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Abstract This study aimed to examine the validity and reproducibility of strain elastography (SE) for detecting prostate cancer (PCa) in patients with elevated prostate-specific antigen (PSA) levels. The study included 107 patients with elevated PSA levels. All eligible patients underwent transrectal ultrasound (TRUS) with real-time elastography (RTE) to detect suspicious lesions. Two readers independently evaluated the lesions and assigned a strain ratio and elastography score to each lesion. Histopathology was used as a reference standard to estimate the validity of RTE in predicting malignant lesions. An intraclass correlation (ICC) was performed to detect reliability of the strain ratios and elastography scores. TRUS-guided biopsy detected malignancies in 64 (59.8%) patients. TRUS with RTE revealed 122 lesions. The strain ratio index (SRI) cut-off values to diagnose malignancy were 4.05 and 4.35, with sensitivity, specificity, and accuracy of 94.7%, 91.3%, and 93.4%, respectively. An elastography score > 3 was the best cut-off value for detecting malignancy. According to readers, the sensitivity, specificity, and accuracy were 91.3–94.7%, 89.5–93.4%, and 91.3–90.9%, respectively. Excellent inter-reader agreement was recorded for SRI and elastography scores, with ICC of 0.937 and 0.800, respectively. SE proves to be an efficient tool for detecting PCa with high accuracy in patients with elevated PSA levels.

[This corrects the article DOI: 10.1371/journal.pone.0300550.].

Egypt is among the world's largest producers of sugarcane. This crop is of great economic importance in the country, as it serves as a primary source of sugar, a vital strategic material. The pre-cutting planting mode is the most used technique for cultivating sugarcane in Egypt. However, this method is plagued by several issues that adversely affect the quality of the crop. A proposed solution to these problems is the implementation of a sugarcane-seed-cutting device, which incorporates automatic identification technology for optimal efficiency. The aim is to enhance the cutting quality and efficiency of the pre-cutting planting mode of sugarcane. The developed machine consists of a feeding system, a node scanning and detection system, a node cutting system, a sugarcane seed counting and monitoring system, and a control system. The current research aims to study the pulse widths (PW) of three-color channels (R, G, and B) of the RGB color sensors under laboratory conditions. The output PW of red, green, and blue channel values were recorded at three color types for hand-colored nodes [black, red, and blue], three speeds of the feeding system [7.5 m/min, 5 m/min, and 4.3 m/min], three installing heights of the RGB color sensors [2.0 cm, 3.0 cm, and 4.0 cm], and three widths of the colored line [10.0 mm, 7.0 mm, and 3.0 mm]. The laboratory test results s to identify hand-colored sugarcane nodes showed that the recognition rate ranged from 95% to 100% and the average scanning time ranged from 1.0 s to 1.75 s. The capacity of the developed machine ranged up to 1200 seeds per hour. The highest performance of the developed machine was 100% when using hand-colored sugarcane stalks with a 10 mm blue color line and installing the RGB color sensor at 2.0 cm in height, as well as increasing the speed of the feeding system to 7.5 m/min. The use of IoT and RGB color sensors has made it possible to get analytical indicators like those achieved with other automatic systems for cutting sugar cane seeds without requiring the use of computers or expensive, fast industrial cameras for image processing.

The current electric vehicles (EVs) market is experiencing significant expansion, underscoring the need to address challenges associated with the limited driving range of EVs. A primary focus in this context is the improvement of the wireless charging process. To contribute to this research area, this study introduces a circular spiral coil design that incorporates transceiver coils. First, an in-depth analysis is conducted using Ansys Maxwell software to assess the effectiveness of the proposed solution through the magnetic field distribution, inductance properties, and mutual inductance between receiver and transmitter coils. In the next step, a direct shielding technique is applied, integrating a ferrite core bar to reduce power leakage and enhance power transmission efficiency. The ferrite magnetic shielding guides magnetic field lines, resulting in a significant reduction in flux leakage and improved power transmission. Lastly, a magnetic resonance series (SS) compensation wireless system is developed to achieve high coupling efficiency and superior performance. The system's effectiveness is evaluated through co-simulation using Ansys Simplorer software. The results confirm the effectiveness of the proposed solution, showing its ability to transmit 3.6 kilowatts with a success rate approaching 99%. This contribution significantly advances the development of wireless charging systems for electric vehicles, addressing concerns and promoting global adoption.

Abstract Floorplan energy assessments present a highly efficient method for evaluating the energy efficiency of residential properties without requiring physical presence. By employing computer modelling, an accurate determination of the building’s heat loss or gain can be achieved, enabling planners and homeowners to devise energy-efficient renovation or redevelopment plans. However, the creation of an AI model for floorplan element detection necessitates the manual annotation of a substantial collection of floorplans, which poses a daunting task. This paper introduces a novel active learning model designed to detect and annotate the primary elements within floorplan images, aiming to assist energy assessors in automating the analysis of such images–an inherently challenging problem due to the time-intensive nature of the annotation process. Our active learning approach initially trained on a set of 500 annotated images and progressively learned from a larger dataset comprising 4500 unlabelled images. This iterative process resulted in mean average precision score of 0.833, precision score of 0.972, and recall score of 0.950. We make our dataset publicly available under a Creative Commons license.

