Testing procedures for selection of new pinewood preservation products

In this work we compare the results of three experimental series of tests used as a first approach on selection of natural products for wood preservation

Mesoscopic mean-field theory for spin-boson chains in quantum optical systems

We present a theoretical description of a system of many spins strongly coupled to a bosonic chain. We rely on the use of a spin-wave theory describing the Gaussian fluctuations around the mean-field solution, and focus on spin-boson chains arising as a generalization of the Dicke Hamiltonian. Our model is motivated by experimental setups such as trapped ions, or atoms/qubits coupled to cavity arrays. This situation corresponds to the cooperative (E⊗β) Jahn-Teller distortion studied in solid-state physics. However, the ability to tune the parameters of the model in quantum optical setups opens up a variety of novel intriguing situations. The main focus of this paper is to review the spin-wave theoretical description of this problem as well as to test the validity of mean-field theory. Our main result is that deviations from mean-field effects are determined by the interplay between magnetic order and mesoscopic cooperativity effects, being the latter strongly size-dependent

Topological edge states in periodically-driven trapped-ion chains

Topological insulating phases are primarily associated with condensed-matter systems, which typically feature short-range interactions. Nevertheless, many realizations of quantum matter can exhibit long-range interactions, and it is still largely unknown the effect that these latter may exert upon the topological phases. In this Letter, we investigate the Su-Schrieffer-Heeger topological insulator in the presence of long-range interactions. We show that this model can be readily realized in quantum simulators with trapped ions by means of a periodic driving. Our results indicate that the localization of the associated edge states is enhanced by the long-range interactions, and that the localized components survive within the ground state of the model. These effects could be easily confirmed in current state-of-the-art experimental implementations

The Interspersed Spin Boson Lattice Model

We describe a family of lattice models that support a new class of quantum magnetism characterized by correlated spin and bosonic ordering [Phys. Rev. Lett. 112, 180405 (2014)]. We explore the full phase diagram of the model using Matrix-Product-State methods. Guided by these numerical results, we describe a modified variational ansatz to improve our analytic description of the groundstate at low boson frequencies. Additionally, we introduce an experimental protocol capable of inferring the low-energy excitations of the system by means of Fano scattering spectroscopy. Finally, we discuss the implementation and characterization of this model with current circuit-QED technology.Comment: Submitted to EPJ ST issue on "Novel Quantum Phases and Mesoscopic Physics in Quantum Gases

Repeated species radiations in the recent evolution of the key marine phytoplankton lineage Gephyrocapsa.

Phytoplankton account for nearly half of global primary productivity and strongly affect the global carbon cycle, yet little is known about the forces that drive the evolution of these keystone microscopic organisms. Here we combine morphometric data from the fossil record of the ubiquitous coccolithophore genus Gephyrocapsa with genomic analyses of extant species to assess the genetic processes underlying Pleistocene palaeontological patterns. We demonstrate that all modern diversity in Gephyrocapsa (including Emiliania huxleyi) originated in a rapid species radiation during the last 0.6 Ma, coincident with the latest of the three pulses of Gephyrocapsa diversification and extinction documented in the fossil record. Our evolutionary genetic analyses indicate that new species in this genus have formed in sympatry or parapatry, with occasional hybridisation between species. This sheds light on the mode of speciation during evolutionary radiation of marine phytoplankton and provides a model of how new plankton species form

Resolving the backbone of the Brassicaceae phylogeny for investigating trait diversity

