Standard Software Research Articles

Epidemiology of Thyroid Diseases in Patients of Heart Failure in a Tertiary Care Hospital of Eastern India: A Cross-sectional Study

Background Thyroid diseases are common in India. Both insufficient and excess thyroid hormones cause adverse cardiovascular events. Subclinical hypothyroidism and hyperthyroidism while undiagnosed can lead to derangement of cardiac functions. This study aimed to explore thyroid function abnormalities among heart failure patients of different severity (NYHA class). Methodology A descriptive, cross-sectional study was undertaken on 492 heart failure patients (246 males and 246 females) of age greater than 18 years. Patients were classified according to NYHA. Serum TSH and FT4 were investigated. Data were analyzed by standard statistical software. Results The mean age of participants was 54.2 ± 13.1 years. Mean FT4 and TSH were 1.36 ± 0.39 ng/dL and 2.89 ± 1.15 mIU/L, respectively. Patients belonged to NYHA class II (35.4%), III (52.4%) and IV (12.2%). Comorbidities included Hypertension (38%), Diabetes Mellitus (29%) and Dyslipidemia (52%). Hypothyroidism and hyperthyroidism were present in 27% and 5% patients respectively in overt or subclinical forms. The prevalence of thyroid disorders among NYHA class II, III and IV patients were 17%, 37%, and 50%, respectively. Conclusion Thyroid disorders were present in about one-third of the patients with heart failure. The prevalence of hypothyroidism was almost five times that of hyperthyroidism. The prevalence of thyroid disorders increased with the severity of heart failure.

An Omitted Variable Bias Framework for Sensitivity Analysis of Instrumental Variables

Abstract We develop an omitted variable bias framework for sensitivity analysis of instrumental variable estimates that naturally handles multiple side-effects (violations of the exclusion restriction assumption) and confounders (violations of the ignorability of the instrument assumption) of the instrument, exploits expert knowledge to bound sensitivity parameters, and can be easily implemented with standard software. Specifically, we introduce sensitivity statistics for routine reporting, such as (extreme) robustness values for instrumental variables, describing the minimum strength that omitted variables need to have to change the conclusions of a study. Next we provide visual displays that fully characterize the sensitivity of point estimates and confidence intervals to violations of the standard instrumental variable assumptions. Finally, we offer formal bounds on the worst possible bias under the assumption that the maximum explanatory power of omitted variables is no stronger than a multiple of the explanatory power of observed variables. Conveniently, many pivotal conclusions regarding the sensitivity of the instrumental variable estimate (e.g. tests against the null hypothesis of zero causal effect) can be reached simply through separate sensitivity analyses of the effect of the instrument on the treatment (the first stage) and the effect of the instrument on the outcome (the reduced form). We apply our methods in a running example that uses proximity to college as an instrumental variable to estimate the returns to schooling.

Best Practice Guidelines for Collecting Microbiome Samples in Research Studies.

Microbiome research has the potential to provide valuable information regarding the complex relationship between microbial communities and the human body. To help facilitate the translation of this potentially revolutionary research to clinical medicine, common guidelines and best practices are necessary. These guidelines should be based on the key findings of recent research in the field and address the primary areas of discrepancy in the previously published literature. Based on this review's findings, future microbiome research should adopt a consistent and clear approach to nomenclature, using standardized terminology to mitigate confusion. Additionally, only sample collection methods proven to reduce the risk of contamination should be utilized. While sample storage often creates difficulty in microbiome research, there are proven techniques that can improve results. With the wide array of research methodologies in the literature, standardization of metadata collection, kits, and analysis software could also be beneficial. Finally, documentation and records are critical in ensuring reproducibility, as is the case with all basic research. This brief review provides a foundation for evidence-based guidelines in microbiome research. PATIENT SUMMARY: This report outlines general guidelines and best practices for conducting microbiome research, which could significantly advance the field of medicine.

Impact of the Cooperative Health Insurance System in Saudi Arabia on Universal Health Coverage-A Systematic Literature Review.

