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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]

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]

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]

Applying malleability to HPC systems can increase their productivity without degrading or even improving the performance of running applications. This paper presents Proteo, a configurable framework that allows to design benchmarks to study the effect of malleability on a system, and also incorporates malleability into a real application. Proteo consists of two modules: SAM allows to emulate the computational behavior of iterative scientific MPI applications, and MaM is able to reconfigure a job during execution, adjusting the number of processes, redistributing data, and resuming execution. An in-depth study of all the possibilities shows that Proteo is able to behave like a real malleable or non-malleable application in the range [0.85, 1.15]. Furthermore, the different methods defined in MaM for process management and data redistribution are analyzed, concluding that asynchronous malleability, where reconfiguration and application execution overlap, results in a 1.15 × speedup. [ABSTRACT FROM AUTHOR]

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The numba-mpi package offers access to the Message Passing Interface (MPI) routines from Python code that uses the Numba just-in-time (JIT) compiler. As a result, high-performance and multi-threaded Python code may utilize MPI communication facilities without leaving the JIT-compiled code blocks, which is not possible with the mpi4py package, a higher-level Python interface to MPI. For debugging or code-coverage analysis purposes, numba-mpi retains full functionality of the code even if the JIT compilation is disabled. The numba-mpi API constitutes a thin wrapper around the C API of MPI and is built around Numpy arrays including handling of non-contiguous views over array slices. Project development is hosted at GitHub leveraging the mpi4py/setup-mpi workflow enabling continuous integration tests on Linux (MPICH, OpenMPI & Intel MPI), macOS (MPICH & OpenMPI) and Windows (MS MPI). The paper covers an overview of the package features, architecture and performance. As of v1.0, the following MPI routines are exposed and covered by unit tests: size/rank, [i]send/[i]recv, wait[all|any], test[all|any], allreduce, bcast, barrier, scatter/[all]gather & wtime. The package is implemented in pure Python and depends on numpy, numba and mpi4py (the latter used at initialization and as a source of utility routines only). The performance advantage of using numba-mpi compared to mpi4py is depicted with a simple example, with entirety of the code included in listings discussed in the text. Application of numba-mpi for handling domain decomposition in numerical solvers for partial differential equations is presented using two external packages that depend on numba-mpi: py-pde and PyMPDATA-MPI.

In this paper, the MPI-based parallel library Free Molecular Dynamics (FMD) is introduced. The general structure of a typical program utilizing FMD to perform classical molecular dynamics (MD) simulation is described. Much of the paper is devoted to one of the advanced features of FMD: its ability to compute physical fields defined on distributed grids (a.k.a. “turies”). Each cell of a distributed grid may be shared among any number of MPI processes. Distributed grids can be used to combine MD with continuum models or to obtain macroscopic quantities, e.g. density, as functions of time and space. The library is designed to provide an easy to use interface for its users, as well as equip its developers with reusable tools required for expanding the code.

Message matching within MPI is an important performance consideration for applications that utilize two-sided semantics. In this work, we present an instrumentation of the CrayMPI library that allows the collection of detailed message-matching statistics as well as an implementation of hashed matching in software. We use this functionality to profile key DOE applications with complex communication patterns to determine under what circumstances an application might benefit from hardware offload capabilities within the NIC to accelerate message matching. We find that there are several applications and libraries that exhibit sufficiently long match list lengths to motivate a Binned Message Matching approach.

Background: Peritonitis is defined as inflammation of the serous membrane that lines the abdominal cavity and the organs contained therein. It is still one of the most important and common infectious problems that a surgeon must face. In this study we have taken into account all the parameters of Mannheim’s peritonitis index for predicting mortality and we have found the positive correlation among them. Aims And Objectives: To evaluate the outcome of patients using MPI and to confirm its predictive value. Materials And Method: It is a prospective study done in department of surgery for an approximate period of two years including patients attending surgery emergency department with features of peritonitis and excluding patients with primary and tertiary peritonitis. The sampling method used is nonrandomised sampling. Results And Observations: In our study we found max. patients in age of 25-35 i.e, 56(37%). Male sex (65%) has predominance. Most common site of perforation was found to be duodenal 53 (35%) and the most common clinical feature was pain in abdomen in 97% and not passing flatus and feces in 80%. Evidence of organ failure was seen in approx. 20% (30) of patients. Presentation of patients within 24 hrs was just 12% (18). Presence of malignancy was found in 12(8%) patients. 79% (118) patients had generalised peritonitis with 56% (84) having purulent exudate. Origin of sepsis was noncolonic in 128(85%) patients. maximum patients had MPI score of <21 i.e, 55% (83). 15(10%) patients expired with maximum patients had MPI of >29. Conclusions: Mannheim Peritonitis index is a useful method to determine study group outcome in patients with peritonitis. All the MPI variables of adverse outcome namely, presence of organ failure; time elapsed > 24hrs; presence of malignancy; age>50 years, generalized extension of peritonitis and type of exudate behaved as expected, except the noncolonic origin of sepsis in peritonitis and female sex. As our study differs in two adverse outcome variables, female sex & noncolonic origin of sepsis, we advocate need for further studies on Mannheim Peritonitis index to include colonic origin of sepsis and to remove female sex as variables of adverse outcome in Mannheim Peritonitis index. [ABSTRACT FROM AUTHOR]

