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Abstract The specificity of gene expression during development requires the insulation of regulatory domains to avoid inappropriate enhancer-gene interactions. In vertebrates, this insulator function is mostly attributed to clusters of CTCF sites located at topologically associating domain (TAD) boundaries. However, TAD boundaries allow some physical crosstalk across regulatory domains, which is at odds with the specific and precise expression of developmental genes. Here we show that developmental genes and nearby clusters of CTCF sites cooperatively foster the robust insulation of regulatory domains. By genetically dissecting a couple of representative loci in mouse embryonic stem cells, we show that CTCF sites prevent undesirable enhancer-gene contacts (i.e. physical insulation), while developmental genes preferentially contribute to regulatory insulation through non-structural mechanisms involving promoter competition rather than enhancer blocking. Overall, our work provides important insights into the insulation of regulatory domains, which in turn might help interpreting the pathological consequences of certain structural variants.

Background/Objectives: The impact of virion maturation on neutralizing antibody responses in HIV treatment is not fully understood. This study examines whether antiretroviral regimens (ART) with boosted protease inhibitors (b-PI), which increase exposure to immature virions, affect neutralization capacity compared to Non-b-PI regimens. Methods: Neutralization activity was assessed in 45 HIV-infected individuals on b-PI regimens and 56 on Non-b-PI regimens, adjusting for factors like infection duration, ART initiation, and immune markers. Individuals on b-PI regimens had significantly lower neutralization scores [mean: 6.1, 95% Confidence Interval (CI): 5.3–6.9] than those on Non-b-PI regimens (mean: 8.9, 95% CI: 8.0–9.9; p < 0.0001). This difference was not explained by infection duration or CD4+ counts. CD4+/CD8+ ratios were positively associated with neutralization, while b-PI use was negatively associated. A regression model indicated that b-PI use significantly predicted lower neutralization scores (beta = −0.30, p = 0.049). Conclusions: These findings suggest that exposure to immature virions via b-PI use reduces neutralizing antibody responses, highlighting the importance of virion maturation in antibody induction. ART regimens promoting exposure to mature virions may enhance neutralization, with potential implications for HIV vaccine design. Further research is needed to explore implications for HIV vaccine design, especially using virus-like particles.

Deep learning is regarded as a promising solution for reversible steganography. There is an accelerating trend of representing a reversible steo-system by monolithic neural networks, which bypass intermediate operations in traditional pipelines of reversible steganography. This end-to-end paradigm, however, suffers from imperfect reversibility. By contrast, the modular paradigm that incorporates neural networks into modules of traditional pipelines can stably guarantee reversibility with mathematical explainability. Prediction-error modulation is a well-established reversible steganography pipeline for digital images. It consists of a predictive analytics module and a reversible coding module. Given that reversibility is governed independently by the coding module, we narrow our focus to the incorporation of neural networks into the analytics module, which serves the purpose of predicting pixel intensities and a pivotal role in determining capacity and imperceptibility. The objective of this study is to evaluate the impacts of different training configurations upon predictive accuracy of neural networks and provide practical insights. In particular, we investigate how different initialisation strategies for input images may affect the learning process and how different training strategies for dual-layer prediction respond to the problem of distributional shift. Furthermore, we compare steganographic performance of various model architectures with different loss functions.

The present work aimed to establish the influence of self-reported trait anxiety on computerized and self-reported measures of executive control, and speed of processing in young adults using latent variable modeling. One hundred and six participants completed the State-trait anxiety questionnaire (STAI-t), the Attentional Control Scale (ACS), and a set of computerized tasks of executive control, tapping into the updating, inhibition, and shifting components. Higher scores in the latent variable of trait anxiety were negatively associated with the self-reported latent variable of attentional control. Notably, self-reported and performance-based indicators of executive control showed no associations at the latent level. Contrary to our hypotheses, higher trait anxiety did not affect any performance-based executive component but was associated with an increase in response times. We show that self-reported trait anxiety is related to a lower self-perceived sense of attentional control and does not affect executive functioning in non-clinical samples. In turn, trait anxiety is mainly associated with a slowed speed of processing. In conclusion, the tendency to experience a negative mood is related with cognitive processing by reducing its speed even in the absence of threatening stimuli.