This systematic review assesses the role of the Cooperative Health Insurance System (CHIS) in achieving Universal Health Coverage (UHC) in Saudi Arabia's evolving healthcare system by consolidating and analyzing findings from diverse studies to provide a comprehensive overview of CHIS's impact and also identifies contextual challenges and practical insights that can inform similar reforms globally. We report results following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The following six databases were searched for relevant studies: PubMed, Scopus, CINAHL, Business Source Complete, APA PsycINFO, and SocIndex. The review protocol was registered with PROSPERO. Inclusion criteria focused on studies examining the impact of CHIS on the UHC dimensions based on the following themes: population covered, affordability, quality, efficiency, access, services covered, and financial coverage. The initial search identified 1316 publications. A total of 30 studies met the inclusion criteria. Our synthesis indicates that CHIS has significantly improved healthcare access and quality, particularly in the private sector. CHIS was also associated with increased healthcare efficiency through standardized benefit packages and reduced out-of-pocket expenditures. However, these studies noted challenges such as rising insurance premiums, infrastructural deficiencies, and cultural barriers. CHIS is integral to Saudi Arabia's healthcare reform, substantially contributing to UHC's objectives. Despite notable advances, continuous efforts are needed to address existing challenges and expand coverage. The findings suggest that enhanced government support and public awareness are crucial for advancing UHC goals in Saudi Arabia.

Enhanced Identity and Access Management with Artificial Intelligence: A Strategic Overview

The article aims to impart an overview of integrating artificial intelligence (AI) with identity and access management (IAM) systems and their strategic view regarding evolution, benefits, and challenges. Solid IAM systems are bulwarks of defense that shield sensitive information from sophisticated digital threats. The evolution of IAM during these last three decades - from modules such as Lightweight Directory Access Protocol (LDAP) to developed systems - has made inroads into handling the contemporary complexities of modern security protocols. It is AI that boosts IAM to real-time threat detection, automated user provisioning, and compliance with regulations through which provisioning is done using machine learning to analyze vast amounts of data for the detection of access patterns that are not usual and from those to predict the breaches, followed by the implementation of solid authentications and controls, including biometric and adaptive verification. Apart from providing benefits, there are challenges to be taken care of in a scenario where the integration of AI will call for advanced hardware, standardized software, and intensive training of users. General AI-driven platforms and tools, like IBM Security Identity Governance and Microsoft Azure Active Directory, add to the strength of IAM by ensuring the automatic detection of potential threats and facilitating proactive actions against them. Moreover, the article elaborates on a few AI-driven IAM applications in the finance and healthcare sectors, which considerably impact data security and fraud reduction. In this regard, addressing the current challenges and developing standardized frameworks is going to be imperative for the extraction of the full potential of AI into IAM.

Overview of the level of knowledge of pregnant women with the utilization of MCH Books in Indonesia: A literature Review

The Maternal and Child Health Handbook (MCH), as stipulated in the Decree of the Minister of Health of the Republic of Indonesia No. 284 of 2004, serves as a tool for the early detection of maternal and child health issues. It is also a communication and educational tool that provides essential information for mothers, families, and communities about maternal and child health services, including referrals, and the standard package of MCH services, nutrition, immunization, and child development [1]. The general benefit of the MCH Handbook is that it provides mothers and children with a comprehensive health record [2]. The research aimed to describe the knowledge levels of pregnant women in utilizing the MCH Handbook in Indonesia. The method employed in this study was a comprehensive literature review. The findings were based on 10 articles published within the last 10 years. The results indicated that seven studies reported pregnant women effectively utilizing the MCH Handbook, while three studies indicated the contrary. A recommendation for pregnant women is to enhance their awareness and habit of reading to fully understand the benefits contained in the MCH Handbook.

