Gas flow and fluidization in a thick dynamic regolith: A new mechanism for the formation of chondritic meteorites

We have previously shown that size and density sorting in a regolith which has been 'fluidized' by the passage or gases from the interior or the body can quantitatively explain metal-silicate fractionation, an important property of ordinary chondrites. Here we discuss whether the flow rates and flux or volatiles expected from a primitive parent body are likely to be sufficient for this mechanism. Many meteorite parent bodies may have contained volatiles. From a consideration of heat diffusion and fluid mechanics, we calculate the gas flow rate of volatiles (e.g., water) in the regolith of an asteroid-sized object heated by Al-26. Our calculations show that the flow velocities and flux of water vapor are sufficient to produce conditions suitable for fluidization. Other heat sources have yet to be considered, but literature work suggests that they may be equally effective

Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks

Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment. Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention. However, classifying suicidal ideation and other mental disorders is challenging as they share similar patterns in language usage and sentimental polarity. This paper enhances text representation with lexicon-based sentiment scores and latent topics and proposes using relation networks to detect suicidal ideation and mental disorders with related risk indicators. The relation module is further equipped with the attention mechanism to prioritize more critical relational features. Through experiments on three real-world datasets, our model outperforms most of its counterparts

Chondrule formation, metamorphism, brecciation, an important new primary chondrule group, and the classification of chondrules

The recently proposed compositional classification scheme for meteoritic chondrules divides the chondrules into groups depending on the composition of their two major phases, olivine (or pyroxene) and the mesostasis, both of which are genetically important. The scheme is here applied to discussions of three topics: the petrographic classification of Roosevelt County 075 (the least-metamorphosed H chondrite known), brecciation (an extremely important and ubiquitous process probably experienced by greater than 40% of all unequilibrated ordinary chondrites), and the group A5 chondrules in the least metamorphosed ordinary chondrites which have many similarities to chondrules in the highly metamorphosed 'equilibrated' chondrites. Since composition provides insights into both primary formation properties of the chondruies and the effects of metamorphism on the entire assemblage it is possible to determine the petrographic type of RC075 as 3.1 with unique certainty. Similarly, the near scheme can be applied to individual chondrules without knowledge of the petrographic type of the host chondrite, which makes it especially suitable for studying breccias. Finally, the new scheme has revealed the existence of chondrules not identified by previous techniques and which appear to be extremely important. Like group A1 and A2 chondrules (but unlike group B1 chondrules) the primitive group A5 chondruies did not supercool during formation, but unlike group A1 and A2 chondrules (and like group B1 chondrules) they did not suffer volatile loss and reduction during formation. It is concluded that the compositional classification scheme provides important new insights into the formation and history of chondrules and chondrites which would be overlooked by previous schemes

Histologic and Clinical Follow-up of Thyroid Fine Needle Aspirates in Pediatric Patients

BACKGROUND Although fine-needle aspiration (FNA) has an important role in evaluating thyroid nodules in adults, there is little published information regarding its utility in the pediatric population. METHODS A retrospective analysis of thyroid FNAs for patients who were 18 years old or younger at 2 institutions was conducted. Aspirates were retrospectively categorized with the Bethesda System for Reporting Thyroid Cytopathology. These diagnoses were then correlated with either final histopathology or clinical follow-up. RESULTS A total of 186 thyroid FNA samples from 154 patients (122 females and 32 males), who ranged in age from 9 months to 18 years (median, 16 years; mean, 14 years), were identified. FNA was performed to evaluate 1 to 3 nodules for each patient. Aspirates were classified as follows: nondiagnostic (n = 27), benign (n = 114), atypia of undetermined significance (AUS; n = 21), follicular neoplasm (FN; n = 8), suspicious for malignancy (n = 3), and malignant (n = 13). Sixty-one samples had a histologic correlation, 68 were followed clinically for ≥2 years, and 57 either had no follow-up or were followed for <2 years. For statistical purposes, FNA diagnoses of suspicious and malignant were considered positive, and benign lesions were considered negative. The accuracy was 99%, and the sensitivity and specificity were 94% and 100%, respectively. The risk of malignancy, not including papillary microcarcinoma, was 2% for benign aspirates, 21% for AUS, 57% for FN, and 100% for suspicious or malignant aspirates. CONCLUSIONS This analysis demonstrates that FNA is a sensitive and highly specific modality for evaluating thyroid nodules in pediatric patients. Each diagnostic category can facilitate communication and guide appropriate management

