TRU-IMP: techniques for reliable use of images in medical physics; a graphical user interface to analyze and compare segmentations in nuclear medicine.

BACKGROUND: In the context of pharmacokinetic analyses, the segmentation method one uses has a large impact on the results obtained, thus the importance of transparency. Innovation: This paper introduces a graphical user interface (GUI), TRU-IMP, that analyzes time-activity curves and segmentations in dynamic nuclear medicine. This GUI fills a gap in the current technological tools available for the analysis of quantitative dynamic nuclear medicine image acquisitions. The GUI includes various techniques of segmentations, with possibilities to compute related uncertainties. Results: The GUI was tested on image acquisitions made on a dynamic nuclear medicine phantom. This allows the comparison of segmentations via their time-activity curves and the extracted pharmacokinetic parameters. Implications: The flexibility and user-friendliness allowed by the proposed interface make the analyses both easy to perform and adjustable to any specific case. This GUI permits researchers to better show and understand the reproducibility, precision, and accuracy of their work in quantitative dynamic nuclear medicine. Availability and Implementation: Source code freely available on GitHub: https://github.com/ArGilfea/TRU-IMP and location of the interface available from there. The GUI is fully compatible with iOS and Windows operating systems (not tested on Linux). A phantom acquisition is also available to test the GUI easily. .

Towards dynamically configured databases for CIFs: the new modulated structures open database at the Bilbao Crystallographic Server.

This article presents a web-based framework to build a database without in-depth programming knowledge given a set of CIF dictionaries and a collection of CIFs. The framework consists of two main elements: the public site that displays the information contained in the CIFs in an ordered manner, and the restricted administrative site which defines how that information is stored, processed and, eventually, displayed. Thus, the web application allows users to easily explore, filter and access the data, download the original CIFs, and visualize the structures via JSmol. The modulated structures open database B-IncStrDB, the official International Union of Crystallography repository for this type of material and available through the Bilbao Crystallographic Server, has been re-implemented following the proposed framework.

SimplySmart_v1, a new tool for the analysis of DNA damage optimized in primary neuronal cultures.

BACKGROUND: The increased interest in research on DNA damage in neurodegeneration has created a need for the development of tools dedicated to the analysis of DNA damage in neurons. Double-stranded breaks (DSBs) are among the most detrimental types of DNA damage and have become a subject of intensive research. DSBs result in DNA damage foci, which are detectable with the marker γH2AX. Manual counting of DNA damage foci is challenging and biased, and there is a lack of open-source programs optimized specifically in neurons. Thus, we developed a new, fully automated application, SimplySmart_v1, for DNA damage quantification and optimized its performance specifically in primary neurons cultured in vitro. RESULTS: Compared with control neurons, SimplySmart_v1 accurately identifies the induction of DNA damage with etoposide in primary neurons. It also accurately quantifies DNA damage in the desired fraction of cells and processes a batch of images within a few seconds. SimplySmart_v1 was also capable of quantifying DNA damage effectively regardless of the cell type (neuron or NSC-34). The comparative analysis of SimplySmart_v1 with other open-source tools, such as Fiji, CellProfiler and a focinator, revealed that SimplySmart_v1 is the most 'user-friendly' and the quickest tool among others and provides highly accurate results free of variability between measurements. In the context of neurodegenerative research, SimplySmart_v1 revealed an increase in DNA damage in primary neurons expressing abnormal TAR DNA/RNA binding protein (TDP-43). CONCLUSIONS: These findings showed that SimplySmart_v1 is a new and effective tool for research on DNA damage and can successfully replace other available software.

SciJava Ops: an improved algorithms framework for Fiji and beyond.

