User-friendly Software Research Articles

AnnoSM: An Automated Annotation Tool for Determining the Substituent Modes on the Parent Skeleton Based on a Characteristic MS/MS Fragment Ion Library.

Mass spectrometry (MS) is a powerful technology for the structural elucidation of known or unknown small molecules. However, the accuracy of MS-based structure annotation is still limited due to the presence of numerous isomers in complex matrices. There are still challenges in automatically interpreting the fine structure of molecules, such as the types and positions of substituents (substituent modes, SMs) in the structure. In this study, we employed flavones, flavonols, and isoflavones as examples to develop an automated annotation method for identifying the SMs on the parent molecular skeleton based on a characteristic MS/MS fragment ion library. Importantly, user-friendly software AnnoSM was built for the convenience of researchers with limited computational backgrounds. It achieved 76.87% top-1 accuracy on the 148 authentic standards. Among them, 22 sets of flavonoid isomers were successfully differentiated. Moreover, the developed method was successfully applied to complex matrices. One such example is the extract of Ginkgo biloba L. (EGB), in which 331 possible flavonoids with SM candidates were annotated. Among them, 23 flavonoids were verified by authentic standards. The correct SMs of 13 flavonoids were ranked first on the candidate list. In the future, this software can also be extrapolated to other classes of compounds.

A Very Deep Graph Convolutional Network for 13C NMR Chemical Shift Calculations with Density Functional Theory Level Performance for Structure Assignment.

Nuclear magnetic resonance (NMR) chemical shift calculations are powerful tools for structure elucidation and have been extensively employed in both natural product and synthetic chemistry. However, density functional theory (DFT) NMR chemical shift calculations are usually time-consuming, while fast data-driven methods often lack reliability, making it challenging to apply them to computationally intensive tasks with a high requirement on quality. Herein, we have constructed a 54-layer-deep graph convolutional network for 13C NMR chemical shift calculations, which achieved high accuracy with low time-cost and performed competitively with DFT NMR chemical shift calculations on structure assignment benchmarks. Our model utilizes a semiempirical method, GFN2-xTB, and is compatible with a broad variety of organic systems, including those composed of hundreds of atoms or elements ranging from H to Rn. We used this model to resolve the controversial J/K ring junction problem of maitotoxin, which is the largest whole molecule assigned by NMR calculations to date. This model has been developed into user-friendly software, providing a useful tool for routine rapid structure validation and assignation as well as a new approach to elucidate the large structures that were previously unsuitable for NMR calculations.

Analysis of Sleep and Circadian Rhythms from Drosophila Activity-Monitoring Data Using SCAMP.

Sleep is a fundamental feature of life for virtually all multicellular animals, but many questions remain about how sleep is regulated and what biological functions it plays. Substantial headway has been made in the study of both circadian rhythms and sleep in the fruit fly Drosophila melanogaster, much of it through studies of individual fly activity using beam break counts from Drosophila activity monitors (DAMs). The number of laboratories worldwide studying sleep in Drosophila has grown from only a few 20 years ago to hundreds today. The utility of these studies is limited by the quality of the metrics that can be extracted from the data. Many software options exist to help analyze DAM data; however, these are often expensive or have significant limitations. Therefore, we describe here a method for analyzing DAM-based data using the sleep and circadian analysis MATLAB program (SCAMP). This user-friendly software has an advantage of combining several analyses of both sleep and circadian rhythms in one package and produces graphical outputs as well as spreadsheets of the outputs for further statistical analysis. The version of SCAMP described here is also the first published software package that can analyze data from multibeam DAM5Ms, enabling determination of positional preference over time.

Managing and Billing Software for Hypermarket

It previews the whole system design while in the free demo session and clear your queries by experiencing Optech’s digital dashboard. In the navigation system, we optimize the UI to be straightforward and comprehend for all sets of people so the employees grasp this entire operation. The Optech software has been developed with VB.net and MY SQL Server. The system is efficient in generating reports, which will help in the maintenance of the hypermarket easily. This Optech software can store a lot of records in the database. It’s a user-friendly software, which can be used for service, sales and report. It has an unlimited storage and application can be used in both online and offline. Reports for billing can be generated by data wise or pricewise.

