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Injection moulding is a polymer processing method of choice for making plastic parts on industrial scale, but its traditional mould is made from tooling steel with time-consuming and costly production. Additive manufacturing technologies arise as an alternative for creating mould inserts at lower costs and shorter lead times. In this context, this study describes a series of stereolithography (SLA)-printed injection mould inserts fabricated from two photopolymer resins, utilised to mould standard tensile specimens of a commercial-grade polypropylene, aiming to evaluate effects on the polymer’s thermal and mechanical properties. Our results demonstrated that the glass fibre-filled resin inserts withstood more moulding cycles before failure, had superior mechanical properties, higher Tg and greater thermal conductivity. Calorimetric data revealed that PP thermal properties and degree of crystallinity were little affected, while mechanical testing suggests a significant effect in the elongation at break. Thus, these findings highlight the importance of adequate heat extraction during injection moulding and endorse further application of SLA mould inserts for the manufacturing of injection-moulded plastic parts in the case of prototypes or small batches, provided suitable cooling is made available, contributing to the feasibility and affordability of employing this approach for an industrial setting. This paper is an extended version of conference paper by M. Hopkins, S. Gunbay, C. Hayes, V.F. Moritz, E. Fuenmayor, J.G. Lyons, D.M. Devine “Stereolithography (SLA) utilised to print injection mould tooling in order to evaluate thermal and mechanical properties of commercial polypropylene”. Procedia Manuf. 2021, 55, 205–212. 10.1016/j.promfg.2021.10.029. yes

peer-reviewed The full text of this article will not be available in ULIR until the embargo expires on the 06/03/2021 One of the many evolved functions of photosynthetic organisms is to synthesize light harvesting nanostructures from photoactive molecules such as porphyrins. Engineering synthetic analogues with optimized molecular order necessary for the efficient capture and harvest of light energy remains challenging. Here, we address this challenge by reporting the self-assembly of zinc(II) meso-tetrakis(4-carboxyphenyl)porphyrins into films of highly ordered nanostructures. The self-assembly process takes place selectively at the interface between two immiscible liquids (water|organic solvent), with kinetically stable interfacial nanostructures formed only at pH values close to the pKa of the carboxyphenyl groups. Molecular dynamics simulations suggest that the assembly process is driven by an interplay between the hydrophobicity gradient at the interface and hydrogen bonding in the formed nanostructure. Ex situ XRD analysis and in situ UV/vis and steady state fluorescence indicates the formation of chlathrate type nanostructures that retain the emission properties of their monomeric constituents. The self-assembly method presented here avoids the use of acidic conditions, additives such as surfactants and external stimuli, offering an alternative for the realization of light-harvesting antennas in artificial photosynthesis technologies.

In this study, we develop a framework for an intelligent and self-supervised industrial pick-and-place operation for cluttered environments. Our target is to have the agent learn to perform prehensile and non-prehensile robotic manipulations to improve the efficiency and throughput of the pick-and-place task. To achieve this target, we specify the problem as a Markov decision process (MDP) and deploy a deep reinforcement learning (RL) temporal difference model-free algorithm known as the deep Q-network (DQN). We consider three actions in our MDP; one is ‘grasping’ from the prehensile manipulation category and the other two are ‘left-slide’ and ‘right-slide’ from the non-prehensile manipulation category. Our DQN is composed of three fully convolutional networks (FCN) based on the memory-efficient architecture of DenseNet-121 which are trained together without causing any bottleneck situations. Each FCN corresponds to each discrete action and outputs a pixel-wise map of affordances for the relevant action. Rewards are allocated after every forward pass and backpropagation is carried out for weight tuning in the corresponding FCN. In this manner, non-prehensile manipulations are learnt which can, in turn, lead to possible successful prehensile manipulations in the near future and vice versa, thus increasing the efficiency and throughput of the pick-and-place task. The Results section shows performance comparisons of our approach to a baseline deep learning approach and a ResNet architecture-based approach, along with very promising test results at varying clutter densities across a range of complex scenario test cases.

—Exactly-once data processing/delivery can be guaranteed in traditional big data processing systems, e.g. Apache Flink. Checkpoint is commonly used as the solution. Each operator in these systems can restart from the last successfully saved state whenever a failure happens. It is not necessary to restore the logical job graph onto the same device(s) in traditional datacentre scenarios with powerful servers close to each other. However, the datacentre oriented solutions are not suitable for IoT collaborative edge computing scenarios. The logical job graph is tightly coupled to the physical topology in IoT networks. Data processing task(s) cannot be placed at a random edge device to recover from a network failure as it needs to evaluate the benefits of transmitting data versus processing/aggregating the data. To address the above challenges, this paper proposes an Information Centric Networking based solution and correspondent protocols to provide Exactly-oncecomputation for the Collaborative Edge in IoT (ECE). It contains a job execution scheme to deliver IoT jobs with exactly once data computation guarantee and a recovery procedure to dynamically change the IoT job execution graph while experiencing link failures. The protocol also provides a checking procedure on data state (received/un-received and computed/un-computed) to prevent any data loss or duplicated data processing due to the updated job graph. A data identification approach based on the job graph is devised to support the ECE functionality. A testbed has been developed on ndnSIM and the simulation results have verified the feasibility and scalability of ECE design. It also evaluates the overhead incurred by the ECE protocol to guarantee exactly once data computation. yes

Poor durability of reinforced concrete structures can lead to serious structural failures. An accurate model to observe the effects of aggressive agents like carbonation, sulfate ingress, and seawater solutions on the solid phase assemblage will help designers and specifiers better understand how cement behaves in these environments. This paper presents the first steps in developing such a model using the PHREEQC geochemical software by accounting for alkali binding and dissolution. It also presents the use of discrete solid phases (DSPs) to account for the solid-solution behaviour of siliceous hydrogarnet and magnesium silicate hydrate (M-S-H). A new thermodynamic description of the vaterite phase has also been developed for this work using the cemdata18 thermodynamic database. The predicted phase assemblages of cements in these environments here agree with previously published findings using a different thermodynamic model supported with experimental data.

Host immunity to infection with SARS-CoV-2 is highly variable, dictating diverse clinical outcomes ranging from asymptomatic to severe disease and death. We previously reported reduced type I interferon in severe COVID-19 patients preceded clinical worsening. Further studies identified genetic mutations in loci of the TLR3- or TLR7-dependent interferon-I pathways, or neutralizing interferon-I autoantibodies as risk factors for development of COVID-19 pneumonia. Here we show in patient cohorts with different severities of COVID-19, that baseline plasma interferon α measures differ according to the immunoassay used, timing of sampling, the interferon α subtype measured, and the presence of autoantibodies. We also show a consistently reduced induction of interferon-I proteins in hospitalized COVID-19 patients upon immune stimulation, that is not associated with detectable neutralizing autoantibodies against interferon α or interferon ω. Intracellular proteomic analysis shows increased monocyte numbers in hospitalized COVID-19 patients but impaired interferon-I response after stimulation. We confirm this by ex vivo whole blood stimulation with interferon-I which induces transcriptomic responses associated with inflammation in hospitalized COVID-19 patients, that is not seen in controls or non-hospitalized moderate cases. These results may explain the dichotomy of the poor clinical response to interferon-I based treatments in late stage COVID-19, despite the importance of interferon-I in early acute infection and may guide alternative therapeutic strategies.

