Miracle-dz/UniFAR-FTUs huggingface dataset:
The tree that hides the forest: identification of common predisposing loci in several hematopoietic cancers and several dog breeds

Histiocytic sarcoma (HS) is a rare but aggressive cancer in humans and dogs. The spontaneous canine model, with the clinical, epidemiological and histological similarities with human HS and specific breed predispositions, is a unique model/opportunity to unravel the genetic bases of this cancer. In this study, we aimed to identify germline risk factors associated with the development of HS in canine predisposed breeds. We used a methodology that combined several genome-wide association studies in a multi-breed and multi-cancer approach, as well as targeted next generation sequencing, and imputation combining several breeds (Bernese mountain dog, Rottweiler, flat coated retriever and golden retriever) and three haematopoietic cancers (HS, lymphoma and mast cell tumor). Results showed that we not only refined the previously identified HS risk CDKN2A locus but we identified new loci on canine chromosomes 2, 5, 12, 14, 20, 26 and X. Capture and targeted sequencing of specific loci pointed towards the existence of regulatory variants in non coding regions and/or methylation mechanisms linked to risk haplotypes, leading to strong cancer predispositions in specific dog breeds. Our results showed that these canine cancer predisposing loci appear to be due to the additive effect of several risk haplotype involved also in other haematopoietic cancers such lymphoma or mast cell tumor, illustrating the pleiotropic nature of these canine cancer loci as observed in human oncology, thus reinforcing the interest of predisposed dog breeds to study cancer initiation and progression.
Genome-wide variation in DNA methylation linked to developmental stage and chromosomal suppression of recombination in white-throated sparrows

DNA methylation is known to play critical roles in key biological processes. Most of our knowledge on regulatory impacts of DNA methylation has come from laboratory-bred model organisms, which may not exhibit the full extent of variation found in wild populations. Here, we investigated naturally-occurring variation in DNA methylation in a wild avian species, the white-throated sparrow (Zonotrichia albicollis). This species offers exceptional opportunities for studying the link between genetic differentiation and phenotypic traits because of a non-recombining chromosome pair linked to both plumage and behavioral phenotypes. Using novel single-nucleotide resolution methylation maps and gene expression data, we show that DNA methylation and the expression of DNA methyltransferases are significantly higher in adults than in nestlings. Genes for which DNA methylation varied between nestlings and adults were implicated in development and cell differentiation and were located throughout the genome. In contrast, differential methylation between plumage morphs was localized to the non-recombining chromosome pair. One subset of CpGs on the non-recombining chromosome was extremely hypomethylated and localized to transposable elements. Changes in methylation predicted changes in gene expression for both chromosomes. In summary, we demonstrate changes in genome-wide DNA methylation that are associated with development and with specific functional categories of genes in white-throated sparrows. Moreover, we observe substantial DNA methylation reprogramming associated with the suppression of recombination, with implications for genome integrity and gene expression divergence. These results offer an unprecedented view of ongoing epigenetic reprogramming in a wild population.
Recommender System Based Tensor Candecomp Parafact Algorithm-ALS to Handle Sparse Data in Food Commerce Information Services

Recommender systems have been widely researched in many applications especially in e-commerce services with the aim to make clear and easy communication between consumer and provider. Simple examples of Recommender systems would include personal and commercial recommendation for the purchase of everyday goods. However, previous studies have not included high order matrix calculation to estimate consumer parameters, e.g., customer behavior, location and their purchase preferences such as those in the field of business online commerce. This study extends the use of Candecomp Parafact algorithm when applied to recommender system to understand consumer preferences. In addition, it is also aimed to produce bilinear models which are constant and stable so that localization can be done to provide more accurate results. As an example the research provides a model based on tensor theory for restaurant and menu selection by analyzing the customer and restaurant parameters.
Substances with Antibacterial Activity in Edible Films – A Review

This article is an overview of literature addressing edible films and substances introduced to films in order to impart them the antimicrobial activity. It describes natural polymers applied for the production of food packages and active substances of natural origin added to them, including: bacteriocins, enzymes, oils, and plant extracts. Further discussion refers to chitosan - a polysaccharide used for film formation and characterised by strong antibacterial and antimycotic properties.
Expressions polylexicales verbales : étude de la variabilité en corpus (Verbal MWEs : a corpus-based study of variability)

La reconnaissance et le traitement approprié des expressions polylexicales (EP) constituent un enjeu pour différentes applications en traitement automatique des langues. Ces expressions sont susceptibles d’apparaître sous d’autres formes que leur forme canonique, d’où l’intérêt d’étudier leur profil de variabilité. Dans cet article, nous proposons de donner un aperçu de motifs de variation syntaxiques et/ou morphologiques d’après un corpus de 4441 expressions polylexicales verbales (EPV) annotées manuellement. L’objectif poursuivi est de générer automatiquement les différentes variantes pour améliorer la performance des techniques de traitement automatique des EPV.
A comparison of gas production during incubation with rumen contents in vitro and nylon bag degradability as predictors of the apparent digestibility in vivo and the voluntary intake of hays

