Analysis Toolbox Research Articles

Pynapple, a toolbox for data analysis in neuroscience.

Datasets collected in neuroscientific studies are of ever-growing complexity, often combining high-dimensional time series data from multiple data acquisition modalities. Handling and manipulating these various data streams in an adequate programming environment is crucial to ensure reliable analysis, and to facilitate sharing of reproducible analysis pipelines. Here, we present Pynapple, the PYthon Neural Analysis Package, a lightweight python package designed to process a broad range of time-resolved data in systems neuroscience. The core feature of this package is a small number of versatile objects that support the manipulation of any data streams and task parameters. The package includes a set of methods to read common data formats and allows users to easily write their own. The resulting code is easy to read and write, avoids low-level data processing and other error-prone steps, and is open source. Libraries for higher-level analyses are developed within the Pynapple framework but are contained within a collaborative repository of specialized and continuously updated analysis routines. This provides flexibility while ensuring long-term stability of the core package. In conclusion, Pynapple provides a common framework for data analysis in neuroscience.

Pynapple, a toolbox for data analysis in neuroscience

Datasets collected in neuroscientific studies are of ever-growing complexity, often combining high-dimensional time series data from multiple data acquisition modalities. Handling and manipulating these various data streams in an adequate programming environment is crucial to ensure reliable analysis, and to facilitate sharing of reproducible analysis pipelines. Here, we present Pynapple, the PYthon Neural Analysis Package, a lightweight python package designed to process a broad range of time-resolved data in systems neuroscience. The core feature of this package is a small number of versatile objects that support the manipulation of any data streams and task parameters. The package includes a set of methods to read common data formats and allows users to easily write their own. The resulting code is easy to read and write, avoids low-level data processing and other error-prone steps, and is open source. Libraries for higher-level analyses are developed within the Pynapple framework but are contained within a collaborative repository of specialized and continuously updated analysis routines. This provides flexibility while ensuring long-term stability of the core package. In conclusion, Pynapple provides a common framework for data analysis in neuroscience.

DarSIA: An Open-Source Python Toolbox for Two-Scale Image Processing of Dynamics in Porous Media

Understanding porous media flow is inherently a multi-scale challenge, where at the core lies the aggregation of pore-level processes to a continuum, or Darcy-scale, description. This challenge is directly mirrored in image processing, where pore-scale grains and interfaces may be clearly visible in the image, yet continuous Darcy-scale parameters may be what are desirable to quantify. Classical image processing is poorly adapted to this setting, as most techniques do not explicitly utilize the fact that the image contains explicit physical processes. Here, we extend classical image processing concepts to what we define as “physical images” of porous materials and processes within them. This is realized through the development of a new open-source image analysis toolbox specifically adapted to time-series of images of porous materials.

MICROSTATELAB: The EEGLAB Toolbox for Resting-State Microstate Analysis

Microstate analysis is a multivariate method that enables investigations of the temporal dynamics of large-scale neural networks in EEG recordings of human brain activity. To meet the enormously increasing interest in this approach, we provide a thoroughly updated version of the first open source EEGLAB toolbox for the standardized identification, visualization, and quantification of microstates in resting-state EEG data. The toolbox allows scientists to (i) identify individual, mean, and grand mean microstate maps using topographical clustering approaches, (ii) check data quality and detect outlier maps, (iii) visualize, sort, and label individual, mean, and grand mean microstate maps according to published maps, (iv) compare topographical similarities of group and grand mean microstate maps and quantify shared variances, (v) obtain the temporal dynamics of the microstate classes in individual EEGs, (vi) export quantifications of these temporal dynamics of the microstates for statistical tests, and finally, (vii) test for topographical differences between groups and conditions using topographic analysis of variance (TANOVA). Here, we introduce the toolbox in a step-by-step tutorial, using a sample dataset of 34 resting-state EEG recordings that are publicly available to follow along with this tutorial. The goals of this manuscript are (a) to provide a standardized, freely available toolbox for resting-state microstate analysis to the scientific community, (b) to allow researchers to use best practices for microstate analysis by following a step-by-step tutorial, and (c) to improve the methodological standards of microstate research by providing previously unavailable functions and recommendations on critical decisions required in microstate analyses.

