Miracle-dz/UniFAR-FTUs huggingface dataset:
Perfect reconstruction FIR filter banks: some properties and factorizations

Perfect reconstruction finite impulse-response (FIR) filter banks are analyzed both in the z-transform and time domains, showing the alternatives between designs in the two domains. Various classes of perfect reconstruction schemes are indicated, and relations between previously known systems are given. Windowed modulated filter banks with low computational complexity and perfect reconstruction are shown. New factorizations of polyphase filter matrices leading in particular to linear-phase filters, are given. The computational complexity and the architecture of the new structures are indicated. >
Conserved features of germination and polarized cell growth: a few insights from a pollen-fern spore comparison.

BACKGROUND The germination of both pollen and fern spores results in the emergence of a cell-pollen tube from pollen, rhizoid from spore-that grows in a polar fashion, primarily at its apical end. In both of these tip-growing cells, the delivery of secretory vesicles to the growing end is guided in part by a calcium gradient, with calcium entering at the tip where it is most highly concentrated. The similarities between the two systems extend beyond tip-focused calcium gradients to encompass signalling pathways and elements including calmodulin, nitric oxide, annexins and Rop-GTPases. SCOPE AND AIMS This review is limited to those pathways and elements that function similarly in fern and pollen systems based on currently available evidence. The aim is to illustrate the common mechanisms by which tip growth occurs, facilitate further investigations into this area, and examine the implications for the evolutionarily conserved control of tip growth. CONCLUSIONS The interplay of calcium, nitric oxide and other effectors in both pollen and fern spores suggests certain signalling pathways became important regulators of germination and growth early in the evolution of land plants. Both large- and small-scale comparative genomic methods have shown to be promising in their ability to find new and relevant comparisons for further research. Cross-species comparisons may serve to speed up this process by highlighting both basic pathways and system-specific deviations.
Evaluation of acridine orange stain for detection of microorganisms in blood cultures

A pH 4.0 buffered solution of the fluorochrome acridine orange was used to stain samples of 2,704 blood cultures that failed to yield visible evidence of growth after 1 day of incubation. Results obtained by the staining method were compared with those obtained by aerobic and anaerobic subcultures simultaneously performed upon the same cultures. Of the 109 culture-positive blood specimens initially detected by the acridine orange and the subculture methods, 85 (78%) were detected by both acridine orange and subcultures techniques, 14 (12.8%) were detected by subculture alone, and 10 (9.2%) were detected by acridine orange alone. The differences between the subculture and acridine orange methods were not found to be statistically significant (P less than 0.1). The acridine orange method represents a rapid and inexpensive alternative to conventional subculture techniques for the detection of bacteria in blood cultures that fail to yield visible evidence of growth after 1 day of incubation.
DiabCare survey of diabetes management and complications in the Gulf countries

Aim: To describe the status of diabetes control and complications, and the quality of diabetes management in Saudi Arabia, Kuwait, and the United Arab Emirates, and to obtain an insight into the relationship between these factors. Methods: Patients with diabetes for>12 months were enrolled from specialist clinics and general hospitals. All available data from the patients’ medical files including patient demographics; glycemic, lipid, and blood pressure status; diabetes-related complications; and diabetes management were recorded in data collection forms and analyzed. Results: Overall, 1290 patients with diabetes were enrolled with a mean (±standard deviation) age of 49.4 ± 12.3 years and duration of diabetes of 8.7 ± 5.9 years. Glycemic control was poor: Mean glycated hemoglobin A1c of 8.3 ± 2.0%, fasting and postprandial plasma glucose levels of 155.9 ± 57.1 mg/dL (8.7 ± 3.2 mmol/L), and 218.2 ± 87.4 mg/dL (12.1 ± 4.9 mmol/L), respectively. Diabetes-related complications such as neuropathy (34.9% of patients), background retinopathy (29.9%), and cataract (14.1%) were common. Cardiovascular complications were reported in <10% of patients, and microalbuminuria was detected in 34.4% of patients. Oral antidiabetic drug (OAD) monotherapy (43.3%) was the most common treatment, followed by insulin + OADs (39.3%) and insulin monotherapy (17.6%). Conclusion: The status of diabetes care was found to be suboptimal. Further improvements in diabetes management are necessary to prevent or delay the development of diabetes-related complications.
Virtuoso: Video-based Intelligence for real-time tuning on SOCs

