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Dynamic Global Vegetation Models (DGVMs) provide a state-of-the-art process-based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single-site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version-controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site-level simulations. We provide input data for 20 established geo-ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines.

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The accuracy of quantum mechanics (QM) simulations depends heavily on the quality of initial input files. Despite the popularity of QM simulation packages, achieving precise results still heavily relies on the user's proficiency in preparing the QM simulation systems. In this work, we present an easy-to-use tool called GUIDE, a YASARA plugin to assist researchers in quantum chemistry workflow automation using ORCA and MOPAC simulation packages. GUIDE lets users compute complex QM calculation workflows via an automated graphical window system. It allows for a more integrated and streamlined research process, as researchers can easily access all the necessary tools within one software without switching between multiple programs. This tool can save time and increase efficiency in computational chemistry methods. GUIDE is written in Python and is freely available for download at https://github.com/YAMACS-SML/GUIDE. The plugin is released under a GPL-3.0 license and is supported on Windows and Linux.

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Mass spectrometry (MS) is an important tool for biological studies because it is capable of interrogating a diversity of biomolecules (proteins, drugs, metabolites) not captured via alternate genomic platforms. Unfortunately, downstream data analysis becomes complicated when attempting to evaluate and integrate measurements of different molecular classes and requires the aggregation of expertise from different relevant disciplines. This complexity represents a significant bottleneck that limits the routine deployment of MS-based multi-omic methods, despite the unmatched biological and functional insight the data can provide. To address this unmet need, our group introduced Omics Notebook as an open-source framework for facilitating exploratory analysis, reporting and integrating MS-based multi-omic data in a way that is automated, reproducible and customizable. By deploying this pipeline, we have devised a framework for researchers to more rapidly identify functional patterns across complex data types and focus on statistically significant and biologically interesting aspects of their multi-omic profiling experiments. This chapter aims to describe a protocol which leverages our publicly accessible tools to analyze and integrate data from high-throughput proteomics and metabolomics experiments and produce reports that will facilitate more impactful research, cross-institutional collaborations, and wider data dissemination.

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Background: Independent Component Analysis (ICA) is the most common and standard technique used in functional neuroscience data analysis. Objective: In this study, two of the significant functional brain techniques are introduced as a model for neuroscience data analysis. Material and Methods: In this experimental and analytical study, Electroencephalography (EEG) signal and functional Magnetic Resonance Imaging (fMRI) were analyzed and managed by the developed tool. The introduced package combines Independent Component Analysis (ICA) to recognize significant dimensions of the data in neuroscience. This study combines EEG and fMRI in the same package for analysis and comparison results. Results: The findings of this study indicated the performance of the ICA, which can be dealt with the presented easy-to-use and learn intuitive toolbox. The user can deal with EEG and fMRI data in the same module. Thus, all outputs were analyzed and compared at the same time; the users can then import the neurofunctional datasets easily and select the desired portions of the functional biosignal for further processing using the ICA method. Conclusion: A new toolbox and functional graphical user interface, running in cross-platform MATLAB, was presented and applied to biomedical engineering research centers.

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As part of a larger effort to aid in seamless integration of Fourier-based multiplexed ion mobility with a range mass analyzers, we have developed an all-in-one graphical user interface tool for FT-IM-MS data analysis that runs directly within a web browser. This tool, FTflow, accepts mzML files and displays necessary information such as mass spectra and extracted ion chromatograms in order to reconstruct arrival time distributions. It also extracts the corresponding mobility-related information (e.g., Ko and CCS) for each of the target ion populations. Furthermore, input fields for experimental conditions are clearly laid out for users and ease-of-use. With flexibility in mind, the processing scripts and GUI interface are written entirely in Python and allows users the option to modify source code to fit their specific needs. While the intention for this tool is to be a starting point for exploratory analysis of FT-IM-MS data, it has the capability to be adapted for use in more automated data processing pipelines through direct access of core processing routines.

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[This corrects the article DOI: 10.3389/fnins.2021.769872.].

