Miracle-dz/UniFAR-FTUs huggingface dataset:
Female Sex Hormones and Cardiac Pressure Overload Independently Contribute to the Cardiogenic Dementia Profile in Yucatan Miniature Swine

Post-menopausal women with heart failure (HF) frequently exhibit cardiogenic dementia. Using a pre-clinical swine model of post-menopausal HF, we recently demonstrated that experimental menopause (ovariectomy; OVX) and HF (6-month cardiac pressure overload/aortic banding; AB) independently altered cerebral vasomotor control and together impaired cognitive function. The purpose of this study was to examine the prefrontal cortex and hippocampus tissues from these animals to assess whether OVX and HF are associated with neurologic alterations that may contribute to cardiogenic dementia. We hypothesized that OVX and HF would independently alter neuronal cell signaling in swine with post-menopausal cardiogenic dementia. Immunoblot analyses revealed OVX was associated with reduced estrogen receptor-α in both brain regions and HF tended to exacerbate OVX-induced deficits in the hippocampus. Further, OVX was associated with a reduction in the ratio of phosphorylated:total Akt and ERK in the hippocampus as well as decreased total Akt and synaptophysin in the prefrontal cortex. In contrast, HF was associated with a trend toward reduced phosphorylated:total ERK in the prefrontal cortex. In addition, HF was associated with decreased β-amyloid (1–38) in the prefrontal cortex and increased β-amyloid (1–38) in the hippocampus. Regional brain lipid analysis revealed OVX tended to increase total, saturated, and monounsaturated fatty acid content in the prefrontal cortex, with the greatest magnitude of change occurring in the AB-OVX group. The data from this study suggest that OVX and HF are independently associated with regional-specific neurologic changes in the brain that contribute to the cardiogenic dementia profile in this model. This pre-clinical swine model may be a useful tool for better understanding post-menopausal cardiogenic dementia pathology and developing novel therapies.
Indentation behaviour of 'soft film on hard substrate' composite system type

This investigation has been carried out in order to analyse and compare the hardness response of different composite systems of the same type, named „soft film on hard substrate“ composite system. Composite systems of monoand multilayered electrodeposited Ni and Cu thin films on (100) and (111)-oriented monocrystalline Si wafers and 100 μm-thick electrodeposited Ni film as the substrates were fabricated. The indentation behaviour of these composite structures was characterized using Berkovich nanohardness and Vickers microhardness testing. The measured hardness is so-called „composite hardness“, because the substrate participates in the plastic deformation during the indentation process. The contribution of the substrate to the measured hardness starts at indentation depths of the order of 0.070. 2 times the film thickness. Dependence of nanohardness and microhardness values on electrodeposition process conditions, substrate and film microstructure, total film thickness, layer thickness and Ni/Cu layer thickness ratio for different composite systems ratio was investigated. Composite hardness model of Chicot-Lesage (C-L) was chosen and applied to experimental data in order to determine absolute film hardness.
Intestinal fatty acidebinding protein levels in patients with chronic renal failure