In hydrating Portland cements, more than one of the AFm family of calcium aluminates may exist. Depending on the amount of carbonate and sulfate present in the cement, the most common phase to precipitate is monosulfate, monocarbonate and/or hemicarbonate. It has been reported in the literature that hemicarbonate often appears in measurements such as XRD but not predicted to form/equilibrate in thermodynamic models. With the ongoing use of commercial cements such as CEM I and CEM II containing more and more limestone, it is important to understand which hydrate solids physically precipitate and numerically predict over time. Using 27 cement samples with three w/c ratios analysed at 1, 3 and 28 days, this paper shows that although hemicarbonate was observed in a hydrating commercial Portland cement, as well as being predicted based on its carbonate (CO2/Al2O3) and sulfate (SO3/Al2O3) ratios, thermodynamic analysis did not predict it to equilibrate and form as a solid hydrate. Regardless of the w/c ratio, thermodynamic analysis did predict hemicarbonate to form for calcite contents < 2 wt.%. It appears that the dominant stability of monocarbonate in thermodynamic models leads to it precipitating and remaining as a persistent phase.

Host immunity to infection with SARS-CoV-2 is highly variable, dictating diverse clinical outcomes ranging from asymptomatic to severe disease and death. We previously reported that reduced blood type I interferon (IFN-I) in severe COVID-19 patients preceded clinical worsening. These results were supported by studies which identified genetic mutations in loci of the TLR3- or TLR7-dependent IFN-I pathways, or autoantibodies neutralizing IFNα or IFNω, as major risk factors for development of severe and critical COVID-19 pneumonia. Here, we analyzed a range of IFN-I associated responses in patient cohorts with different severities of COVID-19, showing that baseline plasma IFNα measures differed significantly according to the immunoassay used, as well as timing of sampling, the IFNα subtype measured, and the presence of autoantibodies. We then compared immune responses induced by ex vivo stimulation between non-hospitalized moderate cases (n=27) and hospitalized (n=17) adult patients that required oxygen supplementation. This showed a consistently reduced induction of IFN-I proteins in hospitalized COVID-19 patients upon stimulation, that was not associated with detectable neutralizing autoantibodies against IFNα or IFNω. We confirmed the poor induction of IFN-I in an independent patient cohort (n=33), and showed it was more pronounced with severe disease. Intracellular proteomic analysis showed that while monocyte numbers were increased in hospitalized COVID-19 patients, they did not secrete IFN-I in response to stimulation. This was further confirmed by ex vivo whole blood stimulation with IFN-I which induced a transcriptomic response associated with inflammation in hospitalized COVID-19 patients, that was not seen in controls or non-hospitalized moderate cases. These results may explain the dichotomy of the poor clinical response to IFN-I based treatments in late stage COVID-19, despite the critical importance of IFN-I in early acute infection. An improved understanding of such variable responses to treatment may help to identify potential alternative therapeutic strategies.

Thermoelectric materials enable the direct conversion of thermal to electrical energy. One application of this is ambient heat energy harvesting where relatively stable temperature gradients existing between the inside and outside of a building could be utilized to produce electricity. Buildings can thus change from energy consumers to energy generators. This could ultimately help reduce the surface temperatures and energy consumption of buildings, especially in urban areas. In this paper, research work carried out on developing and characterizing a cement-based thermoelectric material is presented. Cement-based samples are doped with different metal oxides (Bi2O3 and Fe2O3) to enhance their thermoelectric properties, which are defined through their Seebeck coefficient, electrical conductivity and thermal conductivity. The study also discusses the positive impact of moisture content on the electrical conductivity

Ever since its discovery, the applications of Shape Memory Alloys (SMA) can be found across a range of application domains, from structural design to medical technology. This is based upon the unique and inherent characteristics such as thermal Shape Memory Effect (SME) and Superelasticity (or Pseudoelasticity). While thermal SME is used for shape morphing applications wherein temperature change can govern the shape and dimension of the SMA, Superelasticity allows the alloy to withstand a comparatively very high magnitude of loads without undergoing plastic deformation at higher temperatures. These unique properties in wearables have revolutionized the field, and from fabrics to exoskeletons, SMA has found its place in robotics and cobotics. This review article focuses on the most recent research work in the field of SMA-based smart wearables paired with robotic applications for human-robot interaction. The literature is categorized based on SMA property incorporated and on actuator or sensor-based concept. Further, use-cases or conceptual frameworks for SMA fiber in fabric for ‘Smart Jacket’ and SMA springs in the shoe soles for ‘Smart Shoes’ are proposed. The conceptual frameworks are built upon existing technologies; however, their utility in a smart factory concept is emphasized, and algorithms to achieve the same are proposed. The integration of the two concepts with the Industrial Internet of Things (IIoT) is discussed, specifically regarding minimizing hazards for the worker/user in Industry 5.0. The article aims to propel a discussion regarding the multi-faceted applications of SMAs in human-robot interaction and Industry 5.0. Furthermore, the challenges and the limitations of the smart alloy and the technological barriers restricting the growth of SMA applications in the field of smart wearables are observed and elaborated.

International audience; IRE1 alpha cleaves several mRNAs upon accumulation of misfolded proteins. Here the authors show that active IRE1 alpha cleaves DGAT2 mRNA encoding the rate-limiting enzyme in the synthesis of triacylglycerols, suggesting a role of IRE1 alpha in reprogramming lipid metabolism in cancer cells. IRE1 alpha is constitutively active in several cancers and can contribute to cancer progression. Activated IRE1 alpha cleaves XBP1 mRNA, a key step in production of the transcription factor XBP1s. In addition, IRE1 alpha cleaves select mRNAs through regulated IRE1 alpha-dependent decay (RIDD). Accumulating evidence implicates IRE1 alpha in the regulation of lipid metabolism. However, the roles of XBP1s and RIDD in this process remain ill-defined. In this study, transcriptome and lipidome profiling of triple negative breast cancer cells subjected to pharmacological inhibition of IRE1 alpha reveals changes in lipid metabolism genes associated with accumulation of triacylglycerols (TAGs). We identify DGAT2 mRNA, encoding the rate-limiting enzyme in TAG biosynthesis, as a RIDD target. Inhibition of IRE1 alpha, leads to DGAT2-dependent accumulation of TAGs in lipid droplets and sensitizes cells to nutritional stress, which is rescued by treatment with the DGAT2 inhibitor PF-06424439. Our results highlight the importance of IRE1 alpha RIDD activity in reprograming cellular lipid metabolism.

