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The increase in water demand and the scarcity of fresh water in arid regions have contributed to the depletion of groundwater. Artificial Groundwater Recharge (AGR) is an advanced strategy that contributes to combating water shortage issues. Limited efforts have been exerted to evaluate and demarcate AGR potential zones in the United Arab Emirates (UAE). The current study aims to delineate AGR potential zone mapping using the traditional analytical hierarchy process (AHP) and a hybrid deep learning model namely, Convolutional Neural Network-Xtreme Gradient Boosting (CNN-XGB) was used for the optimal prediction-based suitability assessment. A total of nine hydrogeological factors were considered for AGR mapping. First, the influence of each parameter was determined based on expert opinion and literature reviews for the AHP approach (0.007 consistency ratio). Second, a hybrid CNN-XGB model (90.8 % accuracy) predicted the AGR and non-AGR classes as part of binary classification and generated an AGR potential zone map. Moreover, the contributing factors were analyzed deeply for the AGR site selection to understand the intercorrelation, importance, and prediction interaction. Using both approaches, a comparative assessment was conducted in the eastern, central, and western parts of Sharjah. The AGR zone based on the CNN-XGB model achieved a precision of (0.8168), recall (0.7873), and F1-score (0.8018). The critical contributing factors for AGR mapping were found to be geology (20%), geomorphology (15%), rainfall (10%), and groundwater level (10%). The AGR map is expected to help explore new sites with potentially higher favourability to retain water, deal with water scarcity, and improve water management in the UAE.

The dual use of wind and solar energy holds great promise for low-cost and high-performance green infrastructure. However, for such hybrid systems to operate successfully, comprehensive and simultaneous dimensional planning is required, a goal that single-perspective assessment approaches fail to attain. This paper proposes a novel SpatioTemporal Decision-Making (STDM) model based on Geospatial Artificial Intelligence (GeoAI) for the optimal allocation of onshore wind-solar hybrid plants, with application on a national scale in Iraq. To this end, a wide range of 21 evaluative and restrictive spatial criteria were covered. The temporal synergy factor between renewable resources was considered for the first time in this type of study. Unique global weightings for decision factors were derived using Random Forest (RF) and SHapley Additive exPlanations (SHAP) algorithms supported by sample inventories of wind and solar plants worldwide. Finally, weighted linear combination (WLC) and fuzzy overlay techniques were harnessed in a GIS environment for spatiotemporal suitability mapping of energy systems. According to the RF-SHAP model, the techno-economic criteria demonstrated substantial contributions to the placement of wind and solar systems compared with the socio-environmental criteria. The spatiotemporal suitability map identified three promising opportunities for Iraq at South Dhi-Qar, East Wasit, and West Diyala, with total areas of 780, 2166, and 649 km2, respectively. We anticipate that our findings will encourage government agencies, decision-makers, and stakeholders to increase funding for clean energy transition initiatives.

Date palm plantations in the United Arab Emirates (UAE) are under threat from soil salinity, drought, and date palm weevils. Accordingly, monitoring and conserving date palms are crucial to preserving a vital component of the country’s agricultural heritage, economy, food security, and ecological balance. Previous studies have effectively identified date palm trees using RGB-based aerial and UAV imagery utilizing diverse deep learning methods. However, the utilization of very high-resolution satellite data for delineating individual date palm crowns remains unexplored due to the limited spatial resolution capabilities of existing satellite systems. This study primarily aimed to achieve precise and comprehensive mapping of date palm trees using WorldView-3 (WV-3) satellite data by leveraging the high representational power of the state-of-the-art vision transformers (ViT) in capturing global information from the input data. First, an in-depth analysis assessment of the various transformer-based semantic segmentation architectures, including UperNet with vision transformer and Swin transformer, SegFormer, Mask2Former, and UniFormer, was conducted. Second, the integration of spectral data on the performance of ViTs was evaluated. Moreover, the models’ generalizability and complexity effect on the segmentation effectiveness were assessed. Accordingly, a postprocessing strategy was developed to aid in delineating and counting date palm trees from semantic segmentation outputs. Results demonstrated that integration of WV-3 spectral data into the analysis resulted in a marked improvement in segmentation quality. The UniFormer, UperNet-Swin, and Mask2Former models demonstrated considerable improvements in multispectral data analysis, with increases in mean intersection over union (mIoU) of 2.17% (77.88% mIoU, 86.01% mean F-score [mF-score]), 2% (78.10% mIoU, 86.18% mF-score), and 1.15% (77.36% mIoU, 85.59% mF-score), respectively, compared with their RGB-based results. Evaluations of model transferability also indicated that Mask2Former, UniFormer, and UperNet-Swin transformers efficiently adapted to multispectral data in the Dibba region. These models achieved mIoU scores of 84.36%, 84.25%, and 83.17% and mF-scores of 90.95%, 90.87%, and 90.13%, highlighting their effectiveness and potential for broader regional application. This research highlights the efficacy and feasibility of using ViTs with WV-3 multispectral data for accurate and comprehensive surveying of date palm plantations, enabling the development of palm tree inventories and continuously updating geospatial databases.

Abstract Background The rhizosheath, a cohesive soil layer firmly adhering to plant roots, plays a vital role in facilitating water and mineral uptake. In pearl millet, rhizosheath formation is genetically controlled and influenced by root exudates. Here, we investigated the impact of root exudates on the microbiota composition, interactions, and assembly processes, and rhizosheath structure in pearl millet using four distinct lines with contrasting soil aggregation abilities. Results Utilizing 16S rRNA gene and ITS metabarcoding for microbiota profiling, coupled with FTICR-MS metabonomic analysis of metabolite composition in distinct plant compartments and root exudates, we revealed substantial disparities in microbial diversity and interaction networks. The ß-NTI analysis highlighted bacterial rhizosphere turnover driven primarily by deterministic processes, showcasing prevalent homogeneous selection in root tissue (RT) and root-adhering soil (RAS). Conversely, fungal communities were more influenced by stochastic processes. In bulk soil assembly, a combination of deterministic and stochastic mechanisms shapes composition, with deterministic factors exerting a more pronounced role. Metabolic profiles across shoots, RT, and RAS in different pearl millet lines mirrored their soil aggregation levels, emphasizing the impact of inherent plant traits on microbiota composition and unique metabolic profiles in RT and exudates. Notably, exclusive presence of antimicrobial compounds, including DIMBOA and H-DIMBOA, emerged in root exudates and RT of low aggregation lines. Conclusions This research underscores the pivotal influence of root exudates in shaping the root-associated microbiota composition across pearl millet lines, entwined with their soil aggregation capacities. These findings underscore the interconnectedness of root exudates and microbiota, which jointly shape rhizosheath structure, deepening insights into soil–plant-microbe interactions and ecological processes shaping rhizosphere microbial communities. Deciphering plant–microbe interactions and their contribution to soil aggregation and microbiota dynamics holds promise for the advancement of sustainable agricultural strategies. Video Abstract

The global expansion of solar-powered within distribution networks with Low Voltage (LV) is experiencing substantial expansion. Despite the various advantages offered by solar photovoltaic generation, surpassing the constraints on Hosting Capacity (HC) within these networks persist a significant technical problem in system operation, especially in relation to voltage operation. This research delves into the effectiveness of improving the Hosting Capacity (HC) of a photovoltaic (PV) system within an LV distribution system. It utilizes a Model Predictive Controller (MPC) to achieve this enhancement and contrasts its performance with reactive power control. The study examines scenarios encompassing both linear and non-linear loads to assess the impact of these control strategies on the PV system’s harmonic current in the LV distribution network. Through detailed analysis, the MPC controller demonstrates superior adaptability and responsiveness, maintaining stable active power at 95.5 kW before accommodating a 100% PV system penetration and experiencing a substantial increase to 192 kW. The hosting capacity, thereby, sees a notable 101.05% improvement under MPC control. Additionally, the study reveals that MPC optimizes reactive power utilization, resulting in a 17.9% reduction in reactive power and an 18.3% enhancement in bus voltage compared to reactive power control. Notably, MPC exhibits superior adaptability to both linear and non-linear loads, emphasizing its potential as an effective solution for optimizing the performance of PV systems within LV distribution grids. This research underscores the significance of advanced control strategies in facilitating the integration of renewable energy systems while ensuring grid stability and reliability.

