Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual RNA-seq of maize and H. seropedicae ZAE94 association, in different doses of nitrate, reveals novel insights into Plant-PGPB-environment relationship

The interactions between plants, beneficial bacteria and their environment are profoundly shaped by various environmental factors, including light, temperature, water availability, and soil quality. Despite efforts to elucidate the molecular mechanisms involved in the association between plants and beneficial bacteria, like Plant Growth-Promoting Bacteria (PGPB), with many studies focusing on the transcriptional reprogramming in the plant, there is no report on the modulation of genetic controls from both plant and associated bacteria standpoints, in response to environment. The main goal of this study was to investigate the relationship between plant-bacteria-environment signaling, using as a model maize plants inoculated with H. seropedicae ZAE94 and cultivated with different doses of N (0.3 and 3 mM). For this purpose, we performed rRNA-depleted RNA-seq to determine the global gene expression of both maize roots and associated H. seropedicae ZAE94. Our results revealed a differential modulation of maize nitrogen metabolism, phytohormone and cell wall responses when associated with H. seropedicae ZAE94 at different N concentrations. In parallel, a modulation of the bacterial metabolism could be observed, by regulating genes involved in transport, secretion system, cell mobility, oxidoreductases, and chemotaxis, when bacteria were associated with maize roots and cultivated at different doses of N. The molecular and phenotypic data of maize plantlets suggested that different doses of N fertilization differentially regulated the beneficial effects of bacterial inoculation, as higher doses (3 mM) favored shoot elongation and lower doses (0.3 mM) favored increase in plant biomass. Our results provide a valuable integrated overview of differentially expressed genes in both maize and associated H. seropedicae ZAE94 in response to different N availability, revealing new insights into pathways involved in grass-PGPB associations.
Graphflow: An Active Graph Database

Many applications detect the emergence or deletion of certain subgraphs in their input graphs continuously. In order to evaluate such continuous subgraph queries, these applications resort to inefficient or highly specialized solutions because existing graph databases are passive systems that only support one-time subgraph queries. We demonstrate Graphflow, a prototype active graph data-base that evaluates general one-time and continuous subgraph queries. Graphflow supports the property graph data model and the Cypher++ query language, which extends Neo4j's declarative Cypher language with subgraph-condition-action triggers. At the core of Graphflow's query processor are two worst-case optimal join algorithms called Generic Join and our new Delta Generic Join algorithm for one-time and continuous subgraph queries, respectively.
3D graphics compression and rendering framework

This paper presents a complete system for computer graphics compression and transmission showing MPEG-4 graphics compression standard tool performance to encode 3D graphics objects. We employed a web based receiver to decode and render 3D objects. We provide a compression evaluation of user generated scanned 3D body models, which are huge in size and presented in XML format. A web based daemon is developed that can receive, decode and render such encoded 3D graphics objects.
Relative Value of Inflammatory, Hemostatic, and Rheological Factors for Incident Myocardial Infarction and Stroke: The Edinburgh Artery Study

Background— The aim of our present study was to compare the association of a wide range of 17 biomarkers of inflammation, hemostasis, and blood rheology with incident heart disease and stroke after accounting for an indicator of subclinical atherosclerotic disease and traditional risk factors and also to determine their incremental predictive ability. Methods and Results— We used data from the Edinburgh Artery Study, a population cohort study started in 1987 that comprised 1592 men and women aged 55 to 74 years. Subjects were followed for a mean of 17 years, and 416 of them suffered at least 1 cardiovascular event. In analyses adjusted for cardiovascular risk factors and history of cardiovascular disease (CVD): C-reactive protein, interleukin-6, fibrinogen, fibrin D-dimer, tissue plasminogen activator (t-PA), leukocyte elastase, and lipoprotein(a) (all P<0.01), as well as von Willebrand factor and plasma viscosity (both P<0.05), had significant hazard ratios for incident CVD. Further adjustment for a measure of subclinical atherosclerosis (ankle brachial index) had little impact on these associations. The hazard ratios (95% CI) for incident CVD between top and bottom tertiles in the latter analysis were 1.78 (1.30 to 2.45) for C-reactive protein, 1.85 (1.33 to 2.58) for interleukin-6, and 1.76 (1.35 to 2.31) for fibrinogen. Single biomarkers provided little additional discrimination of incident CVD to that obtained from cardiovascular risk factors and the ankle brachial index. An incremental score of multiple markers [interleukin-6, t-PA, intercellular adhesion molecule 1, and lipoprotein(a)] provided some added discrimination. Conclusions— Several “novel” risk factors predicted CVD after adjustments for conventional risk factors and also for a measure of asymptomatic disease. However, their incremental predictive ability was modest and their clinical utility remains uncertain.
Dynamic self-invalidation: reducing coherence overhead in shared-memory multiprocessors

