The nuclear division takes place in the daughter cell in the basidiomycetous budding yeast Cryptococcus neoformans . Unclustered kinetochores gradually cluster and the nucleus moves to the daughter bud as cells enter mitosis. Here, we show that the evolutionarily conserved Aurora B kinase Ipl1 localizes to the nucleus upon the breakdown of the nuclear envelope during mitosis in C . neoformans . Ipl1 is shown to be required for timely breakdown of the nuclear envelope as well. Ipl1 is essential for viability and regulates structural integrity of microtubules. The compromised stability of cytoplasmic microtubules upon Ipl1 depletion results in a significant delay in kinetochore clustering and nuclear migration. By generating an in silico model of mitosis, we previously proposed that cytoplasmic microtubules and cortical dyneins promote atypical nuclear division in C . neoformans . Improving the previous in silico model by introducing additional parameters, here we predict that an effective cortical bias generated by cytosolic Bim1 and dynein regulates dynamics of kinetochore clustering and nuclear migration. Indeed, in vivo alterations of Bim1 or dynein cellular levels delay nuclear migration. Results from in silico model and localization dynamics by live cell imaging suggests that Ipl1 spatio-temporally influences Bim1 or/and dynein activity along with microtubule stability to ensure timely onset of nuclear division. Together, we propose that the timely breakdown of the nuclear envelope by Ipl1 allows its own nuclear entry that helps in spatio-temporal regulation of nuclear division during semi-open mitosis in C . neoformans .

BackgroundNanoparticle-metal oxide and gold represents a new class of important materials that are increasingly being developed for use in research and health related activities. The biological system being extremely critical requires the fundamental understanding on the influence of inorganic nanoparticles on cellular growth and functions. Our study was aimed to find out the effect of iron oxide (Fe3O4), gold (Au) nanoparticles on cellular growth of Escherichia coli (E. coli) and also try to channelize the obtained result by functionalizing the Au nanoparticle for further biological applications.ResultFe3O4 and Au nanoparticles were prepared and characterized using Transmission electron microscopy (TEM) and Dynamic Light Scattering (DLS). Preliminary growth analysis data suggest that the nanoparticles of iron oxide have an inhibitory effect on E. coli in a concentration dependant manner, whereas the gold nanoparticle directly showed no such activity. However the phase contrast microscopic study clearly demonstrated that the effect of both Fe3O4 and Au nanoparticle extended up to the level of cell division which was evident as the abrupt increase in bacterial cell length. The incorporation of gold nanoparticle by bacterial cell was also observed during microscopic analysis based on which glutathione functionalized gold nanoparticle was prepared and used as a vector for plasmid DNA transport within bacterial cell.ConclusionAltogether the study suggests that there is metal nanoparticle-bacteria interaction at the cellular level that can be utilized for beneficial biological application but significantly it also posses potential to produce ecotoxicity, challenging the ecofriendly nature of nanoparticles.

We explore a standard epidemiological model, known as the SIRD model, to study the COVID-19 infection in India, and a few other countries around the world. We use (a) the stable cumulative infection of various countries and (b) the number of infection versus the tests carried out to evaluate the model. The time-dependent infection rate is set in the model to obtain the best fit with the available data. The model is simulated aiming to project the probable features of the infection in India, various Indian states, and other countries. India imposed an early lockdown to contain the infection that can be treated by its healthcare system. We find that with the current infection rate and containment measures, the total active infection in India would be maximum at the end of June or beginning of July 2020. With proper containment measures in the infected zones and social distancing, the infection is expected to fall considerably from August. If the containment measures are relaxed before the arrival of the peak infection, more people from the susceptible population will fall sick as the infection is expected to see a threefold rise at the peak. If the relaxation is given a month after the peak infection, a second peak with a moderate infection will follow. However, a gradual relaxation of the lockdown started well ahead of the peak infection, leads to a nearly twofold increase of the peak infection with no second peak. The model is further extended to incorporate the infection arising from the population showing no symptoms. The preliminary finding suggests that random testing needs to be carried out within the asymptomatic population to contain the spread of the disease. Our model provides a semi-quantitative overview of the progression of COVID-19 in India, with model projections reasonably replicating the current progress. The projection of the model is highly sensitive to the choice of the parameters and the available data.

