Iron is the most abundant micronutrient in plant mitochondria and it has a crucial role in biochemical reactions involving electron transfer. It has been described in Oryza sativa that Mitochondrial Iron Transporter (MIT) is an essential gene and that knockdown mutant rice plants have a decreased amount of iron in mitochondria, strongly suggesting that OsMIT is involved in mitochondrial iron uptake. In Arabidopsis thaliana, two genes encode MIT homologues. In this study, we analyzed different AtMIT1 and AtMIT2 mutant alleles, confirming that individually AtMIT1 nor AtMIT2 genes are essential. When we generated crosses between Atmit1 and Atmit2 alleles we were able to isolate homozygous double mutant plants. Interestingly, homozygous double mutant plants were obtained only when mutant alleles of Atmit2 with the T-DNA insertion in the intron region were used for crossings, and in these cases a correctly spliced AtMIT2 mRNA was generated, although at a low level. Atmit1 Atmit2 double homozygous mutant plants, which were knockout for AtMIT1 and knockdown for AtMIT2, were grown and chacterised in iron sufficient conditions. Pleiotropic developmental defects were observed including abnormal seeds, increased number of cotyledons, slow growth rate, pinoid stems, defects in flower structures and reduced seed set. We observed a possible phenomenon of T-DNA suppression in the next generation of Atmit1 Atmit2 double homozygous mutant plants, correlating with an increased splicing of the AtMIT2 intron containing the T-DNA. Molecular analysis of gene expression markers for mitochondrial and oxidative stress showed that Atmit1 Atmit2 double homozygous mutant plants express a degree of mitochondrial perturbation. A RNA-Seq study was performed and we could identify more than 760 genes differentially expressed in Atmit1 Atmit2, including genes involved in iron transport, coumarin metabolism, and hormones metabolism, transport and signaling. Our data suggest that some of the phenotypes observed in Atmit1 Atmit2 double homozygous mutant plants are mediated by defects in auxin homeostasis.

Electroencephalography (EEG) is essential for tracking brain activity and identifying seizure effects. However, epileptic behaviour can only be detected after a specialist has carefully analysed all EEG recordings along with a proper history of the patient. A skilled physician is required for the right epilepsy diagnosis and therapy. But most of the time, patients visit the clinician in the interictal stage with no proper history documented. Therefore, it was essential to the automatic prediction of stages of seizure. K nearest neighbours (KNN) and random forest (RF) models using raw EEG signals, preictal, ictal, postictal, and interictal stages were identified in this study. The possibility of these characteristics is explored by examining how well time-domain signals work in the prediction of epileptic stages using intracranial EEG datasets from Freiburg Hospital (FH), Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT), and Temple University Hospital (TUHEEG). To test the viability of this approach, two different types of simulations were carried out on three binary classifications (interictal vs. preictal, interictal vs. ictal, preictal vs. postictal, and interictal vs. postictal), and one four-class problem (interictal vs. preictal vs. ictal vs. postictal) was performed for each model. The average accuracy when using time-domain signals in the FH database was 90.5% and 75.0%; CHB-MIT was 92.87% and 75.9%; and TUHEEG was 94.46% and 76.8%, respectively, for the KNN and RF models.

With the onset of the COVID19 pandemic, information technology has played a critical role in healthcare. A broad spectrum of information technology tools and applications played an essential role to create awareness of the COVID19 vaccination drive and its health benefits. We use the COVID-19 Global Beliefs, Behaviors, and Norms Survey for analysis of prevalence and factors associated with vaccination drives among men and women aged 20-80 years in 60 countries worldwide. Our analysis of the global survey offers a unique perspective about the role of information technology associated with vaccination drives involving social norms and human behavior among 437,236 respondents. The international survey was organized using a pre-registered randomized experiment demonstrating the role of technology in reaching out to people based in diverse communities and evaluating their beliefs, behavior, and social norms. The study shows that vaccine acceptance can vary due to descriptive norms. Our analysis shows 65.06% of people all over the globe are willing to get vaccinated and a large proportion of the population thinks that the COVID19 pandemic is a viable threat to the community and preventive measures need to be taken including vaccination drives.