Miracle-dz/UniFAR-FTUs huggingface dataset:
Deicing Salt Pollution Affects the Foliar Traits and Arthropods' Biodiversity of Lime Trees in Riga's Street Greeneries

Street greeneries and other green spaces within the urban matrix can potentially reduce air pollution and increase urban biodiversity. Yet, these services can be negatively affected by anthropogenic stress factors. In the boreo-nemoral zone, large amounts of salts are spread each year for deicing the pavement. To address the effect of deicing salt on street lines of lime trees and how this cascades up to influence the surrounding arthropod biodiversity, we compared heavily salt-polluted and less polluted sites in the city of Riga, Latvia. We analyzed the impairment of foliar functions and development of aphids’ colonies using a common garden experiment. We found marked variation in the soil physico-chemical properties in polluted versus unpolluted sites. The overall abundance of arthropods, including that of the lime tree aphid specialist, was decreased and, as a consequence, less biotic injury was observed in foliage. The exposure to increased salt levels in the soil caused functional as well as structural injuries within foliage and slowed down the development of aphid colonies. The effects of de-icing salts thus appeared to cascade up through the trophic chain and to negatively affect not only the trees but also the associated arthropod biota. These findings point at the necessity of a wider screening of plant species and cultivars that not only better tolerate anthropogenic stress but also promote the biodiversity in cities.
Functional Dissection of C. elegans bZip-Protein CEBP-1 Reveals Novel Structural Motifs Required for Axon Regeneration and Nuclear Import

The basic leucine-zipper (bZIP) domain transcription factors CCAAT/enhancer-binding proteins (C/EBP) have a variety of roles in cell proliferation, differentiation, and stress response. In the nervous system, several isoforms of C/EBP function in learning and memory, neuronal plasticity, neuroinflammation, and axon regeneration. We previously reported that the Caenorhabditis elegans C/EBP homolog, CEBP-1, is essential for axon regeneration. CEBP-1 consists of 319 amino acids, with its bZIP domain at the C-terminus and a long N-terminal fragment with no known protein motifs. Here, using forward genetic screening with targeted genome editing, we have identified a unique domain in the N-terminus that is critical for its in vivo function. Additionally, we characterized three nuclear localization signals (NLS) in CEBP-1 that act together to mediate CEBP-1’s nuclear import. Moreover, the Importin-α, IMA-3, can bind to CEBP-1 via one of the NLS. ima-3 is ubiquitously expressed in all somatic cells, and ima-3 null mutants are larval lethal. Using Cre-lox dependent neuron-specific deletion strategy, we show that ima-3 is not critical for axon development, but is required for axon regeneration in adults. Together, these data advance our understanding of CEBP-1’s function, and suggest new regulators that remain to be identified to expand the CEBP-1 protein interactome.
Identification of Novel MicroRNAs and Characterization of MicroRNA Expression Profiles in Zebrafish Ovarian Follicular Cells

