MSIGen: An Open-Source Python Package for Processing and Visualizing Mass Spectrometry Imaging Data.

Mass spectrometry imaging (MSI) provides information about the spatial localization of molecules in complex samples with high sensitivity and molecular selectivity. Although point-wise data acquisition, in which mass spectra are acquired at predefined points in a grid pattern, is common in MSI, several MSI techniques use line-wise data acquisition. In line-wise mode, the imaged surface is continuously sampled along consecutive parallel lines and MSI data are acquired as a collection of line scans across the sample. Furthermore, aside from the standard imaging mode in which full mass spectra are acquired, other acquisition modes have been developed to enhance molecular specificity, enable separation of isobaric and isomeric species, and improve sensitivity to facilitate the imaging of low abundance species. These methods, including MS/MS-MSI in both MS2 and MS3 modes, multiple-reaction monitoring (MRM)-MSI, and ion mobility spectrometry (IMS)-MSI have all demonstrated their capabilities, but their broader implementation is limited by the existing MSI analysis software. Here, we present MSIGen, an open-source Python package for the visualization of MSI experiments performed in line-wise acquisition mode containing MS1, MS2, MRM, and IMS data, which is available at https://github.com/LabLaskin/MSIGen. The package supports multiple vendor-specific and open-source data formats and contains tools for targeted extraction of ion images, normalization, and exportation as images, arrays, or publication-style images. MSIGen offers multiple interfaces, allowing for accessibility and easy integration with other workflows. Considering its support for a wide variety of MSI imaging modes and vendor formats, MSIGen is a valuable tool for the visualization and analysis of MSI data.

Bin2cell reconstructs cells from high resolution visium HD data.

SUMMARY: Visium HD by 10X Genomics is the first commercially available platform capable of capturing full scale transcriptomic data paired with a reference morphology image from archived FFPE blocks at sub-cellular resolution. However, aggregation of capture regions to single cells poses challenges. Bin2cell reconstructs cells from the highest resolution data (2 µm bins) by leveraging morphology image segmentation and gene expression information. It is compatible with established Python single cell and spatial transcriptomics software, and operates efficiently in a matter of minutes without requiring a GPU. We demonstrate improvements in downstream analysis when using the reconstructed cells over default 8 µm bins on mouse brain and human colorectal cancer data. AVAILABILITY AND IMPLEMENTATION: Bin2cell is available at https://github.com/Teichlab/bin2cell, along with documentation and usage examples, and can be installed from pip. Probe design functionality is available at https://github.com/Teichlab/gene2probe. SUPPLEMENTARY INFORMATION: Supplementary data are available online.

Exploring the role of topological descriptors to predict physicochemical properties of anti-HIV drugs by using supervised machine learning algorithms.

In order to explore the role of topological indices for predicting physio-chemical properties of anti-HIV drugs, this research uses python program-based algorithms to compute topological indices as well as machine learning algorithms. Degree-based topological indices are calculated using Python algorithm, providing important information about the structural behavior of drugs that are essential to their anti-HIV effectiveness. Furthermore, machine learning algorithms analyze the physio-chemical properties that correspond to anti-HIV activities, making use of their ability to identify complex trends in large, convoluted datasets. In addition to improving our comprehension of the links between molecular structure and effectiveness, the collaboration between machine learning and QSPR research further highlights the potential of computational approaches in drug discovery. This work reveals the mechanisms underlying anti-HIV effectiveness, which paves the way for the development of more potent anti-HIV drugs. This work reveals the mechanisms underlying anti-HIV efficiency, which paves the way for the development of more potent anti-HIV drugs which demonstrates the invaluable advantages of machine learning in assessing drug properties by clarifying the biological processes underlying anti-HIV behavior, which paves the way for the design and development of more effective anti-HIV drugs.

Point Cloud Densification Algorithm for Multiple Cameras and Lidars Data Fusion.