Background: Intestinal fatty acidebinding protein (I-FABP), a biomarker of enterocyte injury, has been reported to be a diagnostic marker of intestinal ischemia and a prognostic marker in critically ill patients. However, the kinetics of I-FABP in renal failure patients is unknown. We sought to identify I-FABP levels in patients with chronic kidney disease (CKD) and end-stage kidney disease (ESKD) on hemodialysis (HD) and to identify the manner in which the I-FABP levels change. Materials and methods: Adult patients who were admitted for elective cardiac surgery with either normal renal function (NRF), CKD, or ESKD on HD were enrolled. Serum I-FABP levels in NRF and CKD patients and in ESKD patients before and after HD were determined. Results: A total of 124 patients were evaluated: 47 NRF, 53 CKD, and 24 ESKD. The I-FABP levels of the CKD patients and pre-HD ESKD patients were significantly higher than those of the NRF patients (P 1⁄4 0.018 and P <0.001, respectively). I-FABP levels were significantly negatively correlated with the estimated glomerular filtration rate in NRF and CKD patients (Spearman’s r 1⁄4 0.313, P 1⁄4 0.002). In addition, I-FABP levels in ESKD patients were significantly lower after HD than those before HD (P <0.001). Conclusions: I-FABP levels in CKD and pre-HD ESKD patients were significantly higher than those in NRF patients. In addition, I-FABP was significantly eliminated by HD in patients with ESKD. Clinicians and researchers should consider this aspect of I-FABP when using it as a diagnostic and prognostic marker in patients with renal insufficiency. a 2018 Elsevier Inc. All rights reserved. * Corresponding author. Division of Intensive Care, Nagasaki University Hospital, 1-7-1 Sakamoto, Nagasaki 852-8501, Japan. Tel. : þ81 95 819 7370; fax: þ81 95 819 7373. E-mail address: m-sekino@nagasaki-u.ac. jp (M. Sekino). Available online at www.sciencedirect. com
Motion Planning of Mobile Robots for Occluded Obstacles

Motion planning of mobile robots for occluded obstacles is a challenge in dynamic environments. The occlusion problem states that if an obstacle suddenly appears from the occluded area, the robot might collide with the obstacle. To overcome this, we propose a novel motion planner, the Velocity Obstacle for occlusion (VOO). The VOO is based on a previous motion planner, the Velocity Obstacle (VO), which is effective for moving obstacles. In the proposed motion planner, information uncertainties about occluded obstacles, such as position, velocity, and moving direction, are quantitatively addressed. Thus, the robot based on the VOO is able to move not only among observed obstacles, but also among the occluded ones. Through simulation experiments, the effectiveness of the VOO for the occlusion problem is demonstrated by comparison with the VO.
Global Cardiovascular Risk of the HIV Positive Patients Receiving Antiretroviral Therapy in Brazzaville

A cross-sectional, descriptive and analytical study was conducted from January to August 2015 at the Brazzaville Ambulatory Treatment Center and at the National Blood Transfusion Center. The objective was to contribute to improving the care of people living with HIV under antiretroviral therapy by assessing their global cardiovascular risk (CVR). The variables studied focused on the epidemiological, clinical and biological aspects. The global CVR was assessed by the Framingham and WHO/ISH scores. There were 135 HIV-positive subjects, including 64 treated patients and 71 untreated HIV+ subjects. The subjects were divided into 83 men (61.5%) and 52 women (38.5%), with an average age of 42.6 ± 2.9 old years. The subjects were single people (62.2%), of a secondary educational level (63.7%), and civil servants (32.6%). The main risk factors found were dyslipidaemia (60%), obesity (36%), smoking (12.6%), hypertension (5.9%), diabetes (0.7%). The metabolic syndrome was found in seven cases (11.3%). The global CVR according to the score of Framingham, initially moderated at 17.2%, and mean at 1.5% within treated patients, was mean at 9.4% and high at 1.6% of the subjects respectively at the sixth month of treatment (p < 0.03). For the score of the WHO/ISH, the risk was high at 2% and very high at 3% within treated patients initially. This risk was increased to 3.1% for the high and very high risk respectively at sixth month of treatment (p < 0.04). In Congo, the HIV population involves a high global CVR under antiretroviral therapy. Preventive actions are highly recommended.
Does Higher-Order Thinking Impinge on Learner-Centric Digital Approach?.

