Evolution of spin correlations in SrDy2O4 in an applied magnetic field

The development of short- and long-range magnetic order induced in a frustrated zig-zag ladder compound SrDy2O4 by an applied field is studied using neutron diffraction techniques. In zero field, SrDy2O4 lacks long-range magnetic order down to temperatures as low as 60 mK, and the observed powder neutron diffraction (PND) patterns are dominated by very broad diffuse scattering peaks. Single crystal neutron diffraction reveals that the zero-field magnetic structure consists of a collection of antiferromagnetic chains running along the c axis and that there is very little correlation between the chains in the ab plane. In an applied magnetic field, the broad diffuse scattering features in PND are gradually replaced by much sharper peaks, however, the pattern remains rather complex, reflecting the highly anisotropic nature of SrDy2O4. Single crystal neutron diffraction shows that a moderate field applied along the b axis induces an up-up-down magnetic order associated with a 1/3-magnetisation plateau, in which magnetic correlation length in the ab plane is significantly increased, but it nevertheless remains finite. The resolution limited k = 0 peaks associated with a ferromagnetic arrangement appear in powder and single crystal neutron diffraction patterns in fields of 2.5 T and above.Comment: 10 pages, 11 figure

The SED Machine: a robotic spectrograph for fast transient classification

Current time domain facilities are finding several hundreds of transient astronomical events a year. The discovery rate is expected to increase in the future as soon as new surveys such as the Zwicky Transient Facility (ZTF) and the Large Synoptic Sky Survey (LSST) come on line. At the present time, the rate at which transients are classified is approximately one order or magnitude lower than the discovery rate, leading to an increasing "follow-up drought". Existing telescopes with moderate aperture can help address this deficit when equipped with spectrographs optimized for spectral classification. Here, we provide an overview of the design, operations and first results of the Spectral Energy Distribution Machine (SEDM), operating on the Palomar 60-inch telescope (P60). The instrument is optimized for classification and high observing efficiency. It combines a low-resolution (R$\sim$100) integral field unit (IFU) spectrograph with "Rainbow Camera" (RC), a multi-band field acquisition camera which also serves as multi-band (ugri) photometer. The SEDM was commissioned during the operation of the intermediate Palomar Transient Factory (iPTF) and has already proved lived up to its promise. The success of the SEDM demonstrates the value of spectrographs optimized to spectral classification. Introduction of similar spectrographs on existing telescopes will help alleviate the follow-up drought and thereby accelerate the rate of discoveries.Comment: 21 pages, 20 figure

Molecular basis of oocyte-paracrine signalling that promotes granulosa cell proliferation

Copyright © 2006 Company of BiologistsOocytes regulate follicle growth by secreting paracrine growth factors that act on neighbouring granulosa cells (GCs). Those factors identified to date are mainly members of the transforming growth factor-ß (TGFß) superfamily, but little is known about which specific receptor/signalling system(s) they employ. This study was conducted to determine the requisite pathways utilised by oocytes to promote GC proliferation. We used an established oocyte-secreted mitogen bioassay, where denuded mouse oocytes are co-cultured with mural GCs. Oocytes, growth differentiation factor-9 (GDF9), TGFß1 and activin-A all promoted GC DNA synthesis, but bone-morphogenetic protein 6 (BMP6) did not. Subsequently, we tested the capacity of various TGFß superfamily receptor ectodomains (ECD) to neutralise oocyte- or specific growth factor-stimulated GC proliferation. The BMP type-II receptor (BMPR-II) ECD antagonised oocyte and GDF9 bioactivity dose-dependently, but had no or minimal effect on TGFß1 and activin-A bioactivity, demonstrating its specificity. The TGFßR-II, activinR-IIA and activinR-IIB ECDs all failed to neutralise oocyte- or GDF9-stimulated GC DNA synthesis, whereas they did antagonise the activity of their respective native ligands. An activin receptor-like kinase (ALK) 4/5/7 inhibitor, SB431542, also antagonised both oocyte and GDF9 bioactivity in a dose-dependent manner. Consistent with these findings, oocytes, GDF9 and TGFß1 all activated SMAD2/3 reporter constructs in transfected GC, and led to phosphorylation of SMAD2 proteins in treated cells. Surprisingly, oocytes did not activate the SMAD1/5/8 pathway in transfected GCs although exogenous BMP6 did. This study indicates that oocyte paracrine factors primarily utilise a similar signalling pathway first identified for GDF9 that employs an unusual combination of TGFß superfamily receptors, the BMPR-II and a SMAD2/3 stimulatory ALK (4, 5 or 7), for transmitting their mitogenic actions in GC. This cell-signalling pathway may also have relevance in the hypothalamic-pituitary axis and in germ-somatic cell interactions in the testis.Robert B. Gilchrist, Lesley J. Ritter, Samu Myllymaa, Noora Kaivo-Oja, Rebecca A. Dragovic, Theresa E. Hickey, Olli Ritvos and David G. Mottershea

#Bieber + #Blast = #BieberBlast: Early Prediction of Popular Hashtag Compounds

Compounding of natural language units is a very common phenomena. In this paper, we show, for the first time, that Twitter hashtags which, could be considered as correlates of such linguistic units, undergo compounding. We identify reasons for this compounding and propose a prediction model that can identify with 77.07% accuracy if a pair of hashtags compounding in the near future (i.e., 2 months after compounding) shall become popular. At longer times T = 6, 10 months the accuracies are 77.52% and 79.13% respectively. This technique has strong implications to trending hashtag recommendation since newly formed hashtag compounds can be recommended early, even before the compounding has taken place. Further, humans can predict compounds with an overall accuracy of only 48.7% (treated as baseline). Notably, while humans can discriminate the relatively easier cases, the automatic framework is successful in classifying the relatively harder cases.Comment: 14 pages, 4 figures, 9 tables, published in CSCW (Computer-Supported Cooperative Work and Social Computing) 2016. in Proceedings of 19th ACM conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2016

