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Nedromi speakers, especially the new generation, tend to correct some mistakes that they believe are stigmatised features in their speech and thus they try to shift to Modern Standard Arabic. This research paper aims at examining the impact of education on language use, which highlights the sociolinguistic variable, mainly that of education and age, and how it may affect the linguistic behaviour in Arabic. A Nedroma speech community is the sample population. This study emphasises phonological, morphological and lexical levels of analysis, trying to make use of both quantitative and qualitative methods. Such an analysis is viewed to help us understand some of the reasons behind such a change in linguistic behaviours essentially motivated by the influence of education. The results show that the choice of certain linguistic features by the individual is determined by the speaker’s age category, his level of education and, most importantly, his attitude towards certain linguistic characteristics. Keywords: Age, Algerian context, education, language variability, linguistic features, Nedromi Arabic

Introduction. Oncolytic virotherapy is one of the approaches in immunotherapy of solid brain tumors. Measles virus vaccine strains are prospective agents for the therapy of cancers such as neuroblastoma, mesothelioma, and glioblastoma multiforme. The hyperexpression of the CD46 and other receptors on the surface of malignant cells allows the measles virus to infect and lyse the tumor, thus inducing an immune response. However, widespread immunization of the population and the resistance of neoplasms to oncolysis present difficulties in clinical practice.Text. This review covers approaches to modifying the measles virus genome in order to increase specificity of virotherapy, overcome existing immunity, and enhance the oncolytic effect. It was shown that expression of proinflammatory cytokines on viral particles leads to tumor regression in mice and triggers a T-cell response. Several approaches have been used to overcome virus-neutralizing antibodies: shielding viral particles, using host cells, and altering the epitope of the protein that enables entry of the virus into the cell. Furthermore, the insertion of reporter genes allows the infection of target cells to be monitored in vivo. A combination with the latest immunotherapies, such as immune checkpoint inhibitors, demonstrates synergistic effects, which suggests the successful use of combined approaches in the therapy of refractory tumors.Conclusion. Measles virus attenuated strains appear to be an easy-to-modify and reliable platform for the therapy of solid brain tumors.

In this letter, we propose a continual learning approach for a set of sequential scene classification tasks, where each task contains a group of land-cover classes. Our aim is to learn new tasks in a continual way without significantly degrading the performances of the old ones, due to the tricky catastrophic forgetting problem inherent to neural networks. To this end, we propose a neural architecture composed of two trainable modules. The first module learns its weights by discriminating between the land-cover classes within the new task while keeping trace of the old ones. On the other side, the second module tries to maximize the separation between the tasks by learning on task-prototypes stored in a linear memory (one prototype per task). The experimental results on two scene data sets (Merced and Optimal31) confirm the promising capability of the proposed method.

When building a model with learning capability, the usual hypothesis is that the data of all the possible situations or tasks are available. Nevertheless, dealing with a massive number of tasks in a sequential manner necessitates preserving the previous tasks data and then retrain the model on it, which is infeasible. Another solution is to retrain the model only on the new task data, but, in this case, the model dramatically collapses when tested on the old tasks data due to the phenomenon known as catastrophic forgetting. To overcome this shortcoming, we propose a novel continual learning technique based on the learned tasks' data auto-generation subnetworks. We sequentially train the proposed model on a set of classification tasks, where each task includes a certain number of remote sensing scenes or classes. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning. In parallel, the second module learns how to reproduce data of the previous tasks by discovering the latent data structure of the new task dataset. Experiments are conducted on two scene datasets (Merced and Optimal31). The experimental results confirm the outperformance and robustness of the proposed model.

Introduction Following traumatic injury, occupational therapists (OTs) are pivotal in supporting people to return-to-work (RTW) and regain independence. Aim: to identify priorities for RTW after traumatic injury and highlight barriers hindering the process. Method We recruited 17 participants with direct (i.e. trauma survivor [ n = 11]; OT trauma survivor [ n = 2]) or indirect (i.e. OT [ n = 4]) experience of returning to work following traumatic injury. Online consultations, lasting 60 min, were guided by a semi-structured script ensuring relevant topics were covered. An inductive approach was used for coding themes using thematic analysis. Findings Sense of purpose, identity and social interaction were greater priorities for RTW, than financial stability. Major barriers were: pain and fatigue; adapting to physical changes; impacts on mental health; and lack of support (healthcare and the workplace). Participants (public and healthcare professionals) reported a lack of vocational rehabilitation in supporting trauma survivors RTW, but advocated occupational therapy for psychological recovery; purposeful engagement in occupations; and community support. Conclusion Restoring an individual’s self-identity and social connections, whilst helping control pain and fatigue, should be prioritised when planning occupational therapy for those returning to work after traumatic injury. This study was limited to ‘returners’, but can inform occupational therapy interventions for RTW.

