Python Package Research Articles

Cyclic-coverings of Line graph L(Sn) and its Disjoint Union

A graph is a particular representation of a static network and labeling of a graph can be think of as automatic routing of data in a network topology. A visual representation of data in the form of a graph help us to look deep insight. The data science (e.g., Python Package: a computer programming language) uses graph theory concepts to study and analyze the networks. Analyzing these network is equivalent of finding a set of edges E′ for a graph G such that every vertex of G is incident with at least one edge in E′. Then E′ is called an edge-covering of G. A spanning tree of a connected graph is an example of edge-covering. A finite simple graph G is an (ad, d)-H-antimagic if the following three conditions are satisfied: G has an H-covering (H a subgraph of G), there exists a bijection α : V (G) ∪ E(G) → {1, 2, 3, . . . , |V (G) ∪ E(G)|} and the H-weights constitute an arithmetic progression with common difference d. The above said labeling is called super if α(V ) = {1, 2, . . . , |V |}. In this research article, we focused on studying super C3-antimagic labeling of line graph of a sun-let graph for several differences and C3-supermagic labeling of its disjoint union.

Classification of Liver Lesion Stages using pyRadiomics Features Combined with 3D-CNN in 3D-CT and US Images

Objectives: The goal of this study is to identify the various stages in liver diseases employing pyRadiomics features by analyzing the 3-dimensional CT and US images. Methods: The study uses Gray level Run Length Matrix for feature extraction and 3D-CNN for classification and compared with methods like Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB) classifiers to assess the efficiency of the proposed model. The suggested algorithm is implemented using the library functions in Tensorflow and Keras packages in Python. Findings: The study utilizes radiomic feature extraction and two-phase classification to detect and classify liver disorders. In the first phase, binary classification is used to classify US and CT images as normal or abnormal, with accuracy rates of 95.4% and 97.9%. This is followed by the second phase classification, which classifies the lesion stages for both US and CT aberrant images. CT images have been shown to be more accurate in categorizing lesion stages than US imaging. CT images have a much lower misclassification rate at each step than US images. Finally, the effectiveness of the 3D-CNN model is compared with SVM, RF, and NB and the suggested method outperforms the other methods. Novelty: The suggested architecture employs radionics feature extraction with two-phase CNN for classification and the suggested method is found to be more efficient than SVM, RF, and NB. Keywords: Radiomics; Feature Extraction; Convolutional Neural Networks (CNN); Classification; Liver Diseases

VI-VS: calibrated identification of feature dependencies in single-cell multiomics.

Unveiling functional relationships between various molecular cell phenotypes from data using machine learning models is a key promise of multiomics. Existing methods either use flexible but hard-to-interpret models or simpler, misspecified models. VI-VS(Variational Inference for Variable Selection) balances flexibility and interpretability to identify relevant feature relationships in multiomic data. It uses deep generative models to identify conditionally dependent features, with false discovery rate control. VI-VS is available as an open-source Python package, providing a robust solution to identify features more likely representing genuine causal relationships.

Tutorial on Molecular Latent Space Simulators (LSSs): Spatially and Temporally Continuous Data-Driven Surrogate Dynamical Models of Molecular Systems.

The inherently serial nature and requirement for short integration time steps in the numerical integration of molecular dynamics (MD) calculations place strong limitations on the accessible simulation time scales and statistical uncertainties in sampling slowly relaxing dynamical modes and rare events. Molecular latent space simulators (LSSs) are a data-driven approach to learning a surrogate dynamical model of the molecular system from modest MD training trajectories that can generate synthetic trajectories at a fraction of the computational cost. The training data may comprise single long trajectories or multiple short, discontinuous trajectories collected over, for example, distributed computing resources. Provided the training data provide sufficient sampling of the relevant thermodynamic states and dynamical transitions to robustly learn the underlying microscopic propagator, an LSS furnishes a global model of the dynamics capable of producing temporally and spatially continuous molecular trajectories. Trained LSS models have produced simulation trajectories at up to 6 orders of magnitude lower cost than standard MD to enable dense sampling of molecular phase space and large reduction of the statistical errors in structural, thermodynamic, and kinetic observables. The LSS employs three deep learning architectures to solve three independent learning problems over the training data: (i) an encoding of the high-dimensional MD into a low-dimensional slow latent space using state-free reversible VAMPnets (SRVs), (ii) a propagator of the microscopic dynamics within the low-dimensional latent space using mixture density networks (MDNs), and (iii) a generative decoding of the low-dimensional latent coordinates back to the original high-dimensional molecular configuration space using conditional Wasserstein generative adversarial networks (cWGANs) or denoising diffusion probability models (DDPMs). In this software tutorial, we introduce the mathematical and numerical background and theory of LSS and present example applications of a user-friendly Python package software implementation to alanine dipeptide and a 28-residue beta-beta-alpha (BBA) protein within simple Google Colab notebooks.