Abstract There is a lack of epidemiological data on fascioliasis in Egypt regarding disease characteristics and treatment outcomes across different governorates. We aimed to identify the demographic, epidemiologic, clinical, laboratory, and radiological characteristics and treatment outcomes of patients diagnosed with fascioliasis in Egypt. Data on human fascioliasis were collected retrospectively from patients’ medical records in the period between January 2018 and January 2020. The study included 261 patients. More than 40% of enrolled patients were in the age group of 21–40 years old. Geographically, 247 (94.6%) were from Assiut Governorate with 69.3% were from rural areas. The most frequent symptoms were right upper quadrant pain (96.9%), and fever (80.1%). Eosinophilia was found in 250 cases (95.8%). Hepatic focal lesions were detected in 131 (50.2%); out of them 64/131 (48.9%) had a single lesion. All patients received a single dose of 10 mg/kg of triclabendazole, 79.7% responded well to a single dose, while in 20.3% a second ± a third dose of treatment was requested. After therapy, there was a reduction in leucocytes, Fasciola antibodies titer, eosinophilic count, bilirubin, and liver enzymes with an increase in hemoglobin level. According to our findings, a high index of suspicion should be raised in cases with fever, right upper abdominal pain, and peripheral eosinophilia, and further imaging workup is mandated to detect hepatic focal lesions. Prompt treatment by triclabendazole can serve as a standard-of-care regimen even for suspected cases.

Abstract Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements. For instance, quantitative predictions of transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, are impossible without accounting for molecular-level interactions. Similarly, inertial confinement fusion simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions. With these two disparate applications in mind, we develop a novel capability which uses an active learning approach to optimize the use of local fine-scale simulations for informing coarse-scale hydrodynamics. Our approach addresses three challenges: forecasting continuum coarse-scale trajectory to speculatively execute new fine-scale molecular dynamics calculations, dynamically updating coarse-scale from fine-scale calculations, and quantifying uncertainty in neural network models.

Abstract This work investigates the effectiveness of solar heating using clear polyethylene bags against rice weevil Sitophilus oryzae (L.), which is one of the most destructive insect pests against many strategic grains such as wheat. In this paper, we aim at finding the key parameters that affect the control heating system against stored grain insects while ensuring that the wheat grain quality is maintained. We provide a new benchmark dataset, where the experimental and environmental data was collected based on fieldwork during the summer in Canada. We measure the effectiveness of the solution using a novel formula to describe the amortising temperature effect on rice weevil. We adopted different machine learning models to predict the effectiveness of our solution in reaching a lethal heating condition for insect pests, and hence measure the importance of the parameters. The performance of our machine learning models has been validated using a 10-fold cross-validation, showing a high accuracy of 99.5% with 99.01% recall, 100% precision and 99.5% F1-Score obtained by the Random Forest model. Our experimental study on machine learning with SHAP values as an eXplainable post-hoc model provides the best environmental conditions and parameters that have a significant effect on the disinfestation of rice weevils. Our findings suggest that there is an optimal medium-sized grain amount when using solar bags for thermal insect disinfestation under high ambient temperatures. Machine learning provides us with a versatile model for predicting the lethal temperatures that are most effective for eliminating stored grain insects inside clear plastic bags. Using this powerful technology, we can gain valuable information on the optimal conditions to eliminate these pests. Our model allows us to predict whether a certain combination of parameters will be effective in the treatment of insects using thermal control. We make our dataset publicly available under a Creative Commons Licence to encourage researchers to use it as a benchmark for their studies.

Abstract The main objective of this study is to model the concentration of ozone in the winter season on air quality through machine learning algorithms, detecting its impact on population health. The study area involves four monitoring stations: Ate, San Borja, Santa Anita and Campo de Marte, all located in Metropolitan Lima during the years 2017, 2018 and 2019. Exploratory, correlational and predictive approaches are presented. The exploratory results showed that ATE is the station with the highest prevalence of ozone pollution. Likewise, in an hourly scale analysis, the pollution peaks were reported at 00:00 and 14:00. Finally, the machine learning models that showed the best predictive capacity for adjusting the ozone concentration were the linear regression and support vector machine.

A lot of oil is leaked into aquatic environments, significantly impacting fish health and, consequently, human populations. This study aimed to introduce an L-phenylalanine-based low-molecular-weight gelator (expressed as Z-Phe-C18) as a smart remediation tool for oil spills. Several groups of Nile tilapia were allocated in aquaria exposed to different doses of crude engine oil with/without the organogelator for 4 weeks. The results revealed a significant increase in biochemical oxygen demand, chemical oxygen demand, electrical conductivity, and total dissolved solids in water samples of fish aquaria exposed to oil pollution. The antioxidant activity levels, micronucleus formation, and expression patterns of stress-related genes were significantly higher in the livers of fish exposed to crude oil than in those of control fish. On the contrary, fish groups exposed to oil pollution and treated with the organogelator indicated that antioxidant enzymes, micronucleus incidence, and gene expression alteration of stress-related genes declined compared with those exposed to oil pollution only. The results suggest that oil pollution can induce oxidative stress via the enhancement of oxygen free radical formation. On the contrary, oil removal by the organogelator decreases oxidative stress and consequently strengthens fish immunity. So, we can conclude that organogelator treatment is promoting oxidative resistance development by increasing the activities of antioxidant enzymes, which are important in protection against oil pollution and preventing peroxidation of fish tissues. Promisingly, the organogelator could be used as a tool for the remediation of oil pollution in aquatic environments.

Due to their simplicity, cheapness, and ease of maintenance, induction motors (IMs) are the most widely used motors in the industry. However, if they are not properly controlled, the load torque and motor speed will fluctuate in an unsatisfactory fashion. To effectively control the load torque and speed of these IMs, it is necessary to use specialized drives. The entire system (IMs + Drives) will experience uncertainty, nonlinearities, and disruptions, which calls for an outstanding performance control structure. The sensorless sliding mode predictive torque control (SSM-PTC) for both AC-DC converter and DC-AC inverter, which are utilized for feeding the IM, is investigated in this work. The AC-DC converter is controlled using the SSM-PTC method in order to follow the DC-link reference voltage throughout any changes in the operating point of the IM. While the DC-AC inverter is controlled using a sensorless predictive power control (SPPC). Within a unity power factor, this SPPC regulates the reactive power flow between the motor and the supply to account for the undesirable harmonic components of the grid current. In addition, an experimental performance improvement of SSM-PTC of IM supplied by a 5-leg AC-DC-AC power converter using extended Kalman filter (EKF) without weighting factor (WF) is also studied in this work. Design and implantation of the suggested control systems are performed using a dSPACE 1104 card. The experimental results of the proposed converter control demonstrate that the suggested approach effectively regulated the DC link, reducing load torque and speed fluctuations. In the context of inverter control, a prompt active power response yields a motor current waveform that resembles a sinusoidal pattern, exhibiting minimal levels of harmonic distortion.