Abstract We investigate the reduction of Feynman integrals to master integrals using Gröbner bases in a rational double-shift algebra Y in which the integration-by-parts (IBP) relations form a left ideal. The problem of reducing a given family of integrals to master integrals can then be solved once and for all by computing the Gröbner basis of the left ideal formed by the IBP relations. We demonstrate this explicitly for several examples. We introduce so-called first-order normal-form IBP relations which we obtain by reducing the shift operators in Y modulo the Gröbner basis of the left ideal of IBP relations. For more complicated cases, where the Gröbner basis is computationally expensive, we develop an ansatz based on linear algebra over a function field to obtain the normal-form IBP relations.

Over the last 30 years, the description of microbial diversity has been mainly based on culture-independent approaches (metabarcoding and metagenomics) allowing an in-depth analysis of microbial diversity that no other approach allows. Bearing in mind that culture-dependent approaches cannot replace culture-independent approaches, we have improved an original method for isolating strains consisting of “culturing” grains of sand directly on Petri dishes (grain-by-grain method). This method allowed to cultivate up to 10% of the bacteria counted on the surface of grains of the three sites studied in the Great Western Erg in Algeria (Timoudi, Béni Abbès, and Taghit), knowing that on average about 10 bacterial cells colonize each grain. The diversity of culturable bacteria (collection of 290 strains) predicted by 16S rRNA gene sequencing revealed that Arthrobacter subterraneus, Arthrobacter tecti, Pseudarthrobacter phenanthrenivorans, Pseudarthrobacter psychrotolerans, and Massilia agri are the dominant species. The comparison of the culture-dependent and -independent (16S rRNA gene metabarcoding) approaches at the Timoudi site revealed 18 bacterial genera common to both approaches with a relative overestimation of the genera Arthrobacter/Pseudarthrobacter and Kocuria, and a relative underestimation of the genera Blastococcus and Domibacillus by the bacterial culturing approach. The bacterial isolates will allow further study on the mechanisms of tolerance to desiccation, especially in Pseudomonadota (Proteobacteria).

Abstract In this work, chaos game optimization (CGO), a robust optimization approach, is employed for efficient design of a novel cascade controller for four test systems with interconnected power systems (IPSs) to tackle load–frequency control (LFC) difficulties. The CGO method is based on chaos theory principles, in which the structure of fractals is seen via the chaotic game principle and the fractals’ self-similarity characteristics are considered. CGO is applied in LFC studies as a novel application, which reveals further research gaps to be filled. For practical implementation, it is also highly desirable to keep the controller structure simple. Accordingly, in this paper, a CGO-based controller of fractional-order (FO) proportional–integral–derivative–FO proportional–integral (FOPID–FOPI) controller is proposed, and the integral time multiplied absolute error performance function is used. Initially, the proposed CGO-based FOPID–FOPI controller is tested with and without the nonlinearity of the governor dead band for a two-area two-source model of a non-reheat unit. This is a common test system in the literature. A two-area multi-unit system with reheater–hydro–gas in both areas is implemented. To further generalize the advantages of the proposed scheme, a model of a three-area hydrothermal IPS including generation rate constraint nonlinearity is employed. For each test system, comparisons with relevant existing studies are performed. These demonstrate the superiority of the proposed scheme in reducing settling time, and frequency and tie-line power deviations.

The greenhouse problem has a significant effect on our communities, health, and climate. So, the capturing techniques for CO2 remain the focus of attention these days. In this work, a Chemical Looping Combustor (CLC) was designed and fabricated with the major geometric sizes at the Faculty of Engineering, Suez Canal University. The system involves two interconnected fluidized beds. Nickel powder with 150 µm diameter as well as brown coal and liquefied petroleum gas were used as oxygen carrier, solid fuel, and gaseous fuel, respectively. The temperature distributions along the fuel reactor for LPG flow rates of 11 and 18 LPM with and without using nickel powder as well as using preheated reactor were discussed and evaluated. The effects of brown coal diameter change with and without using nickel powder were studied. The CO and CO2 concentrations at combustion gases with and without using nickel powder were conducted for LPG and brown coal fuels. A mathematical model was used to simulate the combustion in CLC using combustion and energy code. The obtained results showed that using nickel powder improved the combustion process and in case of using LPG, the flame color changed to blue which is the color of the complete combustion flame. The CO was reduced by 48.4 % and CO2 was augmented by 66.5 %. In case of using brown coal as solid fuel, CO was reduced by 53.7 % and CO2 was increased by 71.9 %. Finally, there is good agreement between the experimental and numerical results based on the determination coefficient.

Summary: We report on synthesis of the heterotrimetallic pentlandite-type material Fe3Co3Ni3S8 (FCNS) in presence of suitable phosphorus-(FCNSP) and nitrogen-(FCNSN) donors for the overall electrochemical water splitting. Throughout the experiments, a preferential incorporation of N into the FCNS-lattice is observed whereas the addition of phosphorus generally leads to metal-phosphate-FCNS composites. The obtained FCNSP, FCNSN, and FCNSNP facilitate the oxygen evolution reaction (OER) at 100 mAcm−2 in 1.0M KOH with overpotentials of 479, 440, and 427 mV, respectively, outperforming the benchmark IrO2 (564 mV) and commercial Ni metal powder (>600 mV). Likewise, FCNSN and FCNSNP reveal an improved performance toward the hydrogen evolution reaction (HER) in 0.5M H2SO4, outperforming the pristine FCNS. All materials revealed high stability and morphological robustness during OER and HER. Notably, DFT calculation suggests that N and P doping boost the OER activity of the pristine FCNS, whereas N doping enhances the HER activity.

The problem of estimating earthquake risk is one of the primary themes for researchers and investigators in the field of geosciences. The combined assessment of spatial probability and the determination of earthquake risk at large scales is challenging. To the best of the authors’ knowledge, there no updated earthquake-hazard-and-risk assessments for the Eurasia region have been published since 1999. Considering that Eurasia is characterized by a seismically active Alpine–Himalayan fault zone and the Pacific Ring of Fire, which are frequently affected by devastating events, a continental-scale risk assessment for Eurasia is necessary to check the global applicability of developed methods and to update the earthquake-hazard, -vulnerability, and -risk maps. The current study proposes an integrated deep-transfer-learning approach called the gated recurrent unit–simple recurrent unit (GRU–SRU) to estimate earthquake risk in Eurasia. In this regard, the GRU model estimates the spatial probability, while the SRU model evaluates the vulnerability. To this end, spatial probability assessment (SPA), and earthquake-vulnerability assessment (EVA) results were integrated to generate risk A, while the earthquake-hazard assessment (EHA) and EVA were considered to generate risk B. This research concludes that in the case of earthquake-risk assessment (ERA), the results obtained for Risk B were better than those for risk A. Using this approach, we also evaluated the stability of the factors and interpreted the interaction values to form a spatial prediction. The accuracy of our proposed integrated approach was examined by means of a comparison between the obtained deep learning (DL)-based results and the maps generated by the Global Earthquake Model (GEM). The accuracy of the SPA was 93.17%, while that of the EVA was 89.33%.

