Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Respiratory Disorders on Smoking Cessation and Re-Initiation in an Italian Cohort Study

The present study aims to prospectively assess the influence of respiratory disorders on smoking cessation and re-initiation. Three population-based Italian cohorts answered a questionnaire on respiratory health and smoking habits during 1998–2001 and after a mean follow-up (SD) of 9.1 (0.8) years. Out of 1874 current smokers and 1166 ex-smokers at baseline, 965 (51.5%) and 735 (63.0%) reported their smoking status at follow-up. From current smokers, 312 had stopped smoking at follow-up, while 86 ex-smokers had resumed smoking. People reporting asthma at baseline were more likely to stop smoking than the other subjects (48.6% vs. 31.7%), while people reporting allergic rhinitis or chronic cough/phlegm had a higher probability to resume smoking (16.7% vs. 10.5% and 20.7% vs. 10.4%, respectively). In the multivariable logistic model, smoking relapse strongly decreased with increasing abstinence duration in people without chronic cough/phlegm (OR for ≥7.5 years vs. <7.5 years = 0.23, 95% CI 0.20–0.27), while no effect was detected in people with chronic cough/phlegm (p for interaction = 0.039). Smoking cessation was enhanced in asthmatic subjects, while people with allergic rhinitis or chronic cough/phlegm were at higher risk to resume smoking. Chronic cough/phlegm blunted the decrease in smoking resumption associated with longer abstinence duration.
Modelling cascading failures in networks with the harmonic closeness

Many studies on cascading failures adopt the degree or the betweenness of a node to define its load. From a novel perspective, we propose an approach to obtain initial loads considering the harmonic closeness and the impact of neighboring nodes. Based on simulation results for different adjustable parameter θ, local parameter δ and proportion of attacked nodes f, it is found that in scale-free networks (SF networks), small-world networks (SW networks) and Erdos-Renyi networks (ER networks), there exists a negative correlation between optimal θ and δ. By the removal of the low load node, cascading failures are more likely to occur in some cases. In addition, we find a valuable result that our method yields better performance compared with other methods in SF networks with an arbitrary f, SW and ER networks with large f. Moreover, the method concerning the harmonic closeness makes these three model networks more robust for different average degrees. Finally, we perform the simulations on twenty real networks, whose results verify that our method is also effective to distribute the initial load in different real networks.
Development of microfluidic devices for biomedical and clinical application

This review focuses on the development and use of microfluidic devices within a clinical setting. The underlying theoretical background of microfluidics is briefly elucidated. The materials and techniques used to fabricate the devices and their applicability to the clinical environment are described. The current research in this area is appraised and projections for future applications are discussed. Copyright © 2010 Society of Chemical Industry
Chemical synthesis of a dextran model, poly‐α‐(1 → 6)‐anhydro‐D‐glucopyranose

Poly‐α‐(1 → 6)‐anhydro‐D‐glucopyranose has been synthesized by the phosphorus pentafluoride‐catalyzed polymerization of l,6‐anhydro‐2,3,4‐tri‐O‐benzyl‐β‐D‐glucopyranose and subsequent debenzylation. Physical characterization establishes its high polymeric character and stereoregularity.
Novel function for anionic clays: selective transition metal cation uptake by diadochy

Here we report for the first time, the extremely high and selective uptake of transition metal cations such as Cu2+, Ni2+, Co2+ and Zn2+ by two anionic clays of nominal composition, [Mg6Al2(OH)16 ]2+[CO3·4H2O]2– and [Mg2Al(OH)6 ]+[NO3·2H2O]–. We postulate that the principal mechanism of this selective cation uptake is by substitution in the anionic clay structure for mainly Mg through a process known as diadochy. This newly discovered function for anionic clays is useful for the decontamination and immobilization of the above transition metals at room temperature and may find applications in the remediation of metal contaminated soils, filtration of drinking water as well as decontamination of waste waters.
On the Origin of the Formation and Stability of Physical Gels of Di-O-benzylidene-d-sorbitol