• The Brassicaceae family comprises ca. 4000 species including economically important crops and the model plant Arabidopsis thaliana. Despite their importance, the relationships among major lineages in the family remain unresolved hampering comparative research.• Here, we inferred a Brassicaceae phylogeny using newly generated targeted enrichment sequence data of 1827 exons (>940,000 bases) representing 63 species as well as sequenced genome data of 16 species, together representing 50 of the 52 currently recognized Brassicaceae tribes. A third of the samples were derived from herbarium material, facilitating broad taxonomic coverage of the family.• Six major clades formed successive sister groups to the rest of Brassicaceae. We also recovered strong support for novel relationships among tribes, and resolved the position of 16 taxa previously not assigned to a tribe. The broad utility of these phylogenetic results is illustrated through a comparative investigation of genome-wide expression signatures that distinguish simple from complex leaves in Brassicaceae.• Our study provides an easily extended dataset for further advances in Brassicaceae systematics and a timely higher-level phylogenetic framework for a wide range of comparative studies of multiple traits in an intensively investigated group of plants. This article is protected by copyright. All rights reserved

Cross-sectional and longitudinal analyses of outdoor air pollution exposure and cognitive function in UK Biobank

Observational studies have shown consistently increased likelihood of dementia or mild cognitive impairment diagnoses in people with higher air pollution exposure history, but evidence has been less consistent for associations with cognitive test performance. We estimated the association between baseline neighbourhood-level exposure to airborne pollutants (particulate matter and nitrogen oxides) and (1) cognitive test performance at baseline and (2) cognitive score change between baseline and 2.8-year follow-up, in 86,759 middle- to older-aged adults from the UK Biobank general population cohort. Unadjusted regression analyses indicated small but consistent negative associations between air pollutant exposure and baseline cognitive performance. Following adjustment for a range of key confounders, associations were inconsistent in direction and of very small magnitude. The largest of these indicated that 1 interquartile range higher air pollutant exposure was associated on average with 0.35% slower reaction time (95% CI: 0.13, 0.57), a 2.92% higher error rate on a visuospatial memory test (95% CI: 1.24, 4.62), and numeric memory scores that were 0.58 points lower (95% CI: −0.96, −0.19). Follow-up analyses of cognitive change scores did not show evidence of associations. The findings indicate that in this sample, which is five-fold larger than any previous cross-sectional study, the association between air pollution exposure and cognitive performance was weak. Ongoing follow-up of the UK Biobank cohort will allow investigation of longer-term associations into old age, including longitudinal tracking of cognitive performance and incident dementia outcomes

Derivative spectrophotometric analysis of benzophenone (as an impurity) in phenytoin

Three simple and rapid spectrophotometric methods were developed for detection and trace determination of benzophenone (the main impurity) in phenytoin bulk powder and pharmaceutical formulations. The first method, zero-crossing first derivative spectrophotometry, depends on measuring the first derivative trough values at 257.6 nm for benzophenone. The second method, zero-crossing third derivative spectrophotometry, depends on measuring the third derivative peak values at 263.2 nm. The third method, ratio first derivative spectrophotometry, depends on measuring the peak amplitudes of the first derivative of the ratio spectra (the spectra of benzophenone divided by the spectrum of 5.0 μg/mL phenytoin solution) at 272 nm. The calibration graphs were linear over the range of 1-10 μg/mL. The detection limits of the first and the third derivative methods were found to be 0.04 μg/mL and 0.11 μg/mL and the quantitation limits were 0.13 μg/mL and 0.34 μg/mL, respectively, while for the ratio derivative method, the detection limit was 0.06 μg/mL and the quantitation limit was 0.18 μg/mL. The proposed methods were applied successfully to the assay of the studied drug in phenytoin bulk powder and certain pharmaceutical preparations. The results were statistically compared to those obtained using a polarographic method and were found to be in good agreement

Predicting AT(N) pathologies in Alzheimer’s disease from blood-based proteomic data using neural networks

Background and objective: Blood-based biomarkers represent a promising approach to help identify early Alzheimer's disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD. Methods: We measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein-protein interaction enrichment analysis. Results: Age and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase-protein kinase B/Akt signaling pathway. Conclusion: Combined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size. Keywords: Alzheimer’s disease; amyloid β; artificial neural networks; machine learning; neurodegeneration; plasma proteomics; ta