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Barley is an important crop worldwide known for its adaptation to harsh environments and used in multiple forms as feed, food and beverages. Its productivity is affected by major abiotic and biotic stresses. Scald caused by hemibiotrophic fungus Rhynchosporium commune is a major foliar disease in many parts of the world. Host plant resistance is targeted by breeders to efficiently control this disease. An association mapping panel of 316 spring barley genotypes (AM2017) was screened for seedling resistance in greenhouse against three R. commune isolates and for adult plant resistance in three field locations in Morocco. The phenotyping results showed different numbers of entries with resistant and moderately resistant reactions at both seedling and adult plant stages. The reactions differed between the isolates with the highest percentage of resistant genotypes observed for isolate SC-S611 (49.4%) and highest percentage of susceptible genotypes (73.8%) for isolate SC-1122. At adult plant stage, the highest percentage of scald resistant genotypes (64.5%) was observed at Rommani site compared to 56% at Guich site and only 28.8% at Marchouch site. Seven genotypes were resistant at the seedling and adult plant stages. Genome wide association study (GWAS) revealed 102 MTA (15 QTL) at the seedling stage, and 25 MTA (12 QTL) associated with scald resistance at the adult plant stage. In addition, the sequences of 92 out of 102 at SRT, and 24 out of 25 significant SNP markers at APR were located in genomic regions enriched with functional proteins involved in diverse cellular processes including disease resistance. These markers span over all chromosomes with the majority of SNPs located on 3H and 7H. This study has verified 18 QTL reported in previous studies. In addition, it was successful in identifying new sources of resistance and novel genomic regions which could help in enhancing scald resistance in barley breeding programs.

Cytotoxic lymphocytes (CLs), specifically cytotoxic T lymphocytes and natural killer cells, are indispensable guardians of the immune system and orchestrate the recognition and elimination of cancer cells. Upon encountering a cancer cell, CLs establish a specialized cellular junction, known as the immunological synapse that stands as a pivotal determinant for effective cell killing. Extensive research has focused on the presynaptic side of the immunological synapse and elucidated the multiple functions of the CL actin cytoskeleton in synapse formation, organization, regulatory signaling, and lytic activity. In contrast, the postsynaptic (cancer cell) counterpart has remained relatively unexplored. Nevertheless, both indirect and direct evidence has begun to illuminate the significant and profound consequences of cytoskeletal changes within cancer cells on the outcome of the lytic immunological synapse. Here, we explore the understudied role of the cancer cell actin cytoskeleton in modulating the immune response within the immunological synapse. We shed light on the intricate interplay between actin dynamics and the evasion mechanisms employed by cancer cells, thus providing potential routes for future research and envisioning therapeutic interventions targeting the postsynaptic side of the immunological synapse in the realm of cancer immunotherapy. This review article highlights the importance of actin dynamics within the immunological synapse between cytotoxic lymphocytes and cancer cells focusing on the less-explored postsynaptic side of the synapse. It presents emerging evidence that actin dynamics in cancer cells can critically influence the outcome of cytotoxic lymphocyte interactions with cancer cells.

Fraudulent internet transactions have caused considerable harm and losses for both people and organizations over time. The growth of cutting-edge technology and worldwide connectivity has exacerbated the rise in online fraud instances. To offset these losses, robust fraud detection systems must be developed. ML and statistical approaches are critical components in properly recognizing fraudulent transactions. However, implementing fraud detection models presents challenges such as limited data availability, data sensitivity, and imbalanced class distributions. The confidentiality of records adds complexity to drawing inferences and constructing improved models in this domain. This research explores multiple algorithms suitable for classifying transactions as either genuine or fraudulent using the Credit Card Fraud dataset. Given the extremely unbalanced nature of the dataset, the SMOTE approach was used for oversampling to alleviate the class distribution imbalance. In addition, feature selection was carried out, and the dataset was divided into training and test data. The experiments utilized NB, RF, and MLP algorithms, all of which demonstrated high accuracy in detecting credit card fraud. MLP method achieved 99.95% accuracy as compared to other methods

Streamflow prediction is crucial for planning future developments and safety measures along river basins, especially in the face of changing climate patterns. In this study, we utilized monthly streamflow data from the United States Bureau of Reclamation and meteorological data (snow water equivalent, temperature, and precipitation) from the various weather monitoring stations of the Snow Telemetry Network within the Upper Colorado River Basin to forecast monthly streamflow at Lees Ferry, a specific location along the Colorado River in the basin. Four machine learning models—Random Forest Regression, Long short-term memory, Gated Recurrent Unit, and Seasonal AutoRegresive Integrated Moving Average—were trained using 30 years of monthly data (1991–2020), split into 80% for training (1991–2014) and 20% for testing (2015–2020). Initially, only historical streamflow data were used for predictions, followed by including meteorological factors to assess their impact on streamflow. Subsequently, sequence analysis was conducted to explore various input-output sequence window combinations. We then evaluated the influence of each factor on streamflow by testing all possible combinations to identify the optimal feature combination for prediction. Our results indicate that the Random Forest Regression model consistently outperformed others, especially after integrating all meteorological factors with historical streamflow data. The best performance was achieved with a 24-month look-back period to predict 12 months of streamflow, yielding a Root Mean Square Error of 2.25 and R-squared (R2) of 0.80. Finally, to assess model generalizability, we tested the best model at other locations—Greenwood Springs (Colorado River), Maybell (Yampa River), and Archuleta (San Juan) in the basin.

