Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of heart-reactive antibody in antisera and hybridoma culture fluids against group A streptococci

Heart-reactive antibody is known to be produced in animals immunized with group A streptococci. However, the detection and quantitation of these antibodies and their respective antigens have been limited to immunofluorescence or immunoprecipitation tests. In this study a more sensitive technique was used to detect heart-reactive antibody, i.e., the enzyme-linked immunosorbent assay (ELISA). Countercurrent immunoelectrophoresis and agar gel immunodiffusion were also used to confirm the reactivity of rabbit antisera to group A streptococci and human heart tissue. Human heart tissue antigen was prepared by Triton X-100 extraction, and checkerboard titrations of heart tissue antigen versus streptococcal antisera revealed optimal concentrations of each for the ELISA. When immune rabbit serum was reacted with heart tissue antigen, a 5- to 10-fold increase was observed over the reactions of preimmune sera controls. Streptococcal antiserum was found to have a six- to eightfold greater reactivity with heart tissue antigen than with similar concentrations of kidney and skeletal muscle antigens. Heart tissue antigen was partially purified by DEAE-cellulose chromatography, and quantities of greater than 10 to 100 ng (dry weight) were shown to react with streptococcal antisera in the ELISA. Heart, kidney, and skeletal muscle antigens were subjected to electrophoresis in polyacrylamide gel slabs and blotted onto a nitrocellulose filter. These filter blots reacted with streptococcal antisera and confirmed the tissue antigen specificity observed with the ELISA. In addition, the ELISA was found to be an effective method for the detection of heart-reactive antibodies produced by murine hybridomas that were producing antibody to group A streptococci.
Identification and Estimation of the Linear-in-Means Model of Social Interactions1

This paper studies identification and estimation of the linear-in-means model of social interactions. The paper emphasizes the different information contained in the betweensocial-group and within-social-group variation of the data. Different identifying assumptions can then be used to deliver exogenous sources of betweenand within-group variation. We provide specific identification and estimation results for grouped cross-section (GCS), repeated grouped cross-section (RGCS), grouped panel (GP), and time series data. Our unifying framework can be used to reinterpret several well-known papers in the social interactions literature and should also facilitate future research.
Neural stem cell replacement: a possible therapy for neurodevelopmental disorders?

Neurodevelopmental disorders are characterized by an abnormal development of the central nervous system, leading to a myriad of symptoms and diseases, including intellectual disability, attention deficits, impairments in learning and memory, speech disorders and repetitive behavior (Telias and Ben-Yosef, 2014). Common major neurodevelopmental disorders include autism and autism spectrum disorders (ASDs), fragile X syndrome (FXS), Down syndrome (DS), and Rett syndrome (RTT). They can be collectively described as disorders in which the plasticity of the brain has been severely impaired. The concept of plasticity refers to the brain's ability to adapt to and process new information and react accordingly, and it can be classified into three categories: a) molecular plasticity, whenever specific receptors, ion channels, enzymes, neurotransmitters or other molecules that participate in neuronal function undergo up- or down-regulation in response to electrochemical inputs or outputs; b) cellular plasticity, when dendrites and axons grow or retract new spines and terminals to develop or eliminate connections within the neuronal network; c) tissue plasticity, when resident neural stem cells (NSCs) in the adult brain differentiate asymmetrically and produce new neurons and glia, which migrate and replenish brain areas in which neurons had died.
Corynebacterium nigricans sp. nov.: Proposed Name for a Black-Pigmented Corynebacterium Species Recovered from the Human Female Urogenital Tract

ABSTRACT Six independent isolates of an unusual black-pigmented Corynebacterium species (strains CN-1, CN-2, CN-3415, W70124, 91-0032, and 92-0360) were recovered from the human female urogenital tract. Four of the six source patients had complications of pregnancy, including spontaneous abortion, preterm labor, and low amniotic fluid volume at the time of the pathogen isolation. One isolate was recovered from a vaginal ulcer. All six strains yielded black-pigmented colonies on sheep blood agar, chocolate agar, and colistin-nalidixic acid agar after 24 to 48 h of incubation at 35°C. The dry, adherent colonies pitted the agar surface. The cells were coccobacillary to rod-shaped, catalase positive, nonmotile, and nonlipophilic. Only five of six isolates were available for characterization. Biochemical and chemotaxonomic studies revealed that the strains belong to the genus Corynebacterium but differ from known corynebacterial species. Comparative 16S rRNA gene sequence analysis showed that the strains are closely related and form a new subline within the genus Corynebacterium. We propose the name Corynebacterium nigricans sp. nov. for this group of coryneforms. The type strain of Corynebacterium nigricans is CN-1. It is deposited in the American Type Culture Collection (assigned strain number ATCC 700975) and in the Institute Pasteur collection (assigned strain number CIP 107346).
Effect Sizes for Interpreting Changes in Health Status