Abstract Two-component systems (TCSs) are ubiquitous signaling pathways, typically comprising a sensory histidine kinase (HK) and a response regulator, which communicate via intermolecular kinase-to-receiver domain phosphotransfer. Hybrid HKs constitute non-canonical TCS signaling pathways, with transmitter and receiver domains within a single protein communicating via intramolecular phosphotransfer. Here, we report how evolutionary relationships between hybrid HKs can be used as predictors of potential intermolecular and intramolecular interactions (‘phylogenetic promiscuity’). We used domain-swap genes chimeras to investigate the specificity of phosphotransfer within hybrid HKs of the GacS–GacA multikinase network of Pseudomonas brassicacearum. The receiver domain of GacS was replaced with those from nine donor hybrid HKs. Three chimeras with receivers from other hybrid HKs demonstrated correct functioning through complementation of a gacS mutant, which was dependent on strains having a functional gacA. Formation of functional chimeras was predictable on the basis of evolutionary heritage, and raises the possibility that HKs sharing a common ancestor with GacS might remain components of the contemporary GacS network. The results also demonstrate that understanding the evolutionary heritage of signaling domains in sophisticated networks allows their rational rewiring by simple domain transplantation, with implications for the creation of designer networks and inference of functional interactions.

Abstract A radiological or nuclear attack could involve such a large number of subjects as to overwhelm the emergency facilities in charge. Resources should therefore be focused on those subjects needing immediate medical attention and care. In such a scenario, for the triage management by first responders, it is necessary to count on efficient biological dosimetry tools capable of early detection of the absorbed dose. At present the validated assays for measuring the absorbed dose are dicentric chromosomes and micronuclei counts, which require more than 2–3 days to obtain results. To overcome this limitation the NATO SPS Programme funded an Italian–Egyptian collaborative project aimed at validating a fast, accurate and feasible tool for assessing the absorbed dose early after radiation exposure. Biomarkers as complete blood cell counts, DNA breaks and radio-inducible proteins were investigated on blood samples collected before and 3 h after the first fraction of radiotherapy in patients treated in specific target areas with doses/fraction of about: 2, 3.5 or > 5 Gy and compared with the reference micronuclei count. Based on univariate and multivariate multiple linear regression correlation, our results identify five early biomarkers potentially useful for detecting the extent of the absorbed dose 3 h after the exposure.

Among all the natural hazards, earthquake prediction is an arduous task. Although many studies have been published on earthquake hazard assessment (EHA), very few have been published on the use of artificial intelligence (AI) in spatial probability assessment (SPA). There is a great deal of complexity observed in the SPA modeling process due to the involvement of seismological to geophysical factors. Recent studies have shown that the insertion of certain integrated factors such as ground shaking, seismic gap, and tectonic contacts in the AI model improves accuracy to a great extent. Because of the black-box nature of AI models, this paper explores the use of an explainable artificial intelligence (XAI) model in SPA. This study aims to develop a hybrid Inception v3-ensemble extreme gradient boosting (XGBoost) model and shapely additive explanations (SHAP). The model would efficiently interpret and recognize factors’ behavior and their weighted contribution. The work explains the specific factors responsible for and their importance in SPA. The earthquake inventory data were collected from the US Geological Survey (USGS) for the past 22 years ranging the magnitudes from 5 Mw and above. Landsat-8 satellite imagery and digital elevation model (DEM) data were also incorporated in the analysis. Results revealed that the SHAP outputs align with the hybrid Inception v3-XGBoost model (87.9% accuracy) explanations, thus indicating the necessity to add new factors such as seismic gaps and tectonic contacts, where the absence of these factors makes the prediction model performs poorly. According to SHAP interpretations, peak ground accelerations (PGA), magnitude variation, seismic gap, and epicenter density are the most critical factors for SPA. The recent Turkey earthquakes (Mw 7.8, 7.5, and 6.7) due to the active east Anatolian fault validate the obtained AI-based earthquake SPA results. The conclusions drawn from the explainable algorithm depicted the importance of relevant, irrelevant, and new futuristic factors in AI-based SPA modeling.

Objective: Carbapenemase-producing bacteria pose a serious public-health threat. This study was performed to understand the emergence and genetic features of NDM-producers in hospital setting. Methods: Samples were collected from a tertiary-care hospital. Isolate identification was performed by 16S rRNA sequencing. The genome of Citrobacter werkmanii (AK-8) was sequenced on an Illumina NextSeq 500 platform. Resistance determinants and pathogenicity islands were determined by ResFinder and PathogenFinder, respectively. MLST, two-component systems and transcription factors were identified by P2RP server, whilst variant calling and insertion sequence (IS) elements were determined by Galaxy and ISfinder, respectively. The genome of AK-8 was compared with uropathogenic Escherichia coli strain 536. Results: This is the first report on whole-genome analysis of extensively drug-resistant NDM-6-producing uropathogenic C. werkmanii ST-104. Resistance genes for all antibiotics except colistin, fosfomycin, fusidic- acid, nitroimidazole, oxazolidinones, tetracycline and glycopeptides were detected in this strain. Genome analysis of AK-8 led to the identification of the BaeSR two-component system regulating production of multidrug efflux proteins. Virulence was regulated by CpxRA, ZraRS, RstAB, UhpAB, AcrAB, RcsBc and UvrY, whereas Bar–UvrY was found to control carbon metabolism, flagellum biosynthesis and biofilm formation. The AK-8 genome encodes 21 chemoreceptors involved in colonisation and pathogenesis. Fur family transcriptional regulator, cAMP receptor protein and RpoS were found to increase the virulence of AK-8. ntBLAST analysis showed 69.60% genetic identity with E. coli 536 as an adaptive feature for survival. Conclusion: The emergence of extensively drug-resistant pathogenic C. werkmanii is alarming and it should not be ignored as commensal.

This work demonstrates the fabrication of a nanoporous iron carbide-iron oxide/reduced graphene oxide (IC-IO/ rGO) hybrid via a controlled one-step thermal treatment of Prussian blue (PB)/GO hybrid at 450 °C under N2 flow. The PB/GO hybrid is initially prepared through the in-situ deposition of PB nanoparticles on the GO sheets through electrostatic interactions. The morphological analysis of the hybrid reveals the uniform coverage of the rGO sheets by IC-IO nanoparticles and the even distribution of carbon (C), oxygen (O), and iron (Fe) on the rGO nanosheets. As a result of the hybrid composition and controlled morphology, the surface area of the obtained IC-IO/rGO hybrid (~40 m2/g) is significantly enhanced compared to those of the calcined GO sheets and PB nanoparticles (without GO).

