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Dynamic (Temporal) graphs capture the valuable evolution of real-world systems, from the continuously evolving patterns of social interactions and genetic pathways to the dynamic fluctuations of economic forces. Detecting communities for such evolving networks poses unique challenges. Detecting and analyzing the evolution of communities within dynamic graphs unlocks valuable insights into the underlying structural and temporal patterns of real-world systems. However, the sheer volume of modern graph data and the inherent complexity of the temporal dimension pose significant challenges to scalable community detection algorithms. Addressing this gap, our work explores the limited landscape of scalable distributed-memory parallel methods specifically designed for dynamic network community detection. We propose a novel parallel algorithm, DyG-DPCD (Dynamic Graph Distributed Parallel Community Detection), to detect communities in dynamic networks using the Message Passing Interface (MPI) framework. We present a vertex-centric approach, allowing us to detect communities through local optimization. Furthermore, we enhance our baseline algorithm by incorporating three heuristics, which improve the algorithm’s performance significantly while maintaining the quality of the solutions. We demonstrate the efficiency of our algorithm by experimenting on several real-world large-scale networks with hundreds of millions of edges spanning diverse domains. Notably, DyG-DPCD achieves speedups between 25 × and 30 × for large networks that we experimented on using NERSC compute nodes. Our algorithm outperforms the STINGER parallel re-agglomeration algorithm by 30 × . [ABSTRACT FROM AUTHOR]

The integrated nested Laplace approximations (INLA) method has become a widely utilized tool for researchers and practitioners seeking to perform approximate Bayesian inference across various fields of application. To address the growing demand for incorporating more complex models and enhancing the method’s capabilities, this paper introduces a novel framework, INLA + , that leverages dense matrices for performing approximate Bayesian inference based on INLA, across multiple computing nodes using high-performance computing (HPC). When dealing with non-sparse precision or covariance matrices, this new approach scales better compared to the current INLA method, capitalizing on the computational power offered by multiprocessors in shared and distributed memory architectures available in contemporary computing resources and specialized dense matrix algebra. To validate the efficacy of this approach, we conduct a simulation study where INLA is compared with INLA + , whereafter it is applied to analyze cancer mortality data in Spain with a three-way spatio-temporal interaction model. [ABSTRACT FROM AUTHOR]

Background: Congenital heart disease (CHD) is the most common fetal malformation, and it can result first in cardiac remodeling and dysfunction and later in cardiac failure and hydrops. A limited number of studies have evaluated cardiac function in fetuses affected by CHD. Functional parameters could potentially identify fetuses at risk of cardiac failure before its development. However, these techniques have not translated from research to clinical settings, due to a lack of standardization and poor repeatability. We seek to evaluate whether application of automated techniques to a cohort with fetal pathology could overcome these factors. Methods: A multicenter cohort study will be carried out in eight teaching hospitals across Europe, Australia, and Middle East. Based on a previous observed standard deviation, a total sample of 381 pregnancies is required to achieve 80% power to detect a difference of 0.03 in mean myocardial performance index (MPI) with a two-sided type I error rate of 5%. After adjustments allowing for patient exclusions or incomplete datasets, a total of 330 healthy singleton pregnancies and 165 diagnosed with CHD will be recruited. Two fetal cardiac function evaluations at 19 + 6–28 + 6 and 32 + 6–36 + 6 weeks will be offered assessing automated pulsed wave doppler (PWD) MPI, spatio-temporal image correlation (STIC) annular and septal plane excursion (TAPSE, MAPSE and SAPSE), alongside cardiac morphometric and Doppler evaluations of flow across the valves. A secondary nested case–control study will evaluate fetuses with hydrops compared to those without. Differences in functional parameters between cases and controls and over time will be assessed using generalized linear mixed models. Logistic regression will estimate the association between cardiac parameters and hydrops’ incidence. Discussion: This study will provide evidence as to whether automated functional parameters could be significantly different in pregnancy affected by CHD versus healthy pregnancies. The primary objective is to compare automated PWD-MPI and STIC TAPSE, MAPSE and SAPSE in fetuses affected by CHD versus healthy. The secondary objective is to estimate whether these automated parameters could improve the predictive value of the classical cardiovascular profile score in case of hydrops. Trial registration: The study protocol has been registered in the ClinicalTrials.gov Protocol Registration System, identification number NCT05698277. [ABSTRACT FROM AUTHOR]

