This study was conducted to evaluate public awareness about COVID with aimed to check public strategies against COVID-19. A semi structured questionnaire was collected and the data was analyzed using some statistical tools (PLS-SEM) and artificial neural networks (ANN). We started by looking at the known causal linkages between the different variables to see if they matched up with the hypotheses that had been proposed. Next, for this reason, we ran a 5,000-sample bootstrapping test to assess how strongly our findings corroborated the null hypothesis. PLS-SEM direct path analysis revealed HRP -> PA-COVID, HI -> PA-COVID, MU -> PA-COVID, PM -> PA-COVID, SD -> PA-COVID. These findings provide credence to the acceptance of hypotheses H1, H3, and H5, but reject hypothesis H2. We have also examined control factors such as respondents' age, gender, and level of education. Age was found to have a positive correlation with PA-COVID, while mean gender and education level were found to not correlate at all with PA-COVID. However, age can be a useful control variable, as a more seasoned individual is likely to have a better understanding of COVID and its effects on independent variables. Study results revealed a small moderation effect in the relationships between understudy independent and dependent variables. Education significantly moderates the relationship of PA-COVID associated with MU, PH, SD, RP, PM, PA-COVID, depicts the moderation role of education on the relationship between MU*Education->PA-COVID, HI*Education->PA.COVID, SD*Education->PA.COVID, HRP*Education->PA.COVID, PM*Education -> PA.COVID. The artificial neural network (ANN) model we've developed for spreading information about COVID-19 (PA-COVID) follows in the footsteps of previous studies. The root means the square of the errors (RMSE). Validity measures how well a model can predict a certain result. With RMSE values of 0.424 for training and 0.394 for testing, we observed that our ANN model for public awareness of COVID-19 (PA-COVID) had a strong predictive ability. Based on the sensitivity analysis results, we determined that PA. COVID had the highest relative normalized relevance for our sample (100%). These factors were then followed by MU (54.6%), HI (11.1%), SD (100.0%), HRP (28.5%), and PM (64.6%) were likewise shown to be the least important factors for consumers in developing countries struggling with diseases caused by contaminated water. In addition, a specific approach was used to construct a goodness-of-fit coefficient to evaluate the performance of the ANN models. The study will aid in the implementation of effective monitoring and public policies to promote the health of local people.
Malignant melanoma is considered one of the deadliest skin diseases if ignored without treatment. The mortality rate caused by melanoma is more than two times that of other skin malignancy diseases. These facts encourage computer scientists to find automated methods to discover skin cancers. Nowadays, the analysis of skin images is widely used by assistant physicians to discover the first stage of the disease automatically. One of the challenges the computer science researchers faced when developing such a system is the un-clarity of the existing images, such as noise like shadows, low contrast, hairs, and specular reflections, which complicates detecting the skin lesions in that images. This paper proposes the solution to the problem mentioned earlier using the active contour method. Still, seed selection in the dynamic contour method has the main drawback of where it should start the segmentation process. This paper uses Gaussian filter-based maximum entropy and morphological processing methods to find automatic seed points for active contour. By incorporating this, it can segment the lesion from dermoscopic images automatically. Our proposed methodology tested quantitative and qualitative measures on standard dataset dermis and used to test the proposed method's reliability which shows encouraging results.
During the COVID-19 pandemic, online teaching modes were found vital to continue students' learning process, but sustainable implementation of online teaching models is an area of concern for policymakers. Psychiatrists are also eager to know students' behavior toward learning and modes of teaching during COVID-19. We have drawn a model based on the big five personality traits to study students' satisfaction with online teaching modes and their adoption intentions toward online teaching modes. We have collected data from 718 bachelor's and master's level students from four different universities. We have applied the SEM-ANN dual-stage approach to test personality traits' influence and ranked them based on their normalized importance. The results revealed that agreeableness, conscientiousness, neuroticism, and openness positively influence students' satisfaction with online teaching models, but that extraversion negatively influences their satisfaction. Agreeableness, extraversion, and neuroticism positively impact, but openness negatively influences. Conscientiousness does not affect adoption intention. Furthermore, agreeableness is the most significant, and conscientiousness is the least important factor for students to adopt online teaching modes. The findings of the study have useful perceptiveness for educational policymakers, academics, and psychiatrists.
Bacillus velezensis FS26 is a bacterium from the genus Bacillus that has been proven as a potential probiotic in aquaculture with a good antagonistic effect on Aeromonas spp. and Vibrio spp. Whole-genome sequencing (WGS) allows a comprehensive and in-depth analysis at the molecular level, and it is becoming an increasingly significant technique in aquaculture research. Although numerous probiotic genomes have been sequenced and investigated recently, there are minimal data on in silico analysis of B. velezensis as a probiotic bacterium isolated from aquaculture sources. Thus, this study aims to analyse the general genome characteristics and probiotic markers from the B. velezensis FS26 genome with secondary metabolites predicted against aquaculture pathogens. The B. velezensis FS26 genome (GenBank Accession: JAOPEO000000000) assembly proved to be of high quality, with eight contigs containing 3,926,371 bp and an average G + C content of 46.5%. According to antiSMASH analysis, five clusters of secondary metabolites from the B. velezensis FS26 genome showed 100% similarity. These clusters include Cluster 2 (bacilysin), Cluster 6 (bacillibactin), Cluster 7 (fengycin), Cluster 8 (bacillaene), and Cluster 9 (macrolactin H), which signify promising antibacterial, antifungal, and anticyanobacterial agents against pathogens in aquaculture. The probiotic markers of B. velezensis FS26 genome for adhesion capability in the hosts' intestine, as well as the acid and bile salt-tolerant genes, were also detected through the Prokaryotic Genome Annotation System (Prokka) annotation pipeline. These results are in agreement with our previous in vitro data, suggesting that the in silico investigation facilitates establishing B. velezensis FS26 as a beneficial probiotic for use in aquaculture.