Decades of iteration on scientific imaging hardware and software has yielded an explosion in not only the size, complexity, and heterogeneity of image datasets but also in the tooling used to analyze this data. This wealth of image analysis tools, spanning different programming languages, frameworks, and data structures, is itself a problem for data analysts who must adapt to new technologies and integrate established routines to solve increasingly complex problems. While many "bridge" layers exist to unify pairs of popular tools, there exists a need for a general solution to unify new and existing toolkits. The SciJava Ops library presented here addresses this need through two novel principles. Algorithm implementations are declared as plugins called Ops, providing a uniform interface regardless of the toolkit they came from. Users express their needs declaratively to the Op environment, which can then find and adapt available Ops on demand. By using these principles instead of direct function calls, users can write streamlined workflows while avoiding the translation boilerplate of bridge layers. Developers can easily extend SciJava Ops to introduce new libraries and more efficient, specialized algorithm implementations, even immediately benefitting existing workflows. We provide several use cases showing both user and developer benefits, as well as benchmarking data to quantify the negligible impact on overall analysis performance. We have initially deployed SciJava Ops on the Fiji platform, however it would be suitable for integration with additional analysis platforms in the future.

Regression of postprandial cardiac hypertrophy in burmese pythons is mediated by FoxO1.

As ambush-hunting predators that consume large prey after long intervals of fasting, Burmese pythons evolved with unique adaptations for modulating organ structure and function. Among these is cardiac hypertrophy that develops within three days following a meal (Andersen et al., 2005, Secor, 2008), which we previously showed was initiated by circulating growth factors (Riquelme et al., 2011). Postprandial cardiac hypertrophy in pythons also rapidly regresses with subsequent fasting (Secor, 2008); however, the molecular mechanisms that regulate the dynamic cardiac remodeling in pythons during digestion are largely unknown. In this study, we employed a multiomics approach coupled with targeted molecular analyses to examine remodeling of the python ventricular transcriptome and proteome throughout digestion. We found that forkhead box protein O1 (FoxO1) signaling was suppressed prior to hypertrophy development and then activated during regression, which coincided with decreased and then increased expression, respectively, of FoxO1 transcriptional targets involved in proteolysis. To define the molecular mechanistic role of FoxO1 in hypertrophy regression, we used cultured mammalian cardiomyocytes treated with postfed python plasma. Hypertrophy regression both in pythons and in vitro coincided with activation of FoxO1-dependent autophagy; however, the introduction of a FoxO1-specific inhibitor prevented both regression of cell size and autophagy activation. Finally, to determine whether FoxO1 activation could induce regression, we generated an adenovirus expressing a constitutively active FoxO1. FoxO1 activation was sufficient to prevent and reverse postfed plasma-induced hypertrophy, which was partially prevented by autophagy inhibition. Our results indicate that modulation of FoxO1 activity contributes to the dynamic ventricular remodeling in postprandial Burmese pythons.

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.

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.

A dataset of the 2023 presidential election in Nigeria.

Nigeria operates under a multi-party system with more than 18 registered political parties. Since the return to democratic rule in 1999, the political scene has been predominantly dominated by two major parties: the People's Democratic Party (PDP) and the All Progressive Congress (APC). Recently, however, emerging parties like The Labour Party (LP) and the New Nigerian People's Party (NNPP) have started gaining traction. Social media has become a pivotal part of modern society. Twitter (now known as X) has emerged as a significant medium for news dissemination, public opinions expression, and emotional responses on various topics. Its ability to allow real-time sharing of views and experiences on current affairs and personal matters has made it a powerful tool in shaping and reflecting public sentiment. The use of Twitter in Nigeria exemplifies its role as a versatile medium for expressing thoughts and feelings, thereby generating a substantial amount of data for sentiment analysis. Deep Learning is a branch of Artificial intelligence that uses multiple layer techniques to extract features from data. It has the capacity to adequately recognize pattern from data to produce insights. There is a dynamic interplay among political developments, social media use, and sentiment analysis using deep learning. This interplay highlights the evolving nature of public discourse and opinion formation in Nigeria. People's opinions about the Nigeria's 2023 Presidential Election were obtained from Twitter using the Twitter API and Python. The dataset contains 364,867 tweets that can be used in predicting the outcome of future elections in Nigeria and for comparing the performances of different models and techniques of sentiment analysis. Sentiment analysis; Deep learning; Python; Twitter.