Broadly neutralizing antibodies (bnAbs) bind and neutralize diverse HIV isolates and demonstrate protective effects in primate models and humans against specific isolates. To develop an effective HIV vaccine, it is widely believed that inducing these antibodies is crucial. However, the high somatic hypermutation in bnAbs and the limited affinity of HIV Env proteins for bnAb germline precursors suggest that extended antigen exposure is necessary for their production. Consequently, HIV vaccine research is exploring complex sequential vaccination strategies to guide the immune response through maturation stages. In this context, the exploration of the factors linked to the generation of these antibodies across diverse age groups becomes critical. In this study, we assessed the anti-HIV-1 neutralization potency and breadth in 108 aviremic adults and 109 aviremic children under 15 years of age who were receiving ART. We used a previously described minipanel of recombinant viruses and investigated the factors associated with neutralization in these individuals. We identified individuals in both groups who were capable of neutralizing viruses from three different subtypes, with greater cross-neutralization observed in the adult group (49.0% vs. 9.2%). In both groups, we observed an inverse association between neutralization breadth and the CD4+/CD8+ ratio, as well as a direct association with the time to ART initiation. However, we found no association with time post-infection, cumulative ART duration, or CD8+ cell levels. The present study demonstrates that children receiving antiretroviral therapy generate broadly neutralizing responses to HIV-1, albeit with lower magnitude compared to adults. We also observed that neutralization breadth is associated with CD4+/CD8+ levels and time to treatment initiation in both children and adults living with HIV-1. Our interpretation of these results is that a delay in ART initiation could have prolonged the antigenic stimulation associated with viral replication and thus facilitate the capacity to elicit long-lasting broadly neutralizing responses. These results corroborate prior findings that show that HIV-1-neutralizing responses can persist for years, even at low antigen levels, implying an HIV-1 vaccine may induce lasting neutralizing antibody response.

This work deals with the validation of a high-fidelity multiphysics system coupling the Serpent 2 Monte Carlo neutron transport code with SUBCHANFLOW, a subchannel thermalhydraulics code, and TRANSURANUS, a fuel-performance analysis code. The results for a full-core pin-by-pin burnup calculation for the ninth operating cycle of the Temelín II VVER-1000 plant, which starts from a fresh core, are presented and assessed using experimental data. A good agreement is found comparing the critical boron concentration and a set of pin-level neutron flux profiles against measurements. In addition, the calculated axial and radial power distributions match closely the values reported by the core monitoring system. To demonstrate the modeling capabilities of the three-code coupling, pin-level neutronic, thermalhydraulic and thermomechanic results are shown as well. These studies are encompassed in the final phase of the EU Horizon 2020 McSAFE project, during which the Serpent-SUBCHANFLOW-TRANSURANUS system was developed.

Purpose: To validate and assess user satisfaction and usability of the New York University (NYU) Langone Eye Test application, a smartphone-based visual acuity (VA) test. Design: Mixed-methods cross-sectional cohort study. Participants: Two hundred forty-four eyes of 125 participants were included. All participants were adults 18 years of age or older. Participants’ eyes with a VA of 20/400 (1.3 logarithm of the minimum angle of resolution [logMAR]) or worse were excluded. Methods: Patients were tested using the clinical standard Rosenbaum near card and the NYU Langone Eye Test application on an iPhone and Android device. Each test was performed twice to measure reliability. Ten patients were selected randomly for subsequent semistructured qualitative interviews with thematic analysis. Main Outcome Measures: Visual acuity was the parameter measured. Bland–Altman analysis was used to measure agreement between the results of the NYU Langone Eye Test application and Rosenbaum card, as well as test–retest reliability of each VA. The correlation between results was calculated using the intraclass correlation coefficient. Satisfaction survey and semistructured interview questions were developed to measure usability and acceptability. Results: Bland–Altman analysis revealed an agreement between the application and the Rosenbaum near card of 0.017 ± 0.28 logMAR (iPhone) and 0.009 ± 0.29 logMAR (Android). The correlation between the application and the Rosenbaum near card was 0.74 for both the iPhone and Android. Test–retest reliability was 0.003 ± 0.22 logMAR (iPhone), 0.01 ± 0.25 logMAR (Android), and 0.01 ± 0.23 logMAR (Rosenbaum card). Of the 125 participants, 97.6% found the application easy to use, and 94.3% were overall satisfied with the application. Thematic analysis yielded 6 key themes: (1) weaknesses of application, (2) benefits of the application, (3) tips for application improvement, (4) difficulties faced while using the application, (5) ideal patient for application, and (6) comparing application with traditional VA testing. Conclusions: The NYU Langone Eye Test application is a user-friendly, accurate, and reliable measure of near VA. The application’s integration with the electronic health record, accessibility, and easy interpretation of results, among other features, make it ideal for telemedicine use.

Designing small and efficient mobile neural networks is difficult because the challenge is to determine the architecture that achieves the best performance under a given limited computational scenario. Previous lightweight neural networks rely on a cell module that is repeated in all stacked layers across the network. These approaches do not permit layer diversity, which is critical for achieving strong performance. This paper presents an experimental study to develop an efficient mobile network using a hierarchical architecture. Our proposed mobile network, called Diversity Network (DivNet), has been shown to perform better than the basic architecture generally employed by the best high-efficiency models–with simply stacked layers–, regarding complexity cost and performance. A set of architectural design decisions are described that reduce the proposed model size while yielding a significant performance improvement. Our experiments on image classification show that compared to, respectively, MobileNetV2, SqueezeNet, and ShuffleNetV2, our proposal DivNet can improve accuracy by 2.09%, 0.76%, and 0.66% on the CIFAR100 dataset, and by 0.05%, 4.96%, and 1.13% on the CIFAR10 dataset. On more complex datasets, e.g., ImageNet, our proposal DivNet achieves 70.65% Top-1 accuracy and 90.23% Top-5 accuracy, still better than other small models like MobilNet, SqueezeNet, ShuffleNet.

