Analysis Package Research Articles

AffineMortality: An R package for estimation, analysis, and projection of affine mortality models

Abstract This paper presents the AffineMortality R package which performs parameter estimation, goodness-of-fit analysis, simulation, and projection of future mortality rates for a set of affine mortality models for use in pricing and reserving. The computational routines build on the univariate Kalman Filtering approach of Koopman and Durbin ((2000). Journal of Time Series Analysis,21(3), 281–296.) along other numerical methods to enhance the robustness of the results. This paper provides a discussion of how the package works in order to effectively estimate and project survival curves, and describes the available functions. Illustration of the package for mortality analysis of the US male data set is provided.

Optimization of Crop Production using Python

The paper critically reviews various methods exclusively used for c r o p planning and points out suggestions for improvement in techniques used for crop planning. Specifically, the study examines scope for optimization of crop plan, objectives and constraints, approaches, seasonality issues, sensitivity analysis and various computer software packages used in computing the optimum models. With such extensive coverage, it intends to help the end users to decide upon an appropriate/suitable method corresponding to their situation and scenarios to frame the best and most practical/realistic optimum crop model.Cropping systems with differential requirement and contribution in modifying the rhizosphere by different crops provide newer challenge as well as opportunity for management to achieve higher input productivity for water and nutrients. Although, more than 250 double cropping systems are adopted in the country, the major contribution to food basket remains with the few cereal based systems such as rice-wheat, rice-rice, rice-gram, rice-sorghum, maize-wheat, maize-gram, soybean-wheat and sugarcane-wheat due to their extent of cultivation

Simplified Workbook in Algebra for Grade 7 Learners

The study aimed to create a Simplified Workbook to improve the performance and retention of Grade 7 learners in Algebra at Solotsolot National High School. It addressed research questions about learner profiles, Algebra performance levels, relationships between profiles and performance, and competencies that were mastered or not. Using descriptive-correlational and descriptive-developmental designs, data was collected through the Test Result and Item Analysis Package (TRIAP) for the second quarter, with analysis using frequency count, percentage, mean, and correlational techniques. The findings showed varied learner profiles in terms of sex, parental education, socio-economic status, and study habits. The performance in Algebra was generally satisfactory, with a grand mean of 27.49, and only the occupation of the mother had a significant relationship with performance. Most Algebra competencies were not mastered, indicating the need for a simplified workbook. The study's conclusions highlighted the need for parental involvement, resource centers, and teacher training to enhance learning outcomes. Recommendations included cooperation between parents and schools to improve discipline and interest in school activities, the use of the Simplified Workbook in Algebra 7, and encouraging active participation in Mathematics-related activities. Additionally, it was suggested that Mathematics teachers attend training sessions to enhance their teaching strategies and consider setting up Mathematics Clubs. Future research could examine other factors affecting mastery of Algebra competencies, such as teaching methodologies, social media exposure, and teacher-related factors.

Comparison of two automated CT perfusion software packages in patients with ischemic stroke presenting within 24 h of onset.

We compared the ischemic core and hypoperfused tissue volumes estimated by RAPID and JLK-CTP, a newly developed automated computed tomography perfusion (CTP) analysis package. We also assessed agreement between ischemic core volumes by two software packages against early follow-up infarct volumes on diffusion-weighted images (DWI). This retrospective study analyzed 327 patients admitted to a single stroke center in Korea from January 2021 to May 2023, who underwent CTP scans within 24 h of onset. The concordance correlation coefficient (ρ) and Bland-Altman plots were utilized to compare the volumes of ischemic core and hypoperfused tissue volumes between the software packages. Agreement with early (within 3 h from CTP) follow-up infarct volumes on diffusion-weighted imaging (n = 217) was also evaluated. The mean age was 70.7 ± 13.0 and 137 (41.9%) were female. Ischemic core volumes by JLK-CTP and RAPID at the threshold of relative cerebral blood flow (rCBF) < 30% showed excellent agreement (ρ = 0.958 [95% CI, 0.949 to 0.966]). Excellent agreement was also observed for time to a maximum of the residue function (T max) > 6 s between JLK-CTP and RAPID (ρ = 0.835 [95% CI, 0.806 to 0.863]). Although early follow-up infarct volume showed substantial agreement in both packages (JLK-CTP, ρ = 0.751 and RAPID, ρ = 0.632), ischemic core volumes at the threshold of rCBF <30% tended to overestimate ischemic core volumes. JLK-CTP and RAPID demonstrated remarkable concordance in estimating the volumes of the ischemic core and hypoperfused area based on CTP within 24 h from onset.