L’analyse statistique est devenue un outil indispensable d'aide à la décision en matière de bonne gestion des ressources en eaux, De ce fait, une analyse statistique des débits maximaux annuels a été réalisée dans le bassin-versant moyen et le bas de Chéliff. L’estimation statistique a été faite en sélectionnant les maxima annuels. Les stations sélectionnées sont au nombre de treize stations hydrométriques. Ces stations disposent de longues séries de mesures de plus de 30 ans. Le choix de la classe d’appartenance de la loi, la plus adéquate aux fins de modélisation des débits maximaux annuels, a été opéré par le système d’aide à la décision (SAD) du logiciel Hyfran plus. Les résultats obtenue montre que toutes les séries des stations appartiennent à la classe D. Pour le choix du meilleur ajustement nous avons utilisé les critères d’information Bayésien (BIC) et d’Akaike (AIC). Ces deux critères permettent de construire un classement des modèles statistiques tenant compte du principe de parcimonie. Le meilleur ajustement, pour chacun des deux critères, correspond à la plus faible valeur de son coefficient. Les principaux résultats des analyses statistiques montrent que les treize stations étudiées sont mieux ajustées à la loi Gamma.

Solar energy, a prominent renewable resource, relies on photovoltaic systems (PVS) to capture energy efficiently. The challenge lies in maximizing power generation, which fluctuates due to changing environmental conditions like irradiance and temperature. Maximum Power Point Tracking (MPPT) techniques have been developed to optimize PVS output. Among these, the incremental conductance (INC) method is widely recognized. However, adapting INC to varying environmental conditions remains a challenge. This study introduces an innovative approach to adaptive MPPT for grid-connected PVS, enhancing classical INC by integrating a PID controller updated through a fuzzy self-tuning controller (INC-FST). INC-FST dynamically regulates the boost converter signal, connecting the PVS's DC output to the grid-connected inverter. A comprehensive evaluation, comparing the proposed adaptive MPPT technique (INC-FST) with conventional MPPT methods such as INC, Perturb & Observe (P&O), and INC Fuzzy Logic (INC-FL), was conducted. Metrics assessed include current, voltage, efficiency, power, and DC bus voltage under different climate scenarios. The proposed MPPT-INC-FST algorithm demonstrated superior efficiency, achieving 99.80%, 99.76%, and 99.73% for three distinct climate scenarios. Furthermore, the comparative analysis highlighted its precision in terms of control indices, minimizing overshoot, reducing rise time, and maximizing PVS power output.

Due to the unpredictability of the majority of green energy sources (GESs), particularly in microgrids (μGs), frequency deviations are unavoidable. These factors include solar irradiance, wind disturbances, and parametric uncertainty, all of which have a substantial impact on the system's frequency. An adaptive load frequency control (LFC) method for power systems is suggested in this paper to mitigate the aforementioned issues. For engineering challenges, soft computing methods like the bat algorithm (BA), where it proves its effectiveness in different applications, consistently produce positive outcomes, so it is used to address the LFC issue. For online gain tuning, an integral controller using an artificial BA is utilized, and this control method is supported by a modification known as the balloon effect (BE) identifier. Stability and robustness of analysis of the suggested BA+BE scheme is investigated. The system with the proposed adaptive frequency controller is evaluated in the case of step/random load demand. In addition, high penetrations of photovoltaic (PV) sources are considered. The standard integral controller and Jaya+BE, two more optimization techniques, have been compared with the suggested BA+BE strategy. According to the results of the MATLAB simulation, the suggested technique (BA+BE) has a significant advantage over other techniques in terms of maintaining frequency stability in the presence of step/random disturbances and PV source. The suggested method successfully keeps the frequency steady over I and Jaya+BE by 61.5% and 31.25%, respectively. In order to validate the MATLAB simulation results, real-time simulation tests are given utilizing a PC and a QUARC pid_e data acquisition card.

L’analyse statistique est devenue un outil indispensable d'aide à la décision en matière de bonne gestion des ressources en eaux, De ce fait, une analyse statistique des débits maximaux annuels a été réalisée dans le bassin-versant moyen et le bas de Chéliff. L’estimation statistique a été faite en sélectionnant les maxima annuels. Les stations sélectionnées sont au nombre de treize stations hydrométriques. Ces stations disposent de longues séries de mesures de plus de 30 ans. Le choix de la classe d’appartenance de la loi, la plus adéquate aux fins de modélisation des débits maximaux annuels, a été opéré par le système d’aide à la décision (SAD) du logiciel Hyfran plus. Les résultats obtenue montre que toutes les séries des stations appartiennent à la classe D. Pour le choix du meilleur ajustement nous avons utilisé les critères d’information Bayésien (BIC) et d’Akaike (AIC). Ces deux critères permettent de construire un classement des modèles statistiques tenant compte du principe de parcimonie. Le meilleur ajustement, pour chacun des deux critères, correspond à la plus faible valeur de son coefficient. Les principaux résultats des analyses statistiques montrent que les treize stations étudiées sont mieux ajustées à la loi Gamma.