Helicoverpa armigera is one of the most harmful pests of tomato. Due to its effectiveness on non-target organisms and increased probability of resistance induction in Helicoverpa armigera, chemical discipline is not a sustainable approach. Moreover, expression of transgenes gradually reduces and finally results in declining the pest resistance in plants. Hereby for the first time, as an integrated pest management strategy, we established that treating the transgenic tomatoes harboring Cry1Ab under control of MPI inducible promoter by methyl jasmonate (MeJA), as a hormonal elicitor stimulating plant defense system, is more effective approach to overcome the pest attack. Cry1Ab integration, its expression, and the presence of Cry1Ab protein were confirmed in T3 transgenic lines. Applying various concentrations of MeJA increasing ROS levels and SOD activity in MeJA treated transgenic (MTT) leaves. The bioassay data indicated that feeding the larvae with MTT leaves resulted in the weakness of digestive system and immunity of Helicoverpa armigera larvae, and subsequently increased the larval mortality rate. The inefficiency of the insect’s digestive and immune system in larvae feeding on MTT leaves led to a decrease in larval growth and development, which was shown in the nutritional indices (Relative Growth Rate [RGR], Relative Consumption Rate [RCR], and Efficiency of Conversion of Ingested Food [ECD]). Finally, utilizing the combination of transgene Cry1Ab with 1 mM MeJA on T3 plants induced 47% larvae death and 53% decrease in larval growth and development during the bioassay. Therefore, the application of MeJA on transgenic plants harboring Cry1Ab could properly enhance tomato resistance against the Helicoverpa armigera via increasing ROS accumulation in transgenic leaves.Key message: Treatment of transgenic tomatoes (MTT) expressing Cry1Ab by methyl jasmonate (MeJA) enhances resistance to Helicoverpa armigera, resulting in 47% larval mortality and a 53% decrease in growth. [ABSTRACT FROM AUTHOR]

Our aim in this study was to investigate whether there is an association between large-for-gestational age (LGA) fetuses and myocardial performance index (MPI). This is a cross-sectional study conducted from July 2022 to July 2023. Prospectively gathered data from 65 LGA cases and 65 age and gestational-age (GA)-matched controls were analyzed. Presence of polyhydramnios and diabetes were recorded in the study group. Fetal left ventricular mod-MPI, peak systolic velocity (PSV) of E and A waves, umbilical and middle cerebral artery (MCA) pulsatility indexes (PI) were sonographically measured. Association between these sonographic measures and LGA fetuses were sought. The LGA group had 33 diabetic cases (22 GDM and 11 PGDM). The LGA group had greater mod-MPI (0.51 vs. 0.45, p = 0.0048). The LGA group also had prolonged isovolumetric contraction time (ICT), compared to controls (37 ms vs. 33 ms, p = 0.008). ICT was longer in LGA fetuses with non-diabetic mothers (38 ms vs. 33 ms, p = 0.009). LGA fetuses with polyhydramnios but without diabetic mothers had also longer ICT (39 ms vs. 33 ms, p = 0.002). Mod-MPI was similar in controls and LGA without diabetes/LGA with polyhydramnios but without diabetes subgroups. Our results indicate that fetal mod-MPI values are higher in LGA fetuses and ICT is prolonged among LGA fetuses irrespective of presence of maternal diabetes. [ABSTRACT FROM AUTHOR]