Recently, data-driven algorithms such as deep neural networks have attracted a lot of attention and have become a popular area of research and development. This interest is driven by several factors, such as recent advances in processing power (cheap and powerful hardware), the availability of large datasets (big data), and several small but important algorithmic advances (e.g., convolutional layers). Nowadays, deep neural networks are the state-of-the-art for several computer vision tasks, such as the ones that require high-level understanding of image semantics, e.g., image classification, object segmentation, saliency detection, and also in low level image processing tasks, such as image denoising, inpainting, and super-resolution, among others. Deep learning can handle large volumes of video data by making use of its powerful non-linear mapping and extracting high-level features with very deep networks. Incorporating deep learning into the IoVT can provide radical innovations in video sensing, coding, enhancing, understanding, and evaluation areas making it easier to handle the enormous growth in video data compared to traditional methods.

A 3D forest monitoring system, called FORSAT (a satellite very high resolution image processing platform for forest assessment), was developed for the extraction of 3D geometric forest information from very high resolution (VHR) satellite imagery and the automatic 3D change detection. FORSAT is composed of two complementary tasks: (1) the geometric and radiometric processing of satellite optical imagery and digital surface model (DSM) reconstruction by using a precise and robust image matching approach specially designed for VHR satellite imagery, (2) 3D surface comparison for change detection. It allows the users to import DSMs, align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes (together with precision values) between epochs. FORSAT is a single source and flexible forest information solution, allowing expert and non-expert remote sensing users to monitor forests in three and four (time) dimensions. The geometric resolution and thematic content of VHR optical imagery are sufficient for many forest information needs such as deforestation, clear-cut and fire severity mapping. The capacity and benefits of FORSAT, as a forest information system contributing to the sustainable forest management, have been tested and validated in case studies located in Austria, Switzerland and Spain.

Establishing and maintaining the supply of skilled STEM workers are issues that many businesses are currently facing. CEOs can provide unique perspectives on the roles of parents, educators and schools, industry and community partners, and government on this issue. To this end, a Qualtrics panel was used to survey 45 CEOs located in the state of Georgia in the southeastern United States. CEOs responded to open-ended questions that were later analyzed using topic modeling techniques to uncover the themes and the factors that have the potential to increase the number of STEM-capable graduates and, ultimately, a sustained STEM workforce. The results of this study indicate that CEOs perceive parents, educators and schools, industry and community partners, and government as key players who are recommended to interact, engage and collaborate to successfully create a sustained pipeline of STEM talent. Business leaders, university business programs, and business faculty should stay abreast of the factors affecting the supply and demand of STEM workers, and this paper adds value by reporting on CEO perspectives on this important problem.

With the advent of 5G technologies, vertical markets have been placed at the forefront, as fundamental drivers and adopters of technical developments and new business models. Small Unmanned Aerial Vehicles (SUAVs) are gaining traction in multiple vertical sectors, as key assets to generate, process, and distribute relevant information for the provision of value-added services. However, the enormous potential of SUAVs to support a flexible, rapid, and cost-effective deployment of vertical applications is still to be exploited. In this paper, we leverage our prior work on Network Functions Virtualization (NFV) and SUAVs to design and build a multi-site experimentation testbed based on open-source technologies. The goal of this testbed is to explore synergies among NFV, SUAVs, and vertical services, following a practical approach primarily governed by experimentation. To verify our testbed design, we realized a reference use case where a number of SUAVs, cloud infrastructures, and communication protocols are used to provide a multi-site vertical service. Our experimentation results suggest the potential of NFV and SUAVs to flexibly support vertical services. The lessons learned have served to identify missing elements in our NFV platform, as well as challenging aspects for potential improvement. These include the development of specific mechanisms to limit processing load and delays of service deployment operations.

This paper presents a watermarking method in the spatial domain with HVS-imperceptibility for High Dynamic Range (HDR) images. The proposed method combines the content readability afforded by invisible watermarking with the visual ownership identification afforded by visible watermarking. The HVS-imperceptibility is guaranteed thanks to a Luma Variation Tolerance (LVT) curve, which is associated with the transfer function (TF) used for HDR encoding and provides the information needed to embed an imperceptible watermark in the spatial domain. The LVT curve is based on the inaccuracies between the non-linear digital representation of the linear luminance acquired by an HDR sensor and the brightness perceived by the Human Visual System (HVS) from the linear luminance displayed on an HDR screen. The embedded watermarks remain imperceptible to the HVS as long as the TF is not altered or the normal calibration and colorimetry conditions of the HDR screen remain unchanged. Extensive qualitative and quantitative evaluations on several HDR images encoded by two widely-used TFs confirm the strong HVS-imperceptibility capabilities of the method, as well as the robustness of the embedded watermarks to tone mapping, lossy compression, and common signal processing operations.