Currently, microservices are trending as the most popular software application design architecture. Software organisations are also being targeted by more cyber-attacks every day and newer security measures are in high demand. One available measure is the application of anomaly detection, which is defined as the discovery of irregular or unusual activity that occurs to a greater or lesser degree than normal occurrences in a data series. In this paper, we continue existing work where various real-world cyber-attacks are executed against a running microservices application, and the application traffic is logged and returned in the form of distributed traces. A Diffusion Convolutional Recurrent Neural Network is used to model the set of distributed traces and learn the spatial and temporal dependencies of the application traffic. Subsequently, the model is used to make predictions for ongoing microservice activity and threshold-based anomaly detection is applied to detect irregular microservice activity indicating the presence of seeded cyber security attacks, or anomalies. The cyber-attacks used to evaluate this approach include a brute force attack, a batch registration of bot accounts and a distributed denial of service attack. yes

Background Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces a complex antibody response that varies by orders of magnitude between individuals and over time. Methods We developed a multiplex serological test for measuring antibodies to 5 SARS-CoV-2 antigens and the spike proteins of seasonal coronaviruses. We measured antibody responses in cohorts of hospitalized patients and healthcare workers followed for up to 11 months after symptoms. A mathematical model of antibody kinetics was used to quantify the duration of antibody responses. Antibody response data were used to train algorithms for estimating time since infection. Results One year after symptoms, we estimate that 36% (95% range, 11%–94%) of anti-Spike immunoglobulin G (IgG) remains, 31% (95% range, 9%–89%) anti-RBD IgG remains, and 7% (1%–31%) of anti-nucleocapsid IgG remains. The multiplex assay classified previous infections into time intervals of 0–3 months, 3–6 months, and 6–12 months. This method was validated using data from a seroprevalence survey in France, demonstrating that historical SARS-CoV-2 transmission can be reconstructed using samples from a single survey. Conclusions In addition to diagnosing previous SARS-CoV-2 infection, multiplex serological assays can estimate the time since infection, which can be used to reconstruct past epidemics., Longitudinal follow-up of individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reveals differences in the duration of antibodies to multiple antigens. Mathematical models of antibody waning and statistical algorithms can estimate an individual’s time since SARS-CoV-2 infection, and previous transmission waves in a population.

International audience; The complexity of karst groundwater flow modelling is reflected by the amount of simulation approaches. The goal of the Karst Modelling Challenge (KMC) is comparing different approaches on one single system using the same data set. Thirteen teams with different computational models for simulating discharge variations at karst springs have applied their respective models on one single data set coming from the Milandre Karst Hydrogeological System (MKHS). The approaches include neural networks, reservoir models, semi-distributed models and fully distributed groundwater models. Four and a half years of hourly or daily meteorological input and hourly discharge data were provided for model calibration. The validation comprised forecasting one year of discharge, without the observed discharge data. The model performance was evaluated using the volume conservation, Nash-Sutcliffe efficiency (NSE) and the Kling-Gupta efficiency (KGE) applied on the total discharge and individual flow components. As a result, the comparison of model performances is a challenging task due to the differences in the model architecture but also required time steps: some of the models require aggregated daily steps while others could be run using hourly data, which provided some interesting differences depending on how the data was transformed. The use of instantaneous data (e.g. value at noon) produces less bias that averaging hourly data over one day. The transformation of hourly into daily data produces a decrease of Nash and KGE of 0.05 to 0.08 (i.e. from 1 to ~0.93). The resulting simulations (forecasted values for year 2016) produced KGEs ranging between 0.83 and 0.37 (0.83 to −0.24 for NSE). Although the simulations matched the monitored flows reasonably well, most models struggled to simulate baseflow conditions accurately. In general, the models that performed the best for this exercise were the global ones (Gardenia and Varkarst), with a limited number of parameters, which can be calibrated using automatic calibration procedures. The neural network models also showed a fair potential, with one providing reasonable results despite the relatively short dataset available for warming-up (4.5 years). Semi-and fully distributed models also suggested that with some more effort they could perform well. The accuracy of model predictions does not seem to increase by using models with more than 9–12 calibration parameters. An evaluation of the relative errors between the forecasted and the observed values revealed that for most models, 50% of the forecasted values contained more than 50% of difference against the observed discharge rate, with 25% having a difference larger than 100%. A significant part of the poorly forecasted values corresponded to base-flow which was surprising given that as base-flow is generally much easier to predict than peak flow. Hence, this shows that modelling approaches and criteria for the calibration are too oriented towards peak-flow sections of the hydrographs, and that improvements could be gained by more focus on the base-flow.

COVID-19 has made eHealth an imperative. The pandemic has been a true catalyst for remote eHealth solutions such as teleHealth. Telehealth facilitates care, diagnoses, and treatment remotely, making them more efficient, accessible, and economical. However, they have a centralized identity management system that restricts the interoperability of patient and healthcare provider identification. Thus, creating silos of users that are unable to authenticate themselves beyond their eHealth application’s domain. Furthermore, the consumers of remote eHealth applications are forced to trust their service providers completely. They cannot check whether their eHealth service providers adhere to the regulations to ensure the security and privacy of their identity information. Therefore, we present a blockchain-based decentralized identity management system that allows patients and healthcare providers to identify and authenticate themselves transparently and securely across different eHealth domains. Patients and healthcare providers are uniquely identified by their health identifiers (healthIDs). The identity attributes are attested by a healthcare regulator, indexed on the blockchain, and stored by the identity owner. We implemented smart contracts on an Ethereum consortium blockchain to facilities identification and authentication procedures. We further analyze the performance using different metrics, including transaction gas cost, transaction per second, number of blocks lost, and block propagation time. Parameters including block-time, gas-limit, and sealers are adjusted to achieve the optimal performance of our consortium blockchain.

The open-source event-driven ECG dataset is available at https://github.com/jedaiproject/Open-Source-Event-Driven-ECG-Dataset; International audience; In this article, non-uniformly sampled electrocardiogram (ECG) signals obtained from level-crossing analog-to-digital converters (LC-ADCs) are analyzed for event-driven classification and compression performance. The signal compression results show that it is important to assess the distortion in event-driven signals when simulating LC-ADC models, especially at lower resolutions and larger quantization steps. The effects of varying the LC-ADC parameters for the application of cardiac arrhythmia classifiers are also assessed using an artificial neural network (ANN) and the MIT-BIH Arrhythmia Database. In comparison with uniformly-sampled data, it is possible to achieve comparable classification accuracy at a much lower complexity with event-driven ECG signals. The results show the best event-driven model achieves over 97% accuracy with 79% reduction in ANN complexity with signal-to-distortion ratio (S/D)≥21dB. For S/D

International audience; We investigate giant magnetoelectric coupling at a Mn spin crossover in [MnL]BPh (L = (3,5-diBr-sal)323) with a field-induced permanent switching of the structural, electric, and magnetic properties. An applied magnetic field induces a first-order phase transition from a high spin/low spin (HS-LS) ordered phase to a HS-only phase at 87.5 K that remains after the field is removed. We observe this unusual effect for DC magnetic fields as low as 8.7 T. The spin-state switching driven by the magnetic field in the bistable molecular material is accompanied by a change in electric polarization amplitude and direction due to a symmetry-breaking phase transition between polar space groups. The magnetoelectric coupling occurs due to a γη coupling between the order parameter γ related to the spin-state bistability and the symmetry-breaking order parameter η responsible for the change of symmetry between polar structural phases. We also observe conductivity occurring during the spin crossover and evaluate the possibility that it results from conducting phase boundaries. We perform ab initio calculations to understand the origin of the electric polarization change as well as the conductivity during the spin crossover. Thus, we demonstrate a giant magnetoelectric effect with a field-induced electric polarization change that is 1/10 of the record for any material.

Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the interactions between the DTs to pave the way for the cyber-physical integration of smart manufacturing. This paper presents a conceptual framework for digital twins collaboration to provide an auto-detection of erratic operational data by utilizing operational data intelligence in the manufacturing systems. The proposed framework provide an interaction mechanism to understand the DT status, interact with other DTs, learn from each other DTs, and share common semantic knowledge. In addition, it can detect the anomalies and understand the overall picture and conditions of the operational environments. Furthermore, the proposed framework is described in the workflow model, which breaks down into four phases: information extraction, change detection, synchronization, and notification. A use case of Energy 4.0 fault diagnosis for wind turbines is described to present the use of the proposed framework and DTs collaboration to identify and diagnose the potential failure, e.g., malfunctioning nodes within the energy industry.