The paper introduces dynamic self-invalidation (DSI), a new technique for reducing cache coherence overhead in shared-memory multiprocessors. DSI eliminates invalidation messages by having a processor automatically invalidate its local copy of a cache block before a conflicting access by another processor. Eliminating invalidation overhead is particularly important under sequential consistency: where the latency of invalidating outstanding copies can increase a program's critical path. DSI is applicable to software, hardware, and hybrid coherence schemes. We evaluate DSI in the context of hardware directory-based write-invalidate coherence protocols. Our results show that DSI reduces execution time of a sequentially consistent full-map coherence protocol by as much as 41%. This is comparable to an implementation of weak consistency that uses a coalescing write-buffer to allow up to 16 outstanding requests for exclusive blocks. When used in conjunction with weak consistency DSI can exploit tear-off blocks-which eliminate both invalidation and acknowledgment messages-for a total reduction in messages of up to 26%.
Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism

Motivation: Identifying drug–drug interactions (DDIs) is a critical process in drug administration and drug development. Clinical support tools often provide comprehensive lists of DDIs, but they usually lack the supporting scientific evidences and different tools can return inconsistent results. In this article, we propose a novel approach that integrates text mining and automated reasoning to derive DDIs. Through the extraction of various facts of drug metabolism, not only the DDIs that are explicitly mentioned in text can be extracted but also the potential interactions that can be inferred by reasoning. Results: Our approach was able to find several potential DDIs that are not present in DrugBank. We manually evaluated these interactions based on their supporting evidences, and our analysis revealed that 81.3% of these interactions are determined to be correct. This suggests that our approach can uncover potential DDIs with scientific evidences explaining the mechanism of the interactions. Contact: luis.tari@roche.com
Shared Decision Making: A Fundamental Tenet in a Conceptual Framework of Integrative Healthcare Delivery

With the increased usage of complementary and alternative medicine (CAM) in the US comes a need for evidence-based and integrated care systems which encourage open communication between patients and providers. This paper introduces a conceptual framework for integrative care delivery, with shared decision making being the “connecting force” between holistic treatment and improved health outcomes for patients.
Dimming in Iran since the 2000s and the potential underlying causes

This study investigates the spatio‐temporal variations of global solar radiation and its relationships with cloud cover (CC) during 1998–2015 in Iran, where no previous information about the variations of solar radiation based on direct measurements exists. The variations in the mentioned variables were detected using a modified Mann–Kendall trend test and the magnitudes of trends were estimated by using the Theil–Sen's slope estimator method. A widespread dimming with a magnitude of about −4.3 and −1.7% per decade is found under all‐ and clear‐sky conditions in Iran since the 2000s (median over nine observation sites). A significant increase in CC was observed at most of the stations studied, at 20.0% per decade (median over nine sites). The decrease in the amount of all‐sky global solar radiation seems to have occurred as a combined effect of aerosol and CC increase over Iran. However, in the south of Iran and at the station Tehran dimming appears to be mostly due to an increase in the amount of aerosol loadings.
Chemolithoautotrophic arsenite oxidation by a thermophilic Anoxybacillus flavithermus strain TCC9-4 from a hot spring in Tengchong of Yunnan, China

