CSV Files Research Articles

NIMG-54. AUTOMATED IMAGING RESPONSE EVALUATION SYSTEM (AIRES) FOR NEURO-ONCOLOGY: PROTOTYPE SOFTWARE TOOL FOR DETERMINING STANDARDIZED RADIOGRAPHIC RESPONSE ASSESSMENT FOR GLIOBLASTOMA CLINICAL TRIALS

Abstract Standardized radiographic response assessment criteria like Modified Response Assessment in Neuro-Oncology (mRANO) and now the RANO 2.0 are utilized for evaluating progression-free survival and objective response rate during glioblastoma clinical trials. After measuring tumor size with either bidimensional diameters or volumetric segmentations, the crucial and final time-consuming step of categorizing progression, stable disease, and response based on multiple criteria and numerical thresholds (e.g. ≥40% volumetric growth = progressive disease) is currently done manually. We developed an application prototype, Automated Imaging Response Evaluation System (AIRES) for Neuro-Oncology, that uses the mRANO criteria to label each timepoint as treatment response, stable disease, or disease progression based on either bidimensional or volumetric measurements as well as optional inputs of clinical status and corticosteroid dosage. These inputs may be uploaded via CSV files, which can integrate with automated tumor segmentation volumetry workflows, or entered in the AIRES graphical user interface (GUI). The AIRES GUI displays a patient report consisting of a table and graph that contain the lesion size, percent change from baseline, percent change from nadir, and the mRANO response category for each timepoint. To test AIRES in a real-world application, tumor size measurements from 367 scans across 41 patients from NUTMEG, a phase II multicenter clinical trial, were used to perform mRANO reads and calculate progression-free survival and time to response. AIRES-assisted reads performed by reader 1 were significantly quicker than fully manual reads performed by reader 2 (37.1±8.6 vs 107.7±58.6 seconds, P<0.0001). These results show that the AIRES app can make the reads quicker and potentially feasible for non-trained personnel, while reducing the amount of error-prone manual steps. Additionally, the underlying logic of AIRES is easily adaptable to integrate other response criteria including but not limited to RANO 2.0 and other future response assessment updates.

Real-time Vehicle Monitoring and Route Prediction using Geolocation through LSTM

Vehicle travel, such as by bus, automobile, etc., is a common way to use public transportation since it is convenient, economical, easily accessible, and eco-friendly. However, there are moments when its challenging to keep an eye on everyone while traveling, avoid traffic bottlenecks, and provide enough service, which makes the passengers bored. In this paper, we offer a real-time geolocation Android application that gathers real-time geolocation data (longitude and latitude), date, time, and speed, and integrates it with Google Firebase to assist predictive analytics for the operational efficiency of a vehicle firm. After being exported from the database as a JSON file, the data must be transformed into a CSV file. The company will be able to anticipate a vehicles future location on a predetermined route based on date, time, and speed by using this database to train an LSTM (Long short-term memory) model, a sort of sequential neural network. Determining the vehicles expected arrival time, allocating passengers, and predicting traffic conditions will therefore be useful in preventing congestion. Utilizing this technology allows a car company to improve scheduling, boost monitoring, and provide better overall service.

Long-Term Outdoor Cultivation of Nannochloropsis in California, Hawaii, and New Mexico

The project “Optimizing Selection Pressures and Pest Management to Maximize Cultivation Yield” (OSPREY, award #DE-EE08902) was undertaken to enhance the annual productivity, stability, and quality of algal production strains for biofuels and bioproducts. The foundation of this project was the year-round cultivation of a Nannochloropsis strain across three outdoor systems in California, Hawaii, and New Mexico. We aimed to leverage environmental selection pressures to drive strain improvement and use metagenomic techniques to inform pest management tools. The resulting dataset includes environmental and biological parameters from these cultivation campaigns, captured in a single CSV file. This dataset aims to serve a wide range of end users, from biologists to algal farmers, addressing the scarcity of publicly available data on algae cultivation. Further data releases will include 16S rRNA amplicon sequencing and shotgun sequencing datasets.

