Formulation and characterization of novel dairy-based dip utilizing heat-acid-coagulated milk gel and whey.

An attempt was made to develop a novel dairy-based dip-like product from heat-acid-induced milk gel and whey. Based upon preliminary trials, the combination of cream (15-35%), whey (60-70%) and common salt (0.8-1.

Checkpoint activation by Spd1: a competition-based system relying on tandem disordered PCNA binding motifs.

DNA regulation, replication and repair are processes fundamental to all known organisms and the sliding clamp proliferating cell nuclear antigen (PCNA) is central to all these processes. S-phase delaying protein 1 (Spd1) from S. pombe, an intrinsically disordered protein that causes checkpoint activation by inhibiting the enzyme ribonucleotide reductase, has one of the most divergent PCNA binding motifs known.

Biosynthesis of lactobionic acid: a systematic review.

Lactobionic acid is a comparatively less explored lactose derivative with impressive biofunctional qualities, and is currently being used by the advanced chemical and pharmaceutical research industries. It is an aldonic acid with probiotics, antimicrobial, antioxidant, calcium chelating activity. In dairy and food products, it can be used to improve flavor, texture, yield and shelf life with additional health benefits.

Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images.

Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a great effect on the specificity of diagnosis, the monitoring of many physiological and pathological procedures, and the response and evaluation of therapeutic effectiveness in various fields of clinical practices, including primary eye diseases and systemic diseases such as diabetes. Therefore, precise diagnosis, classification, and automated image analysis models are crucial.

An Intelligent Healthcare System Using IoT in Wireless Sensor Network.

The Internet of Things (IoT) uses wireless networks without infrastructure to install a huge number of wireless sensors that track system, physical, and environmental factors. There are a variety of WSN uses, and some well-known application factors include energy consumption and lifespan duration for routing purposes. The sensors have detecting, processing, and communication capabilities.

Detection of COVID-19 Case from Chest CT Images Using Deformable Deep Convolutional Neural Network.

The infectious coronavirus disease (COVID-19) has become a great threat to global human health. Timely and rapid detection of COVID-19 cases is very crucial to control its spreading through isolation measures as well as for proper treatment. Though the real-time reverse transcription-polymerase chain reaction (RT-PCR) test is a widely used technique for COVID-19 infection, recent researches suggest chest computed tomography (CT)-based screening as an effective substitute in cases of time and availability limitations of RT-PCR.

An assessment of the intact casein content in natural cheddar cheese to determine its suitability in processed cheeses with desired properties.

As compared to age, intact casein in the natural cheddar cheese is a better index of selection for manufacturing processed cheese. In view of this, the present investigation was designed to establish relation between intact casein of the natural cheese with desirable properties of the processed cheese so that the most desirable intact casein in the natural cheddar cheese can be selected on the basis of required properties of the processed cheese. Processed cheese was prepared by using natural cheddar cheese of different intact casein content (ICC) and analysed for meltability, oiling off and hardness.

IoT-Based Medical Image Monitoring System Using HL7 in a Hospital Database.

In recent years, the healthcare system, along with the technology that surrounds it, has become a sector in much need of development. It has already improved in a wide range of areas thanks to significant and continuous research into the practical implications of biomedical and telemedicine studies. To ensure the continuing technological improvement of hospitals, physicians now also must properly maintain and manage large volumes of patient data.

Trust and Mobility-Based Protocol for Secure Routing in Internet of Things.

In the Internet of Things (IoT), the de facto Routing Protocol for Low Power and Lossy Networks (RPL) is susceptible to several disruptive attacks based on its functionalities and features. Among various RPL security solutions, a trust-based security is easy to adapt for resource-constrained IoT environments. In the existing trust-based security for RPL routing attacks, nodes' mobility is not considered or limited to only the sender nodes.

Deep learning in multimedia healthcare applications: a review.

The increase in chronic diseases has affected the countries' health system and economy. With the recent COVID-19 virus, humanity has experienced a great challenge, which has led to make efforts to detect it and prevent its spread. Hence, it is necessary to develop new solutions that are based on technology and low cost, to satisfy the citizens' needs.

The fission yeast bromodomain protein Bdf2 is required for the growth of cells with circular chromosomes.

Circular chromosomes have frequently been observed in tumors of mesenchymal origin. In the fission yeast Schizosaccharomyces pombe, deletion of pot1+ results in rapid telomere loss, and the resulting survivors have circular chromosomes. Fission yeast has 2 bromodomain and extra-terminal (BET) proteins, Bdf1 and Bdf2; both are required for maintaining acetylated histones.