Dealer Management System

To keep track of all the things happening in the showroom, such as customer maintenance, generating quotations, selling vehicles, tracking customer dues for instalment customers, selling vehicle service and spare parts, keeping track of customer follow-ups, entering customer feedback forms, and employee details, a "Dealer Management System" needs to be built. The Developers behind the Dealer Management System used Visual Studio 2008 (with ASP.NET 3.5), Visual Basic for Windows, and Microsoft SQL Server Express 2005. Easy showroom maintenance is possible because to the system's efficient report generation. This Dealer Management System software can store a lot of records in the database. It’s a user-friendly software which can be used for master, service, sales and report. It has an unlimited storage and application can be used in both online and offline. Reports for billing can be generated by data wise or pricewise.

Muscle geometry and its relevance for sports performance? A perspective of current findings and future opportunities

Muscle geometry and its relevance for sports performance? A perspective of current findings and future opportunities

Implementation of thyroid eye disease registry in Iran: rationale and research protocol.

To describe the implementation of a registry system for patients with thyroid eye disease (TED) in Iran to obtain more information about its nature, prevalence, and annual incidence, as well as extend insight into the etiology, pathogenesis, and eventually make an accurate prognosis of different medical or surgical treatment methods. After receiving approval from the Disease Registry Committee of Iran University of Medical Sciences (IUMS) in 2019 and the Ministry of Health and Medical Education (MOHME) in 2020, the protocol was introduced in three consecutive phases at regional, provincial and national levels. The establishment of a registry committee in Rassoul Akram Hospital, one of the medical centers affiliated to IUMS, was the first step to organizing the registry project's main core. The steering committee included six subgroups of required subject fields. The members are experts in developing a guideline, providing a new dataset, drawing an outline for the next steps, and structuring user-friendly software through several panel discussion meetings. The data is collected from clinical and para-clinical/imaging findings, laboratory evaluations, and their selected treatment strategy, retrospectively and prospectively. The purpose is to broaden our knowledge about the profile of TED; accordingly, data related to patients' demographics, thyroid gland disease (status, duration, treatments, and function tests), general medical and ocular history, along with visual/ocular exams resulting TED status are collected and recorded in a 2- language software. The web-based software system is accessible at https://orc.iums.ac.ir . To maintain data security, prioritized user access was defined for different members. Furthermore, diverse methods, such as employing trained staff and utilizing software validation rules, were implemented to control data quality in every step of data collection, entry, and registration. Medical records of retrospective subjects were also evaluated and entered after accuracy verification. Iran's TED registry provides practitioners with comprehensive data on natural history and phenotype variations in clinical features and outcomes. It facilitates patient recruitment and, consequently, earlier diagnosis on a large scale which helps improve treatment and quality of life for patients.

Conceptualizing and Validating the Trustworthiness of Maps through an Empirical Study on the Influence of Cultural Background on Map Design Perception

Maps are the culmination of numerous choices, with many offering multiple alternatives. Not all of these choices are inherently guided by default, clarity, or universally accepted best practices, guidelines, or recommendations. In the realm of cartography, it is a distinct feature that individual decisions can be made, particularly regarding data preparation and selection and design aspects. As each map is a product of a multitude of decisions, the confidence we place in maps hinges on the reasonableness of these decisions. The trustworthiness of maps depends on whether these decisions are sound, unquestioned, readily accessible, and supported by dependable groups of decision makers whose reliability can be assessed based on their track record as an institution, reputation, and competence. The advent of user-friendly map-making software and data manipulation tools has placed some of these decisions in the hands of the general populace and those interested in using maps to convey specific agendas. This mirrors other forms of communication and has given rise to a growing discourse on “fake news”, “fake media”, and “fake maps”, ultimately prompting us to question how we can trust the information being conveyed and how we can differentiate between “fake” and “trustworthy” maps. This paper highlights the fundamental aspects determined by the pure nature of cartographic modeling, which influences every attempt to understand, analyze, and express the context and trustworthiness of maps. It then identifies fundamental aspects of trustworthiness with respect to maps. Combining these two fundamental considerations represents an epistemological attempt to identify a research portfolio. An example of an empirical study on identifying selected aspects of this portfolio demonstrates the potential of gaining a better understanding of the context given.