The general context of this work is the feedback control of an infinite-dimensional system so that the closed-loop system satisfies a fading-memory property and achieves the setpoint tracking of a given reference signal. More specifically, this paper is concerned with the Proportional Integral (PI) regulation control of the left Neumann trace of a one-dimensional reaction-diffusion equation with a delayed right Dirichlet boundary control. In this setting, the studied reaction-diffusion equation might be either open-loop stable or unstable. The proposed control strategy goes as follows. First, a finite-dimensional truncated model that captures the unstable dynamics of the original infinite-dimensional system is obtained via spectral decomposition. The truncated model is then augmented by an integral component on the tracking error of the left Neumann trace. After resorting to the Artstein transformation to handle the control input delay, the PI controller is designed by pole shifting. Stability of the resulting closed-loop infinite-dimensional system, consisting of the original reaction-diffusion equation with the PI controller, is then established thanks to an adequate Lyapunov function. In the case of a time-varying reference input and a time-varying distributed disturbance, our stability result takes the form of an exponential Input-to-State Stability (ISS) estimate with fading memory. Finally, another exponential ISS estimate with fading memory is established for the tracking performance of the reference signal by the system output. In particular, these results assess the setpoint regulation of the left Neumann trace in the presence of distributed perturbations that converge to a steady-state value and with a time-derivative that converges to zero. Numerical simulations are carried out to illustrate the efficiency of our control strategy., Comment: Preprint

Currently, drones represent a promising technology for combating Coronavirus disease 2019 (COVID-19) due to the transport of goods, medical supplies to a given target location in the quarantine areas experiencing an epidemic outbreak. Drone missions will increasingly rely on drone collaboration, which requires the drones to reduce communication complexity and be controlled in a decentralized fashion. Blockchain technology becomes a must in industrial applications because it provides decentralized data, accessibility, immutability, and irreversibility. Therefore, Blockchain makes data public for all drones and enables drones to log information concerning world states, time, location, resources, delivery data, and drone relation to all neighbors drones. This paper introduces decentralized independent multi-drones to accomplish the task collaboratively. Improving blockchain with a consensus algorithm can improve network partitioning and scalability in order to combat COVID-19. The multi-drones task is to combat COVID-19 via monitoring and detecting, social distancing, sanitization, data analysis, delivering goods and medical supplies, and announcement while avoiding collisions with one another. We discuss End to End (E2E) delivery application of combination blockchain and multi-drone in combating COVID-19 and beyond future pandemics. Furthermore, the challenges and opportunities of our proposed framework are highlighted. yes

The development of the Internet of Things (IoT) technology and their integration in smart cities have changed the way we work and live, and enriched our society. However, IoT technologies present several challenges such as increases in energy consumption, and produces toxic pollution as well as E-waste in smart cities. Smart city applications must be environmentally-friendly, hence require a move towards green IoT. Green IoT leads to an eco-friendly environment, which is more sustainable for smart cities. Therefore, it is essential to address the techniques and strategies for reducing pollution hazards, traffic waste, resource usage, energy consumption, providing public safety, life quality, and sustaining the environment and cost management. This survey focuses on providing a comprehensive review of the techniques and strategies for making cities smarter, sustainable, and eco-friendly. Furthermore, the survey focuses on IoT and its capabilities to merge into aspects of potential to address the needs of smart cities. Finally, we discuss challenges and opportunities for future research in smart city applications.

In recent years, the area of Unmanned Aerial Vehicles (UAVs) has seen rapid growth. There has been a trend to build and produce UAVs that can carry out planetary exploration throughout the past decade. The technology of UAVs has tremendous potential to support various successful space mission solutions. In general, different techniques for observing space objects are available, such as telescopes, probes, and flying spacecraft, orbiters, landers, and rovers. However, a detailed analysis has been carried out due to the benefits of UAVs relative to other planetary exploration techniques. The deployment of UAVs to other solar bodies has been considered by numerous space agencies worldwide, including NASA. This article contributes to investigating the types of UAVs that have been considered for various planetary explorations. This study further investigates the behaviour of UAV prototypes on Mars’ surface in particular. It has been discovered that a prototype UAV flight on Mars has a higher chance of success. In this research, a prototype UAV has been successfully simulated to fly on Mars’ surface. This article discusses the opportunities, challenges, and future scope of deploying UAVs on Mars. yes

Recently, Unmanned Aerial Vehicles (UAVs) present a promising advanced technology that can enhance people life quality and smartness of cities dramatically and increase overall economic efficiency. UAVs have attained a significant interest in supporting many applications such as surveillance, agriculture, communication, transportation, pollution monitoring, disaster management, public safety, healthcare, and environmental preservation. Industry 4.0 applications are conceived of intelligent things that can automatically and collaboratively improve beyond 5G (B5G). Therefore, the Internet of Things (IoT) is required to ensure collaboration between the vast multitude of things efficiently anywhere in real-world applications that are monitored in real-time. However, many IoT devices consume a significant amount of energy when transmitting the collected data from surrounding environments. Due to a drone's capability to fly closer to IoT, UAV technology plays a vital role in greening IoT by transmitting collected data to achieve a sustainable, reliable, eco-friendly Industry 4.0. This survey presents an overview of the techniques and strategies proposed recently to achieve green IoT using UAVs infrastructure for a reliable and sustainable smart world. This survey is different from other attempts in terms of concept, focus, and discussion. Finally, various use cases, challenges, and opportunities regarding green IoT using UAVs are presented.

Modern radio interferometers such as the LOw Frequency ARray (LOFAR) are capable of producing data at hundreds of gigabits to terabits per second. This high data rate makes the analysis of radio data cumbersome and computationally expensive. While high performance computing facilities exist for large national and international facilities, that may not be the case for instruments operated by a single institution or a small consortium. Data rates for next generation radio telescopes are set to eclipse those currently in operation, hence local processing of data will become all the more important. Here, we introduce the REAL-time Transient Acquisition backend (REALTA), a computing backend at the Irish LOFAR station (I-LOFAR) which facilitates the recording of data in near real-time and post-processing. We also present first searches and scientific results of a number of radio phenomena observed by I-LOFAR and REALTA, including pulsars, fast radio bursts (FRBs), rotating radio transients (RRATs), the search for extraterrestrial intelligence (SETI), Jupiter, and the Sun., Comment: 12 pages, 10 figures, accepted for publication in Astronomical instrumentation section of Astronomy & Astrophysics 24/08/2021

International audience; Virtual Reality (VR) has the potential of becoming a game changer in education, with studies showing that VR can lead to better quality of and access to education. One area that is promising, especially for young children, is the use of Virtual Companions that act as teaching assistants and support the learners' educational journey in the virtual environment. However, as it is the case in real life, the appearance of the virtual companions can be critical for the learning experience. This paper studies the impact of the age, gender and general appearance (human-or robot-like) of virtual companions on 9-12 year old children. Our results over two experiments (n=24 and n=13) tend to show that children have a bigger sense of Spatial Presence, Engagement and Ecological Validity when interacting with a human-like Virtual Companion of the Same Age and of a Different Gender.