RamanSPy: An Open-Source Python Package for Integrative Raman Spectroscopy Data Analysis.

Raman spectroscopy is a nondestructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardization, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of tools for spectroscopic analysis that supports day-to-day tasks, integrative analyses, the development of methods and protocols, and the integration of advanced data analytics. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis, and machine learning in Python. RamanSPy is hosted at https://github.com/barahona-research-group/RamanSPy, supplemented with extended online documentation, available at https://ramanspy.readthedocs.io, that includes tutorials, example applications, and details about the real-world research applications presented in this paper.

PESSA: A web tool for pathway enrichment score-based survival analysis in cancer.

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan-Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

Improving the Fuel Economy and Energy Efficiency of Train Cab Climate Systems, Considering Air Recirculation Modes

Current developments in vehicles have generated great interest in the research and optimization of heating, ventilation, and air conditioning (HVAC) systems as a factor to reduce fuel consumption. One of the key trends for finding solutions is the intensive development of electric transport and, consequently, additional requirements for reducing energy consumption and modifying climate systems. Of particular interest is the optimal functioning of comfort and life support systems during air recirculation, i.e., when there is a complete or partial absence of outside air supply, in particular to reduce energy consumption or when the environment is polluted. This work examines numerical models of airfields (temperature, speed, and humidity) and also focuses on the concentration of carbon dioxide and oxygen in the cabin, which is a critical factor for ensuring the health of the driver and passengers. To build a mathematical model, the Navier–Stokes equations with energy, continuity, and diffusion equations are used to simulate the diffusion of gases and air humidity. In the Ansys Fluent finite volume analysis package, the model is solved numerically using averaged RANS equations and k-ω turbulence models. The cabin of a mainline locomotive with two drivers, taking into account their breathing, is used as a transport model. The problem was solved in a nonstationary formulation for the design scenario of summer and winter, the time of stabilization of the fields was found, and graphs were constructed for different points in time. A comparative analysis of the uniformity of fields along the height of the cabin was carried out with different locations of deflectors, and optimal configurations were found. Energy efficiency values of the climate system operation in recirculation operating modes were obtained. A qualitative assessment of the driver’s blowing directions under different circulation and recirculation modes is given from the point of view of the concentration of carbon dioxide in the breathing area. The proposed solution makes it possible to reduce electricity consumption from 3.1 kW to 0.6 kW and in winter mode from 11.6 kW to 3.9 kW and save up to 1.5 L/h of fuel. The conducted research can be used to develop modern energy-efficient and safe systems for providing comfortable climate conditions for drivers and passengers of various types of transport.

Abstract PO4-02-02: Genomic Landscape of ER positive HER2-low early breast cancers in the FLEX Study: MammaPrint, BluePrint and whole transcriptome analysis

