Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of the Airway Microbiota of Healthy Individuals and Patients with Chronic Obstructive Pulmonary Disease by T-RFLP and Clone Sequencing

Chronic obstructive pulmonary disease (COPD) is a progressive, inflammatory lung disease that affects a large number of patients and has significant impact. One hallmark of the disease is the presence of bacteria in the lower airways. Objective: The aim of this study was to analyze the detailed structure of microbial communities found in the lungs of healthy individuals and patients with COPD. Nine COPD patients as compared and 9 healthy individuals underwent flexible bronchoscopy and BAL was performed. Bacterial nucleic acids were subjected to terminal restriction fragment (TRF) length polymorphism and clone library analysis. Overall, we identified 326 T-RFLP band, 159 in patients and 167 in healthy controls. The results of the TRF analysis correlated partly with the data obtained from clone sequencing. Although the results of the sequencing showed high diversity, the genera Prevotella, Sphingomonas, Pseudomonas, Acinetobacter, Fusobacterium, Megasphaera, Veillonella, Staphylococcus, and Streptococcus constituted the major part of the core microbiome found in both groups. A TRF band possibly representing Pseudomonas sp. monoinfection was associated with a reduction of the microbial diversity. Non-cultural methods reveal the complexity of the pulmonary microbiome in healthy individuals and in patients with COPD. Alterations of the microbiome in pulmonary diseases are correlated with disease.
Semantic-based software clustering using hill climbing

Clustering techniques are used for extracting software architecture in reverse engineering process. Extracting the Call Dependency Graph (CDG) from the source code is the first step in the process of software clustering. A CDG indicates the method invocations between software's artifacts. This graph is tightly coupled to the used programming language so that the existing toolsets for constructing a CDG works on the particular programming language. Therefore, using existing CDG extraction toolsets for large-scale software systems, e.g., Mozilla Firefox, which written by different programming languages, is impossible. To overcome this problem, in this paper, we propose a new dependency graph, called semantic dependency graph (SDG), which is independent of the programming languages. The combination of lexical analysis and latent semantic analysis (LSA) generates this graph. Two versions of Hill climbing algorithms are used to compare their performance with SDG and CDG. The results show that the SDG can be replaced instead of CDG. The results of various experiments confirm this claim. The SDG can be independent of programming languages, hence, helps to the software engineer to clustering the large-scale software systems to extract software architecture, aiming to understand and maintain the existing software systems.
Targeted Resequencing of Epilepsy Genes: A Pharmaco-Therapeutic Perspective

More than half of all epilepsies have some genetic basis and single gene defects in ion channels or neurotransmitter receptors are associated with some inherited forms of epilepsy. Genetic research even in the field of epilepsy disorders is increasing in term of testing platform for the investigation of sequence and structural variation. Next generation sequencing (NGS), i.e., high-throughput sequencing technologies now allow analyses that were previously prohibitive. Targeted resequencing methods by diagnostic panels enable to sequence all the genes associated with a certain disease simultaneously within a few weeks. In this review, we will discuss the overall helpfulness and convenience of simultaneous genotyping of multiple epilepsy genes by NGS in a pharmacogenetics perspective.
Improving multiple sclerosis management and collecting safety information in the real world: the MSDS3D software approach

ABSTRACT Introduction: For safety evaluation, randomized controlled trials (RCTs) are not fully able to identify rare adverse events. The richest source of safety data lies in the post-marketing phase. Real-world evidence (RWE) and observational studies are becoming increasingly popular because they reflect usefulness of drugs in real life and have the ability to discover uncommon or rare adverse drug reactions. Areas covered: Adding the documentation of psychological symptoms and other medical disciplines, the necessity for a complex documentation becomes apparent. The collection of high-quality data sets in clinical practice requires the use of special documentation software as the quality of data in RWE studies can be an issue in contrast to the data obtained from RCTs. The MSDS3D software combines documentation of patient data with patient management of patients with multiple sclerosis. Following a continuous development over several treatment-specific modules, we improved and expanded the realization of safety management in MSDS3D with regard to the characteristics of different treatments and populations. Expert opinion: eHealth-enhanced post-authorisation safety study may complete the fundamental quest of RWE for individually improved treatment decisions and balanced therapeutic risk assessment. MSDS3D is carefully designed to contribute to every single objective in this process.
The Role of Endothelial Ca2+ Signaling in Neurovascular Coupling: A View from the Lumen

