Abstract

Bacterial cellulase enzymes are potent candidates for the efficient production of bioethanol, a promising alternative to fossil fuels, from cellulosic biomass. These enzymes catalyze the breakdown of cellulose in plant biomass into simple sugars and then to bioethanol. In the absence of the enzyme, the cellulosic biomass is recalcitrant to decomposition due to fermentationresistant lignin and pectin coatings on the cellulose surface, which make them inaccessible for hydrolysis. Cellobiohydrolase CelS is a microbial enzyme that binds to cellulose fiber and efficiently cleaves it into a simple sugar (cellobiose) by a repeated processive chopping mechanism. The two contributing factors to the catalytic reaction rate and the yield of cellobiose are the efficient product expulsion from the product binding site of CelS and the movement of the substrate or cellulose chain into the active site. Despite progress in understanding product expulsion in other cellulases, much remains to be understood about the molecular mechanism of processive action of these enzymes. Here, nonequilibrium molecular dynamics simulations using suitable reaction coordinates are carried out to investigate the energetics and mechanism of the substrate dynamics and product expulsion in CelS. The calculated free energy barrier for the product expulsion is three times lower than that for the processive action indicating that product removal is relatively easier and faster than the sliding of the substrate to the catalytic active site. The water traffic near the active site in response to the product expulsion and the processive action is also explored.

Abstract

The configurational sampling is central to characterize the equilibrium properties of complex molecular systems, but it remains a significant computational challenge. The conventional molecular dynamics simulations of limited duration often result in inadequate sampling and thus inaccurate equilibrium estimates. Replica exchange with non-equilibrium switches (RENS) is a collective variable-free computational technique to achieve extensive sampling from a sequence of equilibrium and non-equilibrium MD simulations without modifying the underlying potential energy surface of the system. Unlike the conventional replica exchange molecular dynamics (REMD) simulation, which demands a significant number of replicas for better accuracy, RENS employs non-equilibrium heating (forward) and cooling (reverse) work simulations prior to configurational swaps to improve the acceptance probability for replica exchange. Here, we have implemented the RENS algorithm on four model systems and examined its performance against the conventional MD and REMD simulations. The desired equilibrium distributions were generated by RENS for all the model systems, whereas REMD and MD simulations could not do so due to inadequate sampling on the same timescales. The calculated work distributions from RENS obeyed the expected non-equilibrium fluctuation theorem. The results indicate that the switching time of the non-equilibrium simulations can be systematically altered to optimize the acceptance probability and the reduced work of switching. The modular implementation of RENS algorithm not only enables us to readily extend it to multiple replicas, but also paves the way for extension to larger molecular systems in the future

Abstract

In response to the COVID19 pandemic, many countries have implemented lockdowns in multiple phases to ensure social distancing and quarantining of the infected subjects. Subsequent unlocks to reopen the economies started next waves of infection and imposed an extra burden on quarantine to keep the reproduction number (R0) < 1. However, most countries could not efectively contain the infection spread, suggesting identifcation of the potential sources weakening the efect of lockdowns could help design better informed lockdown-unlock cycles in the future. Here, through building quantitative epidemic models and analyzing the metadata of 50 countries from across the continents we frst found that the estimated value of R0, adjusted w.r.t the distribution of medical facilities and virus clades correlates strongly with the testing rates in a country. Since the testing capacity of a country is limited by its medical resources, we investigated if a cost–beneft trade-of can be designed connecting testing rate and extent of unlocking. We present a strategy to optimize this trade-of in a country specifc manner by providing a quantitative estimate of testing and quarantine rates required to allow diferent extents of unlocks while aiming to maintain R0 < 1. We further show that a small fraction of superspreaders can dramatically increase the number of infected individuals even during strict lockdowns by strengthening the positive feedback loop driving infection spread. Harnessing the beneft of optimized country-specifc testing rates would critically require minimizing the movement of these superspreaders via strict social distancing norms, such that the positive feedback driven switch-like exponential spread phase of infection can be avoided/delayed.