Causal mechanism of relationship between the clinical outcome (efficacy or safety endpoints) and putative biomarkers, clinical baseline, and related predictors is usually unknown and must be deduced empirically from experimental data. Such relationships enable the development of tailored therapeutics and implementation of a precision medicine strategy in clinical trials to help stratify patients in terms of disease progression, clinical response, treatment differentiation, and so on. These relationships often require complex modeling to develop the prognostic and predictive signatures. For the purpose of easier interpretation and implementation in clinical practice, defining a multivariate biomarker signature in terms of thresholds (cutoffs/cut points) on individual biomarkers is preferable. In this paper, we propose some methods for developing such signatures in the context of continuous, binary and time-to-event endpoints. Results from simulations and case study illustration are also provided. Copyright © 2017 John Wiley & Sons, Ltd.

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We explore a standard epidemiological model, known as the SIRD model, to study the COVID-19 infection in India, and a few other countries around the world. We use (a) the stable cumulative infection of various countries and (b) the number of infection versus the tests carried out to evaluate the model. The time-dependent infection rate is set in the model to obtain the best fit with the available data. The model is simulated aiming to project the probable features of the infection in India, various Indian states, and other countries. India imposed an early lockdown to contain the infection that can be treated by its healthcare system. We find that with the current infection rate and containment measures, the total active infection in India would be maximum at the end of June or beginning of July 2020. With proper containment measures in the infected zones and social distancing, the infection is expected to fall considerably from August. If the containment measures are relaxed before the arrival of the peak infection, more people from the susceptible population will fall sick as the infection is expected to see a three-fold rise at the peak. If the relaxation is given a month after the peak infection, a second peak with a moderate infection will follow. However, a gradual relaxation of the lockdown started well ahead of the peak infection, leads to a nearly two-fold increase of the peak infection with no second peak. The model is further extended to incorporate the infection arising from the population showing no symptoms. The preliminary finding suggests that random testing needs to be carried out within the asymptomatic population to contain the spread of the disease. Our model provides a semi-quantitative overview of the progression of COVID-19 in India, with model projections reasonably replicating the current progress. The projection of the model is highly sensitive to the choice of the parameters and the available data. Besides, since the pandemic is an ongoing dynamic phenomenon, the reported results are subjected to regular updates in consonance with the acquired real data.

To segregate chromosomes in mitosis, cells assemble mitotic spindle, a molecular machine with centrosomes at two opposing cell poles and chromosomes at the equator. Microtubules and molecular motors connect the poles to kinetochores, specialized protein assemblies on the centromere regions of the chromosomes. Bipolarity of the spindle is crucial for the proper cell division, and two centrosomes in animal cells naturally become two spindle poles. Cancer cells are often multi-centrosomal, yet they are able to assemble bipolar spindles by clustering centrosomes into two spindle poles. Mechanisms of this clustering are debated. In this study, we computationally screen effective forces between a) centrosomes, b) centrosomes and kinetochores, c) centrosomes and chromosome arms, d) centrosomes and cell cortex, to understand mechanics that determines three-dimensional spindle architecture. To do this, we use stochastic Monte Carlo search for stable mechanical equilibria in effective energy landscape of the spindle. We find that the following conditions have to be met to robustly assemble the bipolar spindle in a multi-centrosomal cell: 1) strengths of centrosomes' attraction to each other and to the cell cortex have to be proportional to each other; 2) strengths of centrosomes' attraction to kinetochores and repulsion from the chromosome arms have to be proportional to each other. We also find that three other spindle configurations emerge if these conditions are not met: a) collapsed, b) monopolar, c) multipolar spindles, and the computational screen reveal mechanical conditions for these abnormal spindles. Running title: Multi-centrosomal clusteringSignificance statement: To segregate chromosomes, cells assemble bipolar mitotic spindle. Multiple mechanical forces generated by microtubules and molecular motors in the spindle govern the spindle architecture, but it is unclear what force balances support the bipolarity of the spindle. This problem is especially difficult and important in cancer cells, which often have multiple centrosomes that somehow are able to cluster into two spindle poles. By using stochastic energy minimization in an effective energy landscape of the spindle and computationally screening forces, we find mechanical conditions for mono-, multi-and bi-polar spindles. We predict how microtubule and motor parameters have to be regulated in mitosis in multi-centrosomal cells.