This research paper attempts to examine the impact of education on language use, i.e., highlighting the sociolinguistic variable, mainly that of education and age, and how it may affect the linguistic behavior in the Arabic Nedroma speech community as a sample population. Nedromi speakers, especially the new generation, tend to correct mistakes; they look at them as stigmatized features in their speech and thus, try to shift to Modern Standard Arabic (MSA). In this regard, the researcher attemps to determine the reasons behind such a change in linguistic behaviors essentially motivated by the influence of education. Methodological triangulation was used in this study. Data were collected through observation, questionnaire, and interview. Based on both quantitative and qualitative methods, the findings reveal that the choice of specific linguistic features by the individual is determined by the speaker’s age category, his level of education, and most importantly, his attitude towards specific linguistic characteristics.

This paper addresses the problem of cyber-attacks in discrete-event systems framework. Labeled finite state automata with inputs derived from a particular class of Petri net, called Output Synchronized Petri nets, are used to model a given cyber-physical system along with the information that circulate between controllers and plant. Stealthy cyber-attacks that may alter the control symbols, i.e., the orders sent by the controllers to the actuators, are considered. The objective is to construct an observer that uses both input and output information to provide a state estimation of the system under such stealthy actuators attacks. This observer provides a refined state estimation related to both normal and attack conditions.

Glioblastoma multiforme (GBM) accounts for almost half of all primary malignant brain tumors in adults and has a poor prognosis. Here we demonstrated the oncolytic potential of the L-16 vaccine strain of measles virus (MV) against primary human GBM cells and characterized the genetic patterns that determine the sensitivity of primary human GBM cells to oncolytic therapy. MV replicated in all GBM cells, and seven out of eight cell lines underwent complete or partial oncolysis. RNA-Seq analysis identified about 1200 differentially expressed genes (FDR < 0.05) with at least two-fold expression level change between MV-infected and uninfected cells. Among them, the most significant upregulation was observed for interferon response, apoptosis and cytokine signaling. One out of eight GBM cell lines was defective in type I interferon production and, thus, in the post-interferon response, other cells lacked expression of different cellular defense factors. Thus, none of the cell lines displayed induction of the total gene set necessary for effective inhibition of MV replication. In the resistant cells, we detected aberrant expression of metalloproteinase genes, particularly MMP3. Thus, such genes could be considered intriguing candidates for further study of factors responsible for cell sensitivity and resistance to L-16 MV infection.

As novel SARS-CoV-2 Variants of Concern emerge, the efficacy of existing vaccines against COVID-19 is declining. A possible solution to this problem lies in the development of a live attenuated vaccine potentially able of providing cross-protective activity against a wide range of SARS-CoV-2 antigenic variants. Cold-adapted (ca) SARS-CoV-2 variants, Dubrovka-ca-B4 (D-B4) and Dubrovka-ca-D2 (D-D2), were obtained after long-term passaging of the Dubrovka (D) strain in Vero cells at reduced temperatures. Virulence, immunogenicity, and protective activity of SARS-CoV-2 variants were evaluated in experiments on intranasal infection of Syrian golden hamsters (Mesocricetus auratus). In animal model infecting with ca variants, the absence of body weight loss, the significantly lower viral titer and viral RNA concentration in animal tissues, the less pronounced inflammatory lesions in animal lungs as compared with the D strain indicated the reduced virulence of the virus variant. Single intranasal immunization with D-B4 and D-D2 variants induced the production of neutralizing antibodies in hamsters and protected them from infection with the D strain and the development of severe pneumonia. It was shown that for ca SARS-CoV-2 variants, the temperature-sensitive (ts) phenotype was not obligate for virulence reduction. Indeed, the D-B4 variant, which did not possess the ts phenotype but had lost the ability to infect human lung cells Calu-3, exhibited reduced virulence in hamsters. Consequently, the potential phenotypic markers of attenuation of ca SARS-CoV-2 variants are the ca phenotype, the ts phenotype, and the change in species specificity of the virus. This study demonstrates the great potential of SARS-CoV-2 cold adaptation as a strategy to develop a live attenuated COVID-19 vaccine.

