Miracle-dz/UniFAR-FTUs huggingface dataset:
II . Cross-Talk Between Adipose Tissue and Blood Vessels

_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ Adipose tissue is a critical regulator of vascular function, which until recently had been virtually ignored. Almost all blood vessels are surrounded by perivascular adipose tissue, which is actively involved in the maintenance of vascular homeostasis by producing “vasocrine” signals such as adipokines. Adiponectin and adipocyte fatty acid binding protein (A-FABP), both of which are major adipokines predominantly produced in adipose tissue, have recently been shown to be pivotal modulators of vascular function. Adiponectin has Advances in Pharmacology, Volume 6
SUBSYSTEMS IN NEARBY SOLAR-TYPE WIDE BINARIES

We conducted a deep survey of resolved subsystems among wide binaries with solar-type components within 67 pc of the Sun. Images of 61 stars in the K and H bands were obtained with the Near-Infrared Coronagraphic Imager adaptive-optics instrument on the 8 m Gemini-South telescope. Our maximum detectable magnitude difference is about 5 mag and 7.8 mag at 0. ″15 and 0. ″9 separations, respectively. This enables a complete census of subsystems with stellar companions in the projected separation range from 5 to 100 AU. Out of seven such companions found in our sample, only one was previously known. We determine that the fraction of subsystems with projected separations above 5 AU is 0.12 ± 0.04 and that the distribution of their mass ratio is flat, with a power-law index of 0.2 ± 0.5. Comparing this with the properties of closer spectroscopic subsystems (separations below 1 AU), it appears that the mass-ratio distribution does not depend on the separation. The frequency of subsystems in the separation ranges below 1 AU and between 5 and 100 AU is similar, about 0.15. Unbiased statistics of multiplicity higher than 2, advanced by this work, provide constraints on star formation theory.
The highest velocity and the shortest duration permitting attainment of VO 2 max during running A maior velocidade e o menor tempo de exercício em que o VO 2 max é alcançado na corrida

1 Abstract - The severe-intensity domain has important applications for the prescription of running training and the elaboration of experimental designs. The objectives of this study were: 1) to investigate the validity of a previously proposed model to estimate the shortest exercise duration (T LOW ) and the highest velocity (V HIGH ) at which VO 2 max is reached during running, and 2) to evaluate the effects of aerobic training status on these variables. Eight runners and eight physically active subjects performed several treadmill running exercise tests to exhaustion in order to mathematically estimate and to experimentally determine T LOW and V HIGH . The relationship between the time to achieve VO 2 max and time to exhaustion (Tlim) was used to estimate T LOW . V HIGH was estimated using the critical velocity model. V HIGH was assumed to be the highest velocity at which VO 2 was equal to or higher than the average VO 2 max minus one standard deviation. T LOW was defined as Tlim associated with V HIGH . Runners presented better aerobic fitness and higher V HIGH (22.2 ± 1.9 km.h -1
A new species of pest fruit fly (Diptera: Tephritidae: Dacinae) from Sri Lanka and Africa

A new species of Dacinae, Bactrocera (Bactrocera) invadens sp.n. , from Sri Lanka and Africa, is described and illustrated. This is a pest species that has recently been introduced into eastern Africa and has subsequently made a rapid expansion across tropical Africa. Known distribution and host records are also presented.
AGRICULTURAL AND FOOD SCIENCE

This study evaluated the effects of different lamb production systems on live weight gain (LWG), carcass quality and meat quality. Four production systems for weaned intact male lambs were examined: indoor feeding with grass silage and concentrate (group 1), grazing on cultivated pasture with (group 2) or without (group 3) concentrate, and grazing on semi-natural pasture (group 4). Live weight, carcass weight, dressing percentage, carcass conformation, fatness and pH decline were recorded at slaughter, and M. longissimus thoracis et lumborum was analysed for colour, thawing and cooking loss, pH after 24 hours and 6 days, and Warner-Bratzler shear force. LWG was strongly affected by production system, being highest for group 1 and lowest for group 4 (p<0.001). Group 4 had the lowest conformation (p=0.002) and fat scores (p<0.001). Hence, production system affected age at slaughter, live weight gain, weight at slaughter, carcass conformation and fatness scores, but caused no differences in meat quality attributes in intact male lambs.
AI Aided Noise Processing of Spintronic Based IoT Sensor for Magnetocardiography Application

