Miracle-dz/UniFAR-FTUs huggingface dataset:
Heavy metal–induced toxicity in the Indian green frog: Biochemical and histopathological alterations

Heavy metal contamination may have adverse effects on wetland biota, particularly on amphibians. Severe immunotoxic effects elicited in Euphlyctis hexadactylus (Indian green frog) because of metal exposure (Cd, Cr, Cu, Pb, and Zn) in the Bellanwila‐Attidiya Sanctuary, a polluted urban wetland in Sri Lanka, provided the rationale for the present study. We evaluated the biochemical and histopathological effects of this metal contamination with a reference E. hexadactylus population and a laboratory exposure group that was subjected to 28 d of exposure to a mixture of Cd, Cr, Cu, Pb, and Zn (5 ppm in each mixture). A histopathological scoring for the semiquantification of tissue damage was established. Results of the biochemical and histopathological markers were remarkably consistent between the 2 exposure scenarios, providing validation for the heavy metal exposure hypothesis. Damage to liver, kidney, lung, and skin of metal‐exposed E. hexadactylus quantified multiple impairments absent in the reference frogs. Liver injuries complemented significantly elevated aspartate transaminase (AST), alanine transaminase (ALT), γ‐glutamyl transferase (γ‐GT), and alkaline phosphatases in frog liver homogenate, indicating hepatocellular leakage and loss of functional and structural integrity of the hepatocyte membrane in both field‐ and laboratory‐exposed frogs. Significant elevation of Kupffer cell hypertrophy, pigmentation, inflammatory cell infiltrates and hepatic inflammation, extramedullary hematopoiesis, karyocytomegaly of hepatocytes (p < 0.05) of the liver, and degeneration of epithelia and necrosis of the lung, manifested as impairments in both metal exposure scenarios. Significantly reduced serum total protein and albumin and significantly elevated urea and creatinine in metal‐exposed frogs were indicative of hepatic and renal dysfunction, respectively. The present study affirms histopathology‐related biochemical alterations as potential biomarkers for heavy metal toxicity in amphibians. Environ Toxicol Chem 2017;36:2855–2867. © 2017 SETAC
Complete mitochondrial genome of the Neotropic cormorant (Phalacrocorax brasilianus)

Abstract The complete sequence of the Neotropic cormorant (Phalacrocorax brasilianus) mitochondrial DNA was obtained by the shotgun sequencing approach. The mitogenome is 19 042 bp in length and includes 13 protein-coding genes, 2 ribosomal subunit genes, 22 transfer RNA genes, a control region and a duplicated region of 2418 bp. The base composition is 32.1% for C, 31.8% for A, 22.6% for T and 13.4% for G, with an overall GC content of 45.5%. This is the first mitogenome of the P. brasilianus described and will be a useful tool for further phylogenetic and population genetic studies.
Widely Linear MVDR Beamformers for Noncircular Signals Based on Time-Averaged Second-Order Noncircularity Coefficient Estimation

The optimal widely linear (WL) minimum variance distortionless response (MVDR) beamformer, which has a powerful performance for the reception of a noncircular signal, was proposed by Chevalier in 2009. Nevertheless, in spectrum monitoring or passive listening, the optimal WL MVDR beamformer is hard to implement due to an unknown second-order (SO) noncircularity coefficient. This paper aims at estimating the time-averaged SO noncircularity coefficient of a desired noncircular signal, whose waveform is unknown but whose steering vector is known, in the context of the optimal WL MVDR beamformer. The proposed noncircularity coefficient estimator can process 2N - 1 rectilinear signals at most using an array of N sensors. Moreover, a frequency-shift WL MVDR beamforming algorithm is proposed for a noncircular signal having a nonnull frequency offset or carrier residue, jointly with the estimation of the frequency offset of the rectilinear signal. Due to the inevitable estimation error of the time-averaged SO noncircularity coefficient, a diagonal loading technique is used to enhance the robustness of the optimal WL beamformers. Simulations are shown to verify the effectiveness of the proposed algorithms.
Low latency RNN inference with cellular batching

