Large enhancement of superconducting transition temperature in single-element superconducting rhenium by shear strain

Finding a physical approach for increasing the superconducting transition temperature (Tc) is a challenge in the field of material science. Shear strain effects on the superconductivity of rhenium were investigated using magnetic measurements, X-ray diffraction, transmission electron microscopy, and first-principles calculations. A large shear strain reduces the grain size and simultaneously expands the unit cells, resulting in an increase in Tc. Here we show that this shear strain approach is a new method for enhancing Tc and differs from that using hydrostatic strain. The enhancement of Tc is explained by an increase in net electron–electron coupling rather than a change in the density of states near the Fermi level. The shear strain effect in rhenium could be a successful example of manipulating Bardeen–Cooper–Schrieffer-type Cooper pairing, in which the unit cell volumes are indeed a key parameter

The astrocytic TRPA1 channel mediates an intrinsic protective response to vascular cognitive impairment via LIF production

認知症に対する新たな生体防御機構の発見 --アストロサイトのTRPA1活性化が、LIF産生を介して白質傷害や認知機能障害を防ぐ--. 京都大学プレスリリース. 2023-07-24.Vascular cognitive impairment (VCI) refers to cognitive alterations caused by vascular disease, which is associated with various types of dementia. Because chronic cerebral hypoperfusion (CCH) induces VCI, we used bilateral common carotid artery stenosis (BCAS) mice as a CCH-induced VCI model. Transient receptor potential ankyrin 1 (TRPA1), the most redox-sensitive TRP channel, is functionally expressed in the brain. Here, we investigated the pathophysiological role of TRPA1 in CCH-induced VCI. During early-stage CCH, cognitive impairment and white matter injury were induced by BCAS in TRPA1-knockout but not wild-type mice. TRPA1 stimulation with cinnamaldehyde ameliorated BCAS-induced outcomes. RNA sequencing analysis revealed that BCAS increased leukemia inhibitory factor (LIF) in astrocytes. Moreover, hydrogen peroxide-treated TRPA1-stimulated primary astrocyte cultures expressed LIF, and culture medium derived from these cells promoted oligodendrocyte precursor cell myelination. Overall, TRPA1 in astrocytes prevents CCH-induced VCI through LIF production. Therefore, TRPA1 stimulation may be a promising therapeutic approach for VCI

Striatal TRPV1 activation by acetaminophen ameliorates dopamine D2 receptor antagonists-induced orofacial dyskinesia

ジスキネジア新治療法の発見 --副作用を減らす併用薬から新しい創薬標的へ--. 京都大学プレスリリース. 2021-04-16.Antipsychotics often cause tardive dyskinesia, an adverse symptom of involuntary hyperkinetic movements. Analysis of the U.S. Food and Drug Administration Adverse Event Reporting System and JMDC insurance claims revealed that acetaminophen prevents the dyskinesia induced by dopamine D₂ receptor antagonists. In vivo experiments further showed that a 21-day treatment with haloperidol increased the number of vacuous chewing movements (VCMs) in rats, an effect that was inhibited by oral acetaminophen treatment or intracerebroventricular injection of N-(4-hydroxyphenyl)-arachidonylamide (AM404), an acetaminophen metabolite that acts as an activator of the transient receptor potential vanilloid 1 (TRPV1). In mice, haloperidol-induced VCMs were also mitigated by treatment with AM404 applied to the dorsal striatum, but not in TRPV1-deficient mice. Acetaminophen prevented the haloperidol-induced decrease in the number of c-Fos⁺/preproenkephalin⁺ striatal neurons in wild-type mice but not in TRPV1-deficient mice. Finally, chemogenetic stimulation of indirect-pathway medium spiny neurons in the dorsal striatum decreased haloperidol-induced VCMs. These results suggest that acetaminophen activates the indirect pathway neurons by activating TRPV1 channels via AM404

Androgen’s effects in female

The metabolic effects of androgens and their underlying mechanisms in females have been revealed by recent studies. An excess of androgens can have adverse effects on feeding behavior and metabolic functions and induce metabolic disorders / diseases, such as obesity, insulin resistance, and diabetes, in women and experimental animals of reproductive age. Interestingly, these effects of androgens are not observed in ovariectomized animals, indicating that their effects might be dependent on the estrogen milieu. Central and peripheral mechanisms, such as alterations in the activity of hypothalamic factors, reductions in energy expenditure, skeletal muscle insulin resistance, and β-cell dysfunction, might be related to these androgens’ effects

Down-regulation of GATA1-dependent erythrocyte-related genes in the spleens of mice exposed to a space travel

