PROPOSING BILATERAL INTEGRATION OF TRADITIONAL AND CONVENTIONAL MEDICAL EDUCATION AND PRACTICE PERCEIVING MAHAMANA MALAVIYAS VISION

Total 69% of Allopathic doctors prescribe branded Ayurvedic preparations. In a study in North India, it has been observed that, the prescriptions of Allopathic doctors contained 88% allopathic and 12% Ayurvedic drugs. Another study reported that Ayurvedic drugs were prescribed by 5.26% of allopathic-practitioners. Hence, even without formal knowledge and training, allopathic physicians do not want to refer patients to Ayurvedic doctors but prefer to prescribe Ayurvedic drugs on their own to the patients. This tendency of allopathic doctors is unethical and unwarranted. Similar cross prescriptions are also common among Ayurvedic doctors who frequently prescribe modern drugs, but they are given formal allopathic training during their UG and PG education, which may justify their prescription of Allopathic drugs to some extent. Thus, it is obvious that how important it is for Allopathic practitioners to learn the basics of Ayurveda as its demand is increasing and as it is a fact that the practice of Complementary Ayurveda by Allopathic practitioners is also as important as practice of Complementary Allopathy is by Ayurvedic practitioners for a successful practice. Thus Bilateral Integration of both streams of Medicine in India has now become essential for sustaining the ethical practice with legal provisions in public interest

DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing

Proteins play a critical role in carrying out biological functions, and their 3D structures are essential in determining their functions. Accurately predicting the conformation of protein side-chains given their backbones is important for applications in protein structure prediction, design and protein-protein interactions. Traditional methods are computationally intensive and have limited accuracy, while existing machine learning methods treat the problem as a regression task and overlook the restrictions imposed by the constant covalent bond lengths and angles. In this work, we present DiffPack, a torsional diffusion model that learns the joint distribution of side-chain torsional angles, the only degrees of freedom in side-chain packing, by diffusing and denoising on the torsional space. To avoid issues arising from simultaneous perturbation of all four torsional angles, we propose autoregressively generating the four torsional angles from \c{hi}1 to \c{hi}4 and training diffusion models for each torsional angle. We evaluate the method on several benchmarks for protein side-chain packing and show that our method achieves improvements of 11.9% and 13.5% in angle accuracy on CASP13 and CASP14, respectively, with a significantly smaller model size (60x fewer parameters). Additionally, we show the effectiveness of our method in enhancing side-chain predictions in the AlphaFold2 model. Code will be available upon the accept.Comment: Under revie

Benchmarking Learned Indexes

Recent advancements in learned index structures propose replacing existing index structures, like B-Trees, with approximate learned models. In this work, we present a unified benchmark that compares well-tuned implementations of three learned index structures against several state-of-the-art "traditional" baselines. Using four real-world datasets, we demonstrate that learned index structures can indeed outperform non-learned indexes in read-only in-memory workloads over a dense array. We also investigate the impact of caching, pipelining, dataset size, and key size. We study the performance profile of learned index structures, and build an explanation for why learned models achieve such good performance. Finally, we investigate other important properties of learned index structures, such as their performance in multi-threaded systems and their build times