Miracle-dz/UniFAR-FTUs huggingface dataset:
The Respiratory Phenotype of Rodent Models of Amyotrophic Lateral Sclerosis and Spinocerebellar Ataxia

Amyotrophic lateral sclerosis (ALS) and spinocerebellar ataxia (SCA) are neurodegenerative disorders that result in progressive motor dysfunction and ultimately lead to respiratory failure. Rodent models of neurodegenerative disorders provide a means to study the respiratory motor unit pathology that results in respiratory failure. In addition, they are important for pre-clinical studies of novel therapies that improve breathing, quality of life, and survival. The goal of this review is to compare the respiratory phenotype of two neurodegenerative disorders that have different pathological origins, but similar physiological outcomes. Manuscripts reviewed were identified using specific search terms and exclusion criteria. We excluded manuscripts that investigated novel therapeutics and only included those manuscripts that describe the respiratory pathology. The ALS manuscripts describe pathology in respiratory physiology, the phrenic and hypoglossal motor units, respiratory neural control centers, and accessory respiratory muscles. The SCA rodent model manuscripts characterized pathology in overall respiratory function, phrenic motor units and hypoglossal motor neurons. Overall, a combination of pathology in the respiratory motor units and control centers contribute to devastating respiratory dysfunction.
Eosinophilic Airway Inflammation in Patients with Stable Biomass Smoke- versus Tobacco Smoke-Associated Chronic Obstructive Pulmonary Disease

Background. Chronic obstructive pulmonary disease (COPD) is an inflammatory disease with predominant involvement of neutrophils, macrophages and CD8+ lymphocytes. Eosinophilic airway inflammations are reported in stable state and during acute exacerbations of tobacco smoke-associated COPD (TS-COPD). Women exposed to biomass fuel smoke are known to have eosinophils in sputum. However, little is known about the sputum cellular inflammatory profile in biomass fuel smoke-associated COPD (BMS-COPD). We therefore aimed to compare the sputum cellular inflammatory profile in tobacco smoke- and biomass smoke-associated COPD. Methods. The study was conducted in a tertiary care hospital in Goa, India. A total of 113 patients with stable COPD reporting to the outpatient pulmonary clinic were recruited. All participants were ≥ 40 years of age. Sputum induction studies were performed by the method of Pizzichini et al. after baseline subject characterization. Significant eosinophilia was defined as induced sputum eosinophils ≥ 3%. Results. There were 85 TS-COPD and 28 BMS-COPD patients. The mean age [standard deviation (SD)] was 64.7 (7.8) and 63.0 years (8.3), p = 0.32 in TS and BMS-COPD, respectively. Eighteen subjects (21.1%) were female smokers. The smoking pack-year median [interquartile range (IQR)] was 36 (20, 58) and hour-years of biomass smoke exposure mean (SD) was 192.4 (61). The TS-COPD and BMS-COPD cases showed a post-bronchodilator forced expiratory volume in one second (FEV1%) mean (SD) of 57.9 (17.1), and 62.6 (19.4), p= 0.22, respectively. Both groups had similar symptoms and severity of disease. Induced sputum total cell count per gram of sputum × 106 mean (SD) was 3.05 (1.53) for TS-COPD, and 2.55(1.37) for BMS-COPD p=0.12. The neutrophils % mean (SD) was 86.4 (16.5) and 87.9 (10.2), p = 0.64; eosinophils % median (IQR) was 2.5 (1, 10) and 8 (2, 12.8), p = 0.07; lymphocytes % median (IQR) was 0 (0, 0.75) and 0 (0, 1) p = 0.13; macrophages % median (IQR) was 2.5 (0.75, 5.7) and 1 (0, 4.7) p = 0.13; and significant eosinophilia (eosinophils ≥3%) was 42 (49.4%) and 20 (71%), p=0.04, for TS-COPD and BMS-COPD, respectively. Conclusions. For similar severity of disease and clinical symptoms, significant eosinophilic inflammation was observed in stable BMS-COPD, while both groups had similar neutrophilic inflammation. Participant Consent. Obtained. Ethics Approval. The study was approved by the Institutional Ethics Committee of the Goa Medical College, Goa, India. Competing Interests. The authors declare no competing financial interests.
v 1 2 5 Se p 20 03 Risk trading , network topology , and banking regulation

