Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of Prokaryotic Communities Associated with Organic and Conventional Farming Systems

One of the most important challenges in agriculture is to determine the effectiveness and environmental impact of certain farming practices. The aim of present study was to determine and compare the taxonomic composition of the microbiomes established in soil following long-term exposure (14 years) to a conventional and organic farming systems (CFS and OFS accordingly). Soil from unclared forest next to the fields was used as a control. The analysis was based on RT-PCR and pyrosequencing of 16S rRNA genes of bacteria and archaea. The number of bacteria was significantly lower in CFS than in OFS and woodland. The highest amount of archaea was detected in woodland, whereas the amounts in CFS and OFS were lower and similar. The most common phyla in the soil microbial communities analyzed were Proteobacteria (57.9%), Acidobacteria (16.1%), Actinobacteria (7.9%), Verrucomicrobia (2.0%), Bacteroidetes (2.7%) and Firmicutes (4.8%). Woodland soil differed from croplands in the taxonomic composition of microbial phyla. Croplands were enriched with Proteobacteria (mainly the genus Pseudomonas), while Acidobacteria were detected almost exclusively in woodland soil. The most pronounced differences between the CFS and OFS microbiomes were found within the genus Pseudomonas, which significantly (p<0,05) increased its number in CFS soil compared to OFS. Other differences in microbiomes of cropping systems concerned minor taxa. A higher relative abundance of bacteria belonging to the families Oxalobacteriaceae, Koribacteriaceae, Nakamurellaceae and genera Ralstonia, Paenibacillus and Pedobacter was found in CFS as compared with OFS. On the other hand, microbiomes of OFS were enriched with proteobacteria of the family Comamonadaceae (genera Hylemonella) and Hyphomicrobiaceae, actinobacteria from the family Micrococcaceae, and bacteria of the genera Geobacter, Methylotenera, Rhizobium (mainly Rhizobium leguminosarum) and Clostridium. Thus, the fields under OFS and CFS did not differ greatly for the composition of the microbiome. These results, which were also confirmed by cluster analysis, indicated that microbial communities in the field soil do not necessarily differ largely between conventional and organic farming systems.
The STARK Framework for Spatio-Temporal Data Analytics on Spark

Big Data sets can contain all types of information: from server log files to tracking information of mobile users with their location at a point in time. Apache Spark has been widely accepted for Big Data analytics because of its very fast processing model. However, Spark has no native support for spatial or spatio-temporal data. Spatial filters or joins using, e.g., a contains predicate are not supported and would have to be implemented inefficiently by the users. Also, Spark cannot make use of, e.g., spatial distribution for optimal partitioning. Here we present our STARK framework that adds spatio-temporal support to Spark. It includes spatial partitioners, different modes for indexing, as well as filter, join, and clustering operators. In contrast to existing solutions, STARK integrates seamlessly into any (Scala) Spark program and provides more flexible and comprehensive operators. Furthermore, our experimental evaluation shows that our implementation outperforms existing solutions.
Effect of rifampicin and efavirenz on moxifloxacin concentrations when co-administered in patients with drug-susceptible TB

Objectives We compared the pharmacokinetics of moxifloxacin during rifampicin co-treatment or when dosed alone in African patients with drug-susceptible recurrent TB. Methods Patients in the intervention arm of the Improving Retreatment Success (IMPRESS) randomized controlled TB trial received 400 mg of moxifloxacin, with rifampicin, isoniazid and pyrazinamide in the treatment regimen. Moxifloxacin concentrations were measured in plasma during rifampicin-based TB treatment and again 4 weeks after treatment completion, when given alone as a single dose. Moxifloxacin concentration-time data were analysed using non-linear mixed-effects models. Results We included 58 patients; 42 (72.4%) were HIV co-infected and 40 (95%) of these were on efavirenz-based ART. Moxifloxacin pharmacokinetics was best described using a two-compartment disposition model with first-order lagged absorption and elimination using a semi-mechanistic model describing hepatic extraction. Oral clearance (CL/F) of moxifloxacin during rifampicin-based TB treatment was 24.3 L/h for a typical patient (fat-free mass of 47 kg), resulting in an AUC of 16.5 mg·h/L. This exposure was 7.8% lower than the AUC following the single dose of moxifloxacin given alone after TB treatment completion. In HIV-co-infected patients taking efavirenz-based ART, CL/F of moxifloxacin was increased by 42.4%, resulting in a further 30% reduction in moxifloxacin AUC. Conclusions Moxifloxacin clearance was high and plasma concentrations low in our patients overall. Moxifloxacin AUC was further decreased by co-administration of efavirenz-based ART and, to a lesser extent, rifampicin. The clinical relevance of the low moxifloxacin concentrations for TB treatment outcomes and the need for moxifloxacin dose adjustment in the presence of rifampicin and efavirenz co-treatment need further investigation.
A Platform for Free-Weight Exercise Monitoring with Passive Tags

