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Quantitative Methods

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24 featured work(s)

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preprint2020arXiv

Fluorescence fluctuations-based super-resolution microscopy techniques: an experimental comparative study

Fluorescence fluctuations-based super-resolution microscopy (FF-SRM) is an emerging field promising low-cost and live-cell compatible imaging beyond the resolution of conventional optical microscopy. A comprehensive overview on how the nature of fluctuations, label density, out-of-focus light, sub-cellular dynamics, and the sample itself influence the reconstruction in FF-SRM is crucial to design appropriate biological experiments. We have experimentally compared several of the recently developed FF-SRM techniques (namely ESI, bSOFI, SRRF, SACD, MUSICAL and HAWK) on widefield fluorescence image sequences of a diverse set of samples (namely liposomes, tissues, fixed and living cells), and on three-dimensional simulated data where the ground truth is available. The simulated microscopy data showed that the different techniques have different requirements for signal fluctuation to achieve their optimal performance. While different levels of signal fluctuations had little effect on the SRRF, ESI and SACD images, image reconstructions from both bSOFI and MUSICAL displayed a substantial improvement in their noise rejection, z-sectioning, and overall super-resolution capabilities.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Prevalence and incidence of postpartum depression and environmental factors: the IGEDEPP cohort

Background: IGEDEPP (Interaction of Gene and Environment of Depression during PostPartum) is a prospective multicenter cohort study of 3,310 Caucasian women who gave birth between 2011 and 2016, with follow-up until one year postpartum. The aim of the current study is to describe the cohort and estimate the prevalence and cumulative incidence of early and late postpartum depression (PPD). Methods: Socio-demographic data, personal and family psychiatric history, as well as stressful life events during childhood and pregnancy were evaluated at baseline. Early and late PPD were assessed at 8 weeks and 1 year postpartum respectively, using DSM-5 criteria. Results: The prevalence of early PPD was 8.3% (95%CI 7.3-9.3), and late PPD 12.9% (95%CI 11.5-14.2), resulting in an 8-week cumulative incidence of 8.5% (95%CI 7.4-9.6) and a one-year cumulative incidence of PPD of 18.1% (95%CI: 17.1-19.2). Nearly half of the cohort (N=1571, 47.5%) had a history of at least one psychiatric or addictive disorder, primarily depressive disorder (35%). Almost 300 women in the cohort (9.0%) reported childhood trauma. During pregnancy, 47.7% women experienced a stressful event, 30.2% in the first 8 weeks an

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Pressure Actuated Cellular Structures

This postdoctoral thesis starts by reviewing the historic development of airplane structures and high lift devices from an engineering point of view. However, the main purpose of this document is the development of a novel concept for shape changing, gapless high lift devices that is inspired by the nastic movement of plants. A particular focus is put on the efficient simulation and optimization of compliant pressure actuated cellular structures.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Graph Convolutional Networks Reveal Neural Connections Encoding Prosthetic Sensation

Extracting stimulus features from neuronal ensembles is of great interest to the development of neuroprosthetics that project sensory information directly to the brain via electrical stimulation. Machine learning strategies that optimize stimulation parameters as the subject learns to interpret the artificial input could improve device efficacy, increase prosthetic performance, ensure stability of evoked sensations, and improve power consumption by eliminating extraneous input. Recent advances extending deep learning techniques to non-Euclidean graph data provide a novel approach to interpreting neuronal spiking activity. For this study, we apply graph convolutional networks (GCNs) to infer the underlying functional relationship between neurons that are involved in the processing of artificial sensory information. Data was collected from a freely behaving rat using a four infrared (IR) sensor, ICMS-based neuroprosthesis to localize IR light sources. We use GCNs to predict the stimulation frequency across four stimulating channels in the prosthesis, which encode relative distance and directional information to an IR-emitting reward port. Our GCN model is able to achieve a peak perfo

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Systematic Assessment of Deep Learning Models for Molecule Generation

In recent years the scientific community has devoted much effort in the development of deep learning models for the generation of new molecules with desirable properties (i.e. drugs). This has produced many proposals in literature. However, a systematic comparison among the different VAE methods is still missing. For this reason, we propose an extensive testbed for the evaluation of generative models for drug discovery, and we present the results obtained by many of the models proposed in literature.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Semi-Blind and l1 Robust System Identification for Anemia Management