Abstract

Tissue homeostasis and regeneration depend on the reversible transitions between quiescence (G0) and proliferation. The liver has a remarkable capacity to regenerate after injury or resection by cell growth and division. During regeneration, the liver needs to maintain the essential metabolic tasks and meet the metabolic requirements for hepatocyte growth and division. Understanding the regulatory mechanisms involved in balancing the liver function and proliferation demand after injury or resection is crucial. In this study, we analyzed high-resolution temporal RNA sequencing data of liver regeneration after two-thirds partial hepatectomy (PHx) using network inference and mathematical modeling approaches. The reconstruction of the dynamic regulatory network of liver regeneration reveals the trajectories of different metabolic pathways, protein processing in the endoplasmic reticulum (ER), ribosome biogenesis, RNA transport, spliceosome, immune response, and cell cycle. We further developed a mathematical model of the integrated circuit of liver regeneration that accountsfor underlying features of compensatory metabolism, proliferation, and epithelial-to-mesenchymal transition during liver regeneration. We show that a mutually exclusive behavior emerges due to the bistable inactivation of HNF4A, which controls the initiation and termination of liver regeneration and different population-level expressions observed in single-cell RNA sequencing data of liver regeneration.

Abstract

Estimating brain age and establishing functional biomarkers that are prescient of cognitive declines resulting from aging and dierent neurological diseases are still open research problems. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain functions are also aected by “normal” brain aging. More information is needed on how functional connectivity relates to aging, particularly in the absence of neurodegenerative disorders. Resting-state fMRI enables us to investigate functional brain networks and can potentially help us understand the processes of development as well as aging in terms of how functional connectivity (FC) matures during the early years and declines during the late years. We propose models for estimation of the chronological age of a healthy person from the resting state brain activation (rsfMRI). In this work, we utilized a dataset (N = 638, age-range 20–88) comprising rsfMRI images from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) repository of a healthy population. We propose an age prediction pipeline Ayu which consists of data preprocessing, feature selection, and an attention-based model for deep learning architecture for brain age assessment. We extracted features from the static functional connectivity (sFC) to predict the subject’s age and classified them into dierent age groups (young, middle, middle, and old ages). To the best of our knowledge, a classification accuracy of 72.619 % and a mean absolute error of 6.797, and an r 2 of 0.754 reported by our Ayu pipeline establish competitive benchmark results as compared to the state-of-the-art-approach. Furthermore, it is vital to identify how dierent functional regions of the brain are correlated. We also analyzed how functional regions contribute dierently across ages by applying attention-based networks and integrated gradients. We obtained well-known resting-state networks using the attention model, which maps to within the default mode network, visual network, ventral attention network, limbic network, frontoparietal network, and somatosensory network connected to aging. Our analysis of fMRI data in healthy elderly Age groups revealed that dynamic FC tends to slow down and becomes less complex and more random with increasing

Abstract

The role of non-coding RNAs in neuropsychiatric disorders (NPDs) is an emerging field of study. The long non-coding RNAs (lncRNAs) are shown to sponge the microRNAs (miRNAs) from interacting with their target mRNAs. Investigating the sponge activity of lncRNAs in NPDs will provide further insights into biological mechanisms and help identify disease biomarkers. In this study, a large-scale inference of the lncRNA-related miRNA sponge network of pan-neuropsychiatric disorders, including autism spectrum disorder (ASD), schizophrenia (SCZ), and bipolar disorder (BD), was carried out using brain transcriptomic (RNA-Seq) data. The candidate miRNA sponge modules were identified based on the co-expression pattern of non-coding RNAs, sharing of miRNA binding sites, and sensitivity canonical correlation. miRNA sponge modules are associated with chemical synaptic transmission, nervous system development, metabolism, immune system response, ribosomes, and pathways in cancer. The identified modules showed similar and distinct gene expression patterns depending on the neuropsychiatric condition. The preservation of miRNA sponge modules was shown in the independent brain and blood-transcriptomic datasets of NPDs. We also identified miRNA sponging lncRNAs that may be potential diagnostic biomarkers for NPDs. Our study provides a comprehensive resource on miRNA sponging in NPDs.

