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How does one interpret the observed increase or decrease in COVID-19 case rates? Did the compliance to the non-pharmaceutical interventions, seasonal changes in the temperature influence the transmission rates or are they purely an artefact of the number of tests? To answer these questions, we estimate the effect-sizes from these different factors on the reproduction ratios (Rt) from the different states of the USA during March 9 to August 9. Ideally Rt should be less than 1 to keep the pandemic under control and our model predicts many of these factors contributed significantly to the Rt’s: Post-lockdown opening of the restaurants and nightclubs contributed 0.04 (CI 0.04-0.04) and 0.11 (CI. 0.11-0.11) to Rt. The mask mandates helped reduce Rt by 0.28 (CI 0.28-0.29)), whereas the testing rates which may have influenced the number of infections observed, did not influence Rt beyond 10,000 daily tests 0.07 (CI -0.57-0.42). In our attempt to understand the role of temperature, the contribution to the Rt was found to increase on both sides of 55 F, which we infer as a reflection of the climatization needs. A further analysis using the cooling and heating needs showed contributions of 0.24 (CI 0.18-0.31) and 0.31 (CI 0.28-0.33) respectively. The work thus illustrates a data-driven approach for estimating the effect-sizes on the graded policies, and the possibility of prioritizing the interventions, if necessary by weighing the economic costs and ease of acceptance with them.

Several questions resonate as the governments relax their COVID-19 mitigation policies - is it too early to relax them, were the policies as effective as they could have been. Answering these questions about the past or crafting newer policy decisions in the future requires a quantification of how policy choices affect the spread of the infection. Policy landscape as well as the infection trajectories from different states and countries diverged so fast that comparing and learning from them has not been easy. In this work, we standardize and pool together the ensemble of lockdown and graded re-opening policies adopted by the 50 states of USA in any given week between 9th March and 9th August. Using artificial intelligence (AI) on this pooled data, we build a predictive model (, ) for the weekly-averaged transmission rate of infections. Predictability conceptually raises the possibility of an evidence-based or data-driven mitigation policy-making by evaluating the relative merits of the different policy scenarios. Probing the predictions with interpretable AI highlights how factors such as the closing of bars or the use of masks influence transmission, effects which have been hard to decouple from the ensemble of policy instrument combinations. While acknowledging the limitations of our predictions as well as of the infection testing, we ask the theoretical question if the observed transmission rates in the states were as efficient as they could have been under various levels of restrictions, and if the mitigation policies of the states are ‘overdesigned’. The model can be further refined with a more detailed inclusion of geographies and policy compliances, as well as expanded as newer policies emerge.

Performing a complete deep mutational scan with all single point mutations may not be practical, and may not even be required, especially if predictive computational models can be developed. Computational models are however naive to cellular response in the myriads of assay-conditions. In a realistic paradigm of assay context-aware predictive hybrid models that combine minimal experimental data from deep mutational scans with structure, sequence information and computational models, we define and evaluate different strategies for choosing this minimal set. We evaluated the trivial strategy of a systematic reduction in the number of mutational studies from 85% to 15%, along with several others about the choice of the types of mutations such as random versus site-directed with the same 15% data completeness. Interestingly, the predictive capabilities by training on a random set of mutations and using a systematic substitution of all amino acids to alanine, asparagine and histidine (ANH) were comparable. Another strategy we explored, augmenting the training data with measurements of the same mutants at multiple assay conditions, did not improve the prediction quality. For the six proteins we analyzed, the bin-wise error in prediction is optimal when 50-100 mutations per bin are used in training the computational model, suggesting that good prediction quality may be achieved with a library of 500-1000 mutations.