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Accurate anatomical localization of intracranial electrodes is important for identifying the seizure foci in patients with epilepsy and for interpreting effects from cognitive studies employing intracranial electroencephalography. Localization is typically performed by coregistering postimplant computed tomography (CT) with preoperative magnetic resonance imaging (MRI). Electrodes are then detected in the CT, and the corresponding brain region is identified using the MRI. Many existing software packages for electrode localization chain together separate preexisting programs or rely on command line instructions to perform the various localization steps, making them difficult to install and operate for a typical user. Further, many packages provide solutions for some, but not all, of the steps needed for confident localization. We have developed software, Locate electrodes Graphical User Interface (LeGUI), that consists of a single interface to perform all steps needed to localize both surface and depth/penetrating intracranial electrodes, including coregistration of the CT to MRI, normalization of the MRI to the Montreal Neurological Institute template, automated electrode detection for multiple types of electrodes, electrode spacing correction and projection to the brain surface, electrode labeling, and anatomical targeting. The software is written in MATLAB, core image processing is performed using the Statistical Parametric Mapping toolbox, and standalone executable binaries are available for Windows, Mac, and Linux platforms. LeGUI was tested and validated on 51 datasets from two universities. The total user and computational time required to process a single dataset was approximately 1 h. Automatic electrode detection correctly identified 4362 of 4695 surface and depth electrodes with only 71 false positives. Anatomical targeting was verified by comparing electrode locations from LeGUI to locations that were assigned by an experienced neuroanatomist. LeGUI showed a 94% match with the 482 neuroanatomist-assigned locations. LeGUI combines all the features needed for fast and accurate anatomical localization of intracranial electrodes into a single interface, making it a valuable tool for intracranial electrophysiology research.

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The Python Modular Neural Network Toolbox (PymoNNto) provides a versatile and adaptable Python-based framework to develop and investigate brain-inspired neural networks. In contrast to other commonly used simulators such as Brian2 and NEST, PymoNNto imposes only minimal restrictions for implementation and execution. The basic structure of PymoNNto consists of one network class with several neuron- and synapse-groups. The behaviour of each group can be flexibly defined by exchangeable modules. The implementation of these modules is up to the user and only limited by Python itself. Behaviours can be implemented in Python, Numpy, Tensorflow, and other libraries to perform computations on CPUs and GPUs. PymoNNto comes with convenient high level behaviour modules, allowing differential equation-based implementations similar to Brian2, and an adaptable modular Graphical User Interface for real-time observation and modification of the simulated network and its parameters.

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The current study utilized an artificial neural network (ANN) to generate computational models to achieve process optimization for a previously developed continuous liposome manufacturing system. The liposome formation was based on a continuous manufacturing system with a co-axial turbulent jet in a co-flow technology. The ethanol phase with lipids and aqueous phase resulted in liposomes of homogeneous sizes. The input features of the ANN included critical material attributes (CMAs) (e.g., hydrocarbon tail length, cholesterol percent, and buffer type) and critical process parameters (CPPs) (e.g., solvent temperature and flow rate), while the ANN outputs included critical quality attributes (CQAs) of liposomes (i.e., particle size and polydispersity index (PDI)). Two common ANN architectures, multiple-input-multiple-output (MIMO) models and multiple-input-single-output (MISO) models, were evaluated in this study, where the MISO outperformed MIMO with improved accuracy. Molecular descriptors, obtained from PaDEL-Descriptor software, were used to capture the physicochemical properties of the lipids and used in training of the ANN. The combination of CMAs, CPPs, and molecular descriptors as inputs to the MISO ANN model reduced the training and testing mean relative error. Additionally, a graphic user interface (GUI) was successfully developed to assist the end-user in performing interactive simulated risk analysis and visualizing model predictions.

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Colloidal settlement in natural aqueous suspensions is effectively compensated by diffusive movement if particles resist aggregation - a state known as colloidal stability. However, if the settling velocity increases upon aggregation, complex structural features emerge from the directional movement induced by gravity. We present a comprehensive modeling study on the evolution of an aggregated three-dimensional structure due to diffusion, surface interactions, and gravity. The systematic investigation of particle geometry and size revealed three mechanisms: (I) aggregation due to spatial confinement of settled particles, (II) aggregation due to differential settling, whereby fast and slow particles collide, (III) inhibition of aggregation due to fractionation of particles with different settling velocity. A 3D visualization tool allowed us to follow the subtle interplay of these mechanisms and the highly dynamic hierarchical self-assembly of aggregates. It revealed how the balance of the different interactions determines the actual rate of aggregation.