Emerging trends and hotspots in intestinal microbiota research in sepsis: bibliometric analysis

BackgroundThe association between the gut microbiota and sepsis has garnered attention in the field of intestinal research in sepsis. This study utilizes bibliometric methods to visualize and analyze the literature on gut microbiota research in sepsis from 2011 to 2024, providing a scientific foundation for research directions and key issues in this domain.MethodsOriginal articles and reviews of gut microbiota research in sepsis, which published in English between 2011 and 2024, were obtained from the Web of Science Core Collection on June 21, 2024. Python, VOSviewer, and CiteSpace software were used for the visual analysis of the retrieved data.ResultsA total of 1,031 articles were analyzed, originating from 72 countries or regions, 1,614 research institutions, and 6,541 authors. The articles were published in 434 different journals, covering 89 different research fields. The number of publications and citations in this research area showed a significant growth trend from 2011 to 2024, with China, the United States, and the United Kingdom being the main research forces. Asada Leelahavanichkul from Thailand was identified as the most prolific author, making him the most authoritative expert in this field. “Nutrients” had the highest number of publications, while “Frontiers in Cellular and Infection Microbiology,” “Frontiers in Immunology” and “the International Journal of Molecular Sciences” have shown increasing attention to this field in the past 2 years. Author keywords appearing more than 100 times included “gut microbiota (GM),” “sepsis” and “microbiota.” Finally, this study identified “lipopolysaccharides (LPS),” “short-chain fatty acids (SCFAs),” “probiotics,” “fecal microbiota transplantation (FMT)” and “gut-liver axis” as the research hotspots and potential frontier directions in this field.ConclusionThis bibliometric study summarizes current important perspectives and offers comprehensive guidance between sepsis and intestinal microbiota, which may help researchers choose the most appropriate research directions.

Conditional Flux Balance Analysis (cFBA) Toolbox for python: application to research metabolism in cyclic environments

Abstract Summary We present py_cFBA, a Python-based toolbox for conditional flux balance analysis (cFBA). Our toolbox allows for an easy implementation of cFBA models using a well-documented and modular approach and supports the generation of Systems Biology Markup Language (SBML) models. The toolbox is designed to be user-friendly, versatile, and freely available to non-commercial users, serving as a valuable resource for researchers predicting metabolic behaviour with resource allocation in dynamic-cyclic environments. Availability Extensive documentation, installation steps, tutorials and examples are available at [https://tp-watson-python-cfba.readthedocs.io/en/]. The py_cFBA python package is available at [https://pypi.org/project/py-cfba/].

Analyzing Multilingual French and Russian Text using NLTK, spaCy, and Stanza

This lesson covers tokenization, part-of-speech tagging, and lemmatization, as well as automatic language detection, for non-English and multilingual text. You’ll learn how to use the Python packages NLTK, spaCy, and Stanza to analyze a multilingual Russian and French text.

