Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Smart Stick for Soil Erosion Monitoring

This paper proposed an IOT-based erosion monitoring architecture. The proposed soil erosion monitoring architecture is based on deployed smart sticks. The smart sticks make up a wireless sensor network (WSN) that are deployed on a slope to perform specific tasks such as sensing, data processing, and acknowledgment. The hardware design and software operation was introduced in this study. The experimental results show that the wireless signal transmission of the system is stable and the packet leakage rate is low. The experimental results also prove that the system can instantly detect the amount of erosion change of soil. This study also verified that the TOF sensor of the smart stick is quite accurate and has reasonably high linearity.
Mounting Behaviour Recognition for Pigs Based on Deep Learning

For both pigs in commercial farms and biological experimental pigs at breeding bases, mounting behaviour is likely to cause damage such as epidermal wounds, lameness and fractures, and will no doubt reduce animal welfare. The purpose of this paper is to develop an efficient learning algorithm that is able to detect the mounting behaviour of pigs based on the data characteristics of visible light images. Four minipigs were selected as experimental subjects and were monitored for a week by a camera that overlooked the pen. The acquired videos were analysed and the frames containing mounting behaviour were intercepted as positive samples of the dataset, and the images with inter-pig adhesion and separated pigs were taken as negative samples. Pig segmentation network based on Mask Region-Convolutional Neural Networks (Mask R-CNN) was applied to extract individual pigs in the frames. The region of interest (RoI) parameters and mask coordinates of each pig, from which eigenvectors were extracted, could be obtained. Subsequently, the eigenvectors were classified with a kernel extreme learning machine (KELM) to determine whether mounting behaviour has occurred. The pig segmentation presented considerable accuracy and mean pixel accuracy (MPA) with 94.92% and 0.8383 respectively. The presented method showed high accuracy, sensitivity, specificity and Matthews correlation coefficient with 91.47%, 95.2%, 88.34% and 0.8324 respectively. This method can be an efficient way of solving the problem of segmentation difficulty caused by partial occlusion and adhesion of pig bodies, even if the pig body colour was similar to the background, in recognition of mounting behaviour.
Post-translational modifications of seminal proteins and their importance in male fertility potential

ABSTRACT Introduction: The seminal proteome has been shown to directly influence the male fertile potential. Post-translational modifications (PTMs) are significant changes that play a role in the biological regulation of proteins. Sperm cells are transcriptionally and translationally inactive and these modifications are essential to control protein function. Areas covered: Here we reviewed seven PTMs which importance for male reproductive function investigated in the past decade, namely S-nitrosylation and tyrosine nitration (both occurring by the action of NO), glycosylation, ubiquitination, acetylation, methylation, and SUMOylation. Since they were previously identified in human semen, we focus on their role in sperm function, as well as in physiological and pathophysiological processes which could contribute to the fertility potential. The following keywords were applied: ‘post-translational modification’, ‘sperm’, ‘semen’, ‘seminal plasma’, ‘male infertility’, ‘nitrosylation’, ‘nitration’, ‘histone methylation’, ‘SUMOylation’, ‘ubiquitination’, ‘ubiquitilation’, ‘glycosylation’, and ‘acetylation’. Expert opinion: Most biological processes orchestrated by proteins require PTMs for their activation or inhibition. Most of them are dynamic and occur in mature sperm, modulating protein function, thus exerting a significant role in sperm function and fertility. Finally, the study of PTMs should be also addressed in pathophysiological processes, as different clinical conditions are known to alter the proteome.
Enhanced fibrinolysis detection in a natural occurring canine model with intracavitary effusions: Comparison and degree of agreement between thromboelastometry and FDPs, D-dimer and fibrinogen concentrations