Secondary lymphoid organs are critical for regulating acquired immune responses. The aim of this study was to characterize the impact of spaceflight on secondary lymphoid organs at the molecular level. We analysed the spleens and lymph nodes from mice flown aboard the International Space Station (ISS) in orbit for 35 days, as part of a Japan Aerospace Exploration Agency mission. During flight, half of the mice were exposed to 1 g by centrifuging in the ISS, to provide information regarding the effect of microgravity and 1 g exposure during spaceflight. Whole-transcript cDNA sequencing (RNA-Seq) analysis of the spleen suggested that erythrocyte-related genes regulated by the transcription factor GATA1 were significantly down-regulated in ISS-flown vs. ground control mice. GATA1 and Tal1 (regulators of erythropoiesis) mRNA expression was consistently reduced by approximately half. These reductions were not completely alleviated by 1 g exposure in the ISS, suggesting that the combined effect of space environments aside from microgravity could down-regulate gene expression in the spleen. Additionally, plasma immunoglobulin concentrations were slightly altered in ISS-flown mice. Overall, our data suggest that spaceflight might disturb the homeostatic gene expression of the spleen through a combination of microgravity and other environmental changes

Integrating Statistical Predictions and Experimental Verifications for Enhancing Protein-Chemical Interaction Predictions in Virtual Screening

Predictions of interactions between target proteins and potential leads are of great benefit in the drug discovery process. We present a comprehensively applicable statistical prediction method for interactions between any proteins and chemical compounds, which requires only protein sequence data and chemical structure data and utilizes the statistical learning method of support vector machines. In order to realize reasonable comprehensive predictions which can involve many false positives, we propose two approaches for reduction of false positives: (i) efficient use of multiple statistical prediction models in the framework of two-layer SVM and (ii) reasonable design of the negative data to construct statistical prediction models. In two-layer SVM, outputs produced by the first-layer SVM models, which are constructed with different negative samples and reflect different aspects of classifications, are utilized as inputs to the second-layer SVM. In order to design negative data which produce fewer false positive predictions, we iteratively construct SVM models or classification boundaries from positive and tentative negative samples and select additional negative sample candidates according to pre-determined rules. Moreover, in order to fully utilize the advantages of statistical learning methods, we propose a strategy to effectively feedback experimental results to computational predictions with consideration of biological effects of interest. We show the usefulness of our approach in predicting potential ligands binding to human androgen receptors from more than 19 million chemical compounds and verifying these predictions by in vitro binding. Moreover, we utilize this experimental validation as feedback to enhance subsequent computational predictions, and experimentally validate these predictions again. This efficient procedure of the iteration of the in silico prediction and in vitro or in vivo experimental verifications with the sufficient feedback enabled us to identify novel ligand candidates which were distant from known ligands in the chemical space

Application of GIS and Machine Learning to Predict Flood Areas in Nigeria

Floods are one of the most devastating forces in nature. Several approaches for identifying flood-prone locations have been developed to reduce the overall harmful impacts on humans and the environment. However, due to the increased frequency of flooding and related disasters, coupled with the continuous changes in natural and social-economic conditions, it has become vital to predict areas with the highest probability of flooding to ensure effective measures to mitigate impending disasters. This study predicted the flood susceptible areas in Nigeria based on historical flood records from 1985~2020 and various conditioning factors. To evaluate the link between flood incidence and the fifteen (15) explanatory variables, which include climatic, topographic, land use and proximity information, the artificial neural network (ANN) and logistic regression (LR) models were trained and tested to develop a flood susceptibility map. The receiver operating characteristic curve (ROC) and area under the curve (AUC) were used to evaluate both model accuracies. The results show that both techniques can model and predict flood-prone areas. However, the ANN model produced a higher performance and prediction rate than the LR model, 76.4% and 62.5%, respectively. In addition, both models highlighted that those areas with the highest susceptibility to flood are the low-lying regions in the southern extremities and around water areas. From the study, we can establish that machine learning techniques can effectively map and predict flood-prone areas and serve as a tool for developing flood mitigation policies and plans

Real and illusionary difficulties in conceptual learning in mathematics: comparison between constructivist and inferentialist perspectives

Due to the learning paradox, students cannot have real difficulty in understanding a mathematical concept that they have not yet understood. There is a gap between real difficulties, directly experienced by students, and illusionary ones, only observed by researchers. This paper aims to offer a critical reflection on our understanding of the term difficulty in mathematics education research. We start this paper by arguing that a constructivist perspective, which has often been adopted in researches on mathematical task design, can deal with difficulties in solving a mathematical problem, but it cannot theoretically deal with those in understanding a mathematical concept. Therefore, we need the alternative philosophy of Robert Brandom’s inferentialism to capture students’ real difficulties in conceptual learning. From an inferentialist perspective, we introduce the idea of illusionary and real difficulties. The former is defined as what