New Symmetries in Crystals and Handed Structures

For over a century, the structure of materials has been described by a combination of rotations, rotation-inversions and translational symmetries. By recognizing the reversal of static structural rotations between clockwise and counterclockwise directions as a distinct symmetry operation, here we show that there are many more structural symmetries than are currently recognized in right- or left-handed handed helices, spirals, and in antidistorted structures composed equally of rotations of both handedness. For example, though a helix or spiral cannot possess conventional mirror or inversion symmetries, they can possess them in combination with the rotation reversal symmetry. Similarly, we show that many antidistorted perovskites possess twice the number of symmetry elements as conventionally identified. These new symmetries predict new forms for "roto" properties that relate to static rotations, such as rotoelectricity, piezorotation, and rotomagnetism. They also enable symmetry-based search for new phenomena, such as multiferroicity involving a coupling of spins, electric polarization and static rotations. This work is relevant to structure-property relationships in all material structures with static rotations such as minerals, polymers, proteins, and engineered structures.Comment: 15 Pages, 4 figures, 3 Tables; Fig. 2b has error

Impact of metabolic comorbidity on the association between body mass index and heatlh-related quality of life: a Scotland-wide cross-sectional study of 5,608 participants

<p/>Background: The prevalence of obesity is rising in Scotland and globally. Overall, obesity is associated with increased morbidity, mortality and reduced health-related quality of life. Studies suggest that "healthy obesity" (obesity without metabolic comorbidity) may not be associated with morbidity or mortality. Its impact on health-related quality of life is unknown. <p/>Methods: We extracted data from the Scottish Health Survey on self-reported health-related quality of life, body mass index (BMI), demographic information and comorbidity. SF-12 responses were converted into an overall health utility score. Linear regression analyses were used to explore the association between BMI and health utility, stratified by the presence or absence of metabolic comorbidity (diabetes, hypertension, hypercholesterolemia or cardiovascular disease), and adjusted for potential confounders (age, sex and deprivation quintile). <p/>Results: Of the 5,608 individuals, 3,744 (66.8%) were either overweight or obese and 921 (16.4%) had metabolic comorbidity. There was an inverted U-shaped relationship whereby health utility was highest among overweight individuals and fell with increasing BMI. There was a significant interaction with metabolic comorbidity (p = 0.007). Individuals with metabolic comorbidty had lower utility scores and a steeper decline in utility with increasing BMI (morbidly obese, adjusted coefficient: -0.064, 95% CI -0.115, -0.012, p = 0.015 for metabolic comorbidity versus -0.042, 95% CI -0.067, -0.018, p = 0.001 for no metabolic comorbidity). <p/>Conclusions: The adverse impact of obesity on health-related quality of life is greater among individuals with metabolic comorbidity. However, increased BMI is associated with reduced health-related quality of life even in the absence of metabolic comorbidity, casting doubt on the notion of "healthy obesity"

Gender differences in the association between adiposity and probable major depression: a cross-sectional study of 140,564 UK Biobank participants

<b>Background</b><p></p> Previous studies on the association between adiposity and mood disorder have produced contradictory results, and few have used measurements other than body mass index (BMI). We examined the association between probable major depression and several measurements of adiposity: BMI, waist circumference (WC), waist-hip-ratio (WHR), and body fat percentage (BF%).<p></p> <b>Methods</b><p></p> We conducted a cross-sectional study using baseline data on the sub-group of UK Biobank participants who were assessed for mood disorder. Multivariate logistic regression models were used, adjusting for potential confounders including: demographic and life-style factors, comorbidity and psychotropic medication.<p></p> <b>Results</b><p></p> Of the 140,564 eligible participants, evidence of probable major depression was reported by 30,145 (21.5%). The fully adjusted odds ratios (OR) for obese participants were 1.16 (95% confidence interval (CI) 1.12, 1.20) using BMI, 1.15 (95% CI 1.11, 1.19) using WC, 1.09 (95% CI 1.05, 1.13) using WHR and 1.18 (95% CI 1.12, 1.25) using BF% (all p <0.001). There was a significant interaction between adiposity and gender (p = 0.001). Overweight women were at increased risk of depression with a dose response relationship across the overweight (25.0-29.9 kg/m2), obese I (30.0-34.9 kg/m2), II (35.0-39.9 kg/m2) and III (≥40.0 kg/m2) categories; fully adjusted ORs 1.14, 1.20, 1.29 and 1.48, respectively (all p < 0.001). In contrast, only obese III men had significantly increased risk of depression (OR 1.29, 95% CI 1.08, 1.54, p = 0.006).<p></p> <b>Conclusion</b><p></p> Adiposity was associated with probable major depression, irrespective of the measurement used. The association was stronger in women than men. Physicians managing overweight and obese women should be alert to this increased risk