The Covid-19 pandemic has affected educational systems worldwide, leading some scholars to scrutinise the consequences of lockdown and school closure on learners’ learning habits and teachers’ teaching practices. In this regard, this paper aims to explore the teachers’ challenges while implementing a reading strategy-based instruction for beginners during the Covid-19 pandemic, taking the Algerian middle schools as a case in point. It highlights the difficulties to achieving quality in developing learners’ procedural knowledge and strategic awareness in EFL reading classes. The leading approach to the issue is the interactive approach. To attain the objective of the research, the qualitative method was adopted. Classroom observation and structured interviews were used to collect data. The population targeted was composed of 20 teachers from 16 middle schools in Tizi-Ouzou. The collected data were subjected to qualitative content analysis. The results of the study reveal that most teachers are aware of the importance of reading strategy-based instruction. However, they do not teach them systematically or consistently. Indeed, lack of targeted teacher training, time constraints, and disregard of metacognitive instruction are likely to be obstacles to the efficient implementation of reading strategy-instruction. Furthermore, the Covid-19 pandemic has thrown up several psychological and cognitive learners’ difficulties, including decreased motivation and lack of cognitive focus, making the teaching process more challenging. The results imply a need for a revision of teachers’ professional development programs and a re-consideration of the elementary EFL courses.

This technical note concerns the fault diagnosis of stochastic discrete event systems. Specifically, the goal is to characterize a detected fault by estimating its occurrence date. For that purpose, partially observed stochastic Petri nets are used to model the system, the failure processes, and the sensors. From the proposed modeling and collected dated measurements, the probabilities of consistent trajectories are computed and diagnosis in terms of faults probability is established as a consequence. For each detected fault, the probability density function of its occurrence date is approximated. This estimation improves the diagnosis by providing the most probable time interval of the fault occurrence. The interest of fault datation and the applicability of the proposed approach are showed through a case study that represents a distribution system.

Scene classification is a highly useful task in Remote Sensing (RS) applications. Many efforts have been made to improve the accuracy of RS scene classification. Scene classification is a challenging problem, especially for large datasets with tens of thousands of images with a large number of classes and taken under different circumstances. One problem that is observed in scene classification is the fact that for a given scene, only one part of it indicates which class it belongs to, whereas the other parts are either irrelevant or they actually tend to belong to another class. To address this issue, this paper proposes a deep attention Convolutional Neural Network (CNN) for scene classification in remote sensing. CNN models use successive convolutional layers to learn feature maps from larger and larger regions (or receptive fields) of the scene. The attention mechanism computes a new feature map as a weighted average of these original feature maps. In particular, we propose a solution, named EfficientNet-B3-Attn-2, based on the pre-trained EfficientNet-B3 CNN enhanced with an attention mechanism. A dedicated branch is added to layer 262 of the network, to compute the required weights. These weights are learned automatically by training the whole CNN model end-to-end using the backpropagation algorithm. In this way, the network learns to emphasize important regions of the scene and suppress the regions that are irrelevant to the classification. We tested the proposed EfficientNet-B3-Attn-2 on six popular remote sensing datasets, namely UC Merced, KSA, OPTIMAL-31, RSSCN7, WHU-RS19, and AID datasets, showing its strong capabilities in classifying RS scenes.

The use of digital health products has gained considerable interest as a new way to improve therapeutic research and development. Although these products are being adopted by various industries and stakeholders, their incorporation in clinical trials has been slow due to a disconnect between the promises of digital products and potential risks in using these new technologies in the absence of regulatory support. The Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium hosted a public workshop to address challenges and opportunities in this field. Important characteristics of tool development were addressed in a series of presentations, case studies, and open panel sessions. The workshop participants endorsed the usefulness of an evidentiary criteria framework, highlighted the importance of early patient engagement, and emphasized the potential impact of digital monitoring tools and precompetitive collaborations. Concerns were expressed about the lack of real‐life validation examples and the limitations of legacy standards used as a benchmark for novel tool development and validation. Participants recognized the need for novel analytical and statistical approaches to accommodate analyses of these novel data types. Future directions are to harmonize definitions to build common methodologies and foster multidisciplinary collaborations; to develop approaches toward integrating digital monitoring data with the totality of the data in clinical trials, and to continue an open dialog in the community. There was a consensus that all these efforts combined may create a paradigm shift of how clinical trials are planned, conducted, and results brought to regulatory reviews.

