Python Package Research Articles

Spatial effects of infiltrating T cells on neighbouring cancer cells and prognosis in stage III CRC patients.

Colorectal cancer (CRC) is one of the most frequently occurring cancers, but prognostic biomarkers identifying patients at risk of recurrence are still lacking. In this study, we aimed to investigate in more detail the spatial relationship between intratumoural T cells, cancer cells, and cancer cell hallmarks as prognostic biomarkers in stage III colorectal cancer patients. We conducted multiplexed imaging of 56 protein markers at single-cell resolution on resected fixed tissue from stage III CRC patients who received adjuvant 5-fluorouracil (5FU)-based chemotherapy. Images underwent segmentation for tumour, stroma, and immune cells, and cancer cell 'state' protein marker expression was quantified at a cellular level. We developed a Python package for estimation of spatial proximity, nearest neighbour analysis focusing on cancer cell-T-cell interactions at single-cell level. In our discovery cohort (Memorial Sloan Kettering samples), we processed 462 core samples (total number of cells: 1,669,228) from 221 adjuvant 5FU-treated stage III patients. The validation cohort (Huntsville Clearview Cancer Center samples) consisted of 272 samples (total number of cells: 853,398) from 98 stage III CRC patients. While there were trends for an association between the percentage of cytotoxic T cells (across the whole cancer core), it did not reach significance (discovery cohort: p = 0.07; validation cohort: p = 0.19). We next utilised our region-based nearest neighbour approach to determine the spatial relationships between cytotoxic T cells, helper T cells, and cancer cell clusters. In both cohorts, we found that shorter distance between cytotoxic T cells, T helper cells, and cancer cells was significantly associated with increased disease-free survival. An unsupervised trained model that clustered patients based on the median distance between immune cells and cancer cells, as well as protein expression profiles, successfully classified patients into low-risk and high-risk groups (discovery cohort: p = 0.01; validation cohort: p = 0.003). © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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.

Natural Language Processing Methods Application in Defense Budget Analysis

Abstract Transferring to economy 5.0 makes a great emphasis on Artificial Intelligence technologies implementation in civil and military areas. The aim of the article is to model relation between the Ukrainian Ministry of Defense budget programs and strategic goals and tasks. The classical budget analysis methodology is extended with NLP technics. The analysis is performed for defense budget program 2101020 - Ensuring the activities of the Armed Forces of Ukraine, training of personnel and troops, medical support of personnel, military service veterans and their family members, and war veterans. Either TF-IDF or more advanced NLP methods are used along with Python libraries and packages. It is found that some goals intersect with each other by semantic similarity. Despite the lack of data, we build proof of concept machine learning model and proved its effectiveness.

An Empirical Extinction Curve Revealed by Gaia XP Spectra and LAMOST

We present a direct measurement of extinction curves using corrected Gaia XP spectra of the common sources in Gaia DR3 and LAMOST DR7. Our analysis of approximately 370,000 high-quality samples yielded a high-precision average extinction curve for the Milky Way. After incorporating infrared photometric data from Two Micron All Sky Survey and Wide-field Infrared Survey Explorer, the extinction curve spans wavelengths from 0.336 to 4.6 μm. We determine an average R 55 of 2.730 ± 0.007, corresponding to R V = 3.073 ± 0.009, and a near-infrared power-law index α of 1.935 ± 0.037. Our study confirmed some intermediate-scale structures within the optical range. One new feature was identified at 540 nm, and its intensity exhibited a correlation with extinction. This extinction curve can be used to investigate the characteristics of dust and enhance the extinction correction of Milky Way stars. A Python package for this extinction curve is available.

EncChain: Enhancing Large Language Model Applications with Advanced Privacy Preservation Techniques

In response to escalating concerns about data privacy in the Large Language Model (LLM) domain, we demonstrate EncChain , a pioneering solution designed to bolster data security in LLM applications. EncChain presents an all-encompassing approach to data protection, encrypting both the knowledge bases and user interactions. It empowers confidential computing and implements stringent access controls, offering a significant leap in securing LLM usage. Designed as an accessible Python package, EncChain ensures straightforward integration into existing systems, bolstered by its operation within secure environments and the utilization of remote attestation technologies to verify its security measures. The effectiveness of EncChain in fortifying data privacy and security in LLM technologies underscores its importance, positioning it as a critical advancement for the secure and private utilization of LLMs.