Regular free-weight exercise helps to strengthen natural movements and stabilize muscles that are important to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes for activity sensing, recognition, and counting, etc. However, none of them have incorporated three key factors necessary for a practical free-weight exercise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system provides an integrated free-weight exercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and assessment. The rationale behind FEMO is 1) since each free-weight activity owns unique arm motions, the corresponding Doppler shift profile should be distinguishable to each other. 2) Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the quality of the activity. We implement FEMO with COTS RFID devices and conduct a two-week experiment. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activities, and provide valuable feedbacks for activity alignment.
Endothelin‐1 mediates natriuresis but not polyuria during vitamin D‐induced acute hypercalcaemia

Hypercalcaemia can occur under various pathological conditions, such as primary hyperparathyroidism, malignancy or granulomatosis, and it induces natriuresis and polyuria in various species via an unknown mechanism. A previous study demonstrated that hypercalcaemia induced by vitamin D in rats increased endothelin (ET)‐1 expression in the distal nephron, which suggests the involvement of the ET system in hypercalcaemia‐induced effects. In the present study, we demonstrate that, during vitamin D‐induced hypercalcaemia, the activation of ET system by increased ET‐1 is responsible for natriuresis but not for polyuria. Vitamin D‐treated hypercalcaemic mice showed a blunted response to amiloride, suggesting that epithelial sodium channel function is inhibited. We have identified an original pathway that specifically mediates the effects of vitamin D‐induced hypercalcaemia on sodium handling in the distal nephron without affecting water handling.
Multi-Staged NDVI Dependent Snow-Free Land-Surface Shortwave Albedo Narrowband-to-Broadband (NTB) Coefficients and Their Sensitivity Analysis

Narrowband-to-broadband conversion is a critical procedure for mapping land-surface broadband albedo using multi-spectral narrowband remote-sensing observations. Due to the significant difference in optical characteristics between soil and vegetation, NTB conversion is influenced by the variation in vegetation coverage on different surface types. To reduce this influence, this paper applies an approach that couples NTB coefficient with the NDVI. Multi-staged NDVI dependent NTB coefficient look-up tables (LUT) for Moderate Resolution Imaging Spectroradiometer (MODIS), Polarization and Directionality of Earth’s Reflectance (POLDER) and Advanced Very High Resolution Radiometer (AVHRR) were calculated using 6000 spectra samples collected from two typical spectral databases. Sensitivity analysis shows that NTB conversion is affected more by the NDVI for sensors with fewer band numbers, such as POLDER and AVHRR. Analysis of the validation results based on simulations, in situ measurements and global albedo products indicates that by using the multi-staged NDVI dependent NTB method, the conversion accuracies of these two sensors could be improved by 2%–13% on different NDVI classes compared with the general method. This improvement could be as high as 15%, on average, and 35% on dense vegetative surface compared with the global broadband albedo product of POLDER. This paper shows that it is necessary to consider surface reflectance characteristics associated with the NDVI on albedo-NTB conversion for remote sensors with fewer than five bands.
Bilinear CNN Models for Food Recognition

Due to the diversity of food types and the slight differences between different dishes, the genre of food images becomes a new challenge in the field of computer vision. To tackle this problem, recent efforts are focusing on designing hand-crafted features or extracting features automatically by using deep convolutional neural network. Although these methods have reported a series of success, their general architectures fail to capture the fine-grained features of similar dishes adequately. Inspired by the bilinear CNN models in the field of fine-grained classification, we have exploited such a similar structure in which two deep convolution networks are used as feature extractors and the outputs of them fused to obtain fine-grained features. These features are used to train the food classifier. We have conducted experiments on three publicly benchmark food datasets to evaluate the proposed architecture. The experiments exhibit that our method is comparable to the existing approaches.
Interactive Effects of Arbuscular Mycorrhizal Fungi and Copper Stress on Flowering Phenology and Reproduction of Elsholtzia splendens