L’analyse statistique est devenue un outil indispensable d'aide à la décision en matière de bonne gestion des ressources en eaux, De ce fait, une analyse statistique des débits maximaux annuels a été réalisée dans le bassin-versant moyen et le bas de Chéliff. L’estimation statistique a été faite en sélectionnant les maxima annuels. Les stations sélectionnées sont au nombre de treize stations hydrométriques. Ces stations disposent de longues séries de mesures de plus de 30 ans. Le choix de la classe d’appartenance de la loi, la plus adéquate aux fins de modélisation des débits maximaux annuels, a été opéré par le système d’aide à la décision (SAD) du logiciel Hyfran plus. Les résultats obtenue montre que toutes les séries des stations appartiennent à la classe D. Pour le choix du meilleur ajustement nous avons utilisé les critères d’information Bayésien (BIC) et d’Akaike (AIC). Ces deux critères permettent de construire un classement des modèles statistiques tenant compte du principe de parcimonie. Le meilleur ajustement, pour chacun des deux critères, correspond à la plus faible valeur de son coefficient. Les principaux résultats des analyses statistiques montrent que les treize stations étudiées sont mieux ajustées à la loi Gamma.

L’analyse statistique est devenue un outil indispensable d'aide à la décision en matière de bonne gestion des ressources en eaux, De ce fait, une analyse statistique des débits maximaux annuels a été réalisée dans le bassin-versant moyen et le bas de Chéliff. L’estimation statistique a été faite en sélectionnant les maxima annuels. Les stations sélectionnées sont au nombre de treize stations hydrométriques. Ces stations disposent de longues séries de mesures de plus de 30 ans. Le choix de la classe d’appartenance de la loi, la plus adéquate aux fins de modélisation des débits maximaux annuels, a été opéré par le système d’aide à la décision (SAD) du logiciel Hyfran plus. Les résultats obtenue montre que toutes les séries des stations appartiennent à la classe D. Pour le choix du meilleur ajustement nous avons utilisé les critères d’information Bayésien (BIC) et d’Akaike (AIC). Ces deux critères permettent de construire un classement des modèles statistiques tenant compte du principe de parcimonie. Le meilleur ajustement, pour chacun des deux critères, correspond à la plus faible valeur de son coefficient. Les principaux résultats des analyses statistiques montrent que les treize stations étudiées sont mieux ajustées à la loi Gamma.

The integration of renewable sources (RSs) and the widespread deployment of electric vehicles (EVs) has transitioned from a luxury to a necessity in modern power systems. This results from the sharp increase in electric power demand and public awareness of switching to green energy. However, in addition to load fluctuations and changes in system parameters, these RSs and EVs negatively impact the load frequency (LF). This work presents a LF control for a modern multi-area power system incorporating photovoltaic (PV) and EV chargers. The proposed controller primarily utilizes EV chargers within modern power systems. This approach offers the advantage of using the already present components instead of introducing new ones. The proposed controller comprises the ecological optimization approach (ECO) and the integral controller (I). Both of these components are designed for autonomous vehicle-to-grid (V2G) devices. The proposed control technique is applied to a three-area power system, where the V2G scheme is located in Area-1. Variations in the load, PV power generated, and system parameters are considered to evaluate the effectiveness of the proposed (I+ECO+V2G) controller for controlling the LF. To assess the performance of the proposed I+ECO+V2G system, a comparative analysis is conducted to compare its performance with both the I+ECO system and the standard I-controller. The simulation findings demonstrate that implementing the I+ECO and the proposed I+ECO+V2G strategies results in enhanced system stability and decreased LF fluctuations compared to the conventional I-control approach. Furthermore, while comparing the I+ECO control technique to the suggested control strategy I+ECO+V2G, it was seen that the latter reaches steady state values more quickly. The results validate the robustness and effectiveness of the proposed controller in mitigating the impacts of load disturbances, uncertainties, and nonlinearities within the system. These simulations were performed using MATLAB/SIMULINK. To validate the outcomes of the simulation results, an experimental setup consisting of a real-time dSPACE DS1103 connected to another PC via QUARC pid_e data acquisition card was used. The experimental findings have substantiated the accuracy of the simulation findings about the superiority of the I+ECO+V2G methodology compared to both the I+ECO and I-control methodologies concerning system performance and LF control.

Biometric technology is becoming increasingly prevalent in several vital applications that substitute traditional password and token authentication mechanisms. Recognition accuracy and computational cost are two important aspects that are to be considered while designing biometric authentication systems. Thermal imaging is proven to capture a unique thermal signature for a person and thus has been used in thermal face recognition. However, the literature did not thoroughly analyse the impact of feature selection on the accuracy and computational cost of face recognition which is an important aspect for limited resources applications like IoT ones. Also, the literature did not thoroughly evaluate the performance metrics of the proposed methods/solutions which are needed for the optimal configuration of the biometric authentication systems. This paper proposes a thermal face-based biometric authentication system. The proposed system comprises five phases: a) capturing the user’s face with a thermal camera, b) segmenting the face region and excluding the background by optimized superpixel-based segmentation technique to extract the region of interest (ROI) of the face, c) feature extraction using wavelet and curvelet transform, d) feature selection by employing bio-inspired optimization algorithms: grey wolf optimizer (GWO), particle swarm optimization (PSO) and genetic algorithm (GA), e) the classification (user identification) performed using classifiers: random forest (RF), k-nearest neighbour (KNN), and naive bayes (NB). Upon the public dataset, Terravic Facial IR, the proposed system was evaluated using the metrics: accuracy, precision, recall, F-measure, and receiver operating characteristic (ROC) area. The results showed that the curvelet features optimized using the GWO and classified with random forest could help in authenticating users through thermal images with performance up to 99.5% which is better than the results of wavelet features by 10% while the former used 5% fewer features. In addition, the statistical analysis showed the significance of our proposed model. Compared to the related works, our system showed to be a better thermal face authentication model with a minimum set of features, making it computational-friendly.

