Aggregate Data Research Articles

The use of venous blood gas in assessing arterial acid-base and oxygenation status – an analysis of aggregated data from multiple studies evaluating the venous to arterial conversion (v-TAC) method.

ABSTRACT Background Several methods exist toreduce the number of arterial blood gases (ABGs). One method, Roche v-TAC, hasbeen evaluated in different patient groups. This paper aggregates data from thesestudies, in different patient categories using common analysis criteria. Research Design and Methods We included studiesevaluating v-TAC based on paired arterial and peripheral venous blood samples.Bland-Altman analysis compared measured and calculated arterial values of pH, PCO2and PO2. Subgroup analyses were performed for normal, chronichypercapnia and chronic base excess, acute hyper- and hypocapnia, and acute andchronic base deficit. Results 811 samples from 12 studieswere included. Bias and limits of agreement for measured and calculated values:pH 0.001 (-0.029 to 0.031), PCO2 -0.08 (-0.65 to 0.49) kPa, and PO20.04 (-1.71 to 1.78) kPa, with similar values for all sub-group analyses. Conclusion These data suggest thatv-TAC analysis may have a role in replacing ABGs, avoiding arterial puncture.Substantial data exists in patients with chronic hypercapnia and chronic baseexcess, acute hyper and hypocapnia, and in patients with relatively normal acidbase status, with similar bias and precision across groups and across studydata. Limited data exists for patients with acute and chronic base deficit.

Idiographic artificial intelligence to explain students' self-regulation: Toward precision education

Existing predictive learning analytics models have exclusively relied on aggregate data which not only have obfuscated individual differences but also made replicability and generalizability difficult. This study takes a radical departure and uses a person-specific approach to predicting and explaining students' self-regulation (SRL). A person-specific approach entails developing a predictive model for each individual using their own data (i.e., idiographic, single-subject or N = 1). We also use explainable and interpretable artificial intelligence (AI) models that allow us to identify the variables that explain students' SRL and guide data-informed decisions. Our study has shown that idiographic single-subject models are tenable, informative, and can accurately capture the individualized students' SRL process. Predictions varied vastly across students regarding accuracy and predictors. The traditional average model did not match any student regarding the predictors' order. These findings are a testament that the “average” is rare and often does not represent any individual student. The variability in our study has shown that no single model can accurately and reliably capture all students. To account for the unique learning processes of individual students, idiographic methods could provide a solution. Educational relevance statementIndividualized artificial intelligence is feasible and reliable and can help understand each person using their own data. Using idiographic models, we can deliver solutions that are precise, accurate and interventions that are more likely to work.

A spatially-explicit sensitivity analysis of urban definitions: Uncovering implicit assumptions in the Degree of Urbanisation

We introduce a spatially-explicit sensitivity framework to uncover potential biases in urban delineation approaches. Our starting point is that there is no broadly shared agreement on how to define or delineate urban areas, neither in terms of methods nor in terms of thresholds or criteria. Deciding on delineation criteria thus inevitably involves making certain assumptions that may unwittingly reproduce urban realities experienced by those expressing them, and have spatially unequally distributed implications. Understanding how specific criterion choices shape our understanding of ‘the urban’ and how, why, and – especially – where a definition leads to specific sensitivities is therefore key, especially when the definition is utilised beyond its intended application. Our framework to uncover these sensitivities is spatially explicit in the sense that it does not rely on aggregate statistics but instead focuses on the sensitivity of the ‘urban’ classification of individual spatial units at the finest spatial granularity. Applying the framework to the definition of the Degree of Urbanisation reveals that sensitivity is indeed not equally distributed across the world. Certain regions (e.g., areas around Dallas – Fort Worth) and specific types of urbanisation (e.g., desakota regions in Pacific Asia) exhibit higher sensitivity than others. We discuss how these sensitivities may embody certain implicit assumptions in the definition, and examine their broader theoretical implications.