The revolution of Remotely Piloted Aircraft Systems (RPASs), both in the commercial and the research field, has accelerated the arrival of innovative and complex services to the civilian environment within non-segregated airspace. The extensive deployment of these services will still require solving relevant challenges in several topics, such as regulation, security, or diverse technical defiance. In particular, the services to be provided increasingly demand network resources and performance improvements. This scenario will be strongly exacerbated by the upcoming resources provided by the 5G/6G architectures, where Remotely Piloted Aircrafts (RPAs) will likely support multiple communication interfaces and will be able to establish multi-hop network connectivity with numerous devices leading to an unprecedented hyper-connected RPA environment. In addition, future RPASs will have to enhance the management of their connectivity capabilities to comply with the latest regulations, which demand an uninterrupted link for the Control and Non-Payload Communications (CNPC). This article presents a flexible Communication Infrastructure Manager (CIM) based on Software-Defined Networking (SDN) and virtualization technologies capable of handling the complexity inherent to this ecosystem and being adapted to different operation requirements to cope with all these communication challenges. Finally, the article shows several validation experiences to demonstrate the potential of the CIM versus the standard approach.

Previous studies have shown that teachers and parents of children with language disorders report them to have higher victimization scores, a heightened risk of low-quality friendships and social difficulties, and may be more vulnerable to peer rejection than control peers. However, there are few studies of bullying in children with developmental language disorder (DLD) and reading difficulties (RD), and none has considered the mutual relationships between teacher reports, the perceptions of classmates, and children’s self-reports. We analyzed the experiences of bullying and peer relationships in primary school students with DLD and RD as compared to their age-matched peers using teacher reports, peer reports, and self-reports on victimization. Additionally, we explored how these three perspectives are associated. Results indicated lower levels of peer-rated prosocial skills in DLD and RD students compared to their peers, as well as higher levels of victimization as assessed by peers for students with DLD. In the same line, the teachers’ ratings showed that students with DLD presented poorer social skills, less adaptability, and more withdrawal in social interaction. Contrastingly, self-reports informed of similar rates of interpersonal relationships, social stress, and peer victimization between the three groups. Consequently, we found significant correlations between measures of peer reports and teacher reports that contrasted with the lack of correlations between self and other agents’ reports. These findings stress the importance of using self-reports, peer reports, and teacher reports at the same time to detect bullying situations that might go unnoticed.

Unmanned Aerial Vehicles (UAVs) have progressively been integrated into people lives during the last years. It is quite common now to see UAVs flying in the countryside doing field inspection, in highways for traffic control operations, or above stadiums in sport and music events. It is also common to see spectacular UAV swarm showcases (in most cases they are just performing a choreography) showing the potential of upcoming technologies. This article is focused on multi-UAV scenarios, on the establishment of Flying Ad hoc Networks (FANETs), and on the integration of 5G technologies like Network Function Virtualization (NFV) or Software Defined Networking (SDN). In particular, this article presents a proposal for one of the most common problems that the research and development community has to face at some stage: the validation of the different solutions and deployments. In this area, there is currently a notorious gap between the design phase and the deployment phase, since traditional network simulators are not designed with the constraints imposed by UAVs in mind. Besides, services implementations (that are usually distributed into single-board computers carried as payloads by UAVs) cannot be easily combined with the simulators. VENUE (Virtualized Environment for multi-UAV network emulation) is presented as an experimentation platform that allows testing the integration of multi-UAV FANETs together with network services deployments. VENUE covers from the simulation/emulation phase up to the real equipment integration phase. The validation of the platform is also presented in this article through several UAV use cases that make use of NFV technologies.

Proton therapy has advantages and pitfalls comparing with photon therapy in radiation therapy. Among the limitations of protons in clinical practice we can selectively mention: uncertainties in range, lateral penumbra, deposition of higher LET outside the target, entrance dose, dose in the beam path, dose constraints in critical organs close to the target volume, organ movements and cost. In this review, we combine proposals under study to mitigate those pitfalls by using individually or in combination: (a) biological approaches of beam management in time (very high dose rate “FLASH” irradiations in the order of 100 Gy/s) and (b) modulation in space (a combination of mini-beams of millimetric extent), together with mechanical approaches such as (c) rotational techniques (optimized in partial arcs) and, in an effort to reduce cost, (d) gantry-less delivery systems. In some cases, these proposals are synergic (e.g., FLASH and minibeams), in others they are hardly compatible (mini-beam and rotation). Fixed lines have been used in pioneer centers, or for specific indications (ophthalmic, radiosurgery,…), they logically evolved to isocentric gantries. The present proposals to produce fixed lines are somewhat controversial. Rotational techniques, minibeams and FLASH in proton therapy are making their way, with an increasing degree of complexity in these three approaches, but with a high interest in the basic science and clinical communities. All of them must be proven in clinical applications.

The long-term storage stability of vaccines has a major impact on the roll-out and success of global immunization programs. For the Human Immunodeficiency Virus type 1 (HIV-1) virus-like particle (VLP) vaccine prototype evaluated here, nanoparticle tracking analysis (NTA), and enzyme-linked immunoabsorbent assay (ELISA) results demonstrated a remarkable structural stability. VLPs maintained their integrity and the recognition of relevant B-cell epitopes for three months at 4 and −20 °C. Interestingly, most particles remained intact and preserved the recognition of relevant epitopes even after a week of storage at room temperature.