Health status measures are being used with increasing frequency in clinical research. Up to now the emphasis has been on the reliability and validity of these measures. Less attention has been given to the sensitivity of these measures for detecting clinical change. As health status measures are applied more frequently in the clinical setting, we need a useful way to estimate and communicate whether particular changes in health status are clinically relevant. This report considers effect sizes as a useful way to interpret changes in health status. Effect sizes are defined as the mean change found in a variable divided by the standard deviation of that variable. Effect sizes are used to translate “the before and after changes” in a “one group” situation into a standard unit of measurement that will provide a clearer understanding of health status results. The utility of effect sizes is demonstrated from four different perspectives using three health status data sets derived from arthritis populations administered the Arthritis Impact Measurement Scales (AIMS). The first perspective shows how general and instrument-specific benchmarks can be developed and how they can be used to translate the meaning of clinical change. The second perspective shows how effect sizes can be used to compare traditional clinical measures with health status measures in a standard clinical drug trial. The third application demonstrates the use of effect sizes when comparing two drugs tested in separate drug trials and shows how they can facilitate this type of comparison. Finally, our health status results show how effect sizes can supplement standard statistical testing to give a more complete and clinically relevant picture of health status change. We conclude that effect sizes are an important tool that will facilitate the use and interpretation of health status measures in clinical research in arthritis and other chronic diseases.
Marginalized CNN: Learning Deep Invariant Representations

Training a deep neural network usually requires sufficient annotated samples. The scarcity of supervision samples in practice thus becomes the major bottleneck on performance of the network. In this work, we propose a principled method to circumvent this difficulty through marginalizing all the possible transformations over samples, termed as marginalized Convolutional Neural Network (mCNN). mCNN implicitly considers infinitely many transformed copies of the training data in every training epoch and therefore is able to learn representations invariant for transformation in an end-to-end way. We prove that such marginalization can be understood as a classic CNN with a special form of regularization and thus is efficient for implementation and not restricted to the CNN module used. Experimental results on the MNIST and affNIST digit number datasets demonstrate that mCNN can match or outperform the original CNN with much fewer training samples. Besides, mCNN also performs well for face recognition on the recently released large-scale MS-Cele-1M dataset and outperforms state-of-thearts. Moreover, compared with the traditional CNNs which use data augmentation to improve their performance, the computational cost of mCNN is reduced by a factor of 26.
Deep reinforcement learning boosted by external knowledge

Recent improvements in deep reinforcement learning have allowed to solve problems in many 2D domains such as Atari games. However, in complex 3D environments, numerous learning episodes are required which may be too time consuming or even impossible especially in real-world scenarios. We present a new architecture to combine external knowledge and deep reinforcement learning using only visual input. A key concept of our system is augmenting image input by adding environment feature information and combining two sources of decision. We evaluate the performances of our method in a 3D partially-observable environment from the Microsoft Malmo platform. Experimental evaluation exhibits higher performance and faster learning compared to a single reinforcement learning model.
Time delay estimation via third-order cumulant

Time-delay is a very significant measurement in the field of non-destructive test. The accuracy of time-delay estimation (TDE) directly influences the precision and reliability of the relative measurement method used in non-destructive testing, such as leak location in underground pipeline leak test, ultrasonic blood velocity measurement, ultrasonic material stress measurement etc. In this paper, methods based on third-order cumulant are applied, including Cross Third-order Cumulant Method and Bispectrum Method, to ultrasonic time delay estimation. Higher-order cumulant can effectively suppress big noise interference. According to the principle that the third moment sequence of a zero-mean Gaussian process is identical to zero, we can use third-order cumulant to estimate time delay. Comparing with Generalized Cross-correlation Method, these methods can suppress the effect of correlated Gaussian noise sources and can be used as the substitution of Generalized Cross-correlation Method.
New Statistical And Syntactic Models For Machine Translation