At the sequence level it is hard to describe the complexity of viruses which allows them to challenge host immune system, some for a few weeks and others up to a complete compromise. Paradoxically, viral genomes are both complex and simple. Complex because amino acid mutation rates are very high, and yet viruses remain functional. Simple because they have barely around 10 types of proteins, so viral protein-protein interaction networks are not insightful. In this work we use fine-grained amino acid level information and their evolutionary characteristics obtained from large-scale genomic data to develop a statistical panel, towards the goal of developing quantitative descriptors for the biological complexity of viruses. Networks were constructed from pairwise covariation of amino acids and were statistically analyzed. Three differentiating factors arise: predominantly intra- vs inter-protein covariance relations, the nature of the node degree distribution and network density. Interestingly, the covariance relations were primarily intra-protein in avian influenza and inter-protein in HIV. The degree distributions showed two universality classes: a power-law with exponent −1 in HIV and avian-influenza, random behavior in human flu and dengue. The calculated covariance network density correlates well with the mortality strengths of viruses on the viral-Richter scale. These observations suggest the potential utility of the statistical metrics for describing the covariance patterns in viruses. Our host-virus interaction analysis point to the possibility that host proteins which can interact with multiple viral proteins may be responsible for shaping the inter-protein covariance relations. With the available data, it appears that network density might be a surrogate for the virus Richter scale, however the hypothesis needs a re-examination when large scale complete genome data for more viruses becomes available.

Site-directed and random mutagenesis are biochemical tools to obtain insights into the structure and function of proteins. Recent advances such as deep mutational scan have allowed a complete scan of all the amino acid positions in a protein with each of the 19 possible alternatives. Mapping out the phenotypic consequences of thousands of single point mutations in the same protein is now possible. Visualizing and analysing the rich data offers an opportunity to learn more about the effects of mutations, for a better understanding and engineering of proteins. This work focuses on such visualization analyses applied to the mutational data of TEM-1 β-lactamase. The data is examined in the light of the expected biochemical effects of single point mutations, with the goal of reinforcing or retraining the intuitions. Individual attributes of the amino acid mutations such as the solvent accessible area, charge type change, and distance from the catalytic center capture most of the relevant functional effects. Visualizing the data suggests how combinations of these attributes can be used for a better classification of the effects of mutations, when independently they do not offer a high predictability.

Growth of nanostructured ZnO by solution process always lead to the formation of various kinds of defects. Defect states also can aid in improving different properties of the material. In the case of light-emitting diodes (LEDs), major research is focused on tuning the emission colour so as to achieve white emission without the use of any phosphors. Vertically aligned ZnO nanorods were grown over Mg:GaN substrate by hydrothermal process. High-resolution X-ray diffraction (HRXRD) analysis confirms the epitaxial growth of nanorods over the substrate. The photoluminescence (PL) studies revealed a narrow near band edge emission and a broad defect-induced deep level emission. The intensity of deep level emissions related to Zni, Vo, O i defects decreases on annealing. The V–I characteristics of the heterojunction showed excellent rectifying nature with electroluminescence emission on forward bias. Device fabricated by as-grown ZnO nanorods emits in the UV–blue region and broad emission in the visible region. While the annealed device emitted only in UV–blue region. The emission wavelengths closely matched with that of defect state emissions obtained in the PL studies. By annealing, various defect states density can be controlled, thereby emission colour tuned from white to blue.

Performing deep mutational scan with site-directed mutations of all the amino acid residues in a protein may not be practical, and may not even be required, especially if predictive computational models can be developed. Computational models are however naive to cellular response in the myriads of assay-conditions. We develop a realistic paradigm of assay context-aware predictive hybrid models. Combining minimal deep mutational studies with structure, sequence information and computational models, we predict the phenotypic outcomes quantitatively. It was demonstrated with predictions on the fitness outcomes for a few proteins. Phenotypic fitness data from as few as 15% of the mutations was sufficient for reliable prediction. Interestingly, the predictive capabilities are better with a random set of mutations rather than with a systematic substitution of all amino acids to alanine, asparagine and histidine (ANH). The model can potentially be extended for predicting the phenotypic outcomes at other concentrations of the stressor, such as in dose studies.

Viruses are simultaneously simple and complex. Simple because they have barely around ten types of proteins compared to tens of thousands of proteins in bacteria. Complex because amino acid mutation rates are very high, challenging host immune system and drugs. In this work we use the co-evolution of amino acids and the network characteristics that arise out of it to describe the complexity hidden in the multitude of variations in a viral genome. Using large-scale genomic data, the complexity in several viruses was compared. Interestingly, the co-evolutionary relations were primarily intra-protein in avian influenza and inter-protein in HIV-1. The network degree distributions showed two universality classes: a power-law with exponent −1 in HIV-1 and avian-influenza, random co-evolutionary behavior in human flu and dengue, suggesting the co-evolution as one way to statistically classify the complexity in viruses. The observed correlation between the network densities and the strengths on virus Richter scale raises interesting questions on whether it is possible to define the complexity of viruses using their evolutionary networks.