Background: Neurovascular coupling (NVC) is the mechanism whereby an increase in neuronal activity (NA) leads to local elevation in cerebral blood flow (CBF) to match the metabolic requirements of firing neurons. Following synaptic activity, an increase in neuronal and/or astrocyte Ca2+ concentration leads to the synthesis of multiple vasoactive messengers. Curiously, the role of endothelial Ca2+ signaling in NVC has been rather neglected, although endothelial cells are known to control the vascular tone in a Ca2+-dependent manner throughout peripheral vasculature. Methods: We analyzed the literature in search of the most recent updates on the potential role of endothelial Ca2+ signaling in NVC. Results: We found that several neurotransmitters (i.e., glutamate and acetylcholine) and neuromodulators (e.g., ATP) can induce dilation of cerebral vessels by inducing an increase in endothelial Ca2+ concentration. This, in turn, results in nitric oxide or prostaglandin E2 release or activate intermediate and small-conductance Ca2+-activated K+ channels, which are responsible for endothelial-dependent hyperpolarization (EDH). In addition, brain endothelial cells express multiple transient receptor potential (TRP) channels (i.e., TRPC3, TRPV3, TRPV4, TRPA1), which induce vasodilation by activating EDH. Conclusions: It is possible to conclude that endothelial Ca2+ signaling is an emerging pathway in the control of NVC.
Agent Independent Task Planningt

Agent-Independent Planning is a technique that allows the construction of activity plans without regard to the "agent" that will perform them. Once generated, a plan is then validated and translated into instructions for a particular agent, whether a robot, crewmember, or software-based control system. Because Space Station Freedom (SSF) is planned for orbital operations for approximately thirty years, it will almost certainly experience numerous enhancements and upgrades, including upgrades in robotic manipulators. Agent-Independent Planning provides the capability to construct plans for SSF operations, independent of specific robotic systems, by combining techniques of object-oriented modeling, nonlinear planning and temporal logic. Since a plan is validated using the physical and functional models of a particular agent, new robotic systems can be developed and integrated with existing operations in a robust manner. This technique also provides the capability to generate plans for crewmembers with varying skill levels, and later apply these same plans to more sophisticated robotic manipulators made available by evolutions in technology.
Algorithm-Based Low-Power and High-Performance Multimedia Signal Processing

Low power and high performance are the two most important criteria for many signal-processing system designs, particularly in real-time multimedia applications. There have been many approaches to achieve these two design goals at many different implementation levels ranging from very-large-scale-integration fabrication technology to system design. However, the major drawback is that present approaches are either too costly or not efficient enough. In this paper, we review the works that have been done at various levels and focus on the algorithm-based approaches for low-power and high-performance design of signalprocessing systems. We present the concept of multirate computing that originates from filterbank design, then show how to employ it along with the other algorithmic methods to develop low-power and high-performance signal-processing systems. The proposed multirate design methodology is systematic and applicable to many problems. We demonstrate in this paper that multirate computing is a powerful tool at the algorithmic level that enables designers to achieve either significant power reduction or high throughput depending on their choice. Design examples on basic multimedia processing blocks such as filtering, source coding, and channel coding are given. A digital-signal-processing engine that is an adaptive reconfigurable architecture is also derived from the common features of our approach. Such an architecture forms a new generation of high-performance embedded signal processor based on the adaptive computing model. The goal of this paper is to demonstrate the flexibility and effectiveness of algorithmbased approaches and to show that the multirate approach is an effective and systematic design methodology to achieve lowpower and high-throughput signal processing at the algorithmic and architectural level.
Breed Locally, Disperse Globally: Fine-Scale Genetic Structure Despite Landscape-Scale Panmixia in a Fire-Specialist

An exciting advance in the understanding of metapopulation dynamics has been the investigation of how populations respond to ephemeral patches that go ‘extinct’ during the lifetime of an individual. Previous research has shown that this scenario leads to genetic homogenization across large spatial scales. However, little is known about fine-scale genetic structuring or how this changes over time in ephemeral patches. We predicted that species that specialize on ephemeral habitats will delay dispersal to exploit natal habitat patches while resources are plentiful and thus display fine-scale structure. To investigate this idea, we evaluated the effect of frequent colonization of ephemeral habitats on the fine-scale genetic structure of a fire specialist, the black-backed woodpecker (Picoides arcticus) and found a pattern of fine-scale genetic structure. We then tested for differences in spatial structure between sexes and detected a pattern consistent with male-biased dispersal. We also detected a temporal increase in relatedness among individuals within newly burned forest patches. Our results indicate that specialist species that outlive their ephemeral patches can accrue significant fine-scale spatial structure that does not necessarily affect spatial structure at larger scales. This highlights the importance of both spatial and temporal scale considerations in both sampling and data interpretation of molecular genetic results.
A Homogeneous and Self-Dual Interior-Point Linear Programming Algorithm for Economic Model Predictive Control

