The recent two decades have witnessed tremendous growth in Internet of things (IoT) applications. There are more than 50 billion devices connected globally. IoT applications’ connectivity with the Internet persistently victimized them with a divergent range of traditional threats, including viruses, worms, malware, spyware, Trojans, malicious code injections, and backdoor attacks. Traditional threats provide essential services such as authentication, authorization, and accountability. Authentication and authorization are the process of verifying that a subject is bound to an object. Traditional authentication and authorization mechanisms use three different factors to identity a subject to verify if the subject has the right capability to access the object. Further, it is defined that a computer virus is a type of malware. Malware includes computer viruses, worms, Trojan horses, spyware, and ransomware. There is a high probability that IoT systems can get infected with a more sophisticated form of malware and high-frequency electromagnetic waves. Purpose oriented with distinct nature IoT devices is developed to work in a constrained environment. So there is a dire need to address these security issues because relying on existing traditional techniques is not good. Manufacturers and researchers must think about resolving these security and privacy issues. Most importantly, this study identifies the knowledge and research gap in this area. The primary objective of this systematic literature review is to discuss the divergent types of threats that target IoT systems. Most importantly, the goal is to understand the mode of action of these threats and develop the recovery mechanism to cover the damage. In this study, more than 170 research articles are systematically studied to understand security and privacy issues. Further, security threats and attacks are categorized on a single platform and provide an analysis to explain how and to what extent they damage the targeted IoT systems. This review paper encapsulates IoT security threats and categorizes and analyses them by implementing a comparative study. Moreover, the research work concludes to expand advanced technologies, e.g., blockchain, machine learning, and artificial intelligence, to guarantee security, privacy, and IoT systems.

Clustering plays a particularly fundamental role in exploring data, creating predictions and to overcome the anomalies in the data. Clusters that contain parallel, identical characteristics in a dataset are grouped using reiterative algorithms. As the data in real world is rising day by day so the challenges of perceiving and interpreting the consequential mass of data, which often consists of millions of measurements are increased by the intricacy of a huge number of genes of biological networks. To addressing this challenge, we use clustering algorithms. In this study, we provided a comparative study of the four most popular clustering algorithms: K-Means, PAM, Agglomerative Hierarchical and DIANA and these are evaluated on eight real cancer (four Affymetrix and four cDNA) gene data and simulated data set. The comparative results based upon seven popular cluster validity indices: Average Silhouette Index, Corrected rand Index, Variation of Information, Dunn Index, Calinski-Harabasz Index, Separation Index, and Pearson Gamma. We determine that PAM is best for Affymetrix data set and DIANA is best for cDNA dataset among these four clustering algorithms. This study provides practical evaluation frameworks for accessing clustering results on gene expression cancer datasets.

The novel coronavirus (SARS-CoV-2) has expanded rapidly worldwide. Now it has covered more than 150 countries worldwide. It is referred to as COVID-19. SARS-CoV-2 mainly affects the respiratory systems of humans that can lead up to serious illness or even death in the presence of different comorbidities. However, most COVID-19 infected people show mild to moderate symptoms, and no medication is suggested. Still, drugs of other diseases have been used to treat COVID-19. Nevertheless, the absence of vaccines and proper drugs against the COVID-19 virus has increased the mortality rate. Albeit sex is a risk factor for COVID-19, none of the studies considered this risk factor for identifying biomarkers from the RNASeq count dataset. Men are more likely to undertake severe symptoms with different comorbidities and show greater mortality compared with women. From this standpoint, we aim to identify shared gene signatures between males and females from the human COVID-19 RNAseq count dataset of peripheral blood cells using a robust voom approach. We identified 1341 overlapping DEGs between male and female datasets. The gene ontology (GO) annotation and pathway enrichment analysis revealed that DEGs are involved in various BP categories such as nucleosome assembly, DNA conformation change, DNA packaging, and different KEGG pathways such as cell cycle, ECM-receptor interaction, progesterone-mediated oocyte maturation, etc. Ten hub-proteins (UBC, KIAA0101, APP, CDK1, SUMO2, SP1, FN1, CDK2, E2F1, and TP53) were unveiled using PPI network analysis. The top three miRNAs (mir-17–5p, mir-20a-5p, mir-93–5p) and TFs (PPARG, E2F1 and KLF5) were uncovered. In conclusion, the top ten significant drugs (roscovitine, curcumin, simvastatin, fulvestrant, troglitazone, alvocidib, L-alanine, tamoxifen, serine, and doxorubicin) were retrieved using drug repurposing analysis of overlapping DEGs, which might be therapeutic agents of COVID-19.

