Search Results (14)

Platforms have the ability to create connected digital spaces where different actors co-exist and work together. The paper explores the power of platforms as enablers of a new channel of proximity, called digital proximity. It argues that platforms enable interactions, information flows, and network formation through digital proximity, which can effectively reinforce externalities complementing existing proximity forms or bypassing physical space barriers. Firms and industries adopting platform-based tools can create meaningful channels for increasing their proximity at an intra- and inter-firm level. The study uses data from the Digital Economy and Society database covering 25 EU countries for the years 2019 and 2021. It calculates the degree of adoption by EU firms at the national level for a set of selected platform-based technologies closely related to different proximity forms. It investigates the relationship between digital proximity, firm size, and industry, also introducing a geographical dimension. The evidence suggests that large firms have managed to integrate platform-based technologies to a greater extent, whereas small and medium firms still lack leveraging the full power of platforms. Increased adoption at the country level is also related to increased productivity, indicating the geographical dimension of platforms. The paper argues that platforms can be seen as a new means for balancing uneven spatial capabilities for producing proximity, indicating a high potential for fostering territorial cohesion. It concludes by suggesting that future research should measure the effects of digital proximity on development and their causal relationship to better elaborate on the implications of platforms on development. Full article

Cotton leaf curl Kokhran virus (CLCuKoV) (genus, Begomovirus; family, Geminiviridae) is one of several plant virus pathogens of cotton (Gossypium hirsutum L.) that cause cotton leaf curl disease in Pakistan. Begomoviruses are transmitted by the whitefly Bemisia tabaci cryptic species group and cause economic losses in cotton and other crops worldwide. The CLCuKoV strain, referred to as CLCuKoV-Bur, emerged in the vicinity of Burewala, Pakistan, and was the primary causal virus associated with the second CLCuD epidemic in Pakistan. The monopartite ssDNA genome of (2.7 Kb) contains six open reading frames that encode four predicted proteins. RNA interference (RNAi)-mediated antiviral immunity is a sequence-specific biological process in plants and animals that has evolved to combat virus infection. The objective of this study was to design cotton locus-derived microRNA (ghr-miRNA) molecules to target strains of CLCuKoV, with CLCuKoV-Lu, as a typical CLCuD-begomovirus genome, predicted by four algorithms, miRanda, RNA22, psRNATarget, and RNA hybrid. Mature ghr-miRNA sequences (n = 80) from upland cotton (2n = 4x = 52) were selected from miRBase and aligned with available CLCuKoV-Lu genome sequences. Among the 80 cotton locus-derived ghr-miRNAs analyzed, ghr-miR2950 was identified as the most optimal, effective ghr-miRNA for targeting the CLCuKoV-Lu genome (nucleotide 82 onward), respectively, based on stringent criteria. The miRNA targeting relies on the base pairing of miRNA–mRNA targets. Conservation and potential base pairing of binding sites with the ghr-miR2950 were validated by multiple sequence alignment with all available CLCuKoV sequences. A regulatory interaction network was constructed to evaluate potential miRNA–mRNA interactions with the predicted targets. The efficacy of miRNA targeting of CLCuKoV was evaluated in silico by RNAi-mediated mRNA cleavage. This predicted targets for the development of CLCuD-resistant cotton plants. Full article

The eyes are in constant movement to optimize the interpretation of the visual scene by the brain. Eye movements are controlled by complex neural networks that interact with the rest of the brain. The direction of our eye movements could thus be influenced by our cognitive activity (imagination, internal dialogue, memory, etc.). A given cognitive activity could then cause the gaze to move in a specific direction (a brief movement that would be instinctive and unconscious). Neuro Linguistic Programming (NLP), which was developed in the 1970s by Richard Bandler and John Grinder (psychologist and linguist respectively), issued a comprehensive theory associating gaze directions with specific mental tasks. According to this theory, depending on the visual path observed, one could go back to the participant's thoughts and cognitive processes. Although NLP is widely used in many disciplines (communication, psychology, psychotherapy, marketing, etc), to date, few scientific studies have examined the validity of this theory. Using eye tracking, this study explores one of the hypotheses of this theory, which is one of the pillars of NLP on visual language. We created a protocol based on a series of questions of different types (supposed to engage different brain areas) and we recorded by eye tracking the gaze movements at the end of each question while the participants were thinking and elaborating on the answer. Our results show that (1) complex questions elicit significantly more eye movements than control questions that necessitate little reflection, (2) the movements are not random but are oriented in selected directions, according to the different question types, (3) the orientations observed are not those predicted by the NLP theory. This pilot experiment paves the way for further investigations to decipher the close links between eye movements and neural network activities in the brain. Full article

