Imaging and sensing of pH and chemical state with nuclear-spin-correlated cascade gamma rays via radioactive tracer

Single-photon-emission computed tomography (SPECT) and positron-emission tomography (PET) are highly sensitive molecular detection and imaging techniques that generally measure accumulation of radio-labeled molecules by detecting gamma rays. Quantum sensing of local molecular environment via spin, such as nitrogen vacancy (NV) centers, has also been reported. Here, we describe quantum sensing and imaging using nuclear-spin time-space correlated cascade gamma-rays via a radioactive tracer. Indium-111 (111In) is widely used in SPECT to detect accumulation using a single gamma-ray photon. The time-space distribution of two successive cascade gamma-rays emitted from an 111In atom carries significant information on the chemical and physical state surrounding molecules with double photon coincidence detection. We propose and demonstrate quantum sensing capability of local micro-environment (pH and chelating molecule) in solution along with radioactive tracer accumulation imaging, by using multiple gamma-rays time-and-energy detection. Local molecular environment is extracted through electric quadrupole hyperfine interaction in the intermediate nuclear spin state by the explicit distribution of sub-MeV gamma rays. This work demonstrates a proof of concept, and further work is necessary to increase the sensitivity of the technique for in vivo imaging and to study the effect of scattered radiation for possible application in nuclear medicine.

The role of expert judgments in wine quality assessment: the mismatch between chemical, sensorial and extrinsic cues

Purpose The complexity in determining the quality of a credence good like wine increases due to the lack of mandatory ingredient labeling. This has generated a significant information asymmetry in the wine market, leading consumers to delegate their purchase decisions to expert rankings and wine guides. This paper explores whether expert assessments reduce the information asymmetry caused by the absence of ingredient labeling in the wine market.Design/methodology/approach By employing analysis of variance (ANOVA) in a sample of 304 wines included in the Wine Guide of the Spanish Consumers Organization (OCU), this paper assesses the extent to which expert assessments based on sensory evaluations converge with the objective cues provided by laboratory analysis in wine quality evaluations.Findings Results reveal a mismatch between expert assessments and laboratory analyses. Chemical aspects such as SO2 levels or volatile acidity, sensorial factors such as intensity and persistence, and extrinsic variables such as the region of origin or wine type play an important role in the quality ranking of wines.Originality/value These findings call for the inclusion of objective intrinsic cues in expert sensory assessments to provide consumers reliable information about wines and to resolve the apparent dissonances in wine quality assessments.

The Use of Digitalization (ICTs) in Achieving Sustainable Development Goals

The article examines the use of information and communication technologies (ICT) in achieving sustainable development goals (SDGs). This link is conceptually and contextually analyzed in the global context whereby scholarly opinions and research works are discussed to substantiate the role that ICT can play in achieving SDGs. The article further examines the link in South Africa, using the country as a locus of the study. The article aims to find answers to the following research questions: How is ICT linked to all 17 SDGs? To what extent this link may accelerate SDGs’ implementation? What is the impact of ICT on accelerating SDGs? What are the challenges hampering the efficient utilization of ICT for SDGs’ implementation? The article uses a qualitative approach with desktop analysis to draw significant information. In addition, unobtrusive methods were applied, such as conceptual analysis and document analysis, to draw conclusions based on the findings. The findings confirm that despite ICT policies and infrastructure, the use and impact of ICT on SDGs is not always high. The article suggests that improvements are required at policy, institutional, department and individual levels. The suggestions can be applicable globally, based on the technological advancement and socio-economic development in country-specific contexts.

Diagnosis and Management of Infected or Suspected Patients with Covid-19 a Study of 48 Cases in the UK

Covid-19 is a pandemic infective disease, which has been erupting throughout the whole world from 2020 to 2021. There have been at the time of reviewing more than a hundred million cases (102,399,513) of infection, and more than 2 million deaths (2,217,005) in more than 200 countries; this information is taken from the Covid-19 daily situation report issued on 1st Feb 2021 by WHO. The pandemic was also declared as an outbreak of Public Health Emergency of International Concern on 30th Jan 2020 by WHO. No effective treatment model has yet been confirmed by conventional western medicine, but some herbal treatments used in China can be recognized as having positive results in Covid-19 cases. This pandemic disaster has been severely damaging to quality of life, disturbing social communication, economic development and the progress of humanity. Successful results were first reported from temporary hospitals (Fangcang Hospitals set up in arenas and exhibition spaces) in Wuhan, China, where more than 90% of patients treated with Chinese herbal medicine were prevented by transferring from the minor or milder stages of the disease to the severe or critical stage. In all of them the positive PCR became negative; herbs were involved in the rescue and treatment of severe and critical cases in ICU’s in hospitals as well. It was an important factor in how Covid-19 was controlled so quickly in China. The author recruited both confirmed and suspected Covid-19 patients through social media (WeChat, WhatsApp, Internet, message etc.) as a volunteer TCM consultant from March 2020, during the first Lockdown phase in UK. It was quickly realized that the appearance of the tongue provides special and significant information for identifying whether Covid-19 is present and shows its severity, from a TCM perspective, as the degree and type of dampness accumulated within the body. Herbal prescriptions were sent by post for patients’ treatment according to their clinical findings, virus exposure history and tongue information (from photos). Similar positive effects were found as in China, and treatment reports and results are summarized here. The author believes there are many herbal medicines that can be effective in controlling the Sars-Cov-2 virus, and that prescribing the appropriate formula to match each individual case is the key point for TCM control of Covid-19. Chinese herbal medicine is the most effective current treatment method that can prevent cases in the minor and ordinary stages from progressing to the severe or critical stage, and so can play a significant role in the decrease and cure of Covid-19.