Humans are social beings and the social cognition focuses on how one form impressions of other people, interpret the meaning of other people's behaviour and how people’s behaviour is affected by our attitudes. The school provides complex social situations and in order to thrive, students must possess social cognition, the process of thinking about how other people are likely to think, act, and react. Impact of technology on learning has not been answered clearly many years after the introduction of ICT into classrooms. Today there are optimist and pessimist views regarding the use of ICT in Education. The main objective of Digital classroom is to enhance interactive learning through teaching pedagogies that provide opportunities for authentic, contextualised assessment that supports learning in a digital context. Digital classroom incorporates contemporary teaching and learning strategies. It provides the basis for engaging students in actively constructing and applying rich learning in purposeful and meaningful ways. The integration of ICT in social cognitive development is a complex and multidimensional process, including many dynamics such as ICT tools, teachers, students, school administration, educational programmes and school culture. While using digital media devices, students of today need active participation and involvement rather than playing a passive role. This means integrating the use of smart phones, tablets, and laptops in on-ground classrooms. They are also looking for the emotional satisfaction of using and interacting with these products. The main purpose of this paper is to make an inquiry on how social cognition impinges on learner-centered digital classroom in an Educational view. It also provides an understanding of how the mind is influenced by social interactions and how to implement technology to enhance social
Transitions between Taylor vortices and spirals via wavy Taylor vortices and wavy spirals

We present numerical simulations of closed wavy Taylor vortices and of helicoidal wavy spirals in the Taylor–Couette system. These wavy structures appearing via a secondary bifurcation out of Taylor vortex flow and out of spiral vortex flow, respectively, mediate transitions between Taylor and spiral vortices and vice versa. Structure, dynamics, stability and bifurcation behaviour are investigated in quantitative detail as a function of Reynolds numbers and wave numbers for counter-rotating as well as corotating cylinders. These results are obtained by solving the Navier–Stokes equations subject to axial periodicity for a radius ratio η=0.5 with a combination of a finite differences method and a Galerkin method.
Communication Topologies Between Learning Agents in Deep Reinforcement Learning

A common technique to improve speed and robustness of learning in deep reinforcement learning (DRL) and many other machine learning algorithms is to run multiple learning agents in parallel. A neglected component in the development of these algorithms has been how best to arrange the learning agents involved to better facilitate distributed search. Here we draw upon results from the networked optimization and collective intelligence literatures suggesting that arranging learning agents in less than fully connected topologies (the implicit way agents are commonly arranged in) can improve learning. We explore the relative performance of four popular families of graphs and observe that one such family (Erdos-Renyi random graphs) empirically outperforms the standard fully-connected communication topology across several DRL benchmark tasks. We observe that 1000 learning agents arranged in an Erdos-Renyi graph can perform as well as 3000 agents arranged in the standard fully-connected topology, showing the large learning improvement possible when carefully designing the topology over which agents communicate. We complement these empirical results with a preliminary theoretical investigation of why less than fully connected topologies can perform better. Overall, our work suggests that distributed machine learning algorithms could be made more efficient if the communication topology between learning agents was optimized.
Combining Strengths for Multi-genome Visual Analytics Comparison

The eclosion of data acquisition technologies has shifted the bottleneck in molecular biology research from data acquisition to data analysis. Such is the case in Comparative Genomics, where sequence analysis has transitioned from genes to genomes of several orders of magnitude larger. This fact has revealed the need to adapt software to work with huge experiments efficiently and to incorporate new data-analysis strategies to manage results from such studies. In previous works, we presented GECKO, a software to compare large sequences; now we address the representation, browsing, data exploration, and post-processing of the massive amount of information derived from such comparisons. GECKO-MGV is a web-based application organized as client-server architecture. It is aimed at visual analysis of the results from both pairwise and multiple sequences comparison studies combining a set of common commands for image exploration with improved state-of-the-art solutions. In addition, GECKO-MGV integrates different visualization analysis tools while exploiting the concept of layers to display multiple genome comparison datasets. Moreover, the software is endowed with capabilities for contacting external-proprietary and third-party services for further data post-processing and also presents a method to display a timeline of large-scale evolutionary events. As proof-of-concept, we present 2 exercises using bacterial and mammalian genomes which depict the capabilities of GECKO-MGV to perform in-depth, customizable analyses on the fly using web technologies. The first exercise is mainly descriptive and is carried out over bacterial genomes, whereas the second one aims to show the ability to deal with large sequence comparisons. In this case, we display results from the comparison of the first Homo sapiens chromosome against the first 5 chromosomes of Mus musculus.
Application of Machine Learning Algorithms for Android Malware Detection