A new facultative chemolithoautotrophic arsenite (AsIII)-oxidizing bacterium TCC9-4 was isolated from a hot spring microbial mat in Tengchong of Yunnan, China. This strain could grow with AsIII as an energy source, CO2–HCO3- as a carbon source and oxygen as the electron acceptor in a minimal salts medium. Under chemolithoautotrophic conditions, more than 90% of 100 mg/L AsIII could be oxidized by the strain TCC9-4 in 36 h. Temperature was an important environmental factor that strongly influenced the AsIII oxidation rate and AsIII oxidase (Aio) activity; the highest Aio activity was found at the temperature of 40∘C. Addition of 0.01% yeast extract enhanced the growth significantly, but delayed the AsIII oxidation. On the basis of 16S rRNA phylogenetic sequence analysis, strain TCC9-4 was identified as Anoxybacillus flavithermus. To our best knowledge, this is the first report of arsenic (As) oxidation by A. flavithermus. The Aio gene in TCC9-4 might be quite novel relative to currently known gene sequences. The results of this study expand our current understanding of microbially mediated As oxidation in hot springs.
Concentration-Dependent Mode of Interaction of Angiotensin II Receptor Blockers with Uric Acid Transporter

Serum uric acid (SUA) is currently recognized as a risk factor for cardiovascular disease. It has been reported that an angiotensin II receptor blocker (ARB), losartan, decreases SUA level, whereas other ARBs, such as candesartan, have no lowering effect. Because the renal uric acid transporter (URAT1) is an important factor controlling the SUA level, we examined the involvement of URAT1 in those differential effects of various ARBs on SUA level at clinically relevant concentrations. This study was done by using URAT1-expressing Xenopus oocytes. Losartan, pratosartan, and telmisartan exhibited cis-inhibitory effects on the uptake of uric acid by URAT1, whereas at higher concentrations, only telmisartan did, and these ARBs reduced the uptake in competitive inhibition kinetics. On the other hand, candesartan, EXP3174 [2-n-butyl-4-chloro-1-[(2′-(1H-tetrazol-5-yl)biphenyl-4-yI)methyl]imidazole-5-carboxylic acid] (a major metabolite of losartan), olmesartan, and valsartan were not inhibitory. Preloading of those ARBs in the oocytes enhanced the URAT1-mediated uric acid uptake, showing a trans-stimulatory effect. The present study is a first demonstration of the differential effects of ARBs on URAT1 that some ARBs are both cis-inhibitory and trans-stimulatory, depending on concentration, whereas others exhibit either a trans-stimulatory or cis-inhibitory effect alone, which could explain the clinically observed differential effects of ARBs on SUA level. Furthermore, it was found that such differential effects of ARBs on URAT1 could be predicted from the partial chemical structures of ARBs, which will be useful information for the appropriate use and development of ARBs without an increase of SUA.
A Simple Neural Network Exhibiting Selective Activation of Neuronal Ensembles: From Winner-Take-All to Winners-Share-All

A neuroecological equation of the Lotka-Volterra type for mean firing rate is derived from the conventional membrane dynamics of a neural network with lateral inhibition and self-inhibition. Neural selection mechanisms employed by the competitive neural network receiving external input sare studied with analytic and numerical calculations. A remarkable finding is that the strength of lateral inhibition relative to that of self-inhibition is crucial for determining the steady states of the network among three qualitatively different types of behavior. Equal strength of both types of inhibitory connections leads the network to the well-known winner-take-all behavior. If, however, the lateral inhibition is weaker than the self-inhibition, a certain number of neurons are activated in the steady states or the number of winners is in general more than one (the winners-share-all behavior). On the other hand, if the self-inhibition is weaker than the lateral one, only one neuron is activated, but the winner is not necessarily the neuron receiving the largest input. It is suggested that our simple network model provides a mathematical basis for understanding neural selection mechanisms.
Finite-dimensional Gaussian approximation with linear inequality constraints