The thermal and fluorescence properties of gels formed from 1,3:2,4-di-O-benzylidene-d-sorbitol (DBS) and 1,3:2,4:5,6-tri-O-benzylidene-d-sorbitol (TBS) in ethylene glycol (EG) and other alcoholic solvents were investigated. The fluorescence of the benzylidene ground state dimer was observed even in the solution state, indicating that DBS and TBS tend to aggregate in EG. The concentration dependence of the DBS−EG and TBS−EG fluorescence spectra shows that the main structures of DBS and TBS fibers are identical with one another and that each ten-member bicyclo ring overlaps with other rings: the 1,3-O-benzylidene and 2,4-O-benzylidene groups overlap with each of the other units. The fluorescence results show that some 5,6-O-benzylidene groups of TBS interacted with 1,3- and 2,4-benzylidene groups, which is the reason that TBS−EG gels are less stable than DBS−EG gels. The very slow heating of DBS−EG gels showed that needlelike crystals formed at around 370 K for DBS concentrations of 1, 5, and 10%. This in.
A biologist's guide to model selection and causal inference

A goal of many research programmes in biology is to extract meaningful insights from large, complex datasets. Researchers in ecology, evolution and behavior (EEB) often grapple with long-term, observational datasets from which they construct models to test causal hypotheses about biological processes. Similarly, epidemiologists analyse large, complex observational datasets to understand the distribution and determinants of human health. A key difference in the analytical workflows for these two distinct areas of biology is the delineation of data analysis tasks and explicit use of causal directed acyclic graphs (DAGs), widely adopted by epidemiologists. Here, we review the most recent causal inference literature and describe an analytical workflow that has direct applications for EEB. We start this commentary by defining four distinct analytical tasks (description, prediction, association, causal inference). The remainder of the text is dedicated to causal inference, specifically focusing on the use of DAGs to inform the modelling strategy. Given the increasing interest in causal inference and misperceptions regarding this task, we seek to facilitate an exchange of ideas between disciplinary silos and provide an analytical framework that is particularly relevant for making causal inference from observational data.
Diversification in the Arctic: Biogeography and Systematics of the North American Micranthes (Saxifragaceae)

Abstract Flora endemic to the cold habitats of the Northern Hemisphere provide important models for investigating diversification and disjunctions, given both the intense climatic fluctuations of these areas in the recent past and the fascinating biogeographic patterns of today's Arctic-alpine plant communities. Micranthes Haw. (Saxifragaceae), a clade of small-flowered herbaceous flowering plants comprising ∼80 species, is an ideal group for investigating the evolution and diversification of plants in montane and Arctic ecosystems. Micranthes has proven to be a particularly challenging clade to unravel taxonomically due in part to rampant auto- and allopolyploidy, hybridization, and cryptic speciation. With the goal of providing an updated conspectus for this group, we build upon a recent large phylogenomic analysis to help elucidate the evolution of Micranthes. Here, we present new downstream analyses including diversification analyses, biogeographical reconstructions, and a comparison of methods for dating phylogenomic data sets. To complement these new analyses, we also synthesize chromosomal variation, new observations regarding morphology and species identification, comprehensive field studies, and an extensive review of the literature for Saxifragaceae and Micranthes. A new perspective on the systematics and taxonomy of Micranthes is provided.
Graph NoSQL Data Warehouse Creation

Over the last few years, NoSQL systems are gaining strong popularity and a number of decision makers are using it to implement their warehouses. In the recent years, many web applications are moving towards the use of data in the form of graphs. For example, social media and the emergence of Facebook, LinkedIn and Twitter have accelerated the emergence of the NoSQL database and in particular graph-oriented databases that represent the basic format with which data in these media is stored. Based on these findings and in addition to the absence of a clear approach which allows the creation of data warehouse under NoSQL model, we propose, in this paper, an approach to create a Graph-oriented Data warehouse. We propose the transformation of Dimensional Fact Model into Graph Dimensional Model. Then, we implement the Graph Dimensional Model using java routines in Talend Data Integration tool (TOS).
A Water-Soluble Phospholipid Polymer as a New Biocompatible Synthetic DNA Carrier