Environmental determinants of health: Measuring multiple physical environmental exposures at the United States census tract level

Physical environment plays a key role in determining human health risks. Exposure to toxins, weather extremes, degraded air and water quality, high levels of noise and limited accessibility to green areas can negatively affect health. Furthermore, adverse environmental exposures are often correlated with each other and with socioeconomic status, thereby compounding disadvantages in marginalized populations. Moreover, despite their importance in determining human health risks, the role of multiple environmental exposures is not well studied, and only a few resources contain aggregate environmental exposure data and only for selected areas of the contiguous US. To fill these gaps, we took a cumulative approach to measuring the environment by generating a composite Multi-Exposure Environmental Index (MEEI) as a US Census Tract-level summary of key environmental factors with known health effects. This measure quantifies multiple environmental exposures in the same area that can result in additive and synergistic effects on health outcomes. This information is crucial to better understand and possibly leverage environmental determinants of health for informed policy-making and intervention.

A Secure Data Aggregation Algorithm Based on a Trust Mechanism

Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security.

Somatic and psychiatric comorbidity in people with diagnosed gambling disorder: A Finnish nation-wide register study.

This is the first nation-wide register study based on a total population sample measuring the gender-specific incidences of chronic diseases and conditions among adults diagnosed with gambling disorder (GD). The study used aggregated data for 2011-22 retrieved from the Register of Primary Health Care visits, Care Register for Health Care and Care Register for Social Welfare, including specialized outpatient and inpatient health care, inpatient social care and institutional care and housing services with 24-hour or part-time assistance, set in mainland Finland. Participants comprised people aged 18-90+ years with GD diagnosis [corresponding to pathological gambling, International Classification of Diseases 10th revision (ICD-10) code F63.0, n = 3605; men n = 2574, women n = 1031]and the general population (n = 4 374 192). Incidences of somatic diseases and psychiatric disorders were calculated for the people with diagnosed GD and for the general population, separately for women and men. After standardizing for age, the incidence of each diagnostic group was systematically higher for people with GD compared with the general population, except for cancer. The highest standardized incidence ratio (SIR) values were for psychiatric disorders [SIR = 234.2; 95% confidence interval (CI) = 226.1-242.4], memory disorders (SIR = 172.1; 95% CI = 119.1-234.8), nervous system diseases (SIR = 162.8; 95% CI = 152.8-173.1), chronic respiratory diseases (SIR = 150.6; 95% CI = 137.6-164.2), diabetes (SIR = 141.4; 95% CI = 127.9-155.5) and digestive diseases (SIR = 134.5; 95% CI = 127.1-142.2). In Finland, the incidence of chronic diseases and conditions among people with gambling disorder is higher compared with the general population, apart from cancer.

P-759 Safety and quality of MAR (medically assisted reproduction) provisions in Spain in response to adaptative regulation during a public health emergency: a national registry analysis