Performing inference on pre-trained neural network models must meet the requirement of low-latency, which is often at odds with achieving high throughput. Existing deep learning systems use batching to improve throughput, which do not perform well when serving Recurrent Neural Networks with dynamic dataflow graphs. We propose the technique of cellular batching, which improves both the latency and throughput of RNN inference. Unlike existing systems that batch a fixed set of dataflow graphs, cellular batching makes batching decisions at the granularity of an RNN "cell" (a subgraph with shared weights) and dynamically assembles a batched cell for execution as requests join and leave the system. We implemented our approach in a system called BatchMaker. Experiments show that BatchMaker achieves much lower latency and also higher throughput than existing systems.
MLE-induced Likelihood for Markov Random Fields

Due to the intractable partition function, the exact likelihood function for a Markov random field (MRF), in many situations, can only be approximated. Major approximation approaches include pseudolikelihood and Laplace approximation. In this paper, we propose a novel way of approximating the likelihood function through first approximating the marginal likelihood functions of individual parameters and then reconstructing the joint likelihood function from these marginal likelihood functions. For approximating the marginal likelihood functions, we derive a particular likelihood function from a modified scenario of coin tossing which is useful for capturing how one parameter interacts with the remaining parameters in the likelihood function. For reconstructing the joint likelihood function, we use an appropriate copula to link up these marginal likelihood functions. Numerical investigation suggests the superior performance of our approach. Especially as the size of the MRF increases, both the numerical performance and the computational cost of our approach remain consistently satisfactory, whereas Laplace approximation deteriorates and pseudolikelihood becomes computationally unbearable.
Resveratrol–cyclodextrin complex affects the expression of genes associated with lipid metabolism in bovine in vitro produced embryos

Antioxidants have been widely used during in vitro production to decrease the negative effect of reactive oxygen species. It was reported that the complex resveratrol-methyl β-cyclodextrin (RV-CD) improves resveratrol's stability and bioavailability and increases its antioxidant activity. This study evaluates the effect of RV-CD during in vitro oocyte maturation (IVM) or in vitro embryo culture (IVC) on developmental competence and quantitative changes in gene expression of developmental important genes. In experiment 1, RV-CD was added to IVM media and maturation level, embryo development and oocytes, cumulus cells, and blastocysts gene expression by RT-qPCR were examined. In experiment 2, presumptive zygotes were cultured in SOF supplemented with RV-CD and embryo development and blastocysts gene expression by RT-qPCR were studied. A group without RV-CD (control- ) and a group with cyclodextrin (control+ ) were included. No differences were found in cleavage rate or blastocyst yield between groups. However, the expression of LIPE was higher in blastocysts derived from oocytes treated with resveratrol compared with control groups (p < .05). Blastocysts produced by IVC with resveratrol showed that RV-CD could modify the expression of genes related to lipid metabolism (CYP51A1, PNPLA2 and MTORC1) compared with control groups (p < .05). RV-CD in the IVM and IVC media could reduce accumulated fat by increasing lipolysis and suppressing lipogenesis of blastocysts.
Quantifying beetle-macrofungal associations in a temperate biodiversity hot spot

