Python Programming Research Articles

Age-Related Development During Predictors and Clinical Neurodiagnostic Criteria of Cognitive Impairment in the General Medical Network

Background: prevalence, severity and heterogeneity of cognitive impairment in elderlies along with limited therapy options determine the relevance of the problem of timely diagnostics of cognitive disfunction. The purpose of this study is to identify a combination of the most informative patterns that allow a differentiated approach to the diagnosis of age-related cognitive impairment. Patients and methods: 213 patients were examined (99 patients 50–65 years old, 114 patients over 65 years of age) of “Federal Centre for High Medical Technologies” of Russian Ministry of Health (Kaliningrad). All patients complained for impaired mental performance, memory and attention. A neuropsychologic testing was conducted using next scales: Montreal Cognitive Assessment (MoCA), Hospital Anxiety and Depression Scale (HADS), Multidimensional Fatigue Inventory (MFI-20) and additional cognitive impairment tests. For statistical analysis, machine learning algorithms, Python programming language, and Pandas and SciPy libraries were used. Results: for patients in the 50–65 age category, high relevance was found for executive dysfunction, decreased attention span, fatigue, anxiety, and endocrine system disorders. For patients over 65 years of age, significant features were semantic aphasia, perceptual and memory impairment, hyperlipidemia, history of ischemic stroke, and obesity. A significant negative correlation for the age index was found with the parameters of depression and anxiety; a positive correlation was found with the index of physical asthenia, disorders of perception, memory and semantic processing of information. Conclusion: the results demonstrate prevalence of cognitive dysfunctions in elderly patients. The tests assessing visual perception and semantic information processing can be of interest in early degenerative cognitive impairments diagnosis in elderly age. Discriminant analysis of a wide range of age-related variables will allow to make more effective aging trajectories prediction without any time-consuming diagnostic methods.

Cracking the code: An evidence-based approach to teaching Python in an undergraduate earth science setting

Scientific programming has become increasingly essential for manipulating, visualizing, and interpreting the large volumes of data acquired in earth science research. Yet few discipline-specific instructional approaches have been documented and assessed for their effectiveness in equipping geoscience undergraduate students with coding skills. Here we report on an evidence-based redesign of an introductory Python programming course, taught fully remotely in 2020 in the School of Oceanography at the University of Washington. Key components included a flipped structure, activities infused with active learning, an individualized final research project, and a focus on creating an accessible learning environment. Cloud-based notebooks were used to teach fundamental Python syntax as well as functions from packages widely used in climate-related disciplines. By analyzing quantitative and qualitative data from surveys, online learning platforms, student work, assessments, and a focus group, we conclude that the instructional design facilitated learning and supported self-guided scientific inquiry. Students with less or no prior exposure to coding achieved similar success as peers with more experience, an outcome likely mediated by higher engagement with course resources. We believe that the constructivist approach to teaching introductory programming and data literacy that we present could be broadly applicable across the earth sciences and in other scientific domains.

A new concept for bioenergy yield in anaerobic digestion process: Highlighting current yield challenges

The development of sustainable energy sources including biogas produced from urban and industrial wastes has received much attention over the past few years. Given that biogas can be an alternative to fossil fuels before long, its yield, representing the value of energy production from the substrate, has been of utmost importance. With reference to the current definition, biogas yield simply depends on the maximum biogas produced per unit mass of the substrate. In this line, anaerobic digestion (AD) is a time-consuming process, whose duration affects the biogas production yield as well as the maximum biogas production rate. Against this background, the present study dedicated focus on the time factor to present a new definition of biogas/methane (CH4) production yield in AD. In light of this, the time entailed for the AD process (T95%), including the lag phase time (Tlag), the time in proportion to the logarithmic production of biogas (Tlog), and the biogas stabilization time (reaching 95 % of the maximum biogas production rate, T→95%) were taken into account. In accordance with previous research, four modes, viz., (i) equal maximum biogas-different time, (ii) different maximum biogas-equal time, (iii) different maximum biogas-different time (receding yield) and (iv) different biogas maximum-different time (approaching yield) were assessed. For this purpose, the kinetic models, namely, modified Gompertz (MG) and logistic function (LF), were recruited to determine the maximum biogas production rate (Rm) and the lag phase time (λ). Among the mentioned modes, the yield difference between the samples investigated according to the current and time-based yields was 24 % on average, and reached 85 % in the first and third modes. Evaluating the data from 32 studies, using the Python programming language, correspondingly revealed that the correlation coefficient between the AD process time (T95%) and the current and time-based yields augmented from 7 % to 51 %. This demonstrated the impact of the time factor on the biogas yield. Based on the MG model, the significance level of the Rm parameter increased from 52 % to 94 %, highlighting the value of the logarithmic phase time of biogas in the AD reactor.