Abstract Background: Antibody-drug conjugates (ADCs) continue to emerge for the treatment of a new subset of patients with HER2-low breast cancer. There is limited evidence to demonstrate HER2-low tumors as a distinct biological subtype and why/if these tumors benefit from ADCs. To improve our understanding of this newly defined HER2 category of breast cancers, we evaluated clinical characteristics, MammaPrint (MP), BluePrint (BP), and the whole transcriptomic profile of HER2-low breast cancers in FLEX study. Methods: FLEX (NCT03053193) is a prospective, observational trial that includes stage I-III breast cancer patients who undergo MammaPrint testing (with or without BluePrint) as standard of care, and consent to full transcriptome and clinical data collection. In this study, clinically ER+/HER2- tumors were analyzed. The HER2-low cohort group (n=1698) was defined as HER2 IHC 1+ (ISH positive excluded) and IHC 2+, ISH Negative, and the HER2-0 group (n=1181) was defined as HER2 IHC 0. MP classified tumors as Low Risk or High Risk (further stratified as High 1 and High 2). MP together with BP categorized tumors as Luminal A (MP Low Risk), Luminal B (MP High Risk), HER2 or Basal. Two-tailed proportional z-test was used to compare clinical features and genomic subtypes of HER2-low vs. HER2-0 and the limma R package for differential gene expression analysis (DGEA). P-values were adjusted for multiple testing by Benjamini-Hochberg; significant differentially expressed genes (DEGs) had a p-value &amp;lt; 0.05 and a fold change &amp;gt;2. Results: In this FLEX study, clinically ER+/HER2- tumors showed that the clinical characteristics between HER2-low and HER2-0 did not differ significantly except higher percentage of premenopausal within HER2-low (23% vs 17%, p &amp;lt; 0.01). Of all HER2-low patients, 46% were MP High Risk as within HER2-0 patients (44%, p = 0.27). In both groups with MP High Risk tumors, there was a higher frequency of MP High 1 (83% in HER2-low, 80% in HER2-0) compared to MP High 2 (17% in HER2-low, 20% in HER2-0). BP subtyping showed similar distribution for Luminal subtype between HER2-low and HER2-0, but the frequency of ER+, Basal subtype was lower in HER2-low (2.5%), compared to HER2-0 (4.5%) (p=0.005). Principal component analysis (PCA) of the 500 most variable genes did not reveal a separation of HER2-low and -0 tumors, but clustering was apparent when tumors were classified by BP. DGEA within Basal tumors revealed no DEGs. Within Luminal A tumors, more than 1800 DEGs were identified, and within Luminal B tumors, nearly 300 DEGs were identified. However, all DEGs were &amp;lt; 1.5-fold change: mean, max (1.09, 1.38) for Luminal A and (1.12, 1.44) for Luminal B. In addition, a significant difference (p&amp;lt; 0.01) towards increased ERBB2 (HER2) expression was detected from HER2-0 to HER2-low, but there was a large overlap of expression between the 2 groups. Conclusion: Our study showed that HER2-low and HER2-0 tumors are clinically and biologically similar. The differences in ERBB2 and low-level genome wide expression detected between HER2-low and HER2-0 should be further investigated to determine how these changes affect tumor biology and treatment benefit. The biological heterogeneity among IHC-defined HER2-negative tumors was better captured by MammaPrint and BluePrint than IHC. MammaPrint identified 53% of HER2-low tumors as Low Risk, a subgroup of patients known to have good outcomes without chemotherapy. Our data suggest a subset of patients with HER2-low, MP Low Risk tumors could be spared from the potential toxicities of ADCs. Future studies will investigate the utility of MammaPrint and BluePrint in predicting chemosensitivity and benefit from ADCs, such as T-DXd, in patients with HER2-low tumors. Citation Format: Abirami Sivapiragasam, Adam Brufsky, Hannah Linden, Natasha Hunter, Cynthia Osborne, Sami Diab, Robert Maganini, Joyce O'Shaughnessy, Manojkumar Bupathi, Sung Ho Lee, Theodore Y Kim, William Audeh, Josien Haan, Andrea Menicucci, Lavanya Samraj, FLEX Investigators' Group. Genomic Landscape of ER positive HER2-low early breast cancers in the FLEX Study: MammaPrint, BluePrint and whole transcriptome analysis [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-02-02.

Growth Performance, Carcass Characteristics and Histological Indices of White Fulani (Bunaji) Bull Yearling Calves Fed Graded Levels of Urea and Molasses Ensiled Rice Straw Supplemented with Maize Offal and Cowpea Husk

The study was carried out at Duware farm along Yola-Fufore Road Adamawa state. ix yearling white Fulani (Bunaji) bull calves weighed 80kg-100kg were fed graded levels of urea and molasses ensiled rice straw supplemented with maize offal and cowpea husk to investigate the growth performance, carcass characteristics and histological indices. The treatments diets consist of graded levels of urea and molasses ensiled rice straw supplemented with maize offal for T1, graded levels of urea and molasses ensiled rice straw supplemented with cowpea husk in T2, and graded levels of urea and molasses ensiled rice straw only in T3 (control group). The feeds were offered at 8; 00 am in the morning and 6:00 pm in the evening, remnants were weight before fresh feeds were offered, mineral lick and water was provided ad-libitum, and animals were weight weekly to determine their growth performance. Two weeks adaptation period was carried out, the experiment last for three months including the adaptation period, statistical package for data analysis was completely randomize design (CRD) and the values were subjected to analysis of variance (ANOVA) means were separated using least significant difference (LSD).

Helicobacter pylori CagA Promotes the Formation of Gallstones by Increasing the Permeability of Gallbladder Epithelial Cells.

