User authentication system based on human exhaled breath physics.

This work, in a pioneering approach, attempts to build a biometric system that works purely based on the fluid mechanics governing exhaled breath. We test the hypothesis that the structure of turbulence in exhaled human breath can be exploited to build biometric algorithms. This work relies on the idea that the extrathoracic airway is unique for every individual, making the exhaled breath a biomarker. Methods including classical multi-dimensional hypothesis testing approach and machine learning models are employed in building user authentication algorithms, namely user confirmation and user identification. A user confirmation algorithm tries to verify whether a user is the person they claim to be. A user identification algorithm tries to identify a user's identity with no prior information available. A dataset of exhaled breath time series samples from 94 human subjects was used to evaluate the performance of these algorithms. The user confirmation algorithms performed exceedingly well for the given dataset with over 97% true confirmation rate. The machine learning based algorithm achieved a good true confirmation rate, reiterating our understanding of why machine learning based algorithms typically outperform classical hypothesis test based algorithms. The user identification algorithm performs reasonably well with the provided dataset with over 50% of the users identified as being within two possible suspects. We show surprisingly unique turbulent signatures in the exhaled breath that have not been discovered before. In addition to discussions on a novel biometric system, we make arguments to utilise this idea as a tool to gain insights into the morphometric variation of extrathoracic airway across individuals. Such tools are expected to have future potential in the area of personalised medicines.

Design of spinning disk atomization equipment for synthesis of drug-loaded microparticles.

The synthesis of drug-loaded microparticles with precise control over size distribution and shape is crucial for achieving desired drug distribution in microparticles and tuning drug release profiles. Common large-scale production techniques produce microparticles with a broad particle size distribution and require challenging operating conditions. Recent methods employing microfluidics have enabled the production of microparticles with a uniform size distribution. Still, these methods are limited to low and moderate production rates and can handle fluids with a limited range of physicochemical properties. In this study, we couple the spinning disk atomization (SDA) technique for microdroplet production with a precipitation method to generate drug-loaded polymeric microparticles with a narrow size distribution. The design criteria and fabrication of equipment with a non-contact seal system that integrates spinning disk atomization and precipitation methods for conducting laboratory experiments involving volatile hydrocarbons while ensuring operational and personnel safety are discussed. The production of itraconazole drug-loaded microparticles using the SDA setup that considers the system's operation, maintenance, and safety aspects are discussed, and the system's efficiency is evaluated through material balance. This laboratory equipment is capable of producing drug-loaded microparticles with a narrow size distribution under moderate operating conditions and can be scaled up suitably to meet high production requirements. The applications of this equipment can be explored in various fields, such as the production of drug particles, conversion of waste polymers into microparticles, and microencapsulation of food ingredients.

Perspective on Radiolytic Charging for Redox Flow Battery Electrolytes Using the Nuclear Decay Energy of Spent Nuclear Fuel/Radionuclides.

The conversion of nuclear energy into electricity is facilitated by chemical intermediates and molecular products formed during the radiolysis of water. In this work, we hypothesize a novel radiolytic charging approach for redox flow battery electrolytes. Radiolytically produced ionic intermediates and molecular products oxidize or reduce the metal ion solutes in the electrolytes. A qualitative study for choice of redox couples based on electrochemical principles followed by a feasibility study of radiolytic conversion of active material is presented. A framework for an empirical investigation of radioactive source, equipment and dose, and product characterization techniques is discussed. The proposed method finds application in the utilization of spent nuclear fuel (specifically gamma emissions from fission products in early activity stages) as a radiolysis radiation source for electrolyte charging. We present a perspective on future investigations that are required to harness nuclear energy for charging electrolytes and developing a self-operating RFB system.

Multi-Omic Data Improve Prediction of Personalized Tumor Suppressors and Oncogenes.

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require multiple samples to identify less frequently mutated driver genes. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumor progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify personalized driver genes based on changes in expression. Our method is the first machine learning model to classify genes as tumor suppressor gene (TSG), oncogene (OG), or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalized Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalized driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labeling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared with mutation and expression data, achieving an accuracy ≥ 0.99 for BRCA, LUAD, and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD, and LUAD cancer types reveal commonly altered genes such as TP53 and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9, and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalized driver genes as TSGs and OGs and also identified rare driver genes.

A Computational Framework for Studying Gut-Brain Axis in Autism Spectrum Disorder.