Long-term evolution of binary orbits induced by circumbinary disks

Circumbinary disks are found in a variety of astrophysical scenarios, spanning binary star formation to accreting supermassive black hole binaries. Depending on the characteristics of the system, the interaction with a circumbinary disk can either damp or excite the binary’s eccentricity and can also widen or shrink the orbit. To predict the outcome of the long-term disk-binary interaction, we present a new formalism based on the results of recent suites of hydrodynamic simulations, which resolve the complex geometry of the gas in the vicinity of the binary and fully account for the gravitational and accretion forces. We released a python package, spindler, that implements our model. We show that – under the assumed thin disk model with a fixed thickness and viscosity prescription – accretion onto the binary depletes the disk mass before inducing a significant change in the orbital separation or the mass ratio, unless the mass reservoir feeding the disk is comparable to the mass of the binary. This finding implies that, in most scenarios, an interaction with a circumbinary disk is not an efficient mechanism to shrink the orbit of the binary. However, the interaction can excite the eccentricity up to an equilibrium value, and induce a statistical correlation between the mass ratio and eccentricity, as long as the mass of the disk is at least a few percent of the mass of the binary. We consider the applicability of our model to a variety of astrophysical scenarios: during star formation, in evolved stellar binaries, triples, and in supermassive black hole binaries. We discuss the theoretical and observational implications of our predictions.

RadShap: An Explanation Tool for Highlighting the Contributions of Multiple Regions of Interest to the Prediction of Radiomic Models.

Explaining the decisions made by a radiomic model is of significant interest, as it can provide valuable insights into the information learned by complex models and foster trust in well-performing ones, thereby facilitating their clinical adoption. Promising radiomic approaches that aggregate information from multiple regions within an image currently lack suitable explanation tools that could identify the regions that most significantly influence their decisions. Here we present a model- and modality-agnostic tool (RadShap, https://github.com/ncaptier/radshap), based on Shapley values, that explains the predictions of multiregion radiomic models by highlighting the contribution of each individual region. Methods: The explanation tool leverages Shapley values to distribute the aggregative radiomic model's output among all the regions of interest of an image, highlighting their individual contribution. RadShap was validated using a retrospective cohort of 130 patients with advanced non-small cell lung cancer undergoing first-line immunotherapy. Their baseline PET scans were used to build 1,000 synthetic tasks to evaluate the degree of alignment between the tool's explanations and our data generation process. RadShap's potential was then illustrated through 2 real case studies by aggregating information from all segmented tumors: the prediction of the progression-free survival of the non-small cell lung cancer patients and the classification of the histologic tumor subtype. Results: RadShap demonstrated strong alignment with the ground truth, with a median frequency of 94% for consistently explained predictions in the synthetic tasks. In both real-case studies, the aggregative models yielded superior performance to the single-lesion models (average [±SD] time-dependent area under the receiver operating characteristic curve was 0.66 ± 0.02 for the aggregative survival model vs. 0.55 ± 0.04 for the primary tumor survival model). The tool's explanations provided relevant insights into the behavior of the aggregative models, highlighting that for the classification of the histologic subtype, the aggregative model used information beyond the biopsy site to correctly classify patients who were initially misclassified by a model focusing only on the biopsied tumor. Conclusion: RadShap aligned with ground truth explanations and provided valuable insights into radiomic models' behaviors. It is implemented as a user-friendly Python package with documentation and tutorials, facilitating its smooth integration into radiomic pipelines.

Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systems.