The operation of the system's frequency can be strongly impacted by load change, solar irradiation, wind disturbance, and system parametric uncertainty. In this paper, the application of an adaptive controller based on a hybrid Jaya-Balloon optimizer (JBO) for frequency oscillation mitigation in a single area smart μG system is studied. The proposed adaptive control approach is applied to control the flexible loads such as HPs and EVs by using the JBO which efficiently controls the system frequency. The suggested technique uses the power balance equation to provide a dynamic output feedback controller. The main target is to regulate the frequency and power of an islanded single area μG powered by a PV and a diesel generator with integrations of smart bidirectional loads (HPs and EVs) that are controlled by the proposed adaptive controller in presence of electrical random loads. Moreover, the JBO is designed to minimize the effect of the system load disturbance and parameter variations. For a better assessment, the proposed controller using JBO technique is compared with two other methods which are the coefficient diagram method (CDM) and adaptive one using classical the Jaya technique. In the obtained results, the frequency deviation is found as 0.0015 Hz, which is fully acceptable and in the range of the IEEE standards. The MATLAB simulation results reveal that the suggested technique has a substantial advantage over other techniques in terms of frequency stability in the face of concurrent disturbances and parameter uncertainties. The real-time simulation tests are presented using a dSPACE DS1103 connected to another PC via QUARC pid_e data acquisition card and confirmed the MATLAB simulation results.

Abstract This cone-beam computed tomographic (CBCT) study aimed to evaluate the prevalence of dens invaginatus (DI) and its characteristics in maxillary anterior teeth in a Saudi population. A total of 505 CBCT scans were evaluated, including a total of 2790 maxillary anterior teeth. The patients’ demographic data, including age and sex, were recorded. The presence of DI and the related characteristics including bilateral occurrence; DI type according to Oehlers classification; and presence of periapical radiolucency, open apex, and/or nearby impacted teeth were analyzed. The associations between DI and the other factors were analyzed using the chi-square and fisher exact tests. DI was detected in 7.3% of the patients and 1.6% of the teeth examined. Most of the DI-affected teeth were maxillary lateral incisors (76.1%), followed by mesiodens (19.6%) and maxillary central incisors (4.3%), while no DI was observed in the maxillary canines. Bilateral DI was found in 24.3% of the affected patients. Oehlers type I DI was the most frequent (80%). Periapical radiolucencies, open apices, and nearby impacted teeth were observed in 10.9%, 4.3%, and 30% of the invaginated teeth, respectively. DI was significantly associated with tooth type (P 0.05). Although most of the DI cases are limited to the crown, CBCT imaging is essential for DI evaluation and management.

Carbonate mineral aqueous solubility decreases as carbonates become more Mg-rich during subduction. Coupled with regional variations in amounts of carbon and water subducted, this explains discrepancies in estimates of carbon recycling, suggesting that only around a third returns to the surface.

Chemical elements at high pressure may behave more consistently with their periodic properties than they do at ambient conditions. The authors report the synthesis of PH3 from black phosphorous and hydrogen, and the crystallization of the van der Waals compound (PH3)2H2 which fills a gap in the chemistry of adjacent elements in the periodic table.

In humid and temperate areas, Septoria nodorum blotch (SNB) is a major fungal disease of common wheat (Triticum aestivum L.) in which grain yield is reduced when the pathogen, Parastagonospora nodorum, infects leaves and glumes during grain filling. Foliar SNB susceptibility may be associated with sensitivity to P. nodorum necrotrophic effectors (NEs). Both foliar and glume susceptibility are quantitative, and the underlying genetics are not understood in detail. We genetically mapped resistance quantitative trait loci (QTL) to leaf and glume blotch using a double haploid (DH) population derived from the cross between the moderately susceptible cultivar AGS2033 and the resistant breeding line GA03185-12LE29. The population was evaluated for SNB resistance in the field in four successive years (2018-2021). We identified major heading date (HD) and plant height (PH) variants on chromosomes 2A and 2D, co-located with SNB escape mechanisms. Five QTL with small effects associated with adult plant resistance to SNB leaf and glume blotch were detected on 1A, 1B, and 6B linkage groups. These QTL explained a relatively small proportion of the total phenotypic variation, ranging from 5.6 to 11.8%. The small-effect QTL detected in this study did not overlap with QTL associated with morphological and developmental traits, and thus are sources of resistance to SNB.

Early public health strategies to prevent the spread of COVID-19 in the United States relied on non-pharmaceutical interventions (NPIs) as vaccines and therapeutic treatments were not yet available. Implementation of NPIs, primarily social distancing and mask wearing, varied widely between communities within the US due to variable government mandates, as well as differences in attitudes and opinions. To understand the interplay of trust, risk perception, behavioral intention, and disease burden, we developed a survey instrument to study attitudes concerning COVID-19 and pandemic behavioral change in three states: Idaho, Texas, and Vermont. We designed our survey (n = 1034) to detect whether these relationships were significantly different in rural populations. The best fitting structural equation models show that trust indirectly affects protective pandemic behaviors via health and economic risk perception. We explore two different variations of this social cognitive model: the first assumes behavioral intention affects future disease burden while the second assumes that observed disease burden affects behavioral intention. In our models we include several exogenous variables to control for demographic and geographic effects. Notably, political ideology is the only exogenous variable which significantly affects all aspects of the social cognitive model (trust, risk perception, and behavioral intention). While there is a direct negative effect associated with rurality on disease burden, likely due to the protective effect of low population density in the early pandemic waves, we found a marginally significant, positive, indirect effect of rurality on disease burden via decreased trust (p = 0.095). This trust deficit creates additional vulnerabilities to COVID-19 in rural communities which also have reduced healthcare capacity. Increasing trust by methods such as in-group messaging could potentially remove some of the disparities inferred by our models and increase NPI effectiveness.

In this paper, we will investigate the influence of the microphysics schemes on the rainfall pattern of the extreme storm that impacted Egypt on 12 March 2020. The aim is to improve rainfall forecasting using the numerical Weather Research and Forecasting (WRF) model for an effective Early Warning System (EWS). The performance of six microphysics schemes were evaluated using the Model Object-based Evaluation analysis tool (MODE) forced by three selected satellite-based datasets (CMORPH, PERSIANN, PERSIANN-CCS, etc.) and one reanalysis dataset (ERA5). Six numerical simulations were performed using the WRF model, considering the following microphysics schemes: Lin, WSM6, Goddard, Thompson, Morrison, and NSSL2C. The models were evaluated using both conventional statistical indices and MODE, which is much more suitable in such studies. The results showed that the Lin scheme outperformed the other schemes such as WSM6, Goddard, Thompson, Morrison, and NSSL2C, in rainfall forecasting. The Thompson scheme was found to be the least reliable scheme. An extension for this study is recommended in other regions where the observational rain gauges data are available.