Fusing data from many sources helps to achieve improved analysis and results. In this work, we present a new algorithm to fuse data from multiple cameras with data from multiple lidars. This algorithm was developed to increase the sensitivity and specificity of autonomous vehicle perception systems, where the most accurate sensors measuring the vehicle's surroundings are cameras and lidar devices. Perception systems based on data from one type of sensor do not use complete information and have lower quality. The camera provides two-dimensional images; lidar produces three-dimensional point clouds. We developed a method for matching pixels on a pair of stereoscopic images using dynamic programming inspired by an algorithm to match sequences of amino acids used in bioinformatics. We improve the quality of the basic algorithm using additional data from edge detectors. Furthermore, we also improve the algorithm performance by reducing the size of matched pixels determined by available car speeds. We perform point cloud densification in the final step of our method, fusing lidar output data with stereo vision output. We implemented our algorithm in C++ with Python API, and we provided the open-source library named Stereo PCD. This library very efficiently fuses data from multiple cameras and multiple lidars. In the article, we present the results of our approach to benchmark databases in terms of quality and performance. We compare our algorithm with other popular methods.

BracketMaker: Visualization and optimization of chemical protein synthesis.

Chemical protein synthesis (CPS), in which custom peptide segments of ~20-60 aa are produced by solid-phase peptide synthesis and then stitched together through sequential ligation reactions, is an increasingly popular technique. The workflow of CPS is often depicted with a "bracket" style diagram detailing the starting segments and the order of all ligation, desulfurization, and/or deprotection steps to obtain the product protein. Brackets are invaluable tools for comparing multiple possible synthetic approaches and serve as blueprints throughout a synthesis. Drawing CPS brackets by hand or in standard graphics software, however, is a painstaking and error-prone process. Furthermore, the CPS field lacks a standard bracket format, making side-by-side comparisons difficult. To address these problems, we developed BracketMaker, an open-source Python program with built-in graphic user interface (GUI) for the rapid creation and analysis of CPS brackets. BracketMaker contains a custom graphics engine which converts a text string (a protein sequence annotated with reaction steps, introduced herein as a standardized format for brackets) into a high-quality vector or PNG image. To aid with new syntheses, BracketMaker's "AutoBracket" tool automatically performs retrosynthetic analysis on a set of segments to draft and rank all possible ligation orders using standard native chemical ligation, protection, and desulfurization techniques. AutoBracket, in conjunction with an improved version of our previously reported Automated Ligator (Aligator) program, provides a pipeline to rapidly develop synthesis plans for a given protein sequence. We demonstrate the application of both programs to develop a blueprint for 65 proteins of the minimal Escherichia coli ribosome.

Leveraging Quadratic Polynomials in Python for Advanced Data Analysis.

This research explores the application of quadratic polynomials in Python for advanced data analysis. The study demonstrates how quadratic models can effectively capture nonlinear relationships in complex datasets by leveraging Python libraries such as NumPy, Matplotlib, scikit-learn, and Pandas. The methodology involves fitting quadratic polynomials to the data using least-squares regression and evaluating the model fit using the coefficient of determination (R-squared). The results highlight the strong performance of the quadratic polynomial fit, as evidenced by high R-squared values, indicating the model's ability to explain a substantial proportion of the data variability. Comparisons with linear and cubic models further underscore the quadratic model's balance between simplicity and precision for many practical applications. The study also acknowledges the limitations of quadratic polynomials and proposes future research directions to enhance their accuracy and efficiency for diverse data analysis tasks. This research bridges the gap between theoretical concepts and practical implementation, providing an accessible Python-based tool for leveraging quadratic polynomials in data analysis.

This study examines how quadratic polynomials, which are mathematical equations used to model and understand patterns in data, can be effectively applied using Python, a versatile programming language with libraries suited for mathematical and visual analysis. Researchers have focused on the adaptability of these polynomials in various fields, from software analytics to materials science, in order to provide practical Python code examples. They also discussed the predictive accuracy of the method, confirmed through a statistical measure called R-squared, and acknowledged the need for future research to integrate more complex models for richer data interpretation.

The practical use of artificial intelligence in Transfusion Medicine and Apheresis.

BACKGROUND: Blood and plasma volume calculations are a daily part of practice for many Transfusion Medicine and Apheresis practitioners. Though many formulas exist, each facility may have their own modifications to consider. ChatGPT (Generative Pre-trained Transformer) provides a new and exciting pathway for those with no programming experience to create personalized programs to meet the demands of daily practice. Additionally, this pathway creates computer programs that provide accurate and reproducible outputs. Herein, we aimed to create a step-by-step process for clinicians to create customized computer programs for use in everyday practice. METHODS: We created a process of inputs to ChatGPT-40, which generated computer programming code. This code was copied and pasted into Notepad (and saved as a Python file) and Google Colaboratory to verify functionality. We validated the durability of our process by repeating it over a 5-day timeframe and by recruiting volunteers to reproduce our outputs using the suggested process. RESULTS: Computer code generated by ChatGPT-40 in response to our common language inputs was accurate and durable over time. The code was fully functional in both Python and Colaboratory. Volunteers reproduced our process and outputs with minimal assistance. CONCLUSION: We analyzed the practical application of ChatGPT-40 and artificial intelligence (AI) to perform daily calculations encountered in Transfusion Medicine. Our results provide a proof of concept that people with no programming experience can create customizable solutions for their own facilities. Our future work will expand to the creation of comprehensive and customizable websites designed for each individual user.