Chronic diseases such as cancer, diabetes, heart diseases, chronic kidney disease (CKD) require a drug management system that ensures a stable and robust output of the patient's condition in response to drug dosage. In the case of CKD, the patients suffer from the deficiency of red blood cell count and external human recombinant erythropoietin (EPO) is required to maintain healthy levels of hemoglobin (Hb). Anemia is a common comorbidity in patients with CKD. For an efficient and robust anemia management system for CKD patients instead of traditional population-based approaches, individualized patient-specific approaches are needed. Hence, individualized system (patient) models for patient-specific drug-dose responses are required. In this research, system identification for CKD is performed for individual patients. For control-oriented system identification, two robust identification techniques are applied: (1) l1 robust identification considering zero initial conditions and (2) semi-blind robust system identification considering non-zero initial conditions. The EPO data of patients are used as the input and Hb data is used as the output of the system. For this study, individu

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

GPU-accelerating ImageJ Macro image processing workflows using CLIJ

This chapter introduces GPU-accelerated image processing in ImageJ/FIJI. The reader is expected to have some pre-existing knowledge of ImageJ Macro programming. Core concepts such as variables, for-loops, and functions are essential. The chapter provides basic guidelines for improved performance in typical image processing workflows. We present in a step-by-step tutorial how to translate a pre-existing ImageJ macro into a GPU-accelerated macro.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Topological Data Analysis of Task-Based fMRI Data from Experiments on Schizophrenia

We use methods from computational algebraic topology to study functional brain networks, in which nodes represent brain regions and weighted edges encode the similarity of fMRI time series from each region. With these tools, which allow one to characterize topological invariants such as loops in high-dimensional data, we are able to gain understanding into low-dimensional structures in networks in a way that complements traditional approaches that are based on pairwise interactions. In the present paper, we use persistent homology to analyze networks that we construct from task-based fMRI data from schizophrenia patients, healthy controls, and healthy siblings of schizophrenia patients. We thereby explore the persistence of topological structures such as loops at different scales in these networks. We use persistence landscapes and persistence images to create output summaries from our persistent-homology calculations, and we study the persistence landscapes and images using $k$-means clustering and community detection. Based on our analysis of persistence landscapes, we find that the members of the sibling cohort have topological features (specifically, their 1-dimensional loops)

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Towards Structured Prediction in Bioinformatics with Deep Learning

Using machine learning, especially deep learning, to facilitate biological research is a fascinating research direction. However, in addition to the standard classification or regression problems, in bioinformatics, we often need to predict more complex structured targets, such as 2D images and 3D molecular structures. The above complex prediction tasks are referred to as structured prediction. Structured prediction is more complicated than the traditional classification but has much broader applications, considering that most of the original bioinformatics problems have complex output objects. Due to the properties of those structured prediction problems, such as having problem-specific constraints and dependency within the labeling space, the straightforward application of existing deep learning models can lead to unsatisfactory results. Here, we argue that the following ideas can help resolve structured prediction problems in bioinformatics. Firstly, we can combine deep learning with other classic algorithms, such as probabilistic graphical models, which model the problem structure explicitly. Secondly, we can design the problem-specific deep learning architectures or methods by

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

MutaGAN: A Seq2seq GAN Framework to Predict Mutations of Evolving Protein Populations

The ability to predict the evolution of a pathogen would significantly improve the ability to control, prevent, and treat disease. Despite significant progress in other problem spaces, deep learning has yet to contribute to the issue of predicting mutations of evolving populations. To address this gap, we developed a novel machine learning framework using generative adversarial networks (GANs) with recurrent neural networks (RNNs) to accurately predict genetic mutations and evolution of future biological populations. Using a generalized time-reversible phylogenetic model of protein evolution with bootstrapped maximum likelihood tree estimation, we trained a sequence-to-sequence generator within an adversarial framework, named MutaGAN, to generate complete protein sequences augmented with possible mutations of future virus populations. Influenza virus sequences were identified as an ideal test case for this deep learning framework because it is a significant human pathogen with new strains emerging annually and global surveillance efforts have generated a large amount of publicly available data from the National Center for Biotechnology Information's (NCBI) Influenza Virus Resou

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Effectiveness of Common Fabrics to Block Aqueous Aerosols of COVID Virus-like Nanoparticles

