SSR identification and phylogenetic analysis in four plant species based on complete chloroplast genome sequences.

The effective utilization of traditional Chinese medicine (TCM) has been challenged by the difficulty to accurately distinguish between similar plant varieties. The stability and conservation of the chloroplast genome can aid in resolving genotypes. Previous studies using nuclear sequences and molecular markers have not effectively differentiated the species from related taxa, such as Machilus leptophylla, Hanceola exserta, Rubus bambusarum, and Rubus henryi. This study aimed to characterize the chloroplast genomes of these four plant species, and analyze their simple sequence repeats (SSRs) and phylogenetic positions. The results demonstrated the four chloroplast genomes consisted of 152.624 kb, 153.296 kb, 156.309 kb, and 158.953 kb in length, involving 124, 130, 129, and 131 genes, respectively. They also contained four specific regions with mononucleotide being the class with the most members. Moreover, these repeating types of SSR were various in individual class. Phylogenetic analysis showed that M. leptophylla was clustered with M. yunnanensis, and H. exserta was confirmed as belonging to the family Ocimeae. Additionally, R. bambusarum and R. henryi were grouped together but differed in their SSR features, indicating that they were not the same species. This research provides evidence for resolving species and contributes new genetic information for further studies.

Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda.

The COVID19 pandemic has impacted the global economy, social activities, and Electricity Consumption (EC), affecting the performance of historical data-based Electricity Load Forecasting (ELF) algorithms. This study thoroughly analyses the pandemic's impact on these models and develop a hybrid model with better prediction accuracy using COVID19 data. Existing datasets are reviewed, and their limited generalization potential for the COVID19 period is highlighted. A dataset of 96 residential customers, comprising 36 and six months before and after the pandemic, is collected, posing significant challenges for current models. The proposed model employs convolutional layers for feature extraction, gated recurrent nets for temporal feature learning, and a self-attention module for feature selection, leading to better generalization for predicting EC patterns. Our proposed model outperforms existing models, as demonstrated by a detailed ablation study using our dataset. For instance, it achieves an average reduction of 0.56% & 3.46% in MSE, 1.5% & 5.07% in RMSE, and 11.81% & 13.19% in MAPE over the pre- and post-pandemic data, respectively. However, further research is required to address the varied nature of the data. These findings have significant implications for improving ELF algorithms during pandemics and other significant events that disrupt historical data patterns.

Revealing analgesic and anxiolytic potentials of synthetic benzimidazole analogues: An in-vivo and in-silico study.

Certain drugs have potential to affect and alter individual's behavior. On the other hand, pain is a complex phenomenon with various treatment options; analgesic medicines are the primary source. Therefore, this study was based on examining some of the benzimidazole analogues for their analgesic as well as behavioral potential following Tail immersion test and Open field test respectively. In addition, molecular docking was performed to find the interaction of these compounds with the active site using AutoDock Vina which was further visualized through Discovery Studio Visualizer. It was seen that the cyano-methyl benzimidazole derivatives (CMB1-CMB3) showed relief in pain as compared to benzimidazole derivatives (BI1-BI3), CMB2 demonstrated highly potent analgesic effect. Likewise, all structures except BI1 displayed increase locomotion during open field test and can be offered as anxiolytic compounds. Almost all derivatives showed improve binding energies for the tested proteins where the high analgesic action of CMB2 might be correlated to its high binding affinity and interaction at µOR. It was also noticed that all structures except BI showed possible binding interaction with GABAA receptor and hence possessed anxiolytic like potential. Thus, this study offered benzimidazole analogues for further drug development of analgesic and anxiolytic like compounds.

Dental age estimation of Malaysian children: a comparison of two-dimensional versus three-dimensional imaging analyses of the developing root apices.