Real Time Voltage Stability Estimation under Contingencies by Generalized Curve Fit Method using PMU

An accurate voltage stability monitoring scheme is necessary to take possible remedial action. Recent developments in Phasor Measurement Units (PMUs) technology provide the foundation for online voltage stability monitoring of a power system network. In this paper, the generalized curve fit method using PMUs in the system has been used to access voltage stability of the system and a sensitivity based approach has been used to identify critical buses of the system. Voltage stability margin has been obtained and compared using generalized curve fit method and continuation power flow method using PMUs in the system. Variations in voltage stability margin caused by changing load patterns have also been considered while estimating voltage stability under contingencies. Nose curves obtained using generalized curve fit and continuation power flow methods are also compared. Case studies have been performed on a standard IEEE 14-bus system and practical 246-bus Northern Regional Power Grid (NRPG) system representing power network of nine states of India, with the help of Real Time Digital Simulation (RTDS) and Power System Analysis Toolbox (PSAT) software.

Cardinality optimization in constraint-based modelling: application to human metabolism.

Several applications in constraint-based modelling can be mathematically formulated as cardinality optimization problems involving the minimization or maximization of the number of nonzeros in a vector. These problems include testing for stoichiometric consistency, testing for flux consistency, testing for thermodynamic flux consistency, computing sparse solutions to flux balance analysis problems and computing the minimum number of constraints to relax to render an infeasible flux balance analysis problem feasible. Such cardinality optimization problems are computationally complex, with no known polynomial time algorithms capable of returning an exact and globally optimal solution. By approximating the zero-norm with nonconvex continuous functions, we reformulate a set of cardinality optimization problems in constraint-based modelling into a difference of convex functions. We implemented and numerically tested novel algorithms that approximately solve the reformulated problems using a sequence of convex programs. We applied these algorithms to various biochemical networks and demonstrate that our algorithms match or outperform existing related approaches. In particular, we illustrate the efficiency and practical utility of our algorithms for cardinality optimization problems that arise when extracting a model ready for thermodynamic flux balance analysis given a human metabolic reconstruction. Open source scripts to reproduce the results are here https://github.com/opencobra/COBRA.papers/2023_cardOpt with general purpose functions integrated within the COnstraint-Based Reconstruction and Analysis toolbox: https://github.com/opencobra/cobratoolbox.

New Tools in the Host Cell Protein Analysis Toolbox

New Tools in the Host Cell Protein Analysis Toolbox

A unified power flow controller-based robust damping controller considering time delay in electrical power systems

Nowadays, different power systems are connected to each other due to technical and economic reasons and form a complex developed network. Such networks measure and send system data to decision-making centers for control and protection. This information transmission is accompanied by a delay and causes the performance of the system’s damping controllers to be affected. To improve power system dynamic stability, the supplementary controller in flexible alternating current transmission system (FACTS) devices is designed to account for information transmission delays. In these methods, to increase the controllability and observability of the inter-area mode, the remote signals extracted from the wide area measurement system (WAMS) are used as the input of the modulator. WAMS systems’ time delay reduces power system stability and even causes instability, so it’s crucial to find the maximum delay margin that ensures stability. Therefore, it is necessary to carefully study the role of FACTS tools in stabilizing the power system and damping interregional fluctuations, considering the delay. This research shows that supplementary controller design can dampen frequency and load angle fluctuations in multi-zone power systems despite information transmission delays. The method works in power system analysis toolbox (PSAT) simulation and MATLAB programming.