From STEM-EDXS data to phase separation and quantification using physics-guided NMF

Abstract We present the development of a new algorithm which combines state-of-the-art energy-dispersive X-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the hyperspectral unmixing of scanning transmission electron microscope EDX spectrum images. The algorithm is based on non-negative matrix factorization (NMF) incorporating a physics-guided factorization model. It optimizes a Poisson likelihood, under additional simplex constraint together with user-chosen sparsity-inducing and smoothing regularizations, and is based on iterative multiplicative updates. The fluorescence of X-rays is fully modeled thanks to state-of-the-art theoretical work. It is shown that the output of the algorithm can be used for a direct chemical quantification. With this approach, it is straightforward to include a priori knowledge on the specimen such as the presence or absence of certain chemical elements in some of its phases. This work is implemented within two open-source Python packages, espm and emtables, which are used here for data simulation, data analysis and quantification. Using simulated data, we demonstrate that incorporating physical modeling in the decomposition helps retrieve meaningful components from spatially and spectrally mixed phases, even when the data are very noisy. For synthetic data with a higher signal, the regularizations yield a tenfold increase in the quality of the reconstructed abundance maps compared to standard NMF. Our approach is further validated on experimental data with a known ground truth, where state-of-the art results are achieved by using prior knowledge about the sample. Our model can be generalized to any other scanning spectroscopy techniques where underlying physical modeling can be linearized.

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, COnformational LAndscape Visualization (COLAV), 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 (PTP1B), which regulates insulin signaling, and the SARS CoV-2 Main Protease (MPro). With enough fragment-bound structures, we find that such drug screens enable detailed mapping of proteins' conformational landscapes.

SpottedPy quantifies relationships between spatial transcriptomic hotspots and uncovers environmental cues of epithelial-mesenchymal plasticity in breast cancer.

Spatial transcriptomics is revolutionizing the exploration of intratissue heterogeneity in cancer, yet capturing cellular niches and their spatial relationships remains challenging. We introduce SpottedPy, a Python package designed to identify tumor hotspots and map spatial interactions within the cancer ecosystem. Using SpottedPy, we examine epithelial-mesenchymal plasticity in breast cancer and highlight stable niches associated with angiogenic and hypoxic regions, shielded by CAFs and macrophages. Hybrid and mesenchymal hotspot distribution follows transformation gradients reflecting progressive immunosuppression. Our method offers flexibility to explore spatial relationships at different scales, from immediate neighbors to broader tissue modules, providing new insights into tumor microenvironment dynamics.

INNV-02. BRAINMETS: A CLINICIAN-FRIENDLY SOFTWARE TO MANUALLY SEGMENT BRAIN METASTASES AND DETECT PRIMARY TUMOR ORIGIN USING DEEP LEARNING

Abstract Estimates suggest that around 20%-40% of cancer patients are affected by brain metastases. A subset of these patients first present with central nervous system manifestations with an unknown primary. The issue then arises regarding which region of the body should be imaged, if not the entire body, exposing the patient to more radiation. To address this issue, streamline workflows, and save precious time, we developed a software clinicians can use to identify primary tumor locus. “BrainMets” is a user-friendly software that handles a DICOM input, allows the user to segment the region(s) of interest manually, and uses a deep learning model to predict the most likely tumor origin. The model is a mixed-effects neural network pre-trained on radiomic features from open-source data by Ocaña-Tienda et al. (2023) using 637 high-resolution imaging studies from 10 different medical centers with an accuracy of 90.5%, precision of 92.7%, recall of 87.8%, and F1 score of 90.2%. The software leverages the open-source software 3D-Slicer to help segment regions of interest, extract radiomic features, and make predictions. It is built using Flask, a Python package for backend development. The frontend user interface is created using HTML/CSS and Bootstrap. The “BrainMets” software thus strives to enhance diagnostic workflow significantly, reduce appointment times for scans, and improve patient experience. Future enhancements include integrating more AI models, improving healthcare IT system compatibility, and updating datasets to boost predictive accuracy and generalizability. This tool sets a new standard in personalized medicine, optimizing treatment strategies for cancer patients with brain metastases. As adoption increases, it could substantially improve patient outcomes and reduce healthcare costs associated with misdiagnosis and overtreatment. Further research and development will focus on expanding its applicability across various types of cancers.