A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps

Abstract Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS)—the BIDS App—has provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging—especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad—a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here, we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n = 2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model.

Advancing Reservoir Performance Optimization through UserFriendly Excel VBA Software Development

This study addresses the pressing demand for streamlined field performance analysis within oil and natural gas development, which currently necessitates substantial expertise and time investment. The principal aim involves developing a user-friendly software tool dedicated to optimizing reservoir rendition. Leveraging the Havlena and Odeh material balance straight line equation form, this tool integrates a zero-dimensional reservoir model with Decline Curve Analysis. The implementation of this user-friendly software enables achievable material balance optimization by aligning cumulative produced fluid with historical production data, akin to the widely acknowledged concept of history matching in material balance analysis. This accomplishment not only facilitates further endeavors like pressure simulation and forecasting but also augments the comprehension of reservoir dynamics. The analysis incorporated three datasets: one modeled from L.P. Dake's textbook and two drawn from real-life reservoirs in the Niger Delta. Assessment of estimated water influx and cumulative oil production indicated minimal discrepancies between Np Real and Np model for these reservoirs. Consequently, material balance history matching for these reservoirs seems feasible. Achieving reservoir rendition optimization involved a Microsoft Excel VBA code consisting of two hundred and thirty-five (235) lines, meticulously designed to replicate MBAL functionality. The software demonstrated congruent outcomes with MBAL, affirming its reliability for history matching and enhancing reservoir performance. We strongly advocate the utilization of this software for optimizing reservoir performance across diverse global regions. Its capacity to streamline field analysis could significantly benefit the oil and natural gas industry.

Development of A Neural Boosted Model and JSL Code to Identify “Clean” or “Not Clean” Wells - A West Texas Sperry and Oklahoma Woodford Fractured Wells Coiled Tubing Cleaning Case Study

In a previous study, wellbore cleaning coefficient (WCC) correlations for cleaned wellbores out of debris and bridge plug remnants were developed for three conventional coiled tubing sizes (2.375”, 2.625”, and 2.875”). The following key performance indicators (KPIs): (1) slick water density ( ) ρ f , (2) slick water viscosity ( ) µ f , (3) hydraulic diameter c t (d - d ) between casing inner diameter (dc ) and coil tubing outer diameter (dt ), (4) average annular velocity ( ) v and (5) cleaning pressure gradient ∆P across a measured depth (MD) were employed in the empirical models. The models addressed operational conditions under which fractured wells will be identified as whether “clean” or “not clean”. In this study, the database from 150 wells, in the Spraberry formation in West Texas, was used to develop a predictive model to identify status of cleaned fractured wells: whether “clean” or “not clean”? About 70% of the data (99 wells) was used for training and about 30% (51 wells) for validation. 14 wells from the liquids-rich shale Woodford formation (Oklahoma) were utilized for testing. Six predictive modeling tools were designed to validate the derived empirical correlations. These tools are (1) Fit Stepwise, (2) Neural Boosted, (3) Boosted Tree, (4) Decision Tree (Partition), (5) Generalized Regression Lasso, and K-Nearest Neighbors. In the predictive models, independent variables are the annular velocity (AV), the Reynolds’ Number (Re), the Euler’s Number (Eu), and the coiled tubing roughness to internal radius ratio (ε/D). The dependent variable is well status; “clean” or “not clean”. Jump Scripting Language (JSL) code was used to develop user-friendly software. The software would be utilized to identify the fractured wellbore status, whether “clean” or “not clean”. Operators would be able to use the code to identify working conditions for which completed fractured wells are “clean” out of fracturing debris and remnants of bridge plugs or “not clean”. Input parameters to the code are AV, Re, Eu, and ε/D

ProbBreed: a novel tool for calculating the risk of cultivar recommendation in multienvironment trials.

