One of the most challenging tasks for network operators is implementing accurate per-packet monitoring, looking for signs of performance degradation, security threats, and so on. Upon critical event detection, corrective actions must be taken to keep the network running smoothly. Implementing this mechanism requires the analysis of packet streams in a real-time (or close to) fashion. In a softwarized network context, Stream Processing Systems (SPSs) can be adopted for this purpose. Recent solutions based on traditional SPSs, such as Storm and Flink, can support the definition of general complex queries, but they show poor performance at scale. https://www.selleckchem.com/products/gsk046.html To handle input data rates in the order of gigabits per seconds, programmable switch platforms are typically used, although they offer limited expressiveness. With the proposed approach, we intend to offer high performance and expressive power in a unified framework by solely relying on SPSs for multicores. Captured packets are translated into a proper tuple format, and network monitoring queries are applied to tuple streams. Packet analysis tasks are expressed as streaming pipelines, running on general-purpose programmable network devices, and a second stage of elaboration can process aggregated statistics from different devices. Experiments carried out with an example monitoring application show that the system is able to handle realistic traffic at a 10 Gb/s speed. The same application scales almost up to 20 Gb/s speed thanks to the simple optimizations of the underlying framework. Hence, the approach proves to be viable and calls for the investigation of more extensive optimizations to support more complex elaborations and higher data rates.Critically ill patients with systemic rheumatic diseases (SRDs) have a fair prognosis, while those with interstitial lung disease (ILD) have a poorer outcome. However, the prognosis of SRD patients with ILD admitted to the intensive care unit (ICU) remains unclear. We conducted a case-control study to investigate the outcomes of critically ill SRD-ILD patients. Consecutive SRD-ILD patients admitted to five ICUs from January 2007 to December 2017 were compared to SRD patients without ILD. Mortality rates were compared between groups, and prognostic factors were then identified. One hundred and forty critically ill SRD patients were included in the study. Among the 70 patients with SRD-ILD, the SRDs were connective tissue diseases (56%), vasculitis (29%), sarcoidosis (13%), and spondylarthritis (3%). Patients were mainly admitted for acute exacerbation of SRD-ILD (36%) or infection (34%). ICU, in-hospital, and one-year mortality rates in SRD-ILD patients were higher than in SRD patients without ILD (n = 70) 40% vs. 16% (p less then 0.01), 49% vs. 19% (p less then 0.01), and 66% vs. 40% (p less then 0.01), respectively. Hypoxemia, high sequential organ failure assessment (SOFA) score, and admission for ILD acute exacerbation were associated with ICU mortality. In conclusion, ILD worsened the outcomes of SRD patients admitted to the ICU. Admissions related to SRD-ILD acute exacerbation and the severity of the acute respiratory failure were associated with ICU mortality.In a previous article [...].Identification of causal factors that influence fetal growth and anthropometry at birth is of great importance as they provide information about increased risk of disease throughout life. The association between maternal genetic polymorphism MTHFR(677)C>T and anthropometry at birth has been widely studied because of its key role in the one-carbon cycle. MTHFR(677) CT and TT genotypes have been associated with a greater risk of low birth weight, especially in case of deficient intake of folic acid during pregnancy. This study aimed to analyze the association between the maternal MTHFR(677)C>T genetic polymorphism and anthropometry at birth in a population with adequate folate consumption. We included 694 mother-newborn pairs from a prospective population-based birth cohort in Spain, in the Genetics, Early life enviroNmental Exposures and Infant Development in Andalusia (GENEIDA) project. Women were genotyped for MTHFR(677)C>T SNP by Q-PCR using TaqMan© probes. Relevant maternal and newborn information was obtained from structured questionnaires and medical records. Results showed that maternal MTHFR(677)C>T genotype was associated with newborn anthropometry. Genotypes CT or CT/TT showed statistically significant associations with increased or decreased risk of large-for-gestational-age (LGA) or small-for-gestational-age (SGA) based on weight and height, depending on the newborn's sex, as well as with SGA in premature neonates. The relationships between this maternal genotype and anthropometry at birth remained despite an adequate maternal folate intake.The Bone Morphogenetic Protein 15 (BMP15) gene is known to have multiple single-nucleotide polymorphism sites associated with sheep fecundity. This study used gene sequence analysis and mutation detection assays for BMP15 by using 205 blood samples of ewes with known lambing records. Sequence analysis showed that mutation B1 missed the CTT base in exon 1 at positions 28-30, leading to a leucine deletion in the BMP15 protein. Litter size of ewes differed significantly between BB and B+ genotypes of B1 (p 0.05). Another mutation, T755C, is a T-to-C base change at position 755 of exon 2, resulting in leucine replacement by proline at this position of the BMP15 protein (p.L252P). Two genotypes were identified in the flock heterozygous (E+) and wild-type genotype (++). Ewes with heterozygous (E+) p.L252P had significantly larger litter sizes than those with the wild-type genotype (p less then 0.05). Comprehensive analysis suggests that p.L252P is a mutation that affects fecundity in Cele black sheep.The 3D modelling of indoor environments and the generation of process simulations play an important role in factory and assembly planning. In brownfield planning cases, existing data are often outdated and incomplete especially for older plants, which were mostly planned in 2D. Thus, current environment models cannot be generated directly on the basis of existing data and a holistic approach on how to build such a factory model in a highly automated fashion is mostly non-existent. Major steps in generating an environment model of a production plant include data collection, data pre-processing and object identification as well as pose estimation. In this work, we elaborate on a methodical modelling approach, which starts with the digitalization of large-scale indoor environments and ends with the generation of a static environment or simulation model. The object identification step is realized using a Bayesian neural network capable of point cloud segmentation. We elaborate on the impact of the uncertainty information estimated by a Bayesian segmentation framework on the accuracy of the generated environment model.