Ten hays harvested at three stages (early bloom MB, mid bloom MB or in seed) made from lucerne (Medicago sativaj, sweet clover (Melilotus segetalis), Persian clover (Trifolium resupinatum) and pre-bloom (PB) Italian ryegrass (Lolium multiflorum var.), were offered ad libitum to four Merino male sheep and daily intake (g dry matter (DM) per kg M 0·75 ) and DM apparent digestibility (DMD) were measured. In sacco DM degradation (g per 100 g DM), gas production (ml per 200 mg DM), in vitro digestibility and fibre composition (g/kg DM) of the hays were also studied. Gas production or DM degradation were calculated at 6, 12, 24, 48, 72 or 96 h and their kinetics were described using the equation p = a + b(1 - e -ct ). Intake and in vivo DMD of the hays were variable ( P P 0·05) related to animal performance. Between 12 and 96 h incubation, intake and in vivo DMD were better related to DM degradation (r = 0·79 to 0·83; r = 0·61 to 0·77) than to gas production (r = 0·73 to 0·80; r = 0·58 to 0·78). Prediction of intake and in vivo DMD from the (a + b) values did not provide a great advantage over using some of the static values of gas production or DM degradation. Multiple regression using separated kinetics of degradation resulted in highest accuracy for predicting intake and apparent digestibility from gas production (R2 = 0·63; R2 = 0·78) and nylon bag degradability (R2 = 0·77; R2 = 0·89). It was concluded that the gas test has good potentiality as it was capable of predicting not only apparent digestibility, but also intake to a level close to that of the nylon bag technique.
Stimulation of oxidative metabolism by thyroid hormones in propranolol/alloxan‐treated bony fish, Anabas testudineus (Bloch)

The specific activities of oxidative enzymes, in response to 3,5,3′ triiodo-L-thyronine (T3) and L-thyroxine (T4) administration, were assayed in the teleost, Anabas testudineus pretreated with the beta adrenergic antagonist, propranolol, and the beta islet cell cytotoxin, alloxan. Administration of propranolol or alloxan decreased the activity of cytochrome oxidase, succinate dehydrogenase (SDH), mitochondrial adenosine triphosphatase (Mg2+ ATPase), lactate dehydrogenase (LDH), and cytosolic malate dehydrogenase (cMDH) and did not change that of α-glycerophosphate dehydrogenase (α-GPDH) and glucose-6-phosphate dehydrogenase (G-6-PDH). Propranolol treatment decreased and alloxan administration did not change the mitochondrial MDH (mMDH) activity. A single injection of T3 or T4 significantly stimulated cytochrome oxidase and oc-GPDH activities, and decreased Mg2+ ATPase, cMDH, and mMDH activities but did not affect SDH, G-6-PDH, or LDH. The activity of cytochrome oxidase, oc-GPDH, SDH, and Mg2+ ATPase was increased by T4 or T3 injection into propranolol or alloxan-treated fish. The data suggest that in teleosts, the stimulatory effect of thyroid hormones on oxidative metabolism is not mediated through the potentiation of adrenaline or insulin action. © 1993 Wiley-Liss, Inc.
What Is This Sensor and Does This App Need Access to It?

Mobile sensors have already proven to be helpful in different aspects of people’s everyday lives such as fitness, gaming, navigation, etc. However, illegitimate access to these sensors results in a malicious program running with an exploit path. While the users are benefiting from richer and more personalized apps, the growing number of sensors introduces new security and privacy risks to end users and makes the task of sensor management more complex. In this paper, first, we discuss the issues around the security and privacy of mobile sensors. We investigate the available sensors on mainstream mobile devices and study the permission policies that Android, iOS and mobile web browsers offer for them. Second, we reflect the results of two workshops that we organized on mobile sensor security. In these workshops, the participants were introduced to mobile sensors by working with sensor-enabled apps. We evaluated the risk levels perceived by the participants for these sensors after they understood the functionalities of these sensors. The results showed that knowing sensors by working with sensor-enabled apps would not immediately improve the users’ security inference of the actual risks of these sensors. However, other factors such as the prior general knowledge about these sensors and their risks had a strong impact on the users’ perception. We also taught the participants about the ways that they could audit their apps and their permissions. Our findings showed that when mobile users were provided with reasonable choices and intuitive teaching, they could easily self-direct themselves to improve their security and privacy. Finally, we provide recommendations for educators, app developers, and mobile users to contribute toward awareness and education on this topic.
Recurrent Neural Networks With TF-IDF Embedding Technique for Detection and Classification in Tweets of Dengue Disease