Land Use Land Cover (LULC) mapping has been used in different environmental applications including disaster management, risk analysis, heat island mapping, and the effects of urbanization on environmental changes such as floods and droughts in the recent decade. The earth’s natural surface coverage including urban infrastructure, surface vegetation, bare soil can be identified with LULC maps. Besides, LULC is one of the most important tasks in natural hazards, planning activities, resource management, and global monitoring studies. The present study aimed to improve the level of land used and land cover change mapping in a semi-arid environment using Landsat 8 Operational Land Imager (OLI) image. In this research, several tree-based algorithms for LULC mapping are used and compared. These algorithms are used in a combination model implementation of WEKA 3.8 and R programming language to provide a method of a fit-for-purpose algorithms for LULC mapping. It is found that Reduced Error Pruning Tree (REP Tree) is the best approach according to the training and test datasets which are four classes including the build-up, soil, roads, and vegetation region pixels in a semi-arid environment. For the training dataset, the best values of Overall Accuracy (OA), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) is determined, 99.7313, 0.002 and 0.0354 as well as for the test dataset are 99.7313, 0.002 and 0.0354, respectively. Finally, trained models of the implemented data mining algorithms are used for the classification of a different dataset without using any training data where the Logical Analysis of Data Tree (LAD Tree) algorithm shows the best performance in terms of OA, MAE, and RMSE with values of 99.2815, 0.0057, and 0.0557 respectively.

The reliable and efficient large-scale mapping of date palm trees from remotely sensed data is crucial for developing palm tree inventories, continuous monitoring, vulnerability assessments, environmental control, and long-term management. Given the increasing availability of UAV images with limited spectral information, the high intra-class variance of date palm trees, the variations in the spatial resolutions of the data, and the differences in image contexts and backgrounds, accurate mapping of date palm trees from very-high spatial resolution (VHSR) images can be challenging. This study aimed to investigate the reliability and the efficiency of various deep vision transformers in extracting date palm trees from multiscale and multisource VHSR images. Numerous vision transformers, including the Segformer, the Segmenter, the UperNet-Swin transformer, and the dense prediction transformer, with various levels of model complexity, were evaluated. The models were developed and evaluated using a set of comprehensive UAV-based and aerial images. The generalizability and the transferability of the deep vision transformers were evaluated and compared with various convolutional neural network-based (CNN) semantic segmentation models (including DeepLabV3+, PSPNet, FCN-ResNet-50, and DANet). The results of the examined deep vision transformers were generally comparable to several CNN-based models. The investigated deep vision transformers achieved satisfactory results in mapping date palm trees from the UAV images, with an mIoU ranging from 85% to 86.3% and an mF-score ranging from 91.62% to 92.44%. Among the evaluated models, the Segformer generated the highest segmentation results on the UAV-based and the multiscale testing datasets. The Segformer model, followed by the UperNet-Swin transformer, outperformed all of the evaluated CNN-based models in the multiscale testing dataset and in the additional unseen UAV testing dataset. In addition to delivering remarkable results in mapping date palm trees from versatile VHSR images, the Segformer model was among those with a small number of parameters and relatively low computing costs. Collectively, deep vision transformers could be used efficiently in developing and updating inventories of date palms and other tree species.

Sixteen rice genotypes were planted under normal and high temperature during 2018 and 2019 seasons to understand the mechanisms that make plants tolerant or susceptible to heat stress and methods which may lead to generate new varieties with sustainable yield production. The combined analysis showed significant differences at the level of probability 0.05 and 0.01 among years for all characters except, chlorophyll content and leaf rolling which would indicate wide differences among the weather of both years. Environment’s variance was significant at the level of probability 0.01 over all characteristics. The significant differences at the level of probability 0.01 were recorded among genotypes and G x E interaction variances for all characteristics. The highest values of panicles number plant-1 were obtained from ‘Giza 178’ and ‘Hybrid 2’, however, the highest values of 100-grain weight were recorded with ‘Giza 179’ and ‘Egyptian Yasmine’. The minimum values of sterility% were recorded with ‘Sakha 107’ and ‘IET1444’. Concerning the cultivars performance across two environments, the cultivars ‘Giza 178’, ‘Giza 179’, ‘Sakha 107’ and ‘IET 1444’ gave the best desirable values over natural and heat stress so, those cultivars are considered to play a vital role in breeding program to enhance heat stresses tolerance accompanied with high yield potential.

Background and study aims The goal of this study was to assess surgical resection (SR) of early gastric cancer (EGC) fitting Japanese Gastric Cancer Association (JGCA) endoscopic resection (ER) criteria. Patients and methods We analyzed EGC data from the national Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Results A total of 2219 EGC cases were identified (1074 T1a and 1145 T1b). Of them, 409 met absolute criteria, 219 met expanded 1, 529 expanded 2, and 229 expanded 3. 259 lesions were treated endoscopically while 1007 were surgically resected (20.5 % vs 79.5 %, P = 0.0001). Temporal analysis showed that the frequency of ER steadily increased while SR proportionally decreased during the study period. Cox proportion regression analysis adjusting for confound variables (including age, gender, and race) showed no significant difference in the risk of mortality following either surgery or endoscopy. Conclusions EGC can be safely treated with ER. However, EGC meeting JGCA ER criteria is largely treated with SR in the United States.

Background and study aims Peroral endoscopic myotomy (POEM) is increasingly being used as the preferred treatment option for achalasia. The aim of this systematic review and meta-analysis was to compare the efficacy and safety of POEM versus pneumatic balloon dilation (PD). Methods We performed a comprehensive review of studies that reported clinical outcomes of POEM and PD for the treatment of achalasia. Measured outcomes included clinical success (improvement of symptoms based on a validated scale including an Eckardt score ≤ 3), adverse events, and post-treatment gastroesophageal reflux disease (GERD). Results Sixty-six studies (6268 patients) were included in the final analysis, of which 29 studies (2919 patients) reported on POEM and 33 studies (3050 patients) reported on PD and 4 studies (299 patients) compared POEM versus PD. Clinical success with POEM was superior to PD at 12, 24, and 36 months (92.9 %, vs 76.9 % P = 0.001; 90.6 % vs 74.8 %, P = 0.004; 88.4 % vs 72.2 %, P = 0.006, respectively). POEM was superior to PD in type I, II and III achalasia (92.7 % vs 61 %, P = 0.01; 92.3 % vs 80.3 %, P = 0.01; 92.3 %v 41.9 %, P = 0.01 respectively) Pooled OR of clinical success at 12 and 24 months were significantly higher with POEM (8.97; P = 0.001 & 5.64; P = 0.006). Pooled OR of GERD was significantly higher with POEM (by symptoms: 2.95, P = 0.02 and by endoscopic findings: 6.98, P = 0.001). Rates of esophageal perforation (0.3 % vs 0.6 %, P = 0.8) and significant bleeding (0.4 % vs 0.7 %, P = 0.56) were comparable between POEM and PD groups. Conclusions POEM is more efficacious than PD in the treatment of patients with achalasia during short-term and long-term follow-up, albeit with higher risk of abnormal esophageal acid exposure.

Infections with COVID-19 can induce thrombophilia and increase the risk of thrombotic events. We describe a case of a 64- years-old male who presents with intermediate-risk pulmonary embolism and brachial artery thrombosis secondary to an undiagnosed patent foramen ovale and COVID-19 infection. The patient was successfully treated with thrombolysis and heparin infusions and was discharged home with long-term anticoagulation. Managing patients with multiple pathologies occurring concurrently requires effective inter-speciality working in order to take a holistic approach to managing the patient opposed to managing the conditions in isolation. This case report highlights how clinical guidelines can support decision making of the individual pathologies but working as a team will enable the best care.

