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Previous work has shown that machine learning techniques tend to work well on homogeneous datasets, i.e., data extracted from a single source domain e.g. financial reports in a newspaper. However, the performance suffers when the training and testing datasets are derived from different domains. The problems arising from domain differences for data is referred to as domain adaptation. This thesis investigates domain adaptation methods for dependency parsing and part of speech tagging. The goal is to build a robust and generalizable system by creating domain-specific experts which aid in parsing from a heterogeneous dataset and simultaneously focus on ameliorating the problems arising from differences in annotation styles of different domains, specifically for dependency parsing. For the former problem of multi-domain adaptation, I utilize probabilistic topic models to generate domain experts. I generate topic models to automatically determine domains in the heterogeneous dataset and then train an expert for each of the domains. The test sentences are then asynchronously assigned to the appropriate expert based on similarity. This technique shows a considerable improvement in performance for both the tasks. To mitigate the problem of annotation differences in domains, I formulate a system based on transformation-based error driven learning. I use this method to learn dependency arc and label corrections in a target domain, based on errors made by the source domain parser. This method proved capable of reducing both arc and label errors considerably. This method is also flexible enough to be applied to any domain or language without any major changes to the algorithm.

Lung cancer is one of the most invasive cancers affecting over a million of the population. Non-small cell lung cancer (NSCLC) constitutes up to 85% of all lung cancer cases, and therefore, it is essential to identify predictive biomarkers of NSCLC for therapeutic purposes. Here we use a network theoretical approach to investigate the complex behavior of the NSCLC gene-regulatory interactions. We have used eight NSCLC microarray datasets GSE19188, GSE118370, GSE10072, GSE101929, GSE7670, GSE33532, GSE31547, and GSE31210 and meta-analyzed them to find differentially expressed genes (DEGs) and further constructed a protein–protein interaction (PPI) network. We analyzed its topological properties and identified significant modules of the PPI network using cytoscape network analyzer and MCODE plug-in. From the PPI network, top ten genes of each of the six topological properties like closeness centrality, maximal clique centrality (MCC), Maximum Neighborhood Component (MNC), radiality, EPC (Edge Percolated Component) and bottleneck were considered for key regulator identification. We further compared them with top ten hub genes (those with the highest degrees) to find key regulator (KR) genes. We found that two genes, CDK1 and HSP90AA1, were common in the analysis suggesting a significant regulatory role of CDK1 and HSP90AA1 in non-small cell lung cancer. Our study using a network theoretical approach, as a summary, suggests CDK1 and HSP90AA1 as key regulator genes in complex NSCLC network.

Introduction: Ectodermal dysplasias (EDs) are an inherited group of disorders that share in general developmental defects concerning minimum two of the major structures characteristically held to derive from embryonic ectoderm - hair, teeth, skin and sweat glands. Aim and Objectives: The rationale of this questionnaire study was to assess the knowledge and awareness of dental students regarding ectodermal dysplasia and the management of ectodermal dysplasia patients. Materials and Methods: A cross sectional study was conducted during the academic year in June 2018 among the undergraduate dental students of Awadh Dental College and Hospital, Jamshedpur, Jharkhand. 150 students were randomly enrolled in the study including third year, final year and intern students. All students in the study voluntarily completed a questionnaire consisting of 24 closed ended questions. Results: 67 % of the students had a basic knowledge of the etiology of the disease.82 % of the students were aware of the clinical manifestations of ectodermal dysplasia and 78% of the students respectively answered that they were not aware of precautions that are required to be taken. Conclusions: Most of the dental students had good knowledge about ectodermal dysplasia and dental management of patients with ectodermal dysplasia except for few aspects in treatment.