Face Recognition and Authentication using DLIB for Examination Attendance System

Facial recognition technology enables the identification and detection of individuals based on unique facial features. based on various labels such as name, ID number, etc. In this research, a face recognition system for examination attendance is implemented using the DLIB library. Since manual attendance is a tedious and time-consuming task, this research aims to automate the process, thereby increasing the integrity of examinations. The proposed solution reduces the workload of invigilators by eliminating the need to manually take attendance and fill out attendance sheets. The dataset for this research comprises a combination of student images captured by a camera and images from the database. The system utilizes the DLIB library to generate facial encodings, which are then used to identify students in real-time. Upon recognizing a student, the system logs their attendance by recording their name and the current timestamp in a CSV file. DLIB has pre-trained CNN models that can be used forface detection. This ensures accurate and efficient attendance tracking, providing a reliable record of student presence during examinations.

Genome-Wide Identification of Stable RNA Secondary Structures Across Multiple Organisms Using Chemical Probing Data: Insights into Short Structural Motifs and RNA-Targeting Therapeutics.

Small molecules targeting specific RNA binding sites, including stable and transient RNA structures, are emerging as effective pharmacological approaches for modulating gene expression. However, little is understood about how stable RNA secondary structures are shared across organisms, an important factor in controlling drug selectivity. In this study, I provide an analytical pipeline named RNA Secondary Structure Finder (R2S-Finder) to discover short, stable RNA structural motifs for humans, Escherichia coli ( E. coli ), SARS-CoV-2, and Zika virus by leveraging existing in vivo and in vitro genome-wide chemical RNA-probing datasets. I found several common features across organisms. For example, apart from the well-documented tetraloops, AU-rich tetraloops are widely present in different organisms. I also found that the 5' untranslated region (UTR) contains a higher proportion of stable structures than the coding sequences in humans, SARS-CoV-2, and Zika virus. In general, stable structures predicted from in vitro (protein-free) and in vivo datasets are consistent in humans, E. coli , and SARS-CoV-2, indicating that most stable structure formation were driven by RNA folding alone, while a larger variation was found between in vitro and in vivo data with certain RNA types, such as human long intergenic non-coding RNAs (lincRNAs). Finally, I predicted stable three- and four-way RNA junctions that exist both in vivo and in vitro conditions, which can potentially serve as drug targets. All results of stable sequences, stem-loops, internal loops, bulges, and three- and four-way junctions have been collated in the R2S-Finder database ( https://github.com/JingxinWangLab/R2S-Finder ), which is coded in hyperlinked HTML pages and tabulated in CSV files.

Low-Cost Purpose-Built Ultra-Low-Field NMR Spectrometer.

Low-field NMR has emerged as a new analytical technique for the investigation of molecular structure and dynamics. Here, we introduce a highly integrated ultralow-frequency NMR spectrometer designed for the purpose of ultralow-field NMR polarimetry of hyperpolarized contrast media. The device measures 10 cm × 10 cm × 2.0 cm and weighs only 370 g. The spectrometer's aluminum enclosure contains all components, including an RF amplifier. The device has four ports for connecting to a high-impedance RF transmit-receive coil, a trigger input, a USB port for connectivity to a PC computer, and an auxiliary RS-485/24VDC port for system integration with other devices. The NMR spectrometer is configured for a pulse-wait-acquire-recover pulse sequence, and key sequence parameters are readily controlled by a graphical user interface (GUI) of a Windows-based PC computer. The GUI also displays the time-domain and Fourier-transformed NMR signal and allows autosaving of NMR data as a CSV file. Alternatively, the RS485 communication line allows for operating the device with sequence parameter control and data processing directly on the spectrometer board in a fully automated and integrated manner. The NMR spectrometer, equipped with a 250 ksamples/s 17-bit analog-to-digital signal converter, can perform acquisition in the 1-125 kHz frequency range. The utility of the device is demonstrated for NMR polarimetry of hyperpolarized 129Xe gas and [1-13C]pyruvate contrast media (which was compared to the 13C polarimetry using a more established technology of benchtop 13C NMR spectroscopy, and yielded similar results), allowing reproducible quantification of polarization values and relaxation dynamics. The cost of the device components is only ∼$200, offering a low-cost integrated NMR spectrometer that can be deployed as a plug-and-play device for a wide range of applications in hyperpolarized contrast media production─and beyond.

