Malaxis acuminata D. Don [=Crepidium acuminatum (D. Don) Szlach.] is an endangered medicinal orchid of the Ashtvarga group of plants in Ayurveda (Indian system of traditional medicine). Using a combination of aromatic cytokinin [meta-Topolin (mT)], plant biostimulant (chitosan), auxin [indole-3-butyric acid (IBA)], and a phenolic elicitor [phloroglucinol (PG)], plants of M. acuminata were regenerated in vitro for mass multiplication. The present research reveals the first-ever transcriptome of M. acuminata. A total of 43,111 transcripts encoding 23,951 unigenes were assembled de novo from a total of 815.02 million reads obtained from leaf and pseudobulb of in vitro raised M. acuminata. Expression analysis of genes associated with β-sitosterol and eugenol biosynthesis in leaf and pseudobulb provided vital clues for differential accumulation of metabolites in M. acuminata. Ultra-performance liquid chromatography (UPLC) confirmed higher amounts of β-sitosterol and eugenol content in the leaf as compared to the pseudobulb. Differential expression of transcripts related to starch and sucrose metabolism, plant hormone signal transduction, diterpenoid biosynthesis, phenylalanine metabolism, stilbenoid, diarylheptanoid, and gingerol biosynthesis suggested the operation of differential metabolic pathways in leaf and pseudobulb. The present research provides valuable information on the biosynthesis of secondary metabolites in M. acuminata, which could be used for advanced metabolite bioprospection using cell suspension culture and bioreactor-based approaches. Data also suggested that leaf tissues rather than pseudobulb can be used as an alternate source of bioactive metabolites thereby shifting the need for harvesting the pseudobulb. This will further facilitate the conservation and sustainable utilization of this highly valued medicinal orchid.

Brassica juncea is an important oilseed crop, widely grown as a source of edible oil. Seed size is a pivotal agricultural trait in oilseed Brassicas. However, the regulatory mechanisms underlying seed size determination are poorly understood. To elucidate the transcriptional dynamics involved in the determination of seed size in B. juncea, we performed a comparative transcriptomic analysis using developing seeds of two varieties, small-seeded Early Heera2 (EH2) and bold-seeded Pusajaikisan (PJK), at three distinct stages (15, 30 and 45 days after pollination). We detected 112,550 transcripts, of which 27,186 and 19,522 were differentially expressed in the intra-variety comparisons and inter-variety comparisons, respectively. Functional analysis using pathway, gene ontology, and transcription factor enrichment revealed that cell cycle- and cell division-related transcripts stay upregulated during later stages of seed development in the bold-seeded variety but are downregulated at the same stage in the small-seeded variety, indicating that an extended period of cell proliferation in the later stages increased seed weight in PJK as compared to EH2. Further, k-means clustering and candidate genes-based analyses unravelled candidates for employing in seed size improvement of B. juncea. In addition, candidates involved in determining seed coat color, oil content, and other seed traits were also identified.

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Machine learning is a subfield of artificial intelligence (AI) and computer science that utilizes data and algorithms to imitate how people learn, progressively improving its accuracy. Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights. Detecting fake news comes under a classification problem. Fake news is false or misleading information presented as news. The initial stage in classification is dataset collection, which is followed by preprocessing, feature selection, dataset training and testing, and finally executing the classifier. There is a large amount of written text in the news. This text is processed using NLP. NLP can perform an intelligent analysis of large amounts of plain written text and generate insights from it. It involves methods like data preprocessing and feature selection. Data pre-processing involves data cleaning, removing any incorrect, duplicate, or incomplete data within a dataset. Feature selection is done using the CountVectorizer and TF-IDF Vectorizer. Then comes dataset training and testing and the use of similar data for training and testing reduces the impact of data inconsistencies. After processing the model using the training set, the model is tested by making predictions against the test set. Then, to assess the performance of the classification model for the provided set of test data confusion matrix is used. The primary purpose is to use the Naive Bayes (NB) Classifier technique to generate two classification models one using CountVectorizer and other using TF-IDF Vectorizer and compare their accuracy.