The formation of gallstones is often accompanied by chronic inflammation, and the mechanisms underlying inflammation and stone formation are not fully understood. Our aim is to utilize single-cell transcriptomics, bulk transcriptomics, and microbiome data to explore key pathogenic bacteria that may contribute to chronic inflammation and gallstone formation, as well as their associated mechanisms. scRNA-seq data from a gallstone mouse model were extracted from the Gene Expression Omnibus (GEO) database and analyzed using the FindCluster() package for cell clustering analysis. Bulk transcriptomics data from patients with gallstone were also extracted from the GEO database, and intergroup functional differences were assessed using GO and KEGG enrichment analysis. Additionally, 16S rRNA sequencing was performed on gallbladder mucosal samples from asymptomatic patients with gallstone (n = 6) and liver transplant donor gallbladder mucosal samples (n = 6) to identify key bacteria associated with stone formation and chronic inflammation. Animal models were constructed to investigate the mechanisms by which these key pathogenic bacterial genera promote gallstone formation. Analysis of scRNA-seq data from the gallstone mouse model (GSE179524) revealed seven distinct cell clusters, with a significant increase in neutrophil numbers in the gallstone group. Analysis of bulk transcriptomics data from patients with gallstone (GSE202479) identified chronic inflammation in the gallbladder, potentially associated with dysbiosis of the gallbladder microbiota. 16S rRNA sequencing identified Helicobacter pylori as a key bacterium associated with gallbladder chronic inflammation and stone formation. Dysbiosis of the gallbladder mucosal microbiota is implicated in gallstone disease and leads to chronic inflammation. This study identified H. pylori as a potential key mucosal resident bacterium contributing to gallstone formation and discovered its key pathogenic factor CagA, which causes damage to the gallbladder mucosal barrier. These findings provide important clues for the prevention and treatment of gallstones.

RCPA: An Open-Source R Package for Data Processing, Differential Analysis, Consensus Pathway Analysis, and Visualization.

Identifying impacted pathways is important because it provides insights into the biology underlying conditions beyond the detection of differentially expressed genes. Because of the importance of such analysis, more than 100 pathway analysis methods have been developed thus far. Despite the availability of many methods, it is challenging for biomedical researchers to learn and properly perform pathway analysis. First, the sheer number of methods makes it challenging to learn and choose the correct method for a given experiment. Second, computational methods require users to be savvy with coding syntax, and comfortable with command-line environments, areas that are unfamiliar to most life scientists. Third, as learning tools and computational methods are typically implemented only for a few species (i.e., human and some model organisms), it is difficult to perform pathway analysis on other species that are not included in many of the current pathway analysis tools. Finally, existing pathway tools do not allow researchers to combine, compare, and contrast the results of different methods and experiments for both hypothesis testing and analysis purposes. To address these challenges, we developed an open-source R package for Consensus Pathway Analysis (RCPA) that allows researchers to conveniently: (1) download and process data from NCBI GEO; (2) perform differential analysis using established techniques developed for both microarray and sequencing data; (3) perform both gene set enrichment, as well as topology-based pathway analysis using different methods that seek to answer different research hypotheses; (4) combine methods and datasets to find consensus results; and (5) visualize analysis results and explore significantly impacted pathways across multiple analyses. This protocol provides many example code snippets with detailed explanations and supports the analysis of more than 1000 species, two pathway databases, three differential analysis techniques, eight pathway analysis tools, six meta-analysis methods, and two consensus analysis techniques. The package is freely available on the CRAN repository. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Processing Affymetrix microarrays Basic Protocol 2: Processing Agilent microarrays Support Protocol: Processing RNA sequencing (RNA-Seq) data Basic Protocol 3: Differential analysis of microarray data (Affymetrix and Agilent) Basic Protocol 4: Differential analysis of RNA-Seq data Basic Protocol 5: Gene set enrichment analysis Basic Protocol 6: Topology-based (TB) pathway analysis Basic Protocol 7: Data integration and visualization.

Callsync: An R package for alignment and analysis of multi-microphone animal recordings.