Plant responses to heavy metal contamination may depend on the presence of arbuscular mycorrhizal fungi (AMF). Elsholtzia splendens is an indicator species for the presence of copper (Cu) mines because both its flowering phenology and reproduction are tolerant to heavy metals. To test whether effects of Cu on the flowering phenology and reproduction of E. splendens depend on the presence of AMF, we conducted a factorial experiment with two Cu treatments (with or without Cu addition) crossed with two AMF treatments (with or without AMF inoculation). Without AMF, Cu addition significantly delayed the onset dates, ending dates and peak dates of flowering and decreased flowering duration. However, AMF inoculation reversed the effects of Cu stress, with recovered flowering onset and ending dates and increased the flowering duration. Cu addition significantly decreased inflorescence width and number, inflorescence biomass, vegetative biomass and total seed number, but significantly increased 1000-seed weight. AMF inoculation significantly increased vegetative biomass. Two-way ANOVA results showed that the interactive effects between Cu addition and AMF inoculation were significant on the inflorescence number, vegetative biomass and total seed number. These results indicate that AMF can alleviate the Cu stress on the flowering phenology and reproduction of E. splendens.
The Functional SNPs in the 5’ Regulatory Region of the Porcine PPARD Gene Have Significant Association with Fat Deposition Traits

Peroxisome proliferator-activated receptor delta (PPARD) is a key regulator of lipid metabolism, insulin sensitivity, cell proliferation and differentiation. In this study, we identified two Single Nucleotide Polymorphisms (SNPs, g.1015 A>G and g.1018 T>C) constituting four haplotypes (GT, GC, AC and AT) in the 5’ regulatory region of porcine PPARD gene. Functional analysis of the four haplotypes showed that the transcriptional activity of the PPARD promoter fragment carrying haplotype AC was significantly lower than that of the other haplotypes in 3T3-L1, C2C12 and PK-15 cells, and haplotype AC had the lowest binding capacities to the nuclear extracts. Transcription factor 7-like 2 (TCF7L2) enhanced the transcription activities of promoter fragments of PPARD gene carrying haplotypes GT, GC and AT in C2C12 and 3T3-L1 cells, and increased the protein expression of PPARD gene in C2C12 myoblasts. TCF7L2 differentially bound to the four haplotypes, and the binding capacity of TCF7L2 to haplotype AC was the lowest. There were significant associations between -655A/G and fat deposition traits in three pig populations including the Large White × Meishan F2 pigs, France and American Large White pigs. Pigs with genotype GG had significantly higher expression of PPARD at both mRNA and protein level than those with genotype AG. These results strongly suggested that the SNPs in 5’ regulatory region of PPARD genes had significant impact on pig fat deposition traits.
Termites Are Resistant to the Effects of Fire at Multiple Spatial Scales

Termites play an important ecological role in many ecosystems, particularly in nutrient-poor arid and semi-arid environments. We examined the distribution and occurrence of termites in the fire-prone, semi-arid mallee region of south-eastern Australia. In addition to periodic large wildfires, land managers use fire as a tool to achieve both asset protection and ecological outcomes in this region. Twelve taxa of termites were detected by using systematic searches and grids of cellulose baits at 560 sites, clustered in 28 landscapes selected to represent different fire mosaic patterns. There was no evidence of a significant relationship between the occurrence of termite species and time-since-fire at the site scale. Rather, the occurrence of species was related to habitat features such as the density of mallee trees and large logs (>10 cm diameter). Species richness was greater in chenopod mallee vegetation on heavier soils in swales, rather than Triodia mallee vegetation of the sandy dune slopes. At the landscape scale, there was little evidence that the frequency of occurrence of termite species was related to fire, and no evidence that habitat heterogeneity generated by fire influenced termite species richness. The most influential factor at the landscape scale was the environmental gradient represented by average annual rainfall. Although termites may be associated with flammable habitat components (e.g. dead wood), they appear to be buffered from the effects of fire by behavioural traits, including nesting underground, and the continued availability of dead wood after fire. There is no evidence to support the hypothesis that a fine-scale, diverse mosaic of post-fire age-classes will enhance the diversity of termites. Rather, termites appear to be resistant to the effects of fire at multiple spatial scales.
An Engineered Version of Human PON2 Opens the Way to Understand the Role of Its Post-Translational Modifications in Modulating Catalytic Activity

The human paraoxonase 2 (PON2) has been described as a highly specific lactonase hydrolysing the quorum sensing molecule N-(3-oxododecanoyl)-L-homoserine lactone (3OC12-HSL) and having secondary esterase but not phosphotriesterase activity, in contrast with the related enzymes PON1 and PON3. It has been suggested that PON2 enzyme activity is dependent on glycosylation and its N-terminal region has been recently demonstrated to be a transmembrane domain mediating association to membranes. In the present study we describe a mutated form of PON2, lacking the above N-terminal region, which has been further stabilized by the insertion of six amino acidic substitutions. The engineered version, hence forth called rPON2, has been over-expressed in E.coli, refolded from inclusion bodies and purified, yielding an enzyme with the same characteristics as the full length enzyme. Therefore the first conclusion of this work was that the catalytic activity is independent from the N-terminus and protein glycosylation. The kinetic characterization confirmed the primary activity on 3OC12-HSL; accordingly, in vitro experiments of inhibition of the biofilm formed by Pseudomonas aeruginosa (PAO1) have demonstrated that rPON2 is more effective than PON1. In addition, we observed small but significant activity against organophosphorothiotes pesticides, m-parathion, coumaphos and malathion.The availability of fair amount of active protein allowed to pinpoint, by mass-spectrometry, ubiquitination of Lys 168 induced in rPON2 by HeLa extract and to correlate such post-translational modification to the modulation of catalytic activity. A mutational analysis of the modified residue confirmed the result.
Semantic video classification and feature subset selection under context and concept uncertainty