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression primarily at the post-transcriptional levels and thereby play important roles in regulating many physiological and developmental processes. Oocyte maturation in fish is induced by hormones produced from the hypothalamus, pituitary, and ovary. Gonadotropin-releasing hormone (GnRH) stimulates the secretion of luteinizing hormone (LH), which in turn, induces the secretion of maturation-inducing hormone (MIH) from the ovary. It is documented that small early vitellogenic (or stage IIIa) follicles are unable to undergo oocyte maturation whereas oocytes in mid- to late vitellogenic (stage IIIb) follicles can be induced by LH and MIH to become mature. To determine whether miRNAs may be involved in the growth and acquisition of maturational competency of ovarian follicles, we determined the miRNA expression profiles in follicular cells collected from stage IIIa and IIIb follicles using next-generation sequencing. It was found that miRNAs are abundantly expressed in the follicular cells from both stages IIIa and IIIb follicles. Furthermore, bioinformatics analysis revealed the presence of 214 known, 31 conserved novel and 44 novel miRNAs in zebrafish vitellogenic ovarian follicular cells. Most mature miRNAs in follicular cells were found to be in the length of 22 nucleotides. Differential expression analysis revealed that 11 miRNAs were significantly up-regulated, and 13 miRNAs were significantly down-regulated in the stage IIIb follicular cells as compared with stage IIIa follicular cells. The expression of four of the significantly regulated miRNAs, dre-miR-22a-3p, dre-miR-16a, dre-miR-181a-3p, and dre-miR-29a, was validated by real-time PCR. Finally, gene enrichment and pathway analyses of the predicted targets of the significantly regulated miRNAs supported the involvement of several key signaling pathways in regulating ovarian function, including oocyte maturation. Taken together, this study identifies novel zebrafish miRNAs and characterizes miRNA expression profiles in somatic cells within the zebrafish ovarian follicles. The differential expression of miRNAs between stage IIIa and IIIb follicular cells suggests that these miRNAs are important regulators of zebrafish ovarian follicle development and/or oocyte maturation.
KAE1 Allelic Variants Affect TORC1 Activation and Fermentation Kinetics in Saccharomyces cerevisiae

The eukaryotic domain-conserved TORC1 signalling pathway connects growth with nutrient sufficiency, promoting anabolic processes such as ribosomal biogenesis and protein synthesis. In Saccharomyces cerevisiae, TORC1 is activated mainly by the nitrogen sources. Recently, this pathway has gotten renewed attention but now in the context of the alcoholic fermentation, due to its key role in nitrogen metabolism regulation. Although the distal and proximal effectors downstream TORC1 are well characterised in yeast, the mechanism by which TORC1 is activated by nitrogen sources is not fully understood. In this work, we took advantage of a previously developed microculture-based methodology, which indirectly evaluates TORC1 activation in a nitrogen upshift experiment, to identify genetic variants affecting the activation of this pathway. We used this method to phenotype a recombinant population derived from two strains (SA and WE) with different geographic origins, which show opposite phenotypes for TORC1 activation by glutamine. Using this phenotypic information, we performed a QTL mapping that allowed us to identify several QTLs for TORC1 activation. Using a reciprocal hemizygous analysis, we validated GUS1, KAE1, PIB2, and UTH1 as genes responsible for the natural variation in the TORC1 activation. We observed that reciprocal hemizygous strains for KAE1 (ATPase required for t6A tRNA modification) gene showed the greatest phenotypic differences for TORC1 activation, with the hemizygous strain carrying the SA allele (KAE1SA) showing the higher TORC1 activation. In addition, we evaluated the fermentative capacities of the hemizygous strains under low nitrogen conditions, observing an antagonistic effect for KAE1SA allele, where the hemizygous strain containing this allele presented the lower fermentation rate. Altogether, these results highlight the importance of the tRNA processing in TORC1 activation and connects this pathway with the yeasts fermentation kinetics under nitrogen-limited conditions.
Deep wavelet scattering features for infrasonic threat identification

Infrasonic waves continue to be a staple of threat identification due to their presence in a variety of natural and man-made events, along with their low-frequency characteristics supporting detection over great distances. Considering the large set of phenomena that produce infrasound, it is critical to develop methodologies that exploit the unique signatures generated by such events to aid in threat identification. In this work, we propose a new infrasonic time-series classification technique based on the recently introduced Wavelet Scattering Transform (WST). Leveraging concepts from wavelet theory and signal processing, the WST induces a deep feature mapping on time series that is locally time invariant and stable to time-warping deformations through cascades of signal filtering and modulus operators. We demonstrate that the WST features can be utilized with a variety of classification methods to gain better discrimination. Experimental validation on the Library of Typical Infrasonic Signals (LOTIS)—containing infrasound events from mountain associated waves, microbaroms, internal atmospheric gravity waves and volcanic eruptions—illustrates the effectiveness of our approach and demonstrate it to be competitive with other state-of-the-art classification techniques.
Comparative study on amylosucrases derived from Deinococcus species and catalytic characterization and use of amylosucrase derived from Deinococcus wulumuqiensis