Many asset-intensive organizations implement risk management strategies to mitigate potential hazards associated with physical asset failures, such as infrastructure deterioration or mechanical breakdown. As these physical assets' risks can be treated with maintenance activities, properly evaluating the performance of maintenance management is of interest for risk management. Accordingly, this paper proposes a framework for the determination of Maintenance Performance Indicators (MPIs) to support the risk management of physical assets. The proposed framework included four main processes: Integrate performance evaluation guidelines, Review the maintenance management strategy, Define the performance indicators, and Assess maintenance across the MPIs. The ISO 55000 series for asset management and the Balanced Scorecard (BSC) approach were the guidelines considered. The ISO 55001 standard provides three assessment domains for performance evaluation: asset portfolio, asset management, and asset management system. The BSC approach identifies four performance evaluation perspectives that were integrated to address the requirements of one of these asset management domains. Then, the MPIs were defined for each of the performance evaluation domains in line with the maintenance management strategy toward the risk management of physical assets. Through a case study, the proposed framework was demonstrated considering the operational context of a Brazilian hydroelectric power plant. As a result, the proposed framework was shown to be consistent in systematically determining the MPIs that support risk management in organizations. [ABSTRACT FROM AUTHOR]

The development of new exascale supercomputers has dramatically increased the need for fast, high-performance networking technology. Efficient network topologies, such as Dragonfly+, have been introduced to meet the demands of data-intensive applications and to match the massive computing power of GPUs and accelerators. However, these supercomputers still face performance variability mainly caused by the network that affects system and application performance. This study comprehensively analyzes performance variability on a large-scale HPC system with Dragonfly+ network topology, focusing on factors such as communication patterns, message size, job placement locality, MPI collective algorithms, and overall system workload. The study also proposes an easy-to-measure metric for estimating network background traffic generated by other users, which can be used to estimate the performance of our job accurately. The insights gained from this study contribute to improving performance predictability, enhancing job placement policies and MPI algorithm selection, and optimizing resource management strategies in supercomputers. [ABSTRACT FROM AUTHOR]

The discovery of Allele-Specific Methylation (ASM) is an important research field in biology as it regulates genomic imprinting, which has been identified as the cause of some genetic diseases. Nevertheless, the high computational cost of the bioinformatic tools developed for this purpose prevents their application to large-scale datasets. Hence, much faster tools are required to further progress in this research field. In this work we present PARamrfinder, a parallel tool that applies a statistical model to identify ASM in data from high-throughput short-read bisulfite sequencing. It is based on the state-of-the-art sequential tool amrfinder, which is able to detect ASM at regional level from Bisulfite Sequencing (BS-Seq) experiments in the absence of Single Nucleotide Polymorphism information. PARamrfinder provides the same Allelically Methylated Regions as amrfinder but at significantly reduced runtime thanks to exploiting the compute capabilities of common multicore CPU clusters and MPI RMA operations to attain an efficient dynamic workload balance. As an example, our tool is up to 567 times faster for real data experiments on a cluster with 8 nodes, each one containing two 16-core processors. The source code of PARamrfinder, as well as a reference manual, is available at https://github.com/UDC-GAC/PARamrfinder. [ABSTRACT FROM AUTHOR]

We present two magnetic particle imaging (MPI) systems with bore sizes of 75 mm and 100 mm, respectively, using three-dimensionally arranged permanent magnets for excitation and frequency mixing magnetic detection (FMMD) coils for detection. A rotational and a translational stage were combined to move the field free line (FFL) and acquire the MPI signal, thereby enabling simultaneous overall translation and rotational movement. With this concept, the complex coil system used in many MPI systems, with its high energy consumption to generate the drive field, can be replaced. The characteristic signal of superparamagnetic iron oxide (SPIO) nanoparticles was generated via movement of the FFL and acquired using the FMMD coil. The positions of the stages and the occurrence of the f1 + 2f2 harmonics were mapped to reconstruct the spatial location of the SPIO. Image reconstruction was performed using Radon and inverse Radon transformations. As a result, the presented method based on mechanical movement of permanent magnets can be used to measure the MPI, even for samples as large as 100 mm. Our research could pave the way for further technological developments to make the equipment human size, which is one of the ultimate goals of MPI. [ABSTRACT FROM AUTHOR]

Background: Sarcopenia is associated with adverse health outcomes. Understanding the association between sarcopenia, multidimensional frailty, and prognosis is essential for improving patient care. The aim of this study was to assess the prevalence and prognostic signature of sarcopenia in an acute hospital setting co-led by internists and geriatricians. Methods: Sarcopenia was assessed by applying the European Working Group on Sarcopenia in Older People (EWGSOP2) algorithm, including the SARC-F score, handgrip strength, bioelectrical impedance analysis (BIA), and Timed Up and Go (TUG) test, to 97 older multimorbid inpatients (76.5 ± 6.8 years, 55% women). The patients underwent a Comprehensive Geriatric Assessment (CGA) including an evaluation of Geriatric Syndromes (GSs) and Resources (GR) and prognosis calculation using the CGA-based Multidimensional Prognostic Index (MPI), European Quality of life—5 Dimensions (EQ-5D-5L) scale, Rosenberg Self-Esteem Scale (RSES), and Geriatric Depression Scale (GDS). Information on survival and rehospitalizations was collected 1, 3, and 6 months after discharge. Results: Sarcopenia was present in 63% (95% CI: 54–72%) of patients and categorized as probable (31%), confirmed (13%), and severe sarcopenia (18%). Sarcopenic patients showed significantly higher median MPI-values (p < 0.001), more GSs (p = 0.033), fewer GR (p = 0.003), lower EQ-5D-5L scores (p < 0.001), and lower RSES scores (p = 0.025) than non-sarcopenic patients. Six months after discharge, being sarcopenic at baseline was predictive of falls (p = 0.027) and quality of life (p = 0.043), independent of age, gender, and MPI. Conclusions: Sarcopenia is highly prevalent in older hospitalized multimorbid patients and is associated with poorer prognosis, mood, and quality of life up to 6 months after discharge, independent of age, sex, and MPI as surrogate markers of biological age. [ABSTRACT FROM AUTHOR]