Wmpy-power: A Python package for process-based regional hydropower simulation

wmpy-power: A Python package for process-based regional hydropower simulation

Additions to Space Physics Data Facility and pysatNASA: Increasing Mars Global Surveyor and Mars Atmosphere and Volatile EvolutioN Dataset Utility

The Space Physics Data Facility (SPDF) is a digital archive of space physics data and is useful for the storage, analysis, and dissemination of data. We discuss the process used to create an amended dataset and store it on the SPDF. The operational software to generate the archival data software uses the open-source Python package pysat, and an end-user module has been added to the pysatNASA module. The result is the addition of data products to the Mars Global Surveyor (MGS) magnetometer (MAG) dataset, its archival location on SPDF, and pysat compatibility. The primary and metadata format increases the convenience and efficiency for users of the MGS MAG data. The storage of planetary and heliophysics data in one location supports the use of data throughout the solar system for comparison, while pysat compatibility enables loading data in an identical format for ease of processing. We encourage the use of the outlined process for past, present, and future space science missions of all sizes and funding levels. This includes balloons to Flagship-class missions.

Industrialization and innovation strategy of event resources in ice and snow sports under the background of big data

This article examines the industrialization and innovative strategy of event resources in ice and snow sports in the context of big data. Based on this analysis, the article proposes a path and development strategy for the Chinese sports industry, which includes encouraging the industrialization of the nation’s ice and snow sports industry. It is important to evaluate ice and snow sports in order to improve competitive results and optimize training techniques using big data technologies. Despite using a variety of techniques to evaluate performance, their inability to fully capture the complex patterns found in ice and snow sports events presents difficulties. In order to assess and enhance sports talents, therefore we proposed a new method using ISSE-Apriori algorithms. The pre-processed data gathered before extracting the most important and relevant features. The experiment results section analyzes the resources of ice and snow sports cities. The results examine sports event strategies, taking into account predictions and actual accuracy results, the GDP growth rate, the efficiency of industrial development, and a comparison of Apriori before and after enhancement. The experimental result is validating using measures like recall, accuracy, and precision. In addition, we conducted a comparative analysis with the current approaches to confirm the efficiency and robustness of the proposed methodology. The Suggested approach is implemented with Python Software. The Suggested Approach’s performance is measured in terms of RMSE (0.3524), MAE (0.1832), MAPE (4.24) with large dataset. The results stated that the proposed methodology has provided an accuracy of 98.42%.

Hyperparameter Optimization for Atomic Cluster Expansion Potentials.

Machine learning-based interatomic potentials enable accurate materials simulations on extended time- and length scales. ML potentials based on the atomic cluster expansion (ACE) framework have recently shown promising performance for this purpose. Here, we describe a largely automated computational approach to optimizing hyperparameters for ACE potential models. We extend our openly available Python package, XPOT, to include an interface for ACE fitting, and discuss the optimization of the functional form and complexity of these models based on systematic sweeps across relevant hyperparameters. We showcase the usefulness of the approach for two example systems: the covalent network of silicon and the phase-change material Sb2Te3. More generally, our work emphasizes the importance of hyperparameter selection in the development of advanced ML potential models.

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainty with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of classification of winter precipitation type and regression of surface layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods, while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. In order to encourage broader adoption of evidential deep learning, we have developed a new Python package, MILES-GUESS (https://github.com/ai2es/miles-guess), that enables users to train and evaluate both evidential and ensemble deep learning.

Intelligent rehabilitation assistant: Application of deep learning methods in sports injury recovery

In recent years, sports injury rehabilitation has developed into a specialized field that has forced the combination of an orthopedic surgeon, sports physiotherapist, and sports physician. Determining the appropriate time for an injured athlete to resume practice or competition is regarded as sports rehabilitation. Discovering the best solutions to avoid injuries, maximize recovery, and enhance performance is crucial for sports activities. The study introduced an intelligent rehabilitation assistant (IRA) that leverages advanced deep learning (DL) methods to enhance sports injury recovery. In this study, the IRA incorporates redefined prairie dog optimized bidirectional long-short-term memory (RPDO-Bi-LSTM) to enhance accuracy, predicting sports injury recovery. The study collected data on the state of rehabilitation, physiological parameters, and general health using wearable sensors and movement patterns. The data was preprocessed using a median filter to remove noise from sensor data. Region-based segmentation using segmented images from preprocessed data. Convolutional neural networks (CNN) using extracted features from obtained data. The IRA provides personalized recovery plans and real-time feedback. The framework consists of the components, suggested models to create quality scores for motions, measurements to quantify motion performance, and scoring of performance measurement elements into numerical quality scores. The proposed method is implemented using Python software. RPDO-Bi-LSTM presentation is evaluated by various metrics, such as accuracy 94.2% recall 98.2%, precision 96.5%, and specificity 95.2%, f1 score 95.6%, he planned technique attained good performance and improved the accuracy of sports injury recovery.