Automating Pruning in Top-Down Enumeration for Program Synthesis Problems with Monotonic Semantics

In top-down enumeration for program synthesis, abstraction-based pruning uses an abstract domain to approximate the set of possible values that a partial program, when completed, can output on a given input. If the set does not contain the desired output, the partial program and all its possible completions can be pruned. In its general form, abstraction-based pruning requires manually designed, domain-specific abstract domains and semantics, and thus has only been used in domain-specific synthesizers. This paper provides sufficient conditions under which a form of abstraction-based pruning can be automated for arbitrary synthesis problems in the general-purpose Semantics-Guided Synthesis (SemGuS) framework without requiring manually-defined abstract domains. We show that if the semantics of the language for which we are synthesizing programs exhibits some monotonicity properties, one can obtain an abstract interval-based semantics for free from the concrete semantics of the programming language, and use such semantics to effectively prune the search space. We also identify a condition that ensures such abstract semantics can be used to compute a precise abstraction of the set of values that a program derivable from a given hole in a partial program can produce. These precise abstractions make abstraction-based pruning more effective. We implement our approach in a tool, Moito, which can tackle synthesis problems defined in the SemGuS framework. Moito can automate interval-based pruning without any a-priori knowledge of the problem domain, and solve synthesis problems that previously required domain-specific, abstraction-based synthesizers— e.g., synthesis of regular expressions, CSV file schema, and imperative programs from examples.

Automated Google Scholar Crawling with a Web-Based Tool for Publication Data Management

Here’s the revised abstract in English: The rapid growth of academic publications requires efficient tools for publication data extraction and management, especially from widely used platforms like Google Scholar. To address this need, an automated web-based tool was developed, designed to simplify the processes of data crawling, extraction, and publication data management, allowing researchers to handle large volumes of academic publications more effectively. The tool supports both simple and detailed crawling modes, enabling users to input multiple Google Scholar URLs and neatly organize the extracted data into CSV files. For multiple URLs, the data is compiled into a ZIP file containing separate CSV files for each source, ensuring organized and accessible publication data management. The tool was tested with various dataset sizes. When processing 41 entries, the simple mode completed extraction in 9.054 seconds, while the detailed mode took 71.898 seconds. For smaller datasets of 5 entries, the simple mode executed in 3.283 seconds, while the detailed mode required 11.908 seconds. These results indicate that the tool is efficient and performs well with both small and large datasets. The differences in execution time between the simple and detailed modes offer users flexibility in balancing speed and depth of data extraction according to their research needs. This web-based tool not only automates the data extraction process from Google Scholar but also enhances the organization and accessibility of publication data, making it an asset for researchers and institutions in managing publication data.

The TELCoMB Protocol for High-Sensitivity Detection of ARG-MGE Colocalizations in Complex Microbial Communities.

Understanding the genetic basis of antimicrobial resistance is crucial for developing effective mitigation strategies. One necessary step is to identify the antimicrobial resistance genes (ARGs) within a microbial population, referred to as the resistome, as well as the mobile genetic elements (MGEs) harboring ARGs. Although shotgun metagenomics has been successful in detecting ARGs and MGEs within a microbiome, it is limited by low sensitivity. Enrichment using cRNA biotinylated probes has been applied to address this limitation, enhancing the detection of rare ARGs and MGEs, especially when combined with long-read sequencing. Here, we present the TELCoMB protocol, a Snakemake workflow that elucidates resistome and mobilome composition and diversity and uncovers ARG-MGE colocalizations. The protocol supports both short- and long-read sequencing and does not require enrichment, making it versatile for various genomic data types. TELCoMB generates publication-ready figures and CSV files for comprehensive analysis, improving our understanding of antimicrobial resistance mechanisms and spread. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Installing TELCOMB Locally Alternate Protocol: Installing TELCOMB on a SLURM Cluster Basic Protocol 2: Data Preprocessing Basic Protocol 3: Calculation of Resistome Distribution and Composition Basic Protocol 4: Identification of ARG-MGE Colocalizations.