Introduction: The integrity of the intestinal epithelium is crucial for human health and is harmed in autism spectrum disorder (ASD). An aberrant gut microbial composition resulting in gut-derived metabolic toxins was found to damage the intestinal epithelium, jeopardizing tissue integrity. These toxins further reach the brain via the gut-brain axis, disrupting the normal function of the brain. A mechanistic understanding of metabolic disturbances in the brain and gut is essential to design effective therapeutics and early intervention to block disease progression. Herein, we present a novel computational framework integrating constraint based tissue specific metabolic (CBM) model and whole-body physiological pharmacokinetics (PBPK) modeling for ASD. Furthermore, the role of gut microbiota, diet, and oxidative stress is analyzed in ASD. Methods: A representative gut model capturing host-bacteria and bacteria-bacteria interaction was developed using CBM techniques and patient data. Simultaneously, a PBPK model of toxin metabolism was assembled, incorporating multi-scale metabolic information. Furthermore, dynamic flux balance analysis was performed to integrate CBM and PBPK. The effectiveness of a probiotic and dietary intervention to improve autism symptoms was tested on the integrated model. Results: The model accurately highlighted critical metabolic pathways of the gut and brain that are associated with ASD. These include central carbon, nucleotide, and vitamin metabolism in the host gut, and mitochondrial energy and amino acid metabolisms in the brain. The proposed dietary intervention revealed that a high-fiber diet is more effective than a western diet in reducing toxins produced inside the gut. The addition of probiotic bacteria Lactobacillus acidophilus, Bifidobacterium longum longum, Akkermansia muciniphila, and Prevotella ruminicola to the diet restores gut microbiota balance, thereby lowering oxidative stress in the gut and brain. Conclusion: The proposed computational framework is novel in its applicability, as demonstrated by the determination of the whole-body distribution of ROS toxins and metabolic association in ASD. In addition, it emphasized the potential for developing novel therapeutic strategies to alleviate autism symptoms. Notably, the presented integrated model validates the importance of combining PBPK modeling with COBRA -specific tissue details for understanding disease pathogenesis.

Designing Biological Circuits: From Principles to Applications.

Genetic circuit design is a well-studied problem in synthetic biology. Ever since the first genetic circuits─the repressilator and the toggle switch─were designed and implemented, many advances have been made in this area of research. The current review systematically organizes a number of key works in this domain by employing the versatile framework of generalized morphological analysis. Literature in the area has been mapped on the basis of (a) the design methodologies used, ranging from brute-force searches to control-theoretic approaches, (b) the modeling techniques employed, (c) various circuit functionalities implemented, (d) key design characteristics, and (e) the strategies used for the robust design of genetic circuits. We conclude our review with an outlook on multiple exciting areas for future research, based on the systematic assessment of key research gaps that have been readily unravelled by our analysis framework.

Metabolic modeling of host-microbe interactions for therapeutics in colorectal cancer.

The onset of colorectal cancer (CRC) is often attributed to gut bacterial dysbiosis, and thus gut microbiota are highly relevant in devising treatment strategies. Certain gut microbes, like Enterococcus spp., exhibit remarkable anti-neoplastic and probiotic properties, which can aid in silver nanoparticle (AgNPs) induced reactive oxygen species (ROS)-based CRC treatment. However, the effects of AgNPs on gut microbial metabolism have not been reported thus far. In this study, a detailed systems-level understanding of ROS metabolism in Enterococcus durans (E. durans), a representative gut microbe, was gained using constraint-based modeling, wherein, the critical association between ROS and folate metabolism was established. Experimental studies involving low AgNP concentration treatment of E. durans cultures confirmed these modeling predictions (an increased extracellular folate concentration by 52%, at the 9th h of microbial growth, was observed). Besides, the computational studies established various metabolic pathways involving amino acids, energy metabolites, nucleotides, and SCFAs as the key players in elevating folate levels on ROS exposure. The anti-cancer potential of E. durans was also studied through MTT analysis of HCT 116 cells treated with microbial culture (AgNP treated) supernatant. A decrease in cell viability by 19% implicated the role of microbial metabolites (primarily folate) in causing cell death. The genome-scale modeling approach was then extended to extensively model CRC metabolism, as well as CRC-E. durans interactions in the context of CRC treatment, using tissue-specific metabolic models of CRC and healthy colon. These findings on further validation can facilitate the development of robust and effective cancer therapy.

Novel ratio-metric features enable the identification of new driver genes across cancer types.