The mammalian circadian timing system and metabolism are highly interconnected, and disruption of this coupling is associated with negative health outcomes. Krüppel-like factors (KLFs) are transcription factors that govern metabolic homeostasis in various organs. Many KLFs show a circadian expression in the liver. Here, we show that the loss of the clock-controlled KLF10 in hepatocytes results in extensive reprogramming of the mouse liver circadian transcriptome, which in turn alters the temporal coordination of pathways associated with energy metabolism. We also show that glucose and fructose induce Klf10, which helps mitigate glucose intolerance and hepatic steatosis in mice challenged with a sugar beverage. Functional genomics further reveal that KLF10 target genes are primarily involved in central carbon metabolism. Together, these findings show that in the liver KLF10 integrates circadian timing and sugar metabolism-related signaling, and serves as a transcriptional brake that protects against the deleterious effects of increased sugar consumption.

This paper presents a set of methods, jointly called PGraphD*, which includes two new methods (PGraphDD-QM and PGraphDD-SS) for drift detection and one new method (PGraphDL) for drift localisation in business processes. The methods are based on deep learning and graphs, with PGraphDD-QM and PGraphDD-SS employing a quality metric and a similarity score for detecting drifts, respectively. According to experimental results, PGraphDD-SS outperforms PGraphDD-QM in drift detection, achieving an accuracy score of 100% over the majority of synthetic logs and an accuracy score of 80% over a complex real-life log. Furthermore, PGraphDD-SS detects drifts with delays that are 59% shorter on average compared to the best performing state-of-the-art method.

تمثل المياه الجوفية أحد أهم المحددات الرئيسية في تطور، ونجاح استدامة النشاط الزراعي في ليبيا. كما أن الإدارة الجيدة، والتخطيط السليم لهذا المورد يستوجب معرفة نوعية المياه للحد من المشكلات التي قد تواجه مستعملي تلك المياه. تهدف هذه الدراسة إلى التعرف على التركيب الكيميائي للمياه الجوفية في منطقة جنوب طرابلس، وتقييم جودة المياه الجوفية لأغراض الري. ولتحقيق أهداف هذه الدراسة تم تجميع 31 عينة من مياه آبار جوفية منتشرة في جنوب طرابلس خلال شهر يوليو 2016. أجريت العديد من التحاليل الكيميائية على تلك العينات لتقدير درجة التوصيل الكهربي، ودرجة التفاعل، وتركيز بعض الأيونات الذائبة التي تضمنت كل من الصوديوم، والبوتاسيوم، والكالسيوم، والماغنيسيوم، والكلوريدات، والبيكربونات، والكبريتات، بالإضافة إلى حساب نسبة الصوديوم المدمص. تم تحديد مؤشر جودة مياه الري ذي الخمس معلمات، والتي تشمل كل من (درجة التوصيل الكهربي، الصوديوم، الكلوريدات، البيكربونات، نسبة ادمصاص الصوديوم المعدلة). استخدمت طريقة مقلوب المسافة الوزنية بأس 2، للحصول على خرائط التوزيع المكاني للخصائص الكيميائية، ولمؤشر جودة مياه الري. أظهرت النتائج ارتفاع قيم درجة التوصيل الكهربي عند منتصف منطقة الدراسة، وانخفاضها عند شرقها، وغربها، وأن نمط التوزيع المكاني لها مشابه إلى حد كبير لنمط التوزيع المكاني لكل من أيون الصوديوم، والكلوريد. كما دلت النتائج على أن متوسط الوفرة النسبية للأنيونات الذائبة معبراً عنه بوحدات (ملليمكافيء/ لتر) كانت 65.8، 20.4، 13.8% وذلك للكلوريدات، والكبريتات، والبيكربونات على التوالي. في حين كان متوسط الوفرة النسبية للكاتيونات الذائبة 49.3، 28.1، 21.7، 0.9% وذلك للصوديوم، والماغنيسيوم، والكالسيوم، والبوتاسيوم على التوالي. تراوحت قيم مؤشر جودة مياه الري بين 41.2 و76.6. كما بينت خريطة التوزيع المكاني لمؤشر جودة مياه الري أن 62 % من مساحة منطقة الدراسة صنفت على أنها ذات قيود، ومحددات معتدلة للاستخدام في أغراض الري، وأن 37.5% صنفت على أنها ذات قيود ومحددات مرتفعة.

Coastal countries have traditionally relied on the existing marine resources (e.g., fishing, food, transport, recreation, and tourism) as well as tried to support new economic endeavors (ocean energy, desalination for water supply, and seabed mining). Modern societies and lifestyle resulted in an increased demand for dietary diversity, better health and well-being, new biomedicines, natural cosmeceuticals, environmental conservation, and sustainable energy sources. These societal needs stimulated the interest of researchers on the diverse and underexplored marine environments as promising and sustainable sources of biomolecules and biomass, and they are addressed by the emerging field of marine (blue) biotechnology. Blue biotechnology provides opportunities for a wide range of initiatives of commercial interest for the pharmaceutical, biomedical, cosmetic, nutraceutical, food, feed, agricultural, and related industries. This article synthesizes the essence, opportunities, responsibilities, and challenges encountered in marine biotechnology and outlines the attainment and valorization of directly derived or bio-inspired products from marine organisms. First, the concept of bioeconomy is introduced. Then, the diversity of marine bioresources including an overview of the most prominent marine organisms and their potential for biotechnological uses are described. This is followed by introducing methodologies for exploration of these resources and the main use case scenarios in energy, food and feed, agronomy, bioremediation and climate change, cosmeceuticals, bio-inspired materials, healthcare, and well-being sectors. The key aspects in the fields of legislation and funding are provided, with the emphasis on the importance of communication and stakeholder engagement at all levels of biotechnology development. Finally, vital overarching concepts, such as the quadruple helix and Responsible Research and Innovation principle are highlighted as important to follow within the marine biotechnology field. The authors of this review are collaborating under the European Commission-funded Cooperation in Science and Technology (COST) Action Ocean4Biotech – European transdisciplinary networking platform for marine biotechnology and focus the study on the European state of affairs.

