Early metallurgy in Sardinia: characterizing the evidence from Su Coddu

This paper contextualizes analyses of a collection of metal artifacts and ostensible metallurgical slag from the prehistoric settlement of Su Coddu in south-central Sardinia (ca. 3400–2850 BCE). To characterize the types of metals and associated alloys utilized by the earliest residents of Su Coddu, two pins and an unshaped lump of unknown composition were analyzed using portable XRF spectrometry. In addition to metal artifacts, a large quantity of putative slag was discovered at the site that is consistently cited as the earliest evidence of in situ smelting in prehistoric Sardinia. To reconstruct firing temperatures and characterize mineral phases, four samples of the overfired material were selected for thin section petrography and powder XRD analysis. The results of this study indicate that the two pins were made of copper while the unshaped lump was composed of pure lead, making it the earliest lead-based artifact on Sardinia. XRD and petrographic analyses of the fired “slags” reveal that these samples are unrelated to metallurgical smelting and are likely burnt wall coatings whose mineralogical phases correspond with unfired plasters also recovered from the site. These results in combination contribute towards understanding early metallurgical practices in Sardinia and are relevant in reconstructing the events that have shaped the life history of Su Coddu

Automated workflows for modelling chemical fate, kinetics and toxicity.

Automation is universal in today's society, from operating equipment such as machinery, in factory processes, to self-parking automobile systems. While these examples show the efficiency and effectiveness of automated mechanical processes, automated procedures that support the chemical risk assessment process are still in their infancy. Future human safety assessments will rely increasingly on the use of automated models, such as physiologically based kinetic (PBK) and dynamic models and the virtual cell based assay (VCBA). These biologically-based models will be coupled with chemistry-based prediction models that also automate the generation of key input parameters such as physicochemical properties. The development of automated software tools is an important step in harmonising and expediting the chemical safety assessment process. In this study, we illustrate how the KNIME Analytics Platform can be used to provide a user-friendly graphical interface for these biokinetic models, such as PBK models and VCBA, which simulates the fate of chemicals in vivo within the body and in vitro test systems respectively

Dynamic and volumetric variables reliably predict fluid responsiveness in a porcine model with pleural effusion

Background: The ability of stroke volume variation (SVV), pulse pressure variation (PPV) and global end-diastolic volume (GEDV) for prediction of fluid responsiveness in presence of pleural effusion is unknown. The aim of the present study was to challenge the ability of SVV, PPV and GEDV to predict fluid responsiveness in a porcine model with pleural effusions. Methods: Pigs were studied at baseline and after fluid loading with 8 ml kg−1 6% hydroxyethyl starch. After withdrawal of 8 ml kg−1 blood and induction of pleural effusion up to 50 ml kg−1 on either side, measurements at baseline and after fluid loading were repeated. Cardiac output, stroke volume, central venous pressure (CVP) and pulmonary occlusion pressure (PAOP) were obtained by pulmonary thermodilution, whereas GEDV was determined by transpulmonary thermodilution. SVV and PPV were monitored continuously by pulse contour analysis. Results: Pleural effusion was associated with significant changes in lung compliance, peak airway pressure and stroke volume in both responders and non-responders. At baseline, SVV, PPV and GEDV reliably predicted fluid responsiveness (area under the curve 0.85 (p<0.001), 0.88 (p<0.001), 0.77 (p = 0.007). After induction of pleural effusion the ability of SVV, PPV and GEDV to predict fluid responsiveness was well preserved and also PAOP was predictive. Threshold values for SVV and PPV increased in presence of pleural effusion. Conclusions: In this porcine model, bilateral pleural effusion did not affect the ability of SVV, PPV and GEDV to predict fluid responsiveness

Fuzzy rule-based systems for recognition-intensive classification in granular computing context

In traditional machine learning, classification is typically undertaken in the way of discriminative learning using probabilistic approaches, i.e. learning a classifier that discriminates one class from other classes. The above learning strategy is mainly due to the assumption that different classes are mutually exclusive and each instance is clear-cut. However, the above assumption does not always hold in the context of real-life data classification, especially when the nature of a classification task is to recognize patterns of specific classes. For example, in the context of emotion detection, multiple emotions may be identified from the same person at the same time, which indicates in general that different emotions may involve specific relationships rather than mutual exclusion. In this paper, we focus on classification problems that involve pattern recognition. In particular, we position the study in the context of granular computing, and propose the use of fuzzy rule-based systems for recognition-intensive classification of real-life data instances. Furthermore, we report an experimental study conducted using 7 UCI data sets on life sciences, to compare the fuzzy approach with four popular probabilistic approaches in pattern recognition tasks. The experimental results show that the fuzzy approach can not only be used as an alternative one to the probabilistic approaches but also is capable to capture more patterns which probabilistic approaches cannot achieve

In Heart Failure Patients with Left Bundle Branch Block Single Lead MultiSpot Left Ventricular Pacing Does Not Improve Acute Hemodynamic Response To Conventional Biventricular Pacing. A Multicenter Prospective, Interventional, Non-Randomized Study.