VISION - an open-source software for automated multi-dimensional image analysis of cellular biophysics.

Environment-sensitive probes are frequently used in spectral/multi-channel microscopy to study alterations in cell homeostasis. However, the few open-source packages available for processing of spectral images are limited in scope. Here, we present VISION, a stand-alone software based on Python for spectral analysis with improved applicability. In addition to classical intensity-based analysis, our software can batch-process multidimensional images with an advanced single-cell segmentation capability and apply user-defined mathematical operations on spectra to calculate biophysical and metabolic parameters of single cells. VISION allows for 3D and temporal mapping of properties such as membrane fluidity and mitochondrial potential. We demonstrate the broad applicability of VISION by applying it to study the effect of various drugs on cellular biophysical properties; the correlation between membrane fluidity and mitochondrial potential; protein distribution in cell-cell contacts; and properties of nanodomains in cell-derived vesicles. Together with the code, we provide a graphical user interface for facile adoption.

ResFaultyMan: An intelligent fault detection predictive model in power electronics systems using unsupervised learning isolation forest.

Intelligent fault detection considered as a paramount importance in Power Electronics Systems (PELS) to ensure operational reliability along with rising complexities and critical application demands. However, most of the developed methods in real-world scenarios can have better detection, and accurate diagnosis. In this regard, ResFaultyMan, a novel unsupervised isolation forest-based model, is presented in this paper, for real-world fault/anomaly detection in PELS. Capitalizing on the dynamics of faults, ResFaultyMan utilizes a tree-based structure for effective anomaly isolation, demonstrating adaptability to diverse fault scenarios. The test bench, comprising a load, Triac switch, resistor, voltage source, and Pyboard microcontroller, provides a dynamic setting for performance evaluation. The integration of a Pyboard microcontroller and a Python-to-Python interface facilitates fast data transfer and sampling, enhancing the efficiency of ResFaultyMan in real-time fault detection scenarios. Comparative analysis with OneClassSVM and LocalOutlierFactor, utilizing Key Performance Indicators (KPIs) of Accuracy, Precision, and Recall, as well as F1 Score, manifest ResFaultyMan's fault detection capabilities for fault detection in PELSs, and its performance in the related applications.

PyBrOpS: a Python package for breeding program simulation and optimization for multi-objective breeding.

Plant breeding is a complex endeavor that is almost always multi-objective in nature. In recent years, stochastic breeding simulations have been used by breeders to assess the merits of alternative breeding strategies and assist in decision making. In addition to simulations, visualization of a Pareto frontier for multiple competing breeding objectives can assist breeders in decision making. This paper introduces Python Breeding Optimizer and Simulator (PyBrOpS), a Python package capable of performing multi-objective optimization of breeding objectives and stochastic simulations of breeding pipelines. PyBrOpS is unique among other simulation platforms in that it can perform multi-objective optimizations and incorporate these results into breeding simulations. PyBrOpS is built to be highly modular and has a script-based philosophy, making it highly extensible and customizable. In this paper, we describe some of the main features of PyBrOpS and demonstrate its ability to map Pareto frontiers for breeding possibilities and perform multi-objective selection in a simulated breeding pipeline.

Quality Assessment Framework of Clinical Routine Data for Secondary Use.

To systematically and comprehensively identify data issues in large clinical datasets, we adopted a harmonized data quality assessment framework with Python scripts before integrating the data into FHIR® for secondary use. We also added a preliminary step of categorizing data fields within the database scheme to facilitate the implementation of the data quality framework. As a result, we demonstrated the efficiency and comprehensiveness of detecting data issues using the framework. In future steps, we plan to continually utilize the framework to identify data issues and develop strategies for improving our data quality.

BioEncoder: A metric learning toolkit for comparative organismal biology.