In the context of understanding the nature of the risk transformation process of the financial system we propose an iterative risk-trading game between several agents who build their trading strategies based on a general utility setting. The game is studied numerically for different network topologies. Consequences of topology are shown for the wealth time-series of agents, for the safety and efficiency of various types of networks. The proposed setup allows an analysis of the effects of different approaches to banking regulation as currently suggested by the Basle Committee of Banking Supervision. We find a phase transition-like phenomenon, where the Basle parameter plays the role of temperature and system safety serves as the order parameter. This result suggests the existence of an optimal regulation parameter. As a consequence a tightening of the current regulatory framework does not necessarily lead to an improvement of the safety of the banking system. Moreover, we show that banking systems with local risk-sharing cooperations have higher global default rates than systems with low cyclicality. JEL: C73, G28 PACS: 89.65. Gh, 89.75. Hc, 89.65. -s, 02.50. Le, 05.40. -a
Differences in Inflammatory Breast Cancer Characteristics and Outcomes Between Caucasian and Hispanic Women in the US

Mortality from breast cancer has declined over time in the US, but breast cancer remains the second leading cause of cancer death among women overall and the leading cause of cancer death among Hispanic women. In the US, the proportion of inflammatory breast cancer cases ranges from 2% to 7%. Although inflammatory breast cancer (IBC) is considered to be the most aggressive form of breast cancer, only a small number of studies have attempted to analyze the characteristics and outcomes of inflammatory breast cancer among Hispanic Americans. Comparisons between racial and ethnic groups can propose different approach for research in cancer etiology, prevention, and outcomes and are necessary for determining inconsistencies in healthcare. The goal of this research was to use the Surveillance, Epidemiology, and End Results (SEER) database to evaluating racial disparities in IBC occurrence between Caucasian women and Hispanic women living in the US. I used SEER*Stat to compare the clinicopathologic characteristics and outcomes of Hispanic and Caucasian women between the ages of 20 and 79 years old who were diagnosed between 1992 and 2014. Age at diagnosis was similarly distributed between the two races until 35 years of age where an increase in rate was seen in Caucasian women compared to Hispanic women. Hispanic women displayed a lower incidence rate of IBC compared to Caucasian women. Caucasian and Hispanic women showed no significant differences in 5-year survival. Hispanic women also displayed a lower incidence rate of ER+ tumors compared to Caucasian. Caucasian women had higher incidence rate of PR+ tumors compared to Hispanic women. Understanding the
Parallelizing Simulations of LargeQuantum Circuits

We present a parallelization scheme for classical simulations of quantum circuits. Our scheme is based on a recent method to “cut” large quantum circuits into smaller sub-circuits that can be simulated independently, and whose simulation results can in turn be re-combined to infer the output of the original circuit. The exponentially smaller classical computing resources needed to simulate smaller circuits are counterbalanced by exponential overhead in terms of classical post-processing costs. We discuss how this overhead can be massively parallelized to reduce classical computing costs.
Influence of Knowledge Management Processes on Organizational Performance in Service Sector: An Empirical Study

This research attempts to establish a link between knowledge management processes and organizational performance in knowledge intensive service sectors. The key dimensions of knowledge management processes have been identified which could influence the organizational performance. Metric has been developed for the empirical investigation of the relationships between these research constructs. Structural Equation Modelling (SEM) using partial least square techniques has been used to test these hypotheses with a sample size of 491 knowledge workers (330 Higher educational institutions and 161 from the IT companies) to investigate the empirical relationships between the factors. All the four hypotheses were supported. The testing of the hypotheses justified the identification of the key dimensions of KM as the critical success factors in terms of the organizational performance. Implications of the study would enable the strategic planning managers to make their knowledge management processes more effective so as to enhance the organizational performance.
Deep Learning for Classification of the Chemical Composition of Particle Defects on Semiconductor Wafers