To better understand how vocalisations are used during interactions of multiple individuals, studies are increasingly deploying on-board devices with a microphone on each animal. The resulting recordings are extremely challenging to analyse, since microphone clocks drift non-linearly and record the vocalisations of non-focal individuals as well as noise. Here we address this issue with callsync, an R package designed to align recordings, detect and assign vocalisations to the caller, trace the fundamental frequency, filter out noise and perform basic analysis on the resulting clips. We present a case study where the pipeline is used on a dataset of six captive cockatiels (Nymphicus hollandicus) wearing backpack microphones. Recordings initially had a drift of ~2 min, but were aligned to within ~2 s with our package. Using callsync, we detected and assigned 2101 calls across three multi-hour recording sessions. Two had loud beep markers in the background designed to help the manual alignment process. One contained no obvious markers, in order to demonstrate that markers were not necessary to obtain optimal alignment. We then used a function that traces the fundamental frequency and applied spectrographic cross correlation to show a possible analytical pipeline where vocal similarity is visually assessed. The callsync package can be used to go from raw recordings to a clean dataset of features. The package is designed to be modular and allows users to replace functions as they wish. We also discuss the challenges that might be faced in each step and how the available literature can provide alternatives for each step.

A sensitivity study on the PDFs treating uncertainties in severe accidents for pressurized heavy water reactors

This research article introduces a study regarding the uncertainties treatment during severe accidents for Pressurized Heavy Water Reactors (PHWRs). The present study is focused upon the unmitigated Station BlackOut (SBO) accident analysis for a CANada Deuterium Uranium (CANDU) type reactor emphasizing the impact of the uncertainties treatment on the relevant key timings of the SBO accident progression through different approaches for the uncertainty parameters’ Probabilistic Distribution Functions (PDFs). A comparison between the sensitivity analysis results is provided in the present research study.The uncertainty analysis is performed with the RELAP/SCDAPSIM code with the Integrated Uncertainty Analysis (IUA) package from the code.Results from the research would support the advancements on the best-practices for uncertainty analyses with respect to the parameter's uncertainties distribution functions. Data dispersion is a key element for the realistic quantification of uncertainties in nuclear power plant safety analyses, including severe accidents.

Hematological and biochemical indices, Rumen pH, Ammonia concentration in rumen liquor and Urine pH of white Fulani (Bunaji) bull Yearling calves fed graded levels of urea and molasses ensiled rice straw supplemented with maize offal and cowpea husk

The study was carried out at Duware farm along Yola-Fufore Road Adamawa state. Nigeria to determine the hematological and biochemical indices, rumen pH, ammonia concentration in rumen liquor and urine pH of white Fulani (Bunaji) bull Yearling calves fed graded levels of urea and molasses ensiled rice straw supplemented with maize offal and cowpea husk. Six yearling white Fulani (Bunaji) bull calves weighed 80kg-100kg was used for the research. The treatments diets consist of graded levels of urea and molasses ensiled rice straw supplemented with maize offal in T1, graded levels of urea and molasses ensiled rice straw supplemented with cowpea husk in T2, and graded levels of urea and molasses ensiled rice straw only in T3 (control group). The feeds was offered at 8; 00 am in the morning and 6: 00 pm in the evening, remnants was weight before fresh feeds were offered, mineral lick and water was provided ad-libitum, and animals were weight weekly to determine their growth performance. Two weeks adaptation period was carried out, the experiment last for three months including the adaptation period. Techniques and statistical package for data analysis was completely randomize design (CRD) and the values were subjected to analysis of variance (ANOVA) using least significant difference (LSD) to separate the means. Results revealed that the hematological and biochemical indices PCV, RBC, WBC, HBC, MCV, MCH, MCHC, Total protein, albumin, globulin, creatinine, urea Con. Lymphocytes, Neutrophils, Monocytes were significantly (P&lt;0.05) difference across the treatments means, but are not significantly (P&gt;0.05) difference among T2 and T3 in Eosinophil (%),rumen pH, ammonia concentration in rumen liquor NH3(mg/100ml) and urine pH were not significantly affected (P&gt;0.05) by the inclusion level of urea-molasses in an ensiled rice straw in the diets, it can be concluded that the hematological and biochemical indices fall within the recommended values and white Fulani (Bunaji) bull Yearling calves can be fed graded levels of urea and molasses ensiled rice straw supplemented with maize offal and cowpea husk without any deleterious effects.