Automatic speaker verification (ASV) is one of the most natural and convenient means of biometric person recognition. Unfortunately, just like all other biometric systems, ASV is vulnerable to spoofing, also referred to as "presentation attacks." These vulnerabilities are generally unacceptable and call for spoofing countermeasures or "presentation attack detection" systems. In addition to impersonation, ASV systems are vulnerable to replay, speech synthesis, and voice conversion attacks. The ASVspoof 2019 edition is the first to consider all three spoofing attack types within a single challenge. While they originate from the same source database and same underlying protocol, they are explored in two specific use case scenarios. Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques. Replay spoofing attacks within a physical access (PA) scenario are generated through carefully controlled simulations that support much more revealing analysis than possible previously. Also new to the 2019 edition is the use of the tandem detection cost function metric, which reflects the impact of spoofing and countermeasures on the reliability of a fixed ASV system. This paper describes the database design, protocol, spoofing attack implementations, and baseline ASV and countermeasure results. It also describes a human assessment on spoofed data in logical access. It was demonstrated that the spoofing data in the ASVspoof 2019 database have varied degrees of perceived quality and similarity to the target speakers, including spoofed data that cannot be differentiated from bona-fide utterances even by human subjects., Accepted, Computer Speech and Language. This manuscript version is made available under the CC-BY-NC-ND 4.0. For the published version on Elsevier website, please visit https://doi.org/10.1016/j.csl.2020.101114

There is increasing demand in modern day business applications for communication networks to be robust and reliable due to the complexity and critical nature of such applications. As such, data delivery is expected to be reliable and secure even in the harshest of environments. Software-Defined Networking (SDN) is gaining traction as a promising approach for designing network architectures which are robust and flexible. One reason for this is that separating the data plane from the control plane, increases the controller&rsquo, s ability to configure the network rapidly. When network failure events occur, the network manager may trade-off the optimality of the achieved network reconfiguration with the responsivenss of the reconfiguration process. Responsiveness may be favoured when the network resources are under stress and the failure rate is high. We contribute SDN recovery methods that leverage information about the structure of the network to expedite network restoration when a link failure occurs. They operate by detecting community-like structures in the network topology and then they find alternative paths which have low operation and installation costs using this information. Extensive simulations are conducted to evaluate the proposed SDN recovery methods using open-source simulation tools. They provide evidence that the proposed approaches lead to performance gains when an alternative path is required among a set of candidate paths.

International audience; The development of three-dimensional (3D) porous graphitic structures is of great interest for electrochemical sensing applications as they can support fast charge transfer and mass transport through their extended, large surface area networks. In this work, we present the facile fabrication of conductive and porous graphitic electrodes by direct laser writing techniques. Irradiation of commercial polyimide sheets (Kapton tape) was performed using a low-cost laser engraving machine with visible excitation wavelength (405 nm) at low power (500 mW), leading to formation of 3D laser-induced graphene (LIG) structures. Systematic correlation between applied laser dwell time per pixel ("dwell time") and morphological/structural properties of fabricated electrodes showed that conductive and highly 3D porous structures with spectral signatures of nanocrystalline graphitic carbon materials were obtained at laser dwell times between 20 and 110 ms/pix, with graphenelike carbon produced at 50 ms/pix dwell time, with comparable properties to LIG obtained with high cost CO 2 lasers. Electrochemical characterization with inner and outer sphere mediators showed fast electron transfer rates, comparable to previously reported 2D/3D graphene-based materials and other graphitic carbon electrodes. This work opens the way to the facile fabrication of low-cost, disposable electrochemical sensor platforms for decentralized assays.

Glioblastoma multiforme (GBM) is the most severe primary brain cancer. Despite an aggressive treatment comprising surgical resection and radio/chemotherapy patient’s survival post diagnosis remains short. A limitation for success in finding novel improved therapeutic options for such dismal disease partly lies in the lack of a relevant animal model that accurately recapitulates patient disease and standard of care. In the present study, we have developed a novel immunocompetent GBM model that includes tumor surgery and a radio/chemotherapy regimen resembling the Stupp protocol and we have used this model to test the impact of the pharmacological inhibition of the endoplasmic reticulum (ER) stress sensor IRE1, on treatment efficacy.

Surveillance video recording is a powerful method of deterring unlawful activities. A robust data protection-by-design solution can be helpful in terms of making a captured video immutable, as such recordings cannot become a piece of evidence until proven to be unaltered. Similarly, video sharing from closed-circuit television video recording or in social media interaction requires self-authentication for responsible and reliable data sharing. This article presents a computationally inexpensive method of preserving a chain-of-evidence in surveillance videos by means of hashing and steganography. The method conforms to the data protection regulations, which are increasingly adopted by governments, and is applicable to network edge storage. Encryption keys are stored in a hardware wallet independently of the video capture device itself, while evidential information is stored steganographically within video frames themselves, independently of the content. Added protection is provided by hiding information within the two least-valued of pixel bitplanes, using a newly introduced technique that randomizes the pixel storage locations on a per video frame and video-capture device basis. Overall, the proposed method has turned out to not only preserve the integrity of stored video data but also results in minimal degradation of the video data resulting from steganography. Despite the inclusion of hidden information, video frames will still be available for common image-processing tasks such as tracking and classification, as their objective video quality is almost unchanged. yes

Corresponding authors :Douwe van Sinderen (d.vansinderen@ucc.ie) Marie-Pierre Chapot-Chartier (marie-pierre.chapot-chartier@inra.fr); International audience; In $Lactococcus\ lactis$, cell-wall polysaccharides (CWPSs) act as receptors for many bacteriophages, and their structural diversity among strains explains, at least partially, the narrow host range of these viral predators. Previous studies have reported that lactococcal CWPS consists of two distinct components, a variable chain exposed at the bacterial surface, named polysaccharide pellicle (PSP), and a more conserved rhamnan chain anchored to, and embedded inside, peptidoglycan. These two chains appear to be covalently linked to form a large heteropolysaccharide. The molecular machinery for biosynthesis of both components is encoded by a large gene cluster, named $cwps$. In this study, using a CRISPR/Cas-based method, we performed a mutational analysis of the $cwps$ genes. MALDI-TOF MS-based structural analysis of the mutant CWPS combined with sequence homology, transmission EM, and phage sensitivity analyses enabled us to infer a role for each protein encoded by the $cwps$ cluster. We propose a comprehensive CWPS biosynthesis scheme in which the rhamnan and PSP chains are independently synthesized from two distinct lipid-sugar precursors and are joined at the extracellular side of the cytoplasmic membrane by a mechanism involving a membrane-embedded glycosyltransferase with a GT-C fold. The proposed scheme encompasses a system that allows extracytoplasmic modification of rhamnan by complex substituting oligo-/polysaccharides. It accounts for the extensive diversity of CWPS structures observed among lactococci and may also have relevance to the biosynthesis of complex rhamnose-containing CWPSs in other Gram-positive bacteria.

Best Paper Award; International audience; Lifetime is the main issue of wireless sensors net-works. Since the nodes are often placed in inaccessible places, thereplacement of their battery is not an easy task. Moreover, thenode maintenance is a costly and time consuming operation whenthe nodes are high in numbers. Energy harvesting technologieshave recently been developed to replenish part or all of therequired energy that allows a node to function. In this paper, weuse dedicated chargers carried by drones that can fly over thenetwork and transmit energy to the nodes using radio-frequency(RF) signals. We formulate and optimally solve the OptimalDrone Placement and Planning Problem (OD3P) by using a givennumber of flying drones, in order to efficiently recharge wirelesssensor nodes. Unlike other works in the literature, we assumethat the drones can trade altitude with coverage and rechargepower, while each drone can move across different positions inthe network to extend coverage. We present a linear program aswell as a fast heuristic algorithm to meet the minimum energydemands of the nodes in the shortest possible amount of time.Our simulation results show the effectiveness of our approachesfor network scenarios with up to 50 sensors and a50×50mterrain size.