A comparative analysis of boosting and genetic programming techniques for predicting mechanical properties of soilcrete materials

This study aims to predict compressive strength (CS) and modulus of elasticity (E) of soilcrete mixes to foster their widespread use in the industry. Soilcfigrete has the potential to promote sustainable construction practices by making use of locally available raw materials. However, the accurate determination of mechanical properties of soilcrete mixes is inevitable to foster their widespread use. Thus, this study employs different machine learning algorithms including Extreme Gradient Boosting (XGB), Gene Expression Programming (GEP), AdaBoost, and Multi Expression Programming (MEP) for this purpose. The XGB and AdaBoost algorithms were implemented using python programming language while MEP and GEP were implemented using specialized softwares. The data used for model development was obtained from previously published literature containing five input parameters and two output parameters. This data was split into two sets named training and testing sets for training and testing of the algorithms respectively. The developed models for CS and E prediction were validated using several error metrices and residual comparison. The objective function value which should be closer to zero for an accurate model is the least for XGB model for prediction of both variables (0.0036 for CS and 0.00315 for E). Moreover, shapley analysis was carried out using XGB model to get insights into the underlying model framework. The results highlighted that water-to-binder ratio (W/B), metakaolin (MK), and ultrasonic pulse velocity (UV) are the most significant variables for predicting E and CS of soilcrete materials. These insights can be used practically to optimize the mixture composition of soilcrete mixes according to different site requirements.

Web-based Internet of Things on environmental and lighting control and monitoring system using node-RED, MQTT and Modbus communications within embedded Linux platform

This paper presents our innovative approach in the field of Internet of Things (IoT). Our focus was on enhancing the research environment at Cheng Shiu University's laboratory through the creation of a dynamic network monitoring setup and lighting control system. Our aim was to simplify equipment management and promote energy efficiency. The core of our development is the Raspberry Pi 4B embedded Linux platform, which serves as a host for both a Message Queuing Telemetry Transport (MQTT) broker and a Node-RED server. Python programs, developed in PyCharm IDE, utilize multiple threads for efficient data exchange via Modbus TCP and MQTT protocols, facilitating seamless interaction between PLCs and MQTT broker servers. The Node-RED IoT development tool was instrumental in crafting a cross-platform Human-Machine Interface (HMI) web server with MQTT Publish/Subscribe capabilities, ensuring smooth communication with the MQTT broker server. By configuring the Apache HTTP server to direct to the Node-RED web directory, we can effectively monitor laboratory conditions and manage the lighting system. The experimental results validate the cost-effectiveness of our approach in transforming traditional control systems into a web-based supervisory control system, thereby enhancing the overall functionality of existing equipment. This project underscores our commitment to advancing IoT solutions in practical settings.

Exploratory Data Analytics of Air Pollutant Data for Air Quality Management Application

Purpose: With escalating concerns over the detrimental impacts of air pollution on public health and the environment, this study embarks on a comprehensive exploration of air quality dynamics, focusing on Ogun state metropolis of Nigeria. The primary objective is to contribute to the burgeoning field of air quality management by harnessing the power of data analytics, exploratory data analysis, and advanced python programming libraries. The study seeks to address critical questions regarding the current state of air quality, major sources of pollution, government interventions, and individual contributions to better air quality. Methodology: The research employs Exploratory Data Analysis to unveil intrinsic patterns and trends within historical air quality datasets extracted from IoT devices located across Ogun state. This initial phase aims to discern key statistical parameters, including mean concentrations, variations, and correlations among various pollutants. Subsequently, the study employs Topic Modeling to extract latent themes and sentiments from qualitative data, specifically focusing on public opinions gathered through digital surveys. Resulting topics provide insights into public perceptions regarding air quality, pollution sources, and the efficacy of governmental interventions. Findings: Experimental result is revealing of the diversity of the dataset. The EDA returned a mean Particulate Matter concentration of approximately 12.65 µg/m³, while the mean Nitric Oxide concentration is approximately 13.56 µg/m³. Notable correlations include a strong positive correlation between PM2.5 and PM10 (0.69), indicating a substantial association between fine and coarse particulate matter. Additionally, there is a noteworthy positive correlation between NO and Nox (0.97), suggesting a high degree of correlation between nitric oxide and nitrogen oxide levels. However, negative correlations are observed, such as the substantial negative correlation between RH (relative humidity) and PM2.5 (-0.46), implying an inverse relationship between humidity and fine particulate matter levels. The study develops an innovative air quality management application. Unique Contribution to Theory, Practice and Policy: The application aims to empower both the public and policymakers with actionable insights, fostering informed decision-making and collaborative efforts towards improving air quality. It is highly recommended that industry experts should continue to imbibe ethical standards in data acquisition and deployment practices.