Lung cancer is one of the most invasive cancer affecting over a million of population. Non-small cell lung cancer constitutes up to 85% of all lung cancer cases. Therefore, it is important to identify prognostic biomarkers of NSCLC for therapeutic purpose. The complex behaviour of the NSCLC gene-regulatory network interaction is investigated using a network theoretical approach. We used eight NSCLC microarray datasets GSE19188, GSE118370, GSE10072, GSE101929, GSE7670, GSE33532, GSE31547, GSE31210 and meta analyse them to find differentially expressed genes (DEGs), construct protein-protein interaction (PPI) network, analysed its topological properties, significant modules using network analyser with MCODE, construct a PPI-MCODE network using the genes of the significant modules. We used topological properties such as Maximal Clique Centrality (MCC) and bottleneck from the PPI-MCODE network. We compare them with hub genes (those with highest degrees) to find key regulator (KR) gene. This result is also validated by finding of common genes among top twenty hub genes, genes with highest betweenness, closeness centrality and eigenvector values. It was found that two genes, CDK1 and HSP90AA1 were common in PPI-MCODE combined analysis, and it was also found that CDK1, HSP90AA1 and HSPA8 were common among hub and bottle neck properties and suggesting significant regulatory role of CDK1 in non-small cell lung cancer. After validation, the common genes among top twenty hubs and centrality values like Betweenness Centrality, Closeness Centrality and eigen vector properties, CDK1 again appeared as the common gene. Our study as a summary suggested CDK1 as key regulator gene in complex NSCLC network interaction using network theoretical approach and described the complex topological properties of the network. Competing Interest Statement The authors have declared no competing interest.

We present a new logic-based inference engine for natural language inference (NLI) called MonaLog, which is based on natural logic and the monotonicity calculus. In contrast to existing logic-based approaches, our system is intentionally designed to be as lightweight as possible, and operates using a small set of well-known (surface-level) monotonicity facts about quantifiers, lexical items and tokenlevel polarity information. Despite its simplicity, we find our approach to be competitive with other logic-based NLI models on the SICK benchmark. We also use MonaLog in combination with the current state-of-the-art model BERT in a variety of settings, including for compositional data augmentation. We show that MonaLog is capable of generating large amounts of high-quality training data for BERT, improving its accuracy on SICK.

Purpose Production of a designer crop having added attributes is the primary goal of all plant biotechnologists. Specifically, development of a crop with a simple biotechnological approach and at a rapid pace is most desirable. Genetic engineering enables us to displace genes among species. The newly incorporated foreign gene(s) in the host genome can create a new trait(s) by regulating the genotypes and/or phenotypes. The advent of the CRISPR-Cas9 tools has enabled the modification of a plant genome easily by introducing mutation or replacing genomic fragment. Oilseed mustard varieties (e.g., Brassica juncea, Brassica nigra, Brassica napus , and Brassica carinata ) are one such plants, which have been transformed with different genes isolated from the wide range of species. Current reports proved that the yield and value of oilseed mustard has been tremendously improved by the introduction of stably inherited new traits such as insect and herbicide resistance. However, the genetic transformation of oilseed mustard remains incompetent due to lack of potential plant transformation systems. To solve numerous complications involved in genetically modified oilseed mustard crop varieties regeneration procedures, scientific research is being conducted to rectify the unwanted complications. Thus, this study provides a broader overview of the present status of new traits introduced in each mentioned varieties of oilseed mustard plant by different genetical engineering tools, especially CRISPR-Cas9, which will be useful to improve the transformation system of oilseed mustard crop plants. Methods This review presents recent improvements made in oilseed mustard genetic engineering methodologies by using CRISPR-Cas9 tools, present status of new traits introduced in oilseed mustard plant varieties. Results The review highlighted that the transgenic oilseed mustard production is a challenging process and the transgenic varieties of oilseed mustard provide a powerful tool for enhanced mustard yield. Over expression studies and silencing of desired genes provide functional importance of genes involved in mustard growth and development under different biotic and abiotic stress conditions. Thus, it can be expected that in near future CRISPR can contribute enormously in improving the mustard plant’s architecture and develop stress resilient oilseed mustard plant species.

Ulcerative colitis (UC) is a chronic inflammatory disorder primarily affecting the colonic mucosa and submucosa. Butea monosperma stem bark is traditionally used in Ayurveda for diarrhea, inflammatory diseases, and Grahani Roga , which shares similarities with UC. However, the molecular mechanisms of B. monosperma in UC are unexplored. This study aimed to predict the molecular mechanisms of B. monosperma in UC by integrating network pharmacology, molecular docking, and dynamics-the first systematic effort to decode its phytochemical-target-pathway interactions, bridging traditional knowledge with computational pharmacology. A standardized B. monosperma stem bark was prepared. LC-MS analysis was used to identify phytochemicals in the stem bark, and their potential targets, along with disease targets, were obtained from relevant databases. Protein-protein interaction, Gene Ontology, and KEGG pathway enrichment analysis were performed to identify key biological processes and signaling pathways modulated by stem bark in UC. Molecular docking and dynamics studies predicted interactions between phytoconstituents and key target proteins. The results suggest that stem bark of B. monosperma modulates pathways involved in cell migration, oxidative stress, and epithelial cell apoptosis, particularly via cancer-related pathways, IL-17 signaling, and Th17 cell differentiation, all of which are implicated in UC pathogenesis. Key target proteins, including MAPK1, AKT1, NF-κB, RELA, and MMP9, were predicted to interact with active compounds. These findings suggest a potential molecular basis for the therapeutic effects of B. monosperma stem bark in UC, warranting further experimental validation.