The problem of partitioning dense matrices into sets of sub-matrices has received increased attention recently and is crucial when considering dense linear algebra and kernels with similar communication patterns on heterogeneous platforms. The problem of load balancing and minimizing communication is traditionally reducible to an optimization problem that involves partitioning a square into rectangles. This problem has been proven to be NP-Complete for an arbitrary number of partitions. In this paper, we present recent approaches that relax the restriction that all partitions be rectangles. The first approach uses an original mathematical technique to find the exact optimal partitioning. Due to the complexity of the technique, it has been developed for a small number of partitions only. However, even at a small scale, the optimal partitions found by this approach are often non-rectangular and sometimes non-intuitive. The second approach is the study of approximate partitioning methods utilizing recursive partitioning algorithms. In particular we use the work on optimal partitioning to improve pre-existing algorithms. In this paper we discuss the different perspectives this approach opens and present two algorithms, SNRPP which is a $\sqrt{\frac{3}{2}}$ approximation, and NRPP which is a $\frac{2}{\sqrt{3}}$ approximation. While sub-optimal, the NRRP approach works for an arbitrary number of partitions. We use the first exact approach to analyse how close to the known optimal solutions the NRRP algorithm is for small numbers of partitions.

Triple-negative breast cancer (TNBC) lacks targeted therapies and has a worse prognosis than other breast cancer subtypes, underscoring an urgent need for new therapeutic targets and strategies. IRE1 is an endoplasmic reticulum (ER) stress sensor, whose activation is predominantly linked to the resolution of ER stress and, in the case of severe stress, to cell death. Here we demonstrate that constitutive IRE1 RNase activity contributes to basal production of pro-tumorigenic factors IL-6, IL-8, CXCL1, GM-CSF, and TGFβ2 in TNBC cells. We further show that the chemotherapeutic drug, paclitaxel, enhances IRE1 RNase activity and this contributes to paclitaxel-mediated expansion of tumor-initiating cells. In a xenograft mouse model of TNBC, inhibition of IRE1 RNase activity increases paclitaxel-mediated tumor suppression and delays tumor relapse post therapy. We therefore conclude that inclusion of IRE1 RNase inhibition in therapeutic strategies can enhance the effectiveness of current chemotherapeutics., IRE1/XBP-1 activation has a major role in Triple negative breast cancer (TNBC). Here, the authors show that inhibition of IRE1’s RNase activity attenuates autocrine and paracrine signaling of pro-tumorigenic cytokines and synergizes with paclitaxel to confer potent anti-tumor effects in TNBC.

International audience; All‐optical orthogonal frequency division multiplexing (AO‐OFDM) and optical code‐division multiple‐access system (OCDMA) are combined in the first analytical model, which considers subcarrier hopping by means of advanced two‐dimensional (2D) hybrid‐coded (2D‐HC) signature. The model incorporates probabilistic subcarrier overlapping, multiple‐access interferences and is tested, for the first time, over amplifier‐free long‐reach passive optical networks (LR‐PONs) using cost‐effective intensity modulations and direct detection. For the upstream direction at 40 Gb/s, AO‐OFDM‐OCDMA outperforms a ‘classical’ multi‐channel OCDMA system for low received powers and any number of simultaneous users. In comparison to conventional 1D Walsh–Hadamard, 1D prime code and 2D prime hop system, coding with 2D‐HC can improve the performance of AO‐OFDM‐CDMA, thus allowing a higher number of simultaneous users in LR‐PON without optical amplification. From numerical simulations, the authors show that 16‐quadrature amplitude modulation (16‐QAM) AO‐OFDM‐CDMA with 45 users has comparable performance to conventional multi‐channel 16‐QAM coherent optical OFDM in the downstream direction and up to 58 km with 1:45 split ratio, without employing complex coherent technology. Similarly, based on the feasibility of physical implementation configuration, a budget power calculation is performed showing 108 km as maximum reachability distance for 40 Gb/s QAM signal, 1:64 split ratio when considering standard forward‐error‐correction.

n-3 highly unsaturated fatty acids (n-3 HUFA) directly and indirectly regulate lipid metabolism, energy balance and the inflammatory response. We investigated changes to the n-3 HUFA score of healthy adults, induced by different types and amounts of conjugated linoleic acid (CLA)-enriched (ENCH) cheeses consumed for different periods of time, compared to dietary fish oil (FO) pills (500 mg, each containing 100 mg of eicosapentaenoic and docosahexaenoic acids&mdash, EPA+DHA) or &alpha, linolenic acid (ALA)-rich linseed oil (4 g, containing 2 g of ALA). A significant increase in the n-3 HUFA score was observed, in a dose-dependent manner, after administration of the FO supplement. In terms of the impact on the n-3 HUFA score, the intake of ENCH cheese (90 g/day) for two or four weeks was equivalent to the administration of one or two FO pills, respectively. Conversely, the linseed oil intake did not significantly impact the n-3 HUFA score. Feeding ENCH cheeses from different sources (bovine, ovine and caprine) for two months improved the n-3 HUFA score by increasing plasma DHA, and the effect was proportional to the CLA content in the cheese. We suggest that the improved n-3 HUFA score resulting from ENCH cheese intake may be attributed to increased peroxisome proliferator-activated receptor alpha (PPAR-&alpha, ) activity. This study demonstrates that natural ENCH cheese is an alternative nutritional source of n-3 HUFA in humans.

Polysaccharides are ubiquitous components of the Gram-positive bacterial cell wall. In Lactococcus lactis, a polysaccharide pellicle (PSP) forms a layer at the cell surface. The PSP structure varies among lactococcal strains; in L. lactis MG1363, the PSP is composed of repeating hexasaccharide phosphate units. Here, we report the presence of an additional neutral polysaccharide in L. lactis MG1363 that is a rhamnan composed of α-l-Rha trisaccharide repeating units. This rhamnan is still present in mutants devoid of the PSP, indicating that its synthesis can occur independently of PSP synthesis. High-resolution magic-angle spinning nuclear magnetic resonance (HR-MAS NMR) analysis of whole bacterial cells identified a PSP at the surface of wild-type cells. In contrast, rhamnan was detected only at the surface of PSP-negative mutant cells, indicating that rhamnan is located underneath the surface-exposed PSP and is trapped inside peptidoglycan. The genetic determinants of rhamnan biosynthesis appear to be within the same genetic locus that encodes the PSP biosynthetic machinery, except the gene tagO encoding the initiating glycosyltransferase. We present a model of rhamnan biosynthesis based on an ABC transporter-dependent pathway. Conditional mutants producing reduced amounts of rhamnan exhibit strong morphological defects and impaired division, indicating that rhamnan is essential for normal growth and division. Finally, a mutation leading to reduced expression of lcpA, encoding a protein of the LytR-CpsA-Psr (LCP) family, was shown to severely affect cell wall structure. In lcpA mutant cells, in contrast to wild-type cells, rhamnan was detected by HR-MAS NMR, suggesting that LcpA participates in the attachment of rhamnan to peptidoglycan., IMPORTANCE In the cell wall of Gram-positive bacteria, the peptidoglycan sacculus is considered the major structural component, maintaining cell shape and integrity. It is decorated with other glycopolymers, including polysaccharides, the roles of which are not fully elucidated. In the ovococcus Lactococcus lactis, a polysaccharide with a different structure between strains forms a layer at the bacterial surface and acts as the receptor for various bacteriophages that typically exhibit a narrow host range. The present report describes the identification of a novel polysaccharide in the L. lactis cell wall, a rhamnan that is trapped inside the peptidoglycan and covalently bound to it. We propose a model of rhamnan synthesis based on an ABC transporter-dependent pathway. Rhamnan appears as a conserved component of the lactococcal cell wall playing an essential role in growth and division, thus highlighting the importance of polysaccharides in the cell wall integrity of Gram-positive ovococci.