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The analysis of the SARS-CoV-2 genome datasets has significantly advanced our understanding of the biology and genomic adaptability of the virus. However, the plurality of advanced sequencing datasets-such as short and long reads-presents a formidable computational challenge to uniformly perform quantitative, variant or phylogenetic analysis, thus limiting its application in public health laboratories engaged in studying epidemic outbreaks. We present a computational tool, Infectious Pathogen Detector (IPD), to perform integrated analysis of diverse genomic datasets, with a customized analytical module for the SARS-CoV-2 virus. The IPD pipeline quantitates individual occurrences of 1060 pathogens and performs mutation and phylogenetic analysis from heterogeneous sequencing datasets. Using IPD, we demonstrate a varying burden (5.055-999655.7 fragments per million) of SARS-CoV-2 transcripts across 1500 short- and long-read sequencing SARS-CoV-2 datasets and identify 4634 SARS-CoV-2 variants (~3.05 variants per sample), including 449 novel variants, across the genome with distinct hotspot mutations in the ORF1ab and S genes along with their phylogenetic relationships establishing the utility of IPD in tracing the genome isolates from the genomic data (as accessed on 11 June 2020). The IPD predicts the occurrence and dynamics of variability among infectious pathogens-with a potential for direct utility in the COVID-19 pandemic and beyond to help automate the sequencing-based pathogen analysis and in responding to public health threats, efficaciously. A graphical user interface (GUI)-enabled desktop application is freely available for download for the academic users at http://www.actrec.gov.in/pi-webpages/AmitDutt/IPD/IPD.html and for web-based processing at http://ipd.actrec.gov.in/ipdweb/ to generate an automated report without any prior computational know-how.

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BACKGROUND: Neuromuscular Electrical Stimulation (NMES) has been utilized for many years in cerebral palsy (CP) with limited success despite its inherent potential for improving muscle size and/or strength, inhibiting or reducing spasticity, and enhancing motor performance during functional activities such as gait. While surface NMES has been shown to successfully improve foot drop in CP and stroke, correction of more complex gait abnormalities in CP such as flexed knee (crouch) gait remains challenging due to the level of stimulation needed for the quadriceps muscles that must be balanced with patient tolerability and the ability to deliver NMES assistance at precise times within a gait cycle. METHODS: This paper outlines the design and evaluation of a custom, noninvasive NMES system that can trigger and adjust electrical stimulation in real-time. Further, this study demonstrates feasibility of one possible application for this digitally-controlled NMES system as a component of a pediatric robotic exoskeleton to provide on-demand stimulation to leg muscles within specific phases of the gait cycle for those with CP and other neurological disorders who still have lower limb sensation and volitional control. A graphical user interface was developed to digitally set stimulation parameters (amplitude, pulse width, and frequency), timing, and intensity during walking. Benchtop testing characterized system delay and power output. System performance was investigated during a single session that consisted of four overground walking conditions in a 15-year-old male with bilateral spastic CP, GMFCS Level III: (1) his current Ankle-Foot Orthosis (AFO); (2) unassisted Exoskeleton; (3) NMES of the vastus lateralis; and (4) NMES of the vastus lateralis and rectus femoris. We hypothesized in this participant with crouch gait that NMES triggered with low latency to knee extensor muscles during stance would have a modest but positive effect on knee extension during stance. RESULTS: The system delivers four channels of NMES with average delays of 16.5 ± 13.5 ms. Walking results show NMES to the vastus lateralis and rectus femoris during stance immediately improved mean peak knee extension during mid-stance (p = 0.003*) and total knee excursion (p = 0.009*) in the more affected leg. The electrical design, microcontroller software and graphical user interface developed here are included as open source material to facilitate additional research into digitally-controlled surface stimulation ( github.com/NIHFAB/NMES ). CONCLUSIONS: The custom, digitally-controlled NMES system can reliably trigger electrical stimulation with low latency. Precisely timed delivery of electrical stimulation to the quadriceps is a promising treatment for crouch. Our ultimate goal is to synchronize NMES with robotic knee extension assistance to create a hybrid NMES-exoskeleton device for gait rehabilitation in children with flexed knee gait from CP as well as from other pediatric disorders. TRIAL REGISTRATION: clinicaltrials.gov, ID: NCT01961557 . Registered 11 October 2013; Last Updated 27 January 2020.