Scientometric analysis of global cyber security research output based on Web of Science

Objective. This study aims to analyze global cyber security research using various scientometric indicators for 25 years, from 1999 to 2023. Design/Methodology/Approach. The study used the Web of Science international citation database to retrieve a total of 5,640 records. The records were extracted in the CSV file format and further analyzed using an MS-Excel spreadsheet, VosViewer, and Biblioshiny software. Findings. The findings revealed that a 300% annual growth rate was recorded for the publications in 2002. The USA emerged as the top contributor (31.15%) among the countries. The authors affiliated with the State University System of Florida have more publications (107 contributions). Authors Lingfeng Wang of the University of Wisconsin-Milwaukee and Kim-Kwang Raymond Choo of the University of Texas at San Antonio have contributed the equal and highest number of (21) papers. Originality/Value. Cyber security research helps to guardian the individual or nation’s digital sovereignty, protecting it against sophisticated cyber threats and potential attacks. The discipline has become the subject of global research. This study helps cyber security experts, software or web developers, and researchers identify the primary research evidence and its impact on the research community and policymakers for evidence-based policymaking to combat invisible global threats.

Automated Mass Extraction of Over 680,000 PICOs from Clinical Study Abstracts Using Generative AI: A Proof-of-Concept Study.

Generative artificial intelligence (GenAI) shows promise in automating key tasks involved in conducting systematic literature reviews (SLRs), including screening, bias assessment and data extraction. This potential automation is increasingly relevant as pharmaceutical developers face challenging requirements for timely and precise SLRs using the population, intervention, comparator and outcome (PICO) framework, such as those under the impending European Union (EU) Health Technology Assessment Regulation 2021/2282 (HTAR). This proof-of-concept study aimed to evaluate the feasibility, accuracy and efficiency of using GenAI for mass extraction of PICOs from PubMed abstracts. Abstracts were retrieved from PubMed using a search string targeting randomised controlled trials. A PubMed clinical study 'specific/narrow' filter was also applied. Retrieved abstracts were processed using the OpenAI Batch application programming interface (API), which allowed parallel processing and interaction with Generative Pre-trained Transformer 4 Omni (GPT-4o) via custom Python scripts. PICO elements were extracted using a zero-shot prompting strategy. Results were stored in CSV files and subsequently imported into a PostgreSQL database. The PubMed search returned 682,667 abstracts. PICOs from all abstracts were extracted in < 3 h, with an average processing time of 200 s per 1000 abstracts. A total of 395,992,770 tokens were processed, with an average of 580 tokens per abstract. The total cost was $3390. On the basis of a random sample of 350 abstracts, human verification confirmed that GPT-4o accurately and comprehensively extracted 342 (98%) of all PICOs, with only outcome elements rarely missed. Using GenAI to extract PICOs from clinical study abstracts could fundamentally transform the way SLRs are conducted. By enabling pharmaceutical developers to anticipate PICO requirements, this approach allows for proactive preparation for the EU HTAR process, or other health technology assessments (HTAs), streamlining efficiency and reducing the burden of meeting these requirements.

DoeDat: Enhanced Roundtripping of Crowdsourced Specimen Annotations

The DoeDat platform was launched by Meise Botanic Garden in 2018 to capture label data from imaged herbarium specimens by inviting volunteer contributors (Groom et al. 2018). It has since facilitated data capture from specimens of other natural history collections (Helminger et al. 2020, Mitrache et al. 2023), as well as digitised content from various other disciplines, such as historical photographs, posters and postcards. Volunteers may simply transcribe handwritten and/or typed text, but often also interpret the sparse and scattered information on the image, including trying to georeference its original location. As of April 2024, almost 650.000 tasks have been completed, of which more than 470.000 were herbarium specimens from Meise. DoeDat supports domain standards, including Darwin Core, and follows most of the currently drafted MIDS (Minimum Information about a Digital Specimen) guidelines as to what data is captured for natural history specimens. However, images have to be pre-loaded into the server storage for each project and captured data gets exported as one or more CSV files per project. These data files then still need to be processed before they can be ingested into the local management system (Engledow et al. 2023). Often the data are also subjected to additional quality control before they get openly published. This can result in the pipeline from image to openly published annotations being quite time and labour-consuming. As the biodiversity infrastructure landscape moves more towards FAIR (Findable, Accessible, Interoperable, Reusable) open data, DoeDat will adapt accordingly. This includes digital objects that are easy to annotate. Furthermore, image servers following IIIF (International Image Interoperability Framework) greatly standardise the access and portability of media content, drastically changing the way images are being dealt with. We envision upgrading the DoeDat platform to load images and any required metadata as IIIF manifests, greatly streamlining the process of adding new content and tracking provenance. The transcriptions should be accessible for external systems, loading the updated image manifests and publishing them as annotations such as nanopublications.