Dogs with intracavitary effusion have coagulative abnormalities indicative of primary fibrinolysis/hyperfibrinolysis. The aim of this case control study was to investigate by rotational thromboelastometry (ROTEM) and standard coagulation tests (fibrin-fibrinogen degradation products, D-dimer and fibrinogen) fibrinolysis in dogs with intracavitary effusions. Thirty-two dogs with intracavitary effusion and 32 control sick dogs without effusion were studied. Frequency of fibrinolysis grade of severity (i.e., hypofibrinolysis/basal fibrinolysis vs increased fibrinolysis vs hyperfibrinolysis) by ROTEM and standard coagulation tests were compared between groups. Pattern of fibrinolysis by ROTEM (i.e., late vs intermediate vs fulminant) and type of fibrinolysis by standard coagulation tests (i.e., hypofibrinolysis/basal fibrinolysis vs primary fibrinolysis vs secondary fibrinolysis vs primary hyperfibrinolysis vs secondary hyperfibrinolysis) were also compared between groups. Dogs with intracavitary effusion had a lesser degree of hypofibrinolysis and basal fibrinolysis and a higher degree of increased fibrinolysis and hyperfibrinolysis compared to controls, both by ROTEM and by standard coagulation tests (P = 0.042 and P = 0.017, respectively). Nevertheless, there was a poor agreement between the two classification schemes (34.4%, K = 0.06, 95% CI = -0.14 ‒ +0.26). Dogs with intracavitary effusion showed, by ROTEM, a lesser degree of hypofibrinolysis and basal fibrinolysis and a higher degree of late, intermediate, and fulminant fibrinolysis compared to controls (P = 0.044). Finally, dogs with intracavitary effusion had, by standard coagulation tests, a higher frequency of primary fibrinolysis and primary hyperfibrinolysis and a lower frequency of secondary fibrinolysis compared to controls. Dogs with intracavitary effusion showed an increased frequency and a different and more severe pattern of fibrinolysis compared to controls.
Learning Ocean Dynamical Priors from Noisy Data Using Assimilation-Derived Neural Nets

Recent studies have investigated the identification of governing equations of geophysical systems from data. Here, we investigate such identification issues for ocean surface dy-namcis from ocean remote sensing data. From a methodological point of view, we address the learning of data-driven dynamical models when only provided with a noisy training dataset. We propose a novel architecture that relies on data assimilation schemes to learn the underlying dynamical model through the minimization of a reconstruction cost. We demonstrate the relevance of the proposed architecture with respect to the state-of-the-art approaches in the identification and forecasting of synthetic and real case-studies.
Machine Learning Based App-Collusion Detection in Smartphones

The App-collusion is a multi-app attacking model in which two or more apps work together to achieve a malicious goal. Single app analyses tools are not able to detect app-collusion because they focus on single apps. This paper presents a novel and simple technique to detect collusive app-pairs using machine learning classifiers. The method employs a two-stage classification model. In the first stage, we have logistic regression based classifier to detect single malicious applications. In the second stage, we use the parameters obtained from the first stage to detect collusive app-pairs. In the second stage, we use either logistic regression or perceptron model. Our two-stage classification model is successful in detecting collusive app-pairs.
Two-Stage Short-Term Load Forecasting for Power Transformers Under Different Substation Operating Conditions

The load of transformers shows higher volatility and uncertainty than do the system-level and substation-level loads. This paper proposes a two-stage short-term load forecasting (STLF) model for power transformers. 1) Three state-of-the-art technologies are applied to predict the aggregated substation-level load by taking the historical load, weather, and calendar data as inputs. In this stage, no specific STLF model needs to be developed, which allows the forecasters to select the most accurate prediction results for transformer-level load forecasting. 2) The load distribution factor (LDF) is defined as the ratio of the transformer load to the substation load. The relationship between LDF and substation load is captured by nonlinear regression functions under different substation operating conditions, and the load of each parallel transformer is predicted using these nonlinear regression functions. Each nonlinear function can be accurately established even if the historical load data are scarce under some irregular operating conditions. Three application examples show the effectiveness and rationality of the proposed method. The third example demonstrates that STLF of transformers is necessary because it provides important information for optimizing substation operating schemes and equipment maintenance plans.
Hybrid Spectrum Access of a CR Enabled D2D Network