We develop an efficient homogeneous and self-dual interior-point method (IPM) for the linear programs arising in economic model predictive control of constrained linear systems with linear objective functions. The algorithm is based on a Riccati iteration procedure, which is adapted to the linear system of equations solved in homogeneous and self-dual IPMs. Fast convergence is further achieved using a warm-start strategy. We implement the algorithm in MATLAB and C. Its performance is tested using a conceptual power management case study. Closed loop simulations show that: 1) the proposed algorithm is significantly faster than several state-of-the-art IPMs based on sparse linear algebra and 2) warm-start reduces the average number of iterations by 35%-40%.
A Reconfigurable Simulation Framework for Financial Computation

This paper presents a framework for the acceleration of Monte-Carlo simulations using reconfigurable hardware. Discrete-time random walk simulations are widely used in the financial computation to calculate derivative prices and evaluate portfolio risk, but increases in model complexity and tighter time constraints now require large computer farms to meet operational demands. We present a model for accelerating such tasks with reconfigurable hardware, using an architecture that exploits parallelism at multiple levels, combining fine-grained pipelining, intra-device multi-threading and inter-device distributed processing. The architecture adopts a modular design approach, allowing components to be re-used across different applications, while also allowing automatic design space exploration to maximise performance within different devices. Using our framework, we implement two different discrete-time random walks representative of financial simulations and these show 71 times and 8 times speedup respectively when compared to a C++ software and SSE vectorised implementations
Describing Temperament in an Ungulate: A Multidimensional Approach

Studies on animal temperament have often described temperament using a one-dimensional scale, whereas theoretical framework has recently suggested two or more dimensions using terms like “valence” or “arousal” to describe these dimensions. Yet, the valence or assessment of a situation is highly individual. The aim of this study was to provide support for the multidimensional framework with experimental data originating from an economically important species (Bos taurus). We tested 361 calves at 90 days post natum (dpn) in a novel-object test. Using a principal component analysis (PCA), we condensed numerous behaviours into fewer variables to describe temperament and correlated these variables with simultaneously measured heart rate variability (HRV) data. The PCA resulted in two behavioural dimensions (principal components, PC): novel-object-related (PC 1) and exploration-activity-related (PC 2). These PCs explained 58% of the variability in our data. The animals were distributed evenly within the two behavioural dimensions independent of their sex. Calves with different scores in these PCs differed significantly in HRV, and thus in the autonomous nervous system’s activity. Based on these combined behavioural and physiological data we described four distinct temperament types resulting from two behavioural dimensions: “neophobic/fearful – alert”, “interested – stressed”, “subdued/uninterested – calm”, and “neoophilic/outgoing – alert”. Additionally, 38 calves were tested at 90 and 197 dpn. Using the same PCA-model, they correlated significantly in PC 1 and tended to correlate in PC 2 between the two test ages. Of these calves, 42% expressed a similar behaviour pattern in both dimensions and 47% in one. No differences in temperament scores were found between sexes or breeds. In conclusion, we described distinct temperament types in calves based on behavioural and physiological measures emphasising the benefits of a multidimensional approach.
Relationship between acidification factors and methylene blue uptake by Ca-bentonite: optimisation and kinetic study

This investigation studied the uptake of methylene blue from wastewater by normal and treated bentonites to evaluate the effects of acidification factors on removal efficiency. Hydrochloric, sulphuric and nitric acids were blended in accordance with the response surface methodology to prepare acidic agents. The normal clay was then mixed with the prepared solutions after drying in a laboratory oven. The set-up provided controllable conditions for producing nano-porous powders for which the residence time and temperature were changed. The removal efficiency of the treated powders was assessed by defining an adsorption ratio and determining the optimal composition for acidic agent. Based on the statistical theory and experimental data, nitric acid is a suitable agent for manufacturing porous material to remove methylene blue from wastewater. In addition, the Brunauer-Emmett-Teller method, X-ray diffraction and Fourier transform infrared spectroscopy techniques were applied to identify the structural changes. The experimental data obtained from the batch tests were analysed by a new kinetic model which predicted the data variation with higher regression coefficients and lower relative errors. The proposed procedure can be an important tool in optimising the acidification conditions for manufacturing a nano-porous powder with maximal removal efficiency.
Mechanical and Electrical Properties of Al2O3/SiC Nano-Composites

Mechanical and electrical properties of nano-composite ceramic materials composed of Al2O3 matrix and nano-meter size SiC particles are described. The composites including less than 5vol% SiC particles have the same order of resistivity and dielectricity as those of the non-SiC material, while keeping highly improved-mechanical properties. The incorporation of 5vol% SiC into Al2O3 matrix might be promising for materials used under harsh circumstances.
The need for accurate long-term measurements of water vapor in the upper troposphere and lower stratosphere with global coverage