COVID-19 Vaccine Hesitancy among Temporary Foreign Workers from Bangladesh.

The COVID-19 pandemic poses an extraordinary threat to the health, safety, and freedom of temporary foreign workers (TFWs). Highly effective vaccines against COVID-19 may hold an outsized benefit for TFWs, particularly those living in congregate settings where protective measures such as social distancing are not possible. While some studies of migrant destination countries have included migrants, no study to date has sought to understand variations in vaccine hesitancy among individuals in a single migrant source population across different destinations.

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach.

Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner's premises.

Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning.

Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. There are high chances of getting false positives and false negatives identifying the COVID-19 symptoms through existing medical practices such as PCR (polymerase chain reaction) and RT-PCR (reverse transcription-polymerase chain reaction). It might lead to a community spread of the disease.

An Internet-of-Medical-Things-Enabled Edge Computing Framework for Tackling COVID-19.

Capturing psychological, emotional, and physiological states, especially during a pandemic, and leveraging the captured sensory data within the pandemic management ecosystem is challenging. Recent advancements for the Internet of Medical Things (IoMT) have shown promising results from collecting diversified types of such emotional and physical health-related data from the home environment. State-of-the-art deep learning (DL) applications can run in a resource-constrained edge environment, which allows data from IoMT devices to be processed locally at the edge, and performs inferencing related to in-home health.

Towards the sustainable development of smart cities through mass video surveillance: A response to the COVID-19 pandemic.

Sustainable smart city initiatives around the world have recently had great impact on the lives of citizens and brought significant changes to society. More precisely, data-driven smart applications that efficiently manage sparse resources are offering a futuristic vision of smart, efficient, and secure city operations. However, the ongoing COVID-19 pandemic has revealed the limitations of existing smart city deployment; hence; the development of systems and architectures capable of providing fast and effective mechanisms to limit further spread of the virus has become paramount.

MetaCOVID: A Siamese neural network framework with contrastive loss for -shot diagnosis of COVID-19 patients.

Various AI functionalities such as pattern recognition and prediction can effectively be used to diagnose (recognize) and predict coronavirus disease 2019 (COVID-19) infections and propose timely response (remedial action) to minimize the spread and impact of the virus. Motivated by this, an AI system based on deep meta learning has been proposed in this research to accelerate analysis of chest X-ray (CXR) images in automatic detection of COVID-19 cases. We present a synergistic approach to integrate contrastive learning with a fine-tuned pre-trained ConvNet encoder to capture unbiased feature representations and leverage a Siamese network for final classification of COVID-19 cases.

Privacy-Enhanced Data Fusion for COVID-19 Applications in Intelligent Internet of Medical Things.

With the worldwide large-scale outbreak of COVID-19, the Internet of Medical Things (IoMT), as a new type of Internet of Things (IoT)-based intelligent medical system, is being used for COVID-19 prevention and detection. However, since the widespread use of IoMT will generate a large amount of sensitive information related to patients, it is becoming more and more important yet challenging to ensure data security and privacy of COVID-19 applications in IoMT. The leakage of private information during IoMT data fusion process will cause serious problems and affect people's willingness to contribute data in IoMT.

Convolutional neural network-based models for diagnosis of breast cancer.

Breast cancer is the most prevailing cancer in the world and each year affecting millions of women. It is also the cause of largest number of deaths in women dying in cancers. During the last few years, researchers are proposing different convolutional neural network models in order to facilitate diagnostic process of breast cancer.

COVID-19 Networking Demand: An Auction-Based Mechanism for Automated Selection of Edge Computing Services.

Network and cloud service providers are facing an unprecedented challenge to meet the demand of end-users during the COVID-19 pandemic. Currently, billions of people around the world are ordered to stay at home and use remote connection technologies to prevent the spread of the disease. The COVID-19 crisis brought a new reality to network service providers that will eventually accelerate the deployment of edge computing resources to attract the massive influx of users' traffic.

TermInformer: unsupervised term mining and analysis in biomedical literature.

Terminology is the most basic information that researchers and literature analysis systems need to understand. Mining terms and revealing the semantic relationships between terms can help biomedical researchers find solutions to some major health problems and motivate researchers to explore innovative biomedical research issues. However, how to mine terms from biomedical literature remains a challenge.