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Real-time security surveillance and identity matching using face detection and recognition are central research areas within computer vision. The classical facial detection techniques include Haar-like, MTCNN, AdaBoost, and others. These techniques employ template matching and geometric facial features for detecting faces, striving for a balance between detection time and accuracy. To address this issue, the current research presents an enhanced FaceNet network. The RetinaFace is employed to perform expeditious face detection and alignment. Subsequently, FaceNet, with an improved loss function is used to achieve face verification and recognition with high accuracy. The presented work involves a comparative evaluation of the proposed network framework against both traditional and deep learning techniques in terms of face detection and recognition performance. The experimental findings demonstrate that an enhanced FaceNet can successfully meet the real-time facial recognition requirements, and the accuracy of face recognition is 99.86% which fulfills the actual requirement. Consequently, the proposed solution holds significant potential for applications in face detection and recognition within the education sector for real-time security surveillance. [ABSTRACT FROM AUTHOR]

Most of the studies about NH4+ stress mechanism simply address the effects of free NH4+, failing to recognize the changed nitrogen assimilation products. The objective of this study was to elucidate the effects of glutamate on root growth under high ammonium (NH4+) conditions in winter wheat (Triticum aestivum L.). Hydroponic experiments were conducted using two wheat cultivars, AK58 (NH4+-sensitive) and Xumai25 (NH4+-tolerant) with either 5 mM NH4+ nitrogen (AN) as stress treatment or 5 mM nitrate (NO3-) nitrogen as control. To evaluate the effects of NH4+-assimilation products on plant growth, 1 μM L-methionine sulfoximine (MSO) (an inhibitor of glutamine synthetase (GS)) and 1 mM glutamates (a primary N assimilation product) were added to the solutions, respectively. The AN significantly reduced plant biomass, total root length, surface area and root volume in both cultivars, but less effect was observed in Xumai25. The inhibition effects were alleviated by the application of MSO but strengthened by the application of glutamate. The AN increased the activities of GS, glutamate dehydrogenase (GDH) in both cultivars, resulting in higher glutamate contents. However, its contents were decreased by the application of MSO. Compared to AK58, Xumai25 showed lower glutamate contents due to its higher activities of glutamic-oxaloacetic transaminase (GOT) and glutamic-pyruvic transaminase (GPT). With the indole-3-acetic acid (IAA) contents decreasing in roots, the ratio of shoot to root in IAA was increased, and further increased by the application of glutamate, and reduced by the application of MSO, but the ratio was lower in Xumai25. Meanwhile, the total soluble sugar contents and its root to shoot ratio also showed similar trends. These results indicate that the NH4+-tolerant cultivar has a greater transamination ability to prevent glutamate over-accumulation to maintain higher IAA transport ability, and consequently promoted soluble sugar transport to roots, further maintaining root growth.

Introduction: Children infected with COVID-19 are susceptible to severe manifestations. We aimed to develop and validate a predictive model for severe/ critical pediatric COVID-19 infection utilizing routinely available hospital level data to ascertain the likelihood of developing severe manifestations. Methods: The predictive model was based on an analysis of registry data from COVID-19 positive patients admitted to five tertiary pediatric hospitals across Asia [Singapore, Malaysia, Indonesia (two centers) and Pakistan]. Independent predictors of severe/critical COVID-19 infection were determined using multivariable logistic regression. A training cohort (n = 802, 70%) was used to develop the prediction model which was then validated in a test cohort (n = 345, 30%). The discriminative ability and performance of this model was assessed by calculating the Area Under the Curve (AUC) and 95% confidence interval (CI) from final Receiver Operating Characteristics Curve (ROC). Results: A total of 1147 patients were included in this analysis. In the multivariable model, infant age group, presence of comorbidities, fever, vomiting, seizures and higher absolute neutrophil count were associated with an increased risk of developing severe/critical COVID-19 infection. The presence of coryza at presentation, higher hemoglobin and platelet count were associated with a decreased risk of severe/critical COVID-19 infection. The AUC (95%CI) generated for this model from the training and validation cohort were 0.96 (0.94, 0.98) and 0.92 (0.86, 0.97), respectively. Conclusion: This predictive model using clinical history and commonly used laboratory values was valuable in estimating the risk of developing a severe/critical COVID-19 infection in hospitalized children. Further validation is needed to provide more insights into its utility in clinical practice. [ABSTRACT FROM AUTHOR]