Abstract Study question Did the level of quality and the safety of MAR treatments change in response to the restrictive medical care regulations during the COVID emergency? Summary answer All assessed markers of PMA treatment quality and safety were maintained, indicating a robust quality structure of the Spanish PMA medical system. What is known already Acute public health stressors can affect the provision of effective and safe care in specific healthcare areas, as operational constrains are put in place and resource allocation is shifted to emerging areas of need. During 2020, Spain was heavily affected by the COVID pandemic, and the provision of PMA was constrained both in time and scope for several months. Here, we examine the results of these constrains on the quality and safety of PMA treatments performed in the country through the comparative analysis of pre- and post- emergency national data. Study design, size, duration This retrospective study includes aggregate registry data from 316 PMA centers in Spain, representing 100% of reported IVF/ICSI treatments in the country. Data refer to treatments performed in 2019, 2020 and 2021.Treatment data were compared on an intention to treat basis (ITT) whenever possible. Participants/materials, setting, methods A total of 48.123, 40.453 and 49.734 IVF/ICSI treatments with own oocytes were initiated in 2019, 2020 and 2021 respectively. Oocyte donor stimulation were 15.807, 12.001, and 13.287, respectively. Safety markers analyzed were: oocytes collected (donation), multiple pregnancies and births, complications of treatment requiring hospitalization. Indicators of efficacy were pregnancy and live birth rates, with and without PGT-A, on an ITT basis. Data were compared by ANOVA or t-test. A p < 0.05 was considered significant. Main results and the role of chance The ratio between transfers and clinical results in IVF/ICSI cycles with own eggs, according to treatment intention, has remained stable in both pregnancies [34.4-34.8%] and deliveries [24.7-25.7%] between 2019, 2020 and 2021 Births with multiple fetuses were 11.3%, 9.4% and 8.7% respectively. Live births from PGT-A cycles were 22.4% in 2019 and 23.0% in 2020. The results of cycles with donor eggs remained stable in both pregnancies [56.7-58.7%] and deliveries [43.3-44.2%] between 2019, 2020 and 2021. Births with multiple fetuses were 10.7%, 9.8% and 8.2% respectively. Births of higher order than twins were anecdotal. Complications requiring hospital admission have remained at 0.20% - 0.026% of total treatments, with ovarian hyperstimulation and hemorrhages being the most frequent. Limitations, reasons for caution The aggregate nature of data did not allow for a more in depth associative analysis. Wider implications of the findings The analyzed indicators have remained within the expected ranges, suggesting a low impact of the COVID measures on the operational quality and safety of the TRA units. Trial registration number NA

Assessing economic landscapes: city rankings (2010–2022)

Urban assessment is crucial for understanding the dynamics of city development, identifying areas for improvement, and formulating targeted policies to enhance the quality of life for residents. This study focuses on evaluating Kazakhstan’s megacities across nine indicators grouped into financial and economic condition, business development, and living standards. Using comparative analysis and ranking methods, the study assesses budgetary allocations, population dynamics, industrial growth, and the impact of strategic investments on urban development. Drawing on current economic and demographic trends, the study examines the role of cities as drivers of economic growth and highlights the importance of addressing social and economic inequalities. Through a literature review, the study contextualizes the findings within the broader discourse on urbanization and economic development, emphasizing the need for nuanced strategies to address regional disparities. Methodologically, a rating system based on quantitative indicators from 2010 to 2022 is employed, with a heatmap visualization to illustrate city rankings across various indicators. Using aggregated data on key economic indicators, the cities were classified into three categories: high-ranking, mid-ranking and low-ranking. The study contributes to a deeper understanding of urban development in Kazakhstan and provides insights for policy formulation aimed at promoting sustainable and inclusive growth across its megacities. The city ranking analysis depicts a varied economic landscape in Kazakhstani cities. High-ranked cities exhibit robust economic performance with low poverty rates, strong average salaries, and active retail trade, hinting at promising business prospects. Mid-ranked cities show mixed performances, while low-ranked cities face considerable economic challenges, including higher poverty, lower salaries, and potential declines in trade.

Cervical cancer prevention and care in HIV clinics across sub-Saharan Africa: results of a facility-based survey.

To eliminate cervical cancer (CC), access to and quality of prevention and care services must be monitored, particularly for women living with HIV (WLHIV). We assessed implementation practices in HIV clinics across sub-Saharan Africa (SSA) to identify gaps in the care cascade and used aggregated patient data to populate cascades for WLHIV attending HIV clinics. Our facility-based survey was administered between November 2020 and July 2021 in 30 HIV clinics across SSA that participate in the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium. We performed a qualitative site-level assessment of CC prevention and care services and analysed data from routine care of WLHIV in SSA. Human papillomavirus (HPV) vaccination was offered in 33% of sites. Referral for CC diagnosis (42%) and treatment (70%) was common, but not free at about 50% of sites. Most sites had electronic health information systems (90%), but data to inform indicators to monitor global targets for CC elimination in WLHIV were not routinely collected in these sites. Data were collected routinely in only 36% of sites that offered HPV vaccination, 33% of sites that offered cervical screening and 20% of sites that offered pre-cancer and CC treatment. Though CC prevention and care services have long been available in some HIV clinics across SSA, patient and programme monitoring need to be improved. Countries should consider leveraging their existing health information systems and use monitoring tools provided by the World Health Organization to improve CC prevention programmes and access, and to track their progress towards the goal of eliminating CC.