Abstract Amylosucrase (ASase; EC 2.4.1.4), a versatile enzyme, exhibits three characteristic activities: hydrolysis, isomerization, and transglycosylation. In this study, a novel ASase derived from Deinococcus wulumuquiensis (DWAS) was identified and expressed in Escherichia coli. The optimal reaction temperature and pH for the sucrose hydrolysis activity of DWAS were determined to be 45 °C and 9.0, respectively. DWAS displays relatively high thermostability compared with other ASases, as demonstrated by half-life of 96.7 and 4.7 min at 50 °C and 55 °C, respectively. DWAS fused with 6×His was successfully purified to apparent homogeneity with a molecular mass of approximately 72 kDa by Ni-NTA affinity chromatography and confirmed by SDS-PAGE. DWAS transglycosylation activity can be used to modify isovitexin, a representative flavone C-glucoside contained in buckwheat sprouts to increase its limited bioavailability, which is due to its low absorption rate and unstable structure in the human body. Using isovitexin as a substrate, the major transglycosylation product of DWAS was found to be isovitexin monoglucoside. The comparison of transglycosylation reaction products of DWAS with those of other ASases derived from Deinococcus species revealed that the low sequence homology of loop 8 in ASases may affect the acceptor specificity of ASases and result in a distinctive acceptor specificity of DWAS.
Computational Rheology with Integral Constitutive Equations

Abstract Computational rheology deals with the formulation and solution of constitutive equations for non-Newtonian materials. From these the emphasis is put on polymeric materials, which exhibit both viscous and elastic behaviour in flow and deformation. These materials are often called viscoelastic materials. Polymer solutions and melts (e.g. commercial plastics and rubber) are good examples of viscoelastic materials. Their processing under continuous (e.g. extrusion) or batch (e.g. injection molding) operations is the main occupation of the plastics and rubber industries, but the corresponding modelling and numerical simulation is a difficult task and a relatively recent undertaking. The present work reviews modelling aspects of viscoelasticity and shows how the complex rheology of these materials is best captured through integral constitutive equations with a spectrum of relaxation times. Using such constitutive equations and the Finite Element Method (FEM), the solution of some benchmark problems of rheology becomes feasible. Examples will be shown from the flow of polymer melts and solutions in a 4:1 axisymmetric contraction encountered in standard capillary rheometry, as well as the flow around a sphere falling in a cylindrical tube. The emphasis will be on demonstrating the flow patterns via streamlines and predicting such viscoelastic phenomena as vortex growth, extrudate swell, and reduction of the drag coefficient, which are of particular interest to the rheological community as test cases of computational results.
Genotype by Environment Interaction for Grain Yield of Barley Mutant Lines

Abstract The aim of this study was to examine the genotype by environment interaction for grain yield and to identify high-yielding and stable mutant lines of 6-rowed winter barley under different growing seasons. The study was carried out during 7 growing seasons from 2010 – 2011 to 2016 – 2017 in the experimental field of the Institute of Agriculture – Karnobat, Southeastern Bulgaria. Fourteen advanced mutant lines and the check variety Vesletc were studied using a complete block design with 4 replications. The AMMI analysis of variance indicated that 20.54% of the variation for grain yield was explained by the effect of genotype and 37.34% and 42.12% were attributable to the environmental effects and genotype by environment interaction. The magnitude of the genotype by environment interaction was two times larger than that of genotypes, indicating that there was a substantial difference in genotype response across environments. The AMMI and GGE biplot analyses identified G9 as the highest yielding and stable genotype. This mutant line can be recommended for further evaluation for variety release. The mutant lines G6, G13 and G15 were suggested for inclusion in the breeding program of winter barley due to its high grain yield and intermediate stability.
Privacy-preserving cloud-IoT architecture

