Investigating the Features of the M170 in Congenital Prosopagnosia

Face perception generates specific neural activity as early as 170 ms post-stimulus onset, termed the M170 when measured with Magnetoencephalography (MEG). We examined the M170 in six people with congenital prosopagnosia (CP) and 11 typical controls. Previous research indicates that there are two neural generators for the M170 (one within the right lateral occipital area - rLO and one within the right fusiform gyrus - rFG), and in the current study we explored whether these sources reflect the processing of different types of information. Individuals with CP showed face-selective M170 responses within the rLO and right rFG, which did not differ in magnitude to those of the controls. To examine possible links between neural activity and behavior we correlated the CPs' MEG activity generated within rLO and rFG with their face perception skills. The rLO-M170 correlated with holistic/configural face processing, whereas the rFG-M170 correlated with featural processing. Hence, the results of our study demonstrate that individuals with CP can show an M170 that is within the normal range, and that the M170 in the rLO and rFG are involved in different aspects of face processing

Legio: Fault Resiliency for Embarrassingly Parallel MPI Applications

Due to the increasing size of HPC machines, the fault presence is becoming an eventuality that applications must face. Natively, MPI provides no support for the execution past the detection of a fault, and this is becoming more and more constraining. With the introduction of ULFM (User Level Fault Mitigation library), it has been provided with a possible way to overtake a fault during the application execution at the cost of code modifications. ULFM is intrusive in the application and requires also a deep understanding of its recovery procedures. In this paper we propose Legio, a framework that lowers the complexity of introducing resiliency in an embarrassingly parallel MPI application. By hiding ULFM behind the MPI calls, the library is capable to expose resiliency features to the application in a transparent manner thus removing any integration effort. Upon fault, the failed nodes are discarded and the execution continues only with the non-failed ones. A hierarchical implementation of the solution has been also proposed to reduce the overhead of the repair process when scaling towards a large number of nodes. We evaluated our solutions on the Marconi100 cluster at CINECA, showing that the overhead introduced by the library is negligible and it does not limit the scalability properties of MPI. Moreover, we also integrated the solution in real-world applications to further prove its robustness by injecting faults

Zinc transporter 8 and MAP3865c homologous epitopes are recognized at T1D onset in Sardinian children

Our group has recently demonstrated that Mycobacterium avium subspecies paratuberculosis (MAP) infection significantly associates with T1D in Sardinian adult patients. Due to the potential role played by MAP in T1D pathogenesis, it is relevant to better characterize the prevalence of anti-MAP antibodies (Abs) in the Sardinian population, studying newly diagnosed T1D children. Therefore, we investigated the seroreactivity against epitopes derived from the ZnT8 autoantigen involved in children at T1D onset and their homologous sequences of the MAP3865c protein. Moreover, sera from all individuals were tested for the presence of Abs against: the corresponding ZnT8 C-terminal region, the MAP specific protein MptD, the T1D autoantigen GAD65 and the T1D unrelated Acetylcholine Receptor. The novel MAP3865c281–287 epitope emerges here as the major C-terminal epitope recognized. Intriguingly ZnT8186–194 immunodominant peptide was cross-reactive with the homologous sequences MAP3865c133–141, strengthening the hypothesis that MAP could be an environmental trigger of T1D through a molecular mimicry mechanism. All eight epitopes were recognized by circulating Abs in T1D children in comparison to healthy controls, suggesting that these Abs could be biomarkers of T1D. It would be relevant to investigate larger cohorts of children, followed over time, to elucidate whether Ab titers against these MAP/Znt8 epitopes wane after diagnosis

Recognition of zinc transporter 8 and MAP3865c homologous epitopes by Hashimoto's Thyroiditis subjects from Sardinia: a common target with type 1 diabetes?