Marine organisms produce a vast diversity of metabolites with biological activities useful for humans, e.g., cytotoxic, antioxidant, anti-microbial, insecticidal, herbicidal, anticancer, pro-osteogenic and pro-regenerative, analgesic, anti-inflammatory, anti-coagulant, cholesterol-lowering, nutritional, photoprotective, horticultural or other beneficial properties. These metabolites could help satisfy the increasing demand for alternative sources of nutraceuticals, pharmaceuticals, cosmeceuticals, food, feed, and novel bio-based products. In addition, marine biomass itself can serve as the source material for the production of various bulk commodities (e.g., biofuels, bioplastics, biomaterials). The sustainable exploitation of marine bio-resources and the development of biomolecules and polymers are also known as the growing field of marine biotechnology. Up to now, over 35,000 natural products have been characterized from marine organisms, but many more are yet to be uncovered, as the vast diversity of biota in the marine systems remains largely unexplored. Since marine biotechnology is still in its infancy, there is a need to create effective, operational, inclusive, sustainable, transnational and transdisciplinary networks with a serious and ambitious commitment for knowledge transfer, training provision, dissemination of best practices and identification of the emerging technological trends through science communication activities. A collaborative (net)work is today compelling to provide innovative solutions and products that can be commercialized to contribute to the circular bioeconomy. This perspective article highlights the importance of establishing such collaborative frameworks using the example of Ocean4Biotech, an Action within the European Cooperation in Science and Technology (COST) that connects all and any stakeholders with an interest in marine biotechnology in Europe and beyond.

The aim of this study is to characterize the antimicrobial resistance of Campylobacter jejuni recovered from diarrheal patients in Belgium, focusing on the genetic diversity of resistant strains and underlying molecular mechanisms of resistance among Campylobacter jejuni resistant strains isolated from diarrheal patients in Belgium. Susceptibility profile of 199 clinical C. jejuni isolates was determined by minimum inhibitory concentrations against six commonly-used antibiotics (ciprofloxacin, nalidixic acid, tetracycline, streptomycin, gentamicin, and erythromycin). High rates of resistance were observed against nalidixic acid (56.3%), ciprofloxacin (55.8%) and tetracycline (49.7%); these rates were similar to those obtained from different national reports in broilers intended for human consumption. Alternatively, lower resistance rates to streptomycin (4.5%) and erythromycin (2%), and absolute sensitivity to gentamicin were observed. C. jejuni isolates resistant to tetracycline or quinolones (ciprofloxacin and/or nalidixic acid) were screened for the presence of the tetO gene and the C257T mutation in the quinolone resistance determining region (QRDR) of the gyrase gene gyrA, respectively. Interestingly, some of the isolates that displayed phenotypic resistance to these antimicrobials lacked the corresponding genetic determinants. Among erythromycin-resistant isolates, a diverse array of potential molecular resistance mechanisms was investigated, including the presence of ermB and mutations in the 23S rRNA gene, the rplD and rplV ribosomal genes, and the regulatory region of the cmeABC operon. Two of the four erythromycin-resistant isolates harboured the A2075G transition mutation in the 23S rRNA gene; one of these isolates exhibited further mutations in rplD, rplV and in the cmeABC regulatory region. This study expands the current understanding of how different genetic determinants and particular clones shape the epidemiology of antimicrobial resistance in C. jejuni in Belgium. It also reveals many questions in need of further investigation, such as the role of other undetermined molecular mechanisms that may potentially contribute to the antimicrobial resistance of Campylobacter.

The nature and stability of carbon dioxide under extreme conditions relevant to the Earth’s mantle is still under debate, in view of its possible role within the deep carbon cycle. Here, the authors perform high-pressure experiments providing evidence that polymeric crystalline CO2 is stable under megabaric conditions.

Extreme static pressures exceeding a million atmospheres exist in a variety of natural environments, but obtaining such pressures in a laboratory is still a challenge. Here, the authors develop a toroidal diamond anvil design that allows for the generation of 600 GPa (6 million atmospheres) in routinely used diamond anvil cells.

Automated grading systems using deep convolution neural networks (DCNNs) have proven their capability and potential to distinguish between different breast cancer grades using digitized histopathological images. In digital breast pathology, it is vital to measure how confident a DCNN is in grading using a machine-confidence metric, especially with the presence of major computer vision challenging problems such as the high visual variability of the images. Such a quantitative metric can be employed not only to improve the robustness of automated systems, but also to assist medical professionals in identifying complex cases. In this paper, we propose Entropy-based Elastic Ensemble of DCNN models (3E-Net) for grading invasive breast carcinoma microscopy images which provides an initial stage of explainability (using an uncertainty-aware mechanism adopting entropy). Our proposed model has been designed in a way to (1) exclude images that are less sensitive and highly uncertain to our ensemble model and (2) dynamically grade the non-excluded images using the certain models in the ensemble architecture. We evaluated two variations of 3E-Net on an invasive breast carcinoma dataset and we achieved grading accuracy of 96.15% and 99.50%.

Carbonates are shown to exist in the lower mantle as seen in diamond inclusions, but thermodynamic constraints are poorly understood. Here, the authors synthesise two new iron carbonate compounds and find that self-oxidation-reduction reactions can preserve carbonates in the mantle.