Motivation Mass spectrometry imaging (MSI) characterizes the molecular composition of tissues at spatial resolution, and has a strong potential for distinguishing tissue types, or disease states. This can be achieved by supervised classification, which takes as input MSI spectra, and assigns class labels to subtissue locations. Unfortunately, developing such classifiers is hindered by the limited availability of training sets with subtissue labels as the ground truth. Subtissue labeling is prohibitively expensive, and only rough annotations of the entire tissues are typically available. Classifiers trained on data with approximate labels have sub-optimal performance. Results To alleviate this challenge, we contribute a semi-supervised approach mi-CNN. mi-CNN implements multiple instance learning with a convolutional neural network (CNN). The multiple instance aspect enables weak supervision from tissue-level annotations when classifying subtissue locations. The convolutional architecture of the CNN captures contextual dependencies between the spectral features. Evaluations on simulated and experimental datasets demonstrated that mi-CNN improved the subtissue classification as compared to traditional classifiers. We propose mi-CNN as an important step toward accurate subtissue classification in MSI, enabling rapid distinction between tissue types and disease states. Availability and implementation The data and code are available at https://github.com/Vitek-Lab/mi-CNN_MSI.

Introduction. Neutropenia, which is an abnormally low concentration of neutrophils in the blood, is one of the common side effects in patients receiving radio- or chemotherapy. Neutropenia usually leads to higher risks of severe bacterial and fungal infections. Such medicines as colonystimulating factor filgrastim (and its conjugates) are used to prevent and treat neutropenia in oncology patients. Immunogenicity is a potential concern for any biological product, thus, its assessment is one of the most critical necessities during the development and registration of such products.Aim. The main aim of this study was to validate the ELISA method for anti-pegfilgrastim antibodies detection in human serum samples and to apply the validated method to pegfilgrastim drugs immunogenicity assessment.Materials and methods. To assess pegfilgrastim immunogenicity, the commercial ELISA kit «PEGylated Filgrastim (Neulasta®) ADA ELISA» was used for screening, confirmatory and titer assay. Moreover, to confirm the chosen commercial kit suits the study aims it was revalidated. The absorbance values were obtained using plate immunoassay analyzer Stat Fax 3200, plate washing was performed using an automatic twochannel plate washer.Results and discussion. The ELISA method for anti-pegfilgrastim antibodies determination in human serum samples was validated and applied to the analytical part of the comparative, multicenter, blind, randomized study of pegfilgrastim efficacy and safety in patients with breast cancer, receiving myelosuppressive chemotherapy. Human serum samples were first screened for anti-drug antibodies, then «screening positive» samples were analyzed in confirmatory assay with % inhibition calculation for each sample. The «confirmed positive» samples were further characterized in titer assay.Conclusions. The ELISA method for anti-pegfilgrastim antibodies determination in human serum samples was successfully validated and applied for pegfilgrastim drugs immunogenicity assessment.

Botrytis bunch rot (BBR) of grapevine, caused by Botrytis cinerea, is commonly managed by fungicide (FUN) sprays at flowering (A), at prebunch closure (B), at veraison (C), and before harvest. Applications at A, B, and C are recommended to reduce B. cinerea colonization of bunch trash and the production of conidia during berry ripening. The effects of these applications were previously evaluated as reductions in BBR severity at harvest rather than as reductions in bunch trash colonization and sporulation by B. cinerea. This study investigated the effects of FUNs (a commercial mixture of fludioxonil and cyprodonil), biological control agents (BCAs; Aureobasium pullulans and Trichoderma atroviride), and botanicals (BOTs; a commercial mixture of eugenol, geraniol, and thymol) applied at different timings (A, B, C, or ABC) compared with a nontreated control (NT) on B. cinerea bunch trash colonization and sporulation in vineyards. The ability of B. cinerea to colonize the bunch trash (as indicated by B. cinerea DNA content) and sporulate (as indicated by the number of conidia produced under optimal laboratory conditions) was highly variable, and this variability was higher between years (2015 to 2018) than among the three vineyards and three sampling times (i.e., 1 week after applications at A, B, and C). B. cinerea sporulation on bunch trash was significantly lower in plots treated with FUN than in NT in only 3 of 18 cases (3 vineyards × 2 years × 3 sampling times). FUN applications, however, significantly reduced B. cinerea colonization of bunch trash compared with NT; for colonization, BCA efficacy was similar to that of FUN, but BOT efficacy was variable. For all products, colonization reduction was the same with application at A versus ABC, meaning that the effect of an early season application lasted from flowering to 1 week after veraison. These results indicate that the early season control of B. cinerea is important to reduce the saprophytic colonization of bunch trash, especially when the risk of BBR is high.