A novel positive-negative graph convolutional network-based fault diagnosis method with application to complex systems

Fault diagnosis plays a crucial role in ensuring the safe and stable operation of complex industrial systems. Among various existing methods, graph convolutional network (GCN)-based methods can handle graph-structured data commonly found in industrial systems, due to their capability to capture complex relationships within datasets through association graphs. However, conventional GCN models exclusively consider positive association relationship between data, but do not account for the negative one, resulting in embedding vectors that exhibit clustering tendencies and the over-smoothing issue. To address these challenges, a positive-negative GCN-based fault diagnosis method is proposed in this paper. In the method, the conventional association graph is treated as a positive graph, and a corresponding negative one is constructed to capture negative relationships within the dataset. Furthermore, a graph-level data aggregation strategy is introduced to aggregate the information from both association graphs, and incorporate them into the forward propagation process. Fault diagnosis is achieved using a iteratively trained GCN model. Experimental results on two cases demonstrate that the proposed method outperforms existing fault diagnosis methods in terms of diagnostic performance.

Edge2LoRa: Enabling edge computing on long-range wide-area Internet of Things

Long-Power Wide Area Networks (LPWAN) is a low-cost solution to deploy very-large scale Internet of Things (IoT) infrastructures with minimal requirements following a classic producer/consumer model. Inevitably such deployments will require a shift towards low-latency, distributed and collaborative data aggregation models. The cloud edge computing continuum (CECC) has been proposed as an evolution of the traditional central ultra-high-end processing cloud into a continuum of collaborative processing elements distributed from the cloud to the network edge. Until today, incorporating existing centralized and monolithic LPWAN architectures in the CECC faces multiple security-related implications. We propose Edge2LoRa, a complete secure solution to incorporate LPWAN architectures in CECC enabling faster data processing while reducing the transmission of sensitive data. It improves network performance through data pre-processing, traffic flow optimization, and real-time local analysis. Edge2LoRa gradually transform existing LPWAN deployments into agile and versatile infrastructures that enable the seamless and efficient processing of data throughout the CECC while guaranteeing service continuity and full-backwards compatibility. We implement Edge2LoRa in hardware compliant with the Things Stack and the LoRaWAN v1.0.4 and v1.1. We evaluate the performance in terms of networking and computing resource utilization, quality of service and security. The results provide a clear indication of the improvements to public and private LoRaWAN infrastructures without any disruption or service degradation for existing legacy services. In public LoRaWAN deployments where large-scale IoT data streams drive big data analytics, we demonstrate core network bandwidth usage reductions of up to 90% and data processing latency improvements by a ×10 factor.

Mental Health Hospitalizations in Canadian Children, Adolescents, and Young Adults Over the COVID-19 Pandemic.