Chemical reaction networks (CRNs) play a pivotal role in diverse fields such as systems biology, biochemistry, chemical engineering, and epidemiology. High-level definitions of CRNs enables to use various simulation approaches, including deterministic and stochastic methods, from the same model. However, existing Python tools for simulation of CRN typically wrap external C/C++ libraries for model definition, translation into equations and/or numerically solving them, limiting their extensibility and integration with the broader Python ecosystem. In response, we developed Poincaré and SimBio, two novel Python packages for simulation of dynamical systems and CRNs. Poincaré serves as a foundation for dynamical systems modeling, while SimBio extends this functionality to CRNs, including support for the Systems Biology Markup Language (SBML). Poincaré and SimBio are developed as pure Python packages enabling users to easily extend their simulation capabilities by writing new or leveraging other Python packages. Moreover, this does not compromise the performance, as code can be just-in-time compiled with Numba. Our benchmark tests using curated models from the BioModels repository demonstrate that these tools may provide a potentially superior performance advantage compared to other existing tools. In addition, to ensure a user-friendly experience, our packages use standard typed modern Python syntax that provides a seamless integration with integrated development environments. Our Python-centric approach significantly enhances code analysis, error detection, and refactoring capabilities, positioning Poincaré and SimBio as valuable tools for the modeling community. Poincaré and SimBio are released under the MIT license. Their source code is available on GitHub (https://github.com/maurosilber/poincare and https://github.com/hgrecco/simbio) and can be installed from PyPI or conda-forge.

Spexwavepy: an open-source Python package for X-ray wavefront sensing using speckle-based techniques.

In situ wavefront sensing plays a critical role in the delivery of high-quality beams for X-ray experiments. X-ray speckle-based techniques stand out among other in situ techniques for their easy experimental setup and various data acquisition modes. Although X-ray speckle-based techniques have been under development for more than a decade, there are still no user-friendly software packages for new researchers to begin with. Here, we present an open-source Python package, spexwavepy, for X-ray wavefront sensing using speckle-based techniques. This Python package covers a variety of X-ray speckle-based techniques, provides plenty of examples with real experimental data and offers detailed online documentation for users. We hope it can help new researchers learn and apply the speckle-based techniques for X-ray wavefront sensing to synchrotron radiation and X-ray free-electron laser beamlines.

Gaia DR3 detectability of unresolved binary systems

Context.Gaia cannot individually resolve very close binary systems; however, the collected data can still be used to identify them. A powerful indicator of stellar multiplicity is the sources’ reported re-normalised unit weight error (RUWE), which effectively captures the astrometric deviations from single-source solutions. Aims. We aim to characterise the impact of binarity on the RUWE. By flagging potential binary systems based on RUWE, we aim to determine which of their properties will contribute the most to their detectability. Methods. We developed a model to estimate the RUWEs for observations of Gaia sources, based on the biases to the single-source astrometric track arising from the presence of an unseen companion. Then, using the recipes from previous GaiaUnlimited selection functions, we estimated the selection probability of sources with high RUWEs, and discussed what binary properties contribute to increasing the sources’ RUWEs. Results. We computed the maximum RUWE that is compatible with single-source solutions as a function of their location on-sky. We see that binary systems selected as sources with a RUWE higher than this sky-varying threshold have a strong detectability window in their orbital period distribution, which peaks at periods equal to the Gaia observation time baseline. Conclusions. We demonstrate how our sky-varying RUWE threshold provides a more complete sample of binary systems when compared to single sky-averaged values by studying the unresolved binary population in the Gaia Catalogue of Nearby Stars. We provide the code and tools used in this study, as well as the sky-varying RUWE threshold, through the GaiaUnlimited Python package.

Mapping protein conformational landscapes from crystallographic drug fragment screens.

Proteins are dynamic macromolecules. Knowledge of a protein's thermally accessible conformations is critical to determining important transitions and designing therapeutics. Accessible conformations are highly constrained by a protein's structure such that concerted structural changes due to external perturbations likely track intrinsic conformational transitions. These transitions can be thought of as paths through a conformational landscape. Crystallographic drug fragment screens are high-throughput perturbation experiments, in which thousands of crystals of a drug target are soaked with small-molecule drug precursors (fragments) and examined for fragment binding, mapping potential drug binding sites on the target protein. Here, we describe an open-source Python package, COLAV (COnformational LAndscape Visualization), to infer conformational landscapes from such large-scale crystallographic perturbation studies. We apply COLAV to drug fragment screens of two medically important systems: protein tyrosine phosphatase 1B (PTP-1B), which regulates insulin signaling, and the SARS CoV-2 Main Protease (MPro). With enough fragment-bound structures, we find that such drug screens also enable detailed mapping of proteins' conformational landscapes.