Biomagnifcation and body distribution of ivermectin in dung beetles

We thank the staf of Doñana Biological Reserve (DBR-ICTS), Doñana National Park, and Los Alcornocales Natural Park, especially D. Paz, F. Ibáñez, P. Bayón, M. Malla and D. Ruiz for logistic facilities for the field work and permissions (2019107300000904/IRM/MDCG/mes) to collect cattle dung and dung beetles. We are grateful to J. Castro and A. Rascón for technical assistance. We also thank A. V. Giménez-Gómez for her technical assistance in the laboratory work. We thank also F.-T Krell and the two anonymous reviewers for their constructive comments. Financial support was provided by the project CGL2015-68207-R of the Secretaría de Estado de Investigación–Ministerio de Economía y Competitividad.A terrestrial test system to investigate the biomagnifcation potential and tissue-specifc distribution of ivermectin, a widely used parasiticide, in the non-target dung beetle Thorectes lusitanicus (Jekel) was developed and validated. Biomagnifcation kinetics of ivermectin in T. lusitanicus was investigated by following uptake, elimination, and distribution of the compound in dung beetles feeding on contaminated faeces. Results showed that ivermectin was biomagnifed in adults of T. lusitanicus when exposed to non-lethal doses via food uptake. Ivermectin was quickly transferred from the gut to the haemolymph, generating a biomagnifcation factor (BMFk) three times higher in the haemolymph than in the gut after an uptake period of 12 days. The fat body appeared to exert a major role on the biomagnifcation of ivermectin in the insect body, showing a BMFk 1.6 times higher than in the haemolymph. The results of this study highlight that the biomagnifcation of ivermectin should be investigated from a global dung-based food web perspective and that the use of these antiparasitic substances should be monitored and controlled on a precautionary basis. Thus, we suggest that an additional efort be made in the development of standardised regulatory recommendations to guide biomagnifcation studies in terrestrial organisms, but also that it is necessary to adapt existing methods to assess the efects of such veterinary medical products

Multi-step time series prediction intervals using neuroevolution

Multi-step time series forecasting (TSF) is a crucial element to support tactical decisions (e.g., designing production or marketing plans several months in advance). While most TSF research addresses only single-point prediction, prediction intervals (PIs) are useful to reduce uncertainty related to important decision making variables. In this paper, we explore a large set of neural network methods for multi-step TSF and that directly optimize PIs. This includes multi-step adaptations of recently proposed PI methods, such as lower--upper bound estimation (LUBET), its ensemble extension (LUBEXT), a multi-objective evolutionary algorithm LUBE (MLUBET) and a two-phase learning multi-objective evolutionary algorithm (M2LUBET). We also explore two new ensemble variants for the evolutionary approaches based on two PI coverage--width split methods (radial slices and clustering), leading to the MLUBEXT, M2LUBEXT, MLUBEXT2 and M2LUBEXT2 methods. A robust comparison was held by considering the rolling window procedure, nine time series from several real-world domains and with different characteristics, two PI quality measures (coverage error and width) and the Wilcoxon statistic. Overall, the best results were achieved by the M2LUBET neuroevolution method, which requires a reasonable computational effort for time series with a few hundreds of observations.This article is a result of the project NORTE-01- 0247-FEDER-017497, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We would also like to thank the anonymous reviewers for their helpful suggestionsinfo:eu-repo/semantics/publishedVersio

Adaptation and Selective Information Transmission in the Cricket Auditory Neuron AN2

Sensory systems adapt their neural code to changes in the sensory environment, often on multiple time scales. Here, we report a new form of adaptation in a first-order auditory interneuron (AN2) of crickets. We characterize the response of the AN2 neuron to amplitude-modulated sound stimuli and find that adaptation shifts the stimulus–response curves toward higher stimulus intensities, with a time constant of 1.5 s for adaptation and recovery. The spike responses were thus reduced for low-intensity sounds. We then address the question whether adaptation leads to an improvement of the signal's representation and compare the experimental results with the predictions of two competing hypotheses: infomax, which predicts that information conveyed about the entire signal range should be maximized, and selective coding, which predicts that “foreground” signals should be enhanced while “background” signals should be selectively suppressed. We test how adaptation changes the input–response curve when presenting signals with two or three peaks in their amplitude distributions, for which selective coding and infomax predict conflicting changes. By means of Bayesian data analysis, we quantify the shifts of the measured response curves and also find a slight reduction of their slopes. These decreases in slopes are smaller, and the absolute response thresholds are higher than those predicted by infomax. Most remarkably, and in contrast to the infomax principle, adaptation actually reduces the amount of encoded information when considering the whole range of input signals. The response curve changes are also not consistent with the selective coding hypothesis, because the amount of information conveyed about the loudest part of the signal does not increase as predicted but remains nearly constant. Less information is transmitted about signals with lower intensity

Multiclass classification of microarray data samples with a reduced number of genes

<p>Abstract</p> <p>Background</p> <p>Multiclass classification of microarray data samples with a reduced number of genes is a rich and challenging problem in Bioinformatics research. The problem gets harder as the number of classes is increased. In addition, the performance of most classifiers is tightly linked to the effectiveness of mandatory gene selection methods. Critical to gene selection is the availability of estimates about the maximum number of genes that can be handled by any classification algorithm. Lack of such estimates may lead to either computationally demanding explorations of a search space with thousands of dimensions or classification models based on gene sets of unrestricted size. In the former case, unbiased but possibly overfitted classification models may arise. In the latter case, biased classification models unable to support statistically significant findings may be obtained.</p> <p>Results</p> <p>A novel bound on the maximum number of genes that can be handled by binary classifiers in binary mediated multiclass classification algorithms of microarray data samples is presented. The bound suggests that high-dimensional binary output domains might favor the existence of accurate and sparse binary mediated multiclass classifiers for microarray data samples.</p> <p>Conclusions</p> <p>A comprehensive experimental work shows that the bound is indeed useful to induce accurate and sparse multiclass classifiers for microarray data samples.</p