AIM: This study compared the effectiveness of the three-dimensional (3D) cone beam computed tomography (CBCT) method of age estimation developed by Asif et al. with two-dimensional Cameriere's method. SUBJECTS AND METHODS: CBCT images belonging to 129 Malaysian Chinese and Malay ethnic groups aged 7-14 years were investigated and analysed. RESULTS: The results indicated a strong correlation between chronological age and the predictor variables for both Cameriere's (r = 0.984) and Asif's (r = 0.988) methods of age estimation. Fisher Z test analysis indicated no statistically significant difference in the correlation values between the two methods. Mean absolute error (MAE) value of 0.613 was observed for Cameriere's and 0.290 was observed for Asif's method. CONCLUSIONS: The results indicated that the methods of age estimation from both Asif et al. and Cameriere et al. are applicable on Malaysian children. However, Asif et al.'s 3D CBCT method of age estimation resulted in greater accuracy and reliability in estimating chronological age.

Dental age estimation of Malaysian Indian children and adolescents: applicability of Chaillet and Demirjian's modified method using artificial neural network.

BACKGROUND: Recognising the importance of dental age (DA) estimation in forensic investigations, a variety of methods abound in the literature due to population-specific attributes. A reference eight-tooth method developed by Chaillet and Demirjian estimated the DA of children and adolescents. AIM: This study aims to investigate the applicability of Chaillet and Demirjian's method among Malaysian Indians aged 5.00-17.99 years. SUBJECTS AND METHODS: Dental panoramic tomographs of Malaysian Indians aged 5.00-17.99 years were statistically analysed using paired t-test and artificial neural networks multilayer perceptron (ANN-MLP). RESULTS: A total of 1015 dental panoramic tomographs were analysed. Paired t-test analysis against the reference dental maturity scores revealed underestimation of DA in boys of 1.68 years and girls of 2.56 years indicating inaccurate age estimation. A population-specific prediction model with a new set of dental maturity scores was established on Chaillet and Demirjian's scores using ANN-MLP. The new dental maturity scores showed accurate estimation of DA with differences between CA and DA being 12 and 25 days for boys and girls, respectively. Furthermore, a new DA prediction formula was developed using regression analysis following the establishment of new dental scores based on ANN-MLP. CONCLUSION: A novel Malaysian Indian-specific prediction model that demonstrated accurate DA estimation was established.

AWANet: Attentive-Aware Wide-Kernels Asymmetrical Network with Blended Contour Information for Salient Object Detection.

Although deep learning-based techniques for salient object detection have considerably improved over recent years, estimated saliency maps still exhibit imprecise predictions owing to the internal complexity and indefinite boundaries of salient objects of varying sizes. Existing methods emphasize the design of an exemplary structure to integrate multi-level features by employing multi-scale features and attention modules to filter salient regions from cluttered scenarios. We propose a saliency detection network based on three novel contributions. First, we use a dense feature extraction unit (DFEU) by introducing large kernels of asymmetric and grouped-wise convolutions with channel reshuffling. The DFEU extracts semantically enriched features with large receptive fields and reduces the gridding problem and parameter sizes for subsequent operations. Second, we suggest a cross-feature integration unit (CFIU) that extracts semantically enriched features from their high resolutions using dense short connections and sub-samples the integrated information into different attentional branches based on the inputs received for each stage of the backbone. The embedded independent attentional branches can observe the importance of the sub-regions for a salient object. With the constraint-wise growth of the sub-attentional branches at various stages, the CFIU can efficiently avoid global and local feature dilution effects by extracting semantically enriched features via dense short-connections from high and low levels. Finally, a contour-aware saliency refinement unit (CSRU) was devised by blending the contour and contextual features in a progressive dense connected fashion to assist the model toward obtaining more accurate saliency maps with precise boundaries in complex and perplexing scenarios. Our proposed model was analyzed with ResNet-50 and VGG-16 and outperforms most contemporary techniques with fewer parameters.

A deep fuzzy model for diagnosis of COVID-19 from CT images.