The purpose of this work was the preparation of a water-soluble DNA complex with a biocompatible phospholipid polymer for delivering DNA into a target cell. The phospholipid polymer, poly[2-methacryloyloxyethyl phosphorylcholine (MPC)-co-2-aminoethyl methacrylate (AEMA)] (poly(MPC-co-AEMA)), was synthesized and the functions of poly(MPC-co-AEMA) as a DNA carrier were compared with those of a conventional cationic DNA carrier such as poly(L-lysine). We investigated the toxicity of these polymers and the water solubility, structures, and nuclease resistance of the associated DNA/polymer complexes. An evaluation of toxicity in a colony forming test using Chinese hamster fibroblast cells (V-79 cells) indicated that the toxicity of poly(MPC-co-AEMA) is about 40% of that of poly(L-lysine). In the case of a salmon sperm DNA/poly(L-lysine) complex, a water-insoluble precipitate formed. In contrast, the DNA/poly(MPC-co-AEMA) complex was completely water-soluble. Although the DNA/poly(L-lysine) complex did not provide a CD spectrum, the DNA/poly(MPC-co-AEMA) complex did. DNase I hardly degraded the DNA in the DNA/poly(MPC-co-AEMA) complex. To demonstrate the transient expression of β-galactosidase, a plasmid DNA/poly(MPC-co-AEMA) complex was incubated with V-79 cells with chloroquine treatment. Under a phase-contrast microscope after 5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside (X-gal) staining, more stained cells due to the transient expression of β-galactosidase were observed in a sample with the poly(MPC-co-AEMA) complex than in a sample with the poly-(L-lysine) complex. From these results came the following conclusions: poly(MPC-co-AEMA) is a useful water-soluble polymer that can form a complex with DNA, and DNA/poly(MPC-co-AEMA) has good potential for DNA delivery into cells.
Early Automatic Detection of False Information in Twitter Event Considering Occurrence Scale and Time Series

With the prevalence and rapid proliferation of SNS, dissemination of false information has become a big problem. In this paper, targeting Twitter, we propose a two-step approach for early detection of false information based on machine learning, which considers the event occurrence scale and the time series of tweets that compose the event. In Step 1, in the early stage of an event, whether it is false or true is decided if the prediction probability is high enough. In Step 2, the events whose authenticity cannot be determined in Step 1 are targeted for tracking, and their authenticity is ascertained as the tweets related to the events increase gradually. The experimental results comparing five machine learning models show that SVM is the optimal model for both steps and that our approach can achieve early detection of false information.
Machine Learning as a Service: Challenges in Research and Applications

This study aims to evaluate the current state of research with regards to Machine Learning as a Service (MLaaS) and to identify challenges and research fields of this novel topic. First, a literature review on a basket of eight leading journals was performed. We motivate this study by identifying a lack of studies in the field of MLaaS. The structured literature review was further extended to established scientific databases relevant in this field. We found 30 contributions on MLaaS. As a result of the analysis we grouped them into four key concepts: Platform, Applications; Performance Enhancements and Challenges. Three of the derived concepts are discussed in detail to identify future research areas and to reveal challenges in research as well as in applications.
Fast Clustering of Hypergraphs Based on Bipartite-Edge Restoration and Node Reachability

In recent years, studies on hypergraph that is a generalization of graphs and can represent relationships of two or more nodes, have been actively conducted, however, clustering methods over them have not been established yet. In this study, we propose a fast clustering method where a hypergraph is expanded to a bipartite graph by treating hyperedges as nodes, the relationship between the node and the hyperedge is defined by TFIDF, and the value is treated as the weight of the bipartite edge. Our algorithm can efficiently grasp clusters by restoring bipartite edges in descending order of TFIDF weights and merging nodes reachable along the edges into clusters. Based on the bipartite edge restoration and node reachability, we can realize an efficient and effective clustering for a large-scale hypergraph. Furthermore, by performing our method to hyperedges, one can obtain hard-partitioned hyperedges, and exploiting them, also obtain soft-partitioned nodes. Experimental evaluations using small-scale artificial and large-scale real datasets show that our method outputs more accurate clusters in terms of F1 score between the estimated and the actual clusters, and modularity. In addition, the execution time of our method is significantly faster than the compared existing method. As for the soft-clustering, our method produces results with a more balanced size of clusters.
A GIS-MCDA BASED ASSESSMENT FOR SITING WIND FARMS AND ESTIMATION OF THE TECHNICAL GENERATION POTENTIAL FOR WIND POWER IN SERBIA

