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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.

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.

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.

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.

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.

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.

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.

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/µL. 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.

Clinical trial data collection still relies on a manual entry from information available in the medical record. This process introduces delay and error risk. Automating data transfer from Electronic Health Record (EHR) to Electronic Data Capture (EDC) system, under investigators' supervision, would gracefully solve these issues. The present paper describes the design of the evaluation of a technology allowing EHR to act as eSource for clinical trials. As part of the EHR2EDC project, for 6 ongoing clinical trials, running at 3 hospitals, a parallel semi-automated data collection using such technology will be conducted focusing on a limited scope of data (demographic data, local laboratory results, concomitant medication and vital signs). The evaluation protocol consists in an individual participant data prospective meta-analysis comparing regular clinical trial data collection to the semi-automated one. The main outcome is the proportion of data correctly entered. Data quality and associated workload for hospital staff will be compared as secondary outcomes. Results should be available in 2020.

Grapevine downy mildew caused by Plasmopara viticola is one of the most important diseases in vineyards. Oospores that overwinter in the leaf litter above the soil are the sole source of inoculum for primary infections of P. viticola; in addition to triggering the first infections in the season, the oospores in leaf litter also contribute to disease development during the season. In the current study, a quantitative polymerase chain reaction (qPCR) method that was previously developed to detect P. viticola DNA in fresh grapevine leaves was assessed for its ability to quantify P. viticola oospores in diseased, senescent grapevine leaves. The qPCR assay was specific to P. viticola and sensitive to decreasing amounts of both genomic DNA and numbers of P. viticola oospores used to generate qPCR standard curves. When the qPCR assay was compared to microscope counts of oospores in leaves with different levels of P. viticola infestation, a strong linear relationship (R2 = 0.70) was obtained between the numbers of P. viticola oospores per gram of leaves as determined by qPCR vs. microscopic observation. Unlike microscopic observation, the qPCR assay was able to detect significant differences between leaf samples with a low level of oospore infestation (25% infested leaves and 75% non-infested leaves) vs. samples without infestation, and this ability was not influenced by the weight of the leaf sample. The results indicate that the qPCR method is sensitive and provides reliable estimates of the number of P. viticola oospores in grapevine leaves. Additional research is needed to determine whether the qPCR method is useful for quantifying P. viticola oospores in grapevine leaf litter.

Apple trees require a long exposure to chilling temperature during winter to acquire competency to flower and grow in the following spring. Climate change or adverse meteorological conditions can impair release of dormancy and delay bud break, hence jeopardizing fruit production and causing substantial economic losses. In order to characterize the molecular mechanisms controlling bud dormancy in apple we focused our work on the MADS-box transcription factor gene MdDAM1. We show that MdDAM1 silencing is required for the release of dormancy and bud break in spring. MdDAM1 transcript levels are drastically reduced in the low-chill varieties ‘Anna’ and ‘Dorsett Golden’ compared to ‘Golden Delicious’ corroborating its role as a key genetic factor controlling the release of bud dormancy in Malus species. The functional characterization of MdDAM1 using RNA silencing resulted in trees unable to cease growth in winter and that displayed an evergrowing, or evergreen, phenotype several years after transgenesis. These trees lost their capacity to enter in dormancy and produced leaves and shoots regardless of the season. A transcriptome study revealed that apple evergrowing lines are a genocopy of ‘Golden Delicious’ trees at the onset of the bud break with the significant gene repression of the related MADS-box gene MdDAM4 as a major feature. We provide the first functional evidence that MADS-box transcriptional factors are key regulators of bud dormancy in pome fruit trees and demonstrate that their silencing results in a defect of growth cessation in autumn. Our findings will help producing low-chill apple variants from the elite commercial cultivars that will withstand climate change.

Multi-label classification problem aims to assign multiple class labels to the remote sensing image under analysis, which is more challenging compared to single-label classification. To this end, we propose a neural model based on multiple loss functions. The first loss seeks to increase the similarity between the image with its corresponding labels using a similarity layer. The second one is related to label discrimination, and it is achieved using a modified softmax layer suitable for multi-label classification. The third loss aims to detect automatically the number of labels present in the image through a regression layer. Experimental results on the well known Merced data are reported and discussed.

Scene classification has become an important research topic in remote sensing (RS) field. Typical solution relies on labeling a large enough set of the RS scenes manually using expert opinion if needed, then training the algorithm on this set to learn how to correctly classify other new scenes. The best performance deep learning models required a large labeled dataset for training. Accordingly, there is great need to develop intelligent machine learning algorithm that can learn to classify RS datasets containing new unseen classes from few labeled samples only. This problem is known as few-shot machine learning. In this work we develop a deep few-shot learning method for the classification of RS scenes. The proposed method is based on prototypical deep neural networks combined with SqueezeNet pre-trained CNN for image embedding. In this paper, we report preliminary results using the two RS scene datasets UC Merced and optimal31.