Background Mass spectrometry imaging (MSI) derives spatial molecular distribution maps directly from clinical tissue specimens and thus bears great potential for assisting pathologists with diagnostic decisions or personalized treatments. Unfortunately, progress in translational MSI is often hindered by insufficient quality control and lack of reproducible data analysis. Raw data and analysis scripts are rarely publicly shared. Here, we demonstrate the application of the Galaxy MSI tool set for the reproducible analysis of a urothelial carcinoma dataset. Methods Tryptic peptides were imaged in a cohort of 39 formalin-fixed, paraffin-embedded human urothelial cancer tissue cores with a MALDI-TOF/TOF device. The complete data analysis was performed in a fully transparent and reproducible manner on the European Galaxy Server. Annotations of tumor and stroma were performed by a pathologist and transferred to the MSI data to allow for supervised classifications of tumor vs. stroma tissue areas as well as for muscle-infiltrating and non-muscle infiltrating urothelial carcinomas. For putative peptide identifications, m/z features were matched to the MSiMass list. Results Rigorous quality control in combination with careful pre-processing enabled reduction of m/z shifts and intensity batch effects. High classification accuracy was found for both, tumor vs. stroma and muscle-infiltrating vs. non-muscle infiltrating urothelial tumors. Some of the most discriminative m/z features for each condition could be assigned a putative identity: stromal tissue was characterized by collagen peptides and tumor tissue by histone peptides. Immunohistochemistry confirmed an increased histone H2A abundance in the tumor compared to the stroma tissues. The muscle-infiltration status was distinguished via MSI by peptides from intermediate filaments such as cytokeratin 7 in non-muscle infiltrating carcinomas and vimentin in muscle-infiltrating urothelial carcinomas, which was confirmed by immunohistochemistry. To make the study fully reproducible and to advocate the criteria of FAIR (findability, accessibility, interoperability, and reusability) research data, we share the raw data, spectra annotations as well as all Galaxy histories and workflows. Data are available via ProteomeXchange with identifier PXD026459 and Galaxy results via https://github.com/foellmelanie/Bladder_MSI_Manuscript_Galaxy_links. Conclusion Here, we show that translational MSI data analysis in a fully transparent and reproducible manner is possible and we would like to encourage the community to join our efforts.

Vaccination against COVID-19 is the most effective method of controlling the spread of SARS-CoV-2 and reducing mortality from this disease. The development of vaccines with high protective activity against a wide range of SARS-CoV-2 antigenic variants remains relevant. In this regard, evaluation of the effectiveness of physical methods of virus inactivation, such as ultraviolet irradiation (UV) of the virus stock, remains relevant. This study demonstrates that the UV treatment of SARS-CoV-2 completely inactivates its infectivity while preserving its morphology, antigenic properties, and ability to induce the production of virus-neutralizing antibodies in mice through immunization. Thus, the UV inactivation of SARS-CoV-2 makes it possible to obtain viral material similar in its antigenic and immunogenic properties to the native antigen, which can be used both for the development of diagnostic test systems and for the development of an inactivated vaccine against COVID-19.

BackgroundInborn errors of immunity (IEI) predispose patients to various infectious and non-infectious complications. Thanks to the development and expanding use of flow cytometry and increased awareness, the diagnostic rate of IEI has markedly increased in Algeria the last decade.AimThis study aimed to describe a large cohort of Algerian patients with probable IEI and to determine their clinical characteristics and outcomes.MethodsWe collected and analyzed retrospectively the demographic data, clinical manifestations, immunologic, genetic data, and outcome of Algerian IEI patients - diagnosed in the department of medical immunology of Beni Messous university hospital center, Algiers, from 2008 to 2021.ResultsEight hundred and seven patients with IEI (482 males and 325 females) were enrolled, 9.7% of whom were adults. Consanguinity was reported in 50.3% of the cases and a positive family history in 32.34%. The medium age at disease onset was 8 months and at diagnosis was 36 months. The median delay in diagnosis was 16 months. Combined immunodeficiencies were the most frequent (33.8%), followed by antibody deficiencies (24.5%) and well-defined syndromes with immunodeficiency (24%). Among 287 patients tested for genetic disorders, 129 patients carried pathogenic mutations; 102 having biallelic variants mostly in a homozygous state (autosomal recessive disorders). The highest mortality rate was observed in patients with combined immunodeficiency (70.1%), especially in patients with severe combined immunodeficiency (SCID), Omenn syndrome, or Major Histocompatibility Complex (MHC) class II deficiency.ConclusionThe spectrum of IEI in Algeria is similar to that seen in most countries of the Middle East and North Africa (MENA) region, notably regarding the frequency of autosomal recessive and/or combined immunodeficiencies.