Layered systems of commonly available fabric materials can be used by the public and healthcare providers in face masks to reduce the risk of inhaling viruses with protection about equivalent or better than the filtration and adsorption offered by 5-layer N95 respirators. Over 70 different common fabric combinations and masks were evaluated under steady state, forced convection air flux with pulsed aerosols that simulate forceful respiration. The aerosols contain fluorescent virus-like nanoparticles to track transmission through materials that greatly assist the accuracy of detection, thus avoiding artifacts including pore flooding and the loss of aerosol due to evaporation and droplet break-up. Effective materials comprise both absorbent, hydrophilic layers and barrier, hydrophobic layers. Although the hydrophobic layers can adhere virus-like nanoparticles, they may also repel droplets from adjacent absorbent layers and prevent wicking transport across the fabric system. Effective designs are noted with absorbent layers comprising terry cloth towel, quilting cotton and flannel. Effective designs are noted with barrier layers comprising non-woven polypropylene, polyester and polyar

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Maximum likelihood analysis of non-equilibrium solution-based single-molecule FRET data

Measuring the Förster resonance energy transfer (FRET) efficiency of freely diffusing single molecules provides information about the sampled conformational states of the molecules. Under equilibrium conditions, the distribution of the conformational states is independent of time, whereas it can vary over time under non-equilibrium conditions. In this work, we consider the problem of parameter inference on non-equilibrium solution-based single-molecule FRET data. With a non-equilibrium model for the conformational dynamics and a model for the conformation-dependent FRET efficiency distribution, the likelihood function could be constructed. The model parameters, such as the rate constants of the non-equilibrium conformational dynamics model and the average FRET efficiencies of the different conformational states, have been estimated from the data by maximizing the appropriate likelihood function via the Expectation-Maximization algorithm. We illustrate the likelihood method for a few simple non-equilibrium models and validated the method by simulations. The likelihood method could be applied to study protein folding, macromolecular complex formation, protein conformational dynamics

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Series solution of the Susceptible-Infected-Recovered (SIR) epidemic model with vital dynamics via the Adomian and Laplace-Adomian Decomposition Methods

The Susceptible-Infected-Recovered (SIR) epidemic model as well as its generalizations are extensively used for the study of the spread of infectious diseases, and for the understanding of the dynamical evolution of epidemics. From SIR type models only the model without vital dynamics has an exact analytic solution, which can be obtained in an exact parametric form. The SIR model with vital dynamics, the simplest extension of the basic SIR model, does not admit a closed form representation of the solution. However, in order to perform the comparison with the epidemiological data accurate representations of the time evolution of the SIR model with vital dynamics would be very useful. In the present paper, we obtain first the basic evolution equation of the SIR model with vital dynamics, which is given by a strongly nonlinear second order differential equation. Then we obtain a series representation of the solution of the model, by using the Adomian and Laplace-Adomian Decomposition Methods to solve the dynamical evolution equation of the model. The solutions are expressed in the form of infinite series. The series representations of the time evolution of the SIR model with vital dyn

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Understanding and Modelling the Complexity of the Immune System: Systems Biology for Integration and Dynamical Reconstruction of Lymphocyte Multi-Scale Dynamics

Understanding and modelling the complexity of the immune system is a challenge that is shared by the ImmunoComplexiT$^1$ thematic network from the RNSC. The immune system is a complex biological, adaptive, highly diversified, self-organized and degenerative cognitive network of entities, allowing for a robust and resilient system with emergent properties such as anamnestic responses and regulation. The adaptive immune system has evolved into a complex system of billions of highly diversified lymphocytes all interacting as a connective dynamic, multi-scale organised and distributed system, in order to collectively insure body and species preservation. The immune system is characterized by complexity at different levels: network organisation through fluid cell populations with inter-and intra-cell signalling, lymphocyte receptor diversity, cell clonotype selection and competition at cell level, migration and interaction inside the immunological tissues and fluid dissemination through the organism, homeostatic regulation while rapid adaptation to a changing environment.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Expiratory variability index (EVI) is associated with asthma risk, wheeze and lung function in infants with recurrent respiratory symptoms

