Confinement of Masonry Columns with Natural Lime-Based Mortar Composite: An Experimental Investigation

The paper presents the results of an experimental campaign on the confinement of masonry square columns with fiber-reinforced lime mortar (FRLM) composites made of a natural lime-based matrix. The experimental results show the effectiveness of such a composite for increasing both strength and ductility performances of strengthened columns. Predictive formulas from the literature and from the Italian guidelines CNR-DT 215/2018 do not perfectly fit the experimental outcomes and do not confirm the strength increase of the confined columns. The reason can be attributed to the very low mechanical properties of the natural matrix used to form such a composite. Therefore, considering that the use of a natural and sustainable matrix fully compatible with the masonry substrate is a fundamental requirement for strengthening masonry columns of buildings belonging to architectural heritage, an additional future effort should be made by researchers involved in this field. In particular, for a reliable prediction of the strength of masonry columns confined with composites made of natural matrices, wider experimental campaigns are necessary to refine available formulas with respect to different substrates and component materials.

Prediction of Blood-Brain Barrier Penetration (BBBP) Based on Molecular Descriptors of the Free-Form and In-Blood-Form Datasets

The blood-brain barrier (BBB) controls the entry of chemicals from the blood to the brain. Since brain drugs need to penetrate the BBB, rapid and reliable prediction of BBB penetration (BBBP) is helpful for drug development. In this study, free-form and in-blood-form datasets were prepared by modifying the original BBBP dataset, and the effects of the data modification were investigated. For each dataset, molecular descriptors were generated and used for BBBP prediction by machine learning (ML). For ML, the dataset was split into training, validation, and test data by the scaffold split algorithm MoleculeNet used. This creates an unbalanced split and makes the prediction difficult; however, we decided to use that algorithm to evaluate the predictive performance for unknown compounds dissimilar to existing ones. The highest prediction score was obtained by the random forest model using 212 descriptors from the free-form dataset, and this score was higher than the existing best score using the same split algorithm without using any external database. Furthermore, using a deep neural network, a comparable result was obtained with only 11 descriptors from the free-form dataset, and the resulting descriptors suggested the importance of recognizing the glucose-like characteristics in BBBP prediction.

CRSIPR-A-I: a webtool for the efficacy prediction of CRISPR activation and interference

CRISPR-based gene activation (CRISPRa) or interference (CRISPRi) are powerful and easy-to-use approaches to modify the transcription of endogenous genes in eukaryotes. Successful CRISPRa/i requires sgRNA binding and alteration of local chromatin structure, hence largely depends on the original epigenetic status of the target. Consequently, the efficacy of the CRISPRa/i varies in a wide range when applied to target different gene loci, while a reliable prediction tool is unavailable. To address this problem, we integrated published single cell RNA-Seq data involved CRISPRa/i and epigenomic profiles from K562 cells, identified the significant epigenetic features contributing to CRISPRa/i efficacy by ranking the weight of each feature. We further established a mathematic model and built a user-friendly webtool to predict the CRISPRa/i efficacy of customer-designed sgRNA in different cells. Moreover, we experimentally validated our model by employing CROP-Seq assays. Our work provides both the epigenetic insights into CRISPRa/i and an effective tool for the users.

POINT AND INTERVAL FORECASTS OF DEATH RATES USING NEURAL NETWORKS

The Lee–Carter model has become a benchmark in stochastic mortality modeling. However, its forecasting performance can be significantly improved upon by modern machine learning techniques. We propose a convolutional neural network (NN) architecture for mortality rate forecasting, empirically compare this model as well as other NN models to the Lee–Carter model and find that lower forecast errors are achievable for many countries in the Human Mortality Database. We provide details on the errors and forecasts of our model to make it more understandable and, thus, more trustworthy. As NN by default only yield point estimates, previous works applying them to mortality modeling have not investigated prediction uncertainty. We address this gap in the literature by implementing a bootstrapping-based technique and demonstrate that it yields highly reliable prediction intervals for our NN model.