Collective communication APIs equip MPI vendors with the necessary context to optimize cluster-wide operations on the basis of theoretical complexity models and characteristics of the involved interconnects. Modern HPC runtime systems with a programmability focus can perform dependency analysis to eliminate the need for manual communication entirely. Profiting from optimized collective routines in this context often requires global analysis of the implicit point-to-point communication pattern or tight constrains on the data access patterns allowed inside kernels. The Celerity API provides a high degree of freedom for both runtime implementors and application developers by tieing transparent work assignment to data access patterns through user-defined range-mapper functions. Canonically, data dependencies are resolved through an intra-node coherence model and inter-node point-to-point communication. This paper presents Collective Pattern Discovery (CPD), a fully distributed, coordination-free method for detecting collective communication patterns on parallelized task graphs. Through extensive scheduling and communication microbenchmarks as well as a strong scaling experiment on a compute-intensive application, we demonstrate that CPD can achieve substantial performance gains in the Celerity model. [ABSTRACT FROM AUTHOR]

As a popular linear filter, the mean filter is widely used in different applications as a basic tool for image enhancement. Its main purpose is to reduce the noise in an image and thus to prepare the picture for other image-processing operations depending on the current task. In the last decade, the amount of data, particularly images, that has to be processed in a variety of applications has increased significantly, and thus the usage of effective and fast filtering algorithms has become crucial. The aim of the present research is to identify what type of software (MATLAB, Scilab, or MPI-based) is preferred for reducing the filtering time and consequently save energy. Thus, the aim of the present research corresponds to actual trends in information processing and corresponds to green computing concepts. A set of experimental images divided into two groups—one for small images and a second one for big images—is used for performance evaluation of the recursive mean filter. This type of linear filter was chosen due to its very good denoising characteristics. The filter is implemented in MATLAB and Scilab environments using their specific commands and it is also implemented using the C language with the MPI library to provide the opportunity for parallel execution. Two mobile computer systems are used for experimental performance evaluation and the results indicate that the slowest filtering execution is registered when Scilab is used and the fastest execution is achieved when MPI is used with the C implementation. Depending on the amount and size of the images that have to be filtered, this study formulates advice for achieving effective performance throughout the whole process of working with images. [ABSTRACT FROM AUTHOR]

IntroductionMagnetic 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.MethodThis 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 (fH + 2fL), 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.ResultThe 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).ConclusionsThese 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.

BackgroundFrailty, 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.ObjectivesThis 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.MethodsConsecutive 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.ResultsOne 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.ConclusionsMPI 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.

A comparative analysis of the rapid intensification (RI) of super cyclonic storms Chapala (2015) and Kyarr (2019) in the Arabian Sea is conducted using the North Indian Ocean tropical cyclone data, microwave sounding images, the NOAA OISST data and the ERA5 reanalysis data. Results show that the subtropical westerly jet stream and the Southern Hemisphere anticyclonic circulation led to the formation of an obvious double-channel outflow from the northern and southern sides of the two storm centers, and the substantial inflow appeared at the eastern boundary layer of both storms. These promoted the vertical ascent motion and release of the latent heat of condensation. A warm sea surface is a necessary but not dominant factor for the RI of cyclonic storms in the Arabian Sea. During the RI of Chapala and Kyarr, the deep vertical wind shear was less than 10 m s−1; moreover, the mid-level humidity conditions favored the RI of the two cyclonic storms. Chapala had a single warm core, whereas Kyarr had double warm cores in the vertical direction. The impacts of the latent heat of fusion is more obvious for Chapala, and the potential vorticity in its inner core increases from 4.4 PVU to 8.8 PVU, whereas the potential vorticity and vorticity in the inner core of Kyarr do not change significantly. Microwave detection images show that both Chapala and Kyarr were accompanied by the formation of eyewalls during the RI phase, and the radius of maximum wind decreased and the maximum wind speed increased during the eyewall-thinning process. Both Chapala and Kyarr passed through a positive anomaly region of maximum potential intensity during the RI phase, which increases the possibility to develop to higher intensity after genesis.