Evaluation of ethanol-gasoline blends in SI engines using experimental and ANN techniques

Fuel combustion has become a major global concern, with much research focusing on the various emissions resulting from different types of fuels. Due to the harmful pollutant emissions from fossil fuels, the world has turned to renewable and alternative fuels to limit toxic emissions and greenhouse effects. Ethanol is a biofuel that, when used in spark ignition engines with gasoline can improve the octane number, combustion efficiency, and produce less emissions. The current research studies the effect of different ethanol blends E0, E5, E10, and E15 with gasoline 92 on engine performance parameters and emissions of a GX35 four-stroke engine at different engine speeds. The results along the speed range reveal that increasing ethanol amount leads to an average increase of 2.7%, 1%, and 1.1% in brake power (BP), brake thermal efficiency (BTE), and CO2 emissions, respectively. Meanwhile, it causes an average decrease of 28 °C, 3%, 15 ppm, and 0.18% in exhaust gas temperature (EGT), brake-specific fuel consumption (BSFC), HC, and CO emissions respectively. Moreover, the current study develops an Artificial Neural Networks (ANN) model for predicting the performance and emissions of spark ignition (SI) engines. Python programming language is used for ANN coding to train and validate the ANN model with E15. Regression plots were generated to visualize the correlation between the target and predicted data, indicating outstanding performance. The results confirmed the model’s reliability for BP, EGT, CO, CO2, and HC parameters with R2 values more than 0.99 and with acceptable performance for BSFC and BTE with R2 of 0.9339, and 0.9708, respectively. To ensure that the is no overfitting during the ANN study, we used different statistical methods, such as validation set, cross-validation, and learning curves.

A Model for Detection of Malwares on Edge Devices

Abstract- Malware detection is a significant challenge in today's digital landscape. As new forms of malware are continuously being developed, traditional detection techniques often fall short due to their inability to detect these new strains. This paperintroduces meaningful features that effectively capture various types of malware, including viruses, worms, Trojans and Ransomware on Edge devices. The paper used a model that implemented Random forest classifier for feature selection and a support vector machine (SVM) model for Malware detection. Object-Oriented Analysis and Design (OOAD) methodology was used to as the design methodology, which involved identifying and modeling the different components of the system and their interactions. The system was developed using Python programming language, with an emphasis on model deployment via Python Flask for web-based testing and execution. The experimental results demonstrate the effectiveness of the proposed systems when compared with other existing system. The result gotten from proposed system is better than that of the existing system by achieving a detection accuracy of 99.98% which is better than existing techniques. This dissertation presents a promising direction for improving malware detection using support vector machine (SVM) model and highlights the potential for collaborative learning approaches to overcome the challenges of traditional centralized approaches. This result simulates edge device that performs malware detection. It measures the latency for each detection and prints whether the latency is high or low. After the simulation, it plots a graph to visualize the latency over multiple requests. Which shows that the proposed model had low latency between 0.25secs to 0.15 secs on multiple requests.

TensorFlow Hydrodynamics Analysis for Ly-[formula omitted] Simulations

We introduce the Python program THALAS (TensorFlow Hydrodynamics Analysis for Lyman-Alpha Simulations), which maps baryon fields (baryon density, temperature, and velocity) to Lyα optical depth fields in both real space and redshift space. Unlike previous Lyα codes, THALAS is fully differentiable, enabling a wide variety of potential applications for general analysis of hydrodynamical simulations and cosmological inference. To demonstrate THALAS’s capabilities, we apply it to the Lyα forest inversion problem: given a Lyα optical depth field, we reconstruct the corresponding real-space dark matter density field. Such applications are relevant to both cosmological and three-dimensional tomographic analyses of Lyman Alpha forest data.