As we are about to embark upon the highly hyped “Society 5.0”, powered by the Internet of Things (IoT), traditional ways to monitor human heart signals for tracking cardio-vascular conditions are challenging, particularly in remote healthcare settings. On the merits of low power consumption, portability, and non-intrusiveness, there are no suitable IoT solutions that can provide information comparable to the conventional Electrocardiography (ECG). In this paper, we propose an IoT device utilizing a spintronic-technology-based ultra-sensitive Magnetic Tunnel Junction (MTJ) sensor that measures the magnetic fields produced by cardio-vascular electromagnetic activity, i.e. Magentocardiography (MCG). We treat the low-frequency noise generated by the sensor, which is also a challenge for most other sensors dealing with low-frequency bio-magnetic signals. Instead of relying on generic signal processing techniques such as moving average, we employ deep-learning training on bio-magnetic signals. Using an existing dataset of ECG records, MCG signals are synthesized. A unique deep learning model, composed of a one-dimensional convolution layer, Gated Recurrent Unit (GRU) layer, and a fully-connected neural layer, is trained using the labeled data moving through a striding window, which is able to smartly capture and eliminate the noise features. Simulation results are reported to evaluate the effectiveness of the proposed method that demonstrates encouraging performance.
Bayesian Analogical Cybernetics

It has been argued that all of cognition can be understood in terms of Bayesian inference. It has also been argued that analogy is the core of cognition. Here I will propose that these perspectives are fully compatible, in that analogical reasoning can be described in terms of Bayesian inference and vice versa, and that both of these positions require a thorough cybernetic grounding in order to fulfill their promise as unifying frameworks for understanding minds. From the Bayesian perspective of the Free Energy Principle and Active Inference framework, thought is constituted by dynamics of cascading belief propagation through the nodes of probabilistic generative models specified by a cortical heterarchy "rooted" in action-perception cycles that ground the mind as an embodied control system for an autonomous agent. From the analogical structure mapping perspective, thought is constituted by the alignment and comparison of heterogeneous structural representations. Here I will propose that this core cognitive process for analogical reasoning is naturally implemented by predictive coding mechanisms. However, both Bayesian cognitive science and models of cognitive development via analogical reasoning require rich base domains and priors (or reliably learnable posteriors) from which they can commence the process of bootstrapping minds. Here in the spirit of the work of George Lakoff and Mark Johnson, I propose that embodiment provides many of the inductive biases that are usually described in terms of innate core knowledge. (Please note: this manuscript was written and finalized in 2012.)
Quantum Logic under Semiclassical Limit: Information Loss

We consider the quantum computation efficiency from a new perspective. The efficiency is reduced to its classical counterpart by imposing the semiclassical limit. We show that this reduction is caused by the fact that any elementary quantum logic operation (gate) suffers the information loss during the transition to its classical analog. Amount of the information lost is estimated for any gate from the complete set. We demonstrate that the largest loss is obtained for non-commuting gates. This allows us to consider the non-commutativity as the quantum computational speed-up resource. Our method allows us to quantify advantages of a quantum computation as compared to the classical one by the direct analysis of the involved basic logic. The obtained results are illustrated by the application to a quantum discrete Fourier transform and Grover search algorithms.
Genetic diversity and assessment of drought tolerant sorghum landraces based on morph-physiological traits at different growth stages.

