Université Ibn Zohr

To select candidate populations of wild species to be given priority for conservation, genetic criteria gained from the study of molecular markers may be useful. Traditionally, diversity measures such as expected heterozygosity or percentage of polymorphic loci have been considered. For conservation we propose instead that priority should be given to measures of allelic richness. To standardize the results of allelic richness across populations, we used the technique of rarefaction. This technique allows evaluation of the expected number of different alleles among equal‐sized samples drawn from several different populations. We also show how the contribution of each population to total diversity can be partitioned into two components. The first is related to the level of diversity of the population and the second to its divergence from the other populations. For conservation purposes the uniqueness of a population—in terms of its allelic composition—may be at least as important as its diversity level. These new descriptors are illustrated by means of isozyme and chloroplast DNA data obtained for an endangered tree species, the argan tree of Morocco ( Argania spinosa (L.) Skeels). With these analyses the conservation value of the argan tree populations, especially those of two isolates present in the north of the country, can be better appreciated. The methods proposed to identify priority areas for conservation of the genetic resources of the argan tree are compared to those sometimes advocated in the case of reserve design, where one of the goals is to maximize species richness. Identificacón de Poblaciones para su Conservación en Base a Marcadores Genéticos Los criterios genéticos obtenidos del estudio de marcadores moleculares podrían ser útiles para seleccionar poblaciones de vida silvestre como candidatos con prioridad para su conservación. Tradicionalmente se consideran medidas de la diversidad como son la heterocigocidad esperada, o el porcentaje de loci polimórficos. Para medidas de conservación, nosotros proponemos en su lugar que la prioridad se enfoque en medidas de riqueza alélica. Para estandarizar los resultados de riqueza alélica entre problaciones, utilizamos una técnica de vacuidad. Esta técnica permite evaluar el número esperado de alelos entre muestras de igual tamaño obtenidas de diferentes poblaciones. Mostramos como la contribución de cada población a la diversidad total puede ser repartida en dos componentes; el primero esta relacionado con el nivel de diversidad de la población y el segundo con su divergencia de las otras poblaciones. Para propósitos de conservación, la singularidad de una población (en forma de composición alélica) puede ser por lo menos tan importante como lo es su nivel de diversidad, Estos nuevos elementos descriptivos son ilustrados mediante el uso de datos de DNA de isozima y cloroplasto para una especie de árbol en peligro, el árbol argan de Morocco ( Argania spinosa (L.) Skeels). Con estos análisis, el valor de conservación de las poblaciones del árbol argan puede ser apreciado mejor, especialmente para aquellas provenientes de dos grupos aislados del norte del país. Los métodos propuestos para la identificación de áreas prioritarias para la conservación de los recursos genéticos del árbol argan son comparados con aquellos utilizados en el diseño de reservas, donde una de las metas es la maximización de la riqueza de especies.

Atomically 2D thin-layered structures, such as graphene nanosheets, graphitic carbon nitride nanosheets (g-C3N4), hexagonal boron nitride, and transition metal dichalcogenides are emerging as fascinating materials for a good array of domains owing to their rare physicochemical characteristics. In particular, graphitic carbon nitride has turned into a hot subject in the scientific community due to numerous qualities such as simple preparation, electrochemical properties, high adsorption capacity, good photochemical properties, thermal stability, and acid-alkali chemical resistance, etc. Basically, g-C3N4 is considered as a polymeric material consisting of N and C atoms forming a tri-s-triazine network connected by planar amino groups. In comparison with most C-based materials, g-C3N4 possesses electron-rich characteristics, basic moieties, and hydrogen-bonding groups owing to the presence of hydrogen and nitrogen atoms; therefore, it is taken into account as an interesting nominee to further complement carbon in applications of functional materials. Nevertheless, g-C3N4 has some intrinsic limitations and drawbacks mainly related to a relatively poor specific surface area, rapid charge recombination, a limited light absorption range, and a poor dispersibility in both aqueous and organic mediums. To overcome these shortcomings, numerous chemical modification approaches have been conducted with the aim of expanding the range of application of g-C3N4 and enhancing its properties. In the current review, the comprehensive survey is conducted on g-C3N4 chemical functionalization strategies including covalent and noncovalent approaches. Covalent approaches consist of establishing covalent linkage between the g-C3N4 structure and the chemical modifier such as oxidation/carboxylation, amidation, polymer grafting, etc., whereas the noncovalent approaches mainly consist of physical bonding and intermolecular interaction such as van der Waals interactions, electrostatic interactions, π–π interactions, and so on. Furthermore, the preparation, characterization, and diverse applications of functionalized g-C3N4 in various domains are described and recapped. We believe that this work will inspire scientists and readers to conduct research with the aim of exploring other functionalization strategies for this material in numerous applications.

