Successive phase transitions to antiferromagnetic and weak-ferromagnetic long-range orders in quasi-one-dimensional antiferromagnet Cu3_3Mo2_2O9_9

Investigation of the magnetism of Cu$_3$Mo$_2$O$_9$ single crystal, which has antiferromagnetic (AF) linear chains interacting with AF dimers, reveals an AF second-order phase transition at $T_{\rm N} = 7.9$ K. Although weak ferromagnetic-like behavior appears at lower temperatures in low magnetic fields, complete remanent magnetization cannot be detected down to 0.5 K. However, a jump is observed in the magnetization below weak ferromagnetic (WF) phase transition at $T_{\rm c} \simeq 2.5$ K when a tiny magnetic field along the a axis is reversed, suggesting that the coercive force is very weak. A component of magnetic moment parallel to the chain forms AF long-range order (LRO) below $T_{\rm N}$, while a perpendicular component is disordered above $T_{\rm c}$ at zero magnetic field and forms WF-LRO below $T_{\rm c}$. Moreover, the WF-LRO is also realized with applying magnetic fields even between $T_{\rm c}$ and $T_{\rm N}$. These results are explainable by both magnetic frustration among symmetric exchange interactions and competition between symmetric and asymmetric Dzyaloshinskii-Moriya exchange interactions.Comment: 7 pages, 7 figure

RecipeMeta: Metapath-enhanced Recipe Recommendation on Heterogeneous Recipe Network

Recipe is a set of instructions that describes how to make food. It can help people from the preparation of ingredients, food cooking process, etc. to prepare the food, and increasingly in demand on the Web. To help users find the vast amount of recipes on the Web, we address the task of recipe recommendation. Due to multiple data types and relationships in a recipe, we can treat it as a heterogeneous network to describe its information more accurately. To effectively utilize the heterogeneous network, metapath was proposed to describe the higher-level semantic information between two entities by defining a compound path from peer entities. Therefore, we propose a metapath-enhanced recipe recommendation framework, RecipeMeta, that combines GNN (Graph Neural Network)-based representation learning and specific metapath-based information in a recipe to predict User-Recipe pairs for recommendation. Through extensive experiments, we demonstrate that the proposed model, RecipeMeta, outperforms state-of-the-art methods for recipe recommendation

Glycosylphosphatidylinositol-Anchored Cell Surface Proteins Regulate Position-Specific Cell Affinity in the Limb Bud

AbstractAlthough regional differences in mesenchymal cell affinity in the limb bud represent positional identity, the molecular basis for cell affinity is poorly understood. We found that treatment of the cell surface with bacterial phosphatidylinositol-specific phospholipase C (PI-PLC) could change cell affinity in culture. When PI-PLC was added to the culture medium, segregation of the progress zone (PZ) cells from different stage limb buds was inhibited. Similarly, sorting out of the cells from different positions along the proximodistal (PD) axis of the same stage limb buds was disturbed. Since PI-PLC can remove glycosylphosphatidylinositol (GPI)-anchored membrane bound proteins from the cell surface, the GPI-anchored cell surface proteins may be involved in sorting out. To define the GPI-anchored molecules that determine the segregation of limb mesenchymal cells, we examined the effect of neutralizing antibody on the EphA4 receptor that binds to GPI-anchored cell surface ligands, called ephrin-A. Sorting out of the PZ cells at different stages could be inhibited by the neutralizing antibody to EphA4. These results suggest that EphA4 and its GPI-anchored ligands are, at least in part, involved in sorting out of limb mesenchymal cells with different proximal–distal positional values, and that GPI-anchored cell surface proteins play important roles in determining cell affinity in the limb bud

A Hilbert warping method for camera-based finger-writing recognition, in:

Abstract We propose a time-warping algorithm for recognizing finger actions by a camera. In the proposed method, an input image sequence is aligned to the reference sequences by phase-synchronization of the analytic signals, and then classified by comparing the cumulative distances. A major benefit of this method is that overfitting to sequences of incorrect categories is restricted. The proposed method exhibited high recognition accuracy in finger-writing character recognition

A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive Learning

Medication mistaking is one of the risks that can result in unpredictable consequences for patients. To mitigate this risk, we develop an automatic system that correctly identifies pill-prescription from mobile images. Specifically, we define a so-called pill-prescription matching task, which attempts to match the images of the pills taken with the pills' names in the prescription. We then propose PIMA, a novel approach using Graph Neural Network (GNN) and contrastive learning to address the targeted problem. In particular, GNN is used to learn the spatial correlation between the text boxes in the prescription and thereby highlight the text boxes carrying the pill names. In addition, contrastive learning is employed to facilitate the modeling of cross-modal similarity between textual representations of pill names and visual representations of pill images. We conducted extensive experiments and demonstrated that PIMA outperforms baseline models on a real-world dataset of pill and prescription images that we constructed. Specifically, PIMA improves the accuracy from 19.09% to 46.95% compared to other baselines. We believe our work can open up new opportunities to build new clinical applications and improve medication safety and patient care.Comment: Accepted for publication and presentation at the 19th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2022