Eukaryotes have evolved various quality control mechanisms to promote proteostasis in the endoplasmic reticulum (ER). Selective removal of certain ER domains via autophagy (termed as ER-phagy) has emerged as a major quality control mechanism. However, the degree to which ER-phagy is employed by other branches of ER-quality control remains largely elusive. Here, we identify a cytosolic protein, C53, that is specifically recruited to autophagosomes during ER-stress, in both plant and mammalian cells. C53 interacts with ATG8 via a distinct binding epitope, featuring a shuffled ATG8 interacting motif (sAIM). C53 senses proteotoxic stress in the ER lumen by forming a tripartite receptor complex with the ER-associated ufmylation ligase UFL1 and its membrane adaptor DDRGK1. The C53/UFL1/DDRGK1 receptor complex is activated by stalled ribosomes and induces the degradation of internal or passenger proteins in the ER. Consistently, the C53 receptor complex and ufmylation mutants are highly susceptible to ER stress. Thus, C53 forms an ancient quality control pathway that bridges selective autophagy with ribosome-associated quality control in the ER.

Fault detection and isolation (FDI) in oil pipeline systems (OPS) is a very critical issue because faults in these systems such as leaks or equipment malfunctions may cause significant safety accidents and economic losses. These are the challenging factors, along with the environmental regulations for developing efficient FDI approaches for OPS. This paper proposes a model-based distributed FDI approach, which uses a structural model of the system in conjunction with algorithms to generate diagnostic tests that may be implemented in local diagnosers along the OPS. The proposed approach allows detection and isolation of faults in pipeline sections (pipeline segments), pump stations, as well as process control equipment. In this way, simulation of the obtained diagnostic tests in a benchmark application shows that all faults of interest (pipeline segment faults and sensor faults) are detected and isolated.

In the last few years, we witnessed a growing body of literature about automated negotiation. Mainly, negotiating agents are either purely self-driven by maximizing their utility function or by assuming a cooperative stance by all parties involved in the negotiation. We argue that, while optimizing one’s utility function is essential, agents in a society should not ignore the opponent’s utility in the final agreement to improve the agent’s long-term perspectives in the system. This article aims to show whether it is possible to design a social agent (i.e., one that aims to optimize both sides’ utility functions) while performing efficiently in an agent society. Accordingly, we propose a social agent supported by a portfolio of strategies, a novel tit-for-tat concession mechanism, and a frequency-based opponent modeling mechanism capable of adapting its behavior according to the opponent’s behavior and the state of the negotiation. The results show that the proposed social agent not only maximizes social metrics such as the distance to the Nash bargaining point or the Kalai point but also is shown to be a pure and mixed equilibrium strategy in some realistic agent societies.

At present, new data sharing technologies, such as those used in the Internet of Things (IoT) paradigm, are being extensively adopted. For this reason, intelligent security controls have become imperative. According to good practices and security information standards, particularly those regarding security in depth, several defensive layers are required to protect information assets. Within the context of IoT cyber-attacks, it is fundamental to continuously adapt new detection mechanisms for growing IoT threats, specifically for those becoming more sophisticated within mesh networks, such as identity theft and cloning. Therefore, current applications, such as Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), and Security Information and Event Management Systems (SIEM), are becoming inadequate for accurately handling novel security incidents, due to their signature-based detection procedures using the matching and flagging of anomalous patterns. This project focuses on a seldom-investigated identity attack—the Clone ID attack—directed at the Routing Protocol for Low Power and Lossy Networks (RPL), the underlying technology for most IoT devices. Hence, a robust Artificial Intelligence-based protection framework is proposed, in order to tackle major identity impersonation attacks, which classical applications are prone to misidentifying. On this basis, unsupervised pre-training techniques are employed to select key characteristics from RPL network samples. Then, a Dense Neural Network (DNN) is trained to maximize deep feature engineering, with the aim of improving classification results to protect against malicious counterfeiting attempts.

The Unmanned Aircraft System (UAS) ecosystem is exponentially growing in both recreational and professional fields to provide novel services and applications to consumers from multiple engineering fields. However, this technology has only scraped the surface of its potential, especially in those cases that require fast reaction times. Accordingly, the UAS Traffic Management (UTM) project aims at efficiently managing the air traffic for Unmanned Aerial Vehicle (UAV) operations, including those cases where UAVs might be remotely managed from a completely different geographical location. With these considerations in mind, this article presents a cellular-assisted UAVs testbed used to complete a mission managed beyond the radio line-of-sight (BRLoS), as well as introducing a virtualization platform for deploying services using containerization technology. In addition, the article conducts a communication performance evaluation in order to determine if the testbed equipment meets the requirements to carry out this BRLoS management. Finally, indoor flight operations are carried out to demonstrate the feasibility and proper operation of the testbed.