Manual classification of particle defects on semiconductor wafers is labor-intensive, which leads to slow solutions and longer learning curves on product failures while being prone to human error. This work explores the promise of deep learning for the classification of the chemical composition of these defects to reduce analysis time and inconsistencies due to human error, which in turn can result in systematic root cause analysis for sources of semiconductor defects. We investigate a deep convolutional neural network (CNN) for defect classification based on a combination of scanning electron microscopy (SEM) images and energy-dispersive x-ray (EDX) spectroscopy data. SEM images of sections of semiconductor wafers that contain particle defects are fed into a CNN in which the defects’ EDX spectroscopy data is merged directly with the CNN’s fully connected layer. The proposed CNN classifies the chemical composition of semiconductor wafer particle defects with an industrially pragmatic accuracy. We also demonstrate that merging spectral data with the CNN’s fully connected layer significantly improves classification performance over CNNs that only take either SEM image data or EDX spectral data as an input. The impact of training data collection and augmentation on CNN performance is explored and the promise of transfer learning for improving training speed and testing accuracy is investigated.
Title. Knowledge and participation rights as a predictor of intercultural empathy. A study of participation rights, knowledge and school diversity intercultural empathy among Norwegian and Danish school students

In recent decades Norwegian and Danish schools experience increased cultural diversification among their students. Growing heterogeneity in origin and culture may enhance the risk of students’ marginalization, segregation or exclusion. In response to these challenges, this study is theoretically framed in an inclusive education perspective. It focuses on how knowledge of diversity and participation rights in school predicts student’s intercultural empathy. The study draws on primarily quantitative data gathered on paper questionnaires from 895 students in two upper secondary schools in Denmark and two in Norway. First, knowledge and information about culture and diversity predicts certain aspects of intercultural empathy. Second, participation rights is a more moderate predictor of intercultural empathy. Third, we find that there are substantial differences between boys’ and girls’ scores on intercultural empathy. Fourth school diversity context matters for student’s intercultural empathy. The results and their implications for teaching are discussed.
NEW INTERMEDIATE QUATERNION BASED CONTROL OF SPACECRAFT: PART I – ALMOST GLOBAL ATTITUDE TRACKING

Euler-angle based and conventional quaternion based methods have been extensively employed for spacecraft attitude control. However, the first method suffers from singularity that prohibits large orientation maneuvers, while the second exhibits ambiguity and unwinding phenomena. This work attempts to circumvent these undesirable features in spacecraft attitude control by introducing a simple notion of an intermediate 4-element variable (quaternion) that possesses several beneficial features, among which is the fact that it can be determined uniquely and readily for any attitude spacecraft orientation. This attribute allows for the development of a new attitude control free of ambiguity and unwinding associated with the conventional quaternion based method. Furthermore, since the rate of the proposed intermediate variable is bounded with bounded angular velocity, singularity inherent in the Euler-angle based method is avoided here. These attractive features are theoretically authenticated and numerically verified.
Memory-Based Robotic Motion Primitive Learning for Kicking and Striking Tasks

Robots that can reliably perform manipulation tasks in unstructured environments are vital to fully realize the promise of robotic space exploration, such as orbital satellite repair, assisting astronauts during extra–vehicular activity, or planetary exploration. Dexterous robotic manipulation tasks are among the most challenging robotic motor tasks; for robotic spacecraft, the inherent challenges in dexterous manipulation are even more daunting due to the limited computational power and long communications latency inherent in spacecraft missions. Treating dexterous manipulation as a machine learning problem would allow robots to perform such tasks autonomously with minimal computation. Thus, the application of machine learning techniques to learning motion primitives is an important research area for the development of robotic systems. The primary challenge to applying machine learning to dexterous manipulation is the curse of dimensionality, which appears to require intractably long training periods due to the relatively high dimensionality of such tasks. In this paper we describe an associative memory technique for learning motion primitives that has shown to be remarkably efficient at kicking and striking tasks. We demonstrate the efficacy of this algorithm both by simulation and through hardware trials of learning to kick soccer balls into a goal. We also describe the role that training regimens – the order in which the control manifold for a motion primitive is sampled by the learning system – play in the convergence and rate of learning for our technique. We show that appropriately designed regimens can allow a robotic system to learn a high degree of freedom kicking task with remarkably few trials. We demonstrate the role that randomness plays in the convergence properties of this algorithm, and discuss directions for future work that incorporate ideas from the optimal design of experiments literature that may allow such algorithms to learn complex control manifolds using optimally few trials.
A Machine Learning Approach for Power Gating the FPGA Routing Network