ABSTRACT The share of wind power in energy consumption is continuously increasing at the national level as well as global. Many national strategies plan an increase in the installed capacity of wind farms in the future. Accordingly, this paper provides an assessment of spatial suitability for wind farm siting and estimation of wind power potential for the territory of Serbia. The unsuitable areas (75.91% of the territory) are extracted based on sociopolitical, physical, conservation, and pseudo-economic constraints (22 factors). The available land (24.09%) was evaluated applying the multi-criteria decision analysis (MCDA) in the geographical information system (GIS) environment using several criteria (wind speed, elevation, slope, proximity to roads and power lines, distance from urban areas, land cover). After correction with the suitability factor, the estimated technical generation power potential is 97.57 TWh, i.e. 82.93 TWh with 15% included losses. The most prospective areas for the development of wind projects are the north (Bačka region), northeast (Banat region, along the lower Danube river valley), and east of Serbia (mountains region and Negotinska Krajina region).
Shape analysis of gamma rhythm supports a superlinear inhibitory regime in an inhibition-stabilized network

Visual inspection of stimulus-induced gamma oscillations (30-70 Hz) often reveals a non-sinusoidal shape. Such distortions are a hallmark of non-linear systems and are also observed in mean-field models of gamma oscillations. A thorough characterization of the shape of the gamma cycle can therefore provide additional constraints on the operating regime of such models. However, the gamma waveform has not been quantitatively characterized, partially because the first harmonic of gamma, which arises because of the non-sinusoidal nature of the signal, is typically weak and gets masked due to a broadband increase in power related to spiking. To address this, we recorded local field potential (LFP) from the primary visual cortex (V1) of two awake female macaques while presenting full-field gratings or iso-luminant chromatic hues that produced huge gamma oscillations with prominent peaks at harmonic frequencies in the power spectra. We found that gamma and its first harmonic always maintained a specific phase relationship, resulting in a distinctive shape with a sharp trough and a shallow peak. Interestingly, a Wilson-Cowan (WC) model operating in an inhibition stabilized mode could replicate this shape, but only when the inhibitory population operated in the super-linear regime, as predicted recently. However, another recently developed model of gamma that operates in a linear regime driven by stochastic noise failed to produce salient harmonics or the observed shape. Our results impose additional constraints on models that generate gamma oscillations and their operating regimes.
Predicting soil properties from the Australian soil visible–near infrared spectroscopic database

There are reflectance spectra in the visible and near infrared wavelengths from some 20 000 archived samples of soil in Australia. Their particular forms depend on absorbances at specific wavelengths characteristic of components in the soil such as water, iron oxides, clay minerals and carbon compounds, and so one might expect to be able to predict soil properties from the spectra. We tested a tree‐based technique for the prediction of 24 soil properties. A tree is first constructed by the definition of rules that separate the data into fairly homogeneous groups for any given property on both the absorptions at specified wavelengths and other, categoric, variables. Then within each group the property is predicted from the absorptions at those wavelengths by ordinary least‐squares regression. The spectroscopic predictions of the soil properties were compared with actual values in a subset of sample data separated from the whole data for validation. The criteria of success that we used were the root mean squared error (RMSE) to measure the inaccuracy of our predictions, the mean error (ME) to measure their bias and the standard deviation of the error (SDE) to measure their imprecision. We also used the ratio of performance to deviation (RPD), which is the ratio of the standard deviation of the observed values to the RMSE of the predictions; the larger it is the better does the technique perform. We found good predictions (RPD>2) for clay and total sand content, for total organic carbon and total nitrogen, pH, cation exchange capacity, and exchangeable calcium, magnesium and sodium. Several other properties were moderately well predicted (1.5 ≤ RPD < 2); they included air‐dry water content, volumetric water content at field capacity and wilting point, bulk density, the contents of silt, fine sand and coarse sand, total and exchangeable potassium, total phosphorus and extractable iron. Properties that were poorly predicted (RPD < 1.5) include the carbon‐to‐nitrogen ratio, available phosphorus and exchangeable acidity. We conclude that even though the predictions are less accurate than direct measurements, the spectra are cheap yet valuable sources of information for predicting values of individual soil properties when large numbers of analyses are needed, for example, for soil mapping.
Decomposing the admixture statistic, D, suggests a negligible contribution due to archaic introgression into humans