Evaluating Remote Task Assignment of an Online Engineering Module through Data Mining in a Virtual Communication Platform Environment

E-learning has traditionally emphasised educational resources, web access, student participation, and social interaction. Novel virtual spaces, e-lectures, and digital laboratories have been developed with synchronous or asynchronous practices throughout the migration from face-to-face teaching modes to remote teaching during the pandemic restrictions. This research paper presents a case study concerning the evaluation of the online task assignment of students, using MS Teams as an electronic platform. MS Teams was evaluated to determine whether this communication platform for online lecture delivery and tasks’ assessments could be used to avoid potential problems caused during the teaching process. Students’ data were collected, and after filtering out significant information from the online questionnaires, a statistical analysis, containing a correlation and a reliability analysis, was conducted. The substantial impact of 37 variables was revealed. Cronbach’s alpha coefficient calculation revealed that 89% of the survey questions represented internally consistent and reliable variables, and for the sampling adequacy measure, Bartlett’s test was calculated at 0.816. On the basis of students’ diligence, interaction abilities, and knowledge embedding, two groups of learners were differentiated. The findings of this study shed light on the special features of fully online teaching specifically in terms of improving assessment through digital tools and merit further investigation in virtual and blended teaching spaces, with the goal of extracting outputs that will benefit the educational community.

Recent Developments of Coumarin-based Hybrids in Drug Discovery

Coumarin scaffold is a highly significant O-heterocycle, namely benzopyran-2-ones, form an elite class of naturally occurring compounds that possess promising therapeutic perspectives. Based on its broad spectrum of biological activities, the privileged coumarin scaffold is applied to medicinal and pharmacological treatments by several rational design strategies and approaches. Structure-activity relationships of the coumarin-based hybrids with various bioactivity fragments revealed significant information toward the further development of highly potent and selective disorder therapeutic agents. The molecular docking studies between coumarins and critical therapeutic enzymes demonstrated mode of action by forming noncovalent interactions with more than one receptor, further rationally confirm information about structure-activity relationships. This review summarizes recent developments relating to coumarin-based hybrids with other pharmacophores aiming to numerous feasible therapeutic enzymatic targets to combat various therapeutic fields, including anticancer, antimicrobic, anti-Alzheimer, anti-inflammatory activities.

Research on Fast Compensation Algorithm for Interframe Motion of Multimedia Video Based on Manhattan Distance

To improve the video quality, aiming at the problems of low peak signal-to-noise ratio, poor visual effect, and low bit rate of traditional methods, this paper proposes a fast compensation algorithm for the interframe motion of multimedia video based on Manhattan distance. The absolute median difference based on wavelet transform is used to estimate the multimedia video noise. According to the Gaussian noise variance estimation result, the active noise mixing forensics algorithm is used to preprocess the original video for noise mixing, and the fuzzy C-means clustering method is used to smoothly process the noisy multimedia video and obtain significant information from the multimedia video. The block-based motion idea is to divide each frame of the video sequence into nonoverlapping macroblocks, find the best position of the block corresponding to the current frame in the reference frame according to the specific search range and specific rules, and obtain the relative Manhattan distance between the current frame and the background of multimedia video using the Manhattan distance calculation formula. Then, the motion between the multimedia video frames is compensated. The experimental results show that the algorithm in this paper has a high peak signal-to-noise ratio and a high bit rate, which effectively improves the visual effect of the video.

Hyperspectral Imaging for Clinical Applications

Measuring morphological and biochemical features of tissue is crucial for disease diagnosis and surgical guidance, providing clinically significant information related to pathophysiology. Hyperspectral imaging (HSI) techniques obtain both spatial and spectral features of tissue without labeling molecules such as fluorescent dyes, which provides rich information for improved disease diagnosis and treatment. Recent advances in HSI systems have demonstrated its potential for clinical applications, especially in disease diagnosis and image-guided surgery. This review summarizes the basic principle of HSI and optical systems, deep-learning-based image analysis, and clinical applications of HSI to provide insight into this rapidly growing field of research. In addition, the challenges facing the clinical implementation of HSI techniques are discussed.

Impact of Patient Health Education on the Screening for Disease Test-Outcomes

Screening for diseases is a medical process to predict, prevent, detect, and cure a disease in people at high risk. However, it is limited in the quality and accuracy of the outcomes. The reason for this is the lack of long-term data about the health condition of the patient. Launching modern information and communication technology in the screening process has shown promise of improving the screening outcomes. A previous study has shown that patient education can positively impact the patient behavior face to a disease and can empower the patient to adopt a healthy lifestyle and thus avoid certain diseases. Offering medical education to the patient can positively impact screening outcomes since educated and empowered patients are more aware of certain diseases and can collect significant information. This can minimize the rate of false positive as well as false negative screening results. This chapter analyzes how medical education can contribute to improving screening outcomes.

A Novel Approach of Product Recommendation using Utility Based Association Rules

An exorbitant source of data is easily available but the actual task lies in using this data efficiently. In this article, the aim is to analyse the significant information embedded in the customer purchase behaviour to recommend new products to them. Our proposed scheme is a two-fold approach. First, the authors retrieve various product correlations from the vast library of user transactions. Based on these product correlations, utility based association rules are learned which depict the customer purchase behaviour. These rules are then applied in a recommender system for novel product suggestions to the customers. With improved utility based mining the paper tries to incorporate the usefulness of an item set like cost, profit or any other factor along with their frequency. In this paper the authors have deployed the rules discovered from both the conventional Frequent Item Set Mining and Improved Utility Based Mining on an e-commerce platform to compare the accuracy of the algorithms. The obtained results establish the efficacy of the proposed algorithm.