Power gating is a common approach for reducing circuit static power consumption. In FPGAs, resources that dominate static power consumption lie in the routing network. Researchers have proposed several heuristics for clustering multiplexers in routing network into power-gating regions. In this paper, we propose a fundamentally different approach based on K-means clustering, an algorithm commonly used in machine learning. Experimental results on Titan benchmarks and Stratix-IV FPGA architecture show that our proposed clustering algorithms outperform the state of the art. For example, for 32 power-gating regions in FPGA routing switch matrices, we achieve (on average) almost 1.4× higher savings (37.48% vs. 26.94%) in the static power consumption of the FPGA routing resources at lower area overhead than the most efficient heuristic published so far.
Klassische Homöopathie bei der Mastitis des Rindes

Zusammenfassung Vor dem Hintergrund den Einsatz von Antibiotika in der Nutztierhaltung zu reduzieren, gewinnt die Anwendung von Homöopathika als Alternative bei der Behandlung von bovinen Euterentzündungen an Bedeutung. Der vorliegende Übersichtsartikel stellt Argumente für und wider den Einsatz von Homöopathika dar. Weiterhin werden Studien zur homöopathischen Mastitistherapie vorgestellt, die Schlussfolgerungen in Bezug auf die Voraussetzungen erlauben, die vor der Anwendung von Homöopathika zu erfüllen sind. Auf deren Basis wurde ein Entscheidungsbaum zur sachgerechten Vorgehensweise bei der Behandlung von klinischen Mastitiden unter Anwendung der klassischen Homöopathie erarbeitet, der eine konsequente Diagnostik in den Vordergrund stellt.
A Lesson from Indonesia: Implementing 2006 and 2013 English Curriculum

It is widely assumed that there should be one curriculum in a country to set the standards guiding the teaching and learning process. In Indonesia, however, there are two national curricula implemented at the same time, namely Curriculum 2006 and Curriculum 2013. Given the lack of studies investigating this issue, this article presents a comparative study of these two curricula focusing on English subject in the senior high school context in Indonesia. It aims at examining the distinction between 2006 English Curriculum and 2013 English Curriculum implemented and their implications for English teaching and learning practice. It is found that these two curricula are distinctive in terms of the development, underlying principles, influential factors, both stated and unstated purposes, pedagogy, and assessment carried out. The discussion concludes with a presentation of the idea of the implementation of CORE pedagogy and authentic assessment to reform the education system as well as to address the most urgent educational issue.
SAT0180 Subclinical Inflammation in Rheumatoid Arthritis (RA) in Clinical Remission, Lack of Association between Cytokines Level and Ultrasound-Defined Synovitis