Presently, a significant part of the world population does not have Internet access. The fifth-generation cellular network technology evolution (5G) is focused on reducing latency, increasing the available bandwidth, and enhancing network performance. However, researchers and companies have not invested enough effort into the deployment of the Internet in remote/rural/undeveloped areas for different techno-economic reasons. This article presents the result of a collaboration between Brazil and the European Union, introducing the steps designed to create a fully operational experimentation scenario with the main purpose of integrating the different achievements of the H2020 5G-RANGE project so that they can be trialed together into a 5G networking use case. The scenario encompasses (i) a novel radio access network that targets a bandwidth of 100 Mb/s in a cell radius of 50 km, and (ii) a network of Small Unmanned Aerial Vehicles (SUAV). This set of SUAVs is NFV-enabled, on top of which Virtual Network Functions (VNF) can be automatically deployed to support occasional network communications beyond the boundaries of the 5G-RANGE radio cells. The whole deployment implies the use of a virtual private overlay network enabling the preliminary validation of the scenario components from their respective remote locations, and simplifying their subsequent integration into a single local demonstrator, the configuration of the required GRE/IPSec tunnels, the integration of the new 5G-RANGE physical, MAC and network layer components and the overall validation with voice and data services.

Nowadays, Unmanned Aerial Vehicles (UAV) are frequently present in the civilian environment. However, proper implementations of different solutions based on these aircraft still face important challenges. This article deals with multi-UAV systems, forming aerial networks, mainly employed to provide Internet connectivity and different network services to ground users. However, the mission duration (hours) is longer than the limited UAVs’ battery life-time (minutes). This paper introduces the UAV replacement procedure as a way to guarantee ground users’ connectivity over time. This article also formulates the practical UAV replacements problem in moderately large multi-UAV swarms and proves it to be an NP-hard problem in which an optimal solution has exponential complexity. In this regard, the main objective of this article is to evaluate the suitability of heuristic approaches for different scenarios. This paper proposes betweenness centrality heuristic algorithm (BETA), a graph theory-based heuristic algorithm. BETA not only generates solutions close to the optimal (even with 99% similarity to the exact result) but also improves two ground-truth solutions, especially in low-resource scenarios.

Preventive HIV-1 vaccine strategies rely on the elicitation of broadly neutralizing antibody (bNAb) responses, but their induction in vivo by vaccination remains challenging. Considering that the ability of an epitope to elicit effective humoral immunity depends on its exposure on the virion, we have used a reverse genetics approach to select variants from an HIV-1 AC10_29 randomly mutated envelope library that showed increased affinity for a selected bNAb (4E10 bNAb targeting the HIV-1 MPER region). Isolated envelope sequences were analyzed by deep-sequencing showing a small number of dominant changes, including the loss of four potential N-linked glycosylation sites and disruption of the V1/V2 loop. Accordingly, the dominant variant (LR1-C1), showed not only increased affinity for MPER bNAbs 4E10 and 2F5, but also higher affinity for an additional antibody targeting the V3 loop (447-52D) that could be a consequence of an open conformation tier 1-like Env. Furthermore, the amino acids specific for the selected variant are associated with an increased sensitivity for 4E10 and 2F5 antibodies. In vivo studies showed that sera from mice immunized with LR1-C1 viruses possessed an improved neutralizing activity compared to the wild-type AC10_29 env. While Virus Like Particles (VLPs) carrying this envelope were unable to induce detectable neutralizing activity in immunized rabbits, one animal showed antibody response to the 4E10-proximal region. Our data establish a novel approach that has the potential to yield HIV envelope immunogen sequences that direct antibody responses to specific envelope regions.

The area devoted to growing tobacco (Nicotiana tabacum L.) in the province of Granma, Cuba, accounted for nearly 2600 ha in the year 1997, which represented the 4% of the total tobacco plantation area on the island in that year. Tobacco is tillage-intensive since traditional production systems entail seven or eight cultivation operations before transplanting. The objective of this study was to evaluate two alternative tillage systems in comparison with conventional tillage for tobacco production with the aim to improve soil conditions, reduce the number of tillage operations for soil preparation for transplanting, and increase yields. The treatments studied were: (T1) conventional or traditional tillage, (T2) reduced tillage with a multi-tiller plough, and (T3) reduced tillage with a chisel plough. T1 consisted in disc ploughing twice and disc harrowing twice for primary tillage, while before transplanting plots were ploughed twice with a horse-drawn mouldboard plough and harrowed twice with a horse-drawn spike-tooth harrow. In T2 primary tillage was accomplished with two passes with the multi-tiller followed by disc harrowing twice before transplanting. In T3 chisel ploughing was substituted for multi-tiller ploughing. The two reduced tillage systems improved the physical conditions of the soil, which resulted in: lower bulk density, with average values across the 0-30 cm soil profile of 1.48, 1.34 and 1.30 Mg m-3 in T1, T2 and T3, respectively, at the end of the growing season; higher soil water content in the soil profile in all four sampling dates per season; greater porosity; and lower resistance to penetration with values of 2.48, 2.15 and 1.71 MPa in T1, T2 and T3, respectively, before crop harvesting. Tillage system T3 provided the highest crop yields (2.26 Mg ha-1) compared with T2 (2.14 Mg ha-1) and T1 (1.95 Mg ha-1), for the plants grown on T3 plots had the largest number of leaves. The size of the leaves was similar in all three systems, however.