When users pay for services with their personal data, we recognise the data is valuable resource. Unfortunately, in most current systems, users are given an all-or-nothing choice; if they accept the service conditions, they agree to share their data, otherwise they cannot get any services. Moreover, if they accept the condition to disclose thier data, they are often have no direct control over how their data is used and shared to third parties. The situation is exacerbated by the advent of the Internet of Things (IoT), which permits huge amount of personal data to be collected by devices and transmitted to cloud servers. As a consequence, there is a clear need to develop a Cloud-IoT infrastructures to allow users to control over their data, and to allow users to define the level of trade-off between privacy and benefits. For this, we propose an cloud-IoT architecture, called Data Bank, which permit users to customise data collection and access control policies which suit their privacy need. However, many challenges remain. We, therefore, give a guideline for implementation and highlight technical challenges and open problems.
Entropy based routing for mobile, low power and lossy wireless sensors networks

Routing protocol for low-power and lossy networks is a routing solution specifically developed for wireless sensor networks, which does not quickly rebuild topology of mobile networks. In this article, we propose a mechanism based on mobility entropy and integrate it into the corona RPL (CoRPL) mechanism, which is an extension of the IPv6 routing protocol for low-power and lossy networks (RPL). We extensively evaluated our proposal with a simulator for Internet of Things and wireless sensor networks. The mobility entropy-based mechanism, called CoRPL+E, considers the displacement of nodes as a deciding factor to define the links through which nodes communicate. Simulation results show that the proposed mechanism, when compared to CoRPL mechanism, is effective in reducing packet loss and latency in simulated mobile routing protocol for low-power and lossy networks. From the simulation results, one can see that the CoRPL+E proposal mechanism provides a packet loss reduction rate of up to 50% and delays reduction by up to 25% when compared to CoRPL mechanism.
Characterizing the Global Mobile App Developers: A Large-Scale Empirical Study

The rapid growth of the mobile app ecosystem has attracted a great number of mobile app developers. However, few previous studies have analyzed app developers comprehensively at a large scale, and little is known of the characteristics of mobile app developers. In this work, we take the first step to understand global app developers from the view of mobile app life-cycle. We have created a repository of over 1.03 million Android app developers distributed at Google Play and 16 popular alternative markets, along with over 6.2 million Android apps (including apks and metadata) they created. We then explored various characteristics of these app developers from different angles including developing behaviors, releasing behaviors, app maintenance behaviors and malicious behaviors. The experimental results have revealed various interesting findings, as well as insights for future research directions. Our efforts can contribute to different stakeholders of the mobile app ecosystem.
Human Extraction and Scene Transition utilizing Mask R-CNN

Object detection is a trendy branch of computer vision, especially on human recognition and pedestrian detection. Recognizing the complete body of a person has always been a difficult problem. Over the years, researchers proposed various methods, and recently, Mask R-CNN has made a breakthrough for instance segmentation. Based on Faster R-CNN, Mask R-CNN has been able to generate a segmentation mask for each instance. We propose an application to extracts multiple persons from images and videos for pleasant life scenes to grouping happy moments of people such as family or friends and a community for QOL (Quality Of Life). We likewise propose a methodology to put extracted images of persons into the new background. This enables a user to make a pleasant collection of happy facial expressions and actions of his/her family and friends in his/her life. Mask R-CNN detects all types of object masks from images. Then our algorithm considers only the target person and extracts a person only without obstacles, such as dogs in front of the person, and the user also can select multiple persons as their expectations. Our algorithm is effective for both an image and a video irrespective of the length of it. Our algorithm does not add any overhead to Mask R-CNN, running at 5 fps. We show examples of yoga-person in an image and a dancer in a dance-video frame. We hope our simple and effective approach would serve as a baseline for replacing the image background and help ease future research.
FireBugs: Finding and Repairing Bugs with Security Patterns