μ-opioid Receptor-Mediated Alterations of Allergen-Induced Immune Responses of Bronchial Lymph Node Cells in a Murine Model of Stress Asthma

ABSTRACTBackgroundPsychological stress has a recognized association with asthma symptoms. Using a murine model of allergic asthma, we recently demonstrated the involvement of μ-opioid receptors (MORs) in the central nervous system in the stress-induced exacerbation of airway inflammation. However, the involvement of MORs on neurons and immunological alterations in the stress asthma model remain unclear.MethodsMOR-knockout (MORKO) mice that express MORs only on noradrenergic and adrenergic neurons (MORKO/Tg mice) were produced and characterized for stress responses. Sensitized mice inhaled antigen and were then subjected to restraint stress. After a second antigen inhalation, bronchoalveolar lavage cells were counted. Before the second inhalation, bronchial lymph node (BLN) cells and splenocytes from stressed and non-stressed mice were cultured with antigen, and cytokine levels and the proportions of T cell subsets were measured.ResultsStress-induced worsening of allergic airway inflammation was observed in wild-type and MORKO/Tg mice but not MORKO mice. In wild-type stressed mice, IFN-γ/IL-4 ratios in cell culture supernatants and the proportion of regulatory T cells in BLN cell populations were significantly lower than those in non-stressed mice. These differences in BLN cells were not observed between the stressed and non-stressed MORKO mice. Restraint stress had no effect on cytokine production or T cell subsets in splenocytes.ConclusionsRestraint stress aggravated allergic airway inflammation in association with alterations in local immunity characterized by greater Th2-associated cytokine production and a reduced development of regulatory T cells, mediated by MORs

mediaWalker: Tracking and browsing news video along the topic thread structure *

ABSTRACT We introduce a news video tracking and browsing interface "mediaWalker" that allows users to explore throughout a news video archive by tracking news topics along a chronological semantic structure of news stories

(-)-Pentazocine induces visceral chemical antinociception, but not thermal, mechanical, or somatic chemical antinociception, in μ-opioid receptor knockout mice

<p>Abstract</p> <p>Background</p> <p>(-)-Pentazocine has been hypothesized to induce analgesia via the κ-opioid (KOP) receptor, although the involvement of other opioid receptor subtypes in the effects of pentazocine remains unknown. In this study, we investigated the role of the μ-opioid (MOP) receptor in thermal, mechanical, and chemical antinociception induced by (-)-pentazocine using MOP receptor knockout (MOP-KO) mice.</p> <p>Results</p> <p>(-)-Pentazocine-induced thermal antinociception, assessed by the hot-plate and tail-flick tests, was significantly reduced in heterozygous and abolished in homozygous MOP-KO mice compared with wildtype mice. The results obtained from the (-)-pentazocine-induced mechanical and somatic chemical antinociception experiments, which used the hind-paw pressure and formalin tests, were similar to the results obtained from the thermal antinociception experiments in these mice. However, (-)-pentazocine retained its ability to induce significant visceral chemical antinociception, assessed by the writhing test, in homozygous MOP-KO mice, an effect that was completely blocked by pretreatment with nor-binaltorphimine, a KOP receptor antagonist. <it>In vitro </it>binding and cyclic adenosine monophosphate assays showed that (-)-pentazocine possessed higher affinity for KOP and MOP receptors than for δ-opioid receptors.</p> <p>Conclusions</p> <p>The present study demonstrated the abolition of the thermal, mechanical, and somatic chemical antinociceptive effects of (-)-pentazocine and retention of the visceral chemical antinociceptive effects of (-)-pentazocine in MOP-KO mice. These results suggest that the MOP receptor plays a pivotal role in thermal, mechanical, and somatic chemical antinociception induced by (-)-pentazocine, whereas the KOP receptor is involved in visceral chemical antinociception induced by (-)-pentazocine.</p

Visibility Estimation of Traffic Signals under Rainy Weather Conditions for Smart Driving Support

Abstract-The aim of this work is to support a driver by notifying the information of traffic signals in accordance with their visibility. To avoid traffic accidents, the driver should detect and recognize surrounding objects, especially traffic signals. However, when driving a vehicle under rainy weather conditions, it is difficult for drivers to detect or to recognize objects existing in the road environment in comparison with fine weather conditions. Therefore, this paper proposes a method for estimating the visibility of traffic signals for drivers under rainy weather conditions by image processing. The proposed method is based on the concept of visual noise known in the field of cognitive science, and extracts two types of visual noise features which ware considered that they affect the visibility of traffic signals. We expect to improve the accuracy of visibility estimation by combining the visual noise features with the texture feature introduced in a previous work. Experimental results showed that the proposed method could estimate the visibility of traffic signals more accurately under rainy weather conditions