Efficient and adaptive computer vision systems have been proposed to make computer vision tasks, such as image classification and object detection, optimized for embedded or mobile devices. These solutions, quite recent in their origin, focus on optimizing the model (a deep neural network, DNN) or the system by designing an adaptive system with approximation knobs. In spite of several recent efforts, we show that existing solutions suffer from two major drawbacks. First, the system does not consider energy consumption of the models while making a decision on which model to run. Second, the evaluation does not consider the practical scenario of contention on the device, due to other co-resident workloads. In this work, we propose an efficient and adaptive video object detection system, Virtuoso, which is jointly optimized for accuracy, energy efficiency, and latency. Underlying Virtuoso is a multi-branch execution kernel that is capable of running at different operating points in the accuracy-energy-latency axes, and a lightweight runtime scheduler to select the best fit execution branch to satisfy the user requirement. To fairly compare with Virtuoso, we benchmark 15 state-of-the-art or widely used protocols, including Faster R-CNN (FRCNN), YOLO v3, SSD, EfficientDet, SELSA, MEGA, REPP, FastAdapt, and our in-house adaptive variants of FRCNN+, YOLO+, SSD+, and EfficientDet+ (our variants have enhanced efficiency for mobiles). With this comprehensive benchmark, Virtuoso has shown superiority to all the above protocols, leading the accuracy frontier at every efficiency level on NVIDIA Jetson mobile GPUs. Specifically, Virtuoso has achieved an accuracy of 63.9%, which is more than 10% higher than some of the popular object detection models, FRCNN at 51.1%, and YOLO at 49.5%.
Association of Serum Gamma-Glutamyltransferase With Arterial Stiffness in Established Coronary Artery Disease

Serum gamma-glutamyltransferase (GGT) has been reported to predict vascular risk. We enrolled 978 patients (507 men and 471 women) with established coronary artery disease (CAD). The GGT, brachial–ankle pulse wave velocity ([baPWV] to assess arterial stiffness), and conventional risk factors were evaluated. The means of baPWV tend to increase in both genders according to GGT tertiles. Body mass index, GGT, logarithmical (systolic blood pressure [LnSBP]), uric acid (UA), total bilirubin, Ln (cholinesterase), and Ln (total cholesterol) were correlated with baPWV in men in a multivariate model. However, only GGT, LnSBP, UA, and Ln (high-density lipoprotein cholesterol) were correlated with baPWV in women. The GGT was a significant determinant for increased baPWV both in men (β = 0.017; P < .001) and in women (β = 0.015; P < .001). In conclusion, GGT was independently associated with increased arterial stiffness both in men and in women with established CAD.
Web Pages Classification and Clustering by Means of Genetic Algorithms: A Variable Size Page Representing Approach

Arranging mass of data in related groups is an important way that helps us to decide about them better, clustering and classification are two efficient methods of grouping huge volume of data, most of clustering and classification methods that work on Web pages grouping problems, use fixed size vectors in their learning algorithm. In the real world of WWW this assumption is not reliable. In this paper we use genetic algorithm (GA) for classification and clustering, the algorithm works on variable size vectors. At the GA part we combined standard crossover and mutation operators with K-means algorithm, for improving diversity and correctness of results. By means of this method more accurate classes are achieved, and their subclasses are defined as clusters. This method shows more accurate results than fixed size methods, the accuracy rate is about 90.7%, and also overload of unnecessary elements in vectors is bypassed.
Bioinformatics and Drug Discovery