Neglecting genotype-by-environment interactions in multienvironment trials (MET) increases the risk of flawed cultivar recommendations for growers. Recent advancements in probability theory coupled with cutting-edge software offer a more streamlined decision-making process for selecting suitable candidates across diverse environments. Here, we present the user-friendly ProbBreed package in R, which allows breeders to calculate the probability of a given genotype outperforming competitors under a Bayesian framework. This article outlines the package's basic workflow and highlights its key features, ranging from MET model fitting to estimating the per se and pairwise probabilities of superior performance and stability for selection candidates. Remarkably, only the selection intensity is required to compute these probabilities. By democratizing this complex yet efficient methodology, ProbBreed aims to enhance decision-making and ultimately contribute to more accurate cultivar recommendations in breeding programs.

Diagnostic Utility of Integration of Dynamic Contrast-Enhanced and Dynamic Susceptibility Contrast MR Perfusion Employing Split Bolus Technique in Differentiating High-Grade Glioma.

Background : Despite documented correlation between glioma grades and dynamic contrast-enhanced (DCE) magnetic resonance (MR) perfusion-derived parameters, and its inherent advantages over dynamic susceptibility contrast (DSC) perfusion, the former remains underutilized in clinical practice. Given the inherent spatial heterogeneity in high-grade diffuse glioma (HGG) and assessment of different perfusion parameters by DCE (extravascular extracellular space volume [Ve] and volume transfer constant in unit time [k-trans]) and DSC (rCBV), integration of the two into a protocol could provide a holistic assessment. Considering therapeutic and prognostic implications of differentiating WHO grade 3 from 4, we analyzed the two grades based on a combined DCE and DSC perfusion. Methods : Perfusion sequences were performed on 3-T MR. Cumulative dose of 0.1 mmol/kg of gadodiamide, split into two equal boluses, was administered with an interval of 6 minutes between the DCE and DSC sequences. DCE data were analyzed utilizing commercially available GenIQ software. Results : Of the 41 cases of diffuse gliomas analyzed, 24 were WHO grade III and 17 grade IV gliomas (2016 WHO classification). To differentiate grade III and IV gliomas, Ve cut-off value of 0.178 provided the best combination of sensitivity (88.24%) and specificity (87.50%; AUC: 0.920; p < 0.001). A relative cerebral blood volume (rCBV) of value 3.64 yielded a sensitivity of 70.59% and specificity of 62.50% ( p = 0.018). The k-trans value, although higher in grade III than in grade IV gliomas, did not reach statistical significance ( p = 0.108). Conclusion : Uniqueness of employed combined perfusion technique, treatment naïve patients at imaging, user-friendly postprocessing software utilization, and ability of Ve and rCBV to differentiate between grade III and IV gliomas ( p < 0.05) are the strengths of the present study, contributing to the existing literature and moving a step closer to achieving accurate MR perfusion-based glioma grading.

The dynamic universe: realizing the potential of classical time domain and multimessenger astrophysics