machine translation (SMT) technology, which is currently considered the best way to perform MT of natural languages. The main goal of this thesis is to enhance the classical SMT models, introducing syntactical knowledge in the pre-translation step by reordering the source side of the corpus. To a great extent, our interest is in the value of syntax in reordering for languages with high word order disparity. A secondary objective consists of determining the potential of different language model (LM) enhancement techniques in order to improve the performance and efficiency of SMT systems. We start with a comprehensive study of the SMT state-of-the-art, describing the fundamental models underlying the translation process, along with a brief description of the main methods of automatic evaluation of translation quality. We emphasize phrase-based and N-gram-based SMT, analyzing the major differences between these two approaches. Subsequently, we concentrate on language modeling methods that have not received much attention in the SMT community. We report on experiments in applying N-gram-based SMT system adaptation to a speech transcription task, describe a positive impact of accurate cut-off threshold selection both on the model size and LM noisiness, and finally present a continuous-space LM, estimated in the form of an artificial neural network. Moreover, we propose a novel syntax-based approach to handle the fundamental problem of word ordering for SMT exploiting syntactic representations of source and target texts. The idea of augmenting SMT by using a syntax-based reordering step prior to translation, proposed in recent years, has been quite successful in improving translation quality, especially for translation between languages with high word order disparity. We provide the reader with a thorough study of the state-of-the-art reordering techniques and introduce a new classification of reordering algorithms for SMT. We then propose a new non-deterministic reordering strategy based on a syntactically augmented alignment of source and target texts and automatically extracted hierarchical reordering patterns. In the next step, we couple the novel reordering module with decoding in a deterministic way; our goal in this is to effectively tackle both global and local reordering dependencies. Finally, we propose a novel translation units blending scheme, combining bilingual tuples extracted from the parallel corpora with monotone and reordered source parts. The experiments are carried out on N-gram- and phrase-based SMT systems. We contrast the obtained results with the ones produced by the state-of-the-art reordering algorithms and demonstrate our methods' improvements over alternative distortion models. The major conclusion to be drawn from the thesis is that syntactic information is useful in handling global reordering, and it achieves better MT performance than the standard phrase-based and N-gram-based model.
Similaridad y evolución en la rítmica del flamenco: una incursión de la matemática computacional

presentamos un articulo que es singular por muchas razones. Por un lado, por la procedencia y caracteristicas variadas de sus autores (profesores de Matematica Aplicada de la Universidades de Sevilla y Politecnica de Madrid de Computacion de Queen's y McGill University, una concertista de piano de la Real Escuela Profesional de Danza de Madrid), incluyendo, entre ellos, la figura-senera en Geometria Discreta y Algoritmica- de Godfried Toussaint, que ha desarrollado, desde hace varios anos, una estrecha relacion con la pujante escuela espanoala de Geometria Computacional. Por otro lado, por la tematica elegida, el ritmo flamenco, a cuyo analisis se quiere contribuir (y permitaseme subrayar este termino: contribucion) aqui aportando determinadas herramientas matematicas. Quisiera, como editor de esta Seccion de LA GACETA, dar las gracias a los autores por su original contribucion y, tambien, a los recensores (cuyo nombre, desgraciadamente, no puedo revelar), que han desarrollado una labor critica y constructiva extraordinaria, en un tema tan alejado, aparentemente, de los conocimiento sde la mayoria de los matematicos de mi personal base de datos. Unos y otros me permiten constatar con orgullo que en Espana, hoy, es posible encontrar matematicos de primer nivel que son capaces de aportar comentarios autorizados sobre temas tan singulares e interesante como este.
Effect of sodium butyrate and zinc sulphate supplementation on recombinant human IFN-β production by mammalian cell culture

Recombinant human interferon-β1 (rhIFN-β1) glycoprotein is used as a therapeutic agent for a variety of diseases, such as multiple sclerosis and hepatitis. In the present work, different strategies were applied to produce rhIFN-β1a in mammalian cell cultures. Transfected population of CHO-K1, CHO dhfr - , BHK and HEK cells were compared for their ability to produce rhIFN-β1a, and clones of the most promising cell line (CHO-K1) were isolated by the limit dilution method. Likewise, different culture conditions were assayed by changing the amounts of fetal calf serum, sodium butyrate and/or ZnSO4, to improve cell productivity. The presence of each additive increased the rhIFN-β1a yield ranging from 2 to 8 times, depending on the tested cell clone, but when these components were simultaneously added to the medium, the rhIFN-β1a concentration in the supernatants was even greater.
Increasing knowledge of best practices for occupational therapists treating post-stroke unilateral spatial neglect: results of a knowledge-translation intervention study.

OBJECTIVE The aim of this study was to investigate: (i) the feasibility of delivering a multi-modal knowledge translation intervention specific to the management of acute post-stroke unilateral spatial neglect; and (ii) the impact of the knowledge translation intervention on occupational therapists' knowledge of evidence-based unilateral spatial neglect problem identification, assessment and treatment, and self-efficacy related to evidence-based practice implementation. DESIGN A 3-period (pre-post) repeated measures design. SUBJECTS Acute care occupational therapists treating patients with post-stroke unilateral spatial neglect were recruited from two major Canadian cities. METHODS Participants completed two pre-intervention assessments, took part in a day-long interactive multi-modal knowledge translation intervention and a subsequent 8-week follow-up, and completed a post-intervention assessment. Knowledge of evidence-based problem identification, assessment and treatment of unilateral spatial neglect, and self-efficacy to perform evidence-based practice activities were measured using standard scales. RESULTS The intervention was tested on 20 occupational therapists. Results indicate a significant improvement in knowledge of best practice unilateral spatial neglect management (p < 0.000) and evidence-based practice self-efficacy in carrying out evidence-based practice activities (p < 0.045) post-intervention. CONCLUSION Use of a multi-modal knowledge translation intervention is feasible and can significantly improve occupational therapists' knowledge of unilateral spatial neglect best practices and self-efficacy. The findings should help advance best practices specific to the management of post-stroke unilateral spatial neglect as well as informing knowledge translation studies in other areas of practice.
Proteomics of human cerebrospinal fluid – the good, the bad, and the ugly