The ternary diagram TiO 2 –FeO*–MgO (FeO* = FeO + MnO) is proposed as a quantitative objective tool for distinguishing between primary magmatic biotites and those that are more or less reequilibrated, or possibly neoformed, by or within a hydrothermal fluid. The limit of the domains of the primary magmatic biotites, the reequilibrated biotites and the neoformed biotites were determined on the basis of optical, paragenetic and chemical criteria.

Stigmasterol is an unsaturated phytosterol belonging to the class of tetracyclic triterpenes. It is one of the most common plant sterols, found in a variety of natural sources, including vegetable fats or oils from many plants. Currently, stigmasterol has been examined via in vitro and in vivo assays and molecular docking for its various biological activities on different metabolic disorders. The findings indicate potent pharmacological effects such as anticancer, anti-osteoarthritis, anti-inflammatory, anti-diabetic, immunomodulatory, antiparasitic, antifungal, antibacterial, antioxidant, and neuroprotective properties. Indeed, stigmasterol from plants and algae is a promising molecule in the development of drugs for cancer therapy by triggering intracellular signaling pathways in numerous cancers. It acts on the Akt/mTOR and JAK/STAT pathways in ovarian and gastric cancers. In addition, stigmasterol markedly disrupted angiogenesis in human cholangiocarcinoma by tumor necrosis factor-α (TNF-α) and vascular endothelial growth factor receptor-2 (VEGFR-2) signaling down-regulation. The association of stigmasterol and sorafenib promoted caspase-3 activity and down-regulated levels of the anti-apoptotic protein Bcl-2 in breast cancer. Antioxidant activities ensuring lipid peroxidation and DNA damage lowering conferred to stigmasterol chemoprotective activities in skin cancer. Reactive oxygen species (ROS) regulation also contributes to the neuroprotective effects of stigmasterol, as well as dopamine depletion and acetylcholinesterase inhibition. The anti-inflammatory properties of phytosterols involve the production of anti-inflammatory cytokines, the decrease in inflammatory mediator release, and the inhibition of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2). Stigmasterol exerts anti-diabetic effects by reducing fasting glucose, serum insulin levels, and oral glucose tolerance. Other findings showed the antiparasitic activities of this molecule against certain strains of parasites such as Trypanosoma congolense (in vivo) and on promastigotes and amastigotes of the Leishmania major (in vitro). Some stigmasterol-rich plants were able to inhibit Candida albicans, virusei, and tropicalis at low doses. Accordingly, this review outlines key insights into the pharmacological abilities of stigmasterol and the specific mechanisms of action underlying some of these effects. Additionally, further investigation regarding pharmacodynamics, pharmacokinetics, and toxicology is recommended.

In the last two decades, research in the field of corrosion inhibitors had been directed toward the goal of using cheap effective molecules of low or non-negative environmental impact to replace the environmentally hazardous compounds. One of the encourager compounds which can be used as safe corrosion inhibitors are amino acids. They are environmentally friendly, non-toxic, biodegradable and relatively cheap. On other hand, the development of computational modeling helps to understand the inhibition mechanism of those compounds and to develop the newly designed inhibitors. In this review, most of contribution made in literature on the use of amino acids and their derivatives as corrosion inhibitors for metallic alloys materials were presented and discussed.