A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)

Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.

Discovery and Quantification of Long-Range RNA Base Pairs in Coronavirus Genomes with SEARCH-MaP and SEISMIC-RNA.

RNA molecules perform a diversity of essential functions for which their linear sequences must fold into higher-order structures. Techniques including crystallography and cryogenic electron microscopy have revealed 3D structures of ribosomal, transfer, and other well-structured RNAs; while chemical probing with sequencing facilitates secondary structure modeling of any RNAs of interest, even within cells. Ongoing efforts continue increasing the accuracy, resolution, and ability to distinguish coexisting alternative structures. However, no method can discover and quantify alternative structures with base pairs spanning arbitrarily long distances - an obstacle for studying viral, messenger, and long noncoding RNAs, which may form long-range base pairs. Here, we introduce the method of Structure Ensemble Ablation by Reverse Complement Hybridization with Mutational Profiling (SEARCH-MaP) and software for Structure Ensemble Inference by Sequencing, Mutation Identification, and Clustering of RNA (SEISMIC-RNA). We use SEARCH-MaP and SEISMIC-RNA to discover that the frameshift stimulating element of SARS coronavirus 2 base-pairs with another element 1 kilobase downstream in nearly half of RNA molecules, and that this structure competes with a pseudoknot that stimulates ribosomal frameshifting. Moreover, we identify long-range base pairs involving the frameshift stimulating element in other coronaviruses including SARS coronavirus 1 and transmissible gastroenteritis virus, and model the full genomic secondary structure of the latter. These findings suggest that long-range base pairs are common in coronaviruses and may regulate ribosomal frameshifting, which is essential for viral RNA synthesis. We anticipate that SEARCH-MaP will enable solving many RNA structure ensembles that have eluded characterization, thereby enhancing our general understanding of RNA structures and their functions. SEISMIC-RNA, software for analyzing mutational profiling data at any scale, could power future studies on RNA structure and is available on GitHub and the Python Package Index.

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

Background & aimsWe 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. MethodsWe 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. ResultsBy 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. ConclusionThe 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.

Hf_hydrodata: A Python package for accessing hydrologic simulations and observations across the United States

hf_hydrodata: A Python package for accessing hydrologic simulations and observations across the United States

Conjugacy class fusion from four maximal subgroups of the Monster

We determine the conjugacy class fusion from certain maximal subgroups of the Monster to the Monster, to justify the addition of these data to the Character Table Library in the computational algebra system GAP. The maximal subgroups in question are (PSL2(11)×PSL2(11)):4, 112:(5×2A5), 72:SL2(7), and PSL2(19):2. Our proofs are supported by reproducible calculations carried out using the Python package mmgroup, a computational construction of the Monster recently developed by Seysen.

BreathXplorer: Processing Online Breathomics Data Generated from Direct Analysis Using High-Resolution Mass Spectrometry.

Nontargeted breath analysis in real time using high-resolution mass spectrometry (HRMS) is a promising approach for high coverage profiling of metabolites in human exhaled breath. However, the information-rich and unique non-Gaussian metabolic signal shapes of real-time HRMS-based data pose a significant challenge for efficient data processing. This work takes a typical real-time HRMS technique as an example, i.e. secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS), and presents BreathXplorer, an open-source Python package designed for the processing of real-time exhaled breath data comprising multiple exhalations. BreathXplorer is composed of four main modules. The first module applies either a topological algorithm or a Gaussian mixture model (GMM) to determine the start and end points of each exhalation. Next, density-based spatial clustering of applications with noise (DBSCAN) is employed to cluster m/z values belonging to the same metabolic feature, followed by applying an intensity relative standard deviation (RSD) filter to extract real breath metabolic features. BreathXplorer also offers functions of (1) feature alignment across the samples and (2) associating MS/MS spectra with their corresponding metabolic features for downstream compound annotation. Manual inspection of the metabolic features extracted from SESI-HRMS breath data suggests that BreathXplorer can achieve 100% accuracy in identifying the start and end points of each exhalation and acquire accurate quantitative measurements of each breath feature. In a proof-of-concept study on exercise breathomics using SESI-HRMS, BreathXplorer successfully reveals the significantly changed metabolites that are pertinent to exercise. BreathXplorer is publicly available on GitHub (https://github.com/HuanLab/breathXplorer). It provides a powerful and convenient-to-use tool for the researchers to process breathomics data obtained by directly analysis using HRMS.