Small signal stability and dynamic performance investigation on multi-machines power system including DFIG wind farm

<p>The boundless potential of wind power in augmenting global energy production is a promising prospect. The efficient design and cost-effectiveness of doubly fed induction generator (DFIG) wind systems make them an optimistic solution for incorporating wind power on a massive scale. However, integrating these systems into power grids poses several challenges, including power system stability. This study examines the small signal stability and dynamic performance of a modified Western System Coordinating Council (WSCC) 9-bus system including a DFIG wind farm using load flow analysis, and both electromechanical oscillations and eigenvalue analysis. Three case studies were conducted based on the DFIG location and power increment.The simulation is carried out with the aid of the power system analysis toolbox (PSAT) that operates within the MATLAB environment. The study’s findings suggest that the perturbation and location of the DFIG relative to the system’s load have a minimal influence on the overall system’s stability and efficiency. However, when considering damping ratio, power angle, and rotor speed deviations, generators 1 and 2 with the perturbed DFIG installed on bus 8 are the most sensitive units to instability. Hence, larger perturbations and different DFIG’s location influence on power systems necessitates further analysis.</p>

Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models.

Several studies suggested that transcription factor (TF) binding to DNA may be impaired or enhanced by DNA methylation. We present MeDeMo, a toolbox for TF motif analysis that combines information about DNA methylation with models capturing intra-motif dependencies. In a large-scale study using ChIP-seq data for 335 TFs, we identify novel TFs that show a binding behaviour associated with DNA methylation. Overall, we find that the presence of CpG methylation decreases the likelihood of binding for the majority of methylation-associated TFs. For a considerable subset of TFs, we show that intra-motif dependencies are pivotal for accurately modelling the impact of DNA methylation on TF binding. We illustrate that the novel methylation-aware TF binding models allow to predict differential ChIP-seq peaks and improve the genome-wide analysis of TF binding. Our work indicates that simplistic models that neglect the effect of DNA methylation on DNA binding may lead to systematic underperformance for methylation-associated TFs.

Applications of vertical damping wigglers in an X-ray diffraction limited storage ring

This study presents a lattice design for an X-ray diffraction limited storage ring (DLSR) light source with a circumference of 864 m and achieving an ultralow natural emittance of 25.6 pm·rad (N-IBS). However, the ultra-low emittance and long damping times lead to significant intra-beam scattering (IBS) effects. To address this issue, the study explores the implementation of vertical damping wigglers (VDWs) to decrease damping times, reduce beam horizontal emittance, and generate vertical emittance to achieve a “round beam” in the 864mDLSR. Through theoretical analysis and accelerator toolbox (AT) simulations, the impacts of VDWs with varied parameters were investigated, and the optimal period length, peak field, and total length were determined. Additionally, the twelve quadrupole magnets in both the front and rear cells of the VDWs were employed to correct linear optics. The findings demonstrate that VDWs could significantly reduce the beam's equilibrium emittance and damping times of the 864mDLSR. Furthermore, a comparison between the spectral brightness and flux of the 864mDLSR with and without VDWs revealed that the 864mDLSR employing VDWs yielded higher brightness and flux.

Mathematical Toolbox for Joint Structural and Electrochemical Analysis of Lithium-Ion Battery Cells

Increasing interest in the automotive field for electrification has led to an increased demand for lithium-ion batteries (LIB). LIB remain the most popular and widely applied energy storage technology. The microstructure of the positive electrode of LIBs governs its electrochemical performance. For this reason, it is quite imperative to determine an optimal electrode microstructure design to achieve the best possible energy properties. Mechanistic simulation models can be a powerful tool in analyzing the effect of microstructural parameters on the battery cell performance. Not only do they negate costs for necessary materials, but they also allow analysis and optimization while avoiding time-consuming experiments.Doyle-Fuller-Newman (DFN) models, being pseudo-2-dimensional (P2D), are computationally less expensive than their higher dimension counterparts; however, they often apply the Bruggeman relation to compute effective parameters such as the electrode conductivity of the electrode. Despite its simple implementation (i. e. a correction factor to reflect effective parameters), the Bruggeman relation delivers inaccurate results. [1, 2]In this work [1], we coupled a DFN model with the structure surrogate model from [2], excluding the Bruggeman relation. The model predicts the effective transport parameters and thus the battery performance more accurately. With this coupled model, we investigated the effect of the cathode mass loading and density on the achievable volumetric discharge energy density. [1] Furthermore, we developed a numerical optimization tool, which can predict the optimal structural parameters for maximum energy density. Our findings suggested different optimal designs for different current densities; whereas higher mass loading was preferred at lower C-rates, it was less favorable at higher C-rates. The concentration profiles of lithium-ions across the cathode thickness at the end of discharge suggest transport limitations to be the main reason behind this. As a further step, we carried out an uncertainty analysis to determine the robustness of the optimal designs to structural fluctuations with respect to their energy density.Our findings provide a deeper understanding into the influence of structural parameters on the discharge performance of lithium-ion battery cells, using only simulative methods. Additionally, our methodology of numerical optimization and subsequent uncertainty analysis allows for a quick and simple method for determining optimal structural parameters and their robustness against fluctuations. This approach can assist to produce battery cell electrodes with an optimized design in industry as well as help simulative and experimental researchers to further investigate optimal electrode design.[1] Karaki et al., Energy Technology 2022 [2] Laue et al., E. Acta 2019 Figure 1