An emergent area of cancer genomics is the identification of driver genes. Driver genes confer a selective growth advantage to the cell. While several driver genes have been discovered, many remain undiscovered, especially those mutated at a low frequency across samples. This study defines new features and builds a pan-cancer model, cTaG, to identify new driver genes. The features capture the functional impact of the mutations as well as their recurrence across samples, which helps build a model unbiased to genes with low frequency. The model classifies genes into the functional categories of driver genes, tumour suppressor genes (TSGs) and oncogenes (OGs), having distinct mutation type profiles. We overcome overfitting and show that certain mutation types, such as nonsense mutations, are more important for classification. Further, cTaG was employed to identify tissue-specific driver genes. Some known cancer driver genes predicted by cTaG as TSGs with high probability are ARID1A, TP53, and RB1. In addition to these known genes, potential driver genes predicted are CD36, ZNF750 and ARHGAP35 as TSGs and TAB3 as an oncogene. Overall, our approach surmounts the issue of low recall and bias towards genes with high mutation rates and predicts potential new driver genes for further experimental screening. cTaG is available at https://github.com/RamanLab/cTaG .

Comparison of first trimester dating methods for gestational age estimation and their implication on preterm birth classification in a North Indian cohort.

BACKGROUND: Different formulae have been developed globally to estimate gestational age (GA) by ultrasonography in the first trimester of pregnancy. In this study, we develop an Indian population-specific dating formula and compare its performance with published formulae. Finally, we evaluate the implications of the choice of dating method on preterm birth (PTB) rate. This study's data was from GARBH-Ini, an ongoing pregnancy cohort of North Indian women to study PTB. METHODS: Comparisons between ultrasonography-Hadlock and last menstrual period (LMP) based dating methods were made by studying the distribution of their differences by Bland-Altman analysis. Using data-driven approaches, we removed data outliers more efficiently than by applying clinical parameters. We applied advanced machine learning algorithms to identify relevant features for GA estimation and developed an Indian population-specific formula (Garbhini-GA1) for the first trimester. PTB rates of Garbhini-GA1 and other formulae were compared by estimating sensitivity and accuracy. RESULTS: Performance of Garbhini-GA1 formula, a non-linear function of crown-rump length (CRL), was equivalent to published formulae for estimation of first trimester GA (LoA, - 0.46,0.96 weeks). We found that CRL was the most crucial parameter in estimating GA and no other clinical or socioeconomic covariates contributed to GA estimation. The estimated PTB rate across all the formulae including LMP ranged 11.27-16.50% with Garbhini-GA1 estimating the least rate with highest sensitivity and accuracy. While the LMP-based method overestimated GA by 3 days compared to USG-Hadlock formula; at an individual level, these methods had less than 50% agreement in the classification of PTB. CONCLUSIONS: An accurate estimation of GA is crucial for the management of PTB. Garbhini-GA1, the first such formula developed in an Indian setting, estimates PTB rates with higher accuracy, especially when compared to commonly used Hadlock formula. Our results reinforce the need to develop population-specific gestational age formulae.

On the role of hydrodynamic interactions in the engineered-assembly of droplet ensembles.

Droplets, as they flow inside a microchannel, interact hydrodynamically to result in spatio-temporal patterns. The nature of the interaction decides the type of collective behaviour observed. In this context, we study the application of droplet microfluidics in the area of complex-shape particle synthesis. We show how the dynamics of droplet motion, the steady-state characteristics, the short and long-range hydrodynamics, the dependence on inlet conditions etc. are all related to the features that characterize a device like the functionality (producing many shapes) and robustness (insensitivity to fluctuations). Two primary operating regimes are identified, one where long-range interactions are dominant and the other where they are short-range. In the former, the shapes formed by droplets are steady-state solutions to the governing equations, while in the latter they are a function of how the droplets enter the channel (frequency of entry). We show that identifying the inlet conditions for producing a particle of the desired shape requires the use of a systematic approach to design which involves solving an optimization problem (using genetic algorithms) to identify the optimal operating strategy. With the knowledge of the hydrodynamics between the droplets, we demonstrate how one can reduce the complexity of the design process. We also discuss the control strategies required if one were to realize the identified operating strategy experimentally.

Entrainment of superoxide rhythm by menadione in HCT116 colon cancer cells.

Reactive oxygen species (ROS) are primary effectors of cytotoxicity induced by many anti-cancer drugs. Rhythms in the pseudo-steady-state (PSS) levels of particular intracellular ROS in cancer cells and their relevance to drug effectiveness are unknown thus far. We report that the PSS levels of intracellular superoxide (SOX), an important ROS, exhibit an inherent rhythm in HCT116 colon cancer cells, which is entrained (reset) by the SOX inducer, menadione (MD). This reset was dependent on the expression of p53, and it doubled the sensitivity of the cells to MD. The period of oscillation was found to have a linear correlation with MD concentration, given by the equation, T, in h = 23.52 - 1.05 [MD concentration in µM]. Further, we developed a mathematical model to better understand the molecular mechanisms involved in rhythm reset. Biologically meaningful parameters were obtained through parameter estimation techniques; the model can predict experimental profiles of SOX, establish qualitative relations between interacting species in the system and serves as an important tool to understand the profiles of various species. The model was also able to successfully predict the rhythm reset in MD treated hepatoma cell line, HepG2.