TROPIC: A program for calculating reduced transition probabilities

Measurements of level lifetimes and the extracted transition probabilities are one of the cornerstones of nuclear structure physics. The reduced transition probabilities, B(πλ;Ji→Jf) yield information about the structure, wavefunctions, and matrix elements of excited states connected by electromagnetic transitions in a given nucleus. The arsenal of techniques for measuring lifetimes continues to expand and presently includes a wide range of values from femtoseconds to microseconds. While lifetime measurement techniques vary, the extraction of transition probabilities remains the same. RULER is the program used by the National Nuclear Data Center (NNDC) and ENDSF evaluations, while TRANSNUCLEAR was developed at the University of Cologne and modified by a variety of groups. This paper presents a new program TROPIC (TRansitiOn ProbabIlity Calculator), which is the most modern and efficient way to extract transition probabilities B(πλ). TROPIC is a program written in Python 3 with the NumPy and SciPy libraries. This is in line with the advances that ENSDF and NNDC are making in moving away from the 80-character card punch input formats. Several design features were implemented to provide a streamlined process for the user and mitigate drawbacks that were present in other programs. The results from TROPIC have been compared with TRANSNUCLEAR and RULER. The answers are as expected identical, but the investment of input to output time is significantly reduced. TROPIC will be made available for public domain use, along with a user guide and example files. Program summaryProgram Title: TROPICCPC Library link to program files:https://doi.org/10.17632/958ygp2sb4.1Developer's repository link:https://github.com/ND-fIREBall/TROPICLicensing provisions: GPLv3Programming language: Python 3Nature of problem: An efficient way to calculate multiple reduced transition probabilities with minimal effort invested from the user.Solution method: A Python 3 script has been developed to read in a CSV file containing all necessary input parameters, calculate the transition probabilities listed in the CSV file, and export the results in three different output formats.

Weather Prediction Using Logistic Regression (AI/ML Techniques)

Prediction of ‘Weather or atmospheric condition’ by AI, machine learning techniques is a process of great challenge. Attempts had been made by Computer, Data-Scientists since long, how this condition can be performed successfully .The objective is to predict weather for a place for certain days, here ‘ALIPORE (42807)’.We collected ‘ALIPORE surface data’ (CSV file) for the period, 1969-2023. After collecting this big data, completed process of ‘data mining’ and necessary ‘feature engineering’ steps along with choosing responsible dependent or independent parameters called as predictors to find results or outputs by various machine learning packages of Python like ‘Pandas’, ‘SEABORN’, ‘STATS MODEL’ etc. ,under ‘SCIKIT LEARN’ as well as various ML code and techniques like 'Shape’, ‘drop null values’, ‘Describe’, ‘Label-encoding’, ‘IV- method’, ‘VIF method’ etc. ,some based on statistical theories . Ultimately equation of ‘Logistic Regression’ had been built with test-train split formula to predict future weather as ‘SIGNIFICANT’ or ‘CLEAR’ for certain test array. During analysis, all the weather phenomena as obtained from this big data set, were classified into two categories. No(1)--- ‘Lightning (code 0)’, ‘Drizzle (Code 5)’, ‘Rain (Code 6)’ and ‘Thunderstorm with rain (Code 9)’---for occurrence of any of these weather phenomena ,data were considered as ‘1’ or ‘SIGNIFICANT’ weather and No (2)---On the other hand , all weather except weather as mentioned above ,No (1),were considered as ‘0’ or ‘CLEAR’ weather. Keywords: Confusion-matrix, Heat-map, True-positive, True-negative, False-positive, False-negative, Accuracy- score, Classification-report, Precision, Recall, F1 score.

A Scopus-Based Bibliometric Analysis of the Journal of Craniofacial Surgery From 1990 to 2023.

