Characterization of a novel inulosucrase from Lactiplantibacillus plantarum.

Fructansucrases produce fructans by polymerizing the fructose moiety released from sucrose. Here, we describe the recombinant expression and characterization of a unique fructansucrase from Lactiplantibacillus plantarum DKL3 that showed low sequence similarity with previously characterized fructansucrases. The optimum pH and temperature of fructansucrase were found to be 4.

CSDNet: A Novel Deep Learning Framework for Improved Cataract State Detection.

Cataracts, known for lens clouding and being a common cause of visual impairment, persist as a primary contributor to vision loss and blindness, presenting notable diagnostic and prognostic challenges. This work presents a novel framework called the Cataract States Detection Network (CSDNet), which utilizes deep learning methods to improve the detection of cataract states. The aim is to create a framework that is more lightweight and adaptable for use in environments or devices with limited memory or storage capacity.

Decoding the role of aldosterone in glycation-induced diabetic complications.

Diabetes-mediated development of micro and macro-vascular complications is a global concern. One of the factors is hyperglycemia induced the non-enzymatic formation of advanced glycation end products (AGEs). Accumulated AGEs bind with receptor of AGEs (RAGE) causing inflammation, oxidative stress and extracellular matrix proteins (ECM) modifications responsible for fibrosis, cell damage and tissue remodeling.

Detecting health misinformation: A comparative analysis of machine learning and graph convolutional networks in classification tasks.

In the digital age, the proliferation of health-related information online has heightened the risk of misinformation, posing substantial threats to public well-being. This research conducts a meticulous comparative analysis of classification models, focusing on detecting health misinformation. The study evaluates the performance of traditional machine learning models and advanced graph convolutional networks (GCN) across critical algorithmic metrics.

Towards secure IoT networks: A comprehensive study of metaheuristic algorithms in conjunction with CNN using a self-generated dataset.

The Internet of Things (IoT) has radically reformed various sectors and industries, enabling unprecedented levels of connectivity and automation. However, the surge in the number of IoT devices has also widened the attack surface, rendering IoT networks potentially susceptible to a plethora of security risks. Addressing the critical challenge of enhancing security in IoT networks is of utmost importance.

Exploring the abundance of microplastics in Indian landfill leachate: An analytical study.

Landfills are a major source of many emerging pollutants, including microplastics (MPs). This makes them a potential threat to human and environmental health and calls for a more detailed analysis of their hazard potential. India is a developing country with multiple unscientific waste dumping sites.

Public health nurse perspectives on predicting nonattendance for cervical cancer screening through classification, ensemble, and deep learning models.

Objectives: Women's attendance to cervical cancer screening (CCS) is a major concern for healthcare providers in community. This study aims to use the various algorithms that can accurately predict the most barriers of women for nonattendance to CS.

Design: The real-time data were collected from women presented at OPD of primary health centers (PHCs).

Benzyl isothiocyanate as an alternative to antibiotics? a comparative in vivo study using Pseudomonas aeruginosa infection as a model.

Due to over-prescription of antibiotics, antimicrobial resistance has emerged to be a critical concern globally. Many countries have tightened the control of antibiotic usage, which, in turn, promotes the search for alternatives to antibiotics. Quite a few phytochemicals have been investigated.

Design of antipodal vivaldi antenna with patch and corrugations for 5G applications.

A compact 1 × 4 antipodal Vivaldi antenna (AVA) array designed for 5 G applications is introduced in this study. An elliptical-shaped parasitic patch and corrugation are strategically employed to enhance gain and bandwidth, making it well-suited for 5 G applications. The resulting AVA array with corrugation and parasitic patch (AVA-PC) is designed and simulated on ANSYS HFSS, demonstrating a stable gain ranging from 10 dBi to 11.

Examining the relationship between pro-environmental consumption behaviour and hedonic and eudaimonic motivation.

The present study examines the factors that influence the adoption of environmentally friendly products and environmentally conscious consumption by individuals. The conceptual framework incorporates environmental knowledge, perceived behavioral control, environmental concern, and perceived consumer efficacy in order to highlight the factors that contribute to environmental consciousness. Incorporating the theories of reasoned action and planned behavior, 479 participants completed structured questionnaires as part of this study.

Novel design of cryptographic architecture of nanorouter using quantum-dot cellular automata nanotechnology.

The article introduces a revolutionary Nanorouter structure, which is a crucial component in the Nano communication regime. To complete the connection, many key properties of Nanorouters are investigated and merged. QCA circuits with better speed and reduced power dissipation aid in meeting internet standards.

Ensemble Learning for Higher Diagnostic Precision in Schizophrenia Using Peripheral Blood Gene Expression Profile.

Introduction: Stigma contributes to a significant part of the burden of schizophrenia (SCZ), therefore reducing false positives from the diagnosis would be liberating for the individuals with SCZ and desirable for the clinicians. The stigmatization associated with schizophrenia advocates the need for high-precision diagnosis. In this study, we present an ensemble learning-based approach for high-precision diagnosis of SCZ using peripheral blood gene expression profiles.

In Silico Analysis of Functional SNPs in Genes of Complete Androgen Insensitivity Syndrome (CAIS): A Retrospective, Case-Control Study.