From early 2020, a novel coronavirus disease pneumonia has shown a global "pandemic" trend at an extremely fast speed. Due to the magnitude of its harm, it has become a major global public health event. In the face of dramatic increase in the number of patients with COVID-19, the need for quick diagnosis of suspected cases has become particularly critical. Therefore, this paper constructs a fuzzy classifier, which aims to detect infected subjects by observing and analyzing the CT images of suspected patients. Firstly, a deep learning algorithm is used to extract the low-level features of CT images in the COVID-CT dataset. Subsequently, we analyze the extracted feature information with attribute reduction algorithm to obtain features with high recognition. Then, some key features are selected as the input for the fuzzy diagnosis model to the training model. Finally, several images in the dataset are used as the test set to test the trained fuzzy classifier. The obtained accuracy rate is 94.2%, and the F1-score is 93.8%. Experimental results show that, compared with the deep learning diagnosis methods widely used in medical image analysis, the proposed fuzzy model improves the accuracy and efficiency of diagnosis, which consequently helps to curb the spread of COVID-19.

An Adaptive Game-Based Learning Strategy for Children Road Safety Education and Practice in Virtual Space.

Virtual reality (VR) has been widely used as a tool to assist people by letting them learn and simulate situations that are too dangerous and risky to practice in real life, and one of these is road safety training for children. Traditional video- and presentation-based road safety training has average output results as it lacks physical practice and the involvement of children during training, without any practical testing examination to check the learned abilities of a child before their exposure to real-world environments. Therefore, in this paper, we propose a 3D realistic open-ended VR and Kinect sensor-based training setup using the Unity game engine, wherein children are educated and involved in road safety exercises. The proposed system applies the concepts of VR in a game-like setting to let the children learn about traffic rules and practice them in their homes without any risk of being exposed to the outside environment. Thus, with our interactive and immersive training environment, we aim to minimize road accidents involving children and contribute to the generic domain of healthcare. Furthermore, the proposed framework evaluates the overall performance of the students in a virtual environment (VE) to develop their road-awareness skills. To ensure safety, the proposed system has an extra examination layer for children's abilities evaluation, whereby a child is considered fit for real-world practice in cases where they fulfil certain criteria by achieving set scores. To show the robustness and stability of the proposed system, we conduct four types of subjective activities by involving a group of ten students with average grades in their classes. The experimental results show the positive effect of the proposed system in improving the road crossing behavior of the children.

Federated learning for COVID-19 screening from Chest X-ray images.

Today, the whole world is facing a great medical disaster that affects the health and lives of the people: the COVID-19 disease, colloquially known as the Corona virus. Deep learning is an effective means to assist radiologists to analyze the vast amount of chest X-ray images, which can potentially have a substantial role in streamlining and accelerating the diagnosis of COVID-19. Such techniques involve large datasets for training and all such data must be centralized in order to be processed. Due to medical data privacy regulations, it is often not possible to collect and share patient data in a centralized data server. In this work, we present a collaborative federated learning framework allowing multiple medical institutions screening COVID-19 from Chest X-ray images using deep learning without sharing patient data. We investigate several key properties and specificities of federated learning setting including the not independent and identically distributed (non-IID) and unbalanced data distributions that naturally arise. We experimentally demonstrate that the proposed federated learning framework provides competitive results to that of models trained by sharing data, considering two different model architectures. These findings would encourage medical institutions to adopt collaborative process and reap benefits of the rich private data in order to rapidly build a powerful model for COVID-19 screening.

An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images.