Pyroxene structures, cathodoluminescence and the thermal history of the enstatite chondrites

In order to explore the thermal history of enstatite chondrites, we examined the cathodoluminescence (CL) and thermoluminescence (TL) properties of 15 EH chondrites and 21 EL chondrites, including all available petrographic types, both textural types 3-6 and mineralogical types alpha-delta. The CL properties of EL3(alpha) and EH3(alpha) chondrites are similar. Enstatite grains high in Mn and other transition metals display red CL, while enstatite with low concentrations of these elements show blue CL. A few enstatite grains with greater than 5 wt% FeO display no CL. In contrast, the luminescent properties of the metamorphosed EH chondrites are very different from those of metamorphosed EL chondrites. While the enstatites in metamorphosed EH chondrites display predominantly blue CL, the enstatites in metamorphosed EL chondrites display a distinctive magenta CL with blue and red peaks of approximately equal intensity in their spectra. The TL sensitivities of the enstatite chondrites correlate with the intensity of the blue CL and, unlike other meteorite classes, are not simply related to metamorphism. The different luminescent properties of metamorphosed EH and EL chondrites cannot readily be attributed to compositional differences. But x-ray diffraction data suggests that the enstatite in EH5(gamma),(delta) chondrites is predominantly disordered orthopyroxene, while enstatite in EL6(beta) chondrites is predominantly ordered orthopyroxene. The difference in thermal history of metamorphosed EL and EH chondrites is so marked that the use of single 'petrographic' types is misleading, and separate textural and mineralogical types are preferable. Our data confirm earlier suggestions that metamorphosed EH chondrites underwent relatively rapid cooling, and the metamorphosed EL chondrites cooled more slowly and experienced prolonged heating in the orthopyroxene field

Red thermoluminescence of enstatite from the Chainpur meteorite

For most ordinary chondrites feldspar is mainly responsible for thermoluminescence [TL], but in type 3 ordinary chondrites, especially those which are most primitive, other minerals are important. We observed red TL with a &acd;660nm spectral peak in an ordinary chondrite, Chainpur (LL3.4). The mineral responsible for the red TL was identified as iron-free enstatite. Spatial distribution of TL and cathodoluminescence [CL] for the same specimen was also investigated, and it was found that the red TL areas corresponded to the high-sensitivity areas of red CL

Inulin ameliorates metabolic syndrome in high-fat diet-fed mice by regulating gut microbiota and bile acid excretion

Background: Inulin is a natural plant extract that improves metabolic syndrome by modulating the gut microbiota. Changes in the gut microbiota may affect intestinal bile acids. We suggest that inulin may improve metabolism by inducing bile acid excretion by gut microbes.Methods: Male C57/BL mice were fed either a high-fat diet (60% calories) or a regular diet for 16 weeks, with oral inulin (10% w/w). At the end of the experiment, the gene expression levels (FGF15, CD36, Srebp-1c, FASN, and ACC) in the liver and intestines, as well as the serum levels of triglycerides (TGs), low-density lipoprotein (LDL) cholesterol, total cholesterol, and free fatty acids, were collected. The expression of FGF15 was examined using Western blot analysis. The fat distribution in the liver and groin was detected by oil red and hematoxylin and eosin staining. Simultaneously, the levels of serum inflammatory factors (alanine aminotransferase and aspartate aminotransferase) were detected to explore the side effects of inulin.Results: Inulin significantly improved glucose tolerance and insulin sensitivity, and decreased body weight and serum TG and LDL levels, in mice fed normal diet. Furthermore, inulin increased the α-diversity of the gut microbiota and increased the fecal bile acid and TG excretion in inulin-treated mice. In addition, inulin significantly reduced lipid accumulation in liver and inguinal fat, white fat weight, and hepatic steatosis. Western blot analysis showed that inulin reduced the expression of FGF15, a bile acid reabsorption protein.Conclusion: Inulin ameliorates the glucose and lipid metabolic phenotypes of mice fed a normal diet, including decreased intestinal lipid absorption, increased glucose tolerance, increased insulin sensitivity, and decreased body weight. These changes may be caused by an increase in bile acid excretion resulting from changes in the gut microbiota that affect intestinal lipid absorption