Superior haplotypes to enhance grain filling in rice (Oryza sativa L.) identified through GWAS

BackgroundKey traits for determining rice grain yield include the total number of spikelets per panicle (STOT), the total number of grains per panicle (GTOT), and the percentage of grain filling per panicle (GFTOT). Molecular genetic basis of grain yield is complex and regulates several interconnected genes. Identification of candidate genes and their favourable haplotypes associated with the spikelets, grains and grain filling would accelerate rice grain yield enhancement projects. The interface between genetic and environmental factors often influences the grain filling, affecting overall grain yield. Therefore, we executed a Genome-Wide Association Study (GWAS) to investigate the candidate genes associated with grain-filling traits and to explore genotype × environment (G × E) interactions influencing grain filling traits in rice—STOT, GTOT and GFTOT in the subset of Rice Diversity Panel 1 (RDP1).Methods and resultsA set of 188 RDP1 accessions were evaluated across two environments (wet seasons—2021 and 2022) following the standard package of practices to raise healthy crop. Phenotyping analysis identified promising genotypes for three traits of study—STOT and GTOT (Priano Guaira and Tainan-Iku No. 512) and GFTOT (IR8 and Biser-1). GWAS was carried out using 5.2 M SNP dataset in the Parallel Identification of QTL’s using EMMAX (PIQUE) pipeline. Five QTLs, one for STOT (qSTOT6.6), one for GTOT (qGTOT4.2) and three for GFTOT (qGFTOT3.4, qGFTOT3.5 and qGFTOT12.1), were detected. Further, superior haplotypes for eight candidate genes (LOC_Os6g38850 and LOC_OS6g39050 for STOT; LOC_OS4g11040 for GTOT; LOC_OS3g62720, LOC_OS3g62750, LOC_OS3g62820, LOC_OS12g03450 and LOC_OS12g03470 for GFTOT) were identified.ConclusionsOur results provide key genetic information for the enhancement of grain filling traits in rice. The identified candidate genes and superior haplotypes can possibly be utilized in marker-assisted selection to improve rice grain yield through gene-pyramid breeding/haplotype-assisted breeding.

A Self-supervised Deep Learning Model for Diagonal Sulcus Detection with Limited Labeled Data.

Sulci are a fundamental part of brain morphology, closely linked to brain function, cognition, and behavior. Tertiary sulci, characterized as the shallowest and smallest subtype, pose a challenging task for detection. The diagonal sulcus (ds), located in a crucial area in language processing, has a prevalence between 50% and 60%. Automatic detection of the ds is an unexplored field: while some sulci segmenters include the ds, their accuracy is usually low. In this work, we present a deep learning based model for ds detection using a fine-tuning approach with limited training labeled data. A convolutional autoencoder was employed to learn specific features related to brain morphology with unlabeled data through self-supervised learning. Subsequently, the pre-trained network was fine-tuned to detect the ds using a less extensive labeled dataset. We achieved a mean F1-score of 0.7176 (SD=0.0736) for the test set and a F1-score of 0.72 for a second held-out set, surpassing the results of a standard software and other alternative deep learning models. We conducted an interpretability analysis of the results using occlusion maps and observed that the models focused on adjacent sulci to the ds for prediction, consistent with the approach taken by experts in manual annotation. We also analyzed the challenges of manual labeling by conducting a thorough examination of interrater agreement on a small dataset and its relationship with our model's performance. Finally, we applied our method on a population analysis and reported the prevalence of ds in a case study.

A Comprehensive Life Cycle Carbon Footprint Assessment Model for Electric Power Material Warehouses

Electric power material warehouses are critical to optimizing power grid supply chains and reducing carbon emissions, aiding the power sector’s decarbonization and climate goals. Nevertheless, to our knowledge, there are no comprehensive assessments of the life cycle carbon emissions associated with storage warehouses, so the emission reduction potential of the ever-increasing number of automated technologies is still unknown. This study presents an extensive life cycle carbon footprint assessment model tailored for electric power material warehouses, and it encompasses both traditional and automated frameworks. Utilizing a process-based life cycle assessment (LCA) methodology, carbon emissions across five distinct stages are examined: storage buildings and facilities, loading and unloading, transportation, packaging, and information management systems. For this purpose, warehouses in Jiangsu Province, China, are employed as a case study. The results show that automating warehouses can achieve a reduction in total carbon emissions of 42.85% compared with traditional warehouses, with total life cycle emissions of 39,531.26 tCO2, and the transportation stage is identified as the predominant contributor. This research not only offers actionable recommendations for strategies, including renewable energy integration, intelligent control systems, and standardized packaging protocols, but also establishes a framework for future investigations of refining carbon accounting methodologies—particularly in underexplored domains such as packaging.