Hybrid organic-inorganic perovskite solar cells (PSCs) are the novel fourth-generation solar cells, with impressive progress in the last few years. MAPbI3 is a cost-effective material used as an absorber layer in PSCs. Due to the different diffusion length of carriers, the electron transporting material (ETM) plays a vital role in PSCs' performance. ZnO ETM is a promising candidate for low-cost and high-efficiency photovoltaic technology. In this work, the normal n-i-p planar heterojunction structure has been simulated using SCAPS-1D. The influence of various parameters such as the defect density, the thickness of the MAPbI3 layer, the temperature on fill factor, the open-circuit voltage, the short circuit current density, and the power conversion efficiency are investigated and discussed in detail. We found that a 21.42% efficiency can be obtained under a thickness of around 0.5 μm, and a total defect of 1013 cm−3 at ambient temperature. These simulation results will help fabricate low-cost, high-efficiency, and low-temperature PSCs.

BACKGROUND: Investigations into both the pathophysiology and therapeutic targets in muscle dystrophies have been hampered by the limited proliferative capacity of human myoblasts. Isolation of reliable and stable immortalized cell lines from patient biopsies is a powerful tool for investigating pathological mechanisms, including those associated with muscle aging, and for developing innovative gene-based, cell-based or pharmacological biotherapies. METHODS: Using transduction with both telomerase-expressing and cyclin-dependent kinase 4-expressing vectors, we were able to generate a battery of immortalized human muscle stem-cell lines from patients with various neuromuscular disorders. RESULTS: The immortalized human cell lines from patients with Duchenne muscular dystrophy, facioscapulohumeral muscular dystrophy, oculopharyngeal muscular dystrophy, congenital muscular dystrophy, and limb-girdle muscular dystrophy type 2B had greatly increased proliferative capacity, and maintained their potential to differentiate both in vitro and in vivo after transplantation into regenerating muscle of immunodeficient mice. CONCLUSIONS: Dystrophic cellular models are required as a supplement to animal models to assess cellular mechanisms, such as signaling defects, or to perform high-throughput screening for therapeutic molecules. These investigations have been conducted for many years on cells derived from animals, and would greatly benefit from having human cell models with prolonged proliferative capacity. Furthermore, the possibility to assess in vivo the regenerative capacity of these cells extends their potential use. The innovative cellular tools derived from several different neuromuscular diseases as described in this report will allow investigation of the pathophysiology of these disorders and assessment of new therapeutic strategies.

In semi-arid areas, many ecosystems and activities depend essentially on water availability. In Morocco, the increase of water demands combined to climate change induced decrease of precipitation put a lot of pressure on groundwater. This paper reports the results of updating and evaluation of groundwater datasets with regards to climate scenarios and institutional choices. The continuous imbalance between groundwater extraction and recharge caused a dramatic decline in groundwater levels (20 to 65 m in the past 30 years). Additionally, Morocco suffers from the degradation in groundwater quality due to seawater intrusion, nitrate pollution and natural salinity changes. Climate data analysis and scenarios predict that temperatures will increase by 2 to 4 °C and precipitation will decrease by 53% in all catchments over this century. Consequently, surface water availability will drastically decrease, which will lead to more extensive use of groundwater. Without appropriate measures, this situation will jeopardize water security in Morocco. In this paper, we zoom on the case the Souss-Massa basin, where management plans (artificial recharge, seawater desalination, and wastewater reuse) have been adopted to restore groundwater imbalance or, at least, mitigate the recorded deficits. These plans may save water for future generations and sustain crop production.