The double low-mass white dwarf eclipsing binary system J2102–4145 and its possible evolution

In recent years, about 150 low-mass white dwarfs (WDs), typically with masses below 0.4 M⊙, have been discovered. The majority of these low-mass WDs are observed in binary systems as they cannot be formed through single-star evolution within Hubble time. In this work, we present a comprehensive analysis of the double low-mass WD eclipsing binary system J2102−4145. Our investigation encompasses an extensive observational campaign, resulting in the acquisition of approximately 28 h of high-speed photometric data across multiple nights using NTT/ULTRACAM, SOAR/Goodman, and SMARTS-1m telescopes. These observations have provided critical insights into the orbital characteristics of this system, including parameters such as inclination and orbital period. To disentangle the binary components of J2102−4145, we employed the XTGRID spectral fitting method with GMOS/Gemini-South and X-shooter data. Additionally, we used the PHOEBE package for light curve analysis on NTT/ULTRACAM high-speed time-series photometry data to constrain the binary star properties. Our analysis unveils remarkable similarities between the two components of this binary system. For the primary star, we determine Teff,1 = 13 688−72+65 K, log g1 = 7.36 ± 0.01, R1 = 0.0211 ± 0.0002 R⊙, and M1 = 0.375 ± 0.003 M⊙, while, the secondary star is characterised by Teff,2 = 12952−66+53 K, log g2 = 7.32 ± 0.01, R2 = 0.0203−0.0003+0.0002 R⊙, and M2 = 0.314 ± 0.003 M⊙. Furthermore, we found a notable discrepancy between Teff and R of the less massive WD, compared to evolutionary sequences for WDs from the literature, which has significant implications for our understanding of WD evolution. We discuss a potential formation scenario for this system which might explain this discrepancy and explore its future evolution. We predict that this system will merge in ∼800 Myr, evolving into a helium-rich hot subdwarf star and later into a hybrid He/CO WD.

Selection of Key Socio-Economic Factors for Establishing Relese Criteria Patients after Radioioidine Therapy

Purpose: To assess the association between socioeconomic factors and release criteria after iodine-131 radionuclide therapy in different countries. Material and methods: 15 countries and 104 socioeconomic characteristics were selected for the study. The association between characteristics and the residual activity level at which a patient may be released for different countries was established according to the value of Pearson product moment correlation coefficient (r&gt;0.3). A linear regression model was constructed for the selected characteristics using Microsoft Excel analysis package. The model was used to calculate the acceptable activity level at which a patient may be released for Russia. Results: A fundamental difference between the model used for calculation of release criteria after radionuclide therapy with iodine-131 in Russia and the model recommended by the Nuclear Regulatory Commission was shown: the use of the effective half-life in the second one. Replacing the physical halflife by the effective half life allows increasing the discharge criteria of patients after therapy with Na131I by 30 %. A moderate correlation was found between the value of acceptable activity at patient discharge after radioactive iodine therapy and the following socioeconomic characteristics: scientific activity (r=0.32), quality of higher education (r=0.36), Global Health Security Index (r=0.34), length of roads (r=0.36), population growth rate (r=0.36), average housing area (r=0.38), and average salary per year (r=–0.33). A linear regression was constructed using the factors listed above. The following recommended value of activity and effective dose rate at patient discharge from hospital was obtained: 0.6 GBq and 30 µSv/hour. Conclusion: The study demonstrates that the release criteria for radioactive iodine therapy used in the Russian Federation (0.4 GBq and 20 µSv/h) do not correspond to the standard of living achieved in Russia, which is reflected by the socio-economic factors considered in the paper. The proposed arguments raise the task of revising the currently used model with a consequent increase in the discharge criteria. It was observed that the release criteria in the world are independent of some factors that are directly related to population exposure. The need to develop a unified objective algorithm for establishing release criteria, which takes into account different socio-economic status of states and can be applied for different radionuclides and radiopharmaceuticals, including innovative ones, has been identified.

Reference Data Set for Circular Dichroism Spectroscopy Comprised of Validated Intrinsically Disordered Protein Models.

Circular dichroism (CD) spectroscopy is an analytical technique that measures the wavelength-dependent differential absorbance of circularly polarized light and is applicable to most biologically important macromolecules, such as proteins, nucleic acids, and carbohydrates. It serves to characterize the secondary structure composition of proteins, including intrinsically disordered proteins, by analyzing their recorded spectra. Several computational tools have been developed to interpret protein CD spectra. These methods have been calibrated and tested mostly on globular proteins with well-defined structures, mainly due to the lack of reliable reference structures for disordered proteins. It is therefore still largely unclear how accurately these computational methods can determine the secondary structure composition of disordered proteins. Here, we provide such a required reference data set consisting of model structural ensembles and matching CD spectra for eight intrinsically disordered proteins. Using this set of data, we have assessed the accuracy of several published CD prediction and secondary structure estimation tools, including our own CD analysis package, SESCA. Our results show that for most of the tested methods, their accuracy for disordered proteins is generally lower than for globular proteins. In contrast, SESCA, which was developed using globular reference proteins, but was designed to be applicable to disordered proteins as well, performs similarly well for both classes of proteins. The new reference data set for disordered proteins should allow for further improvement of all published methods.

