A smart privacy preserving framework for industrial IoT using hybrid meta-heuristic algorithm.

Industrial Internet of Things (IIoT) seeks more attention in attaining enormous opportunities in the field of Industry 4.0. But there exist severe challenges related to data privacy and security when processing the automatic and practical data collection and monitoring over industrial applications in IIoT. Traditional user authentication strategies in IIoT are affected by single factor authentication, which leads to poor adaptability along with the increasing users count and different user categories. For addressing such issue, this paper aims to implement the privacy preservation model in IIoT using the advancements of artificial intelligent techniques. The two major stages of the designed system are the sanitization and restoration of IIoT data. Data sanitization hides the sensitive information in IIoT for preventing it from leakage of information. Moreover, the designed sanitization procedure performs the optimal key generation by a new Grasshopper-Black Hole Optimization (G-BHO) algorithm. A multi-objective function involving the parameters like degree of modification, hiding rate, correlation coefficient between the actual data and restored data, and information preservation rate was derived and utilized for generating optimal key. The simulation result establishes the dominance of the proposed model over other state-of the-art models in terms of various performance metrics. In respect of privacy preservation, the proposed G-BHO algorithm has achieved 1%, 15.2%, 12.6%, and 1% enhanced result than JA, GWO, GOA, and BHO, respectively.

BBNSF: Blockchain-Based Novel Secure Framework Using RP2-RSA and ASR-ANN Technique for IoT Enabled Healthcare Systems.

The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the security of these devices has become a growing concern. The lack of security awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest-Shamir-Adleman (RP2-RSA) algorithm for providing security. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. Meanwhile, the abnormal measures are stored in the corresponding patient blockchain. Here, blockchain technology is used to store medical data securely for further analysis. The proposed model has achieved an accuracy of 95.893% and is validated by comparing it with other baseline techniques. On the security front, the proposed RP2-RSA attains a 96.123% security level.

Biocatalyst physiology and interplay: a protagonist of MFC operation.

Microbial fuel cells (MFC) have been foreseen as a sustainable renewable energy resource to meet future energy demand. In the past, several studies have been executed in both benchtop and pilot scale to produce electrical energy from wastewater. The key role players in this technology that leads to the operation are microbes, mainly bacteria. The dominant among them is termed as "exoelectrogens" that have the capability to produce and transport electron by utilizing waste source. The current review focuses on such electrogenic bacteria's involvement for enhanced power generation of MFC. The pathway of electron transfer in their cell along and its conduction to the extracellular environment of the MFC system are critically discussed. The interaction of the microbes in various MFC operational conditions, including the role of substrate and solid electron acceptors, i.e., anode, external resistance, temperature, and pH, was also discussed in depth along with biotechnological advancement and future research perspective.

Neonates Hospitalized with Community-Acquired SARS-CoV-2 in a Colorado Neonatal Intensive Care Unit.

IMPORTANCE: The novel coronavirus 2019 (SARS-CoV-2) has been well described in adults. Further, the impact on older children and during the perinatal time is becoming better studied. As community spread increases, it is important to recognize that neonates are vulnerable to community spread as well. The impact that community-acquired SARS-CoV-2 has in the neonatal time period is unclear, as this population has unique immunity considerations. OBJECTIVE: To report on a case series of SARS-CoV-2 in neonates through community acquisition in the USA. DESIGN: This is an early retrospective study of patients admitted to the Neonatal Intensive Care Unit (NICU) identified as having SAR-CoV-2 through positive real-time polymerase chain reaction assay of nasopharyngeal swabs. FINDINGS: Three patients who required admission to the NICU between the ages of 17 and 33 days old were identified. All 3 had ill contacts in the home or had been to the pediatrician and presented with mild to moderate symptoms including fever, rhinorrhea, and hypoxia, requiring supplemental oxygen during their hospital stay. One patient was admitted with neutropenia, and the other 2 patients became neutropenic during hospitalization. None of the patients had meningitis or multiorgan failure. CONCLUSIONS AND RELEVANCE: Infants with community-acquired SARS-CoV-2 may require hospitalization due to rule-out sepsis guidelines if found to have fever and/or hypoxia. Caregivers of neonates should exercise recommended guidelines before contact with neonates to limit community spread of SARS-CoV-2 to this potentially vulnerable population, including isolation, particularly as asymptomatic cases become prevalent.

Upstream stimulatory factor induces Nr5a1 and Shbg gene expression during the onset of rat Sertoli cell differentiation.

Within the testis, each Sertoli cell can support a finite number of developing germ cells. During development, the cessation of Sertoli cell proliferation and the onset of differentiation establish the final number of Sertoli cells and, thus, the total number of sperm that can be produced. The upstream stimulatory factors 1 and 2 (USF1 and USF2, respectively) differentially regulate numerous Sertoli cell genes during differentiation. To identify genes that are activated by USF proteins during differentiation, studies were conducted in Sertoli cells isolated from 5- and 11-day-old rats, representing proliferating and differentiating cells, respectively. Usf1 mRNA and USF1 protein levels were increased between 5 and 11 days after birth. In vitro studies revealed that USF1 and USF2 DNA-binding activity also increased at 11 days for the promoters of four potential target genes, Fshr, Gata4, Nr5a1, and Shbg. Chromatin immunoprecipitation assays confirmed that USF recruitment increased in vivo between 5 and 11 days after birth at the Fshr, Gata4, and Nr5a1 promoters. Expression of Nr5a1 and Shbg, but not of Fshr or Gata4, mRNAs was elevated in 11-day-old Sertoli cells compared with 5-day-old Sertoli cells. Transient transfection of USF1 and USF2 expression vectors up-regulated Nr5a1 and Shbg promoter activity. RNA interference assays demonstrated that USF1 and USF2 contribute to Nr5a1 and Shbg expression in differentiating cells. Together, these data indicate that increased USF levels induce the expression of Nr5a1 and Shbg during the differentiation of Sertoli cells, whereas Fshr and Gata4 expression is not altered by USF proteins during differentiation.