Abstract Background The main objectives of this study were to compare two in vitro selection procedures for developing drought-tolerant cell lines from rice cultivars, regenerate plants from the selected cells, and use the random amplified polymorphic DNA (RAPD) method for genetic characterization of somaclons. Callus was derived from mature embryo cultures of tow Egyptian rice cultivars, Sakha 102 and Giza 178. Results The direct in vitro selection method indicated that the callus weight of the two cultivars was significantly decreased in response to increasing ABA concentrations. The shoot formation revealed significant differences among ABA concentrations and highly significant for interaction between rice cultivars and ABA concentrations. The percentage of shoot formation either increased or decreased with increasing ABA levels in rice cultivars. However, Giza 178 cultivar was slightly stable for shoot formation than the cultivar Sakha 102. The obtained results for stepwise selection method indicated also that the shoot formation was obtained till 6 mg/L ABA. The results of the RAPD analysis indicated that the rice cultivars and their somaclons were classified into tow clusters. The polymorphic information content (PIC) per primer ranged from 0.715 to 0.906 with an average of 0.860. The results of UPGMA clustering were consistent with those of the PCoA. Conclusions The percentage for Sakha 102 was 10.8, 7.7, and 2.2% on 2, 4, and 6 mg/L ABA, respectively, and was 16.4, 10.0, and 3.4% for Giza 178 cultivar, at the ABA concentrations of 2, 4, and 6 mg/L, respectively. The genetic similarity among the fifteen genotypes ranged from 0.30 to 0.82. Rice cultivars and their somaclons were classified into five clusters.

An assessment of site suitability for wind and solar plants is a strategic step toward ensuring a low-cost, high-performing, and sustainable project. However, these issues are often handled on a local scale using traditional decision-making approaches that involve biased and non-generalizable weightings. This study presents a global wind and solar mapping approach based on eXplainable Artificial Intelligence (XAI). To the best of the author’s knowledge, the current study is the first attempt to create global maps for siting onshore wind and solar power systems and formulate novel weights for decision criteria. A total of 13 conditioning factors (independent variables) defined through a comprehensive literature review and multicollinearity analysis were assessed. Real-world renewable energy experiences (more than 55,000 on-site wind and solar plants worldwide) are exploited to train three machine learning (ML) algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Then, the output of ML models was explained using SHapley Additive exPlanations (SHAP). RF outperformed SVM and MLP in both wind and solar modeling with an overall accuracy of 90% and 89%, kappa coefficient of 0.79 and 0.78, and area under the curve of 0.96 and 0.95, respectively. The high and very high suitability categories accounted for 23.2% (~26.84 million km2) of the site suitability map for wind power plants. In addition, they covered more encouraging areas (24.0% and 19.4%, respectively, equivalent to ~50.31 million km2) on the global map for hosting solar energy farms. SHAP interpretations were consistent with the Gini index indicating the dominance of the weights of technical and economic factors over the spatial assessment under consideration. This study provides support to decision-makers toward sustainable power planning worldwide.

Microbacterium oleivorans A9 cells were exposed or not to 10 µM uranyl nitrate as resting cells in sodium chloride solution. Bacteria exposed to U(VI) and controls were harvested after 0.5, 4, and 24 h of toxicant exposure. Bacteria were subjected to high-throughput proteomics analysis using a Q-Exactive HF high resolution tandem mass spectrometer incorporating an ultra-high-field orbitrap analyzer. MS/MS spectra were assigned with a protein sequence database derived from a draft genome obtained by Illumina sequencing and systematic six-reading frame translation of all the contigs. Proteins identified in bacteria exposed to U(VI) and controls at the three time points allow defining the proteome dynamics upon uranium stress. The data reported here are related to a published study regarding the proteome dynamics of M. oleivorans A9 upon uranium stress by Gallois et al. (in press) entitled “Proteogenomic insights into uranium tolerance of a Chernobyl׳s Microbacterium bacterial isolate”. The data accompanying the manuscript describing the database searches and comparative analysis have been deposited to the ProteomeXchange with identifier PXD005794.

Climate change is undoubtedly affecting the global weather of the Earth. Rapid human civilization has mainly caused this in the last few decades. This research examined the spatial and temporal land surface temperature (LST) in the United Arab Emirates (UAE) coastal cities located in an arid region that faced massive urbanization over the last 50 years. We estimated the LST using by-products of Landsat and MODIS images covering 2000 until 2020. The assessment of LST was performed in two contexts, i.e., spatially and temporally, covering daytime vs. nighttime during the summer and winter seasons. Additionally, a supervised classification technique was adopted to extract the land use and land cover in the study area from the late 1970s until 2018. Unexpectedly, the results indicated that daytime LST in districts near the coastlines (heavily urbanized areas) are lower than the ones far away from the coast (about 9 °C). This observation represents the spatial LST inversion in the study area. Nevertheless, this difference was not observed during nighttime LST temporally, the daytime LST did not increase significantly during either summer or winter seasons. However, the nighttime LST has increased temporally by about 17% since 2000 (the temporal LST inversion). Both LST inversions could be attributed to the uniqueness of the study area, given that bare land desert was replaced by vegetation, high-rise buildings, and industrial activities. Additionally, the wind breeze blowing from the gulf might potentially contribute to cooling the coastal urban area during the daytime. Furthermore, in-depth zonal statistics were conducted to visualize the effect of land use on LST. The study observed that fully built-up areas with vegetation have lower LST than built-up areas without vegetation or a combination of sand and vegetation. The research outcomes are invaluable for decision-makers and researchers in achieving sustainable urban development.

Whitings, the manifestation of high levels of suspended fine-grained calcium carbonate particles in the water, have been reported and studied worldwide. However, the triggering mechanism of whiting occurrences remains uncertain. The current study attempted to analyze potential factors that might account for whiting occurrences in a semi-enclosed gulf (namely the Arabian/Persian Gulf, hereinafter called the Gulf). First, spatial and temporal variability of whiting events and different potential driving factors (i.e., whiting seasonality, wind-induced mixing, sea surface temperature, and bathymetry) were explored and examined for five years (2015–2020). Second, as a general indicator of whiting occurrences in the Gulf, a whiting index (WI) was developed using time-series analysis and decision tree (DT) classification algorithm. Third, the correlation between the proposed WI and the spatial coverage of various whiting events was examined. Time-series analysis showed that whiting events during the winter season are associated with high winds that lasted for several days. Nevertheless, whiting events were rarely observed despite high wind speed and increased potential for CaCO3 precipitation in summer. This finding suggests that wind-driven forces might be potential sources for mixing water columns, resuspension of CaCO3 particles, and the appearance of whiting in the Gulf. The DT classification algorithm demonstrated that a minimum WI value of 1.1 can explain the initiation of most summer and winter whiting events. Furthermore, a Pearson correlation coefficient of 0.73 was measured between WI and the extent of whiting along the UAE and Qatar coastlines in the Gulf. The proposed WI shows a simple yet effective method for identifying and estimating the extent of whiting in the Gulf.

Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.