The development of MALDI ESI in the late 1980s has revolutionized the biological sciences and facilitated the emergence of a new discipline called proteomics. Application of proteomics to human cerebrospinal fluid (CSF) has greatly hastened the advancement of characterizing the CSF proteome as well as revealing novel protein biomarkers that are diagnostic of various neurological diseases. While impressive progressions have been made in this field, it has become increasingly clear that proteomics results generated by various laboratories are highly variable. The underlying issues are vast, including limitations and complications with heterogeneity of patients/testing subjects, experimental design, sample processing, as well as current proteomics technology. Accordingly, this review not only summarizes the current status of characterization of the human CSF proteome and biomarker discovery for major neurodegenerative disorders, i.e., Alzheimer's disease and Parkinson's disease, but also addresses a few essential caveats involved in several steps of CSF proteomics that may contribute to the variable/contradicting results reported by different laboratories. The potential future directions of CSF proteomics are also discussed with this analysis.
Plasma rennin activity: Early indicator of renal injury in bilateral pelviureteric junction obstruction in children

Objective: The objective of the present study is to analyze the early indicators of renal injury in children with bilateral pelviuretric junction obstruction. Materials and Methods: We investigated 23 children, 46 kidney units who were diagnosed with bilateral pelvi-ureteric junction obstruction (PUJO) and underwent unilateral or bilateral pyeloplasty between January 2001 and December 2011. Ipsilateral kidney biopsy was performed during pyeloplasty. Kidney biopsy results were divided into three categories. Pre-operative investigation included ultrasonography with the Society of Fetal Urology (SFU) grading, plasma rennin activity (PRA) and differential renal function (DRF). Results: Out of 23 children there were 17 (73.9%) boys while 6 (26.1%) girls. Median age at operation was 35.4 months (range: 9-60 months). Unilateral pyeloplasty was performed in 14 (60.8%), simultaneous bilateral pyeloplasty in 2 (8.6%) and sequential bilateral pyeloplasty in 7 (30.4%). Conclusion: In bilateral PUJO where DRF and SFU grading of hydronephrosis did not correctly reflect renal injury, PRA showed a significant relationship with renal histopathologic grade and could be an early indicator of renal injury in bilateral PUJO.
Early Administration of Low-Dose Aspirin for the Prevention of Severe and Mild Preeclampsia: A Systematic Review and Meta-Analysis

Abstract Objective To determine whether early administration of aspirin prevents severe and mild preeclampsia. Study Design A systematic review and meta-analysis of randomized controlled trials were performed. Studies in which women were randomized at or before 16 weeks' gestation to low-dose aspirin versus placebo or no treatment were included. The outcomes of interest were severe preeclampsia and mild preeclampsia. Pooled relative risks with their 95% confidence intervals (CIs) were calculated. Results Among 7941 citations retrieved, 352 were completely reviewed and four studies (392 women) fulfilled the inclusion criteria and were analyzed. When compared with controls, aspirin started at ≤16 weeks was associated with a significant reduction in severe (relative risk: 0.22, 95% CI: 0.08 to 0.57) but not mild (relative risk: 0.81, 95% CI: 0.33 to 1.96) preeclampsia. Conclusion Low-dose aspirin initiated at or before 16 weeks reduces the risk of severe preeclampsia, but not mild preeclampsia.
Real-Time technology? Migration of traditional SCADA systems technology. Part1: Concepts & application recommendations

In this technical paper, concepts and applications in electrical systems are presented on two topics: a) the evolution of the operation of electrical systems and b) the optimization of a traditional SCADA system. Also, the terms related and usual in the community of the area of energy and electricity related to: systems of energy management, planning of electrical systems, intelligent networks and micro-grids are presented. The purpose of this paper in this Part 1 is to emphasize that electrical systems need to be updated and improved using tools that allow them to integrate real-time simulation and prediction. The operational reliability, nowadays has an important role in the advances and technological developments of electrical power systems. The contribution of this article is focused on related cases, in implementations of this solution to provide versatility to the electrical systems and their energy management.