In parallel with the multi-messenger revolution, major advances in time-domain astronomy across multiple science disciplines relevant to astrophysics are becoming more urgent to address. Aside from electromagnetic observations of gravitational wave events and explosive counterparts, there are a number of “classical” astrophysical areas that require new thinking for proper exploration in the time domain. How NASA, NSF, ESA, and ESO consider the 2020 USA Decadal Survey within the astronomy community, as well as the worldwide call to support and expand time domain and multi-messenger astrophysics, it is crucial that all areas of astrophysics, including stellar, galactic, Solar System, and exoplanetary science participate in the discussion, and that it not be made into an exclusive preserve of cosmological, high-energy, explosive and transient science. Time domain astronomy is used to explore many aspects of astrophysics–particularly concerning ground- and space-based mission science goals of exploring how the Universe works, understanding how did we get here, and are we alone. Time domain studies are already built into the core operations of many currently operating and future space telescopes (e.g., Roman, PLATO) as well as current and planned large areal ground-based surveys (e.g., Rubin). Time-domain observations designed for one scientific purpose, also lead to great discoveries in many other science areas. The recent advent of user-friendly hardware, software, observational approaches, and online data infrastructure has also helped make time domain observations especially suitable and appealing for citizen science projects. We provide a review of the current state of TDAMM alerts and observational protocols, revealing a wide array of software and applications, much of which is incompatible. Any conversation regarding TDAMM astrophysics should include all aspects of the field, including those aspects seen as classical applications.

SONIA: an immersive customizable virtual reality system for the education and exploration of brain networks

While mastery of neuroanatomy is important for the investigation of the brain, there is an increasing interest in exploring the neural pathways to better understand the roles of neural circuitry in brain functions. To tackle the limitations of traditional 2D-display-based neuronavigation software in intuitively visualizing complex 3D anatomies, several virtual reality (VR) and augmented reality (AR) solutions have been proposed to facilitate neuroanatomical education. However, with the increasing knowledge on brain connectivity and the functioning of the sub-systems, there is still a lack of similar software solutions for the education and exploration of these topics, which demand more elaborate visualization and interaction strategies. To address this gap, we designed the immerSive custOmizable Neuro learnIng plAtform (SONIA), a novel, user-friendly VR software system with a multi-scale interaction paradigm that allowed flexible customization of learning materials. With both quantitative and qualitative evaluations through user studies, the proposed system was shown to have high usability, attractive visual design, and good educational value. As the first immersive system that integrated customizable design and detailed narratives of the brain sub-systems for the education of neuroanatomy and brain connectivity, SONIA showcased new potential directions and provided valuable insights regarding medical learning and exploration in VR.

ExpoSeq: simplified analysis of high-throughput sequencing data from antibody discovery campaigns.

High-throughput sequencing (HTS) offers a modern, fast, and explorative solution to unveil the full potential of display techniques, like antibody phage display, in molecular biology. However, a significant challenge lies in the processing and analysis of such data. Furthermore, there is a notable absence of open-access user-friendly software tools that can be utilized by scientists lacking programming expertise. Here, we present ExpoSeq as an easy-to-use tool to explore, process, and visualize HTS data from antibody discovery campaigns like an expert while only requiring a beginner's knowledge. The pipeline is distributed via GitHub and PyPI, and it can either be installed as a package with pip or the user can choose to clone the repository.

TrajPy: empowering feature engineering for trajectory analysis across domains.

Trajectories, which are sequentially measured quantities that form a path, are an important presence in many different fields, from hadronic beams in physics to electrocardiograms in medicine. Trajectory analysis requires the quantification and classification of curves, either by using statistical descriptors or physics-based features. To date, no extensive and user-friendly package for trajectory analysis has been readily available, despite its importance and potential application across various domains. We have developed TrajPy, a free, open-source Python package that serves as a complementary tool for empowering trajectory analysis. This package features a user-friendly graphical user interface and offers a set of physical descriptors that aid in characterizing these complex structures. TrajPy has already been successfully applied to studies of mitochondrial motility in neuroblastoma cell lines and the analysis of in silico models for cell migration, in combination with image analysis. The TrajPy package is developed in Python 3 and is released under the GNU GPL-3.0 license. It can easily be installed via PyPi, and the development source code is accessible at the repository:https://github.com/ocbe-uio/TrajPy/. The package release is also automatically archived with the DOI 10.5281/zenodo.3656044.