The presence of α-seifertite and seiferite in shocked meteorites are used to determine shock pressures. Here, using high-pressure experiments, the authors find that the presence of α-cristobalite does not exclude high-pressure transformation and seifertite does not necessarily indicate high pressures.

Abstract The exposure of the Earth’s surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(

Heparin induced thrombocytopenia (HIT) is a rare but potentially life threatening adverse drug reaction. We report an unusual case of concomitant subacute coronary stent and femoral artery thrombosis secondary to HIT. In the current era of extensive growth of heparin use and percutaneous coronary interventions, it’s important for clinicians to remember that such complication might occur and should be prevented.

Understanding the dynamics of the magmatic evolution of the interior of the Moon requires accurate knowledge of the viscosity (η) of lunar magmas at high pressure (P) and high temperature (T) conditions. Although the viscosities of terrestrial magmas are relatively well-documented, and their relation to magma composition well-studied, the viscosities of lunar titano-silicate melts are not well-known. Here, we present an experimentally measured viscosity dataset for three end member compositions, characterized by a wide range of titanium contents, at lunar-relevant pressure-temperature range of ∼1.1–2.4 GPa and 1830–2090 K. In situ viscometry using the falling sphere technique shows that the viscosity of lunar melts varies between ∼0.13 and 0.87 Pa-s depending on temperature, pressure and composition. Viscosity decreases with increasing temperature with activation energies for viscous flow of Ea = 201 kJ/mol and Ea = 106 kJ/mol for low-titanium (Ti) and high-Ti melts, respectively. Pressure is found to mildly increase the viscosity of these intermediate polymerized melts by a factor of ∼1.5 between 1.1 and 2.4 GPa. Viscosities of low-Ti and high-Ti magmas at their respective melting temperatures are very close. However at identical P-T conditions (∼1.3 GPa, ∼1840 K) low-Ti magmas are about a factor of three more viscous than high-Ti magmas, reflecting structural effects of Si and Ti on melt viscosity. Measured viscosities differ significantly from empirical models based on measurements of the viscosity of terrestrial basalts, with largest deviations observed for the most Ti-rich and Si-poor composition. Viscosity coefficients for these primitive lunar melts are found to be lower than those of common terrestrial basalts, giving them a high mobility throughout the lunar mantle and onto the surface of the Moon despite their Fe and Ti-rich compositions.

The genetic gain in yield and quality are two major targets of wheat breeding programs around the world. In this study, a high density genetic map consisting of 10,172 SNP markers identified a total of 43 genomic regions associated with three quality traits, three yield traits and two agronomic traits in hard red spring wheat (HRSW). When compared with six grain shape and size traits, the quality traits showed mostly independent genetic control (~18% common loci), while the yield traits showed moderate association (~53% common loci). Association of genomic regions for grain area (GA) and thousand-grain weight (TGW), with yield suggests that targeting an increase in GA may help enhancing wheat yield through an increase in TGW. Flour extraction (FE), although has a weak positive phenotypic association with grain shape and size, they do not share any common genetic loci. A major contributor to plant height was the Rht8 locus and the reduced height allele was associated with significant increase in grains per spike (GPS) and FE, and decrease in number of spikes per square meter and test weight. Stable loci were identified for almost all the traits. However, we could not find any QTL in the region of major known genes like GPC-B1, Ha, Rht-1, and Ppd-1. Epistasis also played an important role in the genetics of majority of the traits. In addition to enhancing our knowledge about the association of wheat quality and yield with grain shape and size, this study provides novel loci, genetic information and pre-breeding material (combining positive alleles from both parents) to enhance the cultivated gene pool in wheat germplasm. These resources are valuable in facilitating molecular breeding for wheat quality and yield improvement.

BACKGROUND:Atrial fibrillation (AF) is a mechanistically heterogeneous disorder, and the ability to identify sub-phenotypes ("endophenotypes") of AF would assist in the delivery of personalized medicine. We used the clinical response to pulmonary vein isolation (PVI) to identify a sub-group of patients with non-PV mediated AF and sought to define the clinical associations. METHODS:Subjects enrolled in the Vanderbilt AF Ablation Registry who underwent a repeat AF ablation due to arrhythmia recurrence were analyzed on the basis of PV reconnection. Subjects who had no PV reconnection were defined as "non-PV mediated AF". A comparison group of subjects were identified who had AF that was treated with PVI-only and experienced no arrhythmia recurrence >12 months. They were considered a group enriched for "PV-mediated AF". Univariate and multivariable binary logistic regression analysis was performed to investigate clinical associations between the PV and non-PV mediated AF groups. RESULTS:Two hundred and twenty nine subjects underwent repeat AF ablation and thirty three (14%) had no PV reconnection. They were compared with 91 subjects identified as having PV-mediated AF. Subjects with non-PV mediated AF were older (64 years [IQR 60,71] vs. 60 [52,67], P = 0.01), more likely to have non-paroxysmal AF (82% [N = 27] vs. 35% [N = 32], P

Carbonates have been proposed as the principal oxidized carbon-bearing phases in the Earth’s interior. Their phase diagram for the high pressure and temperature conditions of the mantle can provide crucial constraints on the deep carbon cycle. We investigated the behavior of MnCO3 at pressures up to 75 GPa and temperatures up to 2200 K. The phase assemblage in the resulting run products was determined in situ by X-ray diffraction (XRD), and the recovered samples were studied by analytical transmission electron microscopy (TEM) and X-ray absorption near edge structure (XANES) imaging. At moderate temperatures below 1400 K and pressures above 50 GPa, MnCO3 transformed into the MnCO3-II phase, with XANES data indicating no change in the manganese oxidation state in MnCO3-II. However, upon heating above 1400 K at the same pressure conditions, both MnCO3 and MnCO3-II undergo decomposition and redox reactions which lead to the formation of manganese oxides and reduced carbon.