Bioinformatic analysis can not only accelerate drug target identification and drug candidate screening and refinement, but also facilitate characterization of side effects and predict drug resistance. High-throughput data such as genomic, epigenetic, genome architecture, cistromic, transcriptomic, proteomic, and ribosome profiling data have all made significant contribution to mechanism-based drug discovery and drug repurposing. Accumulation of protein and RNA structures, as well as development of homology modeling and protein structure simulation, coupled with large structure databases of small molecules and metabolites, paved the way for more realistic protein-ligand docking experiments and more informative virtual screening. I present the conceptual framework that drives the collection of these high-throughput data, summarize the utility and potential of mining these data in drug discovery, outline a few inherent limitations in data and software mining these data, point out news ways to refine analysis of these diverse types of data, and highlight commonly used software and databases relevant to drug discovery.
Molecular and cellular defects of skeletal muscle in an animal model of acute quadriplegic myopathy

Muscle denervation and concomitant high‐dose dexamethasone treatment in rodents produces characteristic pathologic features of severe muscle atrophy and selective myosin heavy filament (MyHC) depletion, identical to those seen in acute quadriplegic myopathy (AQM), also known as critical illness myopathy. We tested the hypothesis that defective pre‐translational processes contribute to the atrophy and selective MyHC depletion in this model. We examined the effects of combined glucocorticoid–denervation treatment on MyHC and actin mRNA populations; we also studied mRNA expression of the myogenic regulatory factors (MRFs), primary transcription factors for MyHC. Adult female rats were subjected to proximal sciatic denervation followed by high‐dose dexamethasone (DD) treatment (5 mg/kg body weight daily) for 7 days. Disease controls included rats treated with denervation alone (DN) or dexamethasone alone (DX). At 1 week the plantaris atrophied by ∼42% in DD muscles. DD treatment resulted in selective MyHC protein depletion; actin protein concentration was not significantly changed. Despite an increase in total RNA concentration in DN and DD muscles, MyHC and actin mRNA concentrations were significantly decreased in these muscles. MyHC mRNA showed a significantly more extensive depletion relative to actin mRNA in DD muscles. Glucocorticoid treatment did not influence a denervation‐induced increase in the mRNA expression of the MRFs. We conclude that a deleterious interaction between glucocorticoid and denervation treatments in skeletal muscle is responsible for pre‐translational defects that reduce actin and MyHC mRNA substrates in a disproportionate fashion. The resultant selective MyHC depletion contributes to the severe muscle atrophy. Muscle Nerve, 2006
Translational control in germ cell development: A role for the RNA‐binding proteins Musashi‐1 and Musashi‐2

Mammalian gametogenesis is a complex process involving specialised cell cycle progression and differentiation. As part of their differentiation, germ cells experience periods of transcriptional inactivation and chromatin inaccessibility whilst continuing to coordinate the correct temporal and spatial expression of genes required for continued development. To overcome these obstacles, mammalian germ cells express a wide variety of sequence‐specific RNA‐binding proteins, which assist in the translational control of many mRNA transcripts which are produced and stored during periods of high mRNA synthesis. In this review we focus on the Musashi family of RNA‐binding proteins, a highly conserved family of translational regulatory proteins whose recent identification in germ cells of Drosophila and Xenopus, as well as their well described role in processes such as cell cycle progression and stem cell identity, has led us to investigate the role of these proteins in mammalian germ cell development. © 2011 IUBMB IUBMB Life, 63(9): 678–685, 2011
Functional assessment of the mouse heart by MRI with a 1‐min acquisition