Paicos: A Python package for analysis of (cosmological) simulations performed with Arepo

Paicos: A Python package for analysis of (cosmological) simulations performed with Arepo

Vibration mitigation of spar-buoy floating wind turbines using a nonlinear energy sink

This work investigates the possibility of mitigating the wind-wave exerted vibration of floating offshore wind turbines using a nonlinear energy sink (NES). The vibration mitigation of floating wind turbines using passive linear control strategies have been intensively investigated, however, the effectiveness of the linear absorbers only works in a narrow frequency band, resulting in limited vibration suppression performance. The NES comprising a mass, a damper and a cubic stiffness element has the ability to mitigate vibrations in a broadband frequency range, which has been adopted in multiple engineering structures with significant performance benefits identified. In this paper, taking spar-buoy floating wind turbines as prototype structure, a simplified model with a NES mounted in nacelle subjected to wind and wave loads is presented. The harmonic balance method is then employed to analyse its nonlinear responses to the influence of NES parameters, together with the pseudo-arc-length method. The optimum NES parameter values are subsequently identified. Results show that the performance improvement can be up to 74.2% and 14.0%, respectively, comparing to that with no absorber and the TMD. Furthermore, to demonstrate the vibration suppression ability of the optimised NES in more realistic spar-buoy wind turbines, Orcaflex-the world’s leading package for the dynamic analysis of offshore systems, is adopted as the simulation tool. The spar-buoy wind turbine as well as the NES are established in Orcaflex and it is demonstrated that the performance benefits with the NES preserve under various loading conditions.

The OMOP common data model in Australian primary care data: Building a quality research ready harmonised dataset.

The use of routinely collected health data for secondary research purposes is increasingly recognised as a methodology that advances medical research, improves patient outcomes, and guides policy. This secondary data, as found in electronic medical records (EMRs), can be optimised through conversion into a uniform data structure to enable analysis alongside other comparable health metric datasets. This can be achieved with the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), which employs a standardised vocabulary to facilitate systematic analysis across various observational databases. The concept behind the OMOP-CDM is the conversion of data into a common format through the harmonisation of terminologies, vocabularies, and coding schemes within a unique repository. The OMOP model enhances research capacity through the development of shared analytic and prediction techniques; pharmacovigilance for the active surveillance of drug safety; and 'validation' analyses across multiple institutions across Australia, the United States, Europe, and the Asia Pacific. In this research, we aim to investigate the use of the open-source OMOP-CDM in the PATRON primary care data repository. We used standard structured query language (SQL) to construct, extract, transform, and load scripts to convert the data to the OMOP-CDM. The process of mapping distinct free-text terms extracted from various EMRs presented a substantial challenge, as many terms could not be automatically matched to standard vocabularies through direct text comparison. This resulted in a number of terms that required manual assignment. To address this issue, we implemented a strategy where our clinical mappers were instructed to focus only on terms that appeared with sufficient frequency. We established a specific threshold value for each domain, ensuring that more than 95% of all records were linked to an approved vocabulary like SNOMED once appropriate mapping was completed. To assess the data quality of the resultant OMOP dataset we utilised the OHDSI Data Quality Dashboard (DQD) to evaluate the plausibility, conformity, and comprehensiveness of the data in the PATRON repository according to the Kahn framework. Across three primary care EMR systems we converted data on 2.03 million active patients to version 5.4 of the OMOP common data model. The DQD assessment involved a total of 3,570 individual evaluations. Each evaluation compared the outcome against a predefined threshold. A 'FAIL' occurred when the percentage of non-compliant rows exceeded the specified threshold value. In this assessment of the primary care OMOP database described here, we achieved an overall pass rate of 97%. The OMOP CDM's widespread international use, support, and training provides a well-established pathway for data standardisation in collaborative research. Its compatibility allows the sharing of analysis packages across local and international research groups, which facilitates rapid and reproducible data comparisons. A suite of open-source tools, including the OHDSI Data Quality Dashboard (Version 1.4.1), supports the model. Its simplicity and standards-based approach facilitates adoption and integration into existing data processes.