A comprehensive comparison of machine learning models for ICH prognostication: Retrospective review of 1501 intra-cerebral hemorrhage patients from the Qatar stroke database.

Multiple prognostic scores have been developed to predict morbidity and mortality in patients with spontaneous intracerebral hemorrhage(sICH). Since the advent of machine learning(ML), different ML models have also been developed for sICH prognostication. There is however a need to verify the validity of these ML models in diverse patient populations. We aim to create machine learning models for prognostication purposes in the Qatari population. By incorporating inpatient variables into model development, we aim to leverage more information. 1501 consecutive patients with acute sICH admitted to Hamad General Hospital(HGH) between 2013 and 2023 were included. We trained, evaluated, and compared several ML models to predict 90-day mortality and functional outcomes. For our dataset, we randomly selected 80% patients for model training and 20% for validation and used k-fold cross validation to train our models. The ML workflow included imbalanced class correction and dimensionality reduction in order to evaluate the effect of each. Evaluation metrics such as sensitivity, specificity, F-1 score were calculated for each prognostic model. Mean age was 50.8(SD 13.1) years and 1257(83.7%) were male. Median ICH volume was 7.5 ml(IQR 12.6). 222(14.8%) died while 897(59.7%) achieved good functional outcome at 90 days. For 90-day mortality, random forest(RF) achieved highest AUC(0.906) whereas for 90-day functional outcomes, logistic regression(LR) achieved highest AUC(0.888). Ensembling provided similar results to the best performing models, namely RF and LR, obtaining an AUC of 0.904 for mortality and 0.883 for functional outcomes. Random Forest achieved the highest AUC for 90-day mortality, and LR achieved the highest AUC for 90-day functional outcomes. Comparing ML models, there is minimal difference between their performance. By creating an ensemble of our best performing individual models we maintained maximum accuracy and decreased variance of functional outcome and mortality prediction when compared with individual models.

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.

RedLionfish - fast Richardson-Lucy Deconvolution package for efficient point spread function suppression in volumetric data.

The experimental limitations with optics observed in many microscopy and astronomy instruments result in detrimental effects for the imaging of objects. This can be generally described mathematically as a convolution of the real object image with the point spread function that characterizes the optical system. The popular Richardson-Lucy (RL) deconvolution algorithm is widely used for the inverse process of restoring the data without these optical aberrations, often a critical step in data processing of experimental data. Here we present the versatile RedLionfish python package, that was written to make the RL deconvolution of volumetric (3D) data easier to run, very fast (by exploiting GPU computing capabilities) and with automatic handling of hardware limitations for large datasets. It can be used programmatically in Python/numpy using conda or PyPi package managers, or with a graphical user interface as a napari plugin.

In order to observe biological phenomena at microscopic scale, light or fluorescent microscopes are often used. These instruments use optical devices such as lenses and mirrors to guide light and help form an image that can be recorded and analyzed. Modern optical methods and techniques have been developed so that scientists can obtain 3D images of microscopic objects of interest, such as confocal microscopy or light sheet microscopy. Currently, optical instruments can readily observe cells and their contents down to a few nanometers resolution (e.g.: chromosomes). However, there are physical limitations that prevent the resolution of images below a nanometer. One such limitation comes from the inherent property of light itself as an electromagnetic wave with wavelengths in the hundreds of nanometers range. Another major limitation comes from the guiding optics used to both illuminate the object and to probe the samples being studied. This results in images that are unavoidably blurry preventing differentiation of small, nearby details. Fortunately, with a good understanding of what causes the blurriness, it is possible to use a filter to reverse it, and recover the image to closely match its non-blurred form. This filter is widely used by scientists and is called the Richardson-Lucy deconvolution algorithm. Although this filter is widely available in many scientific software packages, its implementation is often slow and limited to particular imaging analysis applications, with poor programmatic access. With the popularity of the Python programming language, and an open-source image viewer (napari) we have developed the Redlionfish package to apply the RL filter to 3D image data in a speed optimized manner, while also being easy to use and to install.