Background and Aim: Small ruminants require vaccines to prevent and manage diseases. Unfortunately, no studies have been conducted in Bangladesh to assess the knowledge, attitudes, and practices (KAP) of small ruminant farmers (SRF) regarding vaccine use against infectious diseases, affecting the success of vaccination campaigns. The present study aims to assess SRF’s KAP regarding vaccines, revealing gaps and barriers to efficient vaccination. Materials and Methods: Two hundred and twenty-eight SRF in northern Bangladesh were surveyed in a cross-sectional study. Data were collected from random participants through face-to-face interviews using a structured questionnaire. KAP levels were categorized as “good” or “poor” and “positive” or “negative” using a scoring method with a 60% cutoff. The analysis comprised the utilization of descriptive statistics as well as logistic regression models. Results: Results showed that most participants were female (60.5%), aged 31–40 (34.2%), with secondary education (28.1%), and vaccination training (22.8%). While 75% knew about vaccines, only 37.3% understood their role in preventing infectious diseases, and 63.6% in reducing antibiotic use 68.4% of farmers were aware of negative drawbacks, and 61.8% reported vaccinating their herds. About 42.1% of the farmers had good knowledge, 52.6% had a positive attitude, and 22.8% followed good practices. Female farmers with graduate degrees and 6–10 years of goat farming experience, but not those with vaccination training, demonstrated stronger knowledge. Female farmers with a graduate degree and 6–10 years of goat farming experience displayed positive attitudes. Female goat farmers from Thakurgaon had a higher likelihood of following good vaccination practices than those with vaccination training. Conclusion: The study unearths disparities in KAP scores among farmers. To effectively address KAP gaps concerning vaccine usage and prevent potential infectious diseases, it is essential to design focused educational and training programs. About 52.6% of SRF hold a positive view toward vaccines. Keywords: Bangladesh, knowledge, attitudes, and practices, small ruminant farmers, vaccine use.

Due to the increasing use of information technologies by biomedical experts, researchers, public health agencies, and healthcare professionals, a large number of scientific literatures, clinical notes, and other structured and unstructured text resources are rapidly increasing and being stored in various data sources like PubMed. These massive text resources can be leveraged to extract valuable knowledge and insights using machine learning techniques. Recent advancement in neural network‐based classification models has gained popularity which takes numeric vectors (aka word representation) of training data as the input to train classification models. Better the input vectors, more accurate would be the classification. Word representations are learned as the distribution of words in an embedding space, wherein each word has its vector and the semantically similar words based on the contexts appear nearby each other. However, such distributional word representations are incapable of encapsulating relational semantics between distant words. In the biomedical domain, relation mining is a well‐studied problem which aims to extract relational words, which associates distant entities generally representing the subject and object of a sentence. Our goal is to capture the relational semantics information between distant words from a large corpus to learn enhanced word representation and employ the learned word representation for various natural language processing tasks such as text classification. In this article, we have proposed an application of biomedical relation triplets to learn word representation through incorporating relational semantic information within the distributional representation of words. In other words, the proposed approach aims to capture both distributional and relational contexts of the words to learn their numeric vectors from text corpus. We have also proposed an application of the learned word representations for text classification. The proposed approach is evaluated over multiple benchmark datasets, and the efficacy of the learned word representations is tested in terms of word similarity and concept categorization tasks. Our proposed approach provides better performance in comparison to the state‐of‐the‐art GloVe model. Furthermore, we have applied the learned word representations to classify biomedical texts using four neural network‐based classification models, and the classification accuracy further confirms the effectiveness of the learned word representations by our proposed approach.