Sequence Checking and Deduplication for Existing Fingerprint Databases

Biometric technology is a rapidly evolving field with applications that range from access to devices to border crossing and entry/exit processes. Large-scale applications to collect biometric data, such as border crossings result in multimodal biometric databases containing thousands of identities. However, due to human operator error, these databases often contain many instances of image labeling and classification; this is due to the lack of training and throughput pressure that comes with human error. Multiple entries from the same individual may be assigned to a different identity. Rolled fingerprints may be labeled as flat images, a face image entered into a fingerprint field or images entered in incorrect orientation (such as rotated face images, left or right iris, etc.) are common errors found large database records. Ultimately, these enrollment errors make it impossible to identify that individual upon subsequent identification encounters. Sorting through hundreds of images to check for classification errors is a tedious and time-consuming task, especially when several biometric databases are combined. Our goal is to correctly identify misclassified fingerprints using controlled embeddings and thresholds. This work provides a new perspective on image sorting as it focuses not on the traditional aspects of increasing accuracy metrics but provides a look into multiple factors through various embeddings and thresholds to provide a tool that can be used to scour large datasets with ease to provide what percentage of the images need manual correction. The proposed network provides various metric scores which allowed for analysis on the most effective embedding and thresholds to use, resulting in a proof-of-concept to be used for practical purposes in the real world

A Comprehensive Investigation to Cauliflower Diseases Recognition: An Automated Machine Learning Approach

Vegetables, a significant part of agriculture, are necessary for the general good health of human beings. The use of information technology can help vegetable farmers to reach high yields which can contribute to global food security and sustainable cultivation. Cauliflower (Brassica oleracea var. botrytis) is a popular vegetable that is easily affected by various diseases causing loss of production and quality. However, machine learning-based disease recognition has yet to be developed for cauliflowers which can help farmers to identify cauliflower diseases and enable them to take timely actions. In this paper, an online machine vision-based expert system for recognizing cauliflower diseases is proposed, where a captured image via a smartphone or handheld gadget is processed and then classified to identify disease to assist the cauliflower farmers. Based on the feature extraction, the system classifies four types of diseases namely ‘bacterial soft’, ‘white rust’, ‘black rot’, and ‘downy mildew’ in cauliflowers. A total of 776 images are utilized to implement this experiment. K-means clustering algorithm has been applied on captured images to segment the disease-affected regions before two-type features extraction namely statistical and co-occurrence feature. Six classification algorithms namely BayesNet, Kstar, Random Forest, LMT (Logistic Model Tree), BPN (Back propagation neural network), and J48 were used for disease classification, and we evaluated their performance using seven performance metrics. We found the Random Forest classifier outperforms all other classifiers for cauliflower disease recognition with accuracy approaching 89.00%

Some common deleterious mutations are shared in SARS-CoV-2 genomes from deceased COVID-19 patients across continents

Abstract The identification of deleterious mutations in different variants of SARS-CoV-2 and their roles in the morbidity of COVID-19 patients has yet to be thoroughly investigated. To unravel the spectrum of mutations and their effects within SARS-CoV-2 genomes, we analyzed 5,724 complete genomes from deceased COVID-19 patients sourced from the GISAID database. This analysis was conducted using the Nextstrain platform, applying a generalized time-reversible model for evolutionary phylogeny. These genomes were compared to the reference strain (hCoV-19/Wuhan/WIV04/2019) using MAFFT v7.470. Our findings revealed that SARS-CoV-2 genomes from deceased individuals belonged to 21 Nextstrain clades, with clade 20I (Alpha variant) being the most predominant, followed by clade 20H (Beta variant) and clade 20J (Gamma variant). The majority of SARS-CoV-2 genomes from deceased patients (33.4%) were sequenced in North America, while the lowest percentage (0.98%) came from Africa. The ‘G’ clade was dominant in the SARS-CoV-2 genomes of Asian, African, and North American regions, while the ‘GRY’ clade prevailed in Europe. In our analysis, we identified 35,799 nucleotide (NT) mutations throughout the genome, with the highest frequency (11,402 occurrences) found in the spike protein. Notably, we observed 4150 point-specific amino acid (AA) mutations in SARS-CoV-2 genomes, with D614G (20%) and N501Y (14%) identified as the top two deleterious mutations in the spike protein on a global scale. Furthermore, we detected five common deleterious AA mutations, including G18V, W45S, I33T, P30L, and Q418H, which play a key role in defining each clade of SARS-CoV-2. Our novel findings hold potential value for genomic surveillance, enabling the monitoring of the evolving pattern of SARS-CoV-2 infection, its emerging variants, and their impact on the development of effective vaccination and control strategies

In-silico prediction of highly promising natural fungicides against the destructive blast fungus Magnaporthe oryzae

Magnaporthe oryzae causes destructive blast disease in more than 50 species of the major cereal crops rice, wheat and maize and destroys food of millions of people worldwide. Application of synthetic chemical fungicides are environmentally hazardous and unreliable in controlling M. oryzae. Conversely, naturally occurring biofungicides with multiple modes of actions are needed to be discovered for combatting the blast fungus. To find the effective biofungicides, we performed molecular docking study of some potential antifungal natural compounds targeting two proteins including a single-stranded DNA binding protein MoSub1 (4AGH), and an effector protein AVR-Pik (5E9G) of M. oryzae that regulates transcription in fungus and/or suppresses the host cell immunity. The thirty-nine natural compounds previously shown to inhibit M. oryzae growth and reproduction were put under molecular docking against these two proteins followed by simulation, free energy, and interaction analysis of protein-ligand complexes. The virtual screening revealed that two alkaloidal metabolites, camptothecin and GKK1032A2 showed excellent binding energy with any of these target proteins compared to reference commercial fungicides, azoxystrobin and strobilurin. Of the detected compounds, GKK1032A2 bound to both target proteins of M. oryzae. Both compounds showed excellent bioactivity scores as compared to the reference fungicides. Results of our computational biological study suggest that both camptothecin and GKK1032A2 are potential fungicides that could also be considered as lead compounds to design novel fungicides against the blast fungus. Furthermore, the GKK1032A2 acted as a multi-site mode of action fungicide against M. oryzae