In this paper the performance of a cognitive radio (CR) enabled Device-to-device (D2D) network has been studied. The cellular user (CU) is sharing its resources with the D2D network. A D2D device at first senses the CU channel and accesses it in hybrid spectrum access scheme which is a combination of both underlay as well as overlay schemes. The performance of the D2D network has been investigated with regards to the total throughput for several sensing parameters.
Spatiotemporally random and diverse grid cell spike patterns contribute to the transformation of grid cell to place cell in a neural network model

The medial entorhinal cortex and the hippocampus are brain regions specialized in spatial information processing. While an animal navigates around an environment, grid cells in the medial entorhinal cortex spike at multiple discrete locations, forming hexagonal grid patterns, and each grid cell is spatiotemporally dynamic with a different grid size, spacing, and orientation. In contrast, place cells in the hippocampus spike when an animal is at one or more specific locations, called a “place field”. While an animal traverses through a place field, the place cell’s spike phases relative to the hippocampal theta-frequency oscillation advance in phase, known as the “spike phase precession” phenomenon and each spike encodes the specific location within the place field. Interestingly, the medial entorhinal cortical grid cells and the hippocampal place cells are only one excitatory synapse apart. However, how the spatiotemporally dynamic multi-peaked grid cell activities are transformed into hippocampal place cell activities with spike phase precession phenomenon is yet unknown. To address this question, we construct an anatomically and physiologically realistic neural network model comprised of 10,000 grid cell models, each with a spatiotemporally dynamic grid patterns and a place cell model connected by excitatory synapses. Using this neural network model, we show that grid cells’ spike activities with spatiotemporally random and diverse grid orientation, spacing, and phases as inputs to place cell are able to generate a place field with spike phase precession. These results indicate that spatiotemporally random and diverse grid cell spike activities are essential for the formation of place cell activity observed in vivo.
The Situation of Chemokine Ligands and Receptors Gene Expression, Following the Oral Administration of Drug Mannuronic Acid in Rheumatoid Arthritis Patients

Background: Regarding the leukocytes infiltration into the synovium of Rheumatoid Arthritis (RA) patients is mostly mediated by chemokine ligands and receptors, and following the efficient and motivating results of international Phase III clinical trial of β-D-Mannuronic acid (M2000) patented EP067919 (2017), as a novel anti-inflammatory drug, in patients with RA, the present research was designed. Objectives: This study aimed to assess the oral administration effects of this new drug on gene expression of some chemokine receptors and ligands, including CXCR4, CXCR3, CCR2, CCR5 and CCL2/MCP-1 in PBMCs of patients with active form of RA. Methods: Twelve patients suffering from RA, with inadequate response to conventional drugs were selected (Clinical trial identifier IRCT2017100213739N10) and 1000mg/day of M2000 was orally administrated to them for 12 weeks. The mRNA expression of target molecules was then evaluated in PBMCs of the patients before and after treatment with M2000 using real-time PCR and was compared to healthy controls. Patents related to this study were also reviewed. Results: The results showed that M2000 was able to significantly down-regulate the mRNA expression of CXCR4, CCR2 and CCL2/MCP-1 in the PBMCs of the RA patients. It should be noted that the gene expression situation of the target molecules was in coordinate with the clinical and paraclinical assessments in the patients. Conclusion: Taken together, the results of this investigation revealed the part of molecular and immunological mechanisms of drug Mannuronic acid (M2000) in the treatment of RA, based on chemokine ligands and receptors mediated processes.
Oxidative stress: its impact on cataracts