Root morphology and leaf water relations are important morph-physiological traits for screening various crop plants under water stress. In this study these morph-physiological traits were measured to sort out drought tolerant local landraces (LLRs) of sorghum. The results revealed that the landrace FJSS-1 performed the best for most of the characters followed by FJSS-11 and FJSS-17 which also performed well for various traits contributing towards water stress tolerance at seedling and post-flowering stages. The landrace, FJSS-10 revealed the contrasting parents showing drought susceptibility. Cluster analysis clearly divided the LLRs in two groups out of which Cluster I displayed more scope for selection against water stress. Dry root weight exhibited the highest genotypic coefficient of variation among seedling traits while excise leaf weight loss among the flag leaf related characters. Considerable degree of variation among the LLRs for most of the traits proposed these genotypes as significant source for the selection of water stress tolerance. In the same way, higher amount of heritability and genetic advance for the most of the morph-physiological parameters advocated that elevated amount of genetic gain for these parameters might be possible followed by hybridization. Our results suggest that these morpho-physiological traits could be efficiently used as selection criteria for drought tolerance in sorghum at different growth stages.
Students vs. Professionals in Assisted Requirements Tracing: How Could We Train Our Students?

Assisted requirements tracing (ART) skills are essential for new college graduates joining the software industry as their initial assignments often involve substantial tracing-related activities. Although studying human analysts in ART is an emerging research trend, how students might behave differently from software professionals is yet to be investigated. In this paper, we compare the performances, processes, and strategies between students and software professionals in carrying out ART tasks for an unfamiliar system. We observe that both students and professionals performing ART activities generally follow a generic four-phase problem solving process: define the problem, develop a plan, implement the plan, and evaluate the solution. We find that students show significant deficiency in the overall problem solving process, whereas many professionals follow unique and effective tracing techniques in defining the problem, and in developing and implementing the plan. We identify the improvement areas and propose a set of learning activities for Software Engineering students to enhance their tracing skills. We implement two learning activities in a Software Engineering course and report our experience. Our study contributes to the improvement of training students in performing ART and other information-intensive tasks in Software Engineering.
Evidence of a population of dark subhaloes from Gaia and Pan-STARRS observations of the GD-1 stream

New data from the Gaia satellite, when combined with accurate photometry from the Pan-STARRS survey, allow us to accurately estimate the properties of the GD-1 stream. Here, we analyse the stellar density variations in the GD-1 stream and show that they cannot be due to known baryonic structures such as giant molecular clouds, globular clusters, or the Milky Way’s bar or spiral arms. A joint analysis of the GD-1 and Pal 5 streams instead requires a population of dark substructures with masses ≈107–$10^9 \ \rm {M}_{\odot }$. We infer a total abundance of dark subhaloes normalized to standard cold dark matter $n_{\rm sub}/n_{\rm sub, CDM} = 0.4 ^{+0.3}_{-0.2}$ (68 per cent), which corresponds to a mass fraction contained in the subhaloes $f_{\rm {sub}} = 0.14 ^{+0.11}_{-0.07} {{\ \rm per\ cent}}$, compatible with the predictions of hydrodynamical simulation of cold dark matter with baryons.
Complete cotorsion pairs in the category of complexes

In this paper, we study completeness of cotorsion pairs in the category of complexes of R-modules. Let (A ; B ) be a cotorsion pair in R-Mod. It is shown that the cotorsion pairs ( e A ;dg e B ) and (A ; A ? ) are complete if A is closed under pure submodules and cokernels of pure monomorphisms, where in Gillespie's denitions e A is the class of exact complexes with cycles in A and dg e B is the class of complexes X with components in B such that the complex H om(A;X) is exact for every complex A 2 e A ; and A is the class of all complexes with components in A. Furthermore, they are perfect. As an application, we get that every complex over a right coherent ring has a Gorenstein at cover, which generalizes the well-known results on the existence of Gorenstein at covers.
Toxicity of Particulate Atmospheric Pollution on Installs Respiratory Rabbit