Recurrent respiratory symptoms are common in infants but the paucity of lung function tests suitable for routine use in infants is a widely acknowledged clinical problem. In this study we evaluated tidal breathing variability (expiratory variability index, EVI) measured at home during sleep using impedance pneumography (IP) as a marker of lower airway obstruction in 36 infants (mean age 12.8 [range 6-23] months) with recurrent respiratory symptoms. Lowered EVI was associated with lower lung function (VmaxFRC), higher asthma risk, and obstructive symptoms, but not with nasal congestion. EVI measured using IP is a potential technique for lung function testing in infants.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Data-driven Understanding of COVID-19 Dynamics Using Sequential Genetic Algorithm Based Probabilistic Cellular Automata

COVID-19 pandemic is severely impacting the lives of billions across the globe. Even after taking massive protective measures like nation-wide lockdowns, discontinuation of international flight services, rigorous testing etc., the infection spreading is still growing steadily, causing thousands of deaths and serious socio-economic crisis. Thus, the identification of the major factors of this infection spreading dynamics is becoming crucial to minimize impact and lifetime of COVID-19 and any future pandemic. In this work, a probabilistic cellular automata based method has been employed to model the infection dynamics for a significant number of different countries. This study proposes that for an accurate data-driven modeling of this infection spread, cellular automata provides an excellent platform, with a sequential genetic algorithm for efficiently estimating the parameters of the dynamics. To the best of our knowledge, this is the first attempt to understand and interpret COVID-19 data using optimized cellular automata, through genetic algorithm. It has been demonstrated that the proposed methodology can be flexible and robust at the same time, and can be used to model the daily

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Parametric dynamic causal modelling

This technical note introduces parametric dynamic causal modelling, a method for inferring slow changes in biophysical parameters that control fluctuations of fast neuronal states. The application domain we have in mind is inferring slow changes in variables (e.g., extracellular ion concentrations or synaptic efficacy) that underlie phase transitions in brain activity (e.g., paroxysmal seizure activity). The scheme is efficient and yet retains a biophysical interpretation, in virtue of being based on established neural mass models that are equipped with a slow dynamic on the parameters (such as synaptic rate constants or effective connectivity). In brief, we use an adiabatic approximation to summarise fast fluctuations in hidden neuronal states (and their expression in sensors) in terms of their second order statistics; namely, their complex cross spectra. This allows one to specify and compare models of slowly changing parameters (using Bayesian model reduction) that generate a sequence of empirical cross spectra of electrophysiological recordings. Crucially, we use the slow fluctuations in the spectral power of neuronal activity as empirical priors on changes in synaptic paramete

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Digital pathology-based study of cell- and tissue-level morphologic features in serous borderline ovarian tumor and high-grade serous ovarian cancer

Serous borderline ovarian tumor (SBOT) and high-grade serous ovarian cancer (HGSOC) are two distinct subtypes of epithelial ovarian tumors, with markedly different biologic background, behavior, prognosis, and treatment. However, the histologic diagnosis of serous ovarian tumors can be subjectively variable and labor-intensive as multiple tumor slides/blocks need to be thoroughly examined to search for these features. In this study, we aimed to evaluate technical feasibility of using digital pathological approaches to facilitate objective and scalable diagnosis screening for SBOT and HGSOC. Based on Groovy scripts and QuPath, a novel informatics system was developed to facilitate interactive annotation and imaging data exchange for machine learning purposes. Through this developed system, cellular boundaries were detected and expanded set of cellular features were extracted to represent cell- and tissue-level characteristics. According to our evaluation, cell-level classification was accurately achieved for both tumor and stroma cells with greater than 90% accuracy. Upon further re-examinations, 44.2% of the misclassified cells were due to over-/under-segmentations or low-quality o

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Utilization of 3D segmentation for measurement of pediatric brain tumor outcomes after treatment: review of available free tools, step-by-step instructions, and applications to clinical practice

Volumetric measurements are known to provide more information when it comes to segmenting tumors, in comparison to one- and two-dimensional measurements, and thus can lead to better informed therapy. In this work, we review the free and easily accessible computer platforms available for conducting these 3D measurements, such as Horos and 3D Slicer and compare the segmentations to commercial Visage software. We compare the time for 3D segmentation of tumors and demonstrate how to use a novel plugin that we developed in 3D slicer for the efficient and accurate segmentation of the cystic component of a tumor.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Decontextualized learning for interpretable hierarchical representations of visual patterns