Security is often a critical problem in software systems. The consequences of the failure lead to substantial economic loss or extensive environmental damage. Developing secure software is challenging, and retrofitting existing systems to introduce security is even harder. In this paper, we propose an automated approach for Finding and Repairing Bugs based on security patterns (FireBugs), to repair defects causing security vulnerabilities. To locate and fix security bugs, we apply security patterns that are reusable solutions comprising large amounts of software design experience in many different situations. In the evaluation, we investigated 2,800 Android app repositories to apply our approach to 200 subject projects that use javax.crypto APIs. The vision of our automated approach is to reduce software maintenance burdens where the number of outstanding software defects exceeds available resources. Our ultimate vision is to design more security patterns that have a positive impact on software quality by disseminating correlated sets of best security design practices and knowledge.
An Efficient Intrusion Detection Approach Utilizing Various WEKA Classifiers

Detection of Intrusion is an essential expertise business segment as well as a dynamic area of study and expansion caused by its requirement. Modern day intrusion detection systems still have these limitations of time sensitivity. The main requirement is to develop a system which is able of handling large volume of network data to detect attacks more accurately and proactively. Research conducted by on the KDDCUP99 dataset resulted in a various set of attributes for each of the four major attack types. Without reducing the number of features, detecting attack patterns within the data is more difficult for rule generation, forecasting, or classification. The goal of this research is to present a new method that Compare results of appropriately categorized and inaccurately categorized as proportions and the features chosen. In this research paper we explained our approach “An Efficient Intrusion Detection Approach Utilizing Various WEKA Classifiers” which is proposed to enhance the competence of recognition of intrusion employing different WEKA classifiers on processed KDDCUP99 dataset. During the experiment we employed Adaboost, J48, JRip, NaiveBayes and Random Tree classifiers to categorize the different attacks from the processed KDDCUP99. Keywords : Classifier, Data Mining, IDS, Network Security, Attacks, Cyber Security
A visual-physiology multimodal system for detecting outlier behavior of participants in a reality TV show

This study proposes an outlier detection system based on the visual-physiology multimodal data system for a Korean reality TV show, “Perfect on Paper. ” The goal of this system is to provide the program production team and the master of ceremonies with real-time facial expressions and bio-signal analysis results of program participants. Using this information, the program production team and master of ceremonies can easily identify the participants with unusual behavior. We propose an insole-type hardware that measures the wearer’s skin conductivity, temperature, and motion. We also suggest a dynamic time warp-based clustering algorithm for the outlier and micro-expression detection technique. The system was developed starting from the program planning phase through a 6-month period in collaboration with the production team. The developed system analyzed the biometric information and facial expressions of 88 participants in 11 episodes and provided rapid feedback to the production team at the shooting spot in real time. Finally, we present a case where a visual-physiology multimodal system can be useful for a real TV broadcasting production.
Mobile edge computing resource allocation: A joint Stackelberg game and matching strategy

Mobile edge computing has attracted great interests in the popularity of fifth-generation (5G) networks and Internet of Things. It aims to supply low-latency and high-interaction services for delay-sensitive applications. Utilizing mobile edge computing with Smart Home, which is one of the most important fields of Internet of Things, is a method to satisfy users’ demand for higher computing power and storage capacity. However, due to limited computing resource, how to improve efficiency of resource allocation is a challenge. In this article, we propose a hierarchical architecture in Smart Home with mobile edge computing, providing low-latency services and promoting edge process for smart devices. Based on that, a Stackelberg Game is designed in order to allocate computing resource to devices efficiently. Then, one-to-many matching is established to handle resource allocation problems. It is proved that the allocation strategy can optimize the utility of mobile edge computing server and improve allocating efficiency. Simulation results show the effectiveness of the proposed strategy compared with schemes based on auction game, and present performance with different changing system parameters.