Some plant secondary metabolites, such as procyanidins, have been demonstrated to cause biological denitrification inhibition (BDI) of denitrifiers in soils concomitantly with a gain in plant biomass. The present work evaluated whether procyanidins had an impact on the diversity of nontarget microbial communities that are probably involved in soil fertility and ecosystem services. Lettuce plants were grown in two contrasting soils, namely Manziat (a loamy sand soil) and Serail (a sandy clay loam soil) with and without procyanidin amendment. Microbial diversity was assessed using Illumina sequencing of prokaryotic 16S rRNA gene and fungal ITS regions. We used a functional inference to evaluate the putative microbial functions present in both soils and reconstructed the microbial interaction network. The results showed a segregation of soil microbiomes present in Serail and Manziat that were dependent on specific soil edaphic variables. For example, Deltaproteobacteria was related to total nitrogen content in Manziat, while Leotiomycetes and Firmicutes were linked to Ca2+ in Serail. Procyanidin amendment did not affect the diversity and putative activity of microbial communities. In contrast, microbial interactions differed according to procyanidin amendment, with the results showing an enrichment of Entotheonellaeota and Mucoromycota in Serail soil and of Dependentiae and Rozellomycetes in Manziat soil.

Background and study aims There are numerous studies published on the diagnostic yield of the new fine-needle biopsy (FNB) needles in pancreas masses. However, there are limited studies in suspected gastrointestinal stromal tumors (GIST lesions). The aim of this study was to evaluate the diagnostic yield of a new fork-tip FNB needle. Patients and methods This was a multicenter retrospective study of consecutive patients from prospectively maintained databases comparing endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) versus endoscopic ultrasound-guided FNB (EUS-FNB) using the fork-tip needle. Outcomes measured were cytopathology yield (ability to obtain tissue for analysis of cytology), ability to analyze the tissue for immunohistochemistry (IHC yield), and diagnostic yield (ability to provide a definitive diagnosis). Results A total of 147 patients were included in the study of which 101 underwent EUS-FNB and 46 patients underwent EUS-FNA. Median lesion size in each group was similar (21 mm vs 25 mm, P = 0.25). Cytopathology yield, IHC yield, and diagnostic yield were 92 % vs 46 % (P = 0.001), 89 % vs 41 % (P = 0.001), and 89 % vs 37 % (P = 0.001) between the FNB and FNA groups, respectively. Median number of passes was the same between the two groups at 3.5. Conclusion EUS-FNB is superior to EUS-FNA for diagnostic yield of suspected GIST lesions. This should be confirmed with a prospective study.

Since the last two centuries, concrete has become the number one building material in the world. Therefore, we are interested in studying the mechanical behavior at failure of this material and calculate its toughness. In this paper, we trace the R-curve to determine the critical stress intensity factor and the crack evolution by specifying the three stages: crack stopping stage, stable propagation stage and an unstable propagation stage. The R-curve was obtained from the curve connecting the crack mouth opening to the applied load for prismatic specimens normalized by ASTM and notched laterally (SENB). These specimens are made from concrete with 25 MPa compressive strength and subjected to three-point bending. The dimensionless ratio of the initial notch length and the specimen width is between 0.3 and 0.6. During loading, we can observe an elastic deformation followed by a significant plastic deformation, the average value of the critical stress intensity factor found is 1.05 MPa.m1/2.

Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.

Considering the high-level details in an ultrahigh-spatial-resolution (UHSR) unmanned aerial vehicle (UAV) dataset, detailed mapping of heterogeneous urban landscapes is extremely challenging because of the spectral similarity between classes. In this study, adaptive hierarchical image segmentation optimization, multilevel feature selection, and multiscale (MS) supervised machine learning (ML) models were integrated to accurately generate detailed maps for heterogeneous urban areas from the fusion of the UHSR orthomosaic and digital surface model (DSM). The integrated approach commenced through a preliminary MS image segmentation parameter selection, followed by the application of three supervised ML models, namely, random forest (RF), support vector machine (SVM), and decision tree (DT). These models were implemented at the optimal MS levels to identify preliminary information, such as the optimal segmentation level(s) and relevant features, for extracting 12 land use/land cover (LULC) urban classes from the fused datasets. Using the information obtained from the first phase of the analysis, detailed MS classification was iteratively conducted to improve the classification accuracy and derive the final urban LULC maps. Two UAV-based datasets were used to develop and assess the effectiveness of the proposed framework. The hierarchical classification of the pilot study area showed that the RF was superior with an overall accuracy (OA) of 94.40% and a kappa coefficient (K) of 0.938, followed by SVM (OA = 92.50% and K = 0.917) and DT (OA = 91.60% and K = 0.908). The classification results of the second dataset revealed that SVM was superior with an OA of 94.45% and K of 0.938, followed by RF (OA = 92.46% and K = 0.916) and DT (OA = 90.46% and K = 0.893). The proposed framework exhibited an excellent potential for the detailed mapping of heterogeneous urban landscapes from the fusion of UHSR orthophoto and DSM images using various ML models.

Plant strategies for soil nutrient uptake have the potential to strongly influence plant–microbiota interactions, due to the competition between plants and microorganisms for soil nutrient acquisition and/or conservation. In the present study, we investigate whether these plant strategies could influence rhizosphere microbial activities via root exudation, and contribute to the microbiota diversification of active bacterial communities colonizing the root-adhering soil (RAS) and inhabiting the root tissues. We applied a DNA-based stable isotope probing (DNA-SIP) approach to six grass species distributed along a gradient of plant nutrient resource strategies, from conservative species, characterized by low nitrogen (N) uptake, a long lifespans and low root exudation level, to exploitative species, characterized by high rates of photosynthesis, rapid rates of N uptake and high root exudation level. We analyzed their (i) associated microbiota composition involved in root exudate assimilation and soil organic matter (SOM) degradation by 16S-rRNA-based metabarcoding. (ii) We determine the impact of root exudation level on microbial activities (denitrification and respiration) by gas chromatography. Measurement of microbial activities revealed an increase in denitrification and respiration activities for microbial communities colonizing the RAS of exploitative species. This increase of microbial activities results probably from a higher exudation rate and more diverse metabolites by exploitative plant species. Furthermore, our results demonstrate that plant nutrient resource strategies have a role in shaping active microbiota. We present evidence demonstrating that plant nutrient use strategies shape active microbiota involved in root exudate assimilation and SOM degradation via root exudation.

Milk and dairy products harbor a wide variety of bacterial species that compete for both limited resources and space. Under these competitive conditions, bacteria develop specialized mechanisms to protect themselves during niche colonization and nutrient acquisition processes. The bacterial antagonism mechanisms include the production of antimicrobial agents or molecules that facilitate competitor dispersal. In the present work, a bacterial strain designated RC6 was isolated from Ricotta and identified as Bacillus cereus. It generates antimicrobial peptide (AMP) when grown in the presence of casein. The AMP was active against several species of Bacillus and Listeria monocytogenes. MALDI-TOF analysis of the RP-HPLC purified fractions and amino acid sequencing revealed a molecular mass of 751 Da comprised of a 6-residue sequence, YPVEPF. BLAST analysis showed that the AMP corresponds to the fractions 114–119 of bovine β-casein and represents the product of a specific proteolysis. Analysis of the purified proteolytic fractions from the B. cereus RC6 culture supernatant indicated that the presence of at least two different endoproteases is crucial for the generation of the AMP. Indeed, we were able to identify two new candidate endoproteases by means of genome sequencing and functional assignment using a 3D structural model and molecular docking of misannotated hypothetical proteins. In this light, the capacity of B. cereus RC6 to generate antimicrobial peptides from casein, through the production of extracellular enzymes, presents a new model of antagonistic competition leading to niche colonization. Hence, as a dairy product contaminant, this strategy may enable proteolytic B. cereus RC6 niche specialization in milk matrices.