Introduction: This study aims to evaluate US Department of Defense hospital efficiency. Methods: Drawing on the American Hospital Association's annual survey data, the study employs data envelopment analysis, slack analysis, and the Malmquist Productivity Index to identify the differences in hospital efficiency between Air Force, Army, and Navy hospitals as well as the trends of their efficiency from 2010 to 2021. Results: US Department of Defense hospitals operated inefficiently from 2010 to 2021, although the average technical efficiency of all DOD hospitals increased slightly during this period. The inefficiency of all US Department of Defense hospitals may be due to the lack of pure technical efficiency rather than the suboptimal scale. However, as the efficiency trends in Navy hospitals differ from those in Army and Air Force hospitals, we should be careful in addressing the inefficiency of each type of US Department of Defense hospital. Conclusion: Informed by the findings, this study enhances our understanding of US Department of Defense hospital efficiency and the policy implications, offering practical advice to healthcare policymakers, hospital executives, and managers on managing military hospitals. [ABSTRACT FROM AUTHOR]

Abstract Background Covid-19 is a pandemic viral disease that directly or indirectly affects multiple organ systems, including the cardiovascular system. SPECT GATED MPI is a non-invasive nuclear imaging modality used to assess myocardial perfusion and function. The aim of this study was to evaluate the effect of Covid-19 pandemic on myocardial perfusion imaging in ischemic heart disease. Methods This was a cross-sectional (descriptive-analytical) study of 750 patients requiring myocardial perfusion imaging (MPI) who were referred to the nuclear medicine center of Kowsar Hospital, a teaching hospital in Sanandaj, the center of Kurdistan Province in north-western Iran. Data collection was performed during the Covid-19 pandemic, between 6 April 2020 and 21 March 2021. Data analysis was performed in SPSS using independent samples t-test and chi-squared. Results A total of 750 patients, including 328 (43.7%) Covid-19 positive and 422 (56.3%) Covid-19 negative, were included in the analysis. Although Covid-19 infected obese patients had a significantly higher rate of abnormal MPI (P

The recent surge in high-performance computing (HPC) demands, particularly with the advent of Exascale supercomputers, has highlighted the need for robust parallel systems. Achieving such systems involves not only advancements in hardware but also the integration of multiple programming models to maximize computational efficiency. The dual programming model that combines the Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) is a common approach to enhance parallelism. However, the reliability of these concurrent systems remains a critical concern due to runtime errors, such as deadlocks and race conditions, that are often undetectable by compilers. This paper introduces a novel testing tool that leverages linear temporal logic (LTL) properties to detect runtime errors in hybrid MPI and OpenMP applications. Our tool dynamically tests applications, running in parallel with application threads to ensure portability across different architectures while maintaining an execution overhead of less than 36.94%. By focusing on runtime errors resulting from the integration of MPI and OpenMP, our tool effectively detects deadlocks and race conditions, enhancing system reliability. We provide an implementation and an initial evaluation of the tool, which demonstrates its capability to uncover runtime errors specific to hybrid MPI and OpenMP applications.

Abstract Background Single-photon emission computed tomography (SPECT) analysis relies on qualitative visual assessment or semi-quantitative measures like total perfusion deficit that play a critical role in the non-invasive diagnosis of coronary artery disease by assessing regional blood flow abnormalities. Recently, machine learning (ML) -based analysis of SPECT images for coronary artery disease diagnosis has shown promise, with its utility in predicting long-term patient outcomes (prognosis) remaining an active area of investigation. In this review, we comprehensively examine the current landscape of ML-based analysis of SPECT imaging with an emphasis on prognostication of coronary artery disease. Main body Our systematic search yielded twelve retrospective studies, investigating SPECT-based ML models for prognostic prediction in coronary artery disease patients, with a total sample size of 73,023 individuals. Several of these studies demonstrate the superior prognostic capabilities of ML models over traditional logistic regression (LR) models and total perfusion deficit, especially when incorporating demographic data alongside SPECT imaging. Meta-analysis of 6 studies revealed promising performance of the included ML models, with sensitivity and specificity exceeding 65% for major adverse cardiovascular events and all-cause mortality. Notably, the integration of demographic information with SPECT imaging in ML frameworks shows statistically significant improvements in prognostic performance. Conclusion Our review suggests that ML models either independently or in combination with demographic data enhance prognostic prediction in coronary artery disease.