Leaf Area Calculation Models for Vines Based on Foliar Descriptors

In the case of foliar area studies on vines, with a large number of determinations, a simple, fast, sufficiently accurate and low-cost method is very useful. The typology of leaves on the vine is complex, characterized by several descriptive parameters: median rib; secondary venations of the first and second order; angles between the median rib and the secondary venations; sinuses; length and width of the leaf. The present study aimed to evaluate models for calculating the leaf area based on descriptive parameters and KA (KA as the surface constant used to calculate the leaf area) for six vine cultivars, ‘Cabernet Sauvignon’ (CS), ‘Muscat Iantarnîi’ (MI), ‘Muscat Ottonel’ (MO), ‘Chasselas’ (Ch), ‘Victoria’ (Vi) and ‘Muscat Hamburg’ (MH). The determined KA surface constants had subunit values (0.91 to 0.97), except for the cultivars ‘Muscat Iantarnîi’ and ‘Muscat Ottonel’ where the surface constant KA2 (in relation to the second-order secondary venations) had supraunitary values (1.07 and 1.08, respectively). The determination of the leaf area was possible under different conditions of statistical accuracy (R2 = 0.477, p = 0.0119, up to R2 = 0.988, p < 0.001) in relation to the variety and parametric descriptors considered. The models obtained from the regression analysis facilitated a more reliable prediction of the leaf area based on the elements on the left side of the leaf, in relation to the median rib, compared to those on the right. The accuracy of the results was checked on the basis of minimum error (ME) and confirmed by parameters R2, p and RMSE.

Glass transition temperature prediction of disordered molecular solids

Glass transition temperature, Tg, is the key quantity for assessing morphological stability and molecular ordering of films of organic semiconductors. A reliable prediction of Tg from the chemical structure is, however, challenging, as it is sensitive to both molecular interactions and analysis of the heating or cooling process. By combining a fitting protocol with an automated workflow for forcefield parameterization, we predict Tg with a mean absolute error of ~20 °C for a set of organic compounds with Tg in the 50–230 °C range. Our study establishes a reliable and automated prescreening procedure for the design of amorphous organic semiconductors, essential for the optimization and development of organic light-emitting diodes.

MHD R&D Activities for Liquid Metal Blankets

According to the most recently revised European design strategy for DEMO breeding blankets, mature concepts have been identified that require a reduced technological extrapolation towards DEMO and will be tested in ITER. In order to optimize and finalize the design of test blanket modules, a number of issues have to be better understood that are related to the magnetohydrodynamic (MHD) interactions of the liquid breeder with the strong magnetic field that confines the fusion plasma. The aim of the present paper is to describe the state of the art of the study of MHD effects coupled with other physical phenomena, such as tritium transport, corrosion and heat transfer. Both numerical and experimental approaches are discussed, as well as future requirements to achieve a reliable prediction of these processes in liquid metal blankets.

Haplotype heterogeneity and low linkage disequilibrium reduce reliable prediction of genotypes for the ‑α3.7I form of α-thalassaemia using genome-wide microarray data

Background: The -α3.7I-thalassaemia deletion is very common throughout Africa because it protects against malaria. When undertaking studies to investigate human genetic adaptations to malaria or other diseases, it is important to account for any confounding effects of α-thalassaemia to rule out spurious associations. Methods: In this study, we have used direct α-thalassaemia genotyping to understand why GWAS data from a large malaria association study in Kilifi Kenya did not identify the α-thalassaemia signal. We then explored the potential use of a number of new approaches to using GWAS data for imputing α-thalassaemia as an alternative to direct genotyping by PCR. Results: We found very low linkage-disequilibrium of the directly typed data with the GWAS SNP markers around α-thalassaemia and across the haemoglobin-alpha (HBA) gene region, which along with a complex haplotype structure, could explain the lack of an association signal from the GWAS SNP data. Some indirect typing methods gave results that were in broad agreement with those derived from direct genotyping and could identify an association signal, but none were sufficiently accurate to allow correct interpretation compared with direct typing, leading to confusing or erroneous results. Conclusions: We conclude that going forwards, direct typing methods such as PCR will still be required to account for α-thalassaemia in GWAS studies.