Detecting objects remains one of computer vision and image understanding applications’ most fundamental and challenging aspects. Significant advances in object detection have been achieved through improved object representation and the use of deep neural network models. This paper examines more closely how object detection has evolved in the era of deep learning over the past years. We present a literature review on various state-of-the-art object detection algorithms and the underlying concepts behind these methods. We classify these methods into three main groups: anchor-based, anchor-free, and transformer-based detectors. Those approaches are distinct in the way they identify objects in the image. We discuss the insights behind these algorithms and experimental analyses to compare quality metrics, speed/accuracy tradeoffs, and training methodologies. The survey compares the major convolutional neural networks for object detection. It also covers the strengths and limitations of each object detector model and draws significant conclusions. We provide simple graphical illustrations summarising the development of object detection methods under deep learning. Finally, we identify where future research will be conducted.

Heterogeneous Fenton-like oxidation is one of the advanced oxidation processes (AOPs) that can successfully remove a large number of pollutants from water. Among various materials used as catalysts in this process, spinel ferrite nanoparticles (SFs) have received increasing attention in recent years because of their unique physicochemical properties, low-cost, good catalytic activity and interesting bandgap. In the same vein, their magnetic properties allow them to be easily, rapidly and inexpensively separated from the reaction medium. The present review highlights the unique structure and properties of different spinel ferrite nanoparticles, their synthesis approaches and their applications as a catalyst in heterogeneous Fenton-like and photo-Fenton-like oxidation. It was demonstrated that MFe2O4 have unlimited ability as catalysts in both heterogeneous Fenton-like and photo-Fenton-like oxidation. Thus, it can be deduced that their catalytic activity depends on the nature of divalent metal M, synthesis method, annealing temperature, pollutant nature and concentration, H2O2 concentration, pH, temperature and light source for photo-Fenton-like oxidation.

Crop diseases constitute a serious issue in agriculture, affecting both quality and quantity of agriculture production. Disease control has been a research object in many scientific and technologic domains. Technological advances in sensors, data storage, computing resources and artificial intelligence have shown enormous potential to control diseases effectively. A growing body of literature recognizes the importance of using data from different types of sensors and machine learning approaches to build models for detection, prediction, analysis, assessment, etc. However, the increasing number and diversity of research studies requires a literature review for further developments and contributions in this area. This paper reviews state-of-the-art machine learning methods that use different data sources, applied to plant disease detection. It lists traditional and deep learning methods associated with the main data acquisition modalities, namely IoT, ground imaging, unmanned aerial vehicle imaging and satellite imaging. In addition, this study examines the role of data fusion for ongoing research in the context of disease detection. It highlights the advantage of intelligent data fusion techniques, from heterogeneous data sources, to improve plant health status prediction and presents the main challenges facing this field. The study concludes with a discussion of several current issues and research trends.

The postharvest diseases of citrus fruit cause considerable losses during storage and transportation. These diseases are managed principally by the application of synthetic fungicides. However, the increasing concern for health hazards and environmental pollution due to chemical use has required the development of alternative strategies for the control of postharvest citrus diseases. Management of postharvest diseases using microbial antagonists, natural plant-derived products and Generally Recognized As Safe compounds has been demonstrated to be most suitable to replace the synthetic fungicides, which are either being banned or recommended for limited use. However, application of these alternatives by themselves may not always provide a commercially acceptable level of control of postharvest citrus diseases comparable to that obtained with synthetic fungicides. To provide more effective disease control, a multifaceted approach based on the combination of different postharvest treatments has been adopted. Actually, despite the distinctive features of these alternative methods, several reasons hinder the commercial use of such treatments. Consequently, research should emphasize the development of appropriate tools to effectively implement these alternative methods to commercial citrus production.