Amaç: Bu çalışma, SARS-CoV-2 (COVID-19) pandemisinin sağlık sistemlerinin etkinliği üzerindeki etkisini araştırmayı amaçlamıştır.Yöntem: Çalışmada, aylak tabanlı model, panel veri analizi, Malmquist Toplam Faktör Verimlilik Endeksi ve istatistiksel fark testi kullanılmıştır. Çalışmanın girdi değişkenleri olarak; 1.000 kişiye düşen hekim sayısı, satın alma gücü paritesine göre Amerikan doları olarak kişi başına düşen sağlık harcaması, 1.000.000 kişiye düşen bilgisayarlı tomografi cihazı sayısı; çıktı değişkenleri olarak ise, erkeklerde ve kadınlarda doğumda beklenen yaşam süresi kullanılmıştır.Bulgular: COVID-19 pandemisi aylak tabanlı model ve Malmquist Toplam Faktör Verimlilik Endeksi skorlarını anlamlı bir şekilde azaltmıştır (p

Background: Single-photon emission computed tomography (SPECT) analysis relies on qualitative visual assessment or semi-quantitative measures like total perfusion deficit that play a critical role in the non-invasive diagnosis of coronary artery disease by assessing regional blood flow abnormalities. Recently, machine learning (ML) -based analysis of SPECT images for coronary artery disease diagnosis has shown promise, with its utility in predicting long-term patient outcomes (prognosis) remaining an active area of investigation. In this review, we comprehensively examine the current landscape of ML-based analysis of SPECT imaging with an emphasis on prognostication of coronary artery disease. Main body: Our systematic search yielded twelve retrospective studies, investigating SPECT-based ML models for prognostic prediction in coronary artery disease patients, with a total sample size of 73,023 individuals. Several of these studies demonstrate the superior prognostic capabilities of ML models over traditional logistic regression (LR) models and total perfusion deficit, especially when incorporating demographic data alongside SPECT imaging. Meta-analysis of 6 studies revealed promising performance of the included ML models, with sensitivity and specificity exceeding 65% for major adverse cardiovascular events and all-cause mortality. Notably, the integration of demographic information with SPECT imaging in ML frameworks shows statistically significant improvements in prognostic performance. Conclusion: Our review suggests that ML models either independently or in combination with demographic data enhance prognostic prediction in coronary artery disease. [ABSTRACT FROM AUTHOR]

Background: Frailty, defined as multidimensional prognostic index (MPI), has been recently identified as strong predictor of disability and mortality in the elderly with acute ischemic stroke (AIS). The stress hyperglycemia ratio (SHR) is a recently introduced biomarker significantly associated with poor outcome in AIS. Objectives: This study aimed to investigate in what extent frailty, measured by MPI, and SHR affects the 3-months outcome of patients > 65 years-old with AIS. Methods: Consecutive patients with AIS >65 years-old who underwent intravenous thrombolysis (IVT) from 2015 to 2019 were enrolled in a German and an Italian Stroke Unit. The SHR was calculated by dividing the fasting plasma glucose at admission with glycated hemoglobin. Demographics and clinical premorbid data, stroke-related variables, including baseline and post-treatment NIHSS score were included in a logistic regression model. The 3-months functional outcome was evaluated by using modified Rankin scale (mRS); good outcome was defined as mRS 0–2, poor as mRS ≥ 3. Results: One hundred and fifty-five AIS patients were enrolled in the study. Median MPI was 0.19 [0.13–0.31]; 118 (76.1%) patients were classified as "robust" and 37 (23.9%) as "frail." In regression analysis, age, NIHSS, and MPI demonstrated as the most significant predictor of 3-months good outcome in the whole cohort. In robust patients, SHR values were significantly associated with the outcome. Conclusions: MPI is associated with the 3-months outcome in our cohort, in particular with good outcome. Conversely, SHR seems to be associated with a 3-months poor outcome in "robust" patients but not in frail patients. [ABSTRACT FROM AUTHOR]

