An Attempt to Estimate the Noises in Homeopathic Pathogenetic Trials by Employing the Jaccard Similarity Index and Noise Index

Background The critical task of researchers conducting double-blinded, randomized, placebo-controlled homeopathic pathogenetic trials is to segregate the signals from the noises. The noises are signs and symptoms due to factors other than the trial drug; signals are signs and symptoms due to the trial drug. Unfortunately, the existing tools (criteria for a causal association of symptoms only with the tested medicine, qualitative pathogenetic index, quantitative pathogenetic index, pathogenic index) have limitations in analyzing the symptoms of the placebo group as a comparator, resulting in inadequate segregation of the noises. Hence, the Jaccard similarity index and the Noise index are proposed for analyzing the symptoms of the placebo group as a comparator. Methods The Jaccard similarity index is the ratio of the number of common elements among the placebo and intervention groups to the aggregated number of elements in these groups. The Noise index is the ratio of common elements among the placebo and intervention group to the total elements of the intervention group. Homeopathic pathogenetic trials of Plumbum metallicum, Piper methysticum and Hepatitis C nosode were selected for experimenting with the computation of the Jaccard similarity index and the Noise index. Results Jaccard similarity index calculations show that 8% of Plumbum metallicum's elements, 10.7% of Piper methysticum's elements, and 19.3% of Hepatitis C nosode's elements were similar to the placebo group when elements of both the groups (intervention and placebo) were aggregated. Noise index calculations show that 10.7% of Plumbum metallicum's elements, 13.9% of Piper methysticum's elements and 25.7% of Hepatitis C nosode's elements were similar to those of the placebo group. Conclusion The Jaccard similarity index and the Noise index might be considered an additional approach for analyzing the symptoms of the placebo group as a comparator, resulting in better noise segregation in homeopathic pathogenetic trials.

Species Relationship and Population Differentiation In Stellaria L. (Caryophyllaceae) Of Iran Using Scot Molecular Marker and Morphological Data

Recognition of species is essential in a variety of domains, most remarkably biology, biogeography, ecology, as well as conservation. The genus Stellaria L. (Caryophyllaceae) has over 120 species spread across Europe and Asia's temperate zones. According to the most remarkable current treatments, nine species recognize Stellaria in Iran. These species are categorized into two types. Despite the broad distribution of several Stellaria species in Iran, no research on their genetic variability, method of divergence, or dispersion trends is accessible. As a result, we conducted genetic and morphological research on six Stellaria species and two of their closest relatives gathered from various habitats in Iran. This research aims to 1) Can SCoT markers be utilized to recognize Stellaria species? 2) What are the genetic characteristics of the mentioned taxa in Iran? and 3) To examine the interrelation of the species. In this research, ten SCoT markers were employed for molecular analysis, and 112 accessions were utilized for morphological study. The genetic distances were calculated using the Jaccard similarity coefficient, and descriptive data on the populations were used to estimate genetic parameters. There were 98 polymorphic bands all over. The integration of morphological and SCoT data demonstrated that the Stellaria species of Iran could be delimited and recognized. The Stellaria species are genetically unique; however, they share some similar alleles, according to AMOVA and STRUCTURE analyses. Bangladesh J. Plant Taxon. 28(2): 317-328, 2021 (December)

EXPLORING CONCOMITANT CONCEPTS IN THE DISCUSSION ON THE CIRCULAR ECONOMY: A BIBLIOMETRIC ANALYSIS OF WEB OF SCIENCE, SCOPUS AND TWITTER

A growing interest in the circular economy, which is seen in the intensification of public discourse, could lead to a danger of blurring the concept and introducing inefficiency in implementing circular economy-based solutions. This study explores the trend in concomitant or accompanying concepts of the discussion about the circular economy themes understanding regarding scientific publications (3,486 publications from Web of Science and Scopus) and popular (non-scientific) domain (represented by 106,504 tweets) in the years 2011–2018. By employing text mining, we calculated the Jaccard similarity index divided into years. The results reveal changes over time in themes accompanying the circular economy discussion and a trend of rising recognition of research-related keywords in general public discussion, with unweighted similarity reaching 39.44% in 2018. Our Twitter keyword research perspective indicates the need to consider the consumer’s role in the development of the circular economy – through keywords that are closely related to consumers’ daily activities.

An abundance- and morphology-based similarity index

Classic similarity indices measure community resemblance in terms of incidence (the number of shared species) and abundance (the extent to which the shared species are an equivalently large component of the ecosystem). Here we describe a general method for increasing the amount of information contained in the output of these indices and describe a new “soft” ecological similarity measure (here called “soft Chao-Jaccard similarity”). The new measure quantifies community resemblance in terms of shared species, while accounting for intraspecific variation in abundance and morphology between samples. We demonstrate how our proposed measure can reconstruct short ecological gradients using random samples of taxa, recognizing patterns that are completely missed by classic measures of similarity. To demonstrate the utility of our new index, we reconstruct a morphological gradient driven by river flow velocity using random samples drawn from simulated and real-world data. Results suggest that the new index can be used to recognize complex short ecological gradients in settings where only information about specimens is available. We include open-source R code for calculating the proposed index.