Cataracts, an opaque accumulation of damaged proteins in eye lens, are the major cause of visual decrease in those persons aged over 65. In fact, most of persons included in that age group at least have the onset of cataracts. In such affection are Revista Cubana de Farmacia. 2009; 43(3) http://scielo.sld. cu 2 identified some factors predisposing its appearance. This disease is considered like a multifactorial process, fact corroborated in many opportunities, since it has demonstrated that the preventive use of exogenous antioxidant or stimulation of endogenous antioxidant systems delays appearance of main signs and symptoms of this disease.
An Extensible and Easy-to-use Toolbox for Deep Learning Based Analysis of Remote Sensing Images

Deep Learning (DL) methods are currently the state-of-the-art in Machine Learning and Pattern Recognition. In recent years, DL has been successfully applied to Remote Sensing (RS) image processing for several tasks, from pre-processing to classification. This paper presents DeepGeo, a toolbox that provides state-of-the-art DL algorithms for RS image classification and analysis. DeepGeo focuses on providing easy-to-use and extensible methods, making it easier to those RS analysts without strong programming skills. It is distributed as free and open source package and is available at https: //github.com/rvmaretto/deepgeo.
Effectiveness and Safety of Dorsal Root Ganglion Stimulation for the Treatment of Chronic Pain: A Pooled Analysis

Since it became available in the mid‐2010s, dorsal root ganglion (DRG) stimulation has become part of the armamentarium to treat chronic pain. To date, one randomized controlled trial, and several studies of moderate sample size and various etiologies have been published on this topic. We conducted a pooled analysis to investigate the generalizability of individual studies and to identify differences in outcome between chronic pain etiologic subgroups and/or pain location.
Intelligent Driver Decision Support System in Vehicle Cabin: Reference Model for Dangerous Events Recognition and Learning

A reference model for the driver decision support system that is based on the sensor information provided by the smartphone sensors is proposed in the paper. The system is aimed at dangerous event recognition: drowsiness, distraction, drink driving, determine the heat rate and based on this information generate context-aware recommendations for the driver. The reference model allows to store statistics from the sensor data in the cloud service and process it in offline mode for the further analysis.
Green-Oriented Offloading and Resource Allocation by Reinforcement Learning in MEC

Mobile Edge Computing (MEC) is a promising approach to satisfy the increasing demand of computationintensive applications in fifth-generation (5G) networks. In this MEC networks system, Smart Mobile Devices (SMDs) can implement tasks migration in close proximity to a MEC server via wireless channels. In this paper, we formulate a Green-Oriented Problem (GOP) in MEC networks to minimize the cost of energy consumption for all SMDs in MEC system. In order to address the problem, we jointly optimize the offloading decision, wireless communication resource allocation and computational resource allocation while meeting the latency constraints. Apart from this, noting that finding an optimal policy of GOP in this MEC system is a Mixed Integer Linear Program (MINLP) problem, we use the Reinforcement Learning (RL) method which takes a long-term goal into consideration. Our numerical experiments demonstrate that the proposed algorithm improves energy efficiency of the computation offloading in MEC system.
Functional and expressional analyses of apple FLC-like in relation to dormancy progress and flower bud development.

We previously identified the FLOWERING LOCUS C (FLC)-like gene, a MADS-box transcription factor gene that belongs to Arabidopsis thaliana FLC clade, in apple (Malus x domestica), and its expression in dormant flower buds is positively correlated with cumulative cold exposure. To elucidate the role of the MdFLC-like in the dormancy process and flower development, we first characterized the phenotypes of MdFLC-like overexpressing lines with the Arabidopsis Columbia-0 background. The overexpression of MdFLC-like significantly delayed the bolting date and reduced the plant size, but it did not significantly affect the number of rosette leaves or flower organ formation. Thus, MdFLC-like may affect vegetative growth and development rather than flowering when expressed in Arabidopsis, which is not like Arabidopsis FLC that affects development of flowering. We compared seasonal expression patterns of MdFLC-like in low-chill 'Anna' and high-chill 'Fuji' and 'Tsugaru' apples collected from trees grown in a cold winter region in temperate zone, and found an earlier up-regulation in 'Anna' compared with 'Fuji' and 'Tsugaru'. Expression patterns were also compared in relation to developmental changes in the flower primordia during the chilling accumulation period. Overall, MdFLC-like was progressively up-regulated during flower primordia differentiation and development in autumn to early winter, and reached a maximum expression level at around the same time as the genotype-dependent chilling requirements were fulfilled in high-chill cultivars. Thus, we hypothesize MdFLC-like may be up-regulated in response to cold exposure and flower primordia development during the progress of endodormancy. Our study also suggests MdFLC-like may have a growth inhibiting function during the end of endodormancy and ecodormancy, when the temperature is low and unfavorable for rapid bud outgrowth.
Chromatin dynamics and the transcriptional competence of HSV-1 genomes during lytic infections