Abstract

During healthy ageing, the brain networks undergo various topological and functional alterations. Previous studies have shown that the dedifferentiation of the functional modules could be one of the hallmarks of large-scale brain networks and alterations through the lifespan. This modular organiza- tion and alterations may be critically linked to a variety of neurodegenerative disorders and cognitive deficits encountered during ageing. In spite of accumulating evidence based on tracking static functional connectivity (FC) and modularity in characterizing dedifferentiation associated with ageing, there is a gap in understanding the brain dynamics of modular segregation and integration through the lifespan. Using the Cam-CAN dataset (young: 18-44, mean 32 years, old: 65-88, mean 75 years), we characterize the modular reorganization using dynamic measures like flexibility, to find characteristic nodes that make up the stable core and flexible periphery in the young and old age groups. In this study, we hypothesize that the nodes that exhibit higher flexibility in the older age groups will be negatively correlated with modularity since these nodes ‘compensate’ for the functional integration while ensuring that the segregation is efficient. Our results demonstrate that the regions from the Default Mode network (DMN) show a negative correlation with modularity in the old age groups. Further, nodes from Limbic, SensoriMotor (SMN) and Salience networks show a positive correlation with modularity. These networks that are responsible for higher-order cognitive functions, e.g., decision making, attentional control, cognitive flexibility are found to make up a stable core as evidenced by their low flexibility scores. We also trained various classifiers using node flexibility scores as features for the binary (young vs old) classification task. Support Vector Machine (SVM) with Gaussian kernel trained on a reduced-dimensional feature set gave the best classification results. The features (nodes) that are found to be important for classification concur with those identified through the data-driven network measures based analysis. In summary, we anticipate that these findings can help identify the regions that are responsible for the reorganization

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder predominantly found in children. The current behavior-based diagnosis of ASD is arduous and requires expertise. Therefore, it is appealing to develop an accurate computer-aided tool for diagnosing ASD. Although resting-state functional magnetic resonance imaging (rsfMRI) has proven to be successful in capturing the neural organization of the brain, automated detection of ASD using rsfMRI scans is a challenging task due to heterogeneity in the dataset and limited sample size. This paper proposes a Multilayer Perceptron (MLP) based classification model with auto encoder pretraining for classifying ASD from Typically Developing (TD) using rsfMRI scans obtained from the ABIDE-1 dataset. Our model achieves new state-of-the-art performance on the ABIDE-1 dataset with a 10-fold cross-validation accuracy of 74.82%. Further, we use the Integrated Gradients (IG) and DeepLIFT techniques to identify the correlations between brain regions that contribute most to the classification task. Our analysis identifies the following regions, Left Lingual Gyrus, Right Insula Lobe, Right Cuneus, Right Middle Frontal Gyrus, Left Superior Temporal Gyrus to be associated with ASD. Interestingly, these regions in the brain are primarily responsible for social cognition, language, attention, decision making and visual processing, which are known to be altered in ASD.

Abstract

Machine learning (ML) has recently facilitated many advances in solving problems related to many-body physical systems. Given the intrinsic quantum nature of these problems, it is natural to speculate that quantum-enhanced machine learning will enable us to unveil even greater details than we currently have. With this motivation, this paper examines a quantum machine learning approach based on shallow variational ansatz inspired by tensor networks for supervised learning tasks. In particular, we first look at the standard image classification tasks using the FashionMNIST dataset and study the effect of repeating tensor network layers on ansatz’s expressibility and performance. Finally, we use this strategy to tackle the problem of quantum phase recognition for the transverse-field Ising and Heisenberg spin models in one and two dimensions, where we were able to reach ≥ 98% test-set accuracies with both multi-scale entanglement renormalization ansatz (MERA) and tree tensor network (TTN) inspired parametrized quantum circuits. Keywords: Quantum Computing, Quantum Machine Learning, Quantum Many-body Systems, QuantumClassical Algorithms

Abstract

We discuss the prospects for improving the precision on the hadronic corrections to the anomalous magnetic moment of the muon, and the plans of the Muon g −2 Theory Initiative to update the Standard Model prediction.