Correlation between Laser Fluorescence Readings and Volume of Tooth Preparation in Incipient Occlusal Caries In Vitro

This study evaluated the correlation between laser fluorescence readings and the extent of incipient occlusal caries as measured by the volume of tooth preparation in vitro.One hundred and three permanent molars and premolars containing incipient occlusal pit-and-fissure caries and sound occlusal surfaces (1/4 of the sample, control) were selected. DIAGNOdent (KaVo Dental Corporation, Lake Zurich, IL, USA) readings were obtained according to manufacturer instructions. Caries was removed with 1/4 round burs in high speed. The volume of tooth preparation was measured using a surrogate measure based on the amount of composite needed to fill the preparations. Sensitivity and specificity using different cutoff values were calculated for lesions/preparations extending into dentin. The results were analyzed statistically.The Pearson correlation for preparation volume and DIAGNOdent reading measurements was low ( r  = 0.285). Sensitivity and specificity of DIAGNOdent for detection of dentinal lesions were 0.83 and 0.60, and 0.66 and 0.73 for the cutoff values of 20 and 30, respectively.Within the limitations of this study, laser fluorescence measured with DIAGNOdent does not correlate well with extent of carious tooth structure in incipient occlusal caries.Clinicians should not rely only on DIAGNOdent readings to determine the extension of incipient occlusal caries.( J Esthet Restor Dent 22:31–41, 2010)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78687/1/j.1708-8240.2009.00309.x.pd

Gaming with eutrophication: Contribution to integrating water quantity and quality management at catchment level

The Metropolitan Region of Sao Paulo (MRSP) hosts 18 million inhabitants. A complex system of 23 interconnected reservoirs was built to ensure its water supply. Half of the potable water produced for MRSP's population (35 m3/s) is imported from a neighbour catchment, the other half is produced within the Alto Tietê catchment, where 99% of the population lives. Perimeters of land use restriction were defined to contain uncontrolled urbanization, as domestic effluents were causing increasing eutrophication of some of these reservoirs. In the 90's catchment committees and sub committees were created to promote discussion between stakeholders and develop catchment plans. The committees are very well structured "on paper". However, they are not very well organised and face a lack of experience. The objective of this work was to design tools that would strengthen their discussion capacities. The specific objective of the AguAloca process was to integrate the quality issue and its relation to catchment management as a whole in these discussions. The work was developed in the Alto Tietê Cabeceiras sub-catchment, one of the 5 sub catchments of the Alto-Tietê. It contains 5 interconnected dams, and presents competitive uses such as water supply, industry, effluent dilution and irrigated agriculture. A RPG was designed following a companion modelling approach (Etienne et al., 2003). It contains a friendly game-board, a set of individual and collective rules and a computerized biophysical model. The biophysical model is used to simulate water allocation and quality processes at catchment level. It articulates 3 modules. A simplified nutrient discharge model permits the estimation of land use nutrient exportation. An arc-node model simulates water flows and associated nutrient charges from one point of the hydrographical network to another. The Vollenweider model is used for simulating specific reservoir dynamics. The RPG allows players to make individual and collective decisions related to water allocation and the management of its quality. Impacts of these decisions are then simulated using the biophysical model. Specific indicators of the game are then updated and may influence player's behaviour (actions) in following rounds. To introduce discussions on the management of water quality at a catchment level, an issue that is rarely explicitly dealt with, four game sessions were implemented involving representatives of basin committees and water and sanitation engineers. During the game session, the participants took advantage of the water quality output of the biophysical model to test management alternatives such as rural sewage collection or effluent dilution. The biophysical model accelerated calculations of flows and eutrophication rates that were then returned to the game board with explicit indicators of quantity and quality. Players could easily test decisions impacting on qualitative water processes and visualize the simulation results directly on the game board that was representing a friendly, virtual and simplified catchment. The Agualoca game proved its ability to turn complex water processes understandable for a non totally initiated public. This experience contributed to a better understanding of multiple-use water management and also of joint management of water quality and quantity. (Résumé d'auteur

Training set cleansing of backdoor poisoning by self-supervised representation learning

A backdoor or Trojan attack is an important type of data poisoning attack against deep neural network (DNN) classifiers, wherein the training dataset is poisoned with a small number of samples that each possess the backdoor pattern (usually a pattern that is either imperceptible or innocuous) and which are mislabeled to the attacker's target class. When trained on a backdoor-poisoned dataset, a DNN behaves normally on most benign test samples but makes incorrect predictions to the target class when the test sample has the backdoor pattern incorporated (i.e., contains a backdoor trigger). Here we focus on image classification tasks and show that supervised training may build stronger association between the backdoor pattern and the associated target class than that between normal features and the true class of origin. By contrast, self-supervised representation learning ignores the labels of samples and learns a feature embedding based on images' semantic content. %We thus propose to use unsupervised representation learning to avoid emphasising backdoor-poisoned training samples and learn a similar feature embedding for samples of the same class. Using a feature embedding found by self-supervised representation learning, a data cleansing method, which combines sample filtering and re-labeling, is developed. Experiments on CIFAR-10 benchmark datasets show that our method achieves state-of-the-art performance in mitigating backdoor attacks