Comprehensive single-cell genome analysis at nucleotide resolution using the PTA Analysis Toolbox

Detection of somatic mutations in single cells has been severely hampered by technical limitations of whole-genome amplification. Novel technologies including primary template-directed amplification (PTA) significantly improved the accuracy of single-cell whole-genome sequencing (WGS) but still generate hundreds of artifacts per amplification reaction. We developed a comprehensive bioinformatic workflow, called the PTA Analysis Toolbox (PTATO), to accurately detect single base substitutions, insertions-deletions (indels), and structural variants in PTA-based WGS data. PTATO includes a machine learning approach and filtering based on recurrence to distinguish PTA artifacts from true mutations with high sensitivity (up to 90%), outperforming existing bioinformatic approaches. Using PTATO, we demonstrate that hematopoietic stem cells of patients with Fanconi anemia, which cannot be analyzed using regular WGS, have normal somatic single base substitution burdens but increased numbers of deletions. Our results show that PTATO enables studying somatic mutagenesis in the genomes of single cells with unprecedented sensitivity and accuracy.

Policy transfer and policy experimentation in China: the case of state-sponsored student loans

ABSTRACT As part of efforts to mass higher education, China introduced state-sponsored student loans around the turn of the century, a progressive social policy reflecting the country’s move toward increased marketization and decentralization in delivering educational services. The loan scheme, open to low-income students, is a voluntary policy transfer that draws on lessons from abroad. Unexpectedly, it stalled soon after implementation. Subsequent local experimentation brought it back on track. This article uses the Dolowitz and Marsh policy transfer framework to identify and explain the unsuccessful transfer. It also uses Heilmann’s model to examine the local experimentation that brought about institutional change nationwide. The article argues that policy transfer, in conjunction with policy experimentation, provides practical frameworks for capturing the subtleties of China’s hierarchical governance and the different complications experienced throughout the policy process. The article thus contributes to the integration of policy transfer and experimentation as a broader toolbox for policy analysis.

Stakeholder governance and sustainability in football: A bibliometric analysis

This study presents a bibliometric analysis of stakeholder governance and sustainability in football. The PRISMA statement and the main techniques from the existing bibliometric analysis toolbox are applied to guide the research. The analysis includes 127 documents from Scopus and WoS, covering the period from 2007 to mid-2023, to examine the performance analysis and science mapping of the subject over time. The study's key findings highlight publication-related metrics, citation-and-publication-related metrics, co-authorship analysis, and co-word analysis. The analysis also identifies research gaps, including the need to explore barriers to stakeholder governance in football, the application of stakeholder pressure frameworks in football clubs, the examination of mimetic and normative pressures in the context of sustainability and governance in football, and the involvement of stakeholders and football institutions in collaborative efforts to enhance the effectiveness and impact of sustainability initiatives.

NanopoReaTA: a user-friendly tool for nanopore-seq real-time transcriptional analysis.