A universal optical modulator for synthetic topologically tuneable structured matter

Topologically structured matter, such as metasurfaces and metamaterials, have given rise to impressive photonic functionality, fuelling diverse applications from microscopy and holography to encryption and communication. Presently these solutions are limited by their largely static nature and preset functionality, hindering applications that demand dynamic photonic systems with reconfigurable topologies. Here we demonstrate a universal optical modulator that implements topologically tuneable structured matter as virtual pixels derived from cascading low functionality tuneable devices, altering the paradigm of phase and amplitude control to encompass arbitrary spatially varying retarders in a synthetic structured matter device. Our approach opens unprecedented functionality that is user-defined with high flexibility, allowing our synthetic structured matter to act as an information carrier, beam generator, analyser, and corrector, opening an exciting path to tuneable topologies of light and matter

Transcriptome Analysis of the Octopus vulgaris Central Nervous System

Background: Cephalopoda are a class of Mollusca species found in all the world's oceans. They are an important model organism in neurobiology. Unfortunately, the lack of neuronal molecular sequences, such as ESTs, transcriptomic or genomic information, has limited the development of molecular neurobiology research in this unique model organism. Results: With high-throughput Illumina Solexa sequencing technology, we have generated 59,859 high quality sequences from 12,918,391 paired-end reads. Using BLASTx/BLASTn, 12,227 contigs have blast hits in the Swissprot, NR protein database and NT nucleotide database with E-value cutoff 1e(-5). The comparison between the Octopus vulgaris central nervous system (CNS) library and the Aplysia californica/Lymnaea stagnalis CNS ESTs library yielded 5.93%/13.45% of O. vulgaris sequences with significant matches (1e(-5)) using BLASTn/tBLASTx. Meanwhile the hit percentage of the recently published Schistocerca gregaria, Tilapia or Hirudo medicinalis CNS library to the O. vulgaris CNS library is 21.03%-46.19%. We constructed the Phylogenetic tree using two genes related to CNS function, Synaptotagmin-7 and Synaptophysin. Lastly, we demonstrated that O. vulgaris may have a vertebrate-like Blood-Brain Barrier based on bioinformatic analysis. Conclusion: This study provides a mass of molecular information that will contribute to further molecular biology research on O. vulgaris. In our presentation of the first CNS transcriptome analysis of O. vulgaris, we hope to accelerate the study of functional molecular neurobiology and comparative evolutionary biology.National fund for oceanography research in Public Interest [201005013]; National Key Technology RD Program [2011BAD13

Reshaping Hyperspectral Data into a Two-Dimensional Image for a CNN Model to Classify Plant Species from Reflectance

Leaf-level hyperspectral-based species identification has a long research history. However, unlike hyperspectral image-based species classification models, convolutional neural network (CNN) models are rarely used for the one-dimensional (1D) structured leaf-level spectrum. Our research focuses on hyperspectral data from five laboratories worldwide to test the general use of effective identification of the CNN model by reshaping 1D structure hyperspectral data into two-dimensional greyscale images without principal component analysis (PCA) or downscaling. We compared the performance of two-dimensional CNNs with the deep cross neural network (DCN), support vector machine, random forest, gradient boosting machine, and decision tree in individual tree species classification from leaf-level hyperspectral data. We tested the general performance of the models by simulating an application phase using data from different labs or years as the unseen data for prediction. The best-performing CNN model had validation accuracy of 98.6%, prediction accuracy of 91.6%, and precision of 74.9%, compared to the support vector machine, with 98.6%, 88.8%, and 66.4%, respectively, and DCN, with 94.0%, 85.7%, and 57.1%, respectively. Compared with the reference models, CNNs more efficiently recognized Fagus crenata, and had high accuracy in Quercus rubra identification. Our results provide a template for a species classification method based on hyperspectral data and point to a new way of reshaping 1D data into a two-dimensional image, as the key to better species prediction. This method may also be helpful for foliar trait estimation