Cancer is a major public health problem in Algeria. The aim of this article was to estimate trends in cancer incidence in Oran, Algeria, over the period 1996-2019.Nine cancer sites were analyzed. Incidence data over the 1996-2019 period were collected from the Oran cancer registry. The annual percent changes in incidence rates were evaluated with Joinpoint analysis.Between 1996 and 2019, unfavorable trends in incidence were observed for lung and colorectal cancers in both sexes, as well as female breast cancer. In contrast, a continuous decrease in incidence rates of cervical cancer were observed. As for the prostate, no corresponding trend was emerged for this cancer.The upward trend in incidence rates for some cancers underscores the need to strengthen prevention efforts.

The electrocardiogram (ECG) signal is shown to be promising as a biometric. To this end, it has been demonstrated that the analysis of ECG signals can be considered as a good solution for increasing the biometric security levels. This can be mainly due to its inherent robustness against presentation attacks. In this work, we present a deep contrastive learning-based system for ECG biometric identification. The proposed system consists of three blocks: a feature extraction backbone based on short time Fourier transform (STFT), a contrastive learning network, and a classification network. We evaluated the proposed system on the Heartprint dataset, a new ECG biometrics multi-session dataset. The experimental analysis shows promising capabilities of the proposed method. In particular, it yields an average top1 accuracy of 98.02% on a new dataset built by gathering 1539 ECG records from 199 subjects collected in multiple sessions with an average interval between sessions of 47 days.

Background: The development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions. Methods/design: The Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson's Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability. Discussion: The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility. Trial registration: ISRCTN12051706. This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No. 820820. This JU receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). The funding bodies do not have ultimate authority over any activities (study design, collection, management, analysis, interpretation of data, writing of reports and decision to submit for publication. A draft protocol for the clinical validation was provided as part of the grant/funding application. Content in this publication reflects the authors’ view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein. ISGlobal acknowledges support from the Spanish Ministry of Science, Innovation and Universities through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. Heleen Demeyer is a post-doctoral fellow of the FWO Flanders. Heiko Gaßner is supported by the Fraunhofer Internal Programs under Grant No. Attract 044-602140 and 044-602150

International audience; This paper concerns the security analysis of discrete event systems modeled with a particular class of synchronized Petri nets that include output functions, called Output Synchonized Petri nets. Such a formalism is suitable and tractable to represent a large variety of cyber-physical systems. In particular, we study here cyber-attacks that aim to drive the system from a given normal state to forbidden state. We assume that the attacker has a certain credit to insert and delete input and output events, depending on its own objectives. The proposed analysis aims to evaluate the costs of stealthy attacks on the controlled system depending on the objective of the controller, the structure of the system and the cost of the malicious actions.

Socially assistive robots can play an important role in the monitoring and training of health of older adults. But before their benefits can be reaped, proper usability and a positive user experience need to be ensured. In this study, we tested the usability and user experience of a socially assistive robot (the NAO humanoid robot) to monitor and train the health of frail older adults. They were asked to complete a set of health monitoring and physical training tasks, once provided by the NAO robot, and once provided by a Tablet PC application (as a reference technology). After using each technology, they completed the System Usability Scale for usability, and a set of rating scales for perceived usefulness, enjoyment, and control. Finally, we questioned the participants’ preference for one of the technologies. All interactions were recorded on video and scrutinized for usability issues. Twenty older adults participated. They awarded both technologies ‘average’ usability scores. Perceived usefulness and enjoyment were rated as very positive for both modalities; control was scored positively. Main usability issues for NAO for these tasks were related to speech interaction (e.g., NAO’s limited speech library, NAO’s difficulty to cope with Dutch dialect), older adults’ difficulties with taking their proper role in human-robot interaction, and a lack of affordances of NAO. Seven participants preferred NAO: it was easier to use and more personal. Social robots have the potential to monitor and train the health of frail older adults, but some critical usability challenges need to be overcome first.