While the computational power of HPC clusters breached through the exascale milestone, it highlighted the need for some critical features like fault management. The current de facto standard for inter-process communication at scale, MPI, lacks proper fault management functionalities, precluding result production in the presence of faults. Many efforts have already addressed the problem, with the ULFM extension and Reinit proposal being the two works receiving the most attention. While powerful and effective, they require expertise from the user and significantly impact the application code. For this reason, we presented the Legio fault resilience framework, which can introduce graceful degradation properties in MPI applications with minimal changes. This work summarises the development and evolution of Legio, highlighting successful use cases and providing some tips for users who want to leverage it for their executions. [ABSTRACT FROM AUTHOR]

Today, various applications in different domains increasingly rely on high-performance computing (HPC) to accomplish computations swiftly. Integrating one or more programming models alongside the used programming language enhances system parallelism, thereby improving its performance. However, this integration can introduce runtime errors such as race conditions, deadlocks, or livelocks. Some of these errors may go undetected using conventional testing techniques, necessitating the exploration of additional methods for enhanced reliability. Formal methods, such as temporal logic, can be useful for detecting runtime errors since they have been widely used in real-time systems. Additionally, many software systems must adhere to temporal properties to ensure correct functionality. Temporal logics indeed serve as a formal frame that takes into account the temporal aspect when describing changes in elements or states over time. This paper proposes a temporal-logic-based testing tool utilizing instrumentation techniques designed for a dual-level programming model, namely, Message Passing Interface (MPI) and Open Multi-Processing (OpenMP), integrated with the C++ programming language. After a comprehensive study of temporal logic types, we found and proved that linear temporal logic is well suited as the foundation for our tool. Notably, while the tool is currently in development, our approach is poised to effectively address the highlighted examples of runtime errors by the proposed solution. This paper thoroughly explores various types and operators of temporal logic to inform the design of the testing tool based on temporal properties, aiming for a robust and reliable system. [ABSTRACT FROM AUTHOR]

Yushen Jin,1 Zhongquan Cheng,2 Zhu Yuan,2 Yang Du,3 Jie Tian,3 Bing Shao1,4 1Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Centre for Disease Prevention and Control, Beijing, 100013, People’s Republic of China; 2Department of General Surgery, Capital Medical University, Beijing Friendship Hospital, Beijing, 100050, People’s Republic of China; 3CAS Key Laboratory of Molecular Imaging, Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China; 4College of Veterinary Medicine, China Agricultural University, Beijing, 100193, People’s Republic of ChinaCorrespondence: Bing Shao; Jie Tian, Email shaobingch@sina.com; jietian@ia.ac.cnPurpose: Liver cancer is considered as the third leading cause of cancer-related deaths, with hepatocellular carcinoma (HCC) accounting for approximately 90% of liver cancers. Improving the treatment of HCC is a serious challenge today. The primary objective of this study was to construct SP94-Fe3O4@ICG&DOX nanoparticles and investigate their potential diagnosis and treatment effect benefits on HCC.Methods: Firstly, we synthesized and characterized SP94-Fe3O4@ICG&DOX nanoparticles and confirmed their in vitro release behavior, photothermal and photodynamic performance. Moreover, the in vivo imaging capability was also observed. Finally, the inhibitory effects on Hepa1-6 in vitro and in vivo were observed as well as biosafety.Results: SP94-Fe3O4@ICG&DOX nanoparticles have a size of ~22.1 nm, with an encapsulation efficiency of 45.2% for ICG and 42.7% for DOX, showing excellent in vivo MPI and fluorescence imaging capabilities for precise tumor localization, and synergistic photo-chemotherapy (pH- and thermal-sensitive drug release) against tumors under irradiation. With the assistance of a fluorescence molecular imaging system or MPI scanner, the location and contours of the tumor were clearly visible. Under a constant laser irradiation (808 nm, 0.6 W/cm2) and a set concentration (50 μg/mL), the temperature of the solution could rapidly increase to ~45 °C, which could effectively kill the tumor cells. It could be effectively uptaken by HCC cells and significantly inhibit their proliferation under the laser irradiation (100% inhibition rate for HCC tumors). And most importantly, our nanoparticles exhibited favorable biocompatibility with normal tissues and cells.Conclusion: This versatile agent can serve as an intelligent and promising nanoplatform that integrates multiple accurate diagnoses, precise positioning of cancer tissue, and effective coordination with synergistic tumor photodynamic therapy. Keywords: porous hollow structure, magnetic particle imaging, fluorescence imaging, synergistic treatment, theranostics

Efficient data movement between nodes in a data center is essential for optimal performance of distributed workloads. With advancements in computing interconnection and memory, new opportunities have emerged. We propose a novel inter-node architecture and protocol called Flexible Memory Units (FMU) that uses optically disaggregated memory. FMUs can be dynamically allocated to different nodes during runtime using optical switches. The primary objective of FMUs is to use the disaggregated memory as temporary buffers during inter-node communication. We have implemented Simplecomm, an open-source simulator, to evaluate real MPI benchmarks using FMU. Our evaluation demonstrates significant speedups of up to $5.18\times $ in communication-bound applications and $1.22\times $ on computing-intensive applications, compared to a 100 Gbps InfiniBand interconnect.

In this paper, we present a novel off-chip network architecture, the Tile Serial Peripheral Interface (TSPI), designed for low-power, scalable edge AI accelerators. Our approach modifies the conventional SPI to support a modular network structure that facilitates the scalable connection of multiple accelerators. The TSPI network employs a subset mapping algorithm for efficient routing and integrates the message passing interface (MPI) protocol to ensure rapid data distribution and aggregation. This modular architecture significantly reduces power consumption and improves processing speed. Experimental results demonstrate that our proposed TSPI network achieves a 54.7% reduction in power consumption and an 82.3% decrease in switching power compared to traditional SPI networks, along with a 23% increase in processing speed when utilizing 16 nodes. These advancements make the TSPI network an effective solution for enhancing AI performance in edge computing environments.