This study aims to conduct a Scopus-based bibliometric analysis of the studies published in the Journal of Craniofacial Surgery, which has a very important place in its field. In this bibliometric study, all articles in the Journal of Craniofacial Surgery in the Scopus database on 7 July 2024 were listed. The information of the listed articles was exported in CSV file format. When exporting these articles, the citation information, bibliographic information, abstract and keywords, funding details, and other information options were all selected. These exported CSV data were analyzed with VOSviewer software (version 1.6.18). The bibliometric analysis of the Journal of Craniofacial Surgery from 1990 to 2023 included 15,271 articles listed in the Scopus database. The majority (84.7%) of these articles were original studies. The top 5 countries with the highest number of publications were the United States (4004 articles), Turkey (2124 articles), China (2111 articles), South Korea (1597 articles), and Italy (1109 articles). This comprehensive bibliometric analysis demonstrates the Journal of Craniofacial Surgery's increasing prominence and impact in the field over the years. The diverse international contributions highlight the journal's role in fostering global collaboration and knowledge dissemination in craniofacial and maxillofacial research. While the use of a single database (Scopus) is a limitation, the study provides a detailed overview of the journal's scholarly contributions, influential authors, and publication trends. These findings underscore the journal's pivotal position in advancing craniofacial surgery research and education.

Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes

The agricultural sector is undergoing a transformative paradigm shift with the integration of advanced technologies, particularly artificial intelligence (AI), to enhance data analysis techniques and streamline decision-making processes. This paper delves into the integration of advanced technologies in agriculture, focusing specifically on optimizing data analysis through artificial intelligence (AI) to strengthen decision-making processes in farming. We present a novel AI-powered model that leverages historical agricultural datasets, utilizing a comprehensive array of established machine learning algorithms to enhance the prediction and classification of agricultural data. This work provides tailored algorithm recommendations, bypassing the need to deploy and fine-tune numerous algorithms. We approximate the accuracy of suitable algorithms, highlighting those with the highest precision, thus saving time by leveraging pre-trained AI models on historical agricultural data. Our method involves three phases: collecting diverse agricultural datasets, applying multiple classifiers, and documenting their accuracy. This information is stored in a CSV file, which is then used by AI classifiers to predict the accuracy of new, unseen datasets. By evaluating feature information and various data segmentations, we recommend the configuration that achieves the highest accuracy. This approach eliminates the need for exhaustive algorithm reruns, relying on pre-trained models to estimate outcomes based on dataset characteristics. Our experimentation spans various configurations, including different training–testing splits and feature sets across multiple dataset sizes, meticulously evaluated through key performance metrics such as accuracy, precision, recall, and F-measure. The experimental results underscore the efficiency of our model, with significant improvements in predictive accuracy and resource utilization, demonstrated through comparative performance analysis against traditional methods. This paper highlights the superiority of the proposed model in its ability to systematically determine the most effective algorithm for specific agricultural data types, thus optimizing computational resources and improving the scalability of smart farming solutions. The results reveal that the proposed system can accurately predict a near-optimal machine learning algorithm and data structure for crop data with an accuracy of 89.38%, 87.61%, and 84.27% for decision tree, random forest, and random tree algorithms, respectively.

Modeling Dipolar Molecules with PCP-SAFT: A Vector Group-Contribution Method.

Predicting thermodynamic equilibrium properties is essential to develop chemical and energy conversion processes in the absence of experimental data. For the modeling of thermodynamic properties, statistical associating fluid theory (SAFT)-based equations of state, such as perturbed-chain polar (PCP)-SAFT, have been proven powerful and found broad application. The PCP-SAFT parameters can be predicted by group-contribution (GC) methods. However, their application to the dipole term is substantially limited: current GC methods neglect the dipole term or only allow for a single dipolar group per substance to avoid handling the molecular dipole moment's symmetry effects. Still, substances with multiple dipolar groups are highly relevant, and their description substantially improves by including the dipole term in SAFT models. To overcome these limitations, this work proposes a vector-addition-based (Vector-)GC method for the dipole term of PCP-SAFT that accounts for molecular symmetry. The Vector-GC employs information on the substance's molecular 3D structure to predict the molecular dipole moment through a vector addition of bond contributions. Combining the proposed sum rule for dipole moments with established sum rules for the remaining parameters yields a consistent GC method for PCP-SAFT for dipolar substances. The prediction capabilities of the Vector-GC method are analyzed against experimental data for two substance classes: nonassociating oxygenated and halogenated substances. We demonstrate that the Vector-GC method improves vapor pressure and liquid density predictions compared to neglecting the dipole term. Moreover, we show that the Vector-GC method enables differentiation between cis- and trans-isomers. The Vector-GC method, hence, substantially increases the predictive capabilities and applicability domain of GC methods. All parameters are provided as JSON and CSV files, and the Vector-GC method is available through an open-source python package. Additionally, the developed regression framework for GC methods for PCP-SAFT is openly available. The regression framework can be employed to regress the Vector-GC method to other substance classes and is easily adaptable to other sum rules for PCP-SAFT.