In the realm of biological image analysis, deep learning (DL) has become a core toolkit, for example for segmentation and classification. However, conventional DL methods are challenged by large biodiversity datasets characterized by unbalanced classes and hard-to-distinguish phenotypic differences between them. Here we present BioEncoder, a user-friendly toolkit for metric learning, which overcomes these challenges by focussing on learning relationships between individual data points rather than on the separability of classes. BioEncoder is released as a Python package, created for ease of use and flexibility across diverse datasets. It features taxon-agnostic data loaders, custom augmentation options, and simple hyperparameter adjustments through text-based configuration files. The toolkit's significance lies in its potential to unlock new research avenues in biological image analysis while democratizing access to advanced deep metric learning techniques. BioEncoder focuses on the urgent need for toolkits bridging the gap between complex DL pipelines and practical applications in biological research.

Ichor: A Python library for computational chemistry data management and machine learning force field development.

We present ichor, an open-source Python library that simplifies data management in computational chemistry and streamlines machine learning force field development. Ichor implements many easily extensible file management tools, in addition to a lazy file reading system, allowing efficient management of hundreds of thousands of computational chemistry files. Data from calculations can be readily stored into databases for easy sharing and post-processing. Raw data can be directly processed by ichor to create machine learning-ready datasets. In addition to powerful data-related capabilities, ichor provides interfaces to popular workload management software employed by High Performance Computing clusters, making for effortless submission of thousands of separate calculations with only a single line of Python code. Furthermore, a simple-to-use command line interface has been implemented through a series of menu systems to further increase accessibility and efficiency of common important ichor tasks. Finally, ichor implements general tools for visualization and analysis of datasets and tools for measuring machine-learning model quality both on test set data and in simulations. With the current functionalities, ichor can serve as an end-to-end data procurement, data management, and analysis solution for machine-learning force-field development.

Simple Analysis of Gel Images With IOCBIO Gel Software.

Gel image analyses are often difficult to reproduce, as the most commonly used software, the ImageJ Gels plugin, does not automatically record any steps in the analysis process. This protocol provides detailed steps for image analysis using IOCBIO Gel software with western blot as an example; however, the protocol is applicable to all images obtained by electrophoresis, such as Southern blotting, northern blotting, and isoelectric focusing. IOCBIO Gel allows multiple sample analyses, linking the original image to all the operations performed on it, which can be stored in a central database or on a PC, ensuring ease of access and the possibility to perform corrections at each analysis stage. In addition, IOCBIO Gel is lightweight, with only minimal computer requirements. Key features • Free and open-source software for analyzing gel images. • Reproducibility. • Can be used with images obtained by electrophoresis, such as western blotting, Southern blotting, isoelectric focusing, and more.

Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping.

This study was carried out with the aim of applying Condorcet and Borda scoring algorithms based on Game Theory (GT) to determine flood points and Flood Susceptibility Mapping (FSM) based on Machine Learning Algorithms (MLA) including Random Forest (RF), Support Vector Regression (SVR), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) in the Cheshmeh-Kileh watershed, Iran. Therefore, first, FS conditioning factors including Aspect (As), Elevation (El), Euclidean distance (Euc), Forest (F), NDVI, Precipitation (P), Plan Curvature (PlC), Profile Curvature (PrC), Residential (Re), Rangeland (Rl), Slope (Sl), Stream Power Index (SPI), Topographic Position Index (TPI), and Topographic Wetness Index (TWI) were quantified in each Sub-Watershed (SW). Based on this, flood and non-flood points were identified based on both GT algorithms. In the following, MLAs including Random Forest (RF), Support Vector Regression (SVR), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) were used for the distributional mapping of FS. Finally, based on optimal conjunct approaches, FS maps were presented in the study watershed. Based on the results, among the conjunct algorithms in FS classification, RF-Condorcet and RF-Borda models were selected as the most optimal MLA-GT hybrid models. The upstream SWs were highly susceptible. Also, the effectiveness of NDVI and forest conditioning factors in each classification approach was high. The similarity of SW prioritization based on Condorcet algorithm with RF-Condorcet algorithm was about 86.70%. Meanwhile, the degree of similarity in RF-Borda conjunct algorithm was around 73.33%. These results showed that Condorcet algorithm had an optimal classification compared to Borda scoring algorithm.