Background Lung cancer is a major cause of death worldwide. However, few data on incidence, histologic types and mortality rates of lung cancer were available for Algeria. Methods LuCaReAl is an ongoing descriptive, non-interventional, national, multicenter, prospective and longitudinal study conducted in Algeria, among oncologists and pulmonologists in public community and university hospitals. Median and interquartile ranges are displayed. Results Between July 2016 and July 2017, 897 patients were included. Overall incidence of lung cancer was 3.4 [3.2;3.6] cases per 100,000 inhabitants; overall incidence by sex was 5.8 [5.4;6.2] for males and 1.0 [0.8;1.1] for females. Adenocarcinoma was the most common histologic type of cancer. Most tumors were diagnosed at Stage IV. Conclusion The first results from the LuCaReAl study in Algeria showed that most patients are diagnosed with lung cancer at an advanced stage. The ongoing follow-up will next provide data on the survival and mortality rates.

Background: The use of mobile technologies for data capture and transmission has the potential to streamline clinical trials, but researchers lack methods for collecting, processing, and interpreting data from these tools. Objectives: To assess the performance of a technical platform for collecting and transmitting data from six mobile technologies in the clinic and at home, to apply methods for comparing them to clinical standard devices, and to measure their usability, including how willing subjects were to use them on a regular basis. Methods: In part 1 of the study, conducted over 3 weeks in the clinic, we tested two device pairs (mobile vs. clinical standard blood pressure monitor and mobile vs. clinical standard spirometer) on 25 healthy volunteers. In part 2 of the study, conducted over 3 days both in the clinic and at home, we tested the same two device pairs as in part 1, plus four additional pairs (mobile vs. clinical standard pulse oximeter, glucose meter, weight scale, and activity monitor), on 22 healthy volunteers. Results: Data collection reliability was 98.1% in part 1 of the study and 95.8% in part 2 (the percentages exclude the wearable activity monitor, which collects data continuously). In part 1, 20 of 1,049 overall expected measurements were missing (1.9%), and in part 2, 45 of 1,083 were missing (4.2%). The most common reason for missing data was a single malfunctioning spirometer (13 of 20 total missed readings) in part 1, and that the subject did not take the measurement (22 of 45 total missed readings) in part 2. Also in part 2, a higher proportion of at-home measurements than in-clinic readings were missing (12.6 vs. 2.7%). The data from this experimental study were unable to establish repeatability or agreement for every mobile technology; only the pulse oximeter demonstrated repeatability, and only the weight scale demonstrated agreement with the clinical standard device. Most mobile technologies received high “willingness to use” ratings from the patients on the questionnaires. Conclusions: This study demonstrated that the wireless data transmission and processing platform was dependable. It also identified three critical areas of study for advancing the use of mobile technologies in clinical research: (1) if a mobile technology captures more than one type of endpoint (such as blood pressure and pulse), repeatability and agreement may need to be established for each endpoint to be included in a clinical trial; (2) researchers need to develop criteria for excluding invalid device readings (to be identified by algorithms in real time) for the population studied using ranges based on accumulated subject data and established norms; and (3) careful examination of a mobile technology’s performance (reliability, repeatability, and agreement with accepted reference devices) during pilot testing is essential, even for medical devices approved by regulators.

Background In histopathological routine diagnostics, three-dimensional tissue samples are analyzed histologically and/or immunohistochemically in two-dimensional sectional planes due to the high expenditure of time and the lack of digitization possibilities. Aim Here, we demonstrate the application of three-dimensional reconstruction to solid tumors and analyze inter-/intratumoral heterogeneity with respect to epithelial-mesenchymal transition (EMT). Methods Tissue samples from pancreatic, lung, colorectal, and breast cancers as well as colorectal liver metastases were serially processed in 4μm sections. For individual analyses, alternating stains (cytokeratin AE1/3, zinc finger E‑box-binding homeobox 1 (ZEB1), eCadherin) were performed. Subsequently, the tumor cells were analyzed for their morphology (epitheloid amoeboid, mesenchymal) and the expression of ZEB1 and eCadherin. For statistical analysis, all tumor cell aggregates were hierarchically annotated and analyzed. Results Tumor buds are predominantly associated with the main tumor mass. Furthermore, a shutteling of eCadherin could be observed within tumor cell aggregates smaller than nine cells. ZEB1 is only increasingly expressed in tumor cell groups smaller than five cells. Conclusions The initial tumor budding and the subsequent decoupling of the tumor bud from the main tumor mass is most likely a two-part process. However, the EMT is not statistically significantly increased within the tumor bud detached from the main tumor mass. It could be shown that the currently valid and known definition of a tumor bud as a cell cluster of less than or equal to five cells cannot be completely classified in the concept of EMT represented by eCadherin and ZEB1.