As the popularity of Android smart devises increases, the battle of alleviating Android malware has been considered as a crucial activity with the advent of new attacks including progressively complicated evasion techniques, consequently entailing more cutting-edge detection techniques. Hence, in this paper, two Machine Learning (ML) algorithms, called Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), are applied and evaluated to perform classification of the feature set into either benign or malicious applications (apps) through supervised learning process. This work involves in static analysis of apps, which checks for the presence and frequency of keywords in the Android apps' manifest file and derives the static feature sets from a 400-app dataset to produce better malware detection results. The classification performance of the ML algorithms is measured in terms of accuracy and true positive rate and interpreted to determine which algorithm is more applicable for the Android malware detection. The experimental results for a dataset of real malware and benign apps indicate the average accuracy rate of 79.08% and 80.50% with average true positive rate of over 67.00% and 80.00% using SVM and KNN, respectively.
End-to-end Lyrics Alignment for Polyphonic Music Using an Audio-to-character Recognition Model

Time-aligned lyrics can enrich the music listening experience by enabling karaoke, text-based song retrieval and intra-song navigation, and other applications. Compared to text-to-speech alignment, lyrics alignment remains highly challenging, despite many attempts to combine numerous sub-modules including vocal separation and detection in an effort to break down the problem. Furthermore, training required fine-grained annotations to be available in some form. Here, we present a novel system based on a modified Wave-U-Net architecture, which predicts character probabilities directly from raw audio using learnt multi-scale representations of the various signal components. There are no sub-modules whose interdependencies need to be optimized. Our training procedure is designed to work with weak, line-level annotations available in the real world. With a mean alignment error of 0.35s on a standard dataset our system outperforms the state-of-the-art by an order of magnitude.
Very Large Telescope deep echelle spectroscopy of Galactic planetary nebulae NGC 6153, M 1-42 and Hf 2-2

We present deep spectroscopy of three Galactic planetary nebulae (PNe) with large abundance discrepancy factors (ADFs): NGC6153, M1-42 and Hf2-2. The spectra were obtained with VLT/UVES and cover the whole optical range (3040-11,000 A) with a spectral resolution of ~20,000. For all three PNe, several hundred emission lines were detected and identified, with more than 70 per cent of them as permitted lines. Most of these permitted lines are excited by recombination. Numerous weak optical recombination lines (ORLs) of O II, C II, N II and Ne II were detected in the spectra and accurate fluxes measured. Line flux tables were compiled and ready for use by the community of nebular astrophysics. These ORLs were critically analyzed using the effective recombination coefficients recently calculated for the optical recombination spectrum of N II and O II under the physical conditions of photoionized gaseous nebulae. Plasma diagnostics based on the heavy element ORLs were carried out using the new atomic data. Elemental abundances derived from the ORLs were systematically higher than those derived from the collisionally excited lines (CELs) by a factor of ~10, 22 and 80 for NGC6153, M1-42 and Hf2-2, respectively. The electron temperatures derived from the heavy element ORLs are systematically lower than those derived from the CELs. These ORL versus CEL abundance and temperature discrepancies, previously observed in the three PNe through deep spectroscopy with medium to low spectral resolution, are thus confirmed by our analysis of the deep echelle spectra using the new atomic data.
Unintended Pregnancy in Iran: Prevalence and Risk Factors