Perancangan dan Pembuatan Visual Novel Sejarah L.M.U. Nurtanio Berbasis Android

Air Vice Marshal (Anm.) Nurtanio Pringgoadisuryo is an Indonesian aviation hero who remains largely unknown to the younger generation. To raise awareness about him, an engaging and interactive application is required. A Visual Novel can serve as an ideal medium to introduce or preserve the legacy of Air Vice Marshal (Anm.) Nurtanio. The historical Visual Novel about L.M.U Nurtanio was developed using Ren’Py software, a tool for creating visual novels with a simplified version of the Python programming language. The application development followed the Multimedia Development Life Cycle (MDLC), which includes the stages of Concept, Design, Material Collection, Assembly, Testing, and Distribution. This application is accessible on Android-based devices. The design and development of this Android-based historical Visual Novel about L.M.U Nurtanio provide a more engaging and interactive learning medium for Android users.

Fourier Transform at the Workshop on Physical Optics of NSU

Description of the development of a program in Python for analyzing interference images and generating a graph of the spectrum of a light source for a workshop on physical optics. Calibration of the Fourier transform and determination of frequency components in the composition of light are provided. A visibility graph is constructed and the visibility width is estimated.

PEMPS: a phylogenetic software tool to model the evolution of metabolic pathways

BackgroundMetabolic pathways support the enzyme flux that converts input chemicals into energy and cellular building blocks. With a constant rate of input, steady-state flux is achieved when metabolite concentrations and reaction rates remain constant over time. Individual genes undergo mutation, while selection acts on higher level functions of the pathway, such as steady-state flux where applicable. Modeling the evolution of metabolic pathways through mechanistic sets of ordinary differential equations is a piece of the genotype–phenotype map model for interpreting genetic variation and inter-specific differences. Such models can generate distinct compensatory changes and adaptive changes from directional selection, indicating single nucleotide polymorphisms and fixed differences that could affect phenotype. If used for inference, this would ultimately enable detection of selection on metabolic pathways as well as inference of ancestral states for metabolic pathway function.ResultsA software tool for simulating the evolution of metabolic pathways based upon underlying biochemistry, phylogenetics, and evolutionary considerations is presented. The Python program, Phylogenetic Evolution of Metabolic Pathway Simulator (PEMPS), implements a mutation-selection framework to simulate the evolution of the pathway over a phylogeny by interfacing with COPASI to calculate the steady-state flux of the metabolic network, introducing mutations as alterations in parameter values according to a model, and calculating a fitness score and corresponding probability of fixation based on the change in steady-state flux value(s). Results from simulations are consistent with a priori expectations of fixation probabilities and systematic change in model parameters.ConclusionsThe PEMPS program simulates the evolution of a metabolic pathway with a mutation-selection modeling framework based on criteria like steady-state flux that is designed to work with SBML-formatted kinetic models, and Newick-formatted phylogenetic trees. The Python software is run on the Linux command line and is available at https://github.com/nmccloskey/PEMPS.

Preamplification-free viral RNA diagnostics with single-nucleotide resolution using MARVE, an origami paper-based colorimetric nucleic acid test.

The evolution and mutation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are urgent concerns as they pose the risk of vaccine failure and increased viral transmission. However, affordable and scalable tools allowing rapid identification of SARS-CoV-2 variants are not readily available, which impedes diagnosis and epidemiological surveillance. Here we present a colorimetric nucleic acid assay named MARVE (multiplexed, preamplification-free, single-nucleotide-resolved viral evolution) that is convenient to perform and yields single-nucleotide resolution. The assay integrates nucleic acid strand displacement reactions with enzymatic amplification to colorimetrically sense viral RNA using a metal ion-incorporated DNA probe (TEprobe). We provide detailed guidelines to design TEprobes for discriminating single-nucleotide variations in viral RNAs, and to fabricate a test paper for the detection of SARS-CoV-2 variants of concern. Compared with other nucleic acid assays, our assay is preamplification-free, single-nucleotide-resolvable and results are visible via a color change. Besides, it is smartphone readable, multiplexed, quick and cheap ($0.30 per test). The protocol takes ~2 h to complete, from the design and preparation of the DNA probes and test papers (~1 h) to the detection of SARS-CoV-2 or its variants (30-45 min). The design of the TEprobes requires basic knowledge of molecular biology and familiarity with NUPACK or the Python programming language. The fabrication of the origami papers requires access to a wax printer using the CAD and PDF files provided or requires users to be familiar with AutoCAD to design new origami papers. The protocol is also applicable for designing assays to detect other pathogens and their variants.