Background The presence of subclinical inflammation promotes the progression of morphostructural joint damage in RA patients. Currently, clinical remission is based on the count of swollen and painful joints associated with acute phase reactants. Adding an image technique like grey scale (GS) US and power Doppler (PD) increases the capability to detect active synovitis. There are no studies on the association between active synovitis as defined by PD and more specific markers of inflammation like cytokines in patients with RA in clinical remission Objectives To identify subclinical synovitis in patients with RA in clinical remission using US and to characterize the biological profile of this group of patients by determining inflammatory cytokines Methods RA patients in clinical remission according to ACR/EULAR criteria were enrolled. Clinical evaluation was performed and DAS28 calculated. Cytokines (IL1b, 10, 6. 5, 2, 4, 8, GMCS, TNFa, IFNg,) were determined using ELISA kit (Human ultrasensitive cytokine 10 -plex panel [Invitrogen, CA, USA]). GS and PD ultrasound was assessed in 7 joints according to Backhaus et al (2). Esaote MyLab ® 25 ultrasound machine with 12-18 MHz linear probe was used. Results 21 patients were included. Clinical and demographic data are shown in the table. N=21 Age (years) 44.81 (25–69) Disease duration (months) 100.76 (28–353) Duration in clinical remission (months) 12.15 (1–43) DAS28 1.35 (0.99–2.42) Rheumatoid factor (positive) 95% (n=20) antiCCP (positive) 52% (n=11) GS synovitis was found in 95%, and tenosynovitis in 9.5%. PD was detected in 57%. There was no association between DAS28 and duration in clinical remission, disease duration and US-defined active synovitis (p=0.84, 0.309 and 0.762 respectively). Pro-inflammatory cytokines levels were elevated in patients with less than 6 months and in those with 13 to 24 months in clinical remission. Nevertheless, there was no correlation with US-defined synovitis (p=NS) Conclusions US detected a high percentage of subclinical synovitis in RA patients in clinical remission. Cytokine levels did not correlate with DAS 28 or ultrasound-defined active synovitis. References Brown AK, Conaghan P, Karim Z, et al. An explanation for the apparent dissociation between clinical remission and continues structural deterioration in rheumatoid arthritis. Arthritis Rheum 2008; 58(10): 2958-2967 Backhaus M, Ohrndorf S, Kellner H, et al. Evaluation of a novel 7-joint ultrasound score in daily rheumatologic practice: a pilot project. Arthritis Rheum 2009; 61(9): 1194-1201 Disclosure of Interest None declared DOI 10.1136/annrheumdis-2014-eular.4855
Vandalism detection in crowdsourced knowledge bases

Vandalism Detection in Crowdsourced Knowledge Bases Information systems, such as question answering systems and web search engines, increasingly rely on crowdsourced knowledge bases to answer questions and to display important information about entities. While crowdsourcing enables the collection of vast amounts of information, it also brings along the problem of vandalism and damaging contributions. In this thesis, we focus onWikidata, the largest structured, crowdsourced knowledge base on the web, and develop novel machine learning-based vandalism detectors to reduce the manual reviewing effort. To this end, we carefully develop large-scale vandalism corpora, vandalism detectors with high predictive performance, and vandalism detectors with low bias against certain groups of editors. We extensively evaluate our vandalism detectors in a number of settings, and we compare them to the state of the art represented by the Wikidata Abuse Filter and the Objective Revision Evaluation Service by the Wikimedia Foundation. Our best vandalism detector achieves an area under the curve of the receiver operating characteristics of 0.991, significantly outperforming the state of the art; our fairest vandalism detector achieves a bias ratio of only 5.6 compared to values of up to 310.7 of previous vandalism detectors. Overall, our vandalism detectors enable a conscious trade-off between predictive performance and bias and they might play an important role towards a more accurate and welcoming web in times of fake news and biased AI systems.
Breaking the Curse of Dimensionality (Locally) to Accelerate Conditional Gradients

Conditional gradient methods constitute a class of first order algorithms for solving smooth convex optimization problems that are projection-free and numerically competitive. We present the Locally Accelerated Conditional Gradients algorithmic framework that relaxes the projection-freeness requirement to only require projection onto (typically low-dimensional) simplices and mixes accelerated steps with conditional gradient steps to achieve dimensionindependent local acceleration for smooth strongly convex functions. We prove that the introduced class of methods attains the asymptotically optimal convergence rate. Our theoretical results are supported by numerical experiments that demonstrate a speed-up both in wall-clock time and in per-iteration progress when compared to state-of-the-art conditional gradient variants.