Computational models for prediction of protein-protein interaction in rice and Magnaporthe grisea.

Introduction: Plant-microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice Oryza sativa (O. sativa) due to Magnaporthe grisea (M. grisea) fungus. Protein-protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease. Methods: In this paper, four genomic information-based models such as (i) the interolog, (ii) the domain, (iii) the gene ontology, and (iv) the phylogenetic-based model are developed for predicting the interaction between O. sativa and M. grisea in a whole-genome scale. Results and Discussion: A total of 59,430 interacting pairs between 1,801 rice proteins and 135 blast fungus proteins are obtained from the four models. Furthermore, a machine learning model is developed to assess the predicted interactions. Using composition-based amino acid composition (AAC) and conjoint triad (CT) features, an accuracy of 88% and 89% is achieved, respectively. When tested on the experimental dataset, the CT feature provides the highest accuracy of 95%. Furthermore, the specificity of the model is verified with other pathogen-host datasets where less accuracy is obtained, which confirmed that the model is specific to O. sativa and M. grisea. Understanding the molecular processes behind rice resistance to blast fungus begins with the identification of PPIs, and these predicted PPIs will be useful for drug design in the plant science community.

Internet of Things and Robotics in Transforming Current-Day Healthcare Services.

Technology has become an integral part of everyday lives. Recent years have witnessed advancement in technology with a wide range of applications in healthcare. However, the use of the Internet of Things (IoT) and robotics are yet to see substantial growth in terms of its acceptability in healthcare applications. The current study has discussed the role of the aforesaid technology in transforming healthcare services. The study also presented various functionalities of the ideal IoT-aided robotic systems and their importance in healthcare applications. Furthermore, the study focused on the application of the IoT and robotics in providing healthcare services such as rehabilitation, assistive surgery, elderly care, and prosthetics. Recent developments, current status, limitations, and challenges in the aforesaid area have been presented in detail. The study also discusses the role and applications of the aforementioned technology in managing the current pandemic of COVID-19. A comprehensive knowledge has been provided on the prospect of the functionality, application, challenges, and future scope of the IoT-aided robotic system in healthcare services. This will help the future researcher to make an inclusive idea on the use of the said technology in improving the healthcare services in the future.

Dataset for EEG signals used to detect the effect of coffee consumption on the activation of SSVEP signal.

A set of electroencephalogram (EEG) data was obtained in the National Institute of Technology, Rourkela, India, from six individuals in the presence of seven photic stimuli of different frequencies (range: 3 Hz-30 Hz). The EEG data were recorded prior to, and post-consumption of caffeinated coffee for detecting the influence of coffee consumption on the initiation of steady-state visual evoked potential (SSVEP) signals in different regions of the brain. The data supports the article: "Data mining-based approach to study the effect of consumption of caffeinated coffee on the generation of steady-state visual evoked potential signals" [1]. The obtained dataset can also be used to have more insight into the brain response during the post-consumption of coffee using different feature extraction, classification, and SSVEP signal detection techniques.

Data mining based approach to study the effect of consumption of caffeinated coffee on the generation of the steady-state visual evoked potential signals.

The steady-state visual evoked potentials (SSVEP), are elicited at the parieto-occipital region of the cortex when a light source (3.5-75 Hz), flickering at a constant frequency, stimulates the retinal cells. In the last few decades, researchers have reported that caffeine enhances the vigilance and the executive control of visual attention. However, no study has investigated the effect of caffeinated coffee on the SSVEP response, which is used for controlling the brain-computer interface (BCI) devices for rehabilitative applications. The current work proposes a data mining-based approach to gain insight into the alterations in the SSVEP signals after the consumption of caffeinated coffee. Recurrence quantification analysis (RQA) of the electroencephalogram (EEG) signals was employed for this purpose. The EEG signals were acquired at seven frequencies of photic stimuli. The stimuli frequencies were chosen such that they were distributed throughout the EEG frequency bands. The prominent SSVEP signals were identified using the Canonical Correlation Analysis (CCA) method. Several statistical features were extracted from the recurrence plot of the SSVEP signals. Statistical analyses using the t-test and decision tree-based methods helped to select the most relevant features, which were then classified using Automated Neural Network (ANN). The relevant features could be classified with a maximum accuracy of 97%. This supports our hypothesis that the consumption of caffeinated coffee can alter the SSVEP response. In conclusion, utmost care should be taken in selecting the features for designing BCI devices.

Floristic composition and utilization of ethnomedicinal plant species in home gardens of the Eastern Himalaya.