Development of a technique for molecular typing of <i>Bacillus anthracis</i> strains using new VNTR and INDEL markers

Introduction. Bacillus anthracis, the pathogen of a particularly dangerous zoonotic disease known as anthrax, requires strict epidemiological control and is characterized by high genetic homogeneity, which necessitates the development of genotyping methods. The aim of the study were to to find and characterize the VNTR and INDEL loci of B. anthracis and to develop on their basis a genotyping technique by PCR with electrophoretic detection of the results. Materials and methods. Marker search and phylogenetic analysis were performed on a sample of 388 genomes of B. anthracis strains, 322 from the GenBank collection (RefSeq) and 66 from the collection of the Stavropol Anti-Plague Institute of Rospotrebnadzor. Phylogenetic analysis was performed on the basis of SNP crustal alignment using the Parsnp program. The search for markers was carried out using the Mauve program and author's scripts in Python. PCR was performed using a ScreenMix-HS kit (CJSC "Eurogen", Russia). Results. Genomic variations of B. anthracis strains (SNP — 25,664, SNR — 14,387, VNTR — 693, INDEL — 14,667) were found, bioinformatic analysis of which revealed nine new VNTR and six INDEL molecular markers most suitable for genotyping. The genetic (allelic) variants of the markers are described. Primers were selected for the found markers and a PCR protocol with detection by electrophoresis in agarose gel was developed. When typing using VNTR markers was applied, the strains were divided into nine clusters: A.Br.Ames, A.Br.001/002, A.Br.Aust94, A.Br.005/006, A.Br.008/009 (Tsiankovskii), A.Br.008/009 (STI), A.Br.008/009 (A.Br.125), A.Br.008/009 (strain 228/269), B.Br.001/002. When typing using INDEL markers, the strains were divided into six clusters: A.Br.Ames, A.Br.001/002, A.Br.Aust94, A.Br.008/009(Tsiankovskii), B.Br.001/002(B.Br.014), as well as a cluster comprising several genetic lineages: A.Br.008/009 (STI), A.Br.008/009 (A.Br.125), A.Br.005/006 и B.Br.001/002. Conclusion. The use of the developed methodology for the identification of variable VNTR and INDEL loci makes it possible to reliably determine the phylogenetic position of B. anthracis strains and is promising for use in the epidemiological investigation of anthrax outbreaks.

Tracking Free-Ranging Pantaneiro Sheep during Extreme Drought in the Pantanal through Precision Technologies

The Pantanal has been facing consecutive years of extreme drought, with an impact on the quantity and quality of available pasture. However, little is known about how locally adapted breeds respond to the distribution of forage resources in this extreme drought scenario. This study aimed to evaluate the movement of free-grazing Pantaneiro sheep using a low-cost GPS to assess the main grazing sites, measure the daily distance traveled, and determine the energy requirements for walking with body weight monitoring. In a herd of 100 animals, 31 were selected for weighing, and six ewes were outfitted with GPS collars. GPS data collected on these animals every 10 m from August 2020 to May 2021 was analyzed using the Python programming language. The traveled distance and activity energy requirements (ACT) for horizontal walking (Mcal/d of NEm) were determined. The 31 ewes were weighed at the beginning and end of each season. The available dry matter (DM) and floristic composition of the grazing sites were estimated at the peak of the drought. DM was predicted using power regression with NDVI (normalized difference vegetation index) (R2 = 0.94). DM estimates averaged 450 kg/ha, ranging from traces to 3830 kg/ha, indicating overall very low values. Individual variation in the frequency of use of grazing sites was observed (p < 0.05), reflecting the distances traveled and the energetic cost of the activity. The range of distances traveled by the animals varied from 3.3 to 17.7 km/d, with an average of 5.9 km/d, indicating low energy for walking. However, the traveled distance and ACT remained consistent over time; there were no significant differences observed between seasons (p > 0.05). On average, the ewes’ initial weight did not differ from the weight at the drought peak (p > 0.05), indicating that they maintained their initial weight, which is important for locally adapted breeds as it confers robustness and resilience. This study also highlighted the importance of the breed’s biodiverse diet during extreme drought, which enabled the selection of forage for energy and nutrient supplementation. The results demonstrated that precision tools such as GPS and satellite imagery enabled the study of animals in extensive systems, thereby contributing to decision-making within the production system.

