Approaches in biotechnological applications of natural polymers

Natural polymers, such as gums and mucilage, are biocompatible, cheap, easily available and non-toxic materials of native origin. These polymers are increasingly preferred over synthetic materials for industrial applications due to their intrinsic properties, as well as they are considered alternative sources of raw materials since they present characteristics of sustainability, biodegradability and biosafety. As definition, gums and mucilages are polysaccharides or complex carbohydrates consisting of one or more monosaccharides or their derivatives linked in bewildering variety of linkages and structures. Natural gums are considered polysaccharides naturally occurring in varieties of plant seeds and exudates, tree or shrub exudates, seaweed extracts, fungi, bacteria, and animal sources. Water-soluble gums, also known as hydrocolloids, are considered exudates and are pathological products; therefore, they do not form a part of cell wall. On the other hand, mucilages are part of cell and physiological products. It is important to highlight that gums represent the largest amounts of polymer materials derived from plants. Gums have enormously large and broad applications in both food and non-food industries, being commonly used as thickening, binding, emulsifying, suspending, stabilizing agents and matrices for drug release in pharmaceutical and cosmetic industries. In the food industry, their gelling properties and the ability to mold edible films and coatings are extensively studied. The use of gums depends on the intrinsic properties that they provide, often at costs below those of synthetic polymers. For upgrading the value of gums, they are being processed into various forms, including the most recent nanomaterials, for various biotechnological applications. Thus, the main natural polymers including galactomannans, cellulose, chitin, agar, carrageenan, alginate, cashew gum, pectin and starch, in addition to the current researches about them are reviewed in this article.. }To the Conselho Nacional de Desenvolvimento Cientfíico e Tecnológico (CNPq) for fellowships (LCBBC and MGCC) and the Coordenação de Aperfeiçoamento de Pessoal de Nvíel Superior (CAPES) (PBSA). This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, the Project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and COMPETE 2020 (POCI-01-0145-FEDER-006684) (JAT)

Validation of Deep Learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise

Measure the Performance by Analysis of Different Boosting algorithms on Various Patterns of Phishing datasets

Abstract The internet has become an important component of our daily lives. The most common internet service is web surfing. Many individuals use their browser to do things like online shopping, bill payment, cell phone recharge, and banking transactions. Customers confront different security dangers, such as cyber crime, as a result of the widespread usage of this service. Cyber phishing is a type of web threat that entices users to connect with a false website. The main goal of this research paper is to prevent the user's sensitive information. The proposed model is developed in three steps in step1 we choose a dataset to train on, and then test classifiers on the dataset. In step2 we have applied the three classifiers finding phishing detection accuracy and finally step3 after completing all of the predictions, we discovered that XGBoost outperformed AdaBoost and Gradient boosting machine learning algorithms.</jats:p

Acute Reversal of Warfarin Therapy in Patient with Protein C and S Deficiency Presenting for Emergency Surgery

This is a case report of a 19 year young male presented with swelling and blackening of right upper limb and abdominal pain since 10 days. He was a known case of protein C and protein S deficiency on warfarin therapy, with deranged coagulation parameters posted for amputation fingers in emergenc

The robust quay crane allocation for a discrete bulk material handling port

This study investigates the quay crane allocation problem with respect to vessel assigned to a particular discrete berth at a bulk material handling port. In the proposed model,vessels at the anchorage are berthed on a First in, first out (FIFO) basis at the port, and then the quay cranes are assigned to the berth dynamically before berthing and during unloading of the vessel. To solve the model, we used the Block Based Genetic Algorithm (BBGA) and Genetic Algorithm (GA). Computational study is conducted using the real data provided by a port located on theEastern Coast of India

Effect of Sudarshan Kriya Yoga (SKY) on daytime and situational sleep propensity in novice practitioners: a prospective cohort study

Abstract Objectives Hectic, late-night lifestyle has reduced 90 min sleep in 20% adults resulting in insomnia and excessive daytime sleepiness (EDS). We assess the scope of Sudarshan Kriya Yoga (SKY), a 4-component, breathing process in reducing EDS, generally and situationally. Methods This is a prospective, controlled study involving randomized subjects without any sleep-wake cycle anomalies and prior experience in SKY. Subjects (n=52) performed 30 min of SKY for 6 days/week for 8 weeks, while controls (n=53) performed sitting activity and Suryanamaskar for 4-weeks each. Epworth Sleepiness Scale (ESS) was used to measure EDS at 0, 4, and 8 weeks. Results SKY group showed significant ESS score improvements between 0–4 weeks and 4–8 weeks of 1.22 (p=0.0001) and 1.66 (p=0.001) respectively. Controls however failed to improve with score differences of 0.02 (p=0.892) and 0.02 (p=0.8212) respectively. SKY group showed significant ESS score improvement over controls at 4-weeks (difference=1.74; p=0.013) and 8-weeks (difference eight; p=0.0001). Improvement was most for obese people and those sitting in a halted car. Conclusions Improvement in subjects’ nighttime sleep and daytime wakefulness in SKY practitioners can be attributed to polyvagal theory. Increased heart rate variability (HRV) alterations and sympathetic hyperarousal in chronic insomnia; and cholinergic and GABAergic dysregulation in anxiety disorders are countered by regulated vagal nerve stimulation post SKY. Our study establishes effectivity of SKY in reducing EDS (total and situational), provides a clinical correlation for prior polysomnographic evidence and paves way for larger trials directed towards SKY prescriptions for insomnia. </jats:sec