Tim Sort is a sorting algorithm that combines Merge Sort and Binary Insertion Sort sorting algorithms. Parallel computing is a computational processing technique in parallel or is divided into several parts and carried out simultaneously. The application of parallel computing to algorithms is called parallelization. The purpose of parallelization is to reduce computational processing time, but not all parallelization can reduce computational processing time. Our research aims to analyse the effect of implementing parallel computing on the processing time of the Tim Sort algorithm. The Team Sort algorithm will be parallelized by dividing the flow or data into several parts, then each sorting and recombining them. The libraries we use are OpenMP and MPI, and tests are carried out using up to 16 core processors and data up to 4194304 numbers. The goal to be achieved by comparing the application of OpenMP and MPI to the Team Sort algorithm is to find out and choose which library is better for the case study, so that when there is a similar case, it can be used as a reference for using the library in solving the problem. The results of research for testing using 16 processor cores and the data used prove that the parallelization of the Sort Team algorithm using OpenMP is better with a speed increase of up to 8.48 times, compared to using MPI with a speed increase of 8.4 times. In addition, the increase in speed and efficiency increases as the amount of data increases. However, the increase in efficiency that is obtained by increasing the processor cores decreases.

While the conventional parallel-hole collimators are widely used in myocardial perfusion imaging (MPI) with SPECT, they are suboptimal in balancing the existing sensitivity-resolution compromise. Therefore, multi-pinhole collimation has been proposed to address the problem. In the present study, a channel multi-pinhole collimated SPECT scanner is modeled and then simulated using the GATE Monte Carlo simulation. The multi-pinhole collimator comprises eight apertures. The material, diameter, and height of the apertures were assumed to be varying. A comparison with conventional single-pinhole was also conducted. The results show that increasing the hole diameter leads to degraded spatial resolution for the multi-pinhole collimator. Compared to the single-pinhole, the multi-pinhole collimators suffer from projection overlapping and thus deteriorated spatial resolution. The findings confirm that the channel multi-pinhole collimators outperform the single-pinhole apertures by providing much higher sensitivity at the expense of slightly lower spatial resolution and therefore would be the collimator of choice for MPI with SPECT.

This paper introduces PeriLab, a modern Peridynamics solver developed in the Julia programming language. Emphasizing easy installation, usability, and implementation of new features, the code’s structure is detailed, accompanied by illustrative examples illustrating some of the code’s core functionality. The fully Message Passing Interface (MPI) parallelized code undergoes a separate benchmark problem with two million degrees of freedom, revealing large scale capabilities and analyzing the communication cost occurring in such analysis. The paper highlights key considerations for the adoption of Peridynamics, including the need for a straightforward installation process, user-friendly interfaces, efficient research code development, and well-documented as well as tested functionalities. Overcoming these challenges is crucial for Peridynamics’ widespread acceptance in engineering applications, and PeriLab serves as a valuable contribution to addressing these issues.

In this paper we identify the causes of numerical non-reproducibility in the unstructured mesh computational motif, a class of algorithms commonly used for the solution of PDEs. We introduce a number of parallel and distributed algorithms to address nondeterminism in the order of floating-point computations, in particular, a new graph coloring scheme that produces identical coloring results regardless of how many parts the graph is partitioned to. We implement these in the OP2 domain specific language (DSL) and show how it can be automatically deployed to any application that uses OP2 without user intervention. We contrast differences in results without reproducibility and then demonstrate how bitwise reproducibility can be gained using our methods on a variety of applications including a production CFD application used at Rolls-Royce. We evaluate the performance and overheads of enforcing bitwise reproducibility on a cluster of CPUs and GPUs. [ABSTRACT FROM AUTHOR]

High-performance computing (HPC) and its supercomputers are essential for solving the most difficult issues in many scientific computing domains. The proliferation of computational resources utilized by HPC systems has resulted in an increase in the associated error rates. As such, modern HPC systems promote a hybrid programming style that integrates the message-passing interface (MPI) and open multi-processing (OpenMP). However, this integration often leads to complex defects, such as deadlocks and race conditions, that are challenging to detect and resolve. This paper presents a novel approach: using machine learning algorithms to predict defects in C++-based systems by employing hybrid MPI and OpenMP models. We focus on employing a balanced dataset to enhance prediction accuracy and reliability. Our study highlights the effectiveness of the support vector machine (SVM) classifier, enhanced with term frequency (TF) and recursive feature elimination (RFE) techniques, which demonstrates superior accuracy and performance in defect prediction when compared to other classifiers. This research contributes significantly to the field by providing a robust method for early defect detection in hybrid programming environments, thereby reducing development time, costs and improving the overall reliability of HPC systems. [ABSTRACT FROM AUTHOR]

Urban poverty is a complex phenomenon and people experiencing poverty suffer from various deprivations. Multidimensional poverty measurement has been one of the best indicators of this deprivation. In general, slum dwellers are considered homogenous groups, but it is not valid in multidimensional deprivation. This paper aims to find out the correlates of multidimensional poverty in slums. Spatiality and correlates of poverty in Varanasi City have been tapped using statistical modelling. The paper is based on primary data collected from 384 households through an interview schedule from 12 slums across three geographical zones of the city. The MPI index for slums, based on global MPI, was used to compute MPI for each geographical zone. Further ANOVA and hierarchical regression analysis were performed to find spatiality and correlates of multidimensional deprivation. The paper reveals that socio-religious categories, occupation and geographical location are significant determinants or at least correlates of multidimensional poverty in slums. [ABSTRACT FROM AUTHOR]

