Consequences and conjectures in preordered sets

In a preordered set, or preset, consequence operators in the sense of Tarski, defined on families of subsets, are introduced. From them, the corresponding sets of conjectures, hypotheses, speculations and refutations are considered, studying the relationships between these sets and those previously defined on ortholattices. All the concepts introduced are illustrated with three particular consequence operators, whose behavior is studied in detail. The results obtained are applied to the case of fuzzy sets endowed with the usual pointwise ordering.     

Establishing In-House Cutoffs of CSF Alzheimer’s Disease Biomarkers for the AT(N) Stratification of the Alzheimer Center Barcelona Cohort

Background: Clinical diagnosis of Alzheimer’s disease (AD) increasingly incorporates CSF biomarkers. However, due to the intrinsic variability of the immunodetection techniques used to measure these biomarkers, establishing in-house cutoffs defining the positivity/negativity of CSF biomarkers is recommended. However, the cutoffs currently published are usually reported by using cross-sectional datasets, not providing evidence about its intrinsic prognostic value when applied to real-world memory clinic cases. Methods: We quantified CSF Aβ1-42, Aβ1-40, t-Tau, and p181Tau with standard INNOTEST^® ELISA and Lumipulse G^® chemiluminescence enzyme immunoassay (CLEIA) performed on the automated Lumipulse G600II. Determination of cutoffs included patients clinically diagnosed with probable Alzheimer’s disease (AD, n = 37) and subjective cognitive decline subjects (SCD, n = 45), cognitively stable for 3 years and with no evidence of brain amyloidosis in 18F-Florbetaben-labeled positron emission tomography (FBB-PET). To compare both methods, a subset of samples for Aβ1-42 (n = 519), t-Tau (n = 399), p181Tau (n = 77), and Aβ1-40 (n = 44) was analyzed. Kappa agreement of single biomarkers and Aβ1-42/Aβ1-40 was evaluated in an independent group of mild cognitive impairment (MCI) and dementia patients (n = 68). Next, established cutoffs were applied to a large real-world cohort of MCI subjects with follow-up data available (n = 647). Results: Cutoff values of Aβ1-42 and t-Tau were higher for CLEIA than for ELISA and similar for p181Tau. Spearman coefficients ranged between 0.81 for Aβ1-40 and 0.96 for p181TAU. Passing–Bablok analysis showed a systematic and proportional difference for all biomarkers but only systematic for Aβ1-40. Bland–Altman analysis showed an average difference between methods in favor of CLEIA. Kappa agreement for single biomarkers was good but lower for the Aβ1-42/Aβ1-40 ratio. Using the calculated cutoffs, we were able to stratify MCI subjects into four AT(N) categories. Kaplan–Meier analyses of AT(N) categories demonstrated gradual and differential dementia conversion rates (p = 9.815⁻²⁷). Multivariate Cox proportional hazard models corroborated these findings, demonstrating that the proposed AT(N) classifier has prognostic value. AT(N) categories are only modestly influenced by other known factors associated with disease progression. Conclusions: We established CLEIA and ELISA internal cutoffs to discriminate AD patients from amyloid-negative SCD individuals. The results obtained by both methods are not interchangeable but show good agreement. CLEIA is a good and faster alternative to manual ELISA for providing AT(N) classification of our patients. AT(N) categories have an impact on disease progression. AT(N) classifiers increase the certainty of the MCI prognosis, which can be instrumental in managing real-world MCI subjects.     