ABSTRACT Beetles (Coleoptera) are often among the most abundant and diverse insects that feed on sporocarps of macrofungi, but little is known regarding their relative specialism or generalism in most communities. We surveyed >9000 sporocarps in montane hardwood forest in the Appalachian Mountains (USA) to characterize associations of mycophagous beetles and macrofungi. We used traditional metrics and network analyses to quantify relationships between sporocarp traits (mass, age, persistence, and toughness) and assemblages of adult beetles, drawing from >50 000 beetles collected over two survey years. Strict-sense specificity was rare in these associations: most beetle species were found on multiple fungal genera, and most fungi hosted multiple beetle species. Sporocarp age and fresh mass were positively associated with beetle diversity in fungi with ephemeral sporocarps (here including 12 genera of Agaricales and Russulales), but sporocarp persistence was not. In Polyporales, beetle diversity was greater in softer sporocarps than in tough or woody sporocarps. The increase of beetle diversity in aging sporocarps could not be attributed to increases in sporocarp mass or sampling point in the growing season, suggesting that age-related changes in chemistry or structure may support increasingly diverse beetle communities. Interaction networks differed as a function of sporocarp age, revealing that community-wide measures of generalism (i.e., network connectance) and evenness (i.e., variance in normalized degree) change as sporocarps mature and senesce. Beetles observed on Agaricales and Russulales with more persistent sporocarps had narrower interaction breadth (i.e., were more host-specific) than those on less persistent sporocarps, and beetles on Polyporales with tougher sporocarps had narrower interaction breadth than those on soft sporocarps. In addition to providing a large-scale evaluation of sporocarp use by adult beetles in this temperate biodiversity hot spot, this study shows that characteristics of food organisms are associated with specialism and generalism in interactions relevant to fungal and forest ecology.
Screening for Refrigerant Blends in High-Temperature Lift Heat Pumps for Retrofit in Residential Heating

Providing heat pumps for the retrofit of fuel based boilers in residential heating, is a short or medium term challenge and a major opportunity for a substantial reduction of greenhouse gas emissions. While effective solutions for improved high temperature lift, vapor compression heat pumps have been analyzed elsewhere, this paper specifically deals with the refrigerant selection aspects for alternative blends, using a multi-objective optimization algorithm. The objectives COP and specific heat output are calculated for arbitrarily mixed refrigerant blends, using 17 selected pure fluids. Seasonal mean operating conditions (ambient air 0°C, heating water 50°C) have been used to simulate the heat pump cycle. Blends with a saturation curve extending over the range of –20°C (normal boiling point) and 70°C (critical point) are considered. Alternative blends are compared with the commercial mixture R-407C. The refrigerant mixture with the highest COP shows an improvement of 8% on the low capacity end of the Pareto-optimum curve. By maximizing the specific heat rate, a gain of 83% can be achieved. All the dominating solutions are composed of flammable refrigerants.
Temperature-Stress Equivalency in Nonlinear Viscoelastic Creep Characterization of Thermoplastic/Agro-fiber Composites

The viscoelastic characterization of agro-filler based plastic composites is of paramount importance for the material’s long-term commercial success. To predict creep, it is important to derive a relationship between deformation, time, temperature, relative humidity, and stress. Since temperature shift can interfere with stress shift in creep, the predictive model should incorporate the relationship between these two shifts. Rice husk-HDPE beams were subjected to creep and recovery in the flexural mode and stress/time/temperature-related creep behavior of the same was studied. Temperature-related creep constants and shift factors were determined for the material and the constants were compared against theoretical two-phase constants. The combined effect of temperature and stress on creep strain was accommodated in a single analytical function where the interaction was shown to be additive. This means that the stress equivalency of temperature is possible. This constitutive equation can predict creep in the long run. Although stress dependency is nonlinear, temperature dependency is linear and thermorheologically complex. The ‘single-phase’ material behavior (creep constants) was also compared with a ‘two-phase’ predictive model, where the creep constants were estimated with the ‘theory of mixtures’.
Goldbach's Function Approximation Using Deep Learning

Goldbach conjecture is one of the most famous open mathematical problems. It states that every even number, bigger than two, can be presented as a sum of 2 prime numbers. In this work we present a deep learning based model that predicts the number of Goldbach partitions for a given even number. Surprisingly, our model outperforms all state-of-the-art analytically derived estimations for the number of couples, while not requiring prime factorization of the given number. We believe that building a model that can accurately predict the number of couples brings us one step closer to solving one of the world most famous open problems. To the best of our knowledge, this is the first attempt to consider machine learning based data-driven methods to approximate open mathematical problems in the field of number theory, and hope that this work will encourage such attempts.
Transformation of the Beta Distribution for Color Transfer