Dedicated cardiac SPECT scanners with cadmium-zinc-telluride cameras have shown capabilities for shortened scan times or reduced radiation doses, as well as improved image quality. Since most dedicated scanners do not have integrated CT, image quantification with attenuation correction (AC) is challenging and artifacts are routinely encountered in daily clinical practice. In this work, we demonstrated a direct AC technique using deep learning (DL) for myocardial perfusion imaging (MPI). Methods: In an institutional review board-approved retrospective study, 100 cardiac SPECT/CT datasets with 99mTc-tetrofosmin, obtained using a scanner specifically with a small field of view, were collected at the Yale New Haven Hospital. A convolutional neural network was used for generating DL-based attenuation-corrected SPECT (SPECTDL) directly from noncorrected SPECT (SPECTNC) without undergoing an additional image reconstruction step. The accuracy of SPECTDL was evaluated by voxelwise and segmentwise analyses against the reference, CT-based AC (SPECTCTAC), using the 17-segment myocardial model of the American Heart Association. Polar maps of representative (best, median, and worst) cases were visually compared to illustrate potential benefits and pitfalls of the DL approach. Results: The voxelwise correlations with SPECTCTAC were 92.2% ± 3.7% (slope, 0.87; R2 = 0.81) and 97.7% ± 1.8% (slope, 0.94; R2 = 0.91) for SPECTNC and SPECTDL, respectively. The segmental errors of SPECTNC scattered from -35% to 21% (P < 0.001), whereas the errors of SPECTDL stayed mostly within ±10% (P < 0.001). The average segmental errors (mean ± SD) were -6.11% ± 8.06% and 0.49% ± 4.35% for SPECTNC and SPECTDL, respectively. The average absolute segmental errors were 7.96% ± 6.23% and 3.31% ± 2.87% for SPECTNC and SPECTDL, respectively. Review of polar maps revealed successful reduction of attenuation artifacts; however, the performance of SPECTDL was not consistent for all subjects, likely because of different amounts of attenuation and different uptake patterns. Conclusion: We demonstrated the feasibility of direct AC using DL for SPECT MPI. Overall, our DL approach reduced attenuation artifacts substantially compared with SPECTNC, justifying further studies to establish safety and consistency for clinical applications in stand-alone SPECT systems suffering from attenuation artifacts.

As a popular linear filter, the mean filter is widely used in different applications as a basic tool for image enhancement. Its main purpose is to reduce the noise in an image and thus to prepare the picture for other image-processing operations depending on the current task. In the last decade, the amount of data, particularly images, that has to be processed in a variety of applications has increased significantly, and thus the usage of effective and fast filtering algorithms has become crucial. The aim of the present research is to identify what type of software (MATLAB, Scilab, or MPI-based) is preferred for reducing the filtering time and consequently save energy. Thus, the aim of the present research corresponds to actual trends in information processing and corresponds to green computing concepts. A set of experimental images divided into two groups—one for small images and a second one for big images—is used for performance evaluation of the recursive mean filter. This type of linear filter was chosen due to its very good denoising characteristics. The filter is implemented in MATLAB and Scilab environments using their specific commands and it is also implemented using the C language with the MPI library to provide the opportunity for parallel execution. Two mobile computer systems are used for experimental performance evaluation and the results indicate that the slowest filtering execution is registered when Scilab is used and the fastest execution is achieved when MPI is used with the C implementation. Depending on the amount and size of the images that have to be filtered, this study formulates advice for achieving effective performance throughout the whole process of working with images.

In this study, we present a fast algorithm for the numerical solution of the heat equation. The heat equation models the heat diffusion over time and through a given region. We engage a finite difference method to solve this equation numerically. The performance of its parallel implementation is considered using Message Passing Interface (MPI), Compute Unified Device Architecture (CUDA), and time schemes, such as Forward Euler (FE) and Runge-Kutta (RK) methods. The originality of this study is research on parallel implementations of the fourth-order Runge-Kutta method (RK4) for sparse matrices on Graphics Processing Unit (GPU) architecture. The supreme proprietary framework for GPU computing is CUDA, provided by NVIDIA. We will show three metrics through this parallelization to compare the computing performance: time-to-solution, speed-up, and performance. The spectral method is investigated by utilizing the FFTW software library, based on the computation of the fast Fourier transforms (FFT) in parallel and distributed memory architectures. Our CUDA-based FFT, named CUFFT, is performed in platforms, which is a highly optimized FFTW implementation. We will give numerical tests to reveal that this method is up-and-coming for solving the heat equation. The final result demonstrates that CUDA has a significant advantage and performance since the computational cost is tiny compared with the MPI implementation. This vital performance gain is also achieved through careful attention of managing memory communication and access.