Methane (CH₄) hydrate dissociation and CH₄ release are potential geohazards currently investigated using X-ray computed tomography (XCT). Image segmentation is an important data processing step for this type of research. However, it is often time consuming, computing resource-intensive, operator-dependent, and tailored for each XCT dataset due to differences in greyscale contrast. In this paper, an investigation is carried out using U-Nets, a class of Convolutional Neural Network, to segment synchrotron XCT images of CH₄-bearing sand during hydrate formation, and extract porosity and CH₄ gas saturation. Three U-Net deployments previously untried for this task are assessed: (1) a bespoke 3D hierarchical method, (2) a 2D multi-label, multi-axis method and (3) RootPainter, a 2D U-Net application with interactive corrections. U-Nets are trained using small, targeted hand-annotated datasets to reduce operator time. It was found that the segmentation accuracy of all three methods surpass mainstream watershed and thresholding techniques. Accuracy slightly reduces in low-contrast data, which affects volume fraction measurements, but errors are small compared with gravimetric methods. Moreover, U-Net models trained on low-contrast images can be used to segment higher-contrast datasets, without further training. This demonstrates model portability, which can expedite the segmentation of large datasets over short timespans. Full article

Nowadays, product placements are commonly presented on mobile social media but related studies are rare. The main purpose of the study is to investigate the effects of content likeability, content credibility, and social media engagement on users’ acceptance of product placement in mobile social networks. The results of the online survey indicate that content likeability is an antecedent of social media engagement and content credibility; social media engagement has an influence on content credibility; and content likeability, content credibility, and social media engagement both directly affect user acceptance of product placement in mobile social networks. Furthermore, social media engagement has an interaction effect with content likeability on the content credibility of mobile social networks. The results of the multi-group analysis indicate that young adults show differences with middle-aged adults in the direct effect of content likeability on social media engagement and in the interaction effect of content credibility and social media engagement on the acceptance of product placement in mobile social networks. The implications for practitioners are discussed on the basis of the empirical findings. Full article

In this article, we propose a review of studies evaluating the distribution of distances between elements of a random set independently and uniformly distributed over a region of space in a normed $\mathbb{R}$ -vector space (for example, point events generated by a homogeneous Poisson process in a compact set). The distribution of distances between individuals is present in many situations when interaction depends on distance and concerns many disciplines, such as statistical physics, biology, ecology, geography, networking, etc. After reviewing the solutions proposed in the literature, we present a modern, general and unified resolution method using convolution of random vectors. We apply this method to typical compact sets: segments, rectangles, disks, spheres and hyperspheres. We show, for example, that in a hypersphere the distribution of distances has a typical shape and is polynomial for odd dimensions. We also present various applications of these results and we show, for example, that variance of distances in a hypersphere tends to zero when space dimension increases. Full article

Environmental pollution, global warming and climate change exacerbate the impact of biotic and abiotic stresses on plant growth and yield. Plants have evolved sophisticated defence network, also called innate immune system, in response to ever- changing environmental conditions. Significant progress has been made in identifying the key stress-inducible genes associated with defence response to single stressors. However, relatively little information is available on the signaling crosstalk in response to combined biotic/abiotic stresses. Recent evidence highlights the complex nature of interactions between biotic and abiotic stress responses, significant aberrant signaling crosstalk in response to combined stresses and a degree of overlap, but unique response to each environmental stimulus. Further, the results of simultaneous combined biotic and abiotic stress studies indicate that abiotic stresses particularly heat and drought enhance plant susceptibility to plant pathogens. It is noteworthy that global climate change is predicted to have a negative impact on biotic stress resistance in plants. Therefore, it is vital to conduct plant transcriptome analysis in response to combined stresses to identify general or multiple stress- and pathogen-specific genes that confer multiple stress tolerance in plants under climate change. Here, we discuss the recent advances in our understanding of the molecular mechanisms of crosstalk in response to biotic and abiotic stresses. Pinpointing both, common and specific components of the signaling crosstalk in plants, allows identification of new targets and development of novel methods to combat biotic and abiotic stresses under global climate change. Full article

Many of the major discoveries in the fields of genetics and developmental biology have been made using the fruit fly, Drosophila melanogaster. With regard to heart development, the conserved network of core cardiac transcription factors that underlies cardiogenesis has been studied in great detail in the fly, and the importance of several signaling pathways that regulate heart morphogenesis, such as Slit/Robo, was first shown in the fly model. Recent technological advances have led to a large increase in the genomic data available from patients with congenital heart disease (CHD). This has highlighted a number of candidate genes and gene networks that are potentially involved in CHD. To validate genes and genetic interactions among candidate CHD-causing alleles and to better understand heart formation in general are major tasks. The specific limitations of the various cardiac model systems currently employed (mammalian and fish models) provide a niche for the fly model, despite its evolutionary distance to vertebrates and humans. Here, we review recent advances made using the Drosophila embryo that identify factors relevant for heart formation. These underline how this model organism still is invaluable for a better understanding of CHD. Full article

