A review on deep-learning-based cyberbullying detection

Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, and many others. This paper reviews the existing surveys and identifies the gaps in those studies. We also present a deep-learning-based defense ecosystem for cyberbullying detection, including data representation techniques and different deep-learning-based models and frameworks. We have critically analyzed the existing DL-based cyberbullying detection techniques and identified their significant contributions and the future research directions they have presented. We have also summarized the datasets being used, including the DL architecture being used and the tasks that are accomplished for each dataset. Finally, several challenges faced by the existing researchers and the open issues to be addressed in the future have been presented

Analyses of variability, euclidean clustering and principal components for genetic diversity of eight Tossa Jute (Corchorus olitorius L.) genotypes

An investigation was done to assess the genetic variability, character associations, and genetic diversity of eight jute genotypes for seven morphological traits in a randomised complete block design at Bangladesh Jute Research Institute during 15 March, 2018 to 31 December, 2019. Analyses results revealed significant differences (P<0.01) among all genotypes for studied traits indicating the presence of variability. All the lines performed better than one control (JRO-524), and the line (O-0412-9-4) provided good results for desired traits than all controls. Jute fibre yield showed the highest broad sense heritability (98.54%). The studied jute morphological traits i.e. Plant population, the plant height, green weight, dry fibre yield and dry stick yield gave high heritability along with high genotypic and phenotypic variances, genetic advances in percent of the mean, highly significant and positive correlations. It indicates the possibility of crop improvement through phenotypic selection and maximum genetic gain, simultaneously at the genotypic-phenotypic level. Clustering analysis grouped all genotypes into three distinct clusters. The cluster II showed the highest mean values for all traits followed by cluster I and III. The first two principal components with higher Eigen values (>1.0) accounted for 90.88% of the total variation in the principal component analysis. PCA and cluster analyses indicated that the advanced breeding line O-0412-9-4 made its individual cluster II with higher inter-cluster distance and higher fibre yield (3.12 t ha-1). The investigation was done to select the genotype(s) with good fibre yield and distinct features in respect of developing high yielding Tossa jute variety for cultivation in the farmers’ field. This genotype O-0412-9-4 was selected based on higher plant height, base diameter, fibre yield content. It will be developed as a high yielding variety considering its’ higher genetic variability, heritability, genetic advance, significant associations for desirable characters

In Silico Evaluation of Different Flavonoids from Medicinal Plants for Their Potency against SARS-CoV-2

The ongoing pandemic situation of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a global threat to both the world economy and public health. Therefore, there is an urgent need to discover effective vaccines or drugs to fight against this virus. The flavonoids and their medicinal plant sources have already exhibited various biological effects, including antiviral, anti-inflammatory, antioxidant, etc. This study was designed to evaluate different flavonoids from medicinal plants as potential inhibitors against the spike protein (Sp) and main protease (Mpro) of SARS-CoV-2 using various computational approaches such as molecular docking, molecular dynamics. The binding affinity and inhibitory effects of all studied flavonoids were discussed and compared with some antiviral drugs that are currently being used in COVID-19 treatment namely favipiravir, lopinavir, and hydroxychloroquine, respectively. Among all studies flavonoids and proposed antiviral drugs, luteolin and mundulinol exhibited the highest binding affinity toward Mpro and Sp. Drug-likeness and ADMET studies revealed that the chosen flavonoids are safe and non-toxic. One hundred ns-MD simulations were implemented for luteolin-Mpro, mundulinol-Mpro, luteolin-Sp, and mundulinol-Sp complexes and the results revealed strong stability of these flavonoid-protein complexes. Furthermore, MM/PBSA confirms the stability of luteolin and mundulinol interactions within the active sites of this protein. In conclusion, our findings reveal that the promising activity of luteolin and mundulinol as inhibitors against COVID-19 via inhibiting the spike protein and major protease of SARS CoV-2, and we urge further research to achieve the clinical significance of our proposed molecular-based efficacy

Strategies to tackle SARS-CoV-2 Mu, a newly classified variant of interest likely to resist currently available COVID-19 vaccines