Ethno-Veterinary Survey and Quantitative Study of Medicinal Plants with Anthelmintic Potential Used by Sheep and Goat Breeders in the Cotton Zone of Central Benin (West Africa)

This study presents the diversity of anthelmintic plants in the cotton zone of Central Benin. The aim was to identify the medicinal anthelmintic plants used by small ruminant breeders in cotton zone of Central Benin to treat gastrointestinal parasites. Three hundred and sixty breeders were selected during individual semi-structured face-to-face interviews. Different quantitative indices of cultural importance were calculated in order to determine the level of use of plant species. Jaccard similarity index (JI) was calculated and Pearson’s correlation was determined for Use Value (UV) and Relative Frequency of Citation (RFC). In this study, a total of 99 medicinal species, of which 63 have anthelmintic potential, were listed, including Khaya senegalensis, Launaea taraxacifolia, Napoleonaea vogelii, Momordica charantia and Vernonia amygdalina, which all had UV and RFC above 20%. Each of them had a Fidelity Level above 50% and an Informant Agreement Rate (IAR) value close to one. Pearson’s correlation showed a significant correlation between RFC and UV with r = 0.94, and the studies were clearly independent (IJ < 50%). This study showed that the cotton zone of Central Benin represents 4% of the total flora of Benin, with many anthelmintic plants such as Launaea taraxacifolia and Napoleonaea vogelii that require further investigation.

Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS

In recent years in medical imaging technology, the advancement for medical diagnosis, the initial assessment of the ailment, and the abnormality have become challenging for radiologists. Magnetic resonance imaging is one such predominant technology used extensively for the initial evaluation of ailments. The primary goal is to mechanizean approach that can accurately assess the damaged region of the human brain throughan automated segmentation process that requires minimal training and can learn by itself from the previous experimental outcomes. It is computationally more efficient than other supervised learning strategies such as CNN deep learning models. As a result, the process of investigation and statistical analysis of the abnormality would be made much more comfortable and convenient. The proposed approach’s performance seems to be much better compared to its counterparts, with an accuracy of 77% with minimal training of the model. Furthermore, the performance of the proposed training model is evaluated through various performance evaluation metrics like sensitivity, specificity, the Jaccard Similarity Index, and the Matthews correlation coefficient, where the proposed model is productive with minimal training.

Segmentation of Overlapping Red Blood Cells for Malaria Blood Smear Images by U-Net Architecture

Automated segmentation of red blood cells is a widely applied task in order to evaluate red blood cells for certain diseases. Counting of malaria parasites requires individual red blood cell segmentation in order to evaluate the severity of infection. For such an evaluation, correct segmentation of red blood cells is required. However, it is a difficult task due to the presence of overlapping red blood cells. Existing methodologies employ preprocessing steps in order to segment red blood cells. We propose a deep learning approach that has a U-Net architecture to provide fully automated segmentation of red blood cells without any initial preprocessing. While red blood cells were segmented, irrelevant objects such as white blood cells, platelets and artifacts were removed. The network was trained and tested on 5600 and 600 samples respectively. Segmentation of overlapping red blood cells was achieved with 93.8% Jaccard similarity index. To the best of our knowledge, our results surpassed previous outcomes.

SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations

Medication recommendation is an essential task of AI for healthcare. Existing works focused on recommending drug combinations for patients with complex health conditions solely based on their electronic health records. Thus, they have the following limitations: (1) some important data such as drug molecule structures have not been utilized in the recommendation process. (2) drug-drug interactions (DDI) are modeled implicitly, which can lead to sub-optimal results. To address these limitations, we propose a DDI-controllable drug recommendation model named SafeDrug to leverage drugs’ molecule structures and model DDIs explicitly. SafeDrug is equipped with a global message passing neural network (MPNN) module and a local bipartite learning module to fully encode the connectivity and functionality of drug molecules. SafeDrug also has a controllable loss function to control DDI level in the recommended drug combinations effectively. On a benchmark dataset, our SafeDrug is relatively shown to reduce DDI by 19.43% and improves 2.88% on Jaccard similarity between recommended and actually prescribed drug combinations over previous approaches. Moreover, SafeDrug also requires much fewer parameters than previous deep learning based approaches, leading to faster training by about 14% and around 2× speed-up in inference.

MR Thermometry Accuracy and Prospective Imaging-Based Patient Selection in MR-Guided Hyperthermia Treatment for Locally Advanced Cervical Cancer

The efficacy of a hyperthermia treatment depends on the delivery of well-controlled heating; hence, accurate temperature monitoring is essential for ensuring effective treatment. For deep pelvic hyperthermia, there are no comprehensive and systematic reports on MR thermometry. Moreover, data inclusion generally lacks objective selection criteria leading to a high probability of bias when comparing results. Herein, we studied whether imaging-based data inclusion predicts accuracy and could serve as a tool for prospective patient selection. The accuracy of the MR thermometry in patients with locally advanced cervical cancer was benchmarked against intraluminal temperature. We found that gastrointestinal air motion at the start of the treatment, quantified by the Jaccard similarity coefficient, was a good predictor for MR thermometry accuracy. The results for the group that was selected for low gastrointestinal air motion improved compared to the results for all patients by 50% (accuracy), 26% (precision), and 80% (bias). We found an average MR thermometry accuracy of 2.0 °C when all patients were considered and 1.0 °C for the selected group. These results serve as the basis for comprehensive benchmarking of novel technologies. The Jaccard similarity coefficient also has good potential to prospectively determine in which patients the MR thermometry will be valuable.