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The abundance of RNA species and their response to perturbations are set by the kinetics rates of RNA synthesis, processing, and degradation. However, the visualization, interpretation, and manipulation of these data require familiarity with mathematical modeling and command line tools. INSPEcT-GUI is an R-Shiny interface that allows researchers without specific training to effortlessly explore how the fine kinetic regulation of the RNA life cycle can shape gene expression programs. In particular, it allows to: (i) interactively visualize gene-level RNA dynamics; (ii) refine the model fit of experimental data; (iii) test alternative regulatory models; (iv) explore, independently from the availability of data, how the combined action of the RNA kinetic rates impacts on premature and mature RNA. INSPEcT-GUI is freely available within the R/Bioconductor package INSPEcT at http://bioconductor.org/packages/INSPEcT/. An HTML vignette including documentation on the tool startup and usage, executable examples, and a video demonstration, are available at: http://bioconductor.org/packages/release/bioc/vignettes/INSPEcT/inst/doc/INSPEcT_GUI.html.

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Early diagnosis of SARS-CoV-2 infected patients is essential to control the dynamics of the COVID-19 pandemic. We develop a rapid and accurate one-step multiplex TaqMan probe-based real-time RT-PCR assay, along with a computational tool to systematically analyse the data. Our assay could detect to a limit of 15 copies of SARS-CoV-2 transcripts-based on experiments performed by spiking total human RNA with in vitro synthesized viral transcripts. The assay was evaluated by performing 184 validations for the SARS-CoV-2 Nucleocapsid gene and human RNase P as an internal control reference gene with dilutions ranging from 1-100 ng for human RNA on a cohort of 26 clinical samples. 5 of 26 patients were confirmed to be infected with SARS-CoV-2, while 21 tested negative, consistent with the standards. The accuracy of the assay was found to be 100% sensitive and 100% specific based on the 26 clinical samples that need to be further verified using a large number of clinical samples. In summary, we present a rapid, easy to implement real-time PCR based assay with automated analysis using a novel COVID qPCR Analyzer tool with graphical user interface (GUI) to analyze the raw qRT-PCR data in an unbiased manner at a cost of under $3 per reaction and turnaround time of less than 2h, to enable in-house SARS-CoV-2 testing across laboratories.

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OBJECTIVES: Develop an effective and intuitive Graphical User Interface (GUI) for a Brain-Computer Interface (BCI) system, that achieves high classification accuracy and Information Transfer Rates (ITRs), while using a simple classification technique. Objectives also include the development of an output device, that is capable of real time execution of the selected commands. METHODS: A region based T9 BCI system with familiar face presentation cues capable of eliciting strong P300 responses was developed. Electroencephalogram (EEG) signals were collected from the Oz, POz, CPz and Cz electrode locations on the scalp and subsequently filtered, averaged and used to extract two features. These feature sets were classified using the Nearest Neighbour Approach (NNA). To complement the developed BCI system, a 'drone prototype' capable of simulating six different movements, each over a range of eight distinct selectable distances, was also developed. This was achieved through the construction of a body with 4 movable legs, capable of tilting the main body forward, backward, up and down, as well as a pointer capable of turning left and right. RESULTS: From ten participants, with normal or corrected to normal vision, an average accuracy of 91.3 ± 4.8% and an ITR of 2.2 ± 1.1 commands/minute (12.2 ± 6.0 bits/minute) was achieved. CONCLUSION: The proposed system was shown to elicit strong P300 responses. When compared to similar P300 BCI systems, which utilise a variety of more complex classifiers, competitive accuracy and ITR results were achieved, implying the superiority of the proposed GUI. SIGNIFICANCE: This study supports the hypothesis that more research, time and care should be taken when developing GUIs for BCI systems.