During latent infections with herpes simplex virus 1 (HSV-1), viral transcription is restricted and the genomes are mostly maintained in silenced chromatin, whereas in lytically infected cells all viral genes are transcribed and the genomes are dynamically chromatinized. Histones in the viral chromatin bear markers of silenced chromatin at early times in lytic infection or of active transcription at later times. The virion protein VP16 activates transcription of the immediate-early (IE) genes by recruiting transcription activators and chromatin remodelers to their promoters. Two IE proteins, ICP0 and ICP4 which modulate chromatin epigenetics, then activate transcription of early and late genes. Although chromatin is involved in the mechanism of activation of HSV- transcription, its precise role is not entirely understood. In the cellular genome, chromatin dynamics often modulate transcription competence whereas promoter-specific transcription factors determine transcription activity. Here, biophysical fractionation of serially digested HSV-1 chromatin followed by short-read deep sequencing indicates that nuclear HSV-1 DNA has different biophysical properties than protein-free or encapsidated HSV-1 DNA. The entire HSV-1 genomes in infected cells were equally accessible. The accessibility of transcribed or non-transcribed genes under any given condition did not differ, and each gene was entirely sampled in both the most and least accessible chromatin. However, HSV-1 genomes fractionated differently under conditions of generalized or restricted transcription. Approximately 1/3 of the HSV-1 DNA including fully sampled genes resolved to the most accessible chromatin when HSV-1 transcription was active, but such enrichment was reduced to only 3% under conditions of restricted HSV-1 transcription. Short sequences of restricted accessibility separated genes with different transcription levels. Chromatin dynamics thus provide a first level of regulation on HSV-1 transcription, dictating the transcriptional competency of the genomes during lytic infections, whereas the transcription of individual genes is then most likely activated by specific transcription factors. Moreover, genes transcribed to different levels are separated by short sequences with limited accessibility.
Deep Learning for Agricultural Land Detection in Insular Areas

Nowadays, governmental programs like ESA’s Copernicus provide freely available data that can be easily utilized for earth observation. In the present work, the problem of detecting agricultural and non-agricultural land cover is addressed. The methodology is based on classification with convolutional neural networks (CNNs) and transfer learning using AlexNet. The study area is located at the Ionian Islands, which include several land cover classes according to Copernicus CORINE Land Cover 2018 (CLC 2018). Furthermore, the dataset consists of natural color images acquired by Sentinel-2A multi-spectral instrument. Experimentation proves that extra addition of training data from foreign grounds, unfamiliar to the Greek data, serves much as a confusing agent regarding network performance.
Multi-Source Ocean Gravity Anomaly Data Fusion Processing Method

The ocean gravity field has always been an important part of marine scientific research. Combining ship survey data and satellite data to obtain high-resolution and high-precision gravity data attracts many researchers. In this paper, we present two fusion methods of gravity anomaly data, which are data fusion based on the analysis of error trend surface and the data fusion method based on the combination of net function and fractal interpolation. The experimental results show that the data fusion based on the analysis of error trend surface can effectively improve the effect of data fusion. The data fusion method based on the combination of net function and fractal interpolation is not obvious for accuracy improvement, but it shows great potential in terms of detail.