With the increased usage of Web 2.0 and data-affluent tools such as social media platforms and web blog services, the challenge of extracting public sentiment and disseminating personal health information has become more common than ever in the last decade. This paper proposes a novel model for Dengue disease detection based on social media posts alone. The model does not access any personal information of people or any medical record. The model extracts the presence of patients infected with Dengue disease based on tweets only and decides whether it is a general discussion about the disease, and no one is actually infected, or people are actually infected with that disease. The identification of people infected with Dengue is determined by clinical tests, but the propose technique is used for automatic surveillance and identification of regions where the spread is happening at an alarming rate and guide healthcare professional to take necessary actions to control the spread. This paper uses different machine/deep learning approaches to utilize tweets data for automatic and efficient disease detection. Experimental results demonstrate that the proposed model is able to achieve 92% accuracy compared to the current state-of-the-art techniques in this domain.
Low-Power Artificial Neurons Based on Ag/TiN/HfAlOx/Pt Threshold Switching Memristor for Neuromorphic Computing

Threshold switching (TS) devices are promising candidates to build highly compact and energy efficient artificial neurons. Here, we present a Pt/Ag/TiN/HfAlOx/Pt (PATHP) device with excellent TS characteristics, including a large selectivity(1010), a wide range of operation current from 10 nA to 1 mA, an extremely steep slope (0.63 mV/dec) and fast turn-on speed (50 ns). The stable TS performance can be ascribed to the introduction of TiN buffer layer and the alternate atomic layer deposited HfAlOx layer. Further, we experimentally demonstrate the functions of leaky-integrate-and-fire neurons with low power feature based on a RC circuit and a single device, respectively, which are essential for constructing spiking neuromorphic systems.
TIB-Net: Drone Detection Network With Tiny Iterative Backbone

With the widespread application of drone in commercial and industrial fields, drone detection has received increasing attention in public safety and others. However, due to various appearance of small-size drones, changeable and complex environments, and limited memory resources of edge computing devices, drone detection remains a challenging task nowadays. Although deep convolutional neural network (CNN) has shown powerful performance in object detection in recent years, most existing CNN-based methods cannot balance detection performance and model size well. To solve the problem, we develop a drone detection network with tiny iterative backbone named TIB-Net. In this network, we propose a structure called cyclic pathway, which enhances the capability to extract effective features of small object, and integrate it into existing efficient method Extremely Tiny Face Detector (EXTD). This method not only significantly improves the accuracy of drone detection, but also keeps the model size at an acceptable level. Furthermore, we integrate spatial attention module into our network backbone to emphasize information of small object, which can better locate small-size drone and further improve detection performance. In addition, we present massive manual annotations of object bounding boxes for our collected 2860 drone images as a drone benchmark dataset, which is now publicly available1. In this work, we conduct a series of experiments on our collected dataset to evaluate TIB-Net, and the result shows that our proposed method achieves mean average precision of 89.2% with model size of 697.0KB, which achieves the state-of-the-art results compared with existing methods.
Effects of Host Diversity on Infectious Disease

The dynamics of infectious diseases can be affected by genetic diversity within host populations, species diversity within host communities, and diversity among communities. In principle, diversity can either increase or decrease pathogen transmission and disease risk. Theoretical models and laboratory experiments have demonstrated that a dilution effect (decreased disease risk with increasing diversity) can occur under a wide range of conditions. Field studies of plants, aquatic invertebrates, amphibians, birds, and mammals demonstrate that the phenomenon indeed does occur in many natural systems. A dilution effect is expected when (a) hosts differ in quality for pathogens or vectors; (b) higher quality hosts tend to occur in species-poor communities, whereas lower quality hosts tend to occur in more diverse communities; and (c) lower quality hosts regulate abundance of high-quality hosts or of vectors, or reduce encounter rates between these hosts and pathogens or vectors. Although these conditions characterize many disease systems, our ability to predict when and where the dilution effect occurs remains poor. The life-history traits that cause some hosts to be widespread and resilient might be correlated with those that promote infection and transmission by some pathogens, supporting the notion that the dilution effect might be widespread among disease systems. Criticisms of the dilution effect have focused on whether species richness or species composition (both being metrics of biodiversity) drives disease risk. It is well established, however, that changes in species composition correlate with changes in species richness, and this correlation could explain why the dilution effect appears to be a general phenomenon.
Analysis and Modeling of the Forces Exerted on the Cookware in Induction Heating Applications

