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Many B2B firms have widely accepted AI-based chatbots to provide human-like service interaction at different customer touchpoints in recent years. One of the objectives behind introducing this technology is to provide an enhanced, live channel Customer Experience (CX) all round the clock. Researchers have focused on delivering the CX by improvising the chatbot's internal algorithm, giving limited attention to CX theories from management literature, which leaves a gap. With the proposed paper, we have investigated the influencing factors of AI-based chatbots from the lens of CX theories for B2B firms. In this paper, a model for organizing CX has been proposed using the diffusion of innovation theory, trust commitment theory, information systems success model, and Hoffman & Novak's flow model for the computer-mediated environment and verified using the social media data. The methodology used for this study is the social media analytics-based content analysis method (sentiment analysis, hierarchical clustering, topic modeling) for data preparation, followed by lasso and ridge regression for model verification. The results suggest that CX in B2B enterprises using chatbots is influenced by these bots' overall system design, customers' ability to use technology, and customer trust towards brand and system.

WsWRKY1-mediated transcriptional modulation of Withania somnifera tryptophan decarboxylase gene (WsTDC) helps to regulate fruit-specific tryptamine generation for production of withanamides. Withania somnifera is a highly valued medicinal plant. Recent demonstration of novel indolyl metabolites called withanamides in its fruits (berries) prompted us to investigate its tryptophan decarboxylase (TDC), as tryptophan is invariably a precursor for indole moiety. TDC catalyzes conversion of tryptophan into tryptamine, and the catalytic reaction constitutes a committed metabolic step for synthesis of an array of indolyl metabolites. The TDC gene (WsTDC) was cloned from berries of the plant and expressed in E. coli. The recombinant enzyme was purified and characterized for its catalytic attributes. Catalytic and structural aspects of the enzyme indicated its regulatory/rate-limiting significance in generation of the indolyl metabolites. Novel tissue-wise and developmentally differential abundance of WsTDC transcripts reflected its preeminent role in withanamide biogenesis in the fruits. Transgenic lines overexpressing WsTDC gene showed accumulation of tryptamine at significantly higher levels, while lines silenced for WsTDC exhibited considerably depleted levels of tryptamine. Cloning and sequence analysis of promoter of WsTDC revealed the presence of W-box in it. Follow-up studies on isolation of WsWRKY1 transcription factor and its overexpression in W. somnifera revealed that WsTDC expression was substantially induced by WsWRKY1 resulting in overproduction of tryptamine. The study invokes a key role of TDC in regulating the indolyl secondary metabolites through enabling elevated flux/supply of tryptamine at multiple levels from gene expression to catalytic attributes overall coordinated by WsWRKY1. This is the first biochemical, molecular, structural, physiological and regulatory description of a fruit-functional TDC.

Monitoring buyer experience provides competitive advantages for suppliers as buyers explore the market before reaching a salesperson. Still, not many B2B suppliers monitor their buyers’ expectations throughout their procurement journey, especially in MSMEs and SMEs. In addition, the inductive research on evaluating buyer experience in buyer–supplier relationships is minimal, leaving an unexplored research area. This study explores antecedents of buyer experience during the buyer–supplier relationship in MSMEs and SMEs. Further, we investigate the nature of the influence of extracted precursors on the buyer experience. Firstly, we obtain the possible antecedents from the literature on buyer–supplier experience and supplier selection criteria. We also establish hypotheses based on transaction cost theory, resource-based view (RBV), and information processing view. Secondly, we employ an investigation based on the social media analytics-based approach to uncover the antecedents of buyer experience and their nature of influence on MSMEs and SME suppliers. We found that buyer experience is influenced by sustainable orientation, management capabilities (such as crisis management and process innovation), and suppliers’ technology capabilities (digital readiness, big data analytical capability).Other Information Published in: Annals of Operations Research License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1007/s10479-022-04954-3

Data-driven predictions have become an inseparable part of business decisions. Artificial Intelligence (AI) has started helping the product and support teams perform more accurate experiments in various business settings. This study proposes a framework for businesses based on inductive learnings related to success and barriers shared on social media platforms. Our goal is to analyse the signals emerging from these conversational opinions from the early adoption of AI, with a focus towards facilitators and barriers faced by teams. Factors like efficiency, innovation, business research, product novelty, manual intervention, adaptability, emotion, support, personal growth, experiential learning, fear of failure and fear of upgradation have been identified based on an exploratory study and then a confirmatory study. We present the learnings through a roadmap for practitioners. This study contributes to the IS literature by delineating AI as a determinant of success and introduces a lot of organizational factors into the model.