Effects on magnetocaloric and magnetoresistive properties due to nonmagnetic Zn2+ substitution at Mn-site in charge-ordered Pr0.5Ca0.5MnO3 manganite

This study of nonmagnetic Zn-substitution effect on the charge-ordered behavior of Pr0.5Ca0.5Mn1-xZnxO3 (x = 0.0, 0.1) compounds reveal the weakening of strong charge-ordered antiferromagnetic state of the parent compound (x = 0.0). Magnetocaloric effect (MCE) has been calculated using a Python package from isothermal M-H data for these samples. A prominent ferromagnetic contribution in the antiferromagnetic matrix for Mn-site doping is observed, which removes the inverse MCE observed in the pristine sample below the charge ordering temperature TCO. Development of ferromagnetism in the doped system exhibits a metal-insulator transition at TMI = 90 K under 7 T magnetic field and large magnetoresistance (> 99 %), which is not observed in parent compound. Magnetic field-dependent hysteresis at low temperature reveals the ferromagnetic metastability and first-order nature of magnetic phase transition. Random occupation of Zn at the Mn-site of the parent compound disrupts 1:1 Mn3+/Mn4+ charge-ordered superstructure of the parent compound which weakens the charge-ordered antiferromagnetic strength promoting ferromagnetism and double-exchange interaction through an enhanced 2p-3d hybridization between Mn and O ions.

Packaging code and data for reproducible research: A case study of journey time statistics

The effective and ethical use of data to inform decision-making offers huge value to the public sector, especially when delivered by transparent, reproducible, and robust data processing workflows. One way that governments are unlocking this value is through making their data publicly available, allowing more people and organisations to derive insights. However, open data is not enough in many cases: publicly available datasets need to be accessible in an analysis-ready form from popular data science tools, such as R and Python, for them to realise their full potential. This paper explores ways to maximise the impact of open data with reference to a case study of packaging code to facilitate reproducible analysis. We present the jtstats project, which consists of a main Python package, and a smaller R version, for importing, processing, and visualising large and complex datasets representing journey times, for many transport modes and trip purposes at multiple geographic levels, released by the UK Department for Transport (DfT). jtstats shows how domain specific packages can enable reproducible research within the public sector and beyond, saving duplicated effort and reducing the risks of errors from repeated analyses. We hope that the jtstats project inspires others, particularly those in the public sector, to add value to their data sets by making them more accessible.

Optimization-Based Pairwise Interaction Point Process (O-PIPP): A Precise and Universal Retinal Mosaic Modeling Approach.

A retinal mosaic, the spatial organization of a population of homotypic neurons, is thought to sample a specific visual feature into the feedforward visual pathway. The purpose of this study was to propose a universal modeling approach for precisely generating retinal mosaics and overcoming the limitations of previous models, especially in modeling abnormal mosaic patterns under disease conditions. Here, we developed the optimization-based pairwise interaction point process (O-PIPP). It incorporates optimization techniques into previous simulation approaches, enabling directional control of the simulation process according to the user-designed optimization target. For the convenience of the community, we implemented the O-PIPP approach into a Python package and a website application. We showed that the O-PIPP can generate more precise neural spatial patterns of healthy and diseased mosaics compared to previous phenomenological approaches. Notably, through modeling the retinal neural circuitry with O-PIPP-simulated retinitis pigmentosa cone mosaics, we elucidated how the cone mosaic rearrangement impacted the information processing of ganglion cells. The O-PIPP provides a precise and universal tool to simulate realistic mosaics, which could help to investigate the function of retinal mosaics in vision.