TILLING (Targeting Induced Local Lesions in Genomes) by Sequencing (TbyS) refers to the application of high-throughput sequencing technologies to mutagenised TILLING populations as a tool for functional genomics. TbyS can be used to identify and characterise induced variation in genes (controlling traits of interest) within large mutant populations, and is a powerful approach for the study and harnessing of genetic variation in crop breeding programmes. The extension of existing TILLING platforms by TbyS will accelerate crop functional genomics studies, in concert with the rapid increase in genome editing capabilities and the number and quality of sequenced crop plant genomes. In this mini-review, we provide an overview of the growth of TbyS and its potential applications to crop molecular breeding.

The melt-extrusion process is utilized in the pharmaceutical arena for the manufacturing of a variety of dosage forms and formulations, including solid dispersions. This technology is considered an efficient and continuous dosage form manufacturing method. However, there are potential challenges mainly because, during hot-melt extrusion, polymers are subject to mechanical and thermal degradation. Mechanical degradation may be induced by the shear effects imposed by the rotating screw. Thermal degradation results from high temperatures and includes random scission, scission from the ends of the polymer and unzipping of substitute groups. This paper endeavors to understand the impact of thermal and/or mechanical components of the melt-extrusion process on the stability of a pH sensitive polymer, namely Shellac. Correlation between the screw speeds and processing temperature profile was examined in the context of the overall degradation profile of the polymer. The results suggest that the processing stability of Shellac was reliant on optimization of screw speed (rpm) and process temperature yes

International audience; By representing the constraints and objective function in factorized form, graphical models can concisely define various NP-hard optimization problems. They are therefore extensively used in several areas of computer science and artificial intelligence. Graphical models can be deterministic or stochastic, optimize a sum or product of local functions, defining a joint cost or probability distribution. Simple transformations exist between these two types of models, but also with MaxSAT or linear programming. In this paper, we report on a large comparison of exact solvers which are all state-of-the-art for their own target language. These solvers are all evaluated on deterministic and probabilistic graphical models coming from the Probabilistic Inference Challenge 2011, the Computer Vision and Pattern Recognition OpenGM2 benchmark, the Weighted Partial MaxSAT Evaluation 2013, the MaxCSP 2008 Competition, the MiniZinc Challenge 2012 & 2013, and the CFLib (a library of Cost Function Networks). All 3026 instances are made publicly available in five different formats and seven formulations. To our knowledge, this is the first evaluation that encompasses such a large set of related NP-complete optimization frameworks, despite their tight connections. The results show that a small number of evaluated solvers are able to perform well on multiple areas. By exploiting the variability and complementarity of solver performances, we show that a simple portfolio approach can be very effective. This portfolio won the last UAI Evaluation 2014 (MAP task).

Given the large volume of data that is regularly accumulated, the need to properly manage, efficiently display and correctly interpret, becomes more important. Complex analysis of data is best performed using statistical models and in particular those with a geographical element are best analysed using Spatial Statistical Methods, including local regression. Spatial Statistical Methods are employed in a wide range of disciplines to analyse and interpret data where it is necessary to detect significant spatial patterns or relationships. The topic of the research presented in this thesis is an exploration of the most effective methods of visualising results. A human being is capable of processing a vast amount of data as long as it is effectively displayed. However, the perceptual load will at some point exceed the cognitive processing ability and therefore the ability to comprehend data. Although increases in data scale did increase the cognitive load and reduce processing, prior knowledge of geographical information systems did not result in an overall processing advantage. The empirical work in the thesis is divided into two parts. The first part aims to gain insight into visualisations which would be effective for interpretation and analysis of Geographically Weighted Regression (GWR), a popular Spatial Statistical Method. Three different visualisation techniques; two dimensional, three dimensional and interactive, are evaluated through an experiment comprising two data set sizes. Interactive visualisations perform best overall, despite the apparent lack of researcher familiarity. The increase in data volume can present additional complexity for researchers. Although the evaluation of the first experiment augments understanding of effective visualisation display, the scale at which data can be adequately presented within these visualisations is unclear. Therefore, the second empirical investigation seeks to provide insight into data scalability, and human cognitive limitations associated with data comprehension. The general discussion concludes that there is a need to better inform researchers of the potential of interactive visualisations. People do need to be properly trained to use these systems, but the limits of human perceptual processing also need to be considered in order to permit more efficient and insightful analysis.

International audience; Amorphous calcium carbonate (ACC) is known to interact with proteins, for example, in biogenic ACC, to form stable amorphous phases. The control of amorphous/crystalline and inorganic/organic ratios in inhalable calcium carbonate microparticles may enable particle properties to be adapted to suit the requirements of dry powders for pulmonary delivery by oral inhalation. For example, an amorphous phase can immobilize and stabilize polypeptides in their native structure and amorphous and crystalline phases have different mechanical properties. Therefore, inhalable composite microparticles made of inorganic (i.e., calcium carbonate and calcium formate) and organic (i.e., hyaluronan (HA)) amorphous and crystalline phases were investigated for peptide and protein pulmonary aerosol delivery. The crystalline/amorphous ratio and polymorphic form of the inorganic component was altered by changing the microparticle drying rate and by changing the ammonium carbonate and HA initial concentration. The bioactivity of the model peptide, salmon calcitonin (sCT), coprocessed with alpha-1-antitrypsin (AAT), a model protein with peptidase inhibitor activity, was maintained during processing and the microparticles had excellent aerodynamic properties, making them suitable for pulmonary aerosol delivery. The bioavailability of sCT after aerosol delivery as sCT and AAT-loaded composite microparticles to rats was 4-times higher than that of sCT solution.

Semantic relatedness and disambiguation are fundamental problems for linking text documents to the Web of Data. There are many approaches dealing with both problems but most of them rely on word or concept distribution over Wikipedia. They are therefore not applicable to concepts that do not have a rich textual description. In this paper, we show that semantic relatedness can also be accurately computed by analysing only the graph structure of the knowledge base. In addition, we propose a joint approach to entity and word-sense disambiguation that makes use of graph-based relatedness. As opposed to the majority of state-of-the-art systems that target mainly named entities, we use our approach to disambiguate both entities and common nouns. In our experiments, we first validate our relatedness measure on multiple knowledge bases and ground truth datasets and show that it performs better than related state-of-the-art graph based measures. Afterwards, we evaluate the disambiguation algorithm and show that it also achieves superior disambiguation accuracy with respect to alternative state-of-the- art graph-based algorithms. peer-reviewed

Random indexing (RI) is an incremental method for constructing a vector space model (VSM) with a reduced dimensionality. Previously, the method has been justified using the mathematical framework of Kanerva's sparse distributed memory. This justification, although intuitively plausible, fails to provide the information that is required to set the parameters of the method. In order to suggest criteria for the method's parameters, the RI method is revisited and described using the principles of linear algebra and sparse random projections in Euclidean spaces. These simple mathematics are then employed to suggest criteria for setting the method's parameters and to explain their influence on the estimated distances in the RI-constructed VSMs. The empirical results observed in an evaluation are reported to support the suggested guidelines in the paper. peer-reviewed

A compact singlemode - photonic crystal fibre - singlemode fibre tip (SPST) refractive index sensor is demonstrated in this paper. A CO2 laser cleaving technique is utilsed to provide a clean-cut fibre tip which is then coated by a layer of gold to increase reflection. An average sensitivity of 39.1 nm/RIU and a resolvable index change of 2.56×10-4 are obtained experimentally with a ~3.2 µm diameter SPST. The temperature dependence of this fiber optic sensor probe is presented. The proposed SPST refractometer is also significantly less sensitive to temperature and an experimental demonstration of this reduced sensitivity is presented in the paper. Because of its compactness, ease of fabrication, linear response, low temperature dependency, high sensitivity, easy connectivity to other fibreized optical components and low cost, this refractometer could find various applications in chemical and biological sensing.