Hosting conversational responses on the official websites of products and services companies is an essential marketing aspect. With Artificial Intelligence’s help to make conversational interactivity more intuitive to existing and potential customers visiting the websites, managers can notch up the return on marketing investments. This motivated us to study empirically and develop the MarkBot framework, a chatter robot on the management design principles. The framework uses an Artificial Intelligence application to respond to a website visitor’s browse through the product catalog. Neural network (NN) architectures are known to achieve remarkable performances in synthetic text predictions. We use a long short-term memory recurrent neural network (LSTM) to predict the user’s responses through a chatbot in the current work. The proposed framework reduces the lead time for the firms to adopt MarkBot. We empirically prove using user-generated content on social media platforms like Twitter in responses and queries to digital campaigns on the same product. With new businesses failing to venture into the space of hosting a chatbot owing to no historical data or existing firms yet to host a chatbot, the proposed MarkBot fuelled by user-generated content can have a substantial managerial implication. The management frameworks used to theorize the MarkBot also make it a theoretical contribution for future Information Systems scholars to conceptualize in the marketing field.

The increasing user base of people using social media platforms to interact and report their individual opinions on a subject matter is generating a volume of text data that is nonlinear. This presents an enormous number of possibilities for experts to experiment, build a framework, and make insights related to user behavior. If a piece of information is factual, essential, and helpful, then the right set of words and social networks should be utilized to cascade the information to all. Through current research, we propose a novel deep learning framework to predict information popularity on Twitter, measured through the retweet feature of the tool and algorithmically created features. We perform this research with the hypothesis that retweeting behavior can be an outcome of a writer's practice of semantics and grasp of the language. When we read any sentence, the understanding of the sentence does not start from scratch, but instead builds upon the knowledge in a sequence of reading and interpretation of the phrases used in the text. This rule of semantics can be used to create word features. The extracted features can be utilized to train a deep learning model like long short-term memory that has firmly proven its importance in learning hidden trends in any data. The long short-term memory framework has the capability of storing previous learnings and using them when needed. As an outcome of the experimentation proposed we use the word expletives along with word-embedding features to successfully present a generalizable deep neural network framework to classify tweets with a high potential for being retweeted, and tweets with a low possibility of being retweeted.

Artificial Intelligence (AI) has now evolved from a phase of being merely adopted as a new system to now fueling the decision-systems to generate specific data. Borrowing from the social sciences and emerging sub-body of mathematics research literature and theories around algorithmic knowledge and value realization leads to the formation of perceptions around the confidence of allowing AI to be at the heart of any domain’s decision-making. This amalgamation of AI in decision-making systems (ADMS). The current study has undertaken attempts to link personal attributes to perceptions around ADMS with the boundary constraints of these perceptions, namely the stretch to which these perceptions vary across media and domain contexts. A scenario-based survey instrument has been used to collect the data from two sets of ADMS stakeholders, vis-a-vis owners and end-users of ADMS. Analysis of the collected data from this sample (N = 558) reveals that these stakeholders are by and large anxious about the risks associated with ADMS. They have a mixed and general attitude towards the on-field usefulness and fairness of ADMS outcomes at the societal level. These generic frames of mind are driven mainly by the individual traits and involvement and accountability in the domain like a revenue-generating business, social (healthcare), and the oversight of ADMS. Theoretical, management practice, and societal impacts about these findings are also discussed along the current work’s final sections.

Light is one of the most important environmental signals, which has a profound impact on plant growth and development. Once a seed germinates, the proper emergence and establishment of the seedling into an autotrophic organism is extremely crucial for its survival. Various steps in the seedling establishment process are remarkably influenced by external light conditions. These steps include the dark-adapted growth of seedlings to emerge out of the soil cover; the opening and expansion of their embryonic leaves once they hit the light above the ground; the synthesis and accumulation of chloroplasts in the green parts to initiate photosynthesis; the accumulation of photoprotective compounds to safeguard the seedling from stress induced by sudden and excessive light exposure; the bending of the seedlings in response to the direction of light; the adjustment of root architecture in response to the direction and availability of light; and the morphogenic changes they undergo in a crowded population to avoid shade and reach for the sunlight. Plants possess specialized photoreceptors such as the Phytochromes, Cryptochromes, Phototropins and UVR8 to perceive different wavelengths of the light spectra. Downstream to these photoreceptors, central molecular players such as ELONGATED HYPOCOTYL 5 (HY5) and PHYTOCHROME INTERACTING FACTORS (PIFs) together with numerous other regulatory proteins establish a highly sophisticated signaling network. The light signaling network often culminates in the regulation of different phytohormones to induce major developmental changes in the plant. This chapter will discuss the finely regulated interaction between light and hormonal pathways in determining the developmental plasticity of plants during seedling development.