BACKGROUND: Home gardens are popular micro land-use system and are socioeconomically linked with people for their livelihood. In the foothill region of Eastern Himalaya, very less documentations are available on species richness of the home gardens, particularly on the ethnomedicinal plants. We assumed that the home garden owners of the study site are domesticating ethnomedicinal plants which are not easily accessible to them in the wild due to distant forest. This study was planned to explore and document the diversity and population status of ethnomedicinal plants in the home gardens along with its ethnomedicinal use. METHODS: The present study was conducted in the home gardens of Cooch Behar district of West Bengal from May 2017 to May 2018. A multidisciplinary approach like collection of plant specimen, interview with structured questionnaire for documenting the utilization pattern, and quadrat methods for population study was applied. We selected 150 study sites randomly in the village cluster. The owners of the gardens were the respondents for the household survey. The study documented diversity, population size, and medicinal uses of ethnomedicinal plant species identified by the garden owners growing or being grown in their gardens. RESULTS: A total of 260 plant species were reported, of which, 53 were utilized for different ethnomedicinal applications. These 53 species were represented by 35 families and 45 genera. Most of these ethnomedicinal species were woody perennials (37.73%). Cocus nucifera dominated the list with highest number of use followed by Hibiscus rosa-sinensis. The use value of the species varied from 0.006 to 0.53, while the fidelity value (%) ranged from 2.29 to 93.75%. The leaves of the plants were mostly used for ethnomedicinal applications (19 species) followed by fruits (12 species) and bark (9 species), and the least was the root (7 species). We documented 20 different ailments/diseases cured by using these plants. In some cases, more than one species are used to cure a disease or ailment. As many as 10 species were used to cure only stomach-related problems. Some more diseases like cough and cold and jaundice were treated using six and four species, respectively. CONCLUSION: This documented list of 260 plant species including 53 ethnomedicinal ones from the home gardens of the study area indicates that these gardens are key in maintaining diversity and source of healthcare system in agricultural dominant landscape. Documenting such ecological status and traditional applications becomes a prerequisite for developing conservation and management strategies of home gardens to be included in the mainstream conservation processes.

Indigenous uses of ethnomedicinal plants among forest-dependent communities of Northern Bengal, India.

BACKGROUND: Traditional knowledge on ethnomedicinal plant is slowly eroding. The exploration, identification and documentation on utilization of ethnobotanic resources are essential for restoration and preservation of ethnomedicinal knowledge about the plants and conservation of these species for greater interest of human society. METHODS: The study was conducted at fringe areas of Chilapatta Reserve Forest in the foothills of the eastern sub-Himalayan mountain belts of West Bengal, India, from December 2014 to May 2016. Purposive sampling method was used for selection of area. From this area which is inhabited by aboriginal community of Indo-Mongoloid origin, 400 respondents including traditional medicinal practitioners were selected randomly for personal interview schedule through open-ended questionnaire. The questionnaire covered aspects like plant species used as ethnomedicines, plant parts used, procedure for dosage and therapy. RESULTS: A total number of 140 ethnomedicinal species was documented, in which the tree species (55) dominated the lists followed by herbs (39) and shrubs (30). Among these total planted species used for ethnomedicinal purposes, 52 species were planted, 62 species growing wild or collected from the forest for use and 26 species were both wild and planted. The present study documented 61 more planted species as compared to 17 planted species documented in an ethnomedicinal study a decade ago. The documented species were used to treat 58 human diseases/ailments including nine species used to eight diseases/ailments of domestic animals. Stomach-related problems were treated by maximum number of plants (40 species) followed by cuts and wounds with 27 plant species and least with one species each for 17 diseases or ailments. Maximum number of 12 diseases/ailments was cured by Melia azedarach followed by Centella asiatica and Rauvolfia serpentina which were used to cure 11 diseases/ailments each. CONCLUSIONS: The list of 140 plant species indicates that the Chilapatta Reserve Forest and its fringe areas are rich in biodiversity of ethnobotanical plant species. Rauvolfia serpentina were the most valuable species in terms of its maximal use with higher use value. The documentation of 78 species maintained in the home gardens indicates the community consciousness on the conservation values of these ethnobotanical species. The communities should be encouraged with improved cultivation techniques of commercially viable ethnobotanical species through capacity building, timely policy intervention along with strong market linkage. This will ensure income generation and livelihood improvement and ultimate conservation of these species.

Classification of images based on small local features: a case applied to microaneurysms in fundus retina images.

Convolutional neural networks (CNNs), the state of the art in image classification, have proven to be as effective as an ophthalmologist, when detecting referable diabetic retinopathy. Having a size of [Formula: see text] of the total image, microaneurysms are early lesions in diabetic retinopathy that are difficult to classify. A model that includes two CNNs with different input image sizes, [Formula: see text] and [Formula: see text], was developed. These models were trained using the Kaggle and Messidor datasets and tested independently against the Kaggle dataset, showing a sensitivity [Formula: see text], a specificity [Formula: see text], and an area under the receiver operating characteristics curve [Formula: see text]. Furthermore, by combining these trained models, there was a reduction of false positives for complete images by about 50% and a sensitivity of 96% when tested against the DiaRetDB1 dataset. In addition, a powerful image preprocessing procedure was implemented, improving not only images for annotations, but also decreasing the number of epochs during training. Finally, a feedback method was developed increasing the accuracy of the CNN [Formula: see text] input model.