Enhancing clinical data retrieval with Smart Watchers: a NiFi-based ETL pipeline for Elasticsearch queries.

BACKGROUND: The aim is to develop and deploy an automated clinical alert system to enhance patient care and streamline healthcare operations. Structured and unstructured data from multiple sources are used to generate near real-time alerts for specific clinical scenarios, with an additional goal to improve clinical decision-making through accuracy and reliability. METHODS: The automated clinical alert system, named Smart Watchers, was developed using Apache NiFi and Python scripts to create flexible data processing pipelines and customisable clinical alerts. A comparative analysis between Smart Watchers and the legacy Elastic Watchers was conducted to evaluate performance metrics such as accuracy, reliability, and scalability. The evaluation involved measuring the time taken for manual data extraction through the electronic patient record (EPR) front-end and comparing it with the automated data extraction process using Smart Watchers. RESULTS: Deployment of Smart Watchers showcased a consistent time savings between 90% to 98.67% compared to manual data extraction through the EPR front-end. The results demonstrate the efficiency of Smart Watchers in automating data extraction and alert generation, significantly reducing the time required for these tasks when compared to manual methods in a scalable manner. CONCLUSIONS: The research underscores the utility of employing an automated clinical alert system, and its portability facilitated its use across multiple clinical settings. The successful implementation and positive impact of the system lay a foundation for future technological innovations in this rapidly evolving field.

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.

The quantum hypercube as a k-mer graph.

The application of quantum principles in computing has garnered interest since the 1980s. Today, this concept is not only theoretical, but we have the means to design and execute techniques that leverage the quantum principles to perform calculations. The emergence of the quantum walk search technique exemplifies the practical application of quantum concepts and their potential to revolutionize information technologies. It promises to be versatile and may be applied to various problems. For example, the coined quantum walk search allows for identifying a marked item in a combinatorial search space, such as the quantum hypercube. The quantum hypercube organizes the qubits such that the qubit states represent the vertices and the edges represent the transitions to the states differing by one qubit state. It offers a novel framework to represent k-mer graphs in the quantum realm. Thus, the quantum hypercube facilitates the exploitation of parallelism, which is made possible through superposition and entanglement to search for a marked k-mer. However, as found in the analysis of the results, the search is only sometimes successful in hitting the target. Thus, through a meticulous examination of the quantum walk search circuit outcomes, evaluating what input-target combinations are useful, and a visionary exploration of DNA k-mer search, this paper opens the door to innovative possibilities, laying down the groundwork for further research to bridge the gap between theoretical conjecture in quantum computing and a tangible impact in bioinformatics.

Comparative analysis of COVID-19 pneumonia in pregnant versus matched non-pregnant women: radiologic, laboratory, and clinical perspectives.

This study aimed to assess the severity and outcomes of COVID-19 in pregnant women, focusing on laboratory and radiological discrepancies between pregnant women and matched nonpregnant women. In this retrospective cross-sectional analysis, we matched 107 nonpregnant women with 66 pregnant women in terms of age, comorbidities, and the interval between symptom onset and hospital admission. Demographic, clinical, laboratory, and radiological data were collected, and chest CT scans were evaluated using a severity scale ranging from 0 to 5. Logistic regression and adjusted Cox regression models were used to assess the impact of various factors on pregnancy status and mortality rates. Differences in several laboratory parameters, including the neutrophil-to-lymphocyte ratio, liver aminotransferases, alkaline phosphatase, urea, triglycerides, cholesterol, HbA1c, ferritin, coagulation profiles, and blood gases, were detected. Radiologic exams revealed that nonpregnant women had sharper opacities, whereas pregnant women presented with hazy opacities and signs of crypt-organizing pneumonia. A notable difference was also observed in the pulmonary artery diameter. The mortality rate among pregnant women was 4.62%, which was comparable to the 5.61% reported in nonpregnant patients. Compared with nonpregnant patients, pregnancy did not significantly affect the severity or mortality of COVID-19. Our study revealed discernible differences in specific laboratory and imaging markers between pregnant and nonpregnant COVID-19 patients.

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.

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.