: In this paper, we propose a novel transformation between two Beta distributions. Our transformation progressively and accurately reshapes an input Beta distribution into a target Beta distribution using four intermediate statistical transformations. The key idea of this paper is to adopt the Beta distribution to model the discrete distributions of color and light in images. We design a new Beta transformation which we apply in the context of color transfer between images. Experiments have shown that our method obtains more natural and less saturated results than results of recent state-of-the-art color transfer methods. Moreover, our results portray better both the target color palette and the target contrast.
Optimistic bias in young adults for cancer, cardiovascular and respiratory diseases: A pilot study on smokers and drinkers

Optimistic bias defines the tendency for human beings to underrate risk when it pertains to themselves compared with their view of risk pertaining to other people in the same conditions. The aim of this work is to investigate the optimistic bias in risk perception and health-related behaviours for three specific conditions in a young adult sample: cancer, respiratory disorders and cardiovascular diseases. Young adults showed an optimistic bias related to cancer, and to cardiovascular diseases. Our findings suggest that optimistic bias is linked to specific behavioural patterns, largely widespread in young adults, such as tobacco cigarette smoking and alcohol consumption.
Neck circumference is similarly predicting for impairment of glucose tolerance as classic anthropometric parameters among healthy and obese children and adolescents

Abstract Background: The objective of the study was to investigate the association of neck circumference (NC) to parameters of glucose homeostasis compared to classical anthropometric parameters of lean and obese children. Methods: Three dimensional (3D)-body scanning quantified anthropometric (height, weight, NC, hip/waist circumference, BMI) and metabolic parameters (fasting plasma glucose [FPG], insulin, HbA1c, oGTT, HOMA-IR) were determined cross-sectionally in 1542 participants (5–18 years). Results: NC was positively correlated with all metabolic parameters, except for FPG. For HbA1c there was only a modestly positive correlation. The associations between NC and glucose parameters were rather weak, while the correlation to insulin parameters were stronger. Overall the strongest association to glucose metabolism parameters was found for waist circumference (WC), except for FPG and 2h-postload glucose. In multiple linear regression analyses, NC provided additional benefit beyond classical anthropometric indices to describe impairment of glucose homeostasis. Conclusions: We suggest that NC is comparable or additive to established anthropometric parameters but might not be superior to them. However NC is simple to measure, reproducible and may be considered in clinical practice as an additional measurement tool.
Reordering on Spanish-Basque SMT

In this work we have deal with the reordering problem in Spanish-Basque statistical machine translation, comparing three different approaches and analyzing their strength and weakness. Tested approaches cover the more usual techniques: lexicalized reordering implemented on Moses, preprocessing based on hand deﬁned rules over the syntactic analysis of the source and statistical translation. According with the obtained results, the three reordering techniques improves the results of the baseline. We observe different behaviour at combining techniques. While the use of the Syntax-Based reordered corpus together with the lexicalized reordering get the best results, training the lexicalized reordering on the statistically reordered source does not improve the performance of the single methods.
Prevalence of grade 1, 2 and 3 thinness is associated with lower socio-economic status in children in Shanghai, China

Abstract Objective We aimed to investigate the prevalence of grade 1, 2 and 3 thinness among Chinese children and to explore their associations with socio-economic status (SES). Design A population, school-based cross-sectional study using multistage, stratified cluster random sampling. Grade 1, 2 and 3 thinness, overweight, obesity and severe obesity were defined by the International Obesity Task Force BMI cut-offs. Setting Seven districts of Shanghai, China. Subjects Chinese children aged 3–12 years (n 84 075). Results In boys and girls, respectively, the prevalence of grade 1 thinness was 8·89 % and 11·78 %, of grade 2 thinness was 2·80 % and 3·74 %, and of grade 3 thinness was 2·23 % and 2·93 %. Compared with urban children, suburban children had higher prevalence of thinness. Children whose parent had low education had higher prevalence of grade 1, 2 and 3 thinness than those whose parent had high education. The prevalence of grade 2 and 3 thinness, obesity and severe obesity in low-SES children was higher than that in high-SES children, and the prevalence of grade 1 thinness was lower than that in high-SES children. Particular patterns of prevalence of grade 1, 2 and 3 thinness appeared in low-, middle- and high-SES children. Conclusions The study describes associations of SES with grade 1, 2 and 3 thinness, overweight, obesity and severe obesity in Shanghai children. The patterns of thinness and obesity in Shanghai provide further insights into BMI patterns in mega-cities in developing countries.
Based Adversarial Embedding with Minimum Alteration for Image Steganography