Part 5: Big Data and Machine Learning; International audience; Researchers have been experimenting with various drivers of the diffusion rate like sentiment analysis which only considers the presence of certain words in a tweet. We theorize that the diffusion of particular content on Twitter can be driven by a sequence of nouns, adjectives, adverbs forming a sentence. We exhibit that the proposed approach is coherent with the intrinsic disposition of tweets to a common choice of words while constructing a sentence to express an opinion or sentiment. Through this paper, we propose a Custom Weighted Word Embedding (CWWE) to study the degree of diffusion of content (retweet on Twitter). Our framework first extracts the words, create a matrix of these words using the sequences in the tweet text. To this sequence matrix we further multiply custom weights basis the presence index in a sentence wherein higher weights are given if the impactful class of tokens/words like nouns, adjectives are used at the beginning of the sentence than at last. We then try to predict the possibility of diffusion of information using Long-Short Term Memory Deep Neural Network architecture, which in turn is further optimized on the accuracy and training execution time by a Convolutional Neural Network architecture. The results of the proposed CWWE are compared to a pre-trained glove word embedding. For experimentation, we created a corpus of size 230,000 tweets posted by more than 45,000 users in 6 months. Research experimentations reveal that using the proposed framework of Custom Weighted Word Embedding (CWWE) from the tweet there is a significant improvement in the overall accuracy of Deep Learning framework model in predicting information diffusion through tweets.

In a world of ever-increasing microblogs, the opinions, preferences, support, frustration, anger and other emotions of people regarding various events and individuals, surface in varied ways on social media. The purpose of this research is to find those hidden patterns in raw data, which can explain meaningful insights about its creation, the groups of people who created them and their sentiments which led to the generation of such data. Sentiment analysis has always been an effective methodology for discovering emotion and bias towards or against a situation, topic, thought or initiative and finding other meaningful insights from unstructured data. In this research, we attempted a type of document clustering wherein we attempted to classify the sentiments of the citizens of India as they micro-blogged their opinions, thoughts, views and ideas during the implication of the Citizenship Amendment Act (CAA) on the social networking site, Twitter. By analyzing the tweets of 13,000 twitter users during a specific timeline during which the discussion regarding the CAA was at its peak, we analyzed the sentiment of those twitter users by clustering their tweets (documents) into four sentiment groups with the help of Latent Dirichlet Allocation (LDA) which is an important tool for topic modelling in the domain of sentiment analysis. Using political ritual theory, the present paper examines the sentiments of people who tweeted during a protest in India. After the classification, our research also maps the online political behaviour of these 13,000 social media participants to the postulates of political ritual theory which is explained by previous research regarding the behaviour of physically co-existing political participants and also justifies this display of various sentiments regarding the CAA in the footsteps of political rituals.

Purpose Silver nanoparticles have been extensively used in pharmaceutical sciences as therapeutic agents, including in drug delivery but their toxicity towards healthy tissue is not explored from trials. Being herb, Moringa oleifera lam (MO) is cheap, eco-friendly, non-toxic as compared to synthetic nanoparticles. The antibacterial efficacy of silver nanoparticles of aerial parts of M. oleifera lam and bactericidal mechanisms have not been explored so far. Methods In this study, rapid biosynthesis of M. oleifera lam embedded silver nanoparticles (MONPs) was acquired using M. oleifera lam aerial parts, and characterized by using UV, Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), Transmission electron microscopy (TEM), and X-ray diffractometer (XRD) studies. Well diffusion method was used to assess the zone of inhibition and minimum inhibitory concentration of the MONPs on Escherichia Coli, Pseudomonas aeruginosa, Staphylococcus aureus, Salmonella typhi. The probable mechanism of action was accessed through molecular docking and simulation. Results The size of AgNPs was spherical with less than 50 nm size and showed a crystalline peak at XRD. MONPs at 250 μg/ml inhibit the growth of all the gram-positive and gram-negative bacteria with significant antibacterial affinity. The minimum inhibitory concentration of MONPs was lower in gram-positive bacteria than gram-negative bacteria. The molecular docking studies of phytoconstituents present in MO showed luteolin (−9.2 Kcal/mol) and rutin (−10.1 Kcal/mol) have higher binding energy with DNA gyrase & tyrosyl tRNA synthetase inhibitors respectively. Molecular simulations study confirmed that both ligands have a good stability profile by forming different bonds with active site amino acids. Conclusion Bio-fabricated silver nanoparticles exhibited noteworthy gainful impacts against selected bacterial strains and could serve as a lead for future antibacterial drug discovery.