IPECM Platform: An open-source software for greenhouse environment regulation using machine learning and optimization algorithm

Protected agriculture has emerged as a key solution to address the pressing issue of food scarcity. To enhance crop yield, environmental regulation techniques have been widely employed in protected production. However, the absence of user-friendly, data-driven tools for developing regulation models remains a challenge. This study aims to propose IPECM, an independent and user-friendly software platform for processing and analyzing crop photosynthetic rate (Pn) data and formulating environmental regulation targets. The platform provides functionalities, such as Pn prediction model development, environmental regulation model development and result visualization, supporting various machine learning algorithms and regulation target obtaining algorithms. The IPECM Platform's application is demonstrated through examples of light intensity regulation for cucumber growth and CO2 concentration regulation for tomato growth. The results showcase the software's ability to handle photosynthetic data of any dimension, with the established Pn prediction model achieving a coefficient of determination of 0.98 and a root mean square error lower than 1 μmol·m−2·s−1. The established regulation models can achieve maximum Pn or optimal energy utilization efficiency according to user requirements. IPECM Platform is an independent, automated, and open-source software for protected environmental regulation modeling, providing both the modeling process and results visualization. It offers valuable services for protected agriculture research, eliminating the need for programming knowledge.

Human-verifiable proofs in the theory of word-representable graphs

A graph is word-representable if it can be represented in a certain way using alternation of letters in words. Word-representable graphs generalise several important and well-studied classes of graphs, and they can be characterised by semi-transitive orientations. Recognising word-representability is an NP-complete problem, and the bottleneck of the theory of word-representable graphs is convincing someone that a graph is non-word-representable, keeping in mind that references to (even publicly available and user-friendly) software are not always welcome. (Word-representability can be justified by providing a semi-transitive orientation as a certificate that can be checked in polynomial time). In the literature, a variety of (usually ad hoc) proofs of non-word-representability for particular graphs, or families of graphs, appear, but for a randomly selected graph, one should expect looking at O(2#edges) orientations and justifying that none of them is semi-transitive. Even if computer would print out all these orientations and would point out what is wrong with each of the orientations, such a proof would be essentially non-checkable by a human. In this paper, we develop methods for an automatic search of human-verifiable proofs of graph non-word-representability. As a proof-of-concept, we provide “short” proofs of non-word-representability, generated automatically by our publicly available user-friendly software, of the Shrikhande graph on 16 vertices and 48 edges (6 “lines” of proof) and the Clebsch graph on 16 vertices and 40 edges (10 “lines” of proof). Producing such short proofs for relatively large graphs would not be possible without the instrumental tool we introduce (allowing to assume orientations of several edges in a graph, not just one edge as it was previously used) that is a game changer in the area. As a bi-product of our studies, we correct two mistakes published multiple times (two graphs out of the 25 non-word-representable graphs on 7 vertices were actually word-representable, while two non-word-representable graphs on 7 vertices were missing).

ITaxoTools 1.0: Improved DNA Barcode Exploration with TaxI2.

The overall availability of user-friendly software tools tailored to the analysis of DNA barcodes is limited. Several obvious functions such as detecting and visualizing the DNA barcode gap, the calculation of matrices of pairwise distances at the level of species, or the filtering and decontaminating of sets of sequences based on comparisons with reference databases can typically be carried out only by complex procedures that involve various programs and/or a substantial manual work of formatting. The iTaxoTools project aims at contributing user-friendly software solutions to improve the speed and quality of the workflow of alpha-taxonomy. In this chapter, we provide detailed protocols for the use of a substantially improved version of the tool TaxI2 for distance-based exploration of DNA barcodes. The program calculates genetic distances from prealigned data sets, or based on pairwise alignments, or with an alignment-free approach. Sequence and metadata input can be formatted as tab-delimited files and TaxI2 then computes tables, matrices and graphs of distances, and distance summary statistics within and between species and genera. TaxI2 also includes modes to compare a set of sequences against one or two reference data sets and output lists of best matches or filter data according to thresholds or reciprocal matches. Here, detailed step-by-step protocols are provided for the use of TaxI2, as well as for the interpretation of the program's output.