A stabilized eighteen-node solid element for hyperelastic analysis of shells

The objective of the present study is to develop a solid element for large deformation analysis of hyperelastic shell structures. To attain high computational efficiency and annihilate shear and membrane lockings, a hybrid-strain stabilization approach is adopted. To overcome the thickness locking of the element, the enhanced assumed thickness strain modes are incorporated. Starting from the virtual work principle and a weak form that enforces the equality of the hybrid-strain and the strain arising from the displacement and the enhanced assumed strain, an eighteen-node element for large deformation analysis of hyperelastic shells is developed. The salient feature of the present element for higher computational efficiency is that the element uses only the second-order quadrature for integration along the two in-plane natural coordinates and the stabilization vectors can be formed without using any integration loops. Efficacy of the element is illustrated by popular benchmark problems. © 2003 Elsevier B.V. All rights reserved.postprin

Do temperature changes cause eczema flares? An English cohort study.

BACKGROUND: It is unclear if ambient temperature changes affect eczema. It is also unclear if people with worse disease are more susceptible to weather-related flares, or specific types of emollient offer protection. Substantiating these links may help inform action plans and patients self-management. OBJECTIVE: To investigate the effect of short-term temperature variations on eczema symptoms in children. METHODS: Data from a UK cohort of 519 children (6 months-12 years) with at least mild eczema, participating in a randomised trial comparing four types of emollients on eczema symptoms, were combined with observed temperature data from the Hadley Centre's Integrated Surface Database.Hot & cold weeks were defined by average regional temperature >75th or <25th percentile, January 2018-February 2020. Eczema flares were defined as ≥3 point change in patient-oriented eczema measure (POEM). Random effects logistic regression models were used to estimate the odds ratios of flares in hot & cold weeks (reference group: temperate weeks). The likelihood ratio test assessed for evidence of effect modification by disease severity and emollient type. RESULTS: The baseline mean age was 4.9 years (SD 3.2) and POEM score was 9.2 (SD 5.5), indicating moderate eczema. 90% of participants lived within 20 km of their nearest weather station. From the 519 participants there were 6,796 consecutively paired POEMs and 1,082 flares.Seasonal variation in POEM scores was observed, suggesting symptoms worsening with colder weather in winter and improving with warmer weather in summer. Odds ratios of flares were: 1.15 (p = 0.136, 95%CI 0.96-1.39) in cold weeks, 0.85 (p = 0.045, 95%CI 0.72-1.00) in hot weeks. Likelihood ratio test showed no evidence of this differing by disease severity (p = 0.53) or emollient type used (p = 0.55). CONCLUSIONS: Our findings are consistent with previous studies demonstrating either improvements in eczema symptoms or reduced flares in hot weather. Worse disease and different emollient types did not increase susceptibility or provide protection against temperature changes. Further work should investigate the role of sunlight, humidity, air pollution and other environmental factors

Feeding behavior of the Yellow-chevroned Parakeet at the northeast of the State of Sao Paulo State, Brazil

This research was carried out from March 1990 to December 1993. It was designed to obtain data on the feeding behavior of the Yellow-chevroned Parakeet (Brotogeris chiriri) at four distinct areas of northeast Sao Paulo State. The following aspects of its feeding biology have been addressed: the plant species used as food, the items (flower, fruit, pulp, seed) included in the diet, the methods used to obtain food, and the flock size variation. Brotogeris chiriri used 45 plant species as food resource. From these it used seeds from 27 species, fruit pulp from 19 species and flowers from 16 species. There was a significant statistical variation on the methods used to gather food relative to the food item. The flock size of B. chiriri is highly variable (from one individual eating alone to about 45 individuals) on the same period of the year. The smallest average flock size was found during the breeding period of the species.1519510

RNA-Seq of Huntington's disease patient myeloid cells reveals innate transcriptional dysregulation associated with proinflammatory pathway activation

Innate immune activation beyond the central nervous system is emerging as a vital component of the pathogenesis of neurodegeneration. Huntington's disease (HD) is a fatal neurodegenerative disorder caused by a CAG repeat expansion in the huntingtin gene. The systemic innate immune system is thought to act as a modifier of disease progression; however, the molecular mechanisms remain only partially understood. Here we use RNA-sequencing to perform whole transcriptome analysis of primary monocytes from thirty manifest HD patients and thirty-three control subjects, cultured with and without a proinflammatory stimulus. In contrast with previous studies that have required stimulation to elicit phenotypic abnormalities, we demonstrate significant transcriptional differences in HD monocytes in their basal, unstimulated state. This includes previously undetected increased resting expression of genes encoding numerous proinflammatory cytokines, such as IL6 Further pathway analysis revealed widespread resting enrichment of proinflammatory functional gene sets, while upstream regulator analysis coupled with Western blotting suggests that abnormal basal activation of the NFĸB pathway plays a key role in mediating these transcriptional changes. That HD myeloid cells have a proinflammatory phenotype in the absence of stimulation is consistent with a priming effect of mutant huntingtin, whereby basal dysfunction leads to an exaggerated inflammatory response once a stimulus is encountered. These data advance our understanding of mutant huntingtin pathogenesis, establish resting myeloid cells as a key source of HD immune dysfunction, and further demonstrate the importance of systemic immunity in the potential treatment of HD and the wider study of neurodegeneration