Mycobacterium avium subspecies paratuberculosis (MAP) asymptomatic infection has been previously linked to Type 1 diabetes (T1D) and Multiple Sclerosis. An association between MAP infection and Hashimoto's thyroiditis (HT) was also proposed only in a case report. This study aimed to investigate the robustness of the latter association, testing a large cohort of HT and healthy control (HCs) subjects, all from Sardinia. Prevalence of anti-MAP3865c Abs was assessed by indirect enzyme-linked immunosorbent assay (ELISA). Moreover, given that human ZnT8 is specifically expressed in the pancreatic β-cells, in the follicle epithelial cells and in the parafollicular cells of the thyroid gland, we also tested ZnT8 epitopes homologues to the MAP3865c immunodominant peptides previously identified. Indeed, Abs targeting MAP3865c and ZnT8 homologous regions display similar frequencies in patients and controls, thus suggesting that Abs recognizing these epitopes could be cross-reactive. A statistically significant difference was found between HT patients and HCs when analyzing the humoral response mounted against MAP3865c/ZnT8 homologues epitopes. To our knowledge, this is the first report, which provides statistically significant evidence sustaining the existence of an association between MAP sero-reactivity and HT. Further studies are required to investigate the relevance of MAP to HT, aimed at deciphering if this pathogen can be at play in triggering this autoimmune disease. Likewise, genetic polymorphism of the host, and other environmental factors need to be investigated

Simulation and Post-Processing for Advanced Driver Assistance System (ADAS)

Considering the continuous development in the automotive sector and autonomous driving technology, it is necessary to conduct continuous research to identify the main points that can allow continuous improvement of system autonomy. In addition to designing new components, an important aspect is characterizing the test procedures uniformly. The present work is related to analyzing the testing phases of a vehicle concerning the post-processing of the tests, using suitable software and routines, and creating an overall summary report that includes information on the type of instrumentation and type of test and post-processing results. The paper proposes the generation of an innovative tool designed to improve the generation capacity of test maneuvers for Advanced Driver Assistance Systems (ADASs) and to automate the collection and analysis phase of data relating to tests for a Lane System's Support System (LSS), Autonomous Emergency Braking (AEB), and Car to Pedestrian Nearside Child (CPNC) comply with Euro NCAP LSS 3.0.2, Euro NCAP AEB C2C 3.0.2 and UNECE R-152. The goal was achieved with the collaboration of the company Nardo Technical Center S.r.l. The entire post-processing routine was developed from data relating to experimental tests carried out in the company

An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System

Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for dynamically selecting the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to take tuning decisions on the number of samples improving the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36% and 81%) with respect to a static approach while considering different traffic situations, paths and error requirements. Given the negligible runtime overhead of the proposed approach, it results in an execution-time speedup between 1.5x and 5.1x. This speedup is reflected at infrastructure-level in terms of a reduction of around 36% of the computing resources needed to support the whole navigation pipeline

Out of kernel tuning and optimizations for portable large-scale docking experiments on GPUs

Virtual screening is an early stage in the drug discovery process that selects the most promising candidates. In the urgent computing scenario, finding a solution in the shortest time frame is critical. Any improvement in the performance of a virtual screening application translates into an increase in the number of candidates evaluated, thereby raising the probability of finding a drug. In this paper, we show how we can improve application throughput using Out-of-kernel optimizations. They use input features, kernel requirements, and architectural features to rearrange the kernel inputs, executing them out of order, to improve the computation efficiency. These optimizations’ implementations are designed on an extreme-scale virtual screening application, named LiGen, that can hinge on CUDA and SYCL kernels to carry out the computation on modern supercomputer nodes. Even if they are tailored to a single application, they might also be of interest for applications that share a similar design pattern. The experimental results show how these optimizations can increase kernel performance by 2 X, respectively, up to 2.2X in CUDA and up to 1.9X, in SYCL. Moreover, the reported speedup can be achieved with the best-proposed parameterization, as shown by the data we collected and reported in this manuscript

GPU-optimized approaches to molecular docking-based virtual screening in drug discovery: A comparative analysis

Finding a novel drug is a very long and complex procedure. Using computer simulations, it is possible to accelerate the preliminary phases by performing a virtual screening that filters a large set of drug candidates to a manageable number. This paper presents the implementations and comparative analysis of two GPU-optimized implementations of a virtual screening algorithm targeting novel GPU architectures. This work focuses on the analysis of parallel computation patterns and their mapping onto the target architecture. The first method adopts a traditional approach that spreads the computation for a single molecule across the entire GPU. The second uses a novel batched approach that exploits the parallel architecture of the GPU to evaluate more molecules in parallel. Experimental results showed a different behavior depending on the size of the database to be screened, either reaching a performance plateau sooner or having a more extended initial transient period to achieve a higher throughput (up to 5x), which is more suitable for extreme-scale virtual screening campaigns