Historically, steganographic schemes were designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic methods employ a machine learning (ML) based classifier, it is reasonable to consider countering steganalysis by trying to fool the ML classifiers. However, simply applying perturbations on stego images as adversarial examples may lead to the failure of data extraction and introduce unexpected artefacts detectable by other classifiers. In this paper, we present a steganographic scheme with a novel operation called adversarial embedding, which achieves the goal of hiding a stego message while at the same time fooling a convolutional neural network (CNN) based steganalyzer. The proposed method works under the conventional framework of distortion minimization. Adversarial embedding is achieved by adjusting the costs of image element modifications according to the gradients backpropagated from the CNN classifier targeted by the attack. Therefore, modification direction has a higher probability to be the same as the sign of the gradient. In this way, the so called adversarial stego images are generated. Experiments demonstrate that the proposed steganographic scheme is secure against the targeted adversary-unaware steganalyzer. In addition, it deteriorates the performance of other adversary-aware steganalyzers opening the way to a new class of modern steganographic schemes capable to overcome powerful CNN-based steganalysis. Index Terms Steganography, steganalysis, adversarial machine learning.
Genome-wide Methyl-Seq analysis of blood-brain targets of glucocorticoid exposure

ABSTRACT Chronic exposure to glucocorticoids (GCs) can lead to psychiatric complications through epigenetic mechanisms such as DNA methylation (DNAm). We sought to determine whether epigenetic changes in a peripheral tissue can serve as a surrogate for those in a relatively inaccessible tissue such as the brain. DNA extracted from the hippocampus and blood of mice treated with GCs or vehicle solution was assayed using a genome-wide DNAm platform (Methyl-Seq) to identify differentially methylated regions (DMRs) induced by GC treatment. We observed that ∼70% of the DMRs in both tissues lost methylation following GC treatment. Of the 3,095 DMRs that mapped to the same genes in both tissues, 1,853 DMRs underwent DNAm changes in the same direction. Interestingly, only 209 DMRs (<7%) overlapped in genomic coordinates between the 2 tissues, suggesting tissue-specific differences in GC-targeted loci. Pathway analysis showed that the DMR-associated genes were members of pathways involved in metabolism, immune function, and neurodevelopment. Also, changes in cell type composition of blood and brain were examined by fluorescence-activated cell sorting. Separation of the cortex into neuronal and non-neuronal fractions and the leukocytes into T-cells, B-cells, and neutrophils showed that GC-induced methylation changes primarily occurred in neurons and T-cells, with the blood tissue also undergoing a shift in the proportion of constituent cell types while the proportion of neurons and glia in the brain remained stable. From the current pilot study, we found that despite tissue-specific epigenetic changes and cellular heterogeneity, blood can serve as a surrogate for GC-induced changes in the brain.
Social Emotion Mining Techniques for Facebook Posts Reaction Prediction

As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called `reactions'. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on public pages of firms/companies (like supermarket chains). For this purpose, we collected posts (and their reactions) from Facebook pages of large supermarket chains and constructed a dataset which is available for other researches. In order to predict the distribution of reactions of a new post, neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings. Results of the neural networks were improved by introducing a bootstrapping approach for sentiment and emotion mining on the comments for each post. The final model (a combination of neural network and a baseline emotion miner) is able to predict the reaction distribution on Facebook posts with a mean squared error (or misclassification rate) of 0.135.