In recent years, Online Social Networks (OSNs) have received a great deal of attention for their potential use in the spatial and temporal modeling of events owing to the information that can be extracted from these platforms. Within this context, one of the most latent applications is the monitoring of natural disasters. Vital information posted by OSN users can contribute to relief efforts during and after a catastrophe. Although it is possible to retrieve data from OSNs using embedded geographic information provided by GPS systems, this feature is disabled by default in most cases. An alternative solution is to geoparse specific locations using language models based on Named Entity Recognition (NER) techniques. In this work, a sensor that uses Twitter is proposed to monitor natural disasters. The approach is intended to sense data by detecting toponyms (named places written within the text) in tweets with event-related information, e.g., a collapsed building on a specific avenue or the location at which a person was last seen. The proposed approach is carried out by transforming tokenized tweets into word embeddings: a rich linguistic and contextual vector representation of textual corpora. Pre-labeled word embeddings are employed to train a Recurrent Neural Network variant, known as a Bidirectional Long Short-Term Memory (biLSTM) network, that is capable of dealing with sequential data by analyzing information in both directions of a word (past and future entries). Moreover, a Conditional Random Field (CRF) output layer, which aims to maximize the transition from one NER tag to another, is used to increase the classification accuracy. The resulting labeled words are joined to coherently form a toponym, which is geocoded and scored by a Kernel Density Estimation function. At the end of the process, the scored data are presented graphically to depict areas in which the majority of tweets reporting topics related to a natural disaster are concentrated. A case study on Mexico’s 2017 Earthquake is presented, and the data extracted during and after the event are reported.

This work proposes a persuasion model based on argumentation theory and users’ characteristics for improving the use of resources in bike sharing systems, fostering the use of the bicycles and thus contributing to greater energy sustainability by reducing the use of carbon-based fuels. More specifically, it aims to achieve a balanced network of pick-up and drop-off stations in urban areas with the help of the users, thus reducing the dedicated management trucks that redistribute bikes among stations. The proposal aims to persuade users to choose different routes from the shortest route between a start and an end location. This persuasion is carried out when it is not possible to park the bike in the desired station due to the lack of parking slots, or when the user is highly influenceable. Differently to other works, instead of employing a single criteria to recommend alternative stations, the proposed system can incorporate a variety of criteria. This result is achieved by providing a defeasible logic-based persuasion engine that is capable of aggregating the results from multiple recommendation rules. The proposed framework is showcased with an example scenario of a bike sharing system.

Background and purpose. Intravenous administration of recombinant tissue plasminogen activator (rtPA) is approved for the treatment of patients with acute ischemic stroke. It is still controversial if in patients with ischemic stroke due to atrial fibrillation this treatment has the same efficacy as in patients with ischemic stroke due to other etiologies. The present study aimed to compare the safety and outcome of intravenous thrombolysis with rtPA between patients with stroke due to atrial fibrillation, due to other cardiac sources of emboli and due to large-vessel disease. Methods. Retrospective analysis of 463 patients treated with rtPA in the Stroke Unit of Ramon y Cajal University Hospital, Madrid, between January 2004 and December 2011. Patients were divided in three subgroups according to stroke etiology: cardioembolic stroke due to atrial fibrillation, cardioembolic stroke due to a cardiac disease other than atrial fibrillation, stroke due to large-vessel disease. The outcome measures of the study were: symptomatic intracranial hemorrhage transformation, modified Rankin score and mortality at three months. Results. The results of the multivariate analysis showed that stroke etiology has no influence on functional outcome (p = 0.181), symptomatic intracranial hemorrhage (p = 0.093) and mortality at three months (p = 0.091). Age and baseline NIHSS scores are independent predictors for both functional outcome and mortality. Conclusion. Stroke outcome after intravenous thrombolysis with rtPA is not influenced by the stroke etiology.

In this paper, we present a practical solution to support the adaptable and automated deployment of applications of Small Unmanned Aerial Vehicles (SUAVs). Our solution is based on virtualization technologies, and considers SUAVs as programmable network platforms capable of executing virtual functions and services, which may be dynamically selected according to the requirements specified by the operator of the aerial vehicles. This way, SUAVs can be flexibly and rapidly adapted to different missions with heterogeneous objectives. The design of our solution is based on Network Function Virtualization (NFV) technologies, developed under the umbrella of the fifth generation of mobile networks (5G), as well as on existing Internet protocol standards, including flying ad hoc network routing protocols. We implemented a functional prototype of our solution using well-known open source technologies, and we demonstrated its practical feasibility with the execution of an IP telephony service. This service was implemented as a set of virtualized network functions, which were automatically deployed and interconnected over an infrastructure of SUAVs, being the telephony service tested with real voice-over-IP terminals.

We claim the strong potential of data-centric communications in Unmanned Aircraft Systems (UAS), as a suitable paradigm to enhance collaborative operations via efficient information sharing, as well as to build systems supporting flexible mission objectives. In particular, this paper analyzes the primary contributions to data dissemination in UAS that can be given by the Data Distribution Service (DDS) open standard, as a solid and industry-mature data-centric technology. Our study is not restricted to traditional UAS where a set of Unmanned Aerial Vehicles (UAVs) transmit data to the ground station that controls them. Instead, we contemplate flexible UAS deployments with multiple UAV units of different sizes and capacities, which are interconnected to form an aerial communication network, enabling the provision of value-added services over a delimited geographical area. In addition, the paper outlines an approach to address the issues inherent to the utilization of network-level multicast, a baseline technology in DDS, in the considered UAS deployments. We complete our analysis with a practical experience aiming at validating the feasibility and the advantages of using DDS in a multi-UAV deployment scenario. For this purpose, we use a UAS testbed built up by heterogeneous hardware equipment, including a number of interconnected micro aerial vehicles, carrying single board computers as payload, as well as real equipment from a tactical UAS from the Spanish Ministry of Defense.