A dataset of optical camera and IMU sensor derived kinematics of thirty transtibial prosthesis wearers

Motion analysis has played a crucial role in providing gait analysis for prosthetic users. Understanding kinematics in motion analysis allows for the evaluation of the effects of prostheses and the development of a prosthetic component design that aids in walking within the community. However, there are currently limited open datasets available to study the locomotion of individuals using transtibial prostheses, and most research studies involve a limited number of participants. This dataset shows 30 transtibial prosthesis users walking comfortably on a 10-meter walkway inside a laboratory. We offer a set of joint ankles obtained from the inertial measurement unit (IMU) signals in CSV file format. We also provide the optical motion capture (OMC) system’s raw trajectory marker data in C3D file format and joint angle in CSV file format. This open dataset will provide resources for professionals interested in amputee gait for research in amputee gait detection and tracking algorithms. Moreover, the data will be the control data for comparison in developing advanced prosthetic components.

Get to know your neighbors with a SNAQ™: A framework for single cell spatial neighborhood analysis in immunohistochemical images.

Analyzing the local microenvironment of tumor cells can provide significant insights into their complex interactions with their cellular surroundings, including immune cells. By quantifying the prevalence and distances of certain immune cells in the vicinity of tumor cells through a neighborhood analysis, patterns may emerge that indicate specific associations between cell populations. Such analyses can reveal important aspects of tumor-immune dynamics, which may inform therapeutic strategies. This method enables an in-depth exploration of spatial interactions among different cell types, which is crucial for research in oncology, immunology, and developmental biology. We introduce an R Markdown script called SNAQ™ (Single-cell Spatial Neighborhood Analysis and Quantification), which conducts a neighborhood analysis on immunofluorescent images without the need for extensive coding knowledge. As a demonstration, SNAQ™ was used to analyze images of pancreatic ductal adenocarcinoma. Samples stained for DAPI, PanCK, CD68, and PD-L1 were segmented and classified using QuPath. The resulting CSV files were exported into RStudio for further analysis and visualization using SNAQ™. Visualizations include plots revealing the cellular composition of neighborhoods around multiple cell types within a customizable radius. Additionally, the analysis includes measuring the distances between cells of certain types relative to others across multiple regions of interest. The R Markdown files that comprise the SNAQ™ algorithm and the input data from this paper are freely available on the web at https://github.com/AryehSilver1/SNAQ.

LEVERAGING WEB SCRAPING TO GATHER TOURISM INFORMATION DATA

The influence of Information and Communication Technologies (ICT) on both individuals' daily lives and the economy is of significant importance. In this context, the tourism industry plays a crucial role, and it is essential to recognise the contributions of tourists in terms of sharing their experiences through tourism websites. Analysing this data is key to improving future tourists' experiences. Therefore, the objective of this study is to employ web scraping to gather data on places of interest (POI) and user attributes, specifically in the state of Melaka via the TripAdvisor website. Melaka is chosen as it is one of the places recognised by the United Nations, Educational, Scientific and Cultural Organization (UNESCO). The study focuses on the 200 POI locations (UNESCO) Map, encompassing both Melaka's core and buffer zones. These POIs are categorised into four heritage types: built heritage, natural heritage, personal heritage, and living heritage, with some belonging to more than one category. For the data collection process, this study utilised the TripAdvisor website and extracted a total of 14 attributes. Specifically, 27282 user data entries were collected from 163 POIs in the core zone area, and 8305 data entries from 37 POIs in the buffer zone area. The data is managed and stored in various formats, including CSV, JSON, and Excel files in the repository. The data helps in the development of a tourism application. Furthermore, the tourism industry can benefit from this study by enhancing their services and conserving the cultural heritage.