Compact Si-SiN photonic fiber optic gyroscope transceiver for large volume manufacturing

Miniaturized interferometric fiber optic gyroscopes (IFOGs) providing high-precision angular measurement are highly desired in various smart applications. In this work, we present a high-performance Si-SiN photonic FOG transceiver composed of an optical source, polarizer, splitter, and on-chip germanium (Ge) photodetector (PD). The transceiver is assembled in a standard butterfly package with a thermo-electric cooler (TEC). The optical loss (including two edge couplers, as well as one 3 dB splitter) and polarization extinction ratio (PER) are less than 7 dB and greater than 20 dB at room temperature, respectively. Built with the polarization maintaining (PM) fiber coil with 70 mm average diameter and 580 m length, the transceiver-based IFOG exhibits record-low bias stability of 0.022 deg/h at an integration time of 10 s, the angular random walk (ARW) of 0.0012 deg/h, and the bias instability of 0.003 deg/h, to the best of our knowledge. The preliminary reliability test agrees well with the practical requirements. Our work verifies that the on-chip Ge PD is eligible for high-performance FOG applications. Leveraged with the typical CMOS compatible 8-inch (200 mm diameter wafers) silicon photonics platform and decreased fiber splicing points, the presented transceiver provides a promising solution toward a low-loss and miniaturized FOG system with large volume manufacturing capability.

МикроРНК-143 как биомаркер атерогенных острых сердечно-сосудистых заболеваний

AIM. To evaluate the expression level of microRNA-143 in patients with atherogenic acute cardiovascular diseases. MATERIAL AND METHODS. A total of 133 patients with acute cardiovascular diseases of atherogenic etiology were examined. Group 1 included 81 patients diagnosed with ST-segment elevation myocardial infarction, and Group 2 included 52 patients diagnosed with atherothrombotic subtype of ischemic stroke in the carotid basin. The control group included 27 patients without a history of acute cardiovascular diseases. Upon hospitalization, all patients underwent examinations according to the standard of medical care for patients with stroke and acute coronary syndrome with ST-segment elevation. The degree of stenosis of coronary and internal carotid artery was determined by coronary angiography/duplex scanning. The laboratory diagnostics included an assessment of lipidogram parameters at the time of patent admission. The level of microRNA-143 (miR-143) in blood plasma was determined by real-time PCR. For statistical data processing, standard software packages SPSS Statistics, as well as Pandas and SciPy libraries, were used. RESULTS AND DISCUSSION. A higher level of LDL (p <0.0001) and a prevalence of occlusive atherosclerosis (p < 0.001, p < 0001) were revealed in patients of 1st group compared to the 2nd and control groups; severe stenosis was found in patients of both main groups (p <0.0001, p = 0.0007) compared to the control group with a predominance in patients of 1st group 1 (p = 0.001). Assessment of circulating miR-143 levels demonstrated a higher level of miR-143 (p <0.0001, p = 0.011) in patients of the main groups compared to the control group with a prevalence (p <0.0001) in patients of 1st group. The most significant correlation with the level of miR-143 was observed for LDL (r = 0.484) and the number of patients with severe atherosclerotic lesions of the coronary arteries (r = 0.454) in patients of 1st group, total cholesterol (r = 0.445), presence of diabetes mellitus (r = 0.418) and borderline atherosclerotic stenosis (r = 0.415) of the internal carotid artery - in patients of 2nd group. CONCLUSION. The results of the study allow us to consider the level of miR-143 as one of the important regulators of the process of atherogenesis. The obtained results indicate the necessity to conduct multicenter studies with a larger sample size to determine the potential value of miR-143 as a biomarker of severity and course of acute cardiovascular diseases associated with stenosis of major arteries.