Scorpion envenomation poses a global public health issue, with an estimated 1,500,000 cases worldwide annually resulting in 2600 deaths. North Africa, particularly Morocco, experiences severe envenomations, mainly attributed to Androctonus mauretanicus and Buthus occitanus in Morocco, and Buthus occitanus and Androctonus australis hector in Algeria and Tunisia, with case numbers often underestimated. Current treatment relies mainly on symptomatic approaches, except in Morocco, where management is limited to symptomatic treatment due to controversies regarding specific treatment. In Morocco, between 30,000 and 50,000 scorpion envenomation cases are reported annually, leading to hundreds of deaths, mainly among children. Controversies among clinicians persist regarding the appropriate course of action, often limiting treatments to symptomatic measures. The absence of a specific antivenom for the venoms of the most lethal scorpions further exacerbates the situation. This study aims to address this gap by developing a monovalent antivenom against the endemic and most dangerous scorpion, Androctonus mauretanicus. The antivenom was produced by immunizing albino rabbits with a mixture of Androctonus mauretanicus venom collected from high-risk areas in Morocco. Immunizations were performed by subcutaneous injections at multiple sites near the lymphatic system, following an immunization schedule. Production control of neutralizing antibody titers was conducted through immunodiffusion. Once a sufficient antibody titer was achieved, blood collection was performed, and the recovered plasma underwent affinity chromatography. The efficacy of purified IgG was evaluated by determining the ED50 in mice, complemented by histological and immunohistochemical studies on its ability to neutralize venom-induced tissue alterations and the neutralization of toxins bound to receptors in the studied organs. The monovalent antivenom demonstrated specificity against Androctonus mauretanicus venom and effective cross-protection against the venom of the scorpions Buthus occitanus and Androctonus australis hector, highly implicated in lethal envenomations in the Maghreb. This study shows that the developed monovalent antivenom exhibits notable efficacy against local scorpions and a surprising ability to neutralize the most lethal envenomations in North Africa. These results pave the way for a new, more specific, and promising therapeutic approach to countering severe scorpion envenomations, especially in Morocco, where specific treatment is lacking.

Solar flares are characterized by sudden bursts of electromagnetic radiation from the Sun’s surface, and are caused by the changes in magnetic field states in active solar regions. Earth and its surrounding space environment can suffer from various negative impacts caused by solar flares, ranging from electronic communication disruption to radiation exposure-based health risks to astronauts. In this paper, we address the solar flare prediction problem from magnetic field parameter-based multivariate time series (MVTS) data using multiple state-of-the-art machine learning classifiers that include MINImally RandOm Convolutional KErnel Transform (MiniRocket), Support Vector Machine (SVM), Canonical Interval Forest (CIF), Multiple Representations Sequence Learner (Mr-SEQL), and a Long Short-Term Memory (LSTM)-based deep learning model. Our experiment is conducted on the Space Weather Analytics for Solar Flares (SWAN-SF) benchmark data set, which is a partitioned collection of MVTS data of active region magnetic field parameters spanning over nine years of operation of the Solar Dynamics Observatory (SDO). The MVTS instances of the SWAN-SF dataset are labeled by GOES X-ray flux-based flare class labels, and attributed to extreme class imbalance because of the rarity of the major flaring events (e.g., X and M). As a performance validation metric in this class-imbalanced dataset, we used the True Skill Statistic (TSS) score. Finally, we demonstrate the advantages of the MVTS learning algorithm MiniRocket, which outperformed the aforementioned classifiers without the need for essential data preprocessing steps such as normalization, statistical summarization, and class imbalance handling heuristics.

Solar wind modeling is classified into two main types: empirical models and physics-based models, each designed to forecast solar wind properties in various regions of the heliosphere. Empirical models, which are cost-effective, have demonstrated significant accuracy in predicting solar wind at the L1 Lagrange point. On the other hand, physics-based models rely on magnetohydrodynamics (MHD) principles and demand more computational resources. In this research paper, we build upon our recent novel approach that merges empirical and physics-based models. Our recent proposal involves the creation of a new physics-informed neural network that leverages time series data from solar wind predictors to enhance solar wind prediction. This innovative method aims to combine the strengths of both modeling approaches to achieve more accurate and efficient solar wind predictions. In this work, we show the variability of the proposed physics-informed loss across multiple deep learning models. We also study the effect of training the models on different solar cycles on the model’s performance. This work represents the first effort to predict solar wind by integrating deep learning approaches with physics constraints and analyzing the results across three solar cycles. Our findings demonstrate the superiority of our physics-constrained model over other unconstrained deep learning predictive models.