Plant development is meticulously modulated by interactions between the surrounding environment and the endogenous phytohormones. Light, as an external signal coordinates with the extensive networks of hormones inside the plant to execute its effects on growth and development. Several proteins in plants have been identified for their crucial roles in mediating light regulated development. Among these are the B-box (BBX) family of transcription factors characterized by the presence of zinc-finger B-box domain in their N-terminal region. In Arabidopsis there are 32 BBX proteins that are divided into five structural groups on the basis of the domains present. Several BBX proteins play important roles in seedling photomorphogenesis, neighbourhood detection and photoperiodic regulation of flowering. There is increasing evidence that besides light signaling BBX proteins also play integral roles in several hormone signaling pathways in plants. Here we attempt to comprehensively integrate the roles of multiple BBX proteins in various light and hormone signaling pathways. We further discuss the role of the BBX proteins in mediating crosstalk between the two signaling pathways to harmonize plant growth and development. Finally, we try to analyse the conservation of BBX genes across species and discuss the role of BBX proteins in regulating economically important traits in crop plants.

Tropinone reductases (TRs) are small proteins belonging to the SDR (short chain dehydrogenase/reductase) family of enzymes. TR-I and TR-II catalyze the conversion of tropinone into tropane alcohols (tropine and pseudotropine, respectively). The steps are intermediary enroute to biosynthesis of tropane esters of medicinal importance, hyoscyamine/scopolamine, and calystegins, respectively. Biosynthesis of tropane alkaloids has been proposed to occur in roots. However, in the present report, a tropine forming tropinone reductase (TR-I) cDNA was isolated from the aerial tissue (leaf) of a medicinal plant, Withania coagulans. The ORF was deduced to encode a polypeptide of 29.34 kDa. The complete cDNA (WcTRI) was expressed in E. coli and the recombinant His-tagged protein was purified for functional characterization. The enzyme had a narrow pH range of substantial activity with maxima at 6.6. Relatively superior thermostability of the enzyme (30% retention of activity at 60 °C) was catalytic novelty in consonance with the desert area restricted habitat of the plant. The in vitro reaction kinetics predominantly favoured the forward reaction. The enzyme had wide substrate specificity but did not cover the substrates of other well-known plant SDR related to menthol metabolism. To our knowledge, this pertains to be the first report on any gene and enzyme of secondary metabolism from the commercially and medicinally important vegetable rennet species.

The science of plant metabolomics has revolutionised the underlying platform of research on biological systems. The integration of plant metabolomics with allied branches, namely, system biology, biostatistics and in silico biology, has emerged as significant advancement in comprehensive metabolic profiling of phytomolecules in plant. Further, metabolomics strategies with high level of compositional specificity dealing with both intra- and inter-level organisation of the organism have achieved the predefined standards with overwhelming response. The present chapter entails the achievements and challenges associated with research on plant metabolomics, beginning with a brief introduction on plant metabolomics as interdisciplinary field and recent approaches employed for profiling of plant metabolites. Further, the chapter describes the omics strategies involved in metabolome research and various analytical techniques employed for detection and quantification of plant metabolites. The application ranging from metabolic engineering to abiotic stress response and from in vitro studies to application in functional genomics highlights the rising significance of plant metabolomics as a functional genomics tool. Although studies on plant metabolome have emerged as prospective strategies and contributed tremendously to its growth in recent years, some of the major challenges associated need to be addressed for the science of plant metabolomics to flourish and strengthen in future. Metabolomics is a relatively newer approach aimed at improving understanding of metabolic networks and the subsequent biochemical compositions of the plants and other biological organisms. The important aspects relevant to metabolomics are presented, and perspectives of metabolomics exploitation in the future are outlined. As such, metabolomics is providing new dimensions in the study of systems biology, enabling the in-depth understanding of the intra- and extracellular interactions of plant cells. Metabolomics is also developing into a valuable tool that can be used to monitor and assess gene function and to characterise post-genomic processes from a broad perspective.