Introducing inequality constraints in Gaussian process (GP) models can lead to more realistic uncertainties in learning a great variety of real-world problems. We consider the finite-dimensional Gaussian approach from Maatouk and Bay (2017) which can satisfy inequality conditions everywhere (either boundedness, monotonicity or convexity). Our contributions are threefold. First, we extend their approach in order to deal with general sets of linear inequalities. Second, we explore several Markov Chain Monte Carlo (MCMC) techniques to approximate the posterior distribution. Third, we investigate theoretical and numerical properties of the constrained likelihood for covariance parameter estimation. According to experiments on both artificial and real data, our full framework together with a Hamiltonian Monte Carlo-based sampler provides efficient results on both data fitting and uncertainty quantification.
Privacy-preserving deep learning

Deep learning based on artificial neural networks is a very popular approach to modeling, classifying, and recognizing complex data such as images, speech, and text. The unprecedented accuracy of deep learning methods has turned them into the foundation of new AI-based services on the Internet. Commercial companies that collect user data on a large scale have been the main beneficiaries of this trend since the success of deep learning techniques is directly proportional to the amount of data available for training. Massive data collection required for deep learning presents obvious privacy issues. Users' personal, highly sensitive data such as photos and voice recordings is kept indefinitely by the companies that collect it. Users can neither delete it, nor restrict the purposes for which it is used. Furthermore, centrally kept data is subject to legal subpoenas and extrajudicial surveillance. Many data owners-for example, medical institutions that may want to apply deep learning methods to clinical records-are prevented by privacy and confidentiality concerns from sharing the data and thus benefitting from large-scale deep learning. In this paper, we present a practical system that enables multiple parties to jointly learn an accurate neural-network model for a given objective without sharing their input datasets. We exploit the fact that the optimization algorithms used in modern deep learning, namely, those based on stochastic gradient descent, can be parallelized and executed asynchronously. Our system lets participants train independently on their own datasets and selectively share small subsets of their models' key parameters during training. This offers an attractive point in the utility/privacy tradeoff space: participants preserve the privacy of their respective data while still benefitting from other participants' models and thus boosting their learning accuracy beyond what is achievable solely on their own inputs. We demonstrate the accuracy of our privacy-preserving deep learning on benchmark datasets.
A genome-wide cross-trait analysis identifies shared loci and causal relationships of obesity and lipidemic traits with psoriasis

Background Obesity and dyslipidemia, major global health concerns, have been linked to psoriasis, but previous studies faced methodological limitations and their shared genetic basis remains unclear. This study examines various obesity-related and lipidemic traits as potential contributors to psoriasis development, aiming to clarify their genetic associations and potential causal links. Methods Summary statistics from genome-wide association studies (GWAS) conducted for obesity-related traits (body mass index (BMI), waist-to-hip ratio (WHR), and waist-to-hip ratio adjusted for the body mass index (WHRadjBMI)) and lipidemic traits (high-density lipoprotein (HDL), LDL, triglyceride (TG), total Cholesterol (TC), apolipoprotein A1 (apoA1), apolipoprotein B (apoB), and apolipoprotein E (apoE)) and psoriasis, all in populations of European ancestry, were used. We quantified genetic correlations, identified shared loci and explored causal relationship across traits. Results We found positive genetic correlation between BMI and psoriasis (rg=0.22, p=2.44×10-18), and between WHR and psoriasis (rg=0.19, p=1.41×10-12). We further found the positive genetic correlation between psoriasis and WHRadjBMI(rg=0.07, p=1.81×10-2) the genetic correlation, in while the effect of BMI was controlled for. We identified 14 shared loci underlying psoriasis and obesity-related traits and 43 shared loci between psoriasis and lipidemic traits via cross-trait meta-analysis. Mendelian randomization (MR) supported the causal roles of BMI (IVW OR=1.483, 95%CI=1.333-1.649), WHR (IVW OR=1.393, 95%CI=1.207-1.608) and WHRadjBMI (IVW OR=1.18, 95%CI=1.047-1.329) in psoriasis, but not observe any significant association between lipidemic traits and the risk of psoriasis. Genetic predisposition to psoriasis did not appear to affect the risk of obesity and lipidemic traits. Conclusions An intrinsic link between obesity-related traits and psoriasis has been demonstrated. The genetic correlation and causal role of obesity-related traits in psoriasis highlight the significance of weight management in both the prevention and treatment of this condition.
Decentralized formation of arbitrary multi-robot lattices