Several severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have recently been reported in many countries. These have exacerbated the coronavirus disease 2019 (COVID-19)-induced global health threats and hindered COVID-19 vaccine development and therapeutic progress. This commentary discusses the potential risk of the newly classified Mu variant of interest, seeming a highly vaccine-resistant variant, and the approaches that can be adopted to tackle this variant based on the available evidence. The SARS-CoV-2 B.1.621 (Mu variant) lineage has shown approximately ten times higher resistance to neutralizing sera obtained from COVID-19 survivors or BNT161b2-vaccinated people than the parenteral B.1 lineage. Several urgent and long-term strategic plans, including quick genomic surveillance for uncovering the genetic characteristics of the variants, equitable global mass vaccination, booster dose administration if required, and strict implementation of public health measures or non-pharmaceutical interventions, must be undertaken concertedly to restrict further infections, mutations, or recombination of the SARS-CoV-2 virus and its deadly strains

A Review on Mechanistic Insight of Plant Derived Anticancer Bioactive Phytocompounds and Their Structure Activity Relationship

Cancer is a disorder that rigorously affects the human population worldwide. There is a steady demand for new remedies to both treat and prevent this life-threatening sickness due to toxicities, drug resistance and therapeutic failures in current conventional therapies. Researchers around the world are drawing their attention towards compounds of natural origin. For decades, human beings have been using the flora of the world as a source of cancer chemotherapeutic agents. Currently, clinically approved anticancer compounds are vincristine, vinblastine, taxanes, and podophyllotoxin, all of which come from natural sources. With the triumph of these compounds that have been developed into staple drug products for most cancer therapies, new technologies are now appearing to search for novel biomolecules with anticancer activities. Ellipticine, camptothecin, combretastatin, curcumin, homoharringtonine and others are plant derived bioactive phytocompounds with potential anticancer properties. Researchers have improved the field further through the use of advanced analytical chemistry and computational tools of analysis. The investigation of new strategies for administration such as nanotechnology may enable the development of the phytocompounds as drug products. These technologies have enhanced the anticancer potential of plant-derived drugs with the aim of site-directed drug delivery, enhanced bioavailability, and reduced toxicity. This review discusses mechanistic insights into anticancer compounds of natural origins and their structural activity relationships that make them targets for anticancer treatments

Dominant clade-featured SARS-CoV-2 co-occurring mutations reveal plausible epistasis: An in silico based hypothetical model

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved into eight fundamental clades with four of these clades (G, GH, GR, and GV) globally prevalent in 2020. To explain plausible epistatic effects of the signature co-occurring mutations of these circulating clades on viral replication and transmission fitness, we proposed a hypothetical model using in silico approach. Molecular docking and dynamics analyses showed the higher infectiousness of a spike mutant through more favorable binding of G614 with the elastase-2. RdRp mutation p.P323L significantly increased genome-wide mutations (p \u3c 0.0001), allowing for more flexible RdRp (mutated)-NSP8 interaction that may accelerate replication. Superior RNA stability and structural variation at NSP3:C241T might impact protein, RNA interactions, or both. Another silent 5′-UTR:C241T mutation might affect translational efficiency and viral packaging. These four G-clade-featured co-occurring mutations might increase viral replication. Sentinel GH-clade ORF3a:p.Q57H variants constricted the ion-channel through intertransmembrane–domain interaction of cysteine(C81)-histidine(H57). The GR-clade N:p.RG203-204KR would stabilize RNA interaction by a more flexible and hypo-phosphorylated SR-rich region. GV-clade viruses seemingly gained the evolutionary advantage of the confounding factors; nevertheless, N:p.A220V might modulate RNA binding with no phenotypic effect. Our hypothetical model needs further retrospective and prospective studies to understand detailed molecular events and their relationship to the fitness of SARS-CoV-2

Proceedings of International Conference on Emerging Trends in Engineering and Advanced Science

This conference proceedings contains articles on the various research ideas of the academic community and practitioners presented at the International Conference on Emerging Trends in Engineering and Advanced Science (ICETEAS-2021). ICETEAS-2021 was organized by the Department of Electrical and Electronic Engineering & Department of Mechatronics Engineering, World University of Bangladesh, Dhaka, Bangladesh on July 4th-5th November 2021. Conference Title: International Conference on Emerging Trends in Engineering and Advanced ScienceConference Acronym: ICETEAS-2021Conference Date: 4-5 November 2021Conference Location: Online (Virtual Mode)Conference Organizers: World University of Bangladesh, Dhaka, Bangladesh