Critical Review on Fatty Acid-Based Food and Nutraceuticals as Supporting Therapy in Cancer

Fatty acids have an important place in both biological and nutritional contexts and, from a clinical point of view, they have known consequences for diseases’ onset and development, including cancer. The use of fatty acid-based food and nutraceuticals to support cancer therapy is a multidisciplinary subject, involving molecular and clinical research. Knowledge regarding polyunsaturated fatty acids essentiality/oxidizability and the role of lipogenesis-desaturase pathways for cell growth, as well as oxidative reactivity in cancer cells, are discussed, since they can drive the choice of fatty acids using their multiple roles to support antitumoral drug activity. The central role of membrane fatty acid composition is highlighted for the application of membrane lipid therapy. As fatty acids are also known as biomarkers of cancer onset and progression, the personalization of the fatty acid-based therapy is also possible, taking into account other important factors such as formulation, bioavailability and the distribution of the supplementation. A holistic approach emerges combining nutra- and pharma-strategies in an appropriate manner, to develop further knowledge and applications in cancer therapy.     

Evaluation of Coupling Agents in Poly(propylene)/Fly Ash Composites: Effect on Processing and Mechanical Properties

Composites containing 50 wt.‐% fly ash in a PP homopolymer were prepared via batch mixing and compression moulding. The following coupling agents were evaluated: Lubrizol Solplus C800, N,N′‐(1,3‐phenylene)dimaleimide, γ‐methacryloxypropyltrimethoxysilane and maleic‐anhydride‐grafted PP. At the filler level investigated, C800 gave the best balance of composite strength and toughness. In the latter case filler‐matrix adhesion appeared weaker relative to γ‐MPS, BMI and m‐PP, all of which gave excessively strong filler‐matrix adhesion leading to a reduction in composite toughness. The unexpected weakness of the C800/fly ash interaction may be related to removal of surface calcium ions from the fly ash via reaction of a single calcium ion with two C800 molecules.

     

EXPERIMENTAL STUDY OF THE SELECTIVE HYDROGENATION OF STEAM CRACKING C2CUT. FRONT END AND TAIL END VARIANTS

Selective hydrogenation is the habitual industrial process to eliminate the most unsaturated hydrocarbons, which are harmful for later applications. In this paper, the kinetics of the selective hydrogenation of a C₂ mixture over two palladium/alumina catalysts with both front end and tail end variants, have been studied. Experiments have been carried out to analyse the influence of temperature, hydrogen/acetylene molar ratio, carbon monoxide content in the feed and hydrocarbon volumetric flow rate on the corresponding conversion and selectivity.

The experiments were performed in an integral plug flow reactor and the integral method was used for the kinetic analysis. The minimization of the objective function was made by the Marquardt algorithm for multiple response and the continuity equation set integrated by fourth order Runge-Kutta technique.

The most adequate models were the power law type for the experimental range. The comparison between experimental and observed values of the acetylene and ethane molar fraction in the hydrocarbon mixture, which are used for minimization, confirm the suitability of the fit.     

Rate of evolution of the specific surface area of surface snow layers

The snowpack can impact atmospheric chemistry by exchanging adsorbed or dissolved gases with the atmosphere. Modeling this impact requires the knowledge of the specific surface area (SSA) of snow and its variations with time. We have therefore measured the evolution of the SSA of eight recent surface snow layers in the Arctic and the French Alps, using CH4 adsorption at liquid nitrogen temperature (77 K). The SSA of fresh snow layers was found to decrease with time, from initial values in the range 613-1540 cm2/g to values as low as 257 cm2/g after 6 days. This is explained by snow metamorphism, which causes modifications in crystal shapes, here essentially crystal rounding and the disappearance of microstructures. A parametrization of the rate of SSA decrease is proposed. We fit the SSA decrease to an exponential law and find that the time constant alpha(exp) (day(-1)) depends on temperature according to alpha(exp) = 76.6 exp (-1708/7), with Tin kelvin. Our parametrization predicts that the SSA of a snow layer evolving at -40 degrees C will decrease by a factor of 2 after 14 days, while a similar decrease at -1 degrees C will only require 5 days. Wind was found to increase the rate of SSA decrease, but insufficient data did not allow a parametrization of this effect.     