Retroperitoneal abscess is a rare condition which is difficult to diagnose and treat because of its insidious onset. Herein, we present a case of retroperitoneal abscess secondary to a perforation that occurred during an ERCP. A 54-year-old female patient was admitted to an outside hospital with gallstone pancreatitis and underwent ERCP with sphincterotomy followed by laparoscopic cholecystectomy. An abdominal CT scan was performed at the outside hospital 10 days later for worsening abdominal pain which showed multiple loculated pockets in the right upper and lower quadrant. Her condition improved after IV antibiotics and percutaneous drainage. Her symptoms recurred a month later and she presented to our hospital. Repeat abdominal CT scan at our hospital revealed recurrence of her abscesses. Multiple drains were placed and the abscess cavity was washed out without much improvement. EGD revealed a small mucosal defect in the distal portion of the duodenal bulb which was closed successfully using an over-the-scope clip. Repeat CT scan after 8 weeks from the endoscopic closure showed near complete resolution of the abscess. ERCP-associated perforation is a rare complication and can be challenging to diagnose and treat; prompt recognition is mandatory for favorable prognosis. Our patient was managed successfully via nonsurgical approach.

An unusual genome architecture characterizes the two related human parasitic pathogens Plasmodium falciparum and Toxoplasma gondii. A major fraction of the bulk parasite genome is packaged as transcriptionally permissive euchromatin with few loci embedded in silenced heterochromatin. Primary chromatin shapers include histone modifications at the nucleosome lateral surface close to the DNA but their mode of action remains unclear. We now identify versatile modifications at Lys31 within the globular domain of histone H4 that crucially determine genome organization and expression in Apicomplexa parasites. H4K31 acetylation at the promoter correlates with, and perhaps directly regulates, gene expression in both parasites. By contrast, monomethylated H4K31 is enriched in the core body of T. gondii active genes but inversely correlates with transcription, whereas it is unexpectedly enriched at transcriptionally inactive pericentromeric heterochromatin in P. falciparum, a region devoid of the characteristic H3K9me3 histone mark and its downstream effector HP1.

Root exudation contributes to soil carbon allocation and also to microbial C and energy supply, which subsequently impacts soil aggregation around roots. Biologically-driven soil structural formation is an important driver of soil fertility. Plant genetic determinants of exudation and more generally of factors promoting rhizosphere soil aggregation are largely unknown. Here, we characterized rhizosphere aggregation in a panel of 86 pearl millet inbred lines using a ratio of root-adhering soil dry mass per root tissue dry mass (RAS/RT). This ratio showed significant variations between lines, with a roughly 2-fold amplitude between lowest and highest average values. For 9 lines with contrasting aggregation properties, we then compared the bacterial diversity and composition in root-adhering soil. Bacterial α-diversity metrics increased with the “RAS/RT ratio.” Regarding taxonomic composition, the Rhizobiales were stimulated in lines showing high aggregation level whereas Bacillales were more abundant in lines with low ratio. 184 strains of cultivable exopolysaccharides-producing bacteria have been isolated from the rhizosphere of some lines, including members from Rhizobiales and Bacillales. However, at this stage, we could not find a correlation between abundance of EPS-producing species in bacterial communities and the ratio RAS/RT. These results illustrated the impact of cereals genetic trait variation on soil physical properties and microbial diversity. This opens the possibility of considering plant breeding to help management of soil carbon content and physical characteristics through carbon rhizodeposition in soil.

Seizures is a relatively common presentation with a wide differential diagnosis. However, seizures presenting secondary to underlying pulmonary emboli are rare and, without prompt recognition and management, this easily treatable condition can be potentially fatal. The few available case reports discussing seizures and PE reveal a high mortality rate which underscores the importance of prompt diagnosis. A 38-year-old woman presented to the emergency room having experienced loss of consciousness and a generalized tonic-clonic seizure at home. In the emergency room, her presenting signs and symptoms included tachycardia, worsening dyspnea, mild hypoxemia, and elevated D-dimer. Pertinent history findings revealed she recently received depot hormonal contraceptive treatments. Her initial workup included an EKG which showed sinus tachycardia without evidence of right heart strain. Subsequently a chest CT with angiography revealed massive bilateral pulmonary emboli. DVT studies also revealed a unilateral acute DVT. The patient was promptly started on therapeutic anticoagulation and stabilized. Fortunately, the patient remained symptom-free and eventually was discharged with close follow-up. The goal of this report is to make clinicians more aware of the possibility that seizures, along with the appropriate clinical findings, can be caused by acute PE.

Meeting water demands is a critical pillar for sustaining normal human living standards, industry evolution and agricultural growth. The main obstacles for developing countries in arid regions include unplanned urbanisation and limited water resources. Locating and constructing dams is a strategic priority of countries to preserve and store water. Recent advances in remote sensing, geographic information system (GIS), and machine learning (ML) techniques provide valuable tools for producing a dam site suitability map (DSSM). In this research, a hybrid GIS decision-making technique supported by an ML algorithm was developed to identify the most appropriate location to construct a new dam for Sharjah, one of the major cities in the United Arab Emirates. Nine thematic layers have been considered to prepare the DSSM, including precipitation, drainage stream density, geomorphology, geology, curve number, total dissolved solid elevation, slope and major fracture. The weights of the thematic layers were determined through the analytical hierarchy process supported by several ML techniques, where the best attempted ML technique was the random forest method, with an accuracy of 76%. Precipitation and drainage stream density were the most influential factors affecting the DSSM. The developed DSSM was validated using existing dams across the study area, where the DSSM provides an accuracy of 83% for dams located in the high and moderate zones. Three major sites were identified as suitable locations for constructing new dams in Sharjah. The approach adopted in this study can be applied for any other location globally to identify potential dam construction sites.

Whiting events in seas and lakes are a natural phenomenon caused by suspended calcium carbonate (CaCO3) particles. The Arabian Gulf, which is a semi-enclosed sea, is prone to extensive whiting that covers tens of thousands of square kilometres. Despite the extent and frequency of whiting events in the Gulf, studies documenting the whiting phenomenon are lacking. Therefore, the primary objective of this study was to detect, map and document the spatial and temporal distributions of whiting events in the Gulf using daily images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra and Aqua satellites from 2002 to 2018. A method integrating a geographic object-based image analysis, the correlation-based feature selection technique (CFS), the adaptive boosting decision tree (AdaBoost DT) and the rule-based classification were used in the study to detect, quantify and assess whiting events in the Gulf from the MODIS data. Firstly, a multiresolution segmentation was optimised using unsupervised quality measures. Secondly, a set of spectral bands and indices were investigated using the CFS to select the most relevant feature(s). Thirdly, a generic AdaBoost DT model and a rule-based classification were adopted to classify the MODIS time series data. Finally, the developed classification model was compared with various tree-based classifiers such as random forest, a single DT and gradient boosted DT. Results showed that both the combination of the mean of the green spectral band and the normalised difference index between the green and blue bands (NDGB), or the combination of the NDGB and the colour index for estimating the concentrations of calcium carbonates (CI) of the image objects, were the most significant features for detecting whiting. Moreover, the generic AdaBoost DT classification model outperformed the other tested tree-based classifiers with an overall accuracy of 97.86% and a kappa coefficient of 0.97. The whiting events during the study period (2002−2018) occurred exclusively during the winter season (November to March) and mostly in February. Geographically, the whiting events covered areas ranging from 12,000 km2 to 60,000 km2 and were mainly located along the southwest coast of the Gulf. The duration of most whiting events was 2 to 6 days, with some events extending as long as 8 to 11 days. The study documented the spatiotemporal distribution of whiting events in the Gulf from 2002 to 2018 and presented an effective tool for detecting and motoring whiting events.