A coordinated regulation of different metabolic pathways was highlighted leading to the accumulation of important compounds that may contribute to the final quality of strawberry fruit. Strawberry fruit development and ripening involve complex physiological and biochemical changes, ranging from sugar accumulation to the production of important volatiles compounds that contribute to the final fruit flavor. To better understand the mechanisms controlling fruit growth and ripening in cultivated strawberry (Fragaria × ananassa), we applied a molecular approach combining suppression subtractive hybridization and next generation sequencing to identify genes regulating developmental stages going from fruit set to full ripening. The results clearly indicated coordinated regulation of several metabolic processes such as the biosynthesis of flavonoid, phenylpropanoid and branched-chain amino acids, together with glycerolipid metabolism and pentose and glucuronate interconversion. In particular, genes belonging to the flavonoid pathway were activated in two distinct phases, the first one at the very early stages of fruit development and the second during ripening. The combination of expression analysis with metabolomic data revealed that the functional meaning of these two inductions is different, as during the early stages gene activation of flavonoid pathway leads to the production of proanthocyanidins and ellagic acid-derived tannins, while during ripening anthocyanins are the main product of flavonoid pathway activation. Moreover, the subtractive approach allowed the identification of different members of the same gene family coding for the same or very similar enzymes that in some cases showed opposite regulation during strawberry fruit development. Such regulation is an important trait that can help to understand how plants specifically channel metabolic intermediates towards separate branches of a biosynthetic pathway or use different isoforms of the same enzyme in different organs or developmental stages.

Enteric viruses are the most common cause of acute gastroenteritis (AGE) in young children and a significant public health problem globally. Hospital admissions of children under 5 years of age with diarrhea are primarily associated with group A rotavirus (RVA) infection. In this retrospective study, the population structure of viruses linked to AGE etiology in young children hospitalized with AGE in Moscow was evaluated, and molecular characterization of RVA strains was performed. Fecal specimens were collected from children under 5 years old hospitalized with AGE between 2009 and 2014 in Moscow, Russia. Multiplex real-time reverse transcription PCR was used to detect enteric viruses and for G/[P]-genotyping of isolated RVAs. Sequencing of RVA VP7 and VP4 cDNA fragments was used to validate the data obtained by PCR-genotyping. The main causes for hospitalization of children with AGE were RVA (40.1%), followed by noroviruses (11.4%), while adenoviruses, astroviruses, sapoviruses, enteroviruses, and orthoreoviruses were detected in 4.7%, 1.9%, 1.4%, 1.2%, and 0.2% of samples tested, respectively. Nosocomial infections, predominantly associated with RVAs and noroviruses, were detected in 24.8% of cases and occurred significantly more frequently in younger infants. The predominant RVA genotype was G4P[8], detected in 38.7% of RVA-positive cases, whereas genotypes G1P[8], G9P[8], G3P[8], and G2P[4] were found in 11.8%, 6.6%, 4.2%, and 3.3% of cases, respectively. Together, the presence of circulating RVA strains with rare VP7 and VP4 gene variants (G6 and P[9]) highlights the need to conduct continuous epidemiological monitoring of RVA infection. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12250-018-0043-0) contains supplementary material, which is available to authorized users.

The oncolytic potential of the attenuated mumps virus (MV) vaccine strain Leningrad-3 (L-3) was evaluated in a panel of four human metastatic melanoma cell lines. The lines were shown to be susceptible and permissive to MV infection. Efficient MV replication led to death of melanoma cells, but the effect differed among the cell lines. Possible mechanisms mediating the selectivity of MV L-3 towards the cell lines were explored. Replicative and oncolytic activity of MV was found to depend on the expression pattern of type I interferon genes. None of the melanoma cell lines showed induction of expression of the total spectrum of genes required to inhibit virus replication. Based on the results, MV L-3 was assumed to be a promising oncolytic agent for human melanoma cells.

In this letter, we introduce an asymmetric adaptation neural network (AANN) method for cross-domain classification in remote sensing images. Before the adaptation process, we feed the features obtained from a pretrained convolutional neural network to a denoising autoencoder (DAE) to perform dimensionality reduction. Then the first hidden layer of AANN (placed on the top of DAE) maps the labeled source data to the target space, while the subsequent layers control the separation between the available land-cover classes. To learn its weights, the network minimizes an objective function composed of two losses related to the distance between the source and target data distributions and class separation. The results of experiments conducted on six scenarios built from three benchmark scene remote sensing data sets (i.e., Merced, KSA, and AID data sets) are reported and discussed.