Wearable device data-driven athlete injury detection and rehabilitation monitoring algorithm

In recent years, sports wearable technology has completely changed the way athletes prepare, compete, and recover. Wearable technology has a lot to offer in the rehabilitation process, which is essential to an athlete’s return to their best performance. Wearable devices for athlete injury detection pose potential challenges like data quality, security, and privacy, impacting accuracy, reliability, and effectiveness. To solve these problems, an innovative injury detection and rehabilitation monitoring (IDRM) system was proposed for athletes. By employing an adjustable recurrent neural network (ARNN) to detect anomalies in injury risks such as abnormal joint movements in athletes. In this study, biomechanics data was collected from sports athletes through wearable devices, and the wearable system provided feedback to the user. A redefined convolutional neural network (RCNN) was utilized to monitor the rehabilitation process. This system tracks athlete’s rehabilitation progress and ensures that progress monitors were performed correctly, and the system, feasibility was evaluated on 10 healthy subjects performing 4 different rehabilitation exercises. Each exercise was performed four times monitoring and validation. The data was preprocessed using a Gaussian filter to remove noise from the obtained data. Then the features are extracted using independent component analysis (ICA) for dimensionality reduction from preprocessed data. The proposed method is implemented using Python software. In comparative analysis, the performance of ARNN showed high performance, with an F1-measure of 91.6%, accuracy of 93.5%, recall of 92.8%, and precision of 91.4%. With a 95% accuracy rate, 98% F1 measure, 94% precision, and 93% recall, the RCNN model functioned effectively. The result showed the proposed method achieved better performance in athlete injury detection and accurately recognizing all rehabilitation monitoring. This study provides a complete approach to athlete health management by highlighting the integration of rehabilitation monitoring and injury detection into an overall structure.

Establish a novel framework for enhancing minority music genre identification

The objective of this study is to develop a new framework based on Waterwheel Plant optimization for improving minority music genre classification using Layer-tuned Long Short-Term Memory (WP-LT-LSTM). Chinese minority music includes various musical styles of different ethnic groups in China. It depends on the specific instrumentalities, the distribution of pitch classes and rhythms, and the culture. Specifically, the proposed framework will enhance the ability to efficiently detect under represented music genres, which could have applicability for cultural sustainability and more personalized music recommendation services. For this, we collected a dataset that includes a wide range of different minority music samples in audio format. These include genre labels, artist information and audio features necessary for the training of our suggested model. Using K-fold cross validation to enhances the accuracy. Min-max Normalization is used on the obtained data to perform pre-processing. To extract the important features from the processed data, we used Mel-frequency cepstral coefficients (MFCCs). In our proposed model, the WP algorithm dynamically adjusts LT-LSTM’s internal parameters, enhancing model adaptability. LT-LSTM processes sequential audio data, capturing temporal dependencies crucial for genre classification in minority music genres. The implemented model is executed in Python software. It evaluates the model’s performance across a range of parameters throughout the result analysis phase. We also performed comparison studies using standard methods. The results collected indicate the excellence and effectiveness of the proposed framework for music genre identification.

A Python GPU-accelerated solver for the Gross-Pitaevskii equation and applications to many-body cavity QED

TorchGPE is a general-purpose Python package developed for solving the Gross-Pitaevskii equation (GPE). This solver is designed to integrate wave functions across a spectrum of linear and non-linear potentials. A distinctive aspect of TorchGPE is its modular approach, which allows the incorporation of arbitrary self-consistent and time-dependent potentials, e.g., those relevant in many-body cavity QED models. The package employs a symmetric split-step Fourier propagation method, effective in both real and imaginary time. In our work, we demonstrate a significant improvement in computational efficiency by leveraging GPU computing capabilities. With the integration of the latter technology, TorchGPE achieves a substantial speed-up with respect to conventional CPU-based methods, greatly expanding the scope and potential of research in this field.