Software, Databases, and Information Services

Skyline is a Windows client application for targeted proteomics method creation and quantitative data analysis. It is open source and freely available for academic and commercial use. The Skyline user interface simplifies the development of mass spectrometer methods and the analysis of data from targeted proteomics experiments performed using selected reaction monitoring (SRM). Skyline supports using and creating MS/MS spectral libraries from a wide variety of sources to choose SRM filters and verify results based on previously observed ion trap data. Skyline exports transition lists to and imports the native output files from Agilent, Applied Biosystems, Thermo Fisher Scientific and Waters triple quadrupole instruments, seamlessly connecting mass spectrometer output back to the experimental design document. The fast and compact Skyline file format is easily shared, even for experiments requiring many sample injections. A rich array of graphs displays results and provides powerful tools for inspecting data integrity as data are acquired, helping instrument operators to identify problems early. The Skyline dynamic report designer exports tabular data from the Skyline document model for in-depth analysis with common statistical tools. Single-click, self-updating web installation is available at http://proteome.gs.washington.edu/software/skyline. This web site also provides access to instructional videos, a support board, an issues list and a link to the source code project.

PC/FRAM: a code for the nondestructive measurement of the isotopic composition of actinides for safeguards applications

Program Studi Teknik Informatika Universitas Mataram menerapkan kurikulum Outcome-Based Education (OBE) guna menghasilkan lulusan yang kompeten sesuai standar Merdeka Belajar Kampus Merdeka (MBKM). Website portofolio akhir mata kuliah dikembangkan untuk mengelola data capaian pembelajaran, namun fitur hasil akhir dan form menghadapi kendala antarmuka pengguna (UI) seperti tabel skor yang padat, grafik tidak seragam, serta form dengan dropdown tidak rapi dan input teks terbatas. Hal ini menyulitkan dosen dalam mengelola data capaian pembelajaran. Kegiatan Pengabdian kepada Masyarakat ini bertujuan mengoptimalkan fitur Hasil Akhir dan Form pada website di Mataram, melibatkan enam dosen PSTI sebagai mitra. Metode meliputi identifikasi masalah melalui User Acceptance Testing (UAT I, 8 Januari 2025), diskusi solusi, implementasi perbaikan, dan evaluasi (UAT II). Perbaikan mencakup penambahan filter kelas, pengurutan tabel berdasarkan NIM, grafik seragam, dan Trix editor untuk form. Evaluasi menunjukkan kepuasan pengguna 97,23% berdasarkan survei (skala 1–5). Perbaikan ini mempermudah pengelolaan data capaian pembelajaran lulusan (CPL) dan mata kuliah (CPMK), mendukung implementasi OBE.


 
 
 This text is a report about the CEAMMC PureData distribution - also known as “Pd-ceammc” - and the CEAMMC external library for the official Pure Data distribution by Miller Puckette - also known as “Pd Vanilla”. The CEAMMC Pure Data repository is available at https://github.com/uliss/pure-data. This distribution is a software package that comprises the original Pure Data distribution with several user interface enhancements and the CEAMMC object library plus yet other external libraries. The CEAMMC library itself is also available via the Deken package manager so it can be installed in Pd Vanilla. This paper focuses on our main concepts for this work, current features and future plan.
 
 

Los Alamos National Laboratory is compiling and building tools to simulate infrastructure stability following damage events as part of the National Infrastructure Simulation and Analysis Center (NISAC). The Water Infrastructure Simulation Environment (WISE) project is an analytical framework providing simulation capabilities for drinking water distribution, sewage collection and stormwater infrastructures. The purpose of the WISE framework is to develop within the NISAC toolsets with the capability to represent and study water infrastructures in the context of interdependency. The completed toolset will include tools to manage and visualize data in multiple frameworks and formats, simulate water distribution, sewage collection, and stormwater storage and conveyance infrastructures, and to evaluate interdependencies with electric power, natural gas and other infrastructures. WISE will allow quick and accurate management of the large water distribution and sewage system datasets acquired from metropolitan agencies and pre-process those datasets for use in infrastructure network solvers. Tools developed included ArcWISE, a GIS based graphical user interface that facilitates the acquisition, application and analysis of water infrastructure data in simulation codes, and IEISS Water, a simulation capability for water infrastructure within the Interdependent Energy Infrastructure Simulation System (IEISS) simulation framework. At present, ArcWISE includes tools for estimation of time varying water demand, the improvement of existing water distribution infrastructure and sewage collection GIS data for use in simulation codes, development of input files for those codes, and visualization capabilities. IEISS Water is a water distribution simulation capability for simulating urban scale water infrastructures and their interdependencies.