Plant diseases caused by pathogenic bacteria or fungi cause major economic damage every year and destroy crop yields that could feed millions of people. Only by a thorough understanding of the interaction between plants and phytopathogens can we hope to develop strategies to avoid or treat the outbreak of large-scale crop pests. Here, we studied the interaction of plant-pathogen pairs at the metabolic level. We selected five plant-pathogen pairs, for which both genomes were fully sequenced, and constructed the corresponding genome-scale metabolic networks. We present theoretical investigations of the metabolic interactions and quantify the positive and negative effects a network has on the other when combined into a single plant-pathogen pair network. Merged networks were examined for both the native plant-pathogen pairs as well as all other combinations. Our calculations indicate that the presence of the parasite metabolic networks reduce the ability of the plants to synthesize key biomass precursors. While the producibility of some precursors is reduced in all investigated pairs, others are only impaired in specific plant-pathogen pairs. Interestingly, we found that the specific effects on the host’s metabolism are largely dictated by the pathogen and not by the host plant. We provide graphical network maps for the native plant-pathogen pairs to allow for an interactive interrogation. By exemplifying a systematic reconstruction of metabolic network pairs for five pathogen-host pairs and by outlining various theoretical approaches to study the interaction of plants and phytopathogens on a biochemical level, we demonstrate the potential of investigating pathogen-host interactions from the perspective of interacting metabolic networks that will contribute to furthering our understanding of mechanisms underlying a successful invasion and subsequent establishment of a parasite into a plant host. Full article

In this paper, we analyze a few interrelated concepts about graphs, such as their degree, entropy, or their symmetry/asymmetry levels. These concepts prove useful in the study of different types of Systems, and particularly, in the analysis of Complex Networks. A System can be defined as any set of components functioning together as a whole. A systemic point of view allows us to isolate a part of the world, and so, we can focus on those aspects that interact more closely than others. Network Science analyzes the interconnections among diverse networks from different domains: physics, engineering, biology, semantics, and so on. Current developments in the quantitative analysis of Complex Networks, based on graph theory, have been rapidly translated to studies of brain network organization. The brain's systems have complex network features—such as the small-world topology, highly connected hubs and modularity. These networks are not random. The topology of many different networks shows striking similarities, such as the scale-free structure, with the degree distribution following a Power Law. How can very different systems have the same underlying topological features? Modeling and characterizing these networks, looking for their governing laws, are the current lines of research. So, we will dedicate this Special Issue paper to show measures of symmetry in Complex Networks, and highlight their close relation with measures of information and entropy. Full article

Bacterial histone-like HU proteins are critical to maintenance of the nucleoid structure. In addition, they participate in all DNA-dependent functions, including replication, repair, recombination and gene regulation. In these capacities, their function is typically architectural, inducing a specific DNA topology that promotes assembly of higher-order nucleo-protein structures. Although HU proteins are highly conserved, individual homologs have been shown to exhibit a wide range of different DNA binding specificities and affinities. The existence of such distinct specificities indicates functional evolution and predicts distinct in vivo roles. Emerging evidence suggests that HU proteins discriminate between DNA target sites based on intrinsic flexure, and that two primary features of protein binding contribute to target site selection: The extent to which protein-mediated DNA kinks are stabilized and a network of surface salt-bridges that modulate interaction between DNA flanking the kinks and the body of the protein. These features confer target site selection for a specific HU homolog, they suggest the ability of HU to induce different DNA structural deformations depending on substrate, and they explain the distinct binding properties characteristic of HU homologs. Further divergence is evidenced by the existence of HU homologs with an additional lysine-rich domain also found in eukaryotic histone H1. Full article

This paper presents a new saccade amplitude prediction model. The model is based on a Kalman filter and regression analysis. The aim of the model is to predict a saccade’s am-plitude extremely quickly, i.e., within two eye position samples at the onset of a saccade. Specifically, the paper explores saccade amplitude prediction considering one or two sam-ples at the onset of a saccade. The models’ prediction performance was tested with 35 subjects. The amplitude accuracy results yielded approximately 5.26° prediction error, while the error for direction prediction was 5.3% for the first sample model and 1.5% for the two samples model. The practical use of the proposed model lays in the area of real-time gaze-contingent compression and extreme eye-gaze aware interaction applications. The paper provides theoretical evaluation of the benefits of saccade amplitude prediction to the gaze-contingent multimedia compression, estimating a 21% improvement in com-pression for short network delays. Full article

It is important for electronic commerce companies to understand the service quality of their systems, such as, the response time for users’ interactions, the service delay zone and the number of appropriate users accessing the systems concurrently. The accurate and prompt management of the service quality can greatly help build and operate systems more efficiently. In this paper we present a methodology for the service quality measurement and the user capacity modeling for electronic commerce systems. While most previous researches on this issue have been based on the closed-LAN environment, we conduct experiments under real network environment using sample e-Commerce systems. Specifically, we measure the response times for e-Commerce transactions under Cable, DSL, and wireless networks, and analyze the delay zones in processing the users’ service requests. The discrete event simulation and hybrid simulation are performed to estimate the maximum number of users using a response time limit as the service quality criterion. We also investigate the self-similar characteristics on the response time and the number of users, and the extensive results of the experiment and the simulations are described. Full article