In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and ...cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior R free and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.
In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and ...cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior R free and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.
The main objective of this thesis is to investigate the efficiency of in-situ trainable Convolutional Neural Networks (CNNs) on modern programmable System-on-Chip (SoC) Field Programmable Gate Arrays ...(FPGAs) composed of embedded processors and reconfigurable fabric and to study the robustness of the system when faults happen. One particular characteristic of this work is that CNN is developed exclusively using High-Level Synthesis (HLS), particularly in SystemC, generating Verilog code. In this thesis, the feature maps are also being trained on the FPGA, which is traditionally done offline. The CNN architecture is instantiated on the FPGA and weights are trained through the software model on the ARM processor embedded into the FPGA and updated in the architecture through the AXI bus interface. Moreover, since CNN is implemented in hardware the resource used need to be minimized. This allows to choose a smaller, and cheaper FPGA, as well as reducing the total power consumption. To address this, the effect of bitwidth reduction of the CNN is investigated with respect to the accuracy of handwritten characters recognitions. Finally, the robustness of the CNN is analyzed by breaking internal connection of different neurons studying how the accuracy drops when the fault happens at different layers If the accuracy is reduced, then the CNN is re-trained in-situ to increase the accuracy of the CNN.
Abstract The advent of weather and climate models has equipped us to forecast or project monsoon rainfall patterns over various spatiotemporal scales; however, utilizing a single model is not usually ...sufficient to yield accurate projection due to the inherent uncertainties associated with the individual models. An ensemble of models or model runs is often used for better projections as a multimodel ensemble (MME). This study analyzes the accuracy of MME in simulating the Indian summer monsoon rainfall (ISMR) variability using Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The results highlighted that although the MME primarily reproduces the observed pattern and annual cycle of rainfall, significant biases are noted over homogeneous meteorological regions of India, except northeast India. To overcome this issue, an analysis of variance (ANOVA) and post hoc statistical tests are employed to identify a group of models for which the modified MME gives a better estimate of rainfall and reduces the bias significantly. Our findings underscore the potential of ANOVA and post hoc tests as a practical approach to enhancing the accuracy of multimodel ensemble rainfall for the assessment of model projections.
Context: Molluscum contagiosum (MC) is a common viral cutaneous infection. Despite multiple treatment options, there is no definitive treatment. In some cases, the lesions are severe, recurrent, and ...cosmetically odd. Modified autoinoculation (MAI) is a novel technique that induces cell-mediated immunity resulting in clearance of local as well as distant lesions. Potassium hydroxide (KOH) acts by dissolving the keratin and penetrating deeply destroys the hyperproliferative tissue. We would here like to compare MAI with topical KOH in the treatment of MC. Aims and Objective: The aim of this study was to assess the effectiveness of MAI in treatment of MC and to compare its response with topical KOH application. Settings and Design: This was an open-labeled longitudinal therapeutic outcome study carried out at a tertiary care center over a period of 1 year. Materials and Methods: Hundred consenting MC patients attending the department of dermatology were randomized into Group A and Group B. Group A patients were subjected to MAI and Group B to topical application of 10% KOH. Statistical Analysis Used: The continuous variables are presented as mean ± standard deviation (SD). The difference between the mean score was analyzed using Student's t test for independent variable and paired t test for paired results. Results: At the end of 16 weeks, 91.48% showed complete clearance by MAI compared to 81.64% with topical 10% KOH solution. There was a significant reduction of mean score of lesions in patients treated by MAI compared to patients treated with KOH. Conclusion: MAI therapy provides a promising, easy, cost-effective, daycare option for MC infections.
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