On developing a framework for detection of oscillations in data.

Oscillation is a phenomenon very commonly observed in systems, ranging from simple ones to complex distributed network. Several techniques have been proposed in the literature for detecting oscillations to study their importance in domains ranging from physiology to climate studies. However, there is a lack of a common framework accommodative of important features of data such as non-stationarity, intermittent oscillations, measurement noise, multimodal oscillations, and the like. In this article, we outline a framework that addresses these challenges, the results of which can then be analyzed along with appropriate knowledge about the underlying system. We present results of an extensive simulation study that establishes the robustness and reliability of the proposed technique and demonstrate its applicability to real datasets in climate and in industrial datasets.

A novel approach for benchmarking and assessing the performance of state estimators.

State estimation is a widely adopted soft sensing technique that incorporates predictions from an accurate model of the process and measurements to provide reliable estimates of unmeasured variables. The reliability of such estimators is threatened by measurement related challenges and model inaccuracies. In this article, a method for benchmarking of state estimation techniques is proposed. This method can be used to quantify the performance and hence reliability of an estimator. The Hurst exponents of a posteriori filtering errors are analyzed to characterize a benchmark (minimum mean squared error) estimator, similar to the minimum variance control benchmark developed for control loops. A distance metric is then used to quantify the extent of deviation of an estimator from the benchmark. The proposed technique is developed for linear systems and extended to non-linear systems with single as well as multiple measurable variables. Simulation studies are carried out with Kalman based as well as Monte Carlo based estimators whose computational details are significantly different. Results reveal that the technique serves as a tool that can quantify the performance and assess the reliability of a state estimator. The strengths and limitations of the proposed technique are discussed with guidelines on applications and deployment of the technique in a real life system.

Coalescence of drops in a 2D microchannel: critical transitions to autocatalytic behaviour.

A single coalescence event in a 2D concentrated emulsion in a microchannel can trigger an avalanche of similar events that can destabilize the entire assembly of drops. The sensitive dependence of the process on numerous parameters makes the propagation dynamics appear probabilistic. In this article, a stochastic simulation framework is proposed to understand this collective behavior in a system employing a large number of drops. We discover that the coalescence propagation dynamics exhibit a critical behavior where two outcomes are favored: no avalanche and large avalanches. Our analysis reveals that this behavior is a result of the inherent autocatalytic nature of the process. The effect of the aspect ratio of the drop assembly on the propagation dynamics is studied. We generate a parametric plot that shows the region of the parameter space where the propagation, averaged over the ensemble, is autocatalytic: where the possibility of near destabilization of the drop assembly appears.

Origin of periodic and chaotic dynamics due to drops moving in a microfluidic loop device.

Droplets moving in a microfluidic loop device exhibit both periodic and chaotic behaviors based on the inlet droplet spacing. We observe that the periodic behavior is an outcome of carrier phase mass conservation principle, which translates into a droplet spacing quantization rule. This rule implies that the summation of exit spacing is equal to an integral multiple of inlet spacing. This principle also enables identification of periodicity in experimental systems with input scatter. We find that the origin of chaotic behavior is through intermittency, which arises when drops enter and leave the junctions at the same time. We derive an analytical expression to estimate the occurrence of these chaotic regions as a function of system parameters. We provide experimental, simulation, and analytical results to validate the origin of periodic and chaotic behavior.

Structural properties of gene regulatory networks: definitions and connections.

The study of gene regulatory networks is a significant problem in systems biology. Of particular interest is the problem of determining the unknown or hidden higher level regulatory signals by using gene expression data from DNA microarray experiments. Several studies in this area have demonstrated the critical aspect of the network structure in tackling the network modelling problem. Structural analysis of systems has proved useful in a number of contexts, viz., observability, controllability, fault diagnosis, sparse matrix computations etc. In this contribution, we formally define structural properties that are relevant to Gene Regulatory Networks. We explore the structural implications of certain quantitative methods and explain completely the connections between the identifiability conditions and structural criteria of observability and distinguishability. We illustrate these concepts in case studies using representative biologically motivated network examples. The present work bridges the quantitative modelling methods with those based on the structural analysis.