Multiple-line inference of selection on quantitative traits

Trait differences between species may be attributable to natural selection. However, quantifying the strength of evidence for selection acting on a particular trait is a difficult task. Here we develop a population-genetic test for selection acting on a quantitative trait which is based on multiple-line crosses. We show that using multiple lines increases both the power and the scope of selection inference. First, a test based on three or more lines detects selection with strongly increased statistical significance, and we show explicitly how the sensitivity of the test depends on the number of lines. Second, a multiple-line test allows to distinguish different lineage-specific selection scenarios. Our analytical results are complemented by extensive numerical simulations. We then apply the multiple-line test to QTL data on floral character traits in plant species of the Mimulus genus and on photoperiodic traits in different maize strains, where we find a signatures of lineage-specific selection not seen in a two-line test.Comment: 21 pages, 11 figures; to appear in Genetic

Impact of Educational Intervention Measures on Knowledge regarding HIV/ Occupational Exposure and Post Exposure Prophylaxis among Final Year Nursing Students of a Tertiary Care Hospital in Central India

Amongst the different Health Care Personnel nurses are at a greater risk of being accidentally exposed to HIV and other Blood Borne Pathogens. The present study was conducted among 50 final year nursing students of a Medical College Hospital to assess the knowledge regarding HIV, occupational exposure and Post Exposure Prophylaxis (PEP) among the students and analyses the impact of educational intervention measures on the issues amongst the study subjects. A Pre-designed and Pre-tested semi-structured questionnaire was used to evaluate the level of knowledge before and after educational intervention sessions. Knowledge regarding risk of transmission of HIV by needle-stick injury and body fluids against which universal precautions were mandatory increased by 72% following the intervention sessions (χ2 = 53.202, p <0.001). 72% and 36% respondents correctly knew the duration within which to start PEP and the drugs available for PEP, post educational sessions 98% and 96% students were aware of it: the difference being statistically significant (χ2 = 11.294, p <0.001) and (χ2 = 37.748, p <0.001) respectively. The mean pre-intervention score was 8.32; mean post-intervention score was 14.40: statistical analysis showed the results to be significant (t= 13.857, p< 0.001). The study reflects that there is a dearth of knowledge among the study group. Incorporating the concerned issues in the academic curriculum to provide the students with adequate knowledge and information during their formative years is needed

SkillBot: Towards Data Augmentation using Transformer language model and linguistic evaluation

Creating accurate, closed-domain, and machine learning-based chatbots that perform language understanding (intent prediction/detection) and language generation (response generation) requires significant datasets derived from specific knowledge domains. The common challenge in developing a closed-domain chatbot application is the lack of a comprehensive dataset. Such scarcity of the dataset can be complemented by augmenting the dataset with the use of state- of-the-art technologies existing in the field of Natural Language Processing, called ‘Transformer Models’. Our applied computing project experimented with a ‘Generative Pre-trained Transformer’ model, a unidirectional transformer decoder model for augmenting an original dataset limited in size and manually authored. This model uses unidirectional contextual representation i.e., text input is processed from left to right while computing embeddings corresponding to the input sentences. The primary goal of the project was to leverage the potential of a pre-trained transformer-based language model in augmenting an existing, but limited dataset. Additionally, the idea for using the model for text generation and appending the generated embedding to the input embedding supplied was to preserve the intent for the augmented utterances as well as to find a different form of expressions for the same intent which could be expressed by the potential users in the future. Our experiment showed improved performance for understanding language and generation for the chatbot model trained on the augmented dataset indicating that a pre-trained language model can be beneficial for the effective working of natural language-based applications such as a chatbot model trained on the augmented dataset indicating that a pre-trained language model can be beneficial for the effective working of natural language-based applications such as a chatbo

Entropy and Barrier-Hopping Determine Conformational Viscoelasticity in Single Biomolecules

Biological macromolecules have complex and non-trivial energy landscapes, endowing them a unique conformational adaptability and diversity in function. Hence, understanding the processes of elasticity and dissipation at the nanoscale is important to molecular biology and also emerging fields such as nanotechnology. Here we analyse single molecule fluctuations in an atomic force microscope (AFM) experiment using a generic model of biopolymer viscoelasticity that importantly includes sources of local `internal' conformational dissipation. Comparing two biopolymers, dextran and cellulose, polysaccharides with and without the well-known `chair-to-boat' transition, reveals a signature of this simple conformational change as minima in both the elasticity and internal friction around a characteristic force. A calculation of two-state populations dynamics offers a simple explanation in terms of an elasticity driven by the entropy, and friction by barrier-controlled hopping, of populations on a landscape. The microscopic model, allows quantitative mapping of features of the energy landscape, revealing unexpectedly slow dynamics, suggestive of an underlying roughness to the free energy.Comment: 25 pages, 7 figures, naturemag.bst, modified nature.cls (naturemodified.cls

Plant Nanobionics and Its Applications for Developing Plants with Improved Photosynthetic Capacity

In the present scenario, the ever-growing human population, a decreasing availability of land resources and loss of agricultural productivity are the major global concerns, and these possess a challenge for scientific community. To feed the increasing world population, an increase in the crop productivity with available land resources is one of the essential needs. Crop productivity can be increased by engineering the crop plants for tolerance against various environmental stresses and improving the yield attributes, especially photosynthetic efficiency. Nanomaterials have been developed with new functional properties like improved solar energy harvest. With these nanomaterials, nanobionic plants were developed by the facilitated kinetic trapping of nanomaterials within photosynthetic organelle, that is, chloroplast. The trapping of nanomaterials/nanotubes improved chloroplast carbon capture, that is, photosynthesis by improving chloroplast solar energy harnessing and electron transport rate. Besides improving photosynthesis, nanotubes like poly(acrylic acid) nanoceria (PAA-NC) and single-walled nanotube-nanoceria (SWNT-NC) decrease the amount of reactive oxygen species (ROS) inside extracted chloroplast and influence the sensing process in plants, and these are beneficial for a number of physiological processes. The nanobionic approach to engineer plant functions would lead to an era of plant research at the interface of nanotechnology and plant biology. In this chapter, nanobionic approach, transfer of nanomaterial to plants and their offspring and its potential applications to improve photosynthesis will be discussed