Apart from discriminative models for classification and object detection tasks, the application of deep convolutional neural networks to basic research utilizing natural imaging data has been somewhat limited; particularly in cases where a set of interpretable features for downstream analysis is needed, a key requirement for many scientific investigations. We present an algorithm and training paradigm designed specifically to address this: decontextualized hierarchical representation learning (DHRL). By combining a generative model chaining procedure with a ladder network architecture and latent space regularization for inference, DHRL address the limitations of small datasets and encourages a disentangled set of hierarchically organized features. In addition to providing a tractable path for analyzing complex hierarchal patterns using variation inference, this approach is generative and can be directly combined with empirical and theoretical approaches. To highlight the extensibility and usefulness of DHRL, we demonstrate this method in application to a question from evolutionary biology.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Unsupervised and Supervised Structure Learning for Protein Contact Prediction

Protein contacts provide key information for the understanding of protein structure and function, and therefore contact prediction from sequences is an important problem. Recent research shows that some correctly predicted long-range contacts could help topology-level structure modeling. Thus, contact prediction and contact-assisted protein folding also proves the importance of this problem. In this thesis, I will briefly introduce the extant related work, then show how to establish the contact prediction through unsupervised graphical models with topology constraints. Further, I will explain how to use the supervised deep learning methods to further boost the accuracy of contact prediction. Finally, I will propose a scoring system called diversity score to measure the novelty of contact predictions, as well as an algorithm that predicts contacts with respect to the new scoring system.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Data Mining and Analytical Models to Predict and Identify Adverse Drug-drug Interactions

The use of multiple drugs accounts for almost 30% of all hospital admission and is the 5th leading cause of death in America. Since over 30% of all adverse drug events (ADEs) are thought to be caused by drug-drug interactions (DDI), better identification and prediction of administration of known DDIs in primary and secondary care could reduce the number of patients seeking urgent care in hospitals, resulting in substantial savings for health systems worldwide along with better public health. However, current DDI prediction models are prone to confounding biases along with either inaccurate or a lack of access to longitudinal data from Electronic Health Records (EHR) and other drug information such as FDA Adverse Event Reporting System (FAERS) which continue to be the main barriers in measuring the prevalence of DDI and characterizing the phenomenon in medical care. In this review, analytical models including Label Propagation using drug side effect data and Supervised Learning DDI Prediction model using Drug-Gene interactions (DGIs) data are discussed. Improved identification of DDIs in both of these models compared to previous versions are highlighted while limitations that includ

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preprint2020arXiv

RAMSES: A full-stack application for detecting seizures and reducing data during continuous EEG monitoring

Objective: Continuous EEG (cEEG) monitoring is associated with lower mortality in critically ill patients, however it is underutilized due to the difficulty of manually interpreting prolonged streams of cEEG data. Here we present a novel real-time, machine learning-based alerting and monitoring system for epilepsy and seizures (RAMSES) that dramatically reduces the amount of manual EEG review. Methods: We developed a custom data reduction algorithm using a random forest, and deployed it within an online cloud-based platform which streams data and communicates interactively with caregivers via a web interface to display algorithm results. We validate RAMSES on cEEG recordings from 77 patients undergoing routine scalp ICU EEG monitoring. Results: On subjects with seizures we achieved >80% overall data reduction, while detecting a mean of 84% of seizures across all validation patients, with 19/27 patients achieving 100% seizure detection. On seizure free-patients, the majority of cEEG records, we reduced data requiring manual review by >83%. Conclusion: This study validates a platform for machine-learning assisted data reduction. Significance: This work represents a meaningful step to

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Deep unsupervised learning for Microscopy-Based Malaria detection

Malaria, a mosquito-borne disease caused by a parasite, kills over 1 million people globally each year. People, if left untreated, may develop severe complications, leading to death. Effective and accurate diagnosis is important for the management and control of malaria. Our research focuses on utilizing machine learning to improve the efficiency in Malaria diagnosis. We utilize a modified U-net architecture, as an unsupervised learning model, to conduct cell boundary detection. The blood cells infected by malaria are then identified in chromatic space by a Mahalanobis distance algorithm. Both the cell segmentation and Malaria detection process often requires intensive manual label, which we hope to eliminate via the unsupervised workflow.

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