Расчет плотности гидросети равнинных арк­тических и субарктических территорий России по цифровой модели рельефа с использованием ГИС-технологий (на примере Архангельской области)

The authors have developed a program for calculating the density of the channel network of the current relief using SAGA GIS modules to identify low-amplitude tectonic disturbances in the sedimentary cover of the platform arctic and subarctic territories of the Russian Federation. The program allows automating the calculation of the drainage network density using the sequential execution of standard GIS package modules. The calculation takes into account not only the drainage network, but also linear depressions of the relief, which makes it possible to identify structures with increased activity of current tectonics. The authors have calculated the density of the channel network of the representative territory (Arkhangelsk Region) with different “windows”. Comparison of the results with tectonic disturbances have shown a high degree of coincidence. The proposed methodology is applicable to the plain arctic and subarctic territories of the Russian Federation.

Recovery and potential use for non-polyolefin materials after physical recycling of complex multilayers

Standard flexible packaging in the market contains many components that differ from polyolefins (PO). These components can include barrier materials (Polyamides (PA), Polyethylene-terephthalate (PET), Ethylene-vinyl alcohol (EVOH), Polyvinyl-alcohol (PVOH), and aluminum), inks, lacquers, adhesives, fillers, and pigments. All these materials are insoluble in the solvents used for the physical, dissolution-based, and recycling of polyolefins, leaving them as a solid residue in the process. Characterization of the residue obtained after physical recycling of printed/metallized polyolefin flexible structures using the TNO Möbius dissolution process was performed to select potential applications, stakeholders, and/or secondary valorization routes. The residue resulted in a heterogeneous material composed of PET (60%), inorganic fraction (28%), and other organic components (12%). The potentially valuable materials from this residue are the PET and aluminum fractions. After a desk study, possible secondary recovery techniques for PET and aluminum were selected.

Systems Neuroscience Computing in Python (SyNCoPy): a python package for large-scale analysis of electrophysiological data.

We introduce an open-source Python package for the analysis of large-scale electrophysiological data, named SyNCoPy, which stands for Systems Neuroscience Computing in Python. The package includes signal processing analyses across time (e.g., time-lock analysis), frequency (e.g., power spectrum), and connectivity (e.g., coherence) domains. It enables user-friendly data analysis on both laptop-based and high-performance computing systems. SyNCoPy is designed to facilitate trial-parallel workflows (parallel processing of trials), making it an ideal tool for large-scale analysis of electrophysiological data. Based on parallel processing of trials, the software can support very large-scale datasets via innovative out-of-core computation techniques. It also provides seamless interoperability with other standard software packages through a range of file format importers and exporters and open file formats. The naming of the user functions closely follows the well-established FieldTrip framework, which is an open-source MATLAB toolbox for advanced analysis of electrophysiological data.

Dynamic Control of Cardboard-Blank Picking by Using Reinforcement Learning

Abstract Standard packaging lines with high output rates often struggle when dealing with uncertainties in the conditions of the handled materials. This paper focuses on a piece of machinery in an automatic packaging line, namely, an automated apparatus that extracts cardboard blanks from a buffer and transfers them to the next section through suction cups. In this context, the success of the operation depends on various controllable parameters, disturbances, and time-dependent variables, whose mutual relationships are not easily identifiable and whose understanding has so far been entrusted to the experiential knowledge of human operators. Currently, drops in picking success rates require the machine to be stopped and operators to intervene on-site, making use of their expertise to identify the issue and recalibrate the machine. To address the problem, this paper presents an artificial-intelligence-enabled controller, capable of continuously and autonomously recalibrating the apparatus and compensating for disturbances, in order to avoid missed or incorrectly picked cardboard blanks. In particular, this work exploits experimental data to build a model of the system, on which a reinforcement-learning algorithm is trained. The controller is tasked with regulating the controllable parameters while monitoring process variables. The developed agent is tested on the real apparatus to assess its performance.