This protocol uses the command line tools provided by the Python package TnAtlas to identify and annotate transposon integration events in genomes. Given a set of sequencing reads, transposon sequences and genomes, the protocol looks for positions in the genomes where the transposon sequences have been integrated, annotates the sequencing reads with corresponding features of the genome, and presents a summary of the results as a spreadsheet. Integrations are identified by aligning reads to transposon sequences using NCBI's BLASTN, and annotations are made from reference genomes in GENBANK format. We have used this protocol in our group (https://biocomputationlab.com) to identify the location and gene locus of transposon inserts in the genome of Pseudomonas putida KT2440. However, this script can be used for other genomes for which the genome sequence and annotation are available. This updated version of the protocol is the first to be supported by the Python package TnAtlas, which contains library code which can be used to run the protocol programmatically (in a Python script or notebook), as well as compute and present other statistics about the results themselves. The package TnAtlas requires Python versions greater than or equal to 3.8. The protocol requires at least blastn version greater than or equal to 2.12. Optionally, the protocol also requires sickle version 1.33 or greater, and fastqc (any version), in order to perform the sequence trimming and quality control reporting. This is a description of the LAP entry LAPu-InsertsGenAnnotation-2.0.0 located in the LAP repository, specifically LAPu-InsertsGenAnnotation-3.0.0 and Github Entry TnAtlas, 2 places that you can download directly the tool used and usage examples. The major changes from previous version are: Now shipped as a Python package: The code and tools are packaged and can be installed with Python PIP. New tool for metadata annotation: The old --summary-map system has been replaced with a dedicated tool `tnmeta`, for general metadata annotation of the results. Command line options for saving intermediate files: By default, the tools create far fewer files. The options, --sam, --trim-save, --transposon-save, and --genome-save control the creation of intermediate files. Method of genome alignment: Alignment to the genome is now based on aligning the reads after the transposon sequence that has been found has been removed, giving preference to alignments earlier in the read, and also avoiding position errors of a few base pairs that can affect sequence logo results. Generates annotated genbank sequences: tnfind generates genbank files for each read that includes annotations of the transposon sequence and of the corresponding features from its position in the genome. Annotations from genbank: Annotations come from genbank reference genomes instead of separate CSV files. Multiple genomes: `tnfind` can search through multiple genome sequences at once by passing either passing multiple records in a genbank file, or multiple genbank files, to the `-genome` option.

Successful execution and musical communication require an effective fingering combination for each note the violinist plays. Fingering annotations on the musical score thus serve as important performance reminders for violinists of all abilities, particularly for beginners. However, fingering information is wholly or partially absent from most violin sheet music and existing fingering generation models require extensive preprocessing with low user-accessibility. Here, we showcase a simple graphical user interface (GUI) that automatically annotates violin fingerings on sheet music saved in the widely-adopted MusicXML format and exports the annotated score into a PDF or CSV file. Our fingering recommendation system is based on a deep learning model that further allows customised user inputs and provides three different fingering modes to best suit the unique background, training goals, and abilities of each violinist.

In recent years, the increasing number of ATM frauds has highlighted the limitations of traditional card-and-PIN–based authentication systems. To address these security challenges, this project proposes an enhanced ATM access system that combines card scanning technology with AI/ML-based face recognition to ensure secure and reliable user authentication. The system introduces a multi-layered verification mechanism aimed at preventing unauthorized access while maintaining ease of use for legitimate users.The proposed system is implemented using Python, with Tkinter used to design a graphical user interface that simulates ATM operations such as card scanning, authentication status, and transaction access. User card information and facial data references are maintained using CSV files, providing a lightweight and efficient method for structured data storage. File handling tasks, including storing and managing user images and logs, are performed using the os and shutil modules.

BackgroundProtein–ligand interactions are central to cellular regulation and therapeutic targeting. Experimental identification of binding residues remains costly and time-consuming, motivating the need for efficient computational approaches. We present an interpretable, XGBoost-based framework for residue-level prediction of protein binding sites specific to three ligand classes—metal ions, nucleic acids, and small molecules—emphasizing predictive accuracy and user accessibility.MethodsA benchmark dataset [1] comprising 1314 annotated protein sequences was processed into peptide windows (±5, ±11, ±20 residues) centered on binding sites, maintaining a 1:3 ratio of positive to negative samples. Sequence-derived descriptors were generated using the iLearn toolkit, including amino acid composition (AAC), grouped AAC (GAAC), CTD composition, and BLOSUM62 substitution scores, yielding >8000 features subsequently reduced by feature-importance heuristics. More than 60 classifiers were evaluated; XGBoost consistently achieved superior performance after hyperparameter optimization.ResultsIndependent models trained for each ligand type achieved high predictive power and biological interpretability. For small-molecule binding, the model attained an AUC of 0.99 and F1 of 0.91, with key features involving small residues (G, C, S, A) reflecting steric constraints. The nucleic-acid model reached AUC 0.99 and F1 0.95, highlighting glycine-rich and positively charged motifs typical of RNA/DNA interactions. The metal-ion model (AUC 0.92, F1 0.65) emphasized cysteine and histidine, consistent with metalloprotein binding patterns. Compared with reference CNN models, our framework achieved lower log-loss, greater interpretability, and stable cross-validation.ConclusionA standalone graphical user interface (GUI) implemented in Python allows users to input protein sequences and obtain residue-level binding probabilities, exportable as CSV files. This work demonstrates that well-engineered classical machine learning can rival deep learning in protein binding prediction, offering interpretable, high-performance tools to support biomedical and pharmaceutical research.