BACKGROUND: Transient Receptor Potential (TRP) channels are expressed in many solid tumors. However, their expression in breast cancer remains largely unknown. Here, we investigated the profile expression of 13 TRP channels in human breast ductal adenocarcinoma (hBDA) and performed a correlation between their overexpression and pathological parameters. METHODS: The TRP channels expression was determined by RT-PCR in hBDA tissue, in human breast cancer epithelial (hBCE) primary culture and in MCF-7 cell line. The TRP protein level was evaluated by immunohistochemistry in hBDA tissue samples of 59 patients. RESULTS: TRPC1, TRPC6, TRPM7, TRPM8, and TRPV6 channels were overexpressed in hBDA compared to the adjacent non-tumoral tissue. Most interestingly, TRPC1, TRPM7 and TRPM8 expression strongly correlated with proliferative parameters (SBR grade, Ki67 proliferation index, and tumor size), and TRPV6 was mainly overexpressed in the invasive breast cancer cells. Using laser capture microdissection, we found that TRPV6 expression was higher in invasive areas, compared to the corresponding non-invasive ones. Moreover, TRPV6 silencing inhibited MDA-MB-231 migration and invasion, and MCF-7 migration. CONCLUSION: TRP channels are aberrantly expressed in hBDA, hBCE primary cultures, and cell lines, and associated with pathological parameters. The high expression of TRP channels in tumors suggests the potential of these channels for diagnostic, prognosis and/or therapeutic approaches in human breast ductal adenocarcinoma.

Polymorphisms in the chloroplast genome of the argan tree (Sapotaceae), an endemic species of south-western Morocco, have been detected by restriction site studies of PCR-amplified fragments. A total of 12 chloroplast DNA (cpDNA) and two mitochondrial DNA (mtDNA) fragments were amplified and digested with a single restriction enzyme (HinfI). Polymorphisms were identified in six of the cpDNA fragments, whereas no mtDNA polymorphisms were detected in a survey of 95 individuals from 19 populations encompassing most of the natural range of the species. The cpDNA polymorphisms allowed the identification of 11 haplotypes. Two lineages, one in the south-east and the other in the north-west, divide the range of the argan tree into two distinct areas. The level of genetic differentiation measured at the haplotype level (GSTc = 0.60) (i.e. with unordered haplotypes) was smaller than when phylogenetic relationships were taken into account (NSTc = 0.71-0.74) (ordered haplotypes), indicating that population history must be considered in the study of the geographical distribution of cpDNA lineages in this species. If contrasted with the level of nuclear genetic differentiation measured in a previous study with isozymes (GSTn = 0.25), the results indicate a relatively high level of gene flow by seeds, or conversely a relatively low level of gene flow by pollen, as compared with other tree species. Goats and camels could have played an important role in disseminating the fruits of this tree.

Covid-19 is an acute respiratory disease caused by Coronavirus Sars-Cov-2. Declared recently as pandemic disease, Covid-19 has affected educational systems worldwide. Many countries around the world have closed educational institutions to reduce the spread of this pandemic. Hence, Education in high schools is facing unprecedented challenges. This paper reflects on the role of mobile learning as remote teaching strategies sustaining student-centered learning. The use of mobile learning allows learning anytime, anyplace, and anywhere. Mobile Learning is an unavoidable alternative during COVID-19.

This review presents recent technologies involved in vegetable oil refining as well as quality attributes of crude oils obtained by mechanical and solvent extraction. Usually, apart from virgin oils, crude oils cannot be consumed directly or incorporated into various food applications without technological treatments (refining). Indeed, crude oils like soybean, rapeseed, palm, corn, and sunflower oils must be purified or refined before consumption. The objective of such treatments (chemical and physical refining) is to get a better quality, a more acceptable aspect (limpidity), a lighter odor and color, longer stability, and good safety through the elimination of pollutants while minimizing oil loss during processing. However, the problem is that refining removes some essential nutrients and often generates other undesirable compounds such as 3-MCPD-esters and trans-fatty acids. These compounds directly influence the safety level of refined oil. Advantages and drawbacks of both chemical and physical refining were discussed in the light of recent literature. Physical refining has several advantages over chemical one.