Silk has evolved a unique silk culture in its long history, and silk patterns have condensed the external aesthetics of silk culture and shown distinctive national characteristics. This paper firstly constructs the cultural communication network of the 17th-18th century Maritime Silk Road through big data technology and uses social network analysis to study the structural characteristics of the cultural and artistic communication network. The MPI multidimensional spatio-temporal communication evolution model is used to delineate the network’s layers further, and exploratory spatial data analysis is introduced to explore the spatio-temporal communication evolution pattern of the network. Finally, in the empirical analysis, 200 years of silk pattern data from 1600-1800 in China were selected to establish four dimensions and 11 indicators data for measurement. A comprehensive MPI multidimensional spatio-temporal propagation evolution model was derived by principal component analysis to derive the contribution of each indicator. The results show that the multidimensional cultural dissemination index of traditional silk pattern culture and art from 1600 to 1800 was 0.7461 and approximated to 0.8, and the dissemination influence was 0.2252, respectively, rising to 97%, indicating that the dissemination of silk pattern culture and art in the 17th-18th centuries was a flourishing trend. The MPI multidimensional spatial and temporal communication evolution model proposed in this paper effectively visualizes and analyzes the communication channels of silk pattern culture and art, which provides a guiding reference for the revival and communication of modern Chinese traditional culture.

In this paper, two different parallel approaches for a hybrid fractional order Coronavirus (2019-nCov) mathematical model are presented. Both parallel approaches are implemented using Julia high level language. Parallel algorithm implementations are developed for the HPC cluster using Message Passing Interface (MPI) technology and general-purpose computing on GPUs (GPGPU) using Compute Unified Device Architecture (CUDA) based on hardware environments. The algorithm implementation are used to solve the real-world problem of the hybrid fractional order Coronavirus (2019-nCov) mathematical model and to study the parallel efficiency. The introduced hybrid fractional order derivative is defined as a linear combination of the integral of Riemann-Liouville and the fractional order Caputo derivative. A parallel algorithm is designed based on the predictor-corrector method with the discretization of the Caputo proportional constant fractional hybrid operator for solving the model problem numerically. Simulation results show that, both the new parallel approaches achieve significant efficiency.

AbstractThis study investigated the relationship between agricultural market imperfections and multidimensional rural poverty in Ethiopia’s Sidama Region in 2022. And this study used qualitative and quantitative methodologies. A multi-stage sampling method was used to select 400 sample sizes. Descriptive statistics method was used to find out modes of agricultural market imperfections, and Tobit model was employed to capture the effects of agricultural market imperfections on multidimensional rural poverty. And the multidimensional poverty index (MPI) technique was employed to measure the severity of multidimensional rural poverty. Apart from what they have traditionally done by themselves, about 23% of people lack knowledge about how to produce and where to sell using modernized information. The Tobit model was also employed to figure out how market imperfections affected multidimensional poverty in the study area. This study showed that 43% of farmers transport their goods by carrying them on their shoulders to the market. For farmers with asymmetric information, the multidimensional poverty index (MPI) is expected to have a value of 0.4354 higher than those with symmetric information. Furthermore, the Tobit model’s findings showed that labor, improved seed, farm size, cooperative membership, and commercialization experience significantly negatively impacted multidimensional rural poverty. And asymmetric information, transaction cost, and infrastructure inaccessibility had a statistically significant and positive impact on multidimensional poverty. Hence, improving input usage and creating access to agricultural market information is a foundation for reducing multidimensional rural poverty.

Bottlenecks and imbalance in parallel programs can significantly affect performance of parallel execution. Finding these bottlenecks is a key issue in performance analysis of MPI programs especially on a large scale. One of the ways to discover bottlenecks is to analyze the critical path of the parallel program: the longest execution path in the program activity graph. There are a number of methods of finding the critical path; however, most of them suffer a performance drop when scaled. In this paper, we analyze several methods of critical path finding based on classical Dijkstra and Delta-stepping algorithms along with the proposed algorithm based on topological sorting. Corresponding algorithms for each approach are presented including additional enhancements for increasing performance. The implementation of the algorithms and resulting performance for several benchmark applications (NAS Parallel Benchmarks, CP2K, OpenFOAM, LAMMPS, and MiniFE) are analyzed and discussed. [ABSTRACT FROM AUTHOR]

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Introduction: Congenital glycosylation defects (CDGs) manifest with multisystemic symptoms involving the immune, central nervous, endocrine, and musculoskeletal systems. A total of 137 distinct CDG types have been identified to date. Materials and Methods: Patients diagnosed with CDG in the Division of Pediatric Metabolism and Nutrition, at Çukurova University, between 2013 and 2019 were included in the study. The patients' files were retrospectively reviewed, and demographic, clinical, laboratory and radiological findings and molecular analyses were recorded and evaluated. Results: The mean age at diagnosis for a total of 11 (6 Female; 5 Male) patients (Four with PMM2-CDG, one with MPI-CDG, one with DOLK-CDG, one with B4GALT1-CDG, three with TMEM165-CDG, and one with PIGNCDG) was 6.94 years (ranging from 11 months to 22 years). Amongst the patients, 45% (5 individuals) were male. Sixty-three percent of patients exhibited low weight and height (below the 5th percentile). Elevated liver enzymes were observed in 82% of cases, while 82% showed neurodevelopmental delay, 72% had cerebellar atrophy, and 72% experienced growth retardation. Additionally, 73% of patients displayed hepatomegaly and thrombocytopenia, and 63% had renal involvement. An homozygous p.V129M (c.385G>A) mutation in the PMM2 gene confirmed PMM2-CDG diagnosis in four patients. Furthermore, distinct homozygous mutations were detected: p. I399T (c.1193T>C) in the MPI gene, p. Y441S (c.1322A>C) in the DOLK gene, p. Arg126Cys (c.376C>T) in the TMEM165 gene, a novel p. Tyr239* (c.717T>G) mutation in the B4GALT1 gene, and a novel p. Thr266Ala (c.2356A>G) mutation in the PIGN gene. Conclusion: CDGs exhibit a diverse clinical spectrum, earning them the moniker "the rainbow" of hereditary metabolic disorders. While PMM2-CDG is the most prevalent subtype, only a few instances of other subtypes have been documented. Inverted nipples and abnormal fat pads are primary features of CDGs. The intricate nature of our cases and the rarity of DOLK-CDG, PIGN-CDG, and TMEM-165-CDG diagnoses stand out as notable aspects of this report. [ABSTRACT FROM AUTHOR]