In recent years, online social media information has been the subject of study in several data science fields due to its impact on users as a communication and expression channel. Data gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users’ opinions and make predictions about real events. Cyber-attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber-attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ 1 regularization.

This study evaluated the neutralization breadth in dually infected (DI) HIV-1 long-term non-progressor elite controller patients (LTNP-EC) using a representative minipanel of 6 viruses from 5 different subtypes. Our results showed an improved neutralization breadth in DI LTNP-EC patients when compared with matched LTNP single-infected patients. The role of viral diversity in neutralization was estimated with the Shannon Entropy and the p-distance in viral quasispecies. We found a positive correlation between neutralization breadth and diversity within the viral quasispecies. This correlation could explain why a group of LTNP-EC patients developed a broad neutralizing response despite having undetectable levels of viremia.

With the increasing utilization of medical imaging in clinical practice and the growing dimensions of data volumes generated by various medical imaging modalities, the distribution, storage, and management of digital medical image data sets requires data compression. Over the past few decades, several image compression standards have been proposed by international standardization organizations. This paper discusses the current status of these image compression standards in medical imaging applications together with some of the legal and regulatory issues surrounding the use of compression in medical settings.

Chagas disease kills approximately 45 thousand people annually and affects 10 million people in Latin America and the southern United States. The parasite that causes the disease, Trypanosoma cruzi, can be transmitted by insects of the family Reduviidae, subfamily Triatominae. Any study that attempts to evaluate risk for Chagas disease must focus on the ecology and biogeography of these vectors. Expected distributional shifts of vector species due to climate change are likely to alter spatial patterns of risk of Chagas disease, presumably through northward expansion of high risk areas in North America.We forecast the future (2050) distributions in North America of Triatoma gerstaeckeri and T. sanguisuga, two of the most common triatomine species and important vectors of Trypanosoma cruzi in the southern United States. Our aim was to analyze how climate change might affect the future shift of Chagas disease in North America using a maximum entropy algorithm to predict changes in suitable habitat based on vector occurrence points and predictive environmental variables. Projections based on three different general circulation models (CCCMA, CSIRO, and HADCM3) and two IPCC scenarios (A2 and B2) were analyzed. Twenty models were developed for each case and evaluated via cross-validation. The final model averages result from all twenty of these models. All models had AUC >0.90, which indicates that the models are robust. Our results predict a potential northern shift in the distribution of T. gerstaeckeri and a northern and southern distributional shift of T. sanguisuga from its current range due to climate change.The results of this study provide baseline information for monitoring the northward shift of potential risk from Chagas disease in the face of climate change.

This paper proposes a view-invariant gait recognition framework that employs a unique view invariant model that profits from the dimensionality reduction provided by Direct Linear Discriminant Analysis (DLDA). The framework, which employs gait energy images (GEIs), creates a single joint model that accurately classifies GEIs captured at different angles. Moreover, the proposed framework also helps to reduce the under-sampling problem (USP) that usually appears when the number of training samples is much smaller than the dimension of the feature space. Evaluation experiments compare the proposed framework’s computational complexity and recognition accuracy against those of other view-invariant methods. Results show improvements in both computational complexity and recognition accuracy.

Programmed Death-1 (PD-1) is an inhibitory member of the CD28 family of molecules expressed on CD8+ T cells in response to antigenic stimulation. To better understand the role of PD-1 in antiviral immunity we examined the expression of PD-1 on Epstein-Barr virus (EBV) epitope-specific CD8+ T cells during acute infectious mononucleosis (AIM) and convalescence.Using flow cytometry, we observed higher frequencies of EBV-specific CD8+ T cells and higher intensity of PD-1 expression on EBV-specific CD8+ T cells during AIM than during convalescence. PD-1 expression during AIM directly correlated with viral load and with the subsequent degree of CD8+ T cell contraction in convalescence. Consistent differences in PD-1 expression were observed between CD8+ T cells with specificity for two different EBV lytic antigen epitopes. Similar differences were observed in the degree to which PD-1 was upregulated on these epitope-specific CD8+ T cells following peptide stimulation in vitro. EBV epitope-specific CD8+ T cell proliferative responses to peptide stimulation were diminished during AIM regardless of PD-1 expression and were unaffected by blocking PD-1 interactions with PD-L1. Significant variability in PD-1 expression was observed on EBV epitope-specific CD8+ T cell subsets defined by V-beta usage.These observations suggest that PD-1 expression is not only dependent on the degree of antigen presentation, but also on undefined characteristics of the responding cell that segregate with epitope specificity and V-beta usage.