As large collections of videos become one key component of digital libraries, there is an urgent need of semantic video classification and feature subset selection to enable more effective video database organization and retrieval. However, most existing techniques for classifier training require a large number of labeled samples to learn correctly and suffer from the problems of context and concept uncertainty when only a limited number of labeled samples are available. To address the problems of context and concept uncertainty, we have proposed a novel framework to achieve incremental classifier training by integrating a limited number of labeled samples with a large number of unlabeled samples. Specifically, the contributions of this paper include: (a) Using the salient objects to achieve a middle-level understanding of video contents and enhance the quality of features on discriminating among different semantic video concepts; (b) Modeling the semantic video concepts by using the finite mixture models to approximate the class distributions of the relevant salient objects; (c) Developing an adaptive EM algorithm to integrate the unlabeled samples to enable incremental classifier training and address the problem of context uncertainty; (d) Proposing a cost-sensitive video classification technique to address the problem of concept uncertainty over time; (e) Supporting automatic video annotation via semantic classification. Our experimental results in a certain domain of medical education videos have also been provided a convincing proof of our conclusions.
Measuring the Resilience of Supply Chain Systems Using a Survival Model

Disruptions at any stage of a supply chain system can cause mammoth operational and financial losses to a firm. When there is a disruption with a supply chain system, it is highly desired that the system quickly recover. The ability of recovery is, in short, called resilience. This paper proposes a new measure of the resilience of a supply chain system based on the concept of survival and, subsequently, a survival model [Cox proportional hazard (Cox-PH) model]. The survival model represents a time interval or period from the time the system failed to function to the time the system gets back with its function (i.e., recovery). The input to the model is, thus, a failure event; the output from the model is the recovery time. This model has been implemented. There is a case study to illustrate how the model is used to give a quantitative measurement of resilience, in terms of recovery time.
Subspace Identification for DOA Estimation in Massive/Full-Dimension MIMO Systems: Bad Data Mitigation and Automatic Source Enumeration

In this paper, the direction-of-arrival (DOA) estimation problem for massive multiple-input multiple-output (MIMO) systems with a two dimensional (2D) array (also known as full-dimension MIMO) is investigated, assuming no knowledge of path number, noise power, path gain correlations and bad data statistics. Based on the variational Bayesian framework, a novel iterative algorithm for subspace identification operating on tensor represented data is proposed with integrated features of effective bad data mitigation and automatic source enumeration. The subspace recovered from the proposed algorithm not only enables existing 2D DOA estimators to be readily applied, if the number of signal paths is less than the number of horizontal antennas and vertical antennas, the subspaces in elevation and azimuth domains can be separately estimated, from which one dimensional (1D) DOA estimators can be utilized, thus further lowering the complexity. Simulation results are presented to illustrate the excellent performance of the proposed subspace recovery method and subsequent DOA estimation in terms of accuracy and robustness.
Content-related energy efficiency analysis in cache-enabled device-to-device network

Cache-enabled device-to-device (D2D) communication is a promising technique to offload the core traffic and cache deployment is important in achieving the benefits brought by caching. While rare considered optimal caching deployment from the perspective of energy efficiency. In fact, cache deployment will certainly influence the distribution of active transmitters, network interference and power consumption, contributing to corresponding energy efficiency. In this paper, we first derive the accurate expression of content-related energy efficiency of cache-enabled D2D network as a function of cache deployment based on a proposed content-related interference model. And then optimization problem with respect to optimal caching probability to maximize the content-related energy efficiency is formulated. Due to the complicated function structure and highly coupled variables in objective function and constraints, we turn to three special cases to explore the approximately optimal solutions. It turns out that when the ratio of active intensity of request to cache is too large or too small, the optimization problem can reduce to a concave problem and the optimal caching deployment is achieved using KKT conditions. Numerical and simulation results validate the generality of proposed interference model and demonstrate the optimal caching deployment is related to transmit power and content popularity.