We present a semianalytical model for calculating the forces exerted on cookware in domestic induction heating applications. The developed model is based on the Maxwell’s stress tensor and is also based on the existing semianalytic expressions of the electromagnetic fields in planar induction heating systems, which are expressed in terms of Fourier-Bessel series. Taking advantage of the axial symmetry of usual domestic induction heating systems, the flux of the vertical component of the Maxwell’s stress tensor is analytically integrated and the vertical force is obtained. The proposed model captures both eddy currents and magnetization that occurs in typical ferromagnetic cookware. The model is verified by means of two-dimensional Finite Element simulations and also is tested by means of measurements of the change of the weight experimented by cookware due to the forces during the heating process.
Musical Expertise Shapes Functional and Structural Brain Networks Independent of Absolute Pitch Ability

Professional musicians are a popular model for investigating experience-dependent plasticity in human large-scale brain networks. A minority of musicians possess absolute pitch, the ability to name a tone without reference. The study of absolute pitch musicians provides insights into how a very specific talent is reflected in brain networks. Professional musicians are a popular model for investigating experience-dependent plasticity in human large-scale brain networks. A minority of musicians possess absolute pitch, the ability to name a tone without reference. The study of absolute pitch musicians provides insights into how a very specific talent is reflected in brain networks. Previous studies of the effects of musicianship and absolute pitch on large-scale brain networks have yielded highly heterogeneous findings regarding the localization and direction of the effects. This heterogeneity was likely influenced by small samples and vastly different methodological approaches. Here, we conducted a comprehensive multimodal assessment of effects of musicianship and absolute pitch on intrinsic functional and structural connectivity using a variety of commonly used and state-of-the-art multivariate methods in the largest sample to date (n = 153 female and male human participants; 52 absolute pitch musicians, 51 non-absolute pitch musicians, and 50 non-musicians). Our results show robust effects of musicianship in interhemispheric and intrahemispheric connectivity in both structural and functional networks. Crucially, most of the effects were replicable in both musicians with and without absolute pitch compared with non-musicians. However, we did not find evidence for an effect of absolute pitch on intrinsic functional or structural connectivity in our data: The two musician groups showed strikingly similar networks across all analyses. Our results suggest that long-term musical training is associated with robust changes in large-scale brain networks. The effects of absolute pitch on neural networks might be subtle, requiring very large samples or task-based experiments to be detected. SIGNIFICANCE STATEMENT A question that has fascinated neuroscientists, psychologists, and musicologists for a long time is how musicianship and absolute pitch, the rare talent to name a tone without reference, are reflected in large-scale networks of the human brain. Much is still unknown as previous studies have reported widely inconsistent results based on small samples. Here, we investigate the largest sample of musicians and non-musicians to date (n = 153) using a multitude of established and novel analysis methods. Results provide evidence for robust effects of musicianship on functional and structural networks that were replicable in two separate groups of musicians and independent of absolute pitch ability.
DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features

DORGE predicts cancer-driver genes by integrating the most comprehensive collection of genetic and epigenetic data. Data-driven discovery of cancer driver genes, including tumor suppressor genes (TSGs) and oncogenes (OGs), is imperative for cancer prevention, diagnosis, and treatment. Although epigenetic alterations are important for tumor initiation and progression, most known driver genes were identified based on genetic alterations alone. Here, we developed an algorithm, DORGE (Discovery of Oncogenes and tumor suppressoR genes using Genetic and Epigenetic features), to identify TSGs and OGs by integrating comprehensive genetic and epigenetic data. DORGE identified histone modifications as strong predictors for TSGs, and it found missense mutations, super enhancers, and methylation differences as strong predictors for OGs. We extensively validated DORGE-predicted cancer driver genes using independent functional genomics data. We also found that DORGE-predicted dual-functional genes (both TSGs and OGs) are enriched at hubs in protein-protein interaction and drug-gene networks. Overall, our study has deepened the understanding of epigenetic mechanisms in tumorigenesis and revealed previously undetected cancer driver genes.
Genetic diversity of the mungbean (Vigna radiata, Leguminosae) genepool on the basis of microsatellite analysis

A large representative collection of mungbean (Vigna radiata (L.) Wilczek) consisting of 415 cultivated, 189 wild and 11 intermediate accessions was analysed by using 19 SSR primers. These SSR primers showed polymorphism in wild and cultivated mungbean and were selected from those available for the related species azuki bean (V. angularis (Willd.) Ohwi & Ohwi). One or more SSR primer for each linkage group (on the basis of the azuki linkage map) was analysed. In total, 309 alleles were detected and of these, about twice as many were detected in wild (257 alleles) as in cultivated accessions (138 alleles). The results show that Australia and New Guinea represent a distinct centre of diversity for wild mungbean. Cultivated mungbean has greatest diversity in South Asia, which supports the view that South Asia is where this crop was domesticated. SSR marker allelic diversity for cultivated mungbean has distinct regional variation, with high variation in South and West Asia. The present study represents the first comprehensive analysis of wild and cultivated mungbean germplasm diversity by SSR markers, and highlights specific genetic diversity that might be used to broaden the genetic base of currently grown mungbean cultivars.