Analysis of the genetic locus encompassing a cell wall polysaccharide (CWPS) biosynthesis operon of eight strains of Lactococcus lactis, identified as belonging to the same CWPS type C genotype, revealed the presence of a variable region among the strains examined. The results allowed the identification of five subgroups of the C type named subtypes C1 to C5. This variable region contains genes encoding glycosyltransferases that display low or no sequence homology between the subgroups. In this study, we purified an acidic polysaccharide from the cell wall of L. lactis 3107 (subtype C2) and confirmed that it is structurally different from the previously established CWPS of subtype C1 L. lactis MG1363. The CWPS of L. lactis 3107 is composed of pentasaccharide repeating units linked by phosphodiester bonds with the structure 6-α-Glc-3-β-Galf-3-β-GlcNAc-2-β-Galf-6-α-GlcNAc-1-P. Combinations of genes from the variable region of subtype C2 were introduced into a mutant of subtype C1 L. lactis NZ9000 deficient in CWPS biosynthesis. The resulting recombinant mutant synthesized a polysaccharide with a composition characteristic of that of subtype C2 L. lactis 3107 and not wild-type C1 L. lactis NZ9000. By challenging the recombinant mutant with various lactococcal phages, we demonstrated that CWPS is the host cell surface receptor of tested bacteriophages of both the P335 and 936 groups and that differences between the CWPS structures play a crucial role in determining phage host range., IMPORTANCE Despite the efforts of nearly 80 years of lactococcal phage research, the precise nature of the cell surface receptors of the P335 and 936 phage group receptors has remained elusive. This work demonstrates the molecular nature of a P335 group receptor while bolstering the evidence of its role in host recognition by phages of the 936 group and at least partially explains why such phages have a very narrow host range. The information generated will be instrumental in understanding the molecular mechanisms of how phages recognize specific saccharidic receptors located on the surface of their bacterial host.

Various statistical methods have been developed for local spatial analysis. Among them Geographically Weighted Regression (GWR) is a simple yet powerful method to explore spatially varying relationships between variables. This thesis examines how GWR can be extended to investigate spatially varying relationships at various geographical scales within one model. GWR assumes that observations near to a regression location have more influence on the estimation of local regression coefficients than do observations farther away. A single bandwidth is employed in basic GWR to control the rate of distance-decay in this influence. The magnitude of the bandwidth affects the scale of variation in the estimated regression coefficients and thus usefully reflects the appropriate spatial scale at which the processes being modelled operate. A small bandwidth suggests the processes operate over a local spatial scale, whilst a large bandwidth indicates a more regional process. In practice, a single bandwidth as in basic GWR may not be sufficient to reflect the potentially complex spatial variations in relationships between variables in a multivariate spatial model. Therefore, in order to estimate coefficient surfaces that may vary at different spatial scales for different variables, Flexible Bandwidth GWR (FBGWR) is proposed to allow different bandwidths to be individually specified for each independent variable in a regression framework. An algorithm based on back- fitting is developed to calibrate the FBGWR model. The performance of FBGWR is investigated with simulated datasets where coefficients are predefined at various levels of non-stationarity across space. A case study is then carried out on data relating to the Irish Famine to demonstrate the application of FBGWR to real-world processes. The results suggest that FBGWR can distinguish various scales of non-stationarity in spatial processes and provide an improved model over basic GWR. FBGWR therefore represents a useful development in the modelling of spatially varying processes.

International audience; Sharing of user data has substantially increased over the past few years facilitated by sophisticated Web and mobile applications, including social networks. For instance, users can easily register their trajectories over time based on their daily trips captured with GPS receivers as well as share and relate them with trajectories of other users. Analyzing user trajectories over time can reveal habits and preferences. This information can be used to recommend content to single users or to group users together based on similar trajectories and/or preferences. Recording GPS tracks generates very large amounts of data. Therefore clustering algorithms are required to efficiently analyze such data. In this paper, we focus on investigating ways of efficiently analyzing user trajectories and extracting user preferences from them. We demonstrate an algorithm for clustering user GPS trajectories. In addition, we propose an algorithm to correlate trajectories based on near points between two or more users. The obtained results provided interesting avenues for exploring Location-based Social Network (LBSN) applications.

peer-reviewed Background: The central role of the somatotrophic axis in animal post-natal growth, development and fertility is well established. Therefore, the identification of genetic variants affecting quantitative traits within this axis is an attractive goal. However, large sample numbers are a pre-requisite for the identification of genetic variants underlying complex traits and although technologies are improving rapidly, high-throughput sequencing of large numbers of complete individual genomes remains prohibitively expensive. Therefore using a pooled DNA approach coupled with target enrichment and high-throughput sequencing, the aim of this study was to identify polymorphisms and estimate allele frequency differences across 83 candidate genes of the somatotrophic axis, in 150 Holstein-Friesian dairy bulls divided into two groups divergent for genetic merit for fertility. Results: In total, 4,135 SNPs and 893 indels were identified during the resequencing of the 83 candidate genes. Nineteen percent (n = 952) of variants were located within 5' and 3' UTRs. Seventy-two percent (n = 3,612) were intronic and 9% (n = 464) were exonic, including 65 indels and 236 SNPs resulting in non-synonymous substitutions (NSS). Significant (P < 0.01) mean allele frequency differentials between the low and high fertility groups were observed for 720 SNPs (58 NSS). Allele frequencies for 43 of the SNPs were also determined by genotyping the 150 individual animals (Sequenom® MassARRAY). No significant differences (P > 0.1) were observed between the two methods for any of the 43 SNPs across both pools (i.e., 86 tests in total). Conclusions: The results of the current study support previous findings of the use of DNA sample pooling and high-throughput sequencing as a viable strategy for polymorphism discovery and allele frequency estimation. Using this approach we have characterised the genetic variation within genes of the somatotrophic axis and related pathways, central to mammalian post-natal growth and development and subsequent lactogenesis and fertility. We have identified a large number of variants segregating at significantly different frequencies between cattle groups divergent for calving interval plausibly harbouring causative variants contributing to heritable variation. To our knowledge, this is the first report describing sequencing of targeted genomic regions in any livestock species using groups with divergent phenotypes for an economically important trait. MPM is funded by a Science Foundation Ireland strategic research grant (07/SRC/B1156); CJC is funded under the Science Foundation Ireland (SFI) Stokes lecturer scheme (07/SK/B1236A); Financial support from Research Stimulus Fund (RSF-06-0353; RSF-06-0409) is also acknowledged.