Synchrotron-based characterization of arthroprosthetic CoCrMo particles in human bone marrow

Ti-Zr alloys have gained increasing attention as a new metallic biomaterial, being used as implants for both orthopedics and dentistry. More recently, our group found promising results for the Ti-45Zr alloy, which presented a low elastic modulus, a pronounced and excellent mechanic character, and excellent cell compatibility in vitro. However, its biocompatibility and potential to promote osteogenesis in vivo remained unclear. In the present study, the biocompatibility, osteointegration ability, and immune response effects of the Ti-45Zr alloy were evaluated in animal experiments. The results showed that the alloy had good blood compatibility and no body side effects. After implantation in vivo, the inflammation turned out well and was beneficial to the polarization of macrophages. Additionally, the Ti-45Zr alloy presented a good osteointegration ability. Overall, these results confirmed that the Ti-45Zr alloy can be used as a dental implant material.

     

Learning Fuzzy Set Representations of Partial Shapes on Dual Embedding Spaces

Modeling relations between components of 3D objects is essential for many geometry editing tasks. Existing techniques commonly rely on labeled components, which requires substantial annotation effort and limits components to a dictionary of predefined semantic parts. We propose a novel framework based on neural networks that analyzes an uncurated collection of 3D models from the same category and learns two important types of semantic relations among full and partial shapes: complementarity and interchangeability. The former helps to identify which two partial shapes make a complete plausible object, and the latter indicates that interchanging two partial shapes from different objects preserves the object plausibility. Our key idea is to jointly encode both relations by embedding partial shapes as fuzzy sets in dual embedding spaces. We model these two relations as fuzzy set operations performed across the dual embedding spaces, and within each space, respectively. We demonstrate the utility of our method for various retrieval tasks that are commonly needed in geometric modeling interfaces.     

Natural Polyphenols Selectively Inhibit β‐Carbonic Anhydrase from the Dandruff‐Producing Fungus Malassezia globosa: Activity and Modeling Studies

Around 50 % of the worldwide population is affected by dandruff, which is triggered by a variety of factors. The yeast Malassezia globosa has been labeled as the most probable causative agent for the onset of dandruff. The β‐carbonic anhydrase (CA) of MgCA was recently validated as an anti‐dandruff target, with its inhibition being responsible for in vivo growth defects in the fungus. As classical CA inhibitors of the sulfonamide type give rise to permeability problems through biological membranes, finding non‐sulfonamide alternatives for MgCA inhibition is of considerable interest in the cosmetic field. We recently screened a large library of human (h) CA inhibitors for MgCA inhibition, including different chemotypes, such as monothiocarbamates, dithiocarbamates, phenols, and benzoxaboroles. Herein, we expanded the research toward new MgCA inhibitors by considering a set of natural polyphenols (including flavones, flavonols, flavanones, flavanols, isoflavones, and depsides) that exhibited MgCA inhibitory activity in the micromolar range, as well as selectivity for the fungal isozyme over off‐target human isoforms. The binding mode of representative derivatives within the MgCA catalytic cleft was investigated by docking studies using a homology‐built model.     

Analysis of observer performance in unknown-location tasks for tomographic image reconstruction

Our goal is to optimize regularized image reconstruction for emission tomography with respect to lesion detectability in the reconstructed images. We consider model observers whose decision variable is the maximum value of a local test statistic within a search area. Previous approaches have used simulations to evaluate the performance of such observers. We propose an alternative approach, where approximations of tail probabilities for the maximum of correlated Gaussian random fields facilitate analytical evaluation of detection performance. We illustrate how these approximations, which are reasonably accurate at low probability of false alarm operating points, can be used to optimize regularization with respect to lesion detectability.     

Toward full spatiotemporal control on the nanoscale

We introduce an approach to implement full coherent control on nanometer length scales. It is based on spatiotemporal modulation of the surface plasmon polariton (SPP) fields at the thick edge of a nanowedge. The SPP wavepackets propagating toward the sharp edge of this nanowedge are compressed and adiabatically concentrated at a nanofocus, forming an ultrashort pulse of local fields. The profile of the focused waveform as a function of time and one spatial dimension is completely coherently controlled.     