The current study compared perceived social support, resilience, life satisfaction, and posttraumatic growth among cancer patients, individuals with chronic non-malignant illnesses, and healthy adults. Our study explored significant mean differences for illness types and main demographic factors, including parental status, sibling presence, and marital status, as well as their interactions with illness type, to find variations in psychosocial resources across health conditions. There were 429 adults aged who were purposively recruited from clinics in Kolkata and surrounding suburban areas, age range 18-60, it includes149 cancer patients, 168 patients with chronic illnesses, and 112 healthy people without any known diagnosis. Data was collected from individuals with informed consent, and standardized psychological scales were used. Comparative analyses showed that Illness type showed significant effects on perceived life satisfaction, personal strength, new possibilities, and spiritual influence, while the presence of siblings was significantly associated with new possibilities and several resilience dimensions. Further sibling and illness type interactions showed effects on life satisfaction, spiritual growth, and multiple resilience. Similarly, the presence of children significantly influenced perceived social support from friends, improved relationships, and resilience, while illness type significantly affected life satisfaction, resilience components, and several PTG domains. The interaction effects between children and illness type for perceived social support and multiple resilience dimensions were significant. Marital status showed significant effects on perceived social support, PTG domains, and resilience. Family support was affected by illness type. Marital status and illness type interactions were significant for perceived social support, positive acceptance, new possibilities, and spiritual influence.

The Internet of Things (IoT) paradigm is a predominant research domain for smart cities, smart villages, society, and industry 4.0. The introduction of Unmanned Aircraft Systems (UAS) in an ultra-low latency network with fog, dews, and edge computing gives the researcher ample scope to establish a decentralized architecture for ultra-high-speed message exchange between IoT devices. This work mainly focused on Social Internet of Things ecosystem and its design to efficiently handle large group social gatherings, events, and emergency service management. We propose a layered message transfer framework for the social IoT scenario. We also establish network connection through flying ad hoc network architecture. The standard IoT message transfer protocol is redesigned by amalgamating with an opportunistic routing mechanism and deployed within 6G software-defined network (SDN) slice. We use seven distinguished network slices for different services and corresponding access. The study reveals nearly 99% of message delivery rate with a latency upper bound of 2300 ms by opportunistic message transfer scheme in a dense network scenario for QoS 2. It also shows 95% of the bandwidth utilization per slice and 97% of network coverage under SDN in quality of service level 2.

The onset of malaria causes the induction of various inflammatory markers in the host’s body, which in turn affect the body’s homeostasis and create several cerebral complications. Polarization of myeloid-derived suppressor cells (MDSCs) from the classically activated M1 to alternatively activated M2 phenotype increases the secretion of pro-inflammatory molecules. Treatment with recombinant IL-33 (rIL-33) not only alters this MDSC’s polarization but also targets the glycolysis pathway of the metabolism in MDSCs, rendering them less immunosuppressive. Along with that, the Helper T-cells subset 17 (Th17)/T regulatory cells (Tregs) ratio is skewed towards Th17, which increases inflammation by producing more IL-17. However, treating with rIL-33 also helps to restore this ratio, which brings back homeostasis. During malaria infection, there is an upregulation of IL-12 production from dendritic cells along with a distorted myeloid dendritic cells (mDC)/plasmacytoid dendritic cells (pDC) ratio towards mDCs promoting inflammation. Administering rIL-33 will also subvert this IL-12 production and increase the population of pDC in the host’s immune system during malaria infection, thus restoring mDC/pDC to homeostasis. Therefore, treatment with rIL-33 to reduce the pro-inflammatory signatures and maintenance of immune homeostasis along with the increase in survivability could be a potential therapeutic approach for cerebral malaria.