The COVID-19 pandemic resulted in multiple socially restrictive public health measures and reported negative mental health impacts in youths. Few studies have evaluated incidence rates by sex, region, and social determinants across an entire population. To estimate the incidence of hospitalizations for mental health conditions, stratified by sex, region, and social determinants, in children and adolescents (hereinafter referred to as youths) and young adults comparing the prepandemic and pandemic-prevalent periods. This Canadian population-based repeated ecological cross-sectional study used health administrative data, extending from April 1, 2016, to March 31, 2023. All youths and young adults from 6 to 20 years of age in each of the Canadian provinces and territories were included. Data were provided by the Canadian Institute for Health Information for all provinces except Quebec; the Institut National d'Excellence en Santé et en Services Sociaux provided aggregate data for Quebec. The COVID-19-prevalent period, defined as April 1, 2020, to March 31, 2023. The main outcome measures were the prepandemic and COVID-19-prevalent incidence rates of hospitalizations for anxiety, mood disorders, eating disorders, schizophrenia or psychosis, personality disorders, substance-related disorders, and self-harm. Secondary measures included hospitalization differences by sex, age group, and deprivation as well as emergency department visits for the same mental health conditions. Among Canadian youths and young adults during the study period, there were 218 101 hospitalizations for mental health conditions (ages 6 to 11 years: 5.8%, 12 to 17 years: 66.9%, and 18 to 20 years: 27.3%; 66.0% female). The rate of mental health hospitalizations decreased from 51.6 to 47.9 per 10 000 person-years between the prepandemic and COVID-19-prevalent years. However, the pandemic was associated with a rise in hospitalizations for anxiety (incidence rate ratio [IRR], 1.11; 95% CI, 1.08-1.14), personality disorders (IRR, 1.21; 95% CI, 1.16-1.25), suicide and self-harm (IRR, 1.10; 95% CI, 1.07-1.13), and eating disorders (IRR, 1.66; 95% CI, 1.60-1.73) in females and for eating disorders (IRR, 1.47; 95% CI, 1.31-1.67) in males. In both sexes, there was a decrease in hospitalizations for mood disorders (IRR, 0.84; 95% CI, 0.83-0.86), substance-related disorders (IRR, 0.83; 95% CI, 0.81-0.86), and other mental health disorders (IRR, 0.78; 95% CI, 0.76-0.79). This cross-sectional study of Canadian youths and young adults found a rise in anxiety, personality disorders, and suicidality in females and a rise in eating disorders in both sexes in the COVID-19-prevalent period. These results suggest that in future pandemics, policymakers should support youths and young adults who are particularly vulnerable to deterioration in mental health conditions during public health restrictions, including eating disorders, anxiety, and suicidality.

Performance of Smart Grid Based on Data Aggregation Using Wireless Communication Network Technology

With the advancements in technology, electrical energy has become a basic need to support every field of life including Electric Vehicles (EVs), Industry automation, security networks and communication systems. This, in turn, demands a high order of design, protection and measurement setup of power transmission from the energy source to the end users (load) with minimum loss of energy. All types of loads need a nominal supply voltage to operate, which necessary for its safe operation. Usually the nominal-rated voltage allows ±5% variation tolerance in the operation of loads, without affecting the equipment. The transmission lines that connect several smart grids are called as interconnection, by using it the stability and proficiency can be increased. It should provide reliable and secure communication, with a low-cost solution. Hence, in this research paper, an efficient communication system based on wireless sensor networks will be under consideration, to ensure good quality of service for smart grids. The initial two points that are required to be covered include 1) research on optimum network topology to connect several smart grids and 2) interference cancellation to avoid errors in transmission due to the induced field from the power lines. In addition, 3) the wireless sensor network will be used to support efficient data collection, self-organization of the network and data reduction by removing the redundancy. Finally, 4) development of data prediction algorithm to reduce the transmission rate and latency. Overall, these approaches will help in development of smart network to reduce the energy wastage, ensure nominal supply voltage, increased reliability and improved communication network as compared to existing solutions. The presented work includes the two sorts of wind and solar energy in ordinary working environments and illuminates the energy trade between buyers and power Production Company. The expense of power isn't thought about; however, the various buyers can pick the least expensive energy.

CCOA-DC: A Novel Optimization with NMF Data Compression in WSN Data Aggregation

CCOA-DC: A Novel Optimization with NMF Data Compression in WSN Data Aggregation

Developing an electronic portfolio of learning for family medicine training in South Africa.