Snow metamorphism as revealed by scanning electron microscopy

Current theories of snow metamorphism indicate that sublimating snow crystals have rounded shapes, while growing crystals have shapes that depend on growth rates. At slow growth rates, crystals are rounded. At moderate rates, they have flat faces with rounded edges. At fast growth rates, crystals have flat faces with sharp edges, and they have hollow faces at very fast growth rates. The main growth/sublimation mechanism is thought to be by the homogeneous nucleation of new layers at or near crystal edges. It was also suggested that the equilibrium shape of snow crystals would be temperature dependent: rounded above -10.5 degrees C, and faceted below. To test these paradigms, we have performed SEM investigations of snow samples having undergone metamorphism under natural conditions, and of snow samples subjected to isothermal metamorphism at -4 degrees and -15 degrees C in the laboratory. In general, current theories predicting crystal shapes as a function of growth rates, and of whether crystals are growing or sublimating, are verified. However, the transition in equilibrium shapes from rounded to faceted at -10.5 degrees C is not observed in our isothermal experiments that reveal a predominance of rounded shapes after more than a month of metamorphism at -4 and -15 degrees C. Some small crystals with flat faces that also have sharp angles at -15 degrees C, are observed in our isothermal experiments. These faces are newly formed, and contradict current theory. Several hypotheses are proposed to explain their occurrence. One is that they are due to sublimation at emerging dislocations.     

Understanding the impact of texture on the micromechanical anisotropy of laser powder bed fused Inconel 718

Journal of Materials Science - The manufacturability of metallic alloys using laser-based additive manufacturing methods such as laser powder bed fusion has substantially improved within the last...     

High-Impact PLA in Compatibilized PLA/PCL Blends: Optimization of Blend Composition and Type and Content of Compatibilizer

In this work, the effectiveness of seven commercial compatibilizers is tested in polylactide (PLA)/poly(ε-caprolactone) (PCL) blends with different compositions to obtain a high-impact PLA. None of the compatibilizers is effective for 90/10 and 80/20 PLA/PCL compositions, as no improvement of the impact strength is observed. For the 70/30 composition, compatibilizers having glycidyl methacrylate (GMA) and acrylate groups in their structure are proved the most effective, as the morphological change towards co-continuity induced by them leads to significant impact strength improvements (of ≈345% and 90% with respect to the neat PLA and the noncompatibilized PLA/PCL 70/30 blend, respectively). The 70/30 PLA/PCL composition, as it shows the best balance of properties, and the best compatibilizer (ElvaloyPTW) are chosen to carry out the optimization of the compatibilizer content. It is found that adding 6 phr to the blend results in highly toughened and ductile blends while maintaining a high modulus and yield strength values. Larger compatibilizer contents lead to even higher impact strength values, but the low-strain mechanical properties are notably reduced. Thus, in this work, a simple and easily scalable method to produce high-impact PLA is shown, as it implies the compounding of three commercially available components without involving any toxic solvents.     

Recurrent ANN-based modelling of the dynamic evolution of the surface roughness in grinding

Grinding is critical in modern manufacturing due to its capacity for producing high surface quality and high-precision parts. One of the most important parameters that indicate the grinding quality is the surface roughness (R _a). Analytical models developed to predict surface finish are not easy to apply in the industry. Therefore, many researchers have made use of artificial neural networks. However, all the approaches provide a particular solution for a wheel–workpiece pair, not generalizing to new grinding wheels. Besides, these solutions do not give surface roughness values related to the grinding wheel status. Therefore, in this work the modelling of the dynamic evolution of the surface roughness (R _a) based on recurrent neural networks is presented with the capability to generalize to new grinding wheels and conditions taking into account the wheel wear. Results show excellent prediction of the surface finish dynamic evolution. The absolute maximum error is below 0.49 µm, being the average error around 0.32 µm. Besides, the analysis of the relative importance of the inputs shows that the grinding conditions have higher influence than the wheel characteristics over the prediction of the surface roughness confirming experimental knowledge of grinding technology users.