Esterase Activity of Serum Albumin Studied by 1H NMR Spectroscopy and Molecular Modelling

Serum albumin possesses esterase and pseudo-esterase activities towards a number of endogenous and exogenous substrates, but the mechanism of interaction of various esters and other compounds with albumin is still unclear. In the present study, proton nuclear magnetic resonance (¹H NMR) has been applied to the study of true esterase activity of albumin, using the example of bovine serum albumin (BSA) and p-nitrophenyl acetate (NPA). The site of BSA esterase activity was then determined using molecular modelling methods. According to the data obtained, the accumulation of acetate in the presence of BSA in the reaction mixture is much more intense as compared with the spontaneous hydrolysis of NPA, which indicates true esterase activity of albumin towards NPA. Similar results were obtained for p-nitophenyl propionate (NPP) as substrate. The rate of acetate and propionate release confirms the assumption that there is a site of true esterase activity in the albumin molecule, which is different from the site of the pseudo-esterase activity Sudlow II. The results of molecular modelling of BSA and NPA interaction make it possible to postulate that Sudlow site I is the site of true esterase activity of albumin.     

Genomic Regions Associated with Fusarium Wilt Resistance in Flax

Modern flax cultivars are susceptible to many diseases; arguably, the most economically damaging of these is the Fusarium wilt fungal disease. Over the past decades international flax breeding initiatives resulted in the development of resistant cultivars. However, much remains to be learned about the mechanisms of resistance to Fusarium infection in flax. As a first step to uncover the genetic factors associated with resistance to Fusarium wilt disease, we performed a genome-wide association study (GWAS) using 297 accessions from the collection of the Federal Research Centre of the Bast Fiber Crops, Torzhok, Russia. These genotypes were infected with a highly pathogenic Fusarium oxysporum f.sp. lini MI39 strain; the wilt symptoms were documented in the course of three successive years. Six different single-locus models implemented in GAPIT3 R package were applied to a selected subset of 72,526 SNPs. A total of 15 QTNs (Quantitative Trait Nucleotides) were detected during at least two years of observation, while eight QTNs were found during all three years of the experiment. Of these, ten QTNs occupied a region of 640 Kb at the start of chromosome 1, while the remaining QTNs mapped to chromosomes 8, 11 and 13. All stable QTNs demonstrate a statistically significant allelic effect across 3 years of the experiment. Importantly, several QTNs spanned regions that harbored genes involved in the pathogen recognition and plant immunity response, including the KIP1-like protein (Lus10025717) and NBS-LRR protein (Lus10025852). Our results provide novel insights into the genetic architecture of flax resistance to Fusarium wilt and pinpoint potential candidate genes for further in-depth studies.     

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

Prediction of gas chromatographic retention indices based on compound structure is an important task for analytical chemistry. The predicted retention indices can be used as a reference in a mass spectrometry library search despite the fact that their accuracy is worse in comparison with the experimental reference ones. In the last few years, deep learning was applied for this task. The use of deep learning drastically improved the accuracy of retention index prediction for non-polar stationary phases. In this work, we demonstrate for the first time the use of deep learning for retention index prediction on polar (e.g., polyethylene glycol, DB-WAX) and mid-polar (e.g., DB-624, DB-210, DB-1701, OV-17) stationary phases. The achieved accuracy lies in the range of 16–50 in terms of the mean absolute error for several stationary phases and test data sets. We also demonstrate that our approach can be directly applied to the prediction of the second dimension retention times (GC × GC) if a large enough data set is available. The achieved accuracy is considerably better compared with the previous results obtained using linear quantitative structure-retention relationships and ACD ChromGenius software. The source code and pre-trained models are available online.     

Breathing architecture: Conceptual architectural design based on the investigation into the natural ventilation of buildings

This study explores architectural design by examining air, fluid mechanics, and the natural ventilation of buildings. In this context, this research introduces a new way of dealing with the process of architectural synthesis. The proposed way can be used either to create new architectural projects or to rethink existing ones. This study is supported by previous investigation into the natural ventilation of buildings via computational and laboratory simulation (Stavridou, 2011; Stavridou and Prinos, 2013). The investigation into the natural ventilation of buildings provides information and data that affect architectural design through various parameters. The parameters of architectural synthesis that are influenced and discussed in this paper are the following: (i) inspiration and analogical transfer, (ii) initial conception of the main idea using computational fluid dynamics (digital design), (iii) development of the main idea through an investigatory process toward building form optimization, and (iv) form configuration, shape investigation, and other morphogenetic prospects. This study illustrates the effect of natural ventilation research on architectural design and thus produces a new approach to the architectural design process. This approach leads to an innovative kind of architecture called “breathing architecture.”