The objective of this research work was to determine the characteristic features of the oil content and composition of nutrients of sesame seeds grown in Morocco. Characteristic features of the seed oil revealed a high degree of unsaturation and as determined by gas chromatography reported herein, the major unsaturated fatty acids were linoleic acid (46.9%) followed by oleic acid (37.4%), while the main saturated fatty acid was palmitic acid (9.1%). Sesame seed oil was also found to be rich in tocopherols with a predominance of γ-tocopherol (90.5%). The phytosterol marker β-sitosterol accounted for 59.9% of total sterols contained in sesame seed oil. This oil, therefore, has a potential for its use in human nutrition or industrial applications. Compositional analysis revealed that the sesame seeds contained considerable amounts of protein (22%) and high amounts of lipids (52%). Nutrient information reported herein illustrates the benefits to public health for consumers of these plant seeds. In terms of oil, sesame seed oil may be considered as a valuable source for new multi-purpose products as industrial, cosmetic, and pharmaceutical uses.

Supply chain viability (SCV) is an emerging concept of growing importance in operations management. This paper aims to conceptualize, develop, and validate a measurement scale for SCV. SCV is first defined and operationalized as a construct, followed by content validation and item measure development. Data have been collected through three independent samplings comprising a total of 558 respondents. Both exploratory and confirmatory factor analyses are used in a step-wise manner for scale development. Reliability and validity are evaluated. A nomological model is theorized and tested to evaluate nomological validity. For the first time, our study frames SCV as a novel and distinct construct. The findings show that SCV is a hierarchical and multidimensional construct, reflected in organizational structures, organizational resources, dynamic design capabilities, and operational aspects. The findings reveal that a central characteristic of SCV is the dynamic reconfiguration of SC structures in an adaptive manner to ensure survival in the long-term perspective. This research conceptualizes and provides specific, validated dimensions and item measures for SCV. Practitioner directed guidance and suggestions are offered for improving SCV during the COVID-19 pandemic and future severe disruptions.

Nowadays, network intrusion is considered as one of the major concerns in network communications. Thus, the developed network intrusion detection systems aim to identify attacks or malicious activities in a network environment. Various methods have been already proposed for finding an effective and efficient solution to detect and prevent intrusion in the network, ensuring network security and privacy. Machine learning is an effective analysis framework to detect any anomalous events occurred in the network traffic flow. Based on this framework, the paper in hand evaluates the performance of four well-known classification algorithms; SVM, Naïve Bayes, Decision Tree and Random Forest using Apache Spark, a big data processing tool for intrusion detection in network traffic. The overall performance comparison is evaluated in terms of detection accuracy, building time and prediction time. Experimental results on UNSW-NB15, a recent public dataset for network intrusion detection, show an important advantage for Random Forest classifier among other well-known classifiers in terms of detection accuracy and prediction time, using the complete dataset with all 42 features.

Nowadays, water pollution has been considered a global concern on environmental sustainability, calling for high-performance materials in effective pollution treatments. Adsorption approach has great potential to eliminate persistent inorganic and organic compounds. Activated carbon (AC) materials including their composite materials have been largely investigated under various experimental conditions as low-cost, promising adsorbents to remove contaminants from water resources. In this review, the authors report the most recent development in activated carbon materials for the removal of organic and inorganic pollutants and its modeling counterpart using response surface methodology (RSM) statistical calculations. We also highlights up-to-date studies for the removal of organic pollutants and heavy metals from water using activated carbon materials as adsorbents with a focus on structure-to-properties and the effects of surface functions on adsorption processes. This review also summarizes for the first time the advantages and disadvantages of RSM method in the investigations related to adsorption of pollutants to assess the potential opportunities and challenges for the application of activated carbon materials in wastewater treatment. The critical analyses and conclusions highlighted in this present study should be of benefit to environmental scientists, chemists and engineers interested in the use of AC and optimization tools in environmental remediation.