Evaluation of Antibiofilm and Antiquorum Sensing Activities of Fucoidan Characterized from Padina boryana against Nosocomial Pathogens.

Modern functional chemicals that can be employed in biotechnology, pharmaceutics, and food science are a sustainable source to be found in seaweeds. The bioactivity of the majority of these marine compounds has received scant research. Fucoidan is a highly sulfated polysaccharide with a range of bioactivities, including an antipathogenic effect. There is still much to learn about the relationship between fucoidan structure and its function in pathogen infections. By employing microwave and probe sonication to create crude fucoidan, DEAE-cellulose anion-exchange chromatography was used to further purify the substance. Purified fucoidan was structurally characterized using UV-Visible spectroscopy, Fourier transform infrared spectroscopy, X-ray diffraction, and NMR analysis. The results of the structural analysis demonstrate that sulfates and hydroxyl groups are present in the isolated fucoidan. There are fucose residues, according to the NMR data. The present study investigates the bioactivity of fucoidan, a polysaccharide derived from the brown algae Padina boryana, as a potent weapon against the known nosocomial diseases Proteus vulgaris and Salmonella enterica. Fluorescence microscopy was used to show that fucoidan antibiofilm action is totally effective against Proteus vulgaris and Salmonella enterica biofilm formations as well as planktonic cell growths at dosages over 25 g/mL. Here, using in vitro investigations of the possible inactivation of molecules that are regulated by acyl-homoserine lactone (AHL) in both bacterial species, we explore the antiquarum sensing and antibiofilm capabilities of fucoidan. According to the present study, extracted fucoidan from Padina boryana can be used to generate antibacterial compounds and operate as a quorum-sensing inhibitor to combat side effects and antibiotic resistance.

Electromyography pattern-recognition based prosthetic limb control using various machine learning techniques.

People who have lost their limbs to amputation and neurological disorders confront this loss every morning. As per the literature review, nearly 30% of the Indian population suffered from upper extremity amputation. As a coping-up measure, a force-controlled prosthetic limb has been developed to improve their self-reliance, quality of lifestyle and mental strength. The current prosthetic limb operation is done by residual muscle contraction, which contributes to the activation of the sensor and the motor. But there are some cons, the amputee does not know how much pressure needs to be exerted for holding various objects. Also, the amputee still has to undergo the surgical procedure. However, this paper proposes a way to predict the force which is needed to regulate the voltage for the servomotors using different Machine Learning (ML) regression approaches. Support Vector Regressor (SVR), Linear Regression and Random Forest models have been used to predict that force requirement. After comparing the results, the Random Forest model gave a highly accurate prediction of the force needed to control the voltage for the DC servomotors.

Design and development of a smart blind walking stick using machine learning.

Visually impaired people are often subjugated under extreme circumstances even in their day-to-day life. The daily requirements of a common man appear to be an impediment in their routine life. Simplest of tasks like walking, eating, bathing, conversing and even eating is of utmost difficulty to them. Moreover, with such difficulties their only way-out seems to be dependency on the privileged lot, which further diminishes their confidence in themselves and gradually makes them even more dependent. The conventional devices that are used by visually impaired people include basic walking sticks which fail at the job in hand by not providing adequate stabilisation on rough surfaces and misguiding the users into unfavourable conditions. There is no way for the person to know what the object in front of them is without hitting it with the stick, which could also lead to accidents. To solve these problems, a smart walking stick is developed which not only recognises the object in front of it using Machine Learning (ML) models, but also gives a voice output to alert its user about the particular object thereby limiting the chance of any and all accidents. The concept is realised in hardware and integrated to the walking stick. This helps in stabilisation of phone and to produce better results in object identification. Further an application is developed to alert the user by converting the obtained image into a voice messages.