Objectives: Unintended pregnancy, as one of the main issues in reproductive health, is defined as a mistimed or unwanted pregnancy all over the worlds. This study aimed to determine the prevalence and risk factors of unintended pregnancy among Iranian women. Materials and Methods: As part of a survey on twin or multiple pregnancies in Tehran, Iran, this cross-sectional study considered a total of 5152 deliveries in 103 hospitals during 2015. The required data were gathered at the time of delivery or within the next 2-3 days from physically unstable women. The sampling was carried out within two weeks. All women, regardless of method of delivery, being primiparous or multiparous, and pregnancy outcome were included in the study. Results: According to the results, the prevalence of unintended pregnancy in Iran was estimated 19.81%. Based on univariate analysis, the mean age of mothers, the mean age of fathers, number of pregnancies, and number of deliveries in unintended pregnancies were significantly higher than those in intended pregnancies. In addition, the prevalence of unintended pregnancy among housewives, as well as women with low levels of education and income was high. According to multiple logistic regression analysis, economic status and number of deliveries were the main predicting factors of unintended pregnancy. Conclusions: In general, the prevalence of unintended pregnancy in Iran is lower than that in other countries. However, preventive actions and health education programs still should be undertaken for mothers in order to minimize the prevalence of unintended pregnancies, thereby reducing the consequences for mother and baby.
Threat Detection and Analysis in the Internet of Things using Deep Packet Inspection

Artificial Neural Networks are universal and highly flexible function approximators first used in the fields of cognitive science and engineering. In recent years, Neural Networks have become increasingly popular in finance for tasks such as pattern recognition, classification and time series forecasting. The ability to predict cash requirement within reasonable accuracy of actual demand provides target for supply optimization well in time. Every financial institution (large or small) faces the same daily challenge. While it would be devastating to run out of cash, it is important to keep cash at the right levels to meet customer demand. In such case, it becomes very necessary to have a forecasting system in order to get a clear picture of demand well in advance. This paper presents two neural network models for cash forecasting for a bank branch. One is daily model – taking the parameter values for a day as input to forecast cash requirement for the next day and the other is weekly model, which takes the withdrawal affecting input patterns of a week to pred ict cash requirement for the next week. The system performs better than other cash forecasting systems. This system can be scaled for all branches of a bank in an area by incorporating historical data from these branches.
Microbial population heterogeneity versus bioreactor heterogeneity: Evaluation of Redox Sensor Green as an exogenous metabolic biosensor

Microbial heterogeneity in metabolic performances has attracted a lot of attention, considering its potential impact on industrial bioprocesses. However, little is known about the impact of extracellular perturbations (i.e. bioreactor heterogeneity) on cell‐to‐cell variability in metabolic performances (i.e. microbial population heterogeneity). In this work, we have evaluated the relevance of Redox Sensor Green (RSG) as an exogenous biosensor of metabolic activity at the single‐cell level. RSG signal is proportional to the activity of the electron transport chain and its signal is strongly affected by metabolic burden, availability of electron final acceptor, and side metabolisms (i.e. overflow and mixed acid fermentation). RSG can also be used for the estimation of the impact of scale‐down conditions on microbial metabolic robustness. The relationship linking averaged RSG activity and its cell‐to‐cell variability (noise) has been highlighted but seems unaffected by environmental perturbations.
An Overview of Power Electronics Techniques in Electric Systems for Transport Applications

At present, the airplane, ship and vehicle are the three main types of transport tools, and increasing use of these tools can be observed all over the world, since mobility is one of the most important parameters for achieving economic development. However, according to [1], one third of the total greenhouse gas (GHG) emissions are generated from the transportation system. In order to achieve the ultimate aim of sustainable development, more environmentally friendly means for transportation should be developed. In address to the three forms of transportation mentioned above, the concepts of more-electric aircrafts (MEAs)[2], electric ships (ESs) [3] and electric vehicles (EVs) [4] were proposed, and electric propulsion systems are used to in these applications to replace the original ones based on conventional energy resources for carbon emission reduction.
Magnetic interactions in a ferromagnetic honeycomb nanoscale network

The magnetic structure and magnetization process in a permalloy wire-based honeycomb network have been investigated by means of magnetic-force microscopy (MFM) and magnetoresistance measurement. The MFM measurements show the remanent magnetic structures to be governed by magnetic interaction similar to the ice rule, which provides a direct analogy between the present honeycomb network and an Ising system on a kagom\'e lattice. The magnetoresistance measurements reveal that this interaction also dominates the magnetization processes in the network. By decreasing the exchange energy at the vertices of the network, the ice-rule type of interaction causes a transition of the magnetization process in the network.