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An essential aspect of scientific reproducibility is a coherent and complete acquisition of metadata along with the actual data of an experiment. The high degree of complexity and heterogeneity of neuroscience experiments requires a rigorous management of the associated metadata. The odML framework represents a solution to organize and store complex metadata digitally in a hierarchical format that is both human and machine readable. However, this hierarchical representation of metadata is difficult to handle when metadata entries need to be collected and edited manually during the daily routines of a laboratory. With odMLtables, we present an open-source software solution that enables users to collect, manipulate, visualize, and store metadata in tabular representations (in xls or csv format) by providing functionality to convert these tabular collections to the hierarchically structured metadata format odML, and to either extract or merge subsets of a complex metadata collection. With this, odMLtables bridges the gap between handling metadata in an intuitive way that integrates well with daily lab routines and commonly used software products on the one hand, and the implementation of a complete, well-defined metadata collection for the experiment in a standardized format on the other hand. We demonstrate usage scenarios of the odMLtables tools in common lab routines in the context of metadata acquisition and management, and show how the tool can assist in exploring published datasets that provide metadata in the odML format.

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A typical molecular cloning procedure requires Sanger sequencing for sequence validation, which is cost-prohibitive and labor-intensive for large-scale clone analysis in genotype-phenotype studies. Here we present the cost-effective clone analysis platform TnClone, which uses next-generation sequencing based on Tn5 tagmentation to rapidly analyze a large number of clones from cell lysates. This method bypasses the extensive plasmid purification step. We also developed a user-friendly graphical user interface and provided general guidelines for conducting validation experiments. We tested our program with 1023 plasmids (222 from cell lysates and 801 from purified clones) and achieved 92% and 99.3% sensitivity with cell lysates and purified DNA, respectively. Our platform provides rapid turnaround with minimal hands-on time for secondary evaluation, as next-generation sequencing technology continues to evolve.

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BACKGROUND: Around 125,785 new cases in year 2013-14 of leprosy were detected in India as per WHO report on leprosy in September 2015 which accounts to approximately 62% of the total new cases. Anaesthetic foot caused by leprosy leads to uneven loading of foot leading to ulcer in approximately 20% of the cases. Much efforts have gone in identifying newer techniques to efficiently monitor the progress of ulcer healing. Current techniques followed in measuring the size of ulcers, have not been found to be so accurate but are still is followed by clinicians across the globe. Quantification of prognosis of the condition would be required to understand the efficacy of current treatment methods and plan for further treatment. This study aims at developing a non contact technique to precisely measure the size of ulcer in patients affected by leprosy. METHODS: Using MATLAB software, GUI was designed to process the acquired ulcer image by segmenting and calculating the pixel area of the image. The image was further converted to a standard measurement using a reference object. The developed technique was tested on 16 ulcer images acquired from 10 leprosy patients with plantar ulcers. Statistical analysis was done using MedCalc analysis software to find the reliability of the system. RESULTS: The analysis showed a very high correlation coefficient (r=0.9882) between the ulcer area measurements done using traditional technique and the newly developed technique, The reliability of the newly developed technique was significant with a significance level of 99.9%. CONCLUSIONS: The designed non-contact ulcer area calculating system using MATLAB is found to be a reliable system in calculating the size of ulcers. The technique would help clinicians have a reliable tool to monitor the progress of ulcer healing and help modify the treatment protocol if needed.

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OBJECTIVE: This paper proposes the development of a breast biopsy navigation system with an assisted needle holder tool for a coaxial needle and a graphical user interface, which utilizes an optical tracking device to localize the needle position relative to the ultrasound image with the aim to improve performance especially for a new radiologist or an inexperienced group. MATERIALS AND METHODS: The system consists of an assisted needle holder tool, which as an attachment for the 2D ultrasound transducer and the graphical user interface (GUI) that shows the needle pathway, needle line and warning signs. An optical tracking system is used to track the needle motion, ultrasound image and transform all information to with respect to the technique. The system is evaluated using a phantom made from gel candle. There were nine experienced and eight inexperienced participants who performed the breast biopsy intervention, using three methods: the freehand method, only the needle holder tool guidance, and the whole navigation guidance (GUI + assisted needle holder). RESULTS: The results demonstrate a success rate of over 90% using only assisted needle holder and the whole system to perform breast biopsy for the experienced and inexperienced groups, whereas for the inexperienced group a success rate of 57.5% was achieved using the freehand method. The use of only assisted needle holder for breast biopsy reduces the time for a procedure in the inexperienced group by 6 s when compared to the freehand method. CONCLUSION: The authors believe that this navigation system can be applied in a clinical setting and give an advantage to inexperienced radiologists who must successfully perform clinical breast biopsy.

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A Brain-Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.