Oxford Nanopore Technologies' (ONT) sequencing platform offers an excellent opportunity to perform real-time analysis during sequencing. This feature allows for early insights into experimental data and accelerates a potential decision-making process for further analysis, which can be particularly relevant in the clinical context. Although some tools for the real-time analysis of DNA-sequencing data already exist, there is currently no application available for differential transcriptome data analysis designed for scientists or physicians with limited bioinformatics knowledge. Here, we introduce NanopoReaTA, a user-friendly real-time analysis toolbox for RNA-sequencing data from ONT. Sequencing results from a running or finished experiment are processed through an R Shiny-based graphical user interface with an integrated Nextflow pipeline for whole transcriptome or gene-specific analyses. NanopoReaTA provides visual snapshots of a sequencing run in progress, thus enabling interactive sequencing and rapid decision making that could also be applied to clinical cases. Github https://github.com/AnWiercze/NanopoReaTA; Zenodo https://doi.org/10.5281/zenodo.8099825.

Experimental investigation on the validity of the local thermal equilibrium assumption in ablative-material response models

Thermal Protection Systems (TPS) material response models rely on the assumption of local thermal equilibrium (LTE) between the solid phase and the gas phase. This assumption was challenged and investigated by several authors but a sufficiently precise knowledge of heat transfer coefficients in TPS materials was lacking to reach final conclusions. The objective of this work is to contribute to filling this gap by providing a literature review of available data in other communities (thermal energy storage, heat exchangers) and by performing an experimental characterization of Calcarb, a commercial carbon preform used for manufacturing thermal protection systems. Heat transfer within Calcarb was studied experimentally in the Through-Thickness and in the In-Plane directions for Reynolds numbers of 1 to 4 - representative of the TPS application - using the transient single-blow technique. Numerical parameter estimation was performed using the Porous material Analysis Toolbox based on OpenFoam (PATO) and the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA). The heat transfer coefficient hv is found to be greater than or equal to 108 W/(m3K) and the LTE assumption is shown to be valid in the conditions of the experiment. To assess the validity of the LTE assumption for other conditions, the above bound of hv may now be used in combination with a local thermal non-equilibrium model.

Matlab sensitivity analysis toolbox: an application on faults identification in induction motors

PurposeThis paper aims to introduce the usage of sensitivity analysis (SA) for the problem of faults identification in three-phase induction motors (IMs). These motors are susceptible to different kinds of faults that should be detected in a proper time to keep the systems working in a safety environment.Design/methodology/approachOne of the effective approaches for faults identifications, which is presented in the literature, is a model-based strategy. This strategy mainly depends on using a software model to make an identification decision. Therefore, this work intends to examine the model sensitivity towards variables’ variation. The SA toolbox of Matlab R2017b package is used for this purpose since the Matlab software is a well-known environment, and it is easy for a nonstatistical person to deal with it. As a study case, open-circuit and stator inter-turn faults in the stator windings of a three-phase IM have been chosen.FindingsThe results show that the model-based strategy is considerably speed up by up to 30% when neglecting the trivial model’s parameters with the same accurate identification decision as compared with the results of this strategy without using the SA.Originality/valueThe novelty of this work is summarized in devoting the usage of SA in the field of faults identification to enhance the speed of final decision.

Developing capacitated p-median location-allocation model in the spopt library to allow UCL student teacher placements using public transport

Abstract. Location-allocation is a key tool within the GIS and network analysis toolbox. In this paper we discuss the real world application of a location-allocation case study (approx 800 students, 500 schools) from UCL using public transport. The use of public transportation is key for this case study, as many location-allocation approaches only make use of drive-time or walking-time distances, but the location of UCL in Greater London, UK makes the inclusion of public transport vital for this case study. The location-allocation is implemented as a capacitated p-median location-allocation model, using the spopt library, part of the Python Spatial Analysis Library (PySAL). The capacitated variation of the p-median location-allocation problem is a new addition to the spopt library, which this work will present. The results from the initial version of the capacitated p-median location-allocation problem has shown a marked improvement on public transport travel time, with public transport travel time reduced by 891 minutes overall for an initial sample of 93 students (9.58 minutes per student). Results will be presented below and plans for further improvement shared.

HAT: Hypergraph analysis toolbox.

Recent advances in biological technologies, such as multi-way chromosome conformation capture (3C), require development of methods for analysis of multi-way interactions. Hypergraphs are mathematically tractable objects that can be utilized to precisely represent and analyze multi-way interactions. Here we present the Hypergraph Analysis Toolbox (HAT), a software package for visualization and analysis of multi-way interactions in complex systems.