In recent times, COVID-19, has a great impact on the healthcare sector and results in a wide range of respiratory illnesses. It is a type of Ribonucleic acid (RNA) virus, which affects humans as well as animals. Though several artificial intelligence-based COVID-19 diagnosis models have been presented in the literature, most of the works have not focused on the hyperparameter tuning process. Therefore, this paper proposes an intelligent COVID-19 diagnosis model using a barnacle mating optimization (BMO) algorithm with a cascaded recurrent neural network (CRNN) model, named BMO-CRNN. The proposed BMO-CRNN model aims to detect and classify the existence of COVID-19 from Chest X-ray images. Initially, pre-processing is applied to enhance the quality of the image. Next, the CRNN model is used for feature extraction, followed by hyperparameter tuning of CRNN via the BMO algorithm to improve the classification performance. The BMO algorithm determines the optimal values of the CRNN hyperparameters namely learning rate, batch size, activation function, and epoch count. The application of CRNN and hyperparameter tuning using the BMO algorithm shows the novelty of this work. A comprehensive simulation analysis is carried out to ensure the better performance of the BMO-CRNN model, and the experimental outcome is investigated using several performance metrics. The simulation results portrayed that the BMO-CRNN model has showcased optimal performance with an average sensitivity of 97.01%, specificity of 98.15%, accuracy of 97.31%, and F-measure of 97.73% compared to state-of-the-art methods.

Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data.

The exponential growth in population and their overall reliance on the usage of electrical and electronic devices have increased the demand for energy production. It needs precise energy management systems that can forecast the usage of the consumers for future policymaking. Embedded smart sensors attached to electricity meters and home appliances enable power suppliers to effectively analyze the energy usage to generate and distribute electricity into residential areas based on their level of energy consumption. Therefore, this paper proposes a clustering-based analysis of energy consumption to categorize the consumers' electricity usage into different levels. First, a deep autoencoder that transfers the low-dimensional energy consumption data to high-level representations was trained. Second, the high-level representations were fed into an adaptive self-organizing map (SOM) clustering algorithm. Afterward, the levels of electricity energy consumption were established by conducting the statistical analysis on the obtained clustered data. Finally, the results were visualized in graphs and calendar views, and the predicted levels of energy consumption were plotted over the city map, providing a compact overview to the providers for energy utilization analysis.

Osseous evidence behind micro-osteoperforation technique in accelerating orthodontic tooth movement: A 3-month study.

INTRODUCTION: The study aimed to investigate the effects of micro-osteoperforations (MOPs) on the mandibular bone volume/tissue volume (BV/TV) ratio changes and the rate of orthodontic tooth movement using cone-beam computed tomography images. Another objective was to evaluate the effects of MOP frequency intervals (4 weeks, 8 weeks, and 12 weeks) on the BV/TV ratio and rate of tooth movement. METHODS: In 24 participants, 140-200 g of force was applied for mandibular canine retraction. Three MOPs were made according to the scheduled intervals of the 3 different groups: group 1 (MOP 4 weeks), group 2 (MOP 8 weeks), and group 3 (MOP 12 weeks) directly at the mandibular buccal cortical bone of extracted first premolars sites. Cone-beam computed tomography scans were obtained at the 12th week after MOP application. Computed tomography Analyzer software (version 1.11.0.0; Skyscan, Kontich, Belgium) was used to compute the trabecular alveolar BV/TV ratio. RESULTS: A significant difference was observed in the rate of canine movement between control and MOP. Paired t test analysis showed a significant difference (P = 0.001) in the mean BV/TV ratio between control and MOP sides in all the frequency intervals groups. However, the difference was significant only in group 1 (P = 0.014). A strong negative correlation (r = -0.86) was observed between the rate of canine tooth movement and the BV/TV ratio at the MOP side for group 1 and all frequency intervals together (r = -0.42). CONCLUSIONS: The rate of orthodontic tooth movement can be accelerated by the MOP technique with frequently repeated MOPs throughout the treatment.

Effect of reconstruction parameters on cone beam CT trabecular bone microstructure quantification in sheep.