The fermentation of Malaysian fish sauce (budu) varies from one to twelve months depending on the producer, resulting in inconsistent quality. The microbiota, their predicted metabolic pathways and volatile metabolites profiles were determined at different stages of budu fermentation. Budu fermented for 1 and 3 months were characterized by the presence of Gram negative Enterobacterales, Gammaproteobacteria, and Fusobacteriaceae, which continuously decrease in abundance over fermentation time. The metabolic pathways prediction grouped 1- and 3- month budu in a cluster enriched with degradation reactions. 6-month budu were dominated by Halanaerobium and Staphylococcus, while the 12-month were dominated by Lentibacillus, Bacilli, and Halomonas. Biosynthesis-type predicted pathways involving protein and lipid derivatives were enriched in 6- and 12-month fermented budu, accumulating 2,6-dimethylpyrazine, methyl 2-ethyldecanoate, 2-phenylacetaldehyde, 3-methylbutanal, and 3-methylbutanoic acid. These compounds may indicate budu maturity and quality. This result may assist as a reference for quality control and fermentation monitoring.
The growth performance of red claw crayfish, Cherax quadricarinatus, fed diets with different sources and concentrations of natural calcium was evaluated. Formulated diets containing 30% protein and 8% lipid were prepared using supplementation of 0 (control), 3%, 6%, 9% eggshell, and 6% seashell. C. quadricarinatus juveniles with an average total weight of 0.21 ± 0.01 g and length of 25.0 ± 0.10 mm were obtained from ten ovigerous females bred in captivity. An aquarium with a size of 0.72 m² was divided into eight compartments with an area of 0.09 m² per compartment and was individually stocked with juvenile crayfish. A total of eight juvenile crayfish were used per dietary treatment. There were five dietary treatments in total and each of these treatments were replicated three times. The addition of eggshell and seashell powder exerted positive effects on the growth performance, molting frequency, and survival of the crayfish. Crayfish fed with a 6% eggshell-supplemented diet exhibited the best overall growth performance. The calcium percentage of the carapace increased with the increase in calcium content of the diets. Meanwhile, the performance of diets comprising eggshells and seashells was not significantly different (P > 0.05). Considering the low cost and abundance of eggshells, the addition of 6% of this product to the diet is recommended for the best growth of juvenile C. quadricarinatus.
Cancer is one of the deadliest diseases, causing million of deaths each year globally. Conventional anti-cancer therapies are non-targeted and have systemic toxicities limiting their versatile applications in many cancers. So, there is an unmet need for more specific therapeutic options that will be effective as well as free from toxicities. Antibody-drug conjugates (ADCs) are suitable alternatives with the right potential and improved therapeutic index for cancer therapy. The ADCs are highly precise new class of biopharmaceutical products that covalently linked a monoclonal antibody (mAb) (binds explicitly to a tumor-associated surface antigen) with a customized cytotoxic drug (kills cancer cells) and tied via a chemical linker (releases the drug). Due to its precise design, it brings about the target cell killing sparing the normal counterpart and free from the toxicities of conventional chemotherapy. It has never been so easy to develop potential ADCs for successful therapeutic usage. With relentless efforts, it took almost a century for scientists to advance the formula and design ADCs for its current clinical applications. Until now, several ADCs have passed successfully through preclinical and clinical trials and because of proven efficacy, a few are approved by the FDA to treat various cancer types. Even though ADCs posed some shortcomings like adverse effects and resistance at various stages of development, with continuous efforts most of these limitations are addressed and overcome to improve their efficacy. In this review, the basics of ADCs, physical and chemical properties, the evolution of design, limitations, and future potentials are discussed.
Diagnosing and finding the disease in medical sciences is a complex procedure. The basic steps involved in finding starts with signs, symptoms, and test. This study is based on the diagnosis of a skin disorder. The identification of a disease has been made on the basis of symptoms that sometimes show bipolarity. To address this bipolarity, the bipolar fuzzy sets are used as bipolar fuzzy sets cover the positive as well as negative aspects of a specific symptom. It is combined with the idea of soft sets, which gives more precise results. We have proposed a new technique in which a correlation coefficient is used to measure bipolar fuzzy soft set, which has been applied for diagnosis. The BFSSs deal most effectively with dual and fuzzy information. The correlation coefficient and the weighted correlation coefficient of BFSSs are suggested in this research. Based on said techniques, the decision-making method is suggested under a bipolar fuzzy environment to resolve ambiguous and unclear information. The implementation and effectiveness of the proposed and existing strategy has been checked by numerical computation.