In vivo assessment of heart function in mice is important for basic and translational research in cardiology. MRI is an accurate tool for the investigation of the anatomy and function in the preclinical setting; however, the long scan duration limits its usage. We aimed to reduce the acquisition time of cine MRI to 1 min. We employed spatiotemporal compressed sensing and parallel imaging to accelerate retrospectively gated cine MRI. We compared the functional parameters derived from full and undersampled data in Cartesian and radial MRI by means of Bland–Altman plots. We found that the scan time for the whole heart could be reduced to 2 min with Cartesian sampling and to 1 min with radial sampling. Despite a reduction in the signal‐to‐noise ratio, the accuracy in the estimation of left and right ventricular volumes was preserved for all tested subjects. This method can be used to perform accurate functional MRI examinations in mice for high‐throughput phenotyping or translational studies. Copyright © 2014 John Wiley & Sons, Ltd.
Avaliação da estrutura e do desempenho do sistema tributário Brasileiro: Livro branco da tributação Brasileira

Este trabalho faz um diagnóstico atualizado do sistema tributário brasileiro para subsidiar os debates dos técnicos, das autoridades e dos parlamentares em torno de sua reforma. Isto é feito a partir do levantamento e sistematização de informações sobre a tributação no Brasil. O foco é na caracterização atualizada do sistema tributário - e menos importa sua evolução -, dentro dos limites de disponibilidade das estatísticas. A arrecadação tributária constitui o objeto central, na primeira parte do trabalho. Ele procura detalhar ao máximo a estrutura atual dessa arrecadação: são identificados cada imposto, taxa e contribuição que compõe a arrecadação, e os agrupa por esfera de governo que os coleta e que deles dispõe depois das transferências. Para os principais tributos (federais e ICMS estadual), é também feita uma abertura por modalidade de cobrança e por setor de atividade econômica. Uma segunda abordagem do trabalho é uma a coletânea dos pontos mais polêmicos ou mais citados na agenda de debate tributário - tais como as questões da competitividade, da equidade e da divisão federativa de recursos. A proposta é consolidar a literatura sobre tais temas, com ênfase no levantamento estatístico, das fontes disponíveis.
Recovering Drawing Order of Single-Stroke Handwritten Images Using Probabilistic Tabu Search

This paper considers the problem for recovering a drawing order of static handwritten images with single stroke. Such a stroke may include the so-called double-traced lines (D-lines). The problem is analyzed and solved by employing the graph theoretic approach. Then the central issue is to obtain the smoothest path of stroke from a graph model of input handwritten images. First, the graph model is constructed from an input images by image processing techniques. Then, we locally analyze the structure of graph. In particular, the method to identify D-lines is developed by introducing the idea of 'D-line index'. The method enables us to transform any graphs with D-lines to semi-Eulerian graphs. Then, the restoration problem reduces to the problem of globally computing the maximum weight collection of perfect matchings. For solving such a problem, we propose a method using a probabilistic tabu search algorithm. The effectiveness and usefulness of the proposed method are examined by some experimental studies.
Making an “Attitude Adjustment”: Using a Simulation-Enhanced Interprofessional Education Strategy to Improve Attitudes Toward Teamwork and Communication

Introduction Health care providers must effectively function in highly skilled teams in a collaborative manner, but there are few interprofessional training strategies in place. Interprofessional education (IPE) using simulation technology has gained popularity to address this need because of its inherent ability to impact learners’ cognitive frames and promote peer-to-peer dialog. Provider attitudes toward teamwork have been directly linked to the quality of patient care. Investigators implemented a simulation-enhanced IPE intervention to improve staff attitudes toward teamwork and interprofessional communication in the emergency department setting. Methods The 3-hour course consisted of a didactic session highlighting teamwork and communication strategies, 2 simulation scenarios on septic shock and cardiac arrest, and structured debriefing directed at impacting participant attitudes to teamwork and communication. This was a survey-based observational study. We used the TeamSTEPPS Teamwork Attitudes Questionnaire immediately before and after the session as a measurement of attitude change as well as the Hospital Survey on Patient Safety Culture before the session and 1 year after the intervention for program impact at the behavior level. Results Seventy-two emergency department nurses and resident physicians participated in the course from July to September 2012. Of the 5 constructs in TeamSTEPPS Teamwork Attitudes Questionnaire, 4 had a significant improvement in scores—6.4%, 2.8%, 4.0%, and 4.0% for team structure, leadership, situation monitoring, and mutual support, respectively (P < 0.0001, P = 0.029, P = 0.014, and P = 0.003, respectively). For Hospital Survey on Patient Safety Culture, 3 of 6 composites directly related to teamwork and communication showed a significant improvement—20.6%, 20.5%, and 23.9%, for frequency of event reporting, teamwork within hospital units, and hospital handoffs and transitions, respectively (P = 0.028, P = 0.035, and P = 0.024, respectively). Conclusions A simulation-enhanced IPE curriculum was successful in improving participant attitudes toward teamwork and components of patient safety culture related to teamwork and communication.
A Robust and Alternative Approach to Zero Frequency Filtering Method for Epoch Extraction