BACKGROUND: Cone Beam Computed Tomography (CBCT) is a reliable radiographic modality to assess trabecular bone microarchitecture. The aim of this study was to determine the effect of CBCT image reconstruction parameters, namely, the threshold value and reconstruction voxel size, on trabecular bone microstructure assessment. METHODS: Five sectioned maxilla of adult Dorper male sheep were scanned using a CBCT system with a resolution of 76 µm3 (Kodak 9000). The CBCT images were reconstructed using different reconstruction parameters and analysed. The effect of reconstruction voxel size (76, 100 and 200 µm3) and threshold values (±15% from the global threshold value) on trabecular bone microstructure measurement was assessed using image analysis software (CT analyser version 1.15). RESULTS: There was no significant difference in trabecular bone microstructure measurement between the reconstruction voxel sizes, but a significant difference (Tb.N = 0.03, Tb.Sp = 0.04, Tb.Th = 0.01, BV/TV = 0.00) was apparent when the global threshold value was decreased by 15%. CONCLUSIONS: Trabecular bone microstructure measurements are not compromised by changing the CBCT reconstruction voxel size. However, measurements can be affected when applying a threshold value of less than 15% of the recommended global value.

Violence Detection Using Spatiotemporal Features with 3D Convolutional Neural Network.

The worldwide utilization of surveillance cameras in smart cities has enabled researchers to analyze a gigantic volume of data to ensure automatic monitoring. An enhanced security system in smart cities, schools, hospitals, and other surveillance domains is mandatory for the detection of violent or abnormal activities to avoid any casualties which could cause social, economic, and ecological damages. Automatic detection of violence for quick actions is very significant and can efficiently assist the concerned departments. In this paper, we propose a triple-staged end-to-end deep learning violence detection framework. First, persons are detected in the surveillance video stream using a light-weight convolutional neural network (CNN) model to reduce and overcome the voluminous processing of useless frames. Second, a sequence of 16 frames with detected persons is passed to 3D CNN, where the spatiotemporal features of these sequences are extracted and fed to the Softmax classifier. Furthermore, we optimized the 3D CNN model using an open visual inference and neural networks optimization toolkit developed by Intel, which converts the trained model into intermediate representation and adjusts it for optimal execution at the end platform for the final prediction of violent activity. After detection of a violent activity, an alert is transmitted to the nearest police station or security department to take prompt preventive actions. We found that our proposed method outperforms the existing state-of-the-art methods for different benchmark datasets.

Adaptive Node Clustering Technique for Smart Ocean Under Water Sensor Network (SOSNET).

Abstract: Smart ocean is a term broadly used for monitoring the ocean surface, sea habitat monitoring, and mineral exploration to name a few. Development of an efficient routing protocol for smart oceans is a non-trivial task because of various challenges, such as presence of tidal waves, multiple sources of noise, high propagation delay, and low bandwidth. In this paper, we have proposed a routing protocol named adaptive node clustering technique for smart ocean underwater sensor network (SOSNET). SOSNET employs a moth flame optimizer (MFO) based technique for selecting a near optimal number of clusters required for routing. MFO is a bio inspired optimization technique, which takes into account the movement of moths towards light. The SOSNET algorithm is compared with other bio inspired algorithms such as comprehensive learning particle swarm optimization (CLPSO), ant colony optimization (ACO), and gray wolf optimization (GWO). All these algorithms are used for routing optimization. The performance metrics used for this comparison are transmission range of nodes, node density, and grid size. These parameters are varied during the simulation, and the results indicate that SOSNET performed better than other algorithms.

Pesticide-induced oxidative stress and antioxidant responses in tomato (Solanum lycopersicum) seedlings.