Background: Patients with suspected or known coronary artery disease (CAD) can be challenging to evaluate. Our study aims to assess the efficacy and utility of coronary computed tomographic angiography (CCTA) as a diagnostic method in patients with abnormal myocardial perfusion imaging (MPI) who were initially referred for Invasive Coronary Angiography (ICA). Methods: This is a single-center retrospective descriptive study. We evaluated 210 patients who underwent CCTA instead of ICA. Data of these patients were documented and analyzed accordingly. Results: Of the 210 patients who underwent CCTA, the procedure was found to be diagnostic in 172 (81.9%) patients. Of these 172 patients, 152 (88.4%) had normal coronaries or minor CAD, and 20 (11.6%) had significant disease requiring subsequent ICA. In 38 (18.1%) patients out of the total cohort, the CCTA was not diagnostic or could not be performed due to technical difficulties, requiring either ICA or another ischemia evaluation diagnostic modality. Conclusion: CCTA is a viable and reliable diagnostic tool for evaluating patients with suspected CAD referred for ICA following an abnormal MPI test. It is recommended as an initial test to rule out significant coronary stenosis and as it can avoid unnecessary ICA. [ABSTRACT FROM AUTHOR]

Background and Aim:Two-dimensional (2D) speckle tracking echocardiography (STE) has shown promising results being a recent technology to assess “myocardial performance” in cardiac patients. Three-dimensional echocardiography 3DE has been shown to be accurate in the assessment of left ventricular (LV) systolic function. The multigated acquisition (MUGA) scan provides a more accurate quantification of the ventricular ejection fraction To Assess the role of 2D-STE and 3DE in the assessment of LV systolic function in type II patients with diabetes with negative myocardial perfusion imaging in correlation with the MUGA scan.Materials and Methods:The study included 30 patients with type 2 diabetes mellitus (T2DM) [Group 1, 25 (83%) female and 5 (16%) males with mean age 48.40 ± 7.44], their stress myocardial perfusion imaging was negative for coronary ischemia. The control group included 15 apparently healthy age and sex-matched subjects, [Group 2, 11 (73%) females and 4 (26%) males with mean age 50.20 ± 7.74], LV systolic function was evaluated using conventional, TDI, 2D-STE (LV-GLS), 3-DE and MUGA scan.Results:The group with diabetes showed statistically highly significant reduction in LV-GLS (-18.07 ± 2.73 in group 1 vs -21.24 ± 1.29 in group 2, P < 0.001), and in 3D LVEF (52.30 ± 5.28 in Group 1 vs 58.93 ± 4.69 in Group 2, P < 0.001). We found an agreement between three modalities (speckle tracking, 3DE and MUGA scan) by 33% in 10 patients [3 patients (10%) had impaired LV functions and 7 patients (23%) had preserved LV functions]. There was an agreement between speckle tracking and 3D echo by 76.6% in 23 patients [16 patients (53.3%) had impaired LV functions and 7 patients (23.3%) had preserved LV functions].Conclusion:T2DM is associated with subclinical LV systolic dysfunction that can be assessed by different noninvasive modalities (speckle tracking, 3DE and the MUGA scan). 2D speckle tracking and 3DE might have an edge compared with MUGA scan in the detection of subclinical LV systolic dysfunction.

Introduction: Prognostic scores in patients with local peritonitis (LP) have not yet been studied exhaustively.Aim: We, therefore, aimed in this study to evaluate the ability of several scoring systems to predict death in LP.Materials and methods: A retrospective analysis including 68 patients with LP was conducted at Prof. Dr. Stoyan Kirkovich University Hospital in Stara Zagora from January 2017 to August 2021. Clinical and laboratory data needed for calculating the scoring systems were collected at admission or postoperatively. We compared the prognostic performance of WSES SSS, MPI, SIRS, and qSOFA using area under the receiver operation characteristics (AUROC) curves and bivariate correlation analysis.Results: The observed mortality rate was 8.8%. Among all scores, MPI showed the best prognostic performance (AUROC=0.805, 95% CI 0.660–0.950). A threshold MPI >25 points permitted prediction of adverse outcome with a sensitivity of 66.7% and a specificity of 80.6%. The only significant correlation was found between outcome and MPI (p=0.012, r=0.302). Conclusions: The MPI has the ability to prognosticate mortality in patients with LP unlike WSES SSS, qSOFA and SIRS.