Introduction Recent efforts to increase CRT response by multiSPOT pacing (MSP) from multiple bipols on the same left ventricular lead are still inconclusive. Aim The Left Ventricular (LV) MultiSPOTpacing for CRT (iSPOT) study compared the acute hemodynamic response of MSP pacing by using 3 electrodes on a quadripolar lead compared with conventional biventricular pacing (BiV). Methods Patients with left bundle branch block (LBBB) underwent an acute hemodynamic study to determine the %change in LV+dP/dtmax from baseline atrial pacing compared to the following configurations: BiV pacing with the LV lead in a one of lateral veins, while pacing from the distal, mid, or proximal electrode and all 3 electrodes together (i.e. MSP). All measurements were repeated 4 times at 5 different atrioventricular delays. We also measured QRS-width and individual Q-LV durations. Results Protocol was completed in 24 patients, all with LBBB (QRS width 171±20 ms) and 58% ischemic aetiology. The percentage change in LV+dP/dtmax for MSP pacing was 31.0±3.3% (Mean±SE), which was not significantly superior to any BiV pacing configuration: 28.9±3.2% (LV-distal), 28.3±2.7% (LV-mid), and 29.5±3.0% (LV-prox), respectively. Correlation between LV+dP/dtmax and either QRS-width or Q-LV ratio was poor. Conclusions In patients with LBBB MultiSPOT LV pacing demonstrated comparable improvement in contractility to best conventional BiV pacing. Optimization of atrioventricular delay is important for the best performance for both BiV and MultiSPOT pacing configurations. Trial Registration ClinicalTrials.gov NTC0188314

Mechanical effects of left ventricular midwall fibrosis in non-ischemic cardiomyopathy

Background: Left ventricular (LV) mid-wall fibrosis (MWF), which occurs in about a quarter of patients with non-ischemic cardiomyopathy (NICM), is associated with high risk of pump failure. The mid LV wall is the site of circumferential myocardial fibers. We sought to determine the effect of MWF on LV myocardial mechanics. Methods: Patients with NICM (n = 116; age: 62.8 ± 13.2 years; 67 % male) underwent late gadolinium enhancement cardiovascular magnetic resonance (CMR) and were categorized according to the presence (+) or absence (-) of MWF. Feature tracking (FT) CMR was used to assess myocardial deformation. Results: Despite a similar LVEF (24.3 vs 27.5 %, p = 0.20), patients with MWF (32 [24 %]) had lower global circumferential strain (εcc: -6.6 % vs -9.4 %, P = 0.004), but similar longitudinal (εll: -7.6 % vs. -9.4 %, p = 0.053) and radial (εrr: 14.6 % vs. 17.8 % p = 0.18) strain. Compared with - MWF, + MWF was associated with reduced LV systolic, circumferential strain rate (-0.38 ± 0.1 vs -0.56 ± 0.3 s-1, p = 0.005) and peak LV twist (4.65 vs. 6.31°, p = 0.004), as well as rigid LV body rotation (64 % vs 28 %, P cc: 0.34 vs. 0.46 s-1; DSRll: 0.38 vs. 0.50s-1; DSRrr: -0.55 vs. -0.75 s-1; all

Shape recognition through multi-level fusion of features and classifiers

Shape recognition is a fundamental problem and a special type of image classification, where each shape is considered as a class. Current approaches to shape recognition mainly focus on designing low-level shape descriptors, and classify them using some machine learning approaches. In order to achieve effective learning of shape features, it is essential to ensure that a comprehensive set of high quality features can be extracted from the original shape data. Thus we have been motivated to develop methods of fusion of features and classifiers for advancing the classification performance. In this paper, we propose a multi-level framework for fusion of features and classifiers in the setting of gran-ular computing. The proposed framework involves creation of diversity among classifiers, through adopting feature selection and fusion to create diverse feature sets and to train diverse classifiers using different learn-Xinming Wang algorithms. The experimental results show that the proposed multi-level framework can effectively create diversity among classifiers leading to considerable advances in the classification performance