In this paper, we propose a decentralized algorithm to form arbitrary repeating formations of multiple robots. Methods are known to form specific kinds of repeating structures such as squares, triangles, and hexagons by modeling each robot as a particle that responds to attractive and repulsive forces generated by nearby robots. However, such methods are generally designed by hand for one specific type of lattice. Our approach is more general, in the sense that we present a single algorithm, for which a description of the desired repeating pattern is part of the input. We represent this pattern as a directed graph, in which edges show the desired rigid body transformations between the local frames of pairs of neighbor robots. The robots autonomously organize themselves into a family of rooted trees, and use these trees to perform task assignments locally and without conflicts. We show, via our simulated implementation, that our algorithm works for robot systems with hundreds of robots to form various lattice patterns. Our experiments also show that the approach can recover rapidly from robot failures, even if those failures impact a large fraction of the robot population.
Ontology-Based Management of the Telehealth Smart Home, Dedicated to Elderly in Loss of Cognitive Autonomy

Taking care of an elderly in loss of cognitive autonomy is a challenging task. Artificial agents, such as the Telehealth Smart Home (TSH) system can facilitate that task. However, the design of such a system is, in itself, a challenging task as well. In this paper, we present an ontology-based model of the TSH, which is designed in a modularized approach and implemented in OWL using Protege2000. Our main goal is to take advantage of the full potential of ontologies to describe the domain, in order to provide an effective base for the development, the configuration and the execution of software applications. As an example, instantiations of the ontologies of the TSH are needed to initialize the Bayesian networks used to recognize the activity the patient is probably performing. They are also involved in the learning process of the life habits of the TSH’s occupant, and in the system maintenance.
Privacy Issues with Sharing and Computing Reputation across Communities

Online communities are the new-age platform for sharing information and experiences with groups of people. Members of a community are dealing with the daily conflict of having to decide what information they are willing to disclose in order to increase their reputation. Fear of possible privacy loss may lead a member to avoid sharing information with others, even when acting anonymously. Privacy concerns are amplified when users are members of multiple communities and their overall reputation is obtained from their reputation in each community. Disclosing a piece of reputation-related information in one community may cause privacy loss in another community and vice versa. This paper outlines the privacy concerns in the Cross-Community Reputation (CCR) model for sharing reputation knowledge across communities. These privacy concerns are discussed and modeled, and a policy-based approach that copes with them is presented.
Characterisation of wood-fibre—based materials using image analysis

Wood fibres are the main constituent of paper and are also used to alter properties of plastics in wood-fibre—based composite materials. The manufacturing of these materials involves numerous parameters that determine the quality of the products. The link between the manufacturing parameters and the final products can often be found in properties of the microstructure, which calls for advanced characterisation methods of the materials. Computerised image analysis is the discipline of using computers to automatically extract information from digital images. Computerised image analysis can be used to create automated methods suitable for the analysis of large data volumes. Inherently these methods give reproducible results and are not biased by individual analysts. In this thesis, three-dimensional X-ray computed tomography (CT) at micrometre resolution is used to image paper and composites. Image analysis methods are developed to characterise properties of individual fibres, properties of fibre—fibre bonds, and properties of the whole fibre networks based on these CT images. The main contributions of this thesis is the development of new automated image-analysis methods for characterisation of wood-fibre—based materials. This include the areas of fibre—fibre contacts and the free—fibre lengths. A method for reduction of phase contrast in mixed mode CT images is presented. This method retrieves absorption from images with both absorption and phase contrast. Curvature calculations in volumetric images are discussed and a new method is proposed that is suitable for three-dimensional images of materials with wood fibres, where the surfaces of the objects are close together.