Depression is an illness that can harm someone's life. However, many people still do not know that they are having depression and tend to express their feelings through text or social media. Thus, text-based depression detection could help in identifying the early detection of the illness. Therefore, the research aims to build a depression detection that can identify possible depression cues based on Bahasa Malaysia text. The data, in the form of text, has been collected from depressed and healthy people via a google form. There are three questions asked which are “Apakah kenangan manis yang anda ingat?”, “Apakah rutin harian anda?” and “Apakah keadaan yang membuatkan anda stress?” which obtained 172, 169 and 170 responses for each question respectively. All the datasets are stored in a CSV file. Using Python, TF-IDF was extracted as the feature and pipeline into several classifier models such as Random Forest, Multinomial Naïve Bayes, and Logistic Regression. The results were presented using the classification metrics of confusion matrix, accuracy, and F1-score. Also, another method has been conducted using the text sentiment techniques Vader and Text Blob onto the datasets to identify whether depressive text falls under negative sentiment or vice versa. The percentage differences were determined between the actual sentiment compared to Vader and Text Blob sentiment. From the experiment, the highest score is achieved by AdaBoost Classifier with a 0.66-F1 score. The best model is chosen to be utilized in the Graphical User Interface (GUI).

Clinical laboratories frequently conduct method verification studies to ensure that the process meets quality standards for its intended use, such as patient testing. They play a pivotal role in healthcare, but issues such as accurate statistical assessment and reporting of verification data often make these studies challenging. Missteps can lead to false conclusions about method performance, risking patient safety or leading to incorrect diagnoses. Despite a requirement for accredited labs to document method performance, existing solutions are often expensive and complex. Addressing these issues, we present Method Verification-Graphical User Interface (MV-GUI), a software package designed for ease of use. It is platform-independent, capable of statistical analysis, and generates accreditation-ready reports swiftly and efficiently. Users can input patient data from one or more .CSV files, and MV-GUI will produce comprehensive reports, including statistical comparison tables, regression plots, and Bland–Altman plots. While method validation, which establishes the performance of new diagnostic tools, remains a crucial concern for manufacturers, MV-GUI primarily streamlines the method verification process. The software aids both medical practitioners and researchers and is designed to be user-friendly, even for non-experienced users. Requiring no internet connection, MV-GUI can operate in restricted IT environments, making method verification widely accessible and efficient.

Automatic control on business process with workflow is the core part of a project management system. A process-oriented workflow system model which can support workflow configuration, process control, task assignment, task submitting and approval, and cooperative work is presented. The model is a hierachical architecture that consists of three layers, i.e. database layer, workflow engine layer, and user interface layer. It is based on Browser/Server architecture which can provide multi-user, distributed and access control properties. The workflow engine is based on XPDL and relational database technologies and can support cooperative work, branch selection and workflow configuration. The model is implemented on Windows .NET platform, and is applied in an international enterprise project management system. The application results demonstrate that this workflow system model can satisfy the requirement of cooperative project management.

Advances in camera trap data management tools: Towards collaborative development and integration with GIS

The purpose of this study is to develop the optimal system (AAC device), which helps a person with a profound communication disorder to communicate with other people. Therefore, this system includes the user interface enhancement that is the user adaptation mode algorithm. The symbol is made with a text and an icon which is converted into Korean. The message control operates scanning and adjusts rate control of row-column scanning and linear scanning. This embedded system includes voice input/output and voice recording as well as message display through a graphic LCD. As well as, we suggested method that could apply optimal device access algorithm from clinical environment. Therefore, we are expecting that even the current system itself will be able to improve the life quality of people who need to communicate with the help of devices.

In one year, more than 6.5 million mobile applications have been listed for download on the application stores. That is, they are used by millions (or billions) of users across the world. Users express their daily experience of applications as reviews on those stores. This experience may include reporting bugs, demanding new features, posting feedback with regards to performance, reporting security issues, demanding user interface enhancements, and other needs. Interestingly, reviews could contain valuable information for the interest of application vendors and developers. However, the volume of such data is as huge, that is, traditional searching algorithms may not be efficient in extracting such useful information. Machine learning and data mining techniques are one of the popularly used algorithms to efficiently extracting significant information for Software Requirement Engineering; a key phase in the Software Engineering Life Cycle. In this paper, we experience machine learning algorithms and natural language processing techniques to classify a set of reviews about healthcare-domain applications into multiple types of categories such as bug reports, new feature requests, application performance, and user interface. For this purpose, we could extract more than 7500 reviews of ten different health-related mobile applications. More importantly, those reviews were annotated manually by software experts. In our experiments, we use the Weka tool employing different machine learning algorithms. We will also show what algorithms and features will perform better; in terms of accuracy using different evaluation metrics, when classifying reviews about mobile apps into various classes; bugs, new features, sentimental, general bug, usability, security, and performance. Moreover, the conducted experiments show that the overall performance improves when we use the data subset with highly confident labeling; when two experts agree on the same class. For the imbalanced-data problem, this research will show the effect of applying resampling techniques on improving classification accuracy as well.