Water vapor is the most important greenhouse gas in the atmosphere although changes in carbon dioxide constitute the “control knob” for surface temperatures. While the latter fact is well recognized, resulting in extensive space‐borne and ground‐based measurement programs for carbon dioxide as detailed in the studies by Keeling et al. (1996), Kuze et al. (2009), and Liu et al. (2014), the need for an accurate characterization of the long‐term changes in upper tropospheric and lower stratospheric (UTLS) water vapor has not yet resulted in sufficiently extensive long‐term international measurement programs (although first steps have been taken). Here, we argue for the implementation of a long‐term balloon‐borne measurement program for UTLS water vapor covering the entire globe that will likely have to be sustained for hundreds of years.
A new approach to the massive processing of satellite images

Technological advances in the field of Remote Sensing generate large volumes of geospatial data. Current geographic information systems (GIS) doesn't support the massive processing of satellite imagery, two examples of this kind of software are: (i) the Brazilian Spring project, GIS and remote sensing image processing system (ii) QGIS, a free and open source GIS. To achieve massive processing, we have HIPI framework, it provides a solution for how to store a large collection of images and works on the Hadoop Distributed File System. Currently, HIPI only supports specific image formats, such as, JPEG, PNG and PPM. In this article is presented a new approach to distributed processing of considerable amounts of satellite images. We make an extension of HIPI to support satellite images format, TIFF, this fact helps to preserve the information, process and analyze satellite images massively to have results faster than the traditional way.
Rapid neocortical acquisition of long-term arbitrary associations independent of the hippocampus

Anterograde amnesia following hippocampal damage involves the loss of the capacity to form new declarative memories but leaves nondeclarative memory processes intact. Current theories of declarative memory suggest the existence of two complementary memory systems: a hippocampal-based system that specializes in rapid acquisition of specific events and a neocortical system that slowly learns through environmental statistical regularities and requires the initial support of the hippocampal system. Contrary to this notion, we demonstrate a neurocognitive mechanism that enables rapid acquisition of novel arbitrary associations independently of the hippocampus. This mechanism has been dubbed “fast mapping” (FM) and is believed to support the rapid acquisition of vocabulary in children as young as 16 mo of age. We used FM to teach novel word-picture associations to four profoundly amnesic patients with hippocampal system damage. Patients were able to acquire arbitrary associations through FM normally, despite profound impairment on a matched standard associative memory task. Most importantly, they retained what they learned through FM after a week's delay, when they were around chance level on the standard task. By contrast, two patients with unilateral damage to the left polar temporal neocortex were impaired on FM, suggesting that this cortical region is critical for associative learning through FM. Left perirhinal and entorhinal cortices might also play a role in learning through FM. Contrary to current theories, these findings indicate that rapid acquisition of declarative-like (relational) memory can be accomplished independently of the hippocampus and that neocortical plasticity can be induced rapidly to support novel arbitrary associations.
Adjusting initial weights for Adaboost learning

The Adaboost extracts an optimal set of weak classifiers in stages. On each stage, it chooses the optimal classifier by minimizing the weighted error classification. It also reweights training data so that the next round would focus on data that are difficult to classify. The typical Adaboost algorithm assigns the same weight to each training datum on the first round of a training process. In this paper, we propose to assign different initial weights based on some statistical properties of involved features. In experimental results, we assess that the proposed method shows higher performance than the typical one.
Despeckling Images Using a Preprocessing Filter and Discrete Wavelet Transform-Based Noise Reduction Techniques

Synthetic aperture radar (SAR) images are difficult to analyze due to speckle noise, which is a characteristic of multiplicative noise. Over the last few decades, a number of studies have been performed regarding the removal of speckle noise. However, the existing studies exhibit edge information loss when removing speckle noise. In this paper, we propose an algorithm using speckle reducing anisotropic diffusion (SRAD), soft thresholding, and a guided filter to effectively remove speckle noise from SAR images while preserving edge information. The proposed algorithm first obtains a filtered image by applying an SRAD filter to a noise image. To further remove the multiplicative noise remaining in the filtered image, a logarithmic transformation is applied to convert it into additive noise. The filtering image was decomposed into multiresolution images using discrete wavelet transform (DWT). Soft thresholding and a guided filter were used for each of the high-frequency subimages and the low-frequency subimage. Then, an inverse DWT and an exponential transform are applied to the denoised image. The experimental results indicate that the proposed algorithm shows better performance than the conventional filtering method in terms of both objective and subjective performances.