During production of voiced speech, there exists impulse-like excitations due to abrupt closure of vocal folds. These impulselike excitations are often referred as epochs or glottal closure instants (GCIs). The zero frequency filtering (ZFF) method exploits the properties of impulse-like excitation by passing a speech signal through the resonator whose pole pair is located at 0 Hz. As the resonator is unstable, the polynomial growth/decay is observed in the filtered signal, thus requiring a trend removal operation. It is observed that the length of the window for trend removal operation is critical in speech signals where there are more fluctuations in the fundamental frequency (F0). In this paper, a simple finite impulse response (FIR) implementation is proposed. The FIR filter is designed by placing large number of zeros at fs 2 Hz (fs represents the sampling frequency), closer to the unit circle, in the z-plane. Experimental results show that the proposed method is robust and computationally less complex when compared to the ZFF method.
Threshold-based clustering for intrusion detection systems

Signature-based intrusion detection systems look for known, suspicious patterns in the input data. In this paper we explore compression of labeled empirical data using threshold-based clustering with regularization. The main target of clustering is to compress training dataset to the limited number of signatures, and to minimize the number of comparisons that are necessary to determine the status of the input event as a result. Essentially, the process of clustering includes merging of the clusters which are close enough. As a consequence, we will reduce original dataset to the limited number of labeled centroids. In a complex with k-nearest-neighbor (kNN) method, this set of centroids may be used as a multi-class classifier. Clearly, different attributes have different importance depending on the particular training database. This importance may be regulated in the definition of the distance using linear weight coefficients. The paper introduces special procedure to estimate above weight coefficients. The experiments on the KDD-99 intrusion detection dataset have confirmed effectiveness of the proposed methods.
Thorax in focus: chronic obstructive pulmonary disease

Keeping up to date with scientific developments in any field of medicine is challenging, and chronic obstructive pulmonary disease (COPD) is no exception. Thorax has played an important part in the communication of key developments to its readership. In this article we review original research published in the journal over the last 2-3 years. We consider scientific and clinical developments in the epidemiology, mechanisms and treatment of COPD, placing these articles in the context of other relevant literature in COPD.
Impacts of wind and solar integrations on the dynamic operations of distribution systems

The integration of large-scale distributed generation (DG) and energy storage significantly affect the dynamic behaviors of distribution systems. Distributed systems are facing new challenges in terms of their dynamic behaviors due to the addition of large-scale DG into low-voltage distribution systems. This paper analyses the important effects of the penetration of large-scale wind, solar and energy storage on the dynamic operation of distribution systems during transient conditions. The impact of keeping DG units connected to a distribution grid during and after a grid disturbance is investigated. The behaviors of energy storage devices under fault conditions during a distributed system's islanding are also studied. The impact of the penetration of renewable energy resources on a system is demonstrated through nonlinear simulations using the IEEE benchmark sixteen bus distribution system. The results show that solar, wind and energy storage dynamics have significant impacts on dynamic behaviors and voltage stability of distribution systems.