Large scale mutagenesis experiments are becoming possible owing to the advancement in the sequencing technologies and high throughput screening. Deep mutational scans perform exhaustive single-point muta-tions on a protein and probe their phenotypic effects. Performing a full scan with site-directed mutations of all the amino acid residues in a protein may not be practical, and may not even be required, especially if predictive computational models can be developed. Computational models are however naive to cellular response in the myriads of assay-conditions. In order to develop the realistic paradigm of assay context-aware predictive hybrid models, we combine minimal deep mutational studies with computational models and pre-dict the phenotypic outcomes quantitatively. Structural, sequence and co-evolutionary information along with partial deep mutational scan data was included to capture the phenotypic relevance of the mutations to the specific screening criterion. The model reliably predicts the fitness outcomes of hundreds of randomly selected amino acid mutations in β-lactamase, when the phenotypic fitness data from as few as 15% of the full mutation is available. Interestingly, the predictive capabilities are better with a random set of mutations rather than with a systematic substitution of all amino acids to alanine, asparagine and histidine (ANH). The model can potentially be extended for predicting the phenotypic outcomes at other concentrations of the stressor by carefully analyzing the dose-response curves of a representative set of mutations.Author SummaryMutations are the minor changes in protein sequences, with incommensurately high consequences for their function. Many severe diseases can occur with simple single point mutations. An interesting way of studying these mutations is not to isolate the protein from its natural conditions, but rather study how the fitness of the cell improves or decreases in response to these mutations. Whether it is for understanding disease biology or for bio-engineering applications it is important to quantify the impact of mutations on the cellular fitness. An experimental paradigm has evolved which has improved the ability to sample several hundred thousands of mutation-fitness relations using high throughput screening. However, since these are very specialized experiments, the question is if the number of such experiments required can be minimized, by using computer models to complement the rest of the fitness predictions. In this work we introduce this new paradigm which uses computer model trained on a partial deep mutation scan data, to predict the fitness variations in a full mutations scan that could also be repeated under multiple experimental conditions like drug concentrations.

Bacterial swimming alternates between straight runs for several seconds and tumbles into random directions. Chemotactic bacteria remember nutrient sensing history, change tumble frequency to move toward nutrients. A question that has not been addressed is the significance of the nutrition gain and multiplication of bacterial population with chemotaxis mechanism. To quantify these effects, we introduce a microsimulation model, which seamlessly integrates detailed observations and assumptions about single bacterial tumbles, noisy sensing and nutrient uptake for studying up to a few millions of them in a population. We use the model to simulate absorption of nutrients from lysis and agar plates. Contrary to an intuitive feeling that chemotaxis could be useful under nutrient starvation, we see a significant effect only under nutrient rich conditions where bacteria with chemotaxis outgrow their non-chemotactic counterparts by hundreds of times. The model offers the flexibility to study the consequences of newer assumptions, and experimental conditions.Author SummaryChemotaxis is a mechanism that helps bacteria navigate towards nutrients. Several aspects of the mechanism have been well studied over the past 50 years. As most bacterial mechanisms are helpful evolutionarily to survive and to multiply, it would be a natural question to ask how much this swim helps bacteria to gain nutrition and consequently to multiply. However to our knowledge this question has not been asked. We develop a model that integrates bacterial motion, with sensing and nutrient uptake and show that only under nutrient rich conditions this mechanism helps.

This paper focuses on a nine level multilevel inverter with DC link capacitors at the input side. The main disadvantage of cascaded multilevel inverter is the number of DC sources. So a nine level multilevel inverter with DC link switches, which requires less number of DC sources as compared to the conventional multilevel inverter is proposed. Single carrier method is the control strategy used here. A comprehensive literature survey on existing nine level multilevel inverter is carried out. The feasibility of the proposed nine level inverter is verified through simulation. Proposed nine level inverter is compared with the conventional H bridge multilevel inverter.

This paper proposes a nine level multilevel inverter with DC link switches. Single carrier method is the control strategy used here. The switching sequence to balance the capacitor voltage is also considered. Compared to the exsisting ninelevel multilevel inverter it requires less number of DC sources. The proposed multilevel inverter is compared with five level inverter. The ninelevel inverter produce less distorted waveform compared to fivelevel multilevel inverter. So as the number of level increases harmonic content reduces.