Discovering monotonic stemness marker genes from time-series stem cell microarray data

© 2015 Wang et al.; licensee BioMed Central Ltd. Background: Identification of genes with ascending or descending monotonic expression patterns over time or stages of stem cells is an important issue in time-series microarray data analysis. We propose a method named Monotonic Feature Selector (MFSelector) based on a concept of total discriminating error (DEtotal) to identify monotonic genes. MFSelector considers various time stages in stage order (i.e., Stage One vs. other stages, Stages One and Two vs. remaining stages and so on) and computes DEtotal of each gene. MFSelector can successfully identify genes with monotonic characteristics.Results: We have demonstrated the effectiveness of MFSelector on two synthetic data sets and two stem cell differentiation data sets: embryonic stem cell neurogenesis (ESCN) and embryonic stem cell vasculogenesis (ESCV) data sets. We have also performed extensive quantitative comparisons of the three monotonic gene selection approaches. Some of the monotonic marker genes such as OCT4, NANOG, BLBP, discovered from the ESCN dataset exhibit consistent behavior with that reported in other studies. The role of monotonic genes found by MFSelector in either stemness or differentiation is validated using information obtained from Gene Ontology analysis and other literature. We justify and demonstrate that descending genes are involved in the proliferation or self-renewal activity of stem cells, while ascending genes are involved in differentiation of stem cells into variant cell lineages.Conclusions: We have developed a novel system, easy to use even with no pre-existing knowledge, to identify gene sets with monotonic expression patterns in multi-stage as well as in time-series genomics matrices. The case studies on ESCN and ESCV have helped to get a better understanding of stemness and differentiation. The novel monotonic marker genes discovered from a data set are found to exhibit consistent behavior in another independent data set, demonstrating the utility of the proposed method. The MFSelector R function and data sets can be downloaded from: http://microarray.ym.edu.tw/tools/MFSelector/

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

Long-distance quantum communication with atomic ensembles and linear optics

Quantum communication holds a promise for absolutely secure transmission of secret messages and faithful transfer of unknown quantum states. Photonic channels appear to be very attractive for physical implementation of quantum communication. However, due to losses and decoherence in the channel, the communication fidelity decreases exponentially with the channel length. We describe a scheme that allows to implement robust quantum communication over long lossy channels. The scheme involves laser manipulation of atomic ensembles, beam splitters, and single-photon detectors with moderate efficiencies, and therefore well fits the status of the current experimental technology. We show that the communication efficiency scale polynomially with the channel length thereby facilitating scalability to very long distances.Comment: 2 tex files (Main text + Supplement), 4 figure

Oldest known pantherine skull and evolution of the tiger

The tiger is one of the most iconic extant animals, and its origin and evolution have been intensely debated. Fossils attributable to extant pantherine species-lineages are less than 2 MYA and the earliest tiger fossils are from the Calabrian, Lower Pleistocene. Molecular studies predict a much younger age for the divergence of modern tiger subspecies at <100 KYA, although their cranial morphology is readily distinguishable, indicating that early Pleistocene tigers would likely have differed markedly anatomically from extant tigers. Such inferences are hampered by the fact that well-known fossil tiger material is middle to late Pleistocene in age. Here we describe a new species of pantherine cat from Longdan, Gansu Province, China, Panthera zdanskyi sp. nov. With an estimated age of 2.55–2.16 MYA it represents the oldest complete skull of a pantherine cat hitherto found. Although smaller, it appears morphologically to be surprisingly similar to modern tigers considering its age. Morphological, morphometric, and cladistic analyses are congruent in confirming its very close affinity to the tiger, and it may be regarded as the most primitive species of the tiger lineage, demonstrating the first unequivocal presence of a modern pantherine species-lineage in the basal stage of the Pleistocene (Gelasian; traditionally considered to be Late Pliocene). This find supports a north-central Chinese origin of the tiger lineage, and demonstrates that various parts of the cranium, mandible, and dentition evolved at different rates. An increase in size and a reduction in the relative size of parts of the dentition appear to have been prominent features of tiger evolution, whereas the distinctive cranial morphology of modern tigers was established very early in their evolutionary history. The evolutionary trend of increasing size in the tiger lineage is likely coupled to the evolution of its primary prey species

Energy-efficient scheduling of flexible flow shop of composite recycling

Composite recycling technologies have been developed to tackle the increasing use of composites in industry and as a result of restrictions placed on landfill disposal. Mechanical, thermal and chemical approaches are the existing main recycling techniques to recover the fibres. Some optimisation work for reducing energy consumed by above processes has also been developed. However, the resource efficiency of recycling composites at the workshop level has never been considered before. Considering the current trend of designing and optimising a system in parallel and the future needs of the composite recycling business, a flexible flow shop for carbon fibre reinforced composite recycling is modelled. Optimisation approaches based on non-dominated sorting genetic algorithm II (NSGA-II) have been developed to reduce the time and energy consumed for processing composite wastes by searching for the optimal sub-lot splitting and resource scheduling plans. Case studies on different composite recycling scenarios have been conducted to prove the feasibility of the model and the developed algorithm