It is widely accepted that non-African humans carry a few percent of Neanderthal DNA due to historical inter-breeding. However, methods used to infer a legacy all assume that mutation rate is constant and that back-mutations can be ignored. Here I decompose the widely used admixture statistic, D, in a way that allows the overall signal to be apportioned to different classes of contributing site. I explore three main characteristics: whether the putative Neanderthal allele is likely derived or ancestral; whether an allele is fixed in one of the two human populations; and the type of mutation that created the polymorphism, defined by the base that mutated and immediately flanking bases. The entire signal used to infer introgression can be attributed to a subset of sites where the putative Neanderthal base is common in Africans and fixed in non-Africans. Moreover, the four triplets containing highly mutable CpG motifs alone contribute 29%. In contrast, sites expected to dominate the signal if introgression has occurred, where the putative Neanderthal allele is absent from Africa and rare outside Africa, contribute negligibly. Together, these observations show that D does not capture a signal due to introgression but instead they support an alternative model in which a higher mutation rate in Africa drives increased divergence from the ancestral state.
Disturbance of the Glutamate-Glutamine Cycle, Secondary to Hepatic Damage, Compromises Memory Function

Glutamate fulfils many vital functions both at a peripheral level and in the central nervous system (CNS). However, hyperammonemia and hepatic failure induce alterations in glutamatergic neurotransmission, which may be the main cause of hepatic encephalopathy (HE), an imbalance which may explain damage to both learning and memory. Cognitive and motor alterations in hyperammonemia may be caused by a deregulation of the glutamate-glutamine cycle, particularly in astrocytes, due to the blocking of the glutamate excitatory amino-acid transporters 1 and 2 (EAAT1, EAAT2). Excess extracellular glutamate triggers mechanisms involving astrocyte-mediated inflammation, including the release of Ca2+-dependent glutamate from astrocytes, the appearance of excitotoxicity, the formation of reactive oxygen species (ROS), and cell damage. Glutamate re-uptake not only prevents excitotoxicity, but also acts as a vital component in synaptic plasticity and function. The present review outlines the evidence of the relationship between hepatic damage, such as that occurring in HE and hyperammonemia, and changes in glutamine synthetase function, which increase glutamate concentrations in the CNS. These conditions produce dysfunction in neuronal communication. The present review also includes data indicating that hyperammonemia is related to the release of a high level of pro-inflammatory factors, such as interleukin-6, by astrocytes. This neuroinflammatory condition alters the function of the membrane receptors, such as N-methyl-D-aspartate (NMDA), (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) AMPA, and γ-aminobutyric acid (GABA), thus affecting learning and spatial memory. Data indicates that learning and spatial memory, as well as discriminatory or other information acquisition processes in the CNS, are damaged by the appearance of hyperammonemia and, moreover, are associated with a reduction in the production of cyclic guanosine monophosphate (cGMP). Therefore, increased levels of pharmacologically controlled cGMP may be used as a therapeutic tool for improving learning and memory in patients with HE, hyperammonemia, cerebral oedema, or reduced intellectual capacity.
Editorial: Novel Plant Molecules Regulating the Interaction With Pathogenic and Beneficial Fungi

1 Institute for Sustainable Plant Protection, National Research Council of Italy, Florence, Italy, Department of Ecological and Biological Sciences, University of Tuscia, Viterbo, Italy, 3 Plant-Microbe Interactions, Department of Biology, Utrecht University, Utrecht, Netherlands, 4 Laboratorio de Biotecnología Agrícola y de Alimentos-Ingeniería en Agronomía, Colegio de Ciencias e Ingenierías El Politécnico, Universidad San Francisco de Quito USFQ, Quito, Ecuador