. Various techniques have been developed for T. gondii detection. Objectives The aim of this study was the detection of T. gondii in diabetic patients with RE and B1 genes and the comparison of these two genes for diagnosis using the nested-PCR assay method. Patients and Methods DNA samples from 205 diabetic patients who had been referred to the diabetes center of Ali Asghar hospital in Zahedan, Iran, were collected and analyzed using the nested-PCR assay method. Toxoplasma antibody data gathered using the enzyme-linked immunosorbent assay (ELISA) method from a previous study was used to group patients. The data were analyzed using SPSS 18. The chi-square test was used for comparison. Results Of the diabetic patients selected, the following results were obtained: 53 (IgG+, IgM+); 20 (IgG-, IgM+); 72 (IgG+, IgM-); and 60 (IgG-, IgM-). The nested-PCR detected the following: in the acute group, 21/53 (39.63%), 30/53 (56.60%) (IgM+, IgG+); in the chronic group, 40/72 (55.56%), 51/72 (70.83%), (IgG+, IgM-); in the false positive group, 18/20 (90%), 17/20 (85%) (IgM+, IgG-); and sero-negative samples of 38/60 (63.33%) and 60/ 41 (77.35%) for RE and B1 genes, respectively. The prevalence of toxoplasmosis showed positive in patients with diabetes in the B1 gene 139 (67.8%) and RE gene 117 (57.1%). Conclusions Our study demonstrated that the B1 gene, more so than the RE gene, showed positive samples and can be used to detect toxoplasmosis, although the B1 gene, in comparison to the RE gene, did not show any superiority of molecular diagnosing capability. Results also showed that toxoplasma molecular detection methods can be used instead of routine serological detection methods in a clinical laboratory testing.
LED lighting development for intelligent clothing

LED lighting is applied to clothing. It allows a flexible method to provide illumination of lighting. A family of converter topologies is proposed to give this simply method of power conversion. Analysis for the circuits in terms of output power, efficiency and general operation are provided. Two types of LED lighting clothing are developed. One is using super-bright LED embedded on the cloth. This gives active lighting instead of passive lighting. Another one is using optical fibre with grating to form lighting pattern.
Faster recovery without the use of a tourniquet in total knee arthroplasty

Background and purpose— Tourniquet application is still a common practice in total knee arthroplasty (TKA) surgery despite being associated with several adverse effects. We evaluated the effects of tourniquet use on functional and clinical outcome and on knee range of motion (ROM). Patients and methods— 70 patients who underwent TKA were randomized into a tourniquet group (n = 35) and a non-tourniquet group (n = 35). All operations were performed by the same surgeon and follow-up was for 1 year. Primary outcomes were functional and clinical outcomes, as evaluated by KOOS and knee ROM. Secondary outcomes were intraoperative blood loss, surgical time and visibility, postoperative pain, analgesic consumption, and transfusion requirements. Results— Patients in the non-tourniquet group showed a better outcome in all KOOS subscores and better early knee ROM from surgery to week 8. No difference was detected at the 6- and 12-month follow-ups. Postoperative pain and analgesic consumption were less when a tourniquet was not used. Surgical time and visibility were similar between groups. Intraoperative blood loss was greater when not using a tourniquet, but no postoperative transfusions were required. Interpretation— This study shows that TKA without the use of a tourniquet results in faster recovery in terms of better functional outcome and improved knee ROM. Furthermore, reduced pain and analgesic use were registered and no intraoperative difficulties were encountered.
Business Contracts for B2B

This paper presents an approach for the specification and implementation of business contracts needed for Business-to-Business (B2B) services. We first examine typical elements of business contracts and their usage. This analysis sets a foundation for 1) modeling contracts and 2) developing a role-based architecture that supports typical operations in the contract’s lifetime. We explore how contracts can be encoded in XML and present an approach for monitoring and enforcing of contracts. This approach provides a flexible way of modifying rules of enforcement, as trading arrangements change. A real-world contract example is used to illustrate the concepts described.
Integral Field Unit Observations of NGC 4302: Kinematics of the Diffuse Ionized Gas Halo

We present moderate-resolution spectroscopy of extraplanar diffuse ionized gas (EDIG) emission in the edge-on spiral galaxy NGC 4302. The spectra were obtained with the SparsePak integral field unit (IFU) at the WIYN Observatory. The wavelength coverage of the observations includes the [N II] λ6548, 6583, Hα, and [S II] λ6716, 6731 emission lines. The spatial coverage of the IFU includes the entirety of the EDIG emission noted in previous imaging studies of this galaxy. The spectra are used to construct position-velocity (PV) diagrams at several ranges of heights above the midplane. Azimuthal velocities are directly extracted from the PV diagrams using the envelope-tracing method and indicate an extremely steep drop-off in rotational velocity with increasing height, with magnitude ≈30 km s-1 kpc-1. We find evidence for a radial variation in the velocity gradient on the receding side. We have also performed artificial observations of galaxy models in an attempt to match the PV diagrams. The results of a statistical analysis also favor a gradient of ≈30 km s-1 kpc-1. We compare these results with an entirely ballistic model of disk-halo flow and find a strong dichotomy between the observed kinematics and those predicted by the model. The disagreement is worse than we have found for other galaxies in previous studies. The conclusions of this paper are compared to results for two other galaxies, NGC 5775 and NGC 891. We find that the vertical gradient in rotation speed, per unit EDIG scale height, for all three galaxies is consistent with a constant magnitude (within the errors) of approximately 15-25 km s-1 per scale height, independent of radius. This relationship is also true within the galaxy NGC 4302. We also discuss how the gradient depends on the distribution and morphology of the EDIG and the star formation rates of the galaxies, and consequences for the origin of the gas.