Les etudes epidemiologiques demontrent une correlation entre l'exposition aux particules (P.M.) et les affections respiratoires. Les principaux effets des polluants atmospheriques particulaires sur la sante concernent essentiellement l'appareil respiratoire. Ces manifestations resultent generalement, soit d'une toxicite directe des polluants, soit d'une fragilisation des mecanismes de defense de l'organisme vis a vis des agressions bacteriennes, virales ou allergiques. Nous avons evalue la composition chimique et testees des particules d'origine industrielle sur le lapin et on a etudie leur l'impact sur le parenchyme pulmonaire et tracheal en pratiquant des coupes histologiques. D'apres les observations microscopiques, on a remarque des parenchymes pulmonaire et tracheal tres atteint avec une accumulation des particules industrielles. Notre etude met en evidence cette relation entre la duree de l'exposition aux particules industrielles et les differentes alterations histologiques observees. En effet, l'examen histologique du parenchyme pulmonaire nous revele qu'il est affecte rapidement des 3 jours d'exposition et cette toxicite augmente en fonction du temps. Pollution atmospherique particulaire, toxicite, lapin, appareille respiratoire. The epidemiological studies show a correlation between l' exposure to the particles (PM) and the respiratory affections. The principal effects of the particulate atmospheric pollutants on health relate to primarily the breathing apparatus. These demonstrations generally result, either of a direct toxicity of the pollutants, or of an embitterment of the mechanisms of defence of the organization with respect to the bacterial, viral or allergic aggressions. We evaluated the chemical composition and tested particles of industrial origin on rabbit and one studied their impact on the pulmonary parenchyma and tracheal as a practitioner of the histological cuts. According to the microscopic observations, one noticed parenchyma's pulmonary and tracheal very reached with an accumulation of the industrial particles. Our study highlights this relation between the exposure time to the industrial particles and various histological deteriorations observed. Indeed, the histological examination of the pulmonary parenchyma reveals us that it is affected quickly dice 3 days of exposure and this toxicity increases according to time.
INTERNALIZATION OF VIRUSES AND IDENTIFICATION OF COMPOUNDS EFFECTIVE THEREOF

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Digital blood in massively parallel CPU/GPU systems for the study of platelet transport

We propose a highly versatile computational framework for the simulation of cellular blood flow focusing on extreme performance without compromising accuracy or complexity. The tool couples the lattice Boltzmann solver Palabos for the simulation of blood plasma, a novel finite-element method (FEM) solver for the resolution of deformable blood cells, and an immersed boundary method for the coupling of the two phases. The design of the tool supports hybrid CPU–GPU executions (fluid, fluid–solid interaction on CPUs, deformable bodies on GPUs), and is non-intrusive, as each of the three components can be replaced in a modular way. The FEM-based kernel for solid dynamics outperforms other FEM solvers and its performance is comparable to state-of-the-art mass–spring systems. We perform an exhaustive performance analysis on Piz Daint at the Swiss National Supercomputing Centre and provide case studies focused on platelet transport, implicitly validating the accuracy of our tool. The tests show that this versatile framework combines unprecedented accuracy with massive performance, rendering it suitable for upcoming exascale architectures.
Rules Induced by Symbolic Machine Learning Algorithms Using Rough Sets Reducts for Selecting Features : An Empirical Comparison with Other Filters

Feature Subset Selection — FSS — is an important problem within the Machine Learning —ML— area where the learning algorithm is faced with the problem of selecting relevant features while ignoring the rest. Rough Sets Theory is a mathematical tool to deal with vagueness and uncertainty information. One of the main features of this approach are the reducts. A reduct is a minimal feature set that preserves the ability to discern each object from the others. This work presents several experiments run one nine natural datasets, results and comparisons using Rough Sets Reducts and other Filters for FSS. After FSS the reduced datasets are used as input to two symbolic ML algorithms that induce if then rules. The purpose of this work is to investigate not only the accuracy of the induced classifiers but also its complexity in terms of the number of rules in each classifier.
Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from “Waiters are very friendly and the pasta is simply average” could be (‘Waiters’, positive, ‘friendly’). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.