The electrocardiogram (ECG) signal produced by the human heart is an emerging biometric modality that can play an important role in the future generation’s identity recognition with the support of machine learning techniques. One of the major obstacles in the progress of this modality is the lack of public datasets with a long interval between sessions of data acquisition to verify the uniqueness and permanence of the biometric signature of the heart of a subject. To address this issue, we put forward Heartprint, a large biometric database of multisession ECG signals comprising 1539 records captured from the fingers of 199 healthy subjects. The capturing time for each record was 15 s, and recordings were made in resting and reading conditions. They were collected in multiple sessions over ten years, and the average interval between first session (S1) and third session (S3L) was 1572.2 days. The dataset also covers several demographic classes such as genders, ethnicities, and age groups. The combination of raw ECG signals and demographic information turns the Heartprint dataset, which is made publicly available online, into a valuable resource for the development and evaluation of biometric recognition algorithms.

Parkinson’s disease (PD) is a progressive movement disorder characterized by tremors at rest, bradykinesia, and stifness. The alteration of handwriting (HW) faculties is one of the earliest motor symptoms in PD patients. This characteristic can be exploited to develop an automatic aid system for early detection of this pathologie. This article aims to assess the importance of diacritics and punctuation marks (DPM) in the PD patients and healthy controls (HCs) discrimination problem, by comparing the classification results obtained from three components: text carrying DPM, text without DPM, as well as only DPM. This work includes the Arabic manuscripts of 31 PD patients and 31 HCs. Furthermore, kinematic, mechanic, and inclination features were calculated for each component. Then, Adaboost models have been constructed on different feature sets, as well as on reduced sets formed in incremental manner using mRMR ranked-feature selection method. From the obtained results, it was concluded that the separating power of HW features in the classification problem of PD patients and HCs is present in all components of the Arabic text, but in varying degrees of importance. Despite the simple graphical nature of DPM, they are carrying of relevant diagnostic information, and effectively contributing to the improvement of PD detection performance. The highest accuracy of 93.54% was achieved for this component.

Parkinson’s disease (PD) is a neurodegenerative disorder that affects a person’s movement. As the early diagnosis of the disease is crucial, the main aim of this work is to implement an online analysis system of patients’ handwriting, through computer vision and signal processing techniques, using the database collected in the neurology department of the University Hospital Center Hassan II in Fez. For this, we studied the handwriting tests on a WACOM graphic tablet to retrieve the spatiotemporal data (position, pressure and angles of inclination), for each point (P(n)) of the trajectory. The features vector was obtained basing on five types of features: (a) Kinematic features related to the dynamics of spiral design, (b) Mechanical based on the pressure exerted on the writing surface, (c) Inclination angles, (d) Spatial interrelation feature and (e) Pen-Up. The used classification and clustering algorithms are respectively the Hoeffding tree and the FarthestFirst clusters. We observed coherence between the classification results and the clustering ones, thus the results being encouraging and promising with a recognition rate of 98.36%

Deep learning models suffer from catastrophic forgetting and collapse dramatically when they are subjected to continual learning process. To overcome this handicap, we propose a novel continual learning approach based on previously seen data auto-generation sub-networks. The proposed model continually learns a set of sequential classification tasks or classes, where each classification task includes a certain number of remote sensing scenes or classes. The proposed neural networks architecture is composed of two trainable sub-networks. The first module adjusts its weights by minimizing the discrimination between the land-cover classes error during the new task learning. In parallel, the second module attempts to learn how to reproduce the task data by discovering the latent data structure of the new task dataset. Experiments are conducted on Merced dataset. The experimental results confirm the outperformance and robustness of the proposed model.

International audience; This paper concerns discrete event systems modeled with a particular class of synchronized Petri nets that include output functions, called Output Synchronized Petri nets. Such a formalism is suitable and tractable to represent a large variety of Cyber-Physical Systems.As a preliminary result on Output Synchronized Petri nets, we propose a method to design observers for such systems based on the input control and output signal that circulate between the controller and the plant.