Omics-Based Approaches for Rice Improvement Somnath Roy, Amrita Banerjee, Somnath Bhattacharya, Arunava Pattanayak, and Kailash C Bansal Practical Omics Approaches for Drought Tolerance in Rice Prashant Vikram, B P Mallikarjuna Swamy, and Arvind Kumar Omics Approaches in Maize Improvement Pawan K Agrawal, Navinder Saini, B Kalyana Babu, and Jagdish C Bhatt Omics Approaches in Pulses Abhishek Bohra, Uday Chand Jha, Balwant Singh, Khela Ram Soren, Indra Prakash Singh, Sushil Kumar Chaturvedi, Nagaswamy Nadarajan, and Debmalya Barh Genetic Engineering in Potato Improvement Elena Rakosy-Tican Omics Applications in Brassica Species Xiaonan Li, Nirala Ramchiary, Vignesh Dhandapani, Su Ryun Choi, and Yong Pyo Lim Integrating Omics Approaches in Sugarcane Improvement Rachayya M Devarumath, Sachin B Kalwade, Pranali A Kulkarni,Suman S Sheelavanthmath, and Penna Suprasanna Recent Advances in Temperate Fruit Crops: An Omics Perspective Md Abdur Rahim and Livio Trainotti Omics Approaches in Tropical Fruit Crops Kundapura V Ravishankar, Kanupriya, Ajitha-kumar Rekha, Anuradha Upadhyay,Chinmaiyan Vasugi, N Vijayakumari, Pooja Kishnani, and Makki R Dinesh Catharanthus roseus: The Metabolome That Represents a Unique Reservoir of Medicinally Important Alkaloids under Precise Genomic Regulation Ashutosh K Shukla and Suman PS Khanuja Omics of Secondary Metabolic Pathways in Withania somnifera Dunal (ashwagandha) Neelam S Sangwan, Laxmi N Misra, Sandhya Tripathi, and Amit K Kushwaha Genetic Engineering in Ornamental Plants Rajesh Kumar Dubey, Simrat Singh, Gurupkar Singh Sidhu, and Manisha Dubey Omics Advances in Tea (Camellia sinensis) Mainaak Mukhopadhyay, Bipasa Sarkar, and Tapan Kumar Mondal Omics Approaches to Improving Fiber Qualities in Cotton Tianzhen Zhang, Xiangdong Chen, and Wangzhen Guo Forestry and Engineered Forest Trees Mohammed Ellatifi Application of Omics Technologies in Forage Crop Improvement Suresh Kumar and Vishnu Bhat Bioenergy Crops Enter the Omics Era Atul Grover, Patade Vikas Yadav, Maya Kumari, Sanjay Mohan Gupta,Mohommad Arif, and Zakwan Ahmed Molecular Farming in the Decades of Omics Dinesh K Yadav, Neelam Yadav, and SM Paul Khurana Natural Pesticidome Replacing Conventional Pesticides Daiane Hansen Intellectual Property Rights in Plant Biotechnology: Relevance, Present Status, and Future Prospects Dinesh Yadav, Gautam Anand, Sangeeta Yadav, Amit K Dubey, Naveen C Bisht,and Bijaya K Sarangi Index

Withania somnifera is one of the most reputed medicinal plants of Indian systems of medicine synthesizing diverse types of secondary metabolites such as withanolides, alkaloids, withanamides etc. Present study comprises cloning and E. coli over-expression of a tropinone reductase gene (WsTR-I) from W. somnifera, and elucidation of biochemical characteristics and physiological role of tropinone reductase enzyme in tropane alkaloid biosynthesis in aerial tissues of the plant. The recombinant enzyme was demonstrated to catalyze NADPH-dependent tropinone to tropine conversion step in tropane metabolism, through TLC, GC and GC-MS-MS analyses of the reaction product. The functionally active homodimeric ~60 kDa enzyme catalyzed the reaction in reversible manner at optimum pH 6.7. Catalytic kinetics of the enzyme favoured its forward reaction (tropine formation). Comparative 3-D models of landscape of the enzyme active site contours and tropinone binding site were also developed. Tissue-wide and ontogenic stage-wise assessment of WsTR-I transcript levels revealed constitutive expression of the gene with relatively lower abundance in berries and young leaves. The tissue profiles of WsTR-I expression matched those of tropine levels. The data suggest that, in W. somnifera, aerial tissues as well possess tropane alkaloid biosynthetic competence. In vivo feeding of U-[(14)C]-sucrose to orphan shoot (twigs) and [(14)C]-chasing revealed substantial radiolabel incorporation in tropinone and tropine, confirming the de novo synthesizing ability of the aerial tissues. This inherent independent ability heralds a conceptual novelty in the backdrop of classical view that these tissues acquire the alkaloids through transportation from roots rather than synthesis. The TR-I gene expression was found to be up-regulated on exposure to signal molecules (methyl jasmonate and salicylic acid) and on mechanical injury. The enzyme's catalytic and structural properties as well as gene expression profiles are discussed with respect to their physiological overtones.