Workplace-based assessment has become increasingly crucial in the postgraduate training of specialists in South Africa, particularly for family physicians. The development of a Portfolio of Learning (PoL) has been a central focus within the discipline of family medicine for over a decade. Initially, a paper-based portfolio was adopted to collect evidence of learning for 50 out of 85 agreed exit-level outcomes. Stellenbosch University led the conversion of this portfolio into an electronic format, known as e-PoL, utilising Scorion software. The e-PoL was successfully implemented in the Western and Eastern Cape regions and was subsequently adopted nationally under the coordination of the South African Academy of Family Physicians. In 2023, the e-PoL underwent a redesign to gather evidence of learning for 22 entrustable professional activities (EPAs). Key insights from this development process underscore the importance of the PoL in supporting assessment-for-learning rather than merely assessment-of-learning. This necessitates features for feedback and interaction, ensuring that the PoL functions beyond a mere repository of forms. Additionally, the e-PoL should facilitate triangulation, aggregation, and saturation of data points to effectively measure EPAs. Furthermore, the PoL has not only documented learning but has also played a pivotal role in guiding the development of clinical training by explicitly outlining expectations for both registrars and supervisors. While the initial design and development costs are significant, operational costs become affordable when shared across all training programmes.

Assessment of centanafadine in adults with ADHD: a matching adjusted indirect comparison versus methylphenidate hydrochloride extended release (Concerta)

Objective: To compare the safety and efficacy of centanafadine versus methylphenidate hydrochloride extended release (ER; Concerta) in adults with ADHD. Methods: In the absence of head-to-head trials, anchored matching-adjusted indirect comparisons (MAIC) were used to compare rates of adverse events reported across trials and mean change from baseline in Adult ADHD Investigator Symptom Rating Scale (AISRS) score between centanafadine and methylphenidate hydrochloride ER. Pooled patient-level data from two centanafadine trials (NCT03605680, NCT03605836) and aggregate data from one published methylphenidate hydrochloride ER trial (NCT00937040) were used. Characteristics of individual patients from the centanafadine trials were matched to aggregate baseline characteristics from the methylphenidate hydrochloride ER trial using propensity score weighting. A sensitivity analysis assessed the robustness of the results to the capping of extreme weights (i.e., above the 99th percentile). Results: Compared with methylphenidate hydrochloride ER, centanafadine was associated with significantly lower risk of dry mouth (risk difference [RD] in percentage points: -11.95), initial insomnia (-11.10), decreased appetite (-8.05), anxiety (-5.39), palpitations (-5.25), and feeling jittery (-4.73) though a significantly smaller reduction in AISRS score (4.16-point). In the sensitivity analysis, the safety results were consistent with the primary analysis but there was no significant difference in efficacy between centanafadine and methylphenidate hydrochloride ER. Conclusion: In this anchored MAIC, centanafadine had a better safety profile and possibly lower efficacy than methylphenidate hydrochloride ER. While the safety results were robust across analyses, there was no difference in efficacy between centanafadine and methylphenidate hydrochloride ER in the sensitivity analysis. Considering its favorable safety profile, centanafadine may be preferred among patients for whom treatment-related adverse events are a concern.

Efficient and Secure Data Aggregation for Resource-Constrained IoT Environments

Efficient and Secure Data Aggregation for Resource-Constrained IoT Environments

Blockchain‐based secure multifunctional data aggregation for fog‐IoT environments

SummaryData aggregation, in its basic form, has been widely used, and several solutions have been proposed for IoT environments. However, to calculate statistical metrics, detect anomalies, and predict future trends, we need to perform various data analysis functions on the aggregated data. Recently, multifunctional data aggregation (MFDA) has been proposed to calculate various statistical functions such as sum, mean, variance, covariance, and analyze of variance (ANOVA). The purpose of MFDA is to enable the improvement of decision making, resource allocation and system performance by providing diverse and varied statistical data. However, the existing solutions involving MFDA generate significant communication and calculation costs. Furthermore, they cannot prevent malicious aggregators from sending fake data. Recently, the Fog computing paradigm has been adopted in IoT environments to address various challenges and enhance the efficiency of data processing and storage. The blockchain technology has been integrated in various IoT applications to enhance the security, increase transparency, and facilitate decentralized data exchange and transactions. In this article, we propose BMDA, a blockchain‐based secure multifunctional data aggregation method for IoT‐Fog environments. BMDA employs an encoding function to structure the data before their transmission. Furthermore, to ensure privacy preservation, authentication, data integrity and to resist malicious aggregators, we employ Paillier homomorphic encryption, BLS signature, and blockchain technology. The security analysis demonstrates the robustness of our proposal, and the performance analysis in terms of computations and communications shows the effectiveness of BMDA compared to existing solutions.