International audience

In a quickly growing world, there is increasing demand for a secure food supply, a reduction in the intensive use of natural resources, and the enhancement of sustainability for future long-term maintenance. In this regard, plant health, including fruit and foliar diseases, which can cause a vast amount of crop loss, potentially has a huge effect on food security. The integration of new, innovative technological tools and data management techniques into the traditional agricultural practices is a promising approach to combat future food shortages. The use of the same principles of precision agriculture to “do the right thing, at the right time, in the right place” will allow for providing detailed, real-time information that will help farmers to protect their crops and choose healthier, as well as more productive, farming methods. The presented narrative review reports on several items of innovation, including monitoring and surveillance, diagnostic, and decision-making tools, with a specific focus devoted to digital solutions that can be applied in agriculture in order to improve the quality and the speed of the decision-making process and specifically, to set up a digital collaboration that can be crucial under certain circumstances to reach sustainability goals, particularly in the Near East and North Africa (NENA) Region, where an effective and rapid solution for phytosanitary control is needed.

This paper deals with the planning of collision-free trajectory for autonomous vehicle on highway. The problem is formulated using Model Predictive Control (MPC) where a simplified prediction model that integrates the relative positions and velocities between the surrounding vehicles and the ego vehicle is employed. The MPC provides the necessary lateral and longitudinal accelerations to the ego vehicle to track the reference path and to drive at the desired speed without hitting the road boundaries or colliding with the neighbouring vehicles. The collision avoidance is guaranteed through the integration of a time varying, Sigmoid-based, safety constraint. This constraint plays the role of an S-shaped barrier, enforcing the host vehicle to move apart and change the lane when the inter-vehicular safety distance is not kept. The proposed approach is focused on avoiding collision by performing lane change and overtaking maneuvers since in many traffic situations, an imminent collision cannot be avoided by braking. Satisfactory results are obtained from the set of the simulated scenarios.

This paper addresses the control problem of controlled generalized batches Petri nets. The considered problem is to reach a given steady state from an initial one by controlling the firing flow of transitions. The proposed strategy is event-driven and the control actions are updated only when specific events occur. It is based on an algorithm with a linear programming problem that computes a new control firing flow vector when the considered events occur. The convergence of the marking to its steady state marking by the proposed strategy is proved and an example is provided to illustrate the computation steps.

Nanopore sequencing of virus genomes represented by segmented RNA (e.g. rotaviruses) requires the development of specific approaches. Due to the massive use of rotavirus vaccines, the relevance of monitoring the genetic diversity of circulating strains of group A rotaviruses (RVA) increased. The WHO recommended method of multiplex type-specific PCR does not allow genotyping of all clinically significant strains of RVA and identifying inter-strain differences within the genotype. We have described a new principle of amplification of RVA gene segments using six primers for reverse transcription and one universal primer for PCR for nanopore sequencing. The amplification of RVA genome was tested on clinical samples and three phylogenetically distant laboratory RVA strains, Wa (G1P[8]), DS-1 (G2P[4]) and 568 (G3P[3]). The developed protocol of sample preparation and nanopore sequencing allowed obtaining full-length sequences for gene segments of RVA, including the diagnostically significant segments 9 (VP7), 4 (VP4) and 6 (VP6) with high accuracy and coverage. The accuracy of sequencing of the rotavirus genome exceeded 99.5 %, and the genome coverage varied for different strains from 59.0 to 99.6 % (on average 86 %). The developed approach of nanopore sequencing of RVA genome could be a prospective tool for epidemiological studies and surveillance of rotavirus infection.

A real-time loop-mediated isothermal amplification (LAMP) assay was evaluated for the detection of Botrytis cinerea in grapevine bunch trash, immature berries, and ripening berries. A simple method for the preparation of crude extracts of grape tissue was also developed for on-site LAMP analysis. When tested with 14 other fungal species frequently found in grapevines, the LAMP assay was specific and sensitive to a B. cinerea DNA quantity of 0.1 ng/&micro, L. The sensitivity was further tested using bunch trash samples with B. cinerea colonization levels between 6 and 100% and with bulk-berry samples composed of 4 pathogen-free berries or 4 berries among which 25 to 100% had been inoculated with B. cinerea. The LAMP assay detected the lowest B. cinerea colonization level tested in bunch trash and in immature and mature berries in less than 20 min. In single-berry experiments, LAMP amplified B. cinerea DNA from all artificially inoculated individual immature and mature berries. No amplification occurred in B. cinerea-free material. The real-time LAMP assay has the potential to be used as a rapid on-site diagnostic tool for assessing B. cinerea colonization in bunch trash and B. cinerea latent infections in berries, which represent critical stages for decision-making about disease management.