Background: Complete androgen insensitivity syndrome (CAIS) is one of the categories of androgen insensitivity syndrome (AIS) described as complete failure of the cell to react to androgens with external genitalia of a normal female. People with AIS condition are genetically male, with XY karyotype in each cell, but their bodies are unable to respond to male sex hormones (called androgens). It is associated with infertility as well as developing cancerous conditions.

Quality of Life and Its Association With Time in Range Among People With Type 2 Diabetes Mellitus Following Different Dietary Interventions: A Crossover Clinical Trial.

Background Quality of Life (QoL) is an essential consideration in healthcare. Numerous studies have examined QoL in India; however, data on QoL following different dietary interventions are lacking. Similarly, the use of technology such as continuous glucose monitoring (CGM) for diabetes care has independently demonstrated improvements in glycemic control; however, its association with QoL remains limited.

A sodium ion conducting gel polymer electrolyte with counterbalance between 1-ethyl-3-methylimidazolium tetrafluoroborate and tetra ethylene glycol dimethyl ether for electrochemical applications.

For sodium batteries, the development of gel polymer electrolytes (GPEs) with remarkable electrochemical properties is in its early stage and persists to be a challenge. In this report we have synthesized a series of GPEs containing a poly(vinyllidene fluoride--hexafluoropropylene) (PVdF-HFP) and poly (methyl methacrylate) (PMMA) as blend polymer, sodium perchlorate (NaClO) as ion-conducting salt and 1-ethyl-3-methylimidazolium tetrafluoroborate (EMIM-BF) and tetra ethylene glycol dimethyl ether (TEGDME) as molecular solvents. The counter balance between EMIM-BF and TEGDME is maintained by the electrolyte, which is formed through the optimal weight ratio of 2 : 1.

A comparison of deep transfer learning backbone architecture techniques for printed text detection of different font styles from unstructured documents.

Object detection methods based on deep learning have been used in a variety of sectors including banking, healthcare, e-governance, and academia. In recent years, there has been a lot of attention paid to research endeavors made towards text detection and recognition from different scenesor images of unstructured document processing. The article's novelty lies in the detailed discussion and implementation of the various transfer learning-based different backbone architectures for printed text recognition.

Neutrosophic robust ratio type estimator for estimating finite population mean.

Various authors have put their sincere efforts into proposing ratio estimators for estimating the population's mean and variance under different situations and sampling methods. But the problem arises when data is unstable, imprecise, ambiguous, incomplete and vague. In such situations, classical methods of estimation do not yield precise results, as these methods are not meant for such problems.

The effect of porcelain filler particulates madar fiber reinforced epoxy composite - A comprehensive study for biomedical applications.

Environmental consciousness motivates scientists to devise an alternative method for producing natural fiber composite materials in order to decrease the demand for synthetic fibers. This study explores the potential of a novel composite material derived from madar fiber-reinforced epoxy with porcelain filler particulates, designed specifically for biomedical instrumentation applications. The primary focus is to assess the material's structural, mechanical, and antibacterial properties.

Surface roughness prediction of AISI D2 tool steel during powder mixed EDM using supervised machine learning.

Surface integrity is one of the key elements used to judge the quality of machined surfaces, and surface roughness is one such quality parameter that determines the pass level of the machined product. In the present study, AISI D2 steel was machined with electric discharge at different process parameters using Jatropha and EDM oil. Titanium dioxide (TiO) nanopowder was added to the dielectric to improve surface integrity.

A Scoping Review of GIS Mapping of Type 1 Diabetes in Children: Identifying Current Gaps and Future Research Directions Using PRISMA-ScR.

Background: Type 1 Diabetes poses a significant public health threat, especially in low-and-middle countries, where resources are limited. The use of geographical information systems in diabetes research has shown the potential to reveal several epidemiological risk factors.

Aims: This scoping review aimed to identify the scope and extent of the current literature and explore its limitations on the geographical mapping of children with type 1 diabetes.

A narrative review on the Synchrobreathe™: A novel breath-actuated pressurised metered-dose inhaler for the treatment of obstructive airway diseases.

Pressurised metered-dose inhalers (pMDIs) and dry powder inhalers (DPIs), are widely used to deliver drugs for the treatment of asthma and chronic obstructive pulmonary disease (COPD). Incorrect use of inhalers is one of the main obstacles to achieving better clinical control. Indeed, with pMDIs, patients fail to synchronise actuation with inhalation due to a lack of coordination and with DPIs insufficient inspiratory effort compromises drug deposition in lungs.

A novel front in sustainable microbial management: computational analysis of curcumin and mangiferin's synergistic action against .

Background: Microorganisms are crucial in our ecosystem, offering diverse functions and adaptability. The UNGA Science Summit has underscored the importance of understanding microbes in alignment with the UN Sustainable Development Goals. poses significant challenges among various microorganisms due to its harmful effects on both soil and public health.

Data mining approaches to pneumothorax detection: Integrating mask-RCNN and medical transfer learning techniques.

With the medical condition of pneumothorax, also known as collapsed lung, air builds up in the pleural cavity and causes the lung to collapse. It is a critical disorder that needs to be identified and treated right as it can cause breathing difficulties, low blood oxygen levels, and, in extreme circumstances, death. Chest X-rays are frequently used to diagnose pneumothorax.

Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model.

According to experts in neurology, brain tumours pose a serious risk to human health. The clinical identification and treatment of brain tumours rely heavily on accurate segmentation. The varied sizes, forms, and locations of brain tumours make accurate automated segmentation a formidable obstacle in the field of neuroscience.