This article compares the properties of individual- and household-based multidimensional child poverty approaches. Specifically, it contrasts UNICEF's multiple overlapping deprivation analysis (MODA) with the Global Multidimensional Poverty Index (MPI) developed by the Oxford Poverty and Human Development Initiative. MODA focuses on children and is rooted in the child rights approach, while MPI has been developed for households and follows Sen's (1985) capabilities approach. We demonstrate their similarities and differences using two recent multiple indicator cluster surveys: Sierra Leone and Lao People's Democratic Republic. The analysis suggests that MODA tends to produce higher multidimensional child poverty headcount rates than MPI, both because of the differences in the survey items used to construct the indicators of deprivation and because of how the indicators are aggregated and weighted. The study also shows that both MODA and MPI are highly sensitive to the exclusion of any one indicator from the analysis. Thus it is crucial to have valid information on the same indicators when tracking multidimensional poverty over time, e.g. for monitoring progress towards the sustainable development goals. Yet they are both robust to reductions in deprivation on just one indicator, suggesting that policies targeting only one component of the overall index would have a limited impact on the MD deprivation rate. [ABSTRACT FROM AUTHOR]

Introduction: Magnetic particle imaging (MPI), a radiation-free, dynamic, and targeted imaging technique, has gained significant traction in both research and clinical settings worldwide. Signal-to-noise ratio (SNR) is a crucial factor influencing MPI image quality and detection sensitivity, and it is affected by ambient noise, system thermal noise, and the magnetization response of superparamagnetic nanoparticles. Therefore to address the high amplitude system and inherent thermal noise present in conventional MPI systems is essential to improve detection sensitivity and imaging resolution. Method: This study introduces a novel open-loop, narrow-band MPI signal acquisition system based on mixed-frequency harmonic magnetization response. Allowing superparamagnetic nanoparticles to be excited by low frequency, high amplitude magnetic fields and high frequency, low amplitude magnetic fields, the excitation coil generates a mixed excitation magnetic field at a mixed frequency of 8.664 kHz (f H + 2 f L ), and the tracer of superparamagnetic nanoparticles can generate a locatable superparamagnetic magnetization signal with rich harmonic components in the mixed excitation magnetic field and positioning magnetic field. The third harmonic signal is detected by a Gradiometer coil with high signal-to-noise ratio, and the voltage cloud image is formed. Result: The experimental results show that the external noise caused by the excitation coil can be effectively reduced from 12 to about 1.5 μV in the imaging area of 30 mm × 30 mm, which improves the stability of the detection signal of the Gradiometer coil, realizes the detection of high SNR, and makes the detection sensitivity reach 10 μg Fe. By mixing excitation, the total intensity of the excitation field is reduced, resulting in a slight improvement of the resolution under the same gradient field, and the spatial resolution of the image reconstruction is increased from 2 mm under the single frequency excitation (20.7 kHz) in the previous experiment to 1.5 mm under the mixed excitation (8.664 kHz). Conclusions: These experimental results highlight the effectiveness of the proposed open-loop narrowband MPI technique in improving signal detection sensitivity, achieving high signal-to-noise ratio detection and improving the quality of reconstructed images by changing the excitation magnetic field frequency of the excitation coil, providing novel design ideas and technical pathways for future MPI systems. [ABSTRACT FROM AUTHOR]