Random Effects Meta-Analysis of Contingency Tables with Complete and Partially Complete Data, with Application to COVID-19 Research

We present a random effects meta-analytic approach for analyzing exchangeable 2 × 2 × 2 tables of COVID-19 deaths classified by two comorbidities, for example, diabetes and hypertension. We take the marginal tables of comorbidities as multinomial with table-specific cell probabilities drawn from a Dirichlet distribution. Conditionally hereon, we model the death counts for cell in the 2 × 2 table of possible comorbidity combinations as independent binomial random variables. We allow for a randomly drawn normally distributed frailty to model the correlation of the death counts within the same study. For complete tables, this model can be fitted by standard statistical software. We propose an approximate maximum likelihood (ML) imputation procedure by which tables with missing entries can be completed enabling the use of the same standard procedures and giving a better estimate of model parameters than one would get by leaving partial tables out. The properties of the method are illustrated by simulations.

A cross-sectional study to characterise the topical steroid dependent face by dermoscopy at a tertiary care centre

The occurrence of topical steroid dependent face (TSDF) in India has become widespread because of the abuse of creams containing steroids. Of late, many studies have been undertaken about topical steroid dependent face. Only few studies have demonstrated the dermoscopic features of this condition. Hence, we undertake a research to study the characteristic features of topical steroid dependent face by dermoscopy.A total of 398 patients who have applied topical steroid creams for more than one month and with symptoms indicative of topical steroid dependent face like redness, itching, burning, increased hair growth, acne were included in the study. The demographic, clinical and dermoscopic features were noted in such patients. These dermoscopic features were correlated with the clinical features and potency of steroid creams using Chi- square test. Statistical analysis was done using standard statistical software, ‘p’ value less than 0.05 was considered statistically significant.398 cases were included in the study. The mean age of the patients was 30.22 ± 5.7 years. The duration of topical corticosteroid application was 1-3 months in 30.15% patients, 3-6 months in 32.66% patients. Melasma and other hyperpigmentary causes was the most common cause of steroid usage in 32.16% patients. The most prevalent steroid used was 0.1% Betamethasone valerate. The most common presenting complaints were with itching (70.35%) and redness (69.84%). The dermoscopic features included mainly diffuse red areas (93.96%), terminal hair (75.87%), telangiectasias (74.87%) and brown globules (73.86%). Terminal hair, telangiectasias, focal white areas, white hair were seen on dermoscopy in statistically higher proportion of cases.Atrophy of skin, telangiectasias, red skin, hypertrichosis, easy bruisability and acneiform eruptions are the common clinical features of TSDF in our cohort. Dermoscopic findings along with these clinical features aid in meticulous diagnosis, differentiating it from other dermatological disorders. Hence, dermoscopic evaluation of TSDF provides an easy, diagnostic and time saving procedure that aids in quick detection and helps in management of such cases.

Accounting for bias due to outcome data missing not at random: comparison and illustration of two approaches to probabilistic bias analysis: a simulation study

BackgroundBias from data missing not at random (MNAR) is a persistent concern in health-related research. A bias analysis quantitatively assesses how conclusions change under different assumptions about missingness using bias parameters that govern the magnitude and direction of the bias. Probabilistic bias analysis specifies a prior distribution for these parameters, explicitly incorporating available information and uncertainty about their true values. A Bayesian bias analysis combines the prior distribution with the data’s likelihood function whilst a Monte Carlo bias analysis samples the bias parameters directly from the prior distribution. No study has compared a Monte Carlo bias analysis to a Bayesian bias analysis in the context of MNAR missingness.MethodsWe illustrate an accessible probabilistic bias analysis using the Monte Carlo bias analysis approach and a well-known imputation method. We designed a simulation study based on a motivating example from the UK Biobank study, where a large proportion of the outcome was missing and missingness was suspected to be MNAR. We compared the performance of our Monte Carlo bias analysis to a principled Bayesian bias analysis, complete case analysis (CCA) and multiple imputation (MI) assuming missing at random.ResultsAs expected, given the simulation study design, CCA and MI estimates were substantially biased, with 95% confidence interval coverages of 7–48%. Including auxiliary variables (i.e., variables not included in the substantive analysis that are predictive of missingness and the missing data) in MI’s imputation model amplified the bias due to assuming missing at random. With reasonably accurate and precise information about the bias parameter, the Monte Carlo bias analysis performed as well as the Bayesian bias analysis. However, when very limited information was provided about the bias parameter, only the Bayesian bias analysis was able to eliminate most of the bias due to MNAR whilst the Monte Carlo bias analysis performed no better than the CCA and MI.ConclusionThe Monte Carlo bias analysis we describe is easy to implement in standard software and, in the setting we explored, is a viable alternative to a Bayesian bias analysis. We caution careful consideration of choice of auxiliary variables when applying imputation where data may be MNAR.

