Shilajit is a pale-brown to blackish-brown exudation, of variable consistency, exuding from layers of rocks in many mountain ranges of the world, especially the Himalayas and Hindukush ranges of the Indian subcontinent. It has been found to consist of a complex mixture of organic humic substances and plant and microbial metabolites occurring in the rock rhizospheres of its natural habitat. Shilajit has been used as a rejuvenator and an adaptogen for thousands of years, in one form or another, as part of traditional systems of medicine in a number of countries. Many therapeutic properties have been ascribed to it, a number of which have been verified by modern scientific evaluation. Shilajit has been attributed with many miraculous healing properties.

In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task. Stock forecasting is complex, given the stochastic dynamics and non-stationary behavior of the market. Stock movements are influenced by varied factors beyond the conventionally studied historical prices, such as social media and correlations among stocks. The rising ubiquity of online content and knowledge mandates an exploration of models that factor in such multimodal signals for accurate stock forecasting. We introduce an architecture that achieves a potent blend of chaotic temporal signals from financial data, social media, and inter-stock relationships via a graph neural network in a hierarchical temporal fashion. Through experiments on real-world S&P 500 index data and English tweets, we show the practical applicability of our model as a tool for investment decision making and trading.

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Polymeric hydrophilic matrices are widely used for controlled-release preparations. The process of drug release is controlled by matrix swelling or polymer dissolution. It has been shown that the swelling of guar gum is affected by concentration of drug and viscosity grade of the polymer. This study examines the mechanism of behavior of guar gum in a polymer-drug matrix. The swelling action of guar gum, in turn, is controlled by the rate of water uptake into the matrices. An inverse relationship exists between the drug concentration in the gel and matrix swelling. This implies that guar gum swelling is one of the factors affecting drug release. The swelling behavior of guar gum is therefore useful in predicting drug release.

The aim of the present investigation was to prepare and evaluate novel bioadhesive vaginal microspheres containing Terbinafine (TBH) in order to provide long-term therapeutic activity at the site of infection and prove the same using in vitro anti fungal activity of the same using Candida albicans. Microspheres were prepared by the Solvent extraction technique using sodium hyaluronate and Arlacel A. Microspheres were characterized by SEM, DSC, FTIR, particle size analysis and evaluated for percentage yield, drug loading, encapsulation efficiency and in vitro drug release. FTIR and DSC studies showed that no chemical changes or alterations occurred in the drug and polymers. The sphericity factor indicated that the prepared microspheres were spherical. Formulation (TBHMs) indicated a controlled in vitro drug release and good bioadhesive strength. The in vitro anti fungal activity confirmed for a controlled and prolonged capacity of the prepared novel formulation. The results indicated that this drug delivery system can be explored for controlled intra-vaginal drug release.

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Quantitative trading and investment decision making are intricate financial tasks that rely on accurate stock selection. Despite advances in deep learning that have made significant progress in the complex and highly stochastic stock prediction problem, modern solutions face two significant limitations. They do not directly optimize the target of investment in terms of profit, and treat each stock as independent from the others, ignoring the rich signals between related stocks' temporal price movements. Building on these limitations, we reformulate stock prediction as a learning to rank problem and propose STHAN-SR, a neural hypergraph architecture for stock selection. The key novelty of our work is the proposal of modeling the complex relations between stocks through a hypergraph and a temporal Hawkes attention mechanism to tailor a new spatiotemporal attention hypergraph network architecture to rank stocks based on profit by jointly modeling stock interdependence and the temporal evolution of their prices. Through experiments on three markets spanning over six years of data, we show that STHAN-SR significantly outperforms state-of-the-art neural stock forecasting methods. We validate our design choices through ablative and exploratory analyses over STHAN-SR's spatial and temporal components and demonstrate its practical applicability.

The aggregation of a number of penicillins, both in water and in electrolyte solution, has been examined by total intensity light scattering methods. Micellar association was noted for carfecillin, flucloxacillin cloxacillin, phenethicillin and penicillin V and critical micelle concentrations and micellar aggregation numbers were determined. Association of penicillin G in water and electrolyte was limited to dimer and trimer formation.