Automatic motion estimation with applications to hiPSC-CMs.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are an effective tool for studying cardiac function and disease, and hold promise for screening drug effects on human tissue. Understanding alterations in motion patterns within these cells is crucial for comprehending how the administration of a drug or the onset of a disease can impact the rhythm of the human heart. However, quantifying motion accurately and efficiently from optical measurements using microscopy is currently time consuming. In this work, we present a unified framework for performing motion analysis on a sequence of microscopically obtained images of tissues consisting of hiPSC-CMs. We provide validation of our developed software using a synthetic test case and show how it can be used to extract displacements and velocities in hiPSC-CM microtissues. Finally, we show how to apply the framework to quantify the effect of an inotropic compound. The described software system is distributed as a python package that is easy to install, well tested and can be integrated into any python workflow.

Stopping criteria for neutron spectrum unfolding algorithms.

Neutron spectrum unfolding is a crucial process in radiation protection and dosimetry. Unfolding codes using iterative algorithms require a criterion to stop the iterations. One approach often relies on the Root Mean Square Error (RMSE) criterion to assess the convergence of iterative algorithms. The aim of this work is to present a new criteria: Average Ratio Scaled (AVGS) and Relative Change in AVGS (dAVGS) to address specific challenges associated with RMSE. Extensive validation tests were conducted, covering a range of scenarios with results showing high level of agreement between the unfolded spectra and the reference.

Genetic algorithms applied to translational strategy in metabolic-dysfunction associated steatohepatitis (MASH). Learning from mouse models.

BACKGROUND & AIMS: We previously identified subsets of patients with metabolic (dysfunction)-associated steatotic liver disease (MASLD) with different metabolic phenotypes. Here, we aimed to refine this classification based on genetic algorithms implemented in a Python package. The use of these genetic algorithms can help scientists to solve problems which cannot be solved with other methods. We present this package and its capabilities with specific problems. The name, PyGenMet, comes from its main goal, solving problems in Python with Genetic Algorithms and Metabolomics data. METHODS: We collected serum from methionine adenosyltransferase 1a knockout (Mat1a-KO) mice, which have chronically low level of hepatic S-adenosylmethionine (SAMe) and the metabolomes of all samples were determined. We also analyzed serum metabolomes of 541 patients with biopsy proven MASLD (182 with simple steatosis and 359 with metabolic (dysfunction)-associated steatohepatitis or MASH) and compared them with the serum metabolomes of this specific MASLD mouse model using Genetic Algorithms in order to select patients with a specific phenotype. RESULTS: By applying genetic algorithms, we have found a subgroup of patients with a lipid profile similar to that observed in the mouse model. When analyzing the two groups of patients, we have seen that patients with a lipid profile reflecting the mouse model characteristics show significant differences in lipoproteins, especially in LDL-4, LDL-5, and LDL-6 associated with atherogenic risk. CONCLUSION: The results show that the application of genetic algorithms to subclassify patients with MASLD (or other metabolic disease) give consistent results and are a good approximation for the treatment of large volumes of data such as those from omics sciences and patient classification.

BuildAMol: a versatile Python toolkit for fragment-based molecular design.

In recent years computational methods for molecular modeling have become a prime focus of computational biology and cheminformatics. Many dedicated systems exist for modeling specific classes of molecules such as proteins or small drug-like ligands. These are often heavily tailored toward the automated generation of molecular structures based on some meta-input by the user and are not intended for expert-driven structure assembly. Dedicated manual or semi-automated assembly software tools exist for a variety of molecule classes but are limited in the scope of structures they can produce. In this work we present BuildAMol, a highly flexible and extendable, general-purpose fragment-based molecular assembly toolkit. Written in Python and featuring a well-documented, user-friendly API, BuildAMol empowers researchers with a framework for detailed manual or semi-automated construction of diverse molecular models. Unlike specialized software, BuildAMol caters to a broad range of applications. We demonstrate its versatility across various use cases, encompassing generating metal complexes or the modeling of dendrimers or integrated into a drug discovery pipeline. By providing a robust foundation for expert-driven model building, BuildAMol holds promise as a valuable tool for the continuous integration and advancement of powerful deep learning techniques.Scientific contributionBuildAMol introduces a cutting-edge framework for molecular modeling that seamlessly blends versatility with user-friendly accessibility. This innovative toolkit integrates modeling, modification, optimization, and visualization functions within a unified API, and facilitates collaboration with other cheminformatics libraries. BuildAMol, with its shallow learning curve, serves as a versatile tool for various molecular applications while also laying the groundwork for the development of specialized software tools, contributing to the progress of molecular research and innovation.