ScCross: efficient search for rare subpopulations across multiple single-cell samples.

Identifying rare cell types is an important task to capture the heterogeneity of single-cell data, such as scRNA-seq. The widespread availability of such data enables to aggregate multiple samples, corresponding for example to different donors, into the same study. Yet, such aggregated data is often subject to batch effects between samples. Clustering it therefore generally requires the use of data integration methods, which can lead to overcorrection, making the identification of rare cells difficult. We present scCross, a biclustering method identifying rare subpopulations of cells present across multiple single-cell samples. It jointly identifies a group of cells with specific marker genes by relying on a global sum criterion, computed over entire subpopulation of cells, rather than pairwise comparisons between individual cells. This proves robust with respect to the high variability of scRNA-seq data, in particular batch effects. We show through several case studies that scCross is able to identify rare subpopulations across multiple samples without performing prior data integration. Namely, it identifies a cilium subpopulation with potential new ciliary genes from lung cancer cells, which is not detected by typical alternatives. It also highlights rare subpopulations in human pancreas samples sequenced with different protocols, despite visible shifts in expression levels between batches. We further show that scCross outperforms typical alternatives at identifying a target rare cell type in a controlled experiment with artificially created batch effects. This shows the ability of scCross to efficiently identify rare cell subpopulations characterized by specific genes despite the presence of batch effects. The R and Scala implementation of scCross is freely available on GitHub, at https://github.com/agerniers/scCross/. A snapshot of the code and the data underlying this article are available on Zenodo, at https://zenodo.org/doi/10.5281/zenodo.10471063. Supplementary data are available at Bioinformatics online.

American Heart Association Cardiogenic Shock Registry: Design and Implementation.

Cardiogenic shock is a morbid complication of heart disease that claims the lives of more than 1 in 3 patients presenting with this syndrome. Supporting a unique collaboration across clinical specialties, federal regulators, payors, and industry, the American Heart Association volunteers and staff have launched a quality improvement registry to better understand the clinical manifestations of shock phenotypes, and to benchmark the management patterns, and outcomes of patients presenting with cardiogenic shock to hospitals across the United States. Participating hospitals will enroll consecutive hospitalized patients with cardiogenic shock, regardless of etiology or severity. Data are collected through individual reviews of medical records of sequential adult patients with cardiogenic shock. The electronic case record form was collaboratively designed with a core minimum data structure and aligned with Shock Academic Research Consortium definitions. This registry will allow participating health systems to evaluate patient-level data including diagnostic approaches, therapeutics, use of advanced monitoring and circulatory support, processes of care, complications, and in-hospital survival. Participating sites can leverage these data for onsite monitoring of outcomes and benchmarking versus other institutions. The registry was concomitantly designed to provide a high-quality longitudinal research infrastructure for pragmatic randomized trials as well as translational, clinical, and implementation research. An aggregate deidentified data set will be made available to the research community on the American Heart Association's Precision Medicine Platform. On March 31, 2022, the American Heart Association Cardiogenic Shock Registry received its first clinical records. At the time of this submission, 100 centers are participating. The American Heart Association Cardiogenic Shock Registry will serve as a resource using consistent data structure and definitions for the medical and research community to accelerate scientific advancement through shared learning and research resulting in improved quality of care and outcomes of shock patients.