This work reports the successful preparation of nanoporous iron oxide/carbon composites through the in-situ growth of Prussian blue (PB) nanoparticles on the surface of graphene oxide (GO) nanosheets. The applied thermal treatment allows the conversion of PB nanoparticles into iron oxide (Fe2O3) nanoparticles. The resulting iron oxide/carbon composite exhibits higher specific capacitance at all scan rates than pure GO and Fe2O3 electrodes due to the synergistic contribution of electric double-layer capacitance from GO and pseudocapacitance from Fe2O3. Notably, even at a high current density of 20 A g−1, the iron oxide/carbon composite still shows a high capacitance retention of 51%, indicating that the hybrid structure provides a highly accessible path for diffusion of electrolyte ions.

Accurate mapping of date palm trees is essential for their sustainable management, yield estimation, and environmental studies. In this study, we integrated geographic object-based image analysis, class-specific accuracy measures, fractional factorial design, metaheuristic feature-selection technique, and rule-based classification to detect and map date palm trees from very-high-spatial-resolution (VHSR) aerial images of two study areas. First, multiresolution segmentation was optimized through the synergy of the F1-score accuracy measure and the robust Taguchi design. Second, ant colony optimization (ACO) was adopted to select the most significant features. Out of 31 features, only 12 significant color invariants and textural features were selected. Third, based on the selected features, the rule-based classification with the aid of a decision tree algorithm was applied to extract date palm trees. The proposed methodology was developed on a subset of the first study area, and ultimately applied to the second study area to investigate its efficiency and transferability. To evaluate the proposed classification scheme, various supervised object-based algorithms, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (k-NN), were applied to the first study area. The result of image segmentation optimization demonstrated that segmentation optimization based on an integrated F1-score class-specific accuracy measure and Taguchi statistical design showed improvement compared with objective function, along with the Taguchi design. Moreover, the result of the feature selection by ACO outperformed, with almost 88% overall accuracy, several feature-selection techniques, such as chi-square, correlation-based feature selection, gain ratio, information gain, support vector machine, and principal component analysis. The integrated framework for palm tree detection outperformed RF, SVM, and k-NN classification algorithms with an overall accuracy of 91.88% and 87.03%, date palm class-specific accuracies of 0.91 and 0.89, and kappa coefficients of 0.90 and 0.85 for the first and second study areas, respectively. The proposed integrated methodology demonstrated a highly efficient and promising tool to detect and map date palm trees from VHSR aerial images.

A Weyl arrangement is the arrangement defined by the root system of a finite Weyl group. When a set of positive roots is an ideal in the root poset, we call the corresponding arrangement an ideal subarrangement. Our main theorem asserts that any ideal subarrangement is a free arrangement and that its exponents are given by the dual partition of the height distribution, which was conjectured by Sommers-Tymoczko. In particular, when an ideal subarrangement is equal to the entire Weyl arrangement, our main theorem yields the celebrated formula by Shapiro, Steinberg, Kostant, and Macdonald. The proof of the main theorem is classification-free. It heavily depends on the theory of free arrangements and thus greatly differs from the earlier proofs of the formula.

Ramlibacter tataouinensis TTB310(T) (strain TTB310), a betaproteobacterium isolated from a semi-arid region of South Tunisia (Tataouine), is characterized by the presence of both spherical and rod-shaped cells in pure culture. Cell division of strain TTB310 occurs by the binary fission of spherical "cyst-like" cells ("cyst-cyst" division). The rod-shaped cells formed at the periphery of a colony (consisting mainly of cysts) are highly motile and colonize a new environment, where they form a new colony by reversion to cyst-like cells. This unique cell cycle of strain TTB310, with desiccation tolerant cyst-like cells capable of division and desiccation sensitive motile rods capable of dissemination, appears to be a novel adaptation for life in a hot and dry desert environment. In order to gain insights into strain TTB310's underlying genetic repertoire and possible mechanisms responsible for its unusual lifestyle, the genome of strain TTB310 was completely sequenced and subsequently annotated. The complete genome consists of a single circular chromosome of 4,070,194 bp with an average G+C content of 70.0%, the highest among the Betaproteobacteria sequenced to date, with total of 3,899 predicted coding sequences covering 92% of the genome. We found that strain TTB310 has developed a highly complex network of two-component systems, which may utilize responses to light and perhaps a rudimentary circadian hourglass to anticipate water availability at the dew time in the middle/end of the desert winter nights and thus direct the growth window to cyclic water availability times. Other interesting features of the strain TTB310 genome that appear to be important for desiccation tolerance, including intermediary metabolism compounds such as trehalose or polyhydroxyalkanoate, and signal transduction pathways, are presented and discussed.

In RNA silencing, small RNAs produced by the RNase-III Dicer guide Argonaute-like proteins as part of RNA-induced silencing complexes (RISC) to regulate gene expression transcriptionally or post-transcriptionally. Here, we have characterized the RNA silencing machinery and exhaustive small RNAome of Toxoplasma gondii, member of the Apicomplexa, a phylum of animal- and human-infecting parasites that cause extensive health and economic damages to human populations worldwide. Remarkably, the small RNA-generating machinery of Toxoplasma is phylogenetically and functionally related to that of plants and fungi, and accounts for an exceptionally diverse array of small RNAs. This array includes conspicuous populations of repeat-associated small interfering RNA (siRNA), which, as in plants, likely generate and maintain heterochromatin at DNA repeats and satellites. Toxoplasma small RNAs also include many microRNAs with clear metazoan-like features whose accumulation is sometimes extremely high and dynamic, an unexpected finding given that Toxoplasma is a unicellular protist. Both plant-like heterochromatic small RNAs and metazoan-like microRNAs bind to a single Argonaute protein, Tg-AGO. Toxoplasma miRNAs co-sediment with polyribosomes, and thus, are likely to act as translational regulators, consistent with the lack of catalytic residues in Tg-AGO. Mass spectrometric analyses of the Tg-AGO protein complex revealed a common set of virtually all known RISC components so far characterized in human and Drosophila, as well as novel proteins involved in RNA metabolism. In agreement with its loading with heterochromatic small RNAs, Tg-AGO also associates substoichiometrically with components of known chromatin-repressing complexes. Thus, a puzzling patchwork of silencing processor and effector proteins from plant, fungal and metazoan origin accounts for the production and action of an unsuspected variety of small RNAs in the single-cell parasite Toxoplasma and possibly in other apicomplexans. This study establishes Toxoplasma as a unique model system for studying the evolution and molecular mechanisms of RNA silencing among eukaryotes.

Bacteria of the Thiomonas genus are ubiquitous in extreme environments, such as arsenic-rich acid mine drainage (AMD). The genome of one of these strains, Thiomonas sp. 3As, was sequenced, annotated, and examined, revealing specific adaptations allowing this bacterium to survive and grow in its highly toxic environment. In order to explore genomic diversity as well as genetic evolution in Thiomonas spp., a comparative genomic hybridization (CGH) approach was used on eight different strains of the Thiomonas genus, including five strains of the same species. Our results suggest that the Thiomonas genome has evolved through the gain or loss of genomic islands and that this evolution is influenced by the specific environmental conditions in which the strains live.