Multisegment Overlap–Save Method for Coherent Dedispersion

Dispersion occurs due to the interstellar medium, which functions as a prism and causes different time delays in radio waves of varying frequencies. The coherent dedispersion technique is often used in pulsar and fast radio burst observations to mitigate this phenomenon. The widely recognized Overlap–Save approach enables this dedispersion algorithm to efficiently perform long linear convolution. However, with the present implementation, the necessary filter length for dedispersion can reach 100 million points or more. The corresponding fast Fourier transform (FFT) points should be larger than this value and a GPU cluster can be used to tackle this demanding process. This study presents the Multisegment Overlap–Save Method (MS-OSM) to effectively address this problem. Our algorithm divides signal bands into separate short segments based on frequency component delays during dedispersion. By using the short segments shuffling technique with the Overlap-Save structure, MS-OSM can greatly reduce the FFT and inverse FFT points required down to fewer than 65,536 points. To evaluate the performance of MS-OSM, a synthetic pulsar signal is created and verified using standard software tools like DSPSR and PRESTO. The results show that MS-OSM maintains the same resolution while reducing execution time and resource usage. The validation of MS-OSM is taken by processing real pulsar observation data.

Abstract 4136204: Clearing Senescent Cells Improves Mouse Survival Rate Post Myocardial Infarction through Alteration of Cardiomyocyte and Immune Cell Subpopulations

Introduction: Cellular senescence often involves a p16-pathway, and p16 overexpression is a hallmark of senescent cells. The role of cellular senescence in myocardial infarction (MI) and any mediating mechanisms remain unclear. Aims: To investigate the effect of p16 + cell clearance on survival post MI and elucidate underlying mechanisms. Methods: We utilized INK-ATTAC transgenic mice, in which p16 + cells undergo targeted apoptosis upon exposure to AP20187 (AP). Sham and MI mice were treated with AP or vehicle (V) twice-weekly for one month, starting 3-4 hours post-MI. Survival rate improvement post MI in the AP group (Fig A, P<0.001) prompted us to evaluate effects of senescent cell clearance at the cardiac cellular level and its involvement in MI-associated pathways at 3 days post-MI. The infarct and peri-infarct areas were collected and snap-frozen. Nuclei were extracted with modified gentleMACS methods; libraries were constructed using 10X Genomics. Standard software (CellRanger, Seurat) were employed for single-nuclei-RNA-seq data analysis. Results: Cardiomyocytes (CMs) showed 5 distinct subsets. CM subclusters 1 and 2 showed enrichments of genes characteristic of infarct boarder zones, while most sham CMs were found in subcluster 0, reflecting healthy-CMs. AP-treated mice exhibited a higher proportion of healthy CMs (Fig B). Differentially expressed genes (DEGs) between V and AP group were enriched in the healthy CMs, suggesting that clearance of p16 + cells improves the status of CMs post MI (Fig C). Immune-cell profiling showed that by far the largest number of DEG for AP vs vehicle were expressed in the (arginase-1) Arg1 macrophage subgroup (Fig D). The percentage of Arg1 macrophages decreased in the AP group (FDR=0.059) (Fig E). We noted significant enrichment in the hypoxia and cardiac rupture pathways in Arg1 macrophages (Fig F), correlating with the high death rate from myocardial rupture in V-mice. Conclusion: These results suggest a significant role of p16 + senescent cells in post-MI mortality, potentially through effects on cardiomyocytes and a specific macrophage subpopulation, Arg1 macrophages.