Excessive use of pesticides can adversely affect the growth of non-target host plants in different ways. Pesticide-induced stress can affect non-target plants through elevated levels of reactive oxygen species (ROS) responsible for detrimental effects on cell metabolism, biochemical and other physiological activities. In response to oxidative stress, plant activates antioxidant defense system consisting of both enzymatic and non-enzymatic components. In the present investigation, three commonly used pesticides, emamectin benzoate, alpha-cypermethrin and imidacloprid, were assessed for causing oxidative stress in tomato. The oxidative damage induced by these pesticides at five different concentrations i.e. 1/4X, 1/2X, recommended application dose (X), 2X and 4X in the root and shoot tissues of tomato plant/seedlings were evaluated. Following pesticide exposure for 35 days, cell viability, cell injury, total soluble sugar (TSS) and total soluble proteins (TSP) were measured. Antioxidant activities were estimated by measuring activity levels of superoxide dismutase (SOD), catalase (CAT), glutathione reductase (GR) peroxidase (POD), ascorbate peroxidase (APX) and proline. Hydrogen peroxide (H2O2) levels were analysed as ROS, lipid peroxidation was measured in term of thiobarbituric acid reactive substances (TBARS) as membrane damage caused by ROS was also assessed. Analysis of the data revealed that pesticides application at higher concentrations significantly elevated ROS levels and caused membrane damage by the formation of TBARS, increased cell injury and reduced cell viability both in root and shoot tissues compared with non-treated plants. Moreover, a gradual decrease in the levels of TSS and TSP was observed in plants subjected to increasing doses of pesticides. To cope with pesticide-induced oxidative stress, a significant increase in levels of antioxidants was observed in the plants exposed to higher doses of pesticides. Shoot tissues responded more drastically by producing higher levels of antioxidants as compared to root tissues indicating the direct exposure of shoots to foliar application of pesticides. Taken together, these results strongly suggested that the application of pesticides above the recommended dose can provoke the state of oxidative stress and can cause oxidative damages in non-target host plants.

OsGPAT3 Plays a Critical Role in Anther Wall Programmed Cell Death and Pollen Development in Rice.

In flowering plants, ideal male reproductive development requires the systematic coordination of various processes, in which timely differentiation and degradation of the anther wall, especially the tapetum, is essential for both pollen formation and anther dehiscence. Here, we show that OsGPAT3, a conserved glycerol-3-phosphate acyltransferase gene, plays a critical role in regulating anther wall degradation and pollen exine formation. The gpat3-2 mutant had defective synthesis of Ubisch bodies, delayed programmed cell death (PCD) of the inner three anther layers, and abnormal degradation of micropores/pollen grains, resulting in failure of pollen maturation and complete male sterility. Complementation and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated 9 (Cas9) experiments demonstrated that OsGPAT3 is responsible for the male sterility phenotype. Furthermore, the expression level of tapetal PCD-related and nutrient metabolism-related genes changed significantly in the gpat3-2 anthers. Based on these genetic and cytological analyses, OsGPAT3 is proposed to coordinate the differentiation and degradation of the anther wall and pollen grains in addition to regulating lipid biosynthesis. This study provides insights for understanding the function of GPATs in regulating rice male reproductive development, and also lays a theoretical basis for hybrid rice breeding.

Ecotoxicological evaluation of two anti-dandruff hair shampoos using Lemna minor.

Hair shampoos, a mixture of various organic and organic compounds, are commonly used personnel care products. Since shampoos are used in almost every household and beauty shop, their ingredients are common components of domestic and municipal wastewater. However, studies on the effect of shampoos to aquatic plants can hardly be found in literature. Therefore, the present study was conducted to investigate the phytotoxic effects of two commonly used anti-dandruff shampoos (named here AD 1 and AD 2) using Lemna minor as a biotest organism. For toxicity assessment, frond number, fresh and dry biomass, and light-harvesting pigments (chlorophyll a, b and total carotenoids) of Lemna were used as end points. Five different concentrations (0.001, 0.01, 0.1, 1, and 5%) of each shampoo were tested in comparison to the control. At lower concentrations of shampoos, some minor and non-significant stimulatory effects were observed in some parameters, but at concentrations above 0.01% both the shampoos significantly inhibited almost all parameters in Lemna. The EC50 values obtained for frond number were 0.034 and 0.11% for AD 1 and AD 2, respectively. The fresh biomass gave EC50 values of 0.07 and 0.066% for AD 1 and AD 2, respectively. Based on the preset study, it can be speculated that shampoo contamination at higher concentrations in water bodies can be a threat to aquatic organisms. This study can be used as a baseline to further investigate shampoo toxicity using other species and to explore the mechanism of shampoo toxicity in aquatic plants.