Deep learning approach to femoral AVN detection in digital radiography: differentiating patients and pre-collapse stages

ObjectiveThis study aimed to evaluate a new deep-learning model for diagnosing avascular necrosis of the femoral head (AVNFH) by analyzing pelvic anteroposterior digital radiography.MethodsThe study sample included 1167 hips. The radiographs were independently classified into 6 stages by a radiologist using their simultaneous MRIs. After that, the radiographs were given to train and test the deep learning models of the project including SVM and ANFIS layer using the Python programming language and TensorFlow library. In the last step, the test set of hip radiographs was provided to two independent radiologists with different work experiences to compare their diagnosis performance to the deep learning models’ performance using the F1 score and Mcnemar test analysis.ResultsThe performance of SVM for AVNFH detection (AUC = 82.88%) was slightly higher than less experienced radiologists (79.68%) and slightly lower than experienced radiologists (88.4%) without reaching significance (p-value > 0.05). Evaluation of the performance of SVM for pre-collapse AVNFH detection with an AUC of 73.58% showed significantly higher performance than less experienced radiologists (AUC = 60.70%, p-value < 0.001). On the other hand, no significant difference is noted between experienced radiologists and SVM for pre-collapse detection. ANFIS algorithm for AVNFH detection with an AUC of 86.60% showed significantly higher performance than less experienced radiologists (AUC = 79.68%, p-value = 0.04). Although reaching less performance compared to experienced radiologists statistically not significant (AUC = 88.40%, p-value = 0.20).ConclusionsOur study has shed light on the remarkable capabilities of SVM and ANFIS as diagnostic tools for AVNFH detection in radiography. Their ability to achieve high accuracy with remarkable efficiency makes them promising candidates for early detection and intervention, ultimately contributing to improved patient outcomes.

Mixed Convection Flow of MHD Casson Nanofluid over a Vertically Extending Sheet with Effects of Hall, Ion Slip and Nonlinear Thermal Radiation

This manuscript investigates the effects of viscous dissipation, Ohmic heating, nonlinear thermal radiation, chemical reactions, Hall current, and ion slip on the mixed convection flow of magnetohydrodynamic (MHD) Casson nanofluid over a vertically stretching sheet in a spongy medium. The strongly nonlinear ordinary differential equations (ODEs) derived from the governing equations are solved using a sixth-order, seven-stage Runge–Kutta (RK6) method in conjunction with the shooting technique. Numerical simulations are conducted using an open-source Python programming language. The study evaluates temperature, velocity, nanoparticle concentration, skin friction coefficients, Nusselt number (rate of heat transfer), and Sherwood number (rate of mass transfer) based on dimensionless parameters. Key findings reveal that increasing ion slip and Hall currents enhance the velocity profile in the x-direction, while the Casson parameter exerts a diminishing effect. The fluid temperature decreases with higher Hall current, Casson parameter, and ion slip values but increases due to nonlinear thermal radiation, the temperature ratio parameter, and the Eckert number. Furthermore, the chemical reaction parameter and Schmidt number reduce nanoparticle concentration within the boundary layer, with the Casson parameter amplifying this effect. The study also examines the rates of mass, heat, and momentum transfer near the surface under varying parameter influences, presenting results through comprehensive graphs and tables. To validate the numerical method, a comparative analysis is conducted against previous studies, demonstrating excellent agreement and thus confirming the reliability and accuracy of the computational approach.

The Long-Term Survival of LVAD Patients-A TriNetX Database Analysis.

Background: Donor shortage limits the utilization of heart transplantation, making it available for only a fraction of the patients on the transplant waiting list. Therefore, continuous-flow left ventricular assist devices (CF-LVADs) have evolved as a standard of care for end-stage heart failure. It is imperative therefore to investigate long-term survival in this population. Methods: This study assesses the impact of demographics, infections, comorbidities, types of cardiomyopathies, arrhythmias, and end-organ dysfunction on the long-term survival of LVAD recipients. The TriNetX database comprises de-identified patient information across healthcare organizations. The log-rank test assessed post-implant survival effects, while Cox regression was used in the univariate analysis to obtain the Hazard Ratio (HR). All analyses were conducted using the Python programming language and the lifelines library. Results: This study identified CMV, hepatitis A exposure, atrial fibrillation, paroxysmal ventricular tachycardia, ischemic cardiomyopathy, renal dysfunction, diabetes, COPD, mitral valve disease, and essential hypertension as risk factors that impact long-term survival. Interestingly, hypokalemia seems to have a protective effect and gender does not affect survival significantly. Conclusions: This is the first report of a detailed long-term survival assessment of the LVAD population using a decoded database.

Novel Methodology for the Design of Personalized Cancer Vaccine Targeting Neoantigens: Application to Pancreatic Ductal Adenocarcinoma.

Personalized cancer vaccines have emerged as a promising avenue for cancer treatment or prevention strategies. This approach targets the specific genetic alterations in individual patient's tumors, offering a more personalized and effective treatment option. Previous studies have shown that generalized peptide vaccines targeting a limited scope of gene mutations were ineffective, emphasizing the need for personalized approaches. While studies have explored personalized mRNA vaccines, personalized peptide vaccines have not yet been studied in this context. Pancreatic ductal adenocarcinoma (PDAC) remains challenging in oncology, necessitating innovative therapeutic strategies. In this study, we developed a personalized peptide vaccine design methodology, employing RNA sequencing (RNAseq) to identify prevalent gene mutations underlying PDAC development in a patient solid tumor tissue. We performed RNAseq analysis for trimming adapters, read alignment, and somatic variant calling. We also developed a Python program called SCGeneID, which validates the alignment of the RNAseq analysis. The Python program is freely available to download. Using chromosome number and locus data, SCGeneID identifies the target gene along the UCSC hg38 reference set. Based on the gene mutation data, we developed a personalized PDAC cancer vaccine that targeted 100 highly prevalent gene mutations in two patients. We predicted peptide-MHC binding affinity, immunogenicity, antigenicity, allergenicity, and toxicity for each epitope. Then, we selected the top 50 and 100 epitopes based on our previously published vaccine design methodology. Finally, we generated pMHC-TCR 3D molecular model complex structures, which are freely available to download. The designed personalized cancer vaccine contains epitopes commonly found in PDAC solid tumor tissue. Our personalized vaccine was composed of neoantigens, allowing for a more precise and targeted immune response against cancer cells. Additionally, we identified mutated genes, which were also found in the reference study, where we obtained the sequencing data, thus validating our vaccine design methodology. This is the first study designing a personalized peptide cancer vaccine targeting neoantigens using human patient data to identify gene mutations associated with the specific tumor of interest.

A CFD simulation platform for surface finishing processes in advanced manufacturing

Products created by additive manufacturing often have surface imperfections that require post-processing operations to remove extraneous material in order to meet design specifications. The usage of computational fluid dynamics (CFD) simulations to predict material removal rates of components, allows practitioners to optimize the setup and usage of post-processing equipment. However, those without in-depth knowledge of CFD or the related specialized software, require an easy-to-use and cost-effective application to manage the computational workflow. The two specific surface finishing applications investigated here, are, abrasive flow machining (AFM) and robotic stream finishing (RSF). In order to satisfy user requirements, a modular, threaded, fault-tolerant and object-oriented project management application, written with the Python programming language and PyQt6 framework, has been developed to conduct surface finishing-related CFD simulations using OpenFOAM®. The advantages of the proposed software are: 1) the modern PyQt6 framework is used to develop a cross-platform and user-friendly application which employs the model-view class architectural paradigm for data management and its display, 2) step-by-step interactive project workflows have been tailored specifically for AFM and RSF simulations, 3) the developed steady-state viscoelastic flow solver for AFM and continuum-based steady-state dense granular flow solver for RSF, offer advantages over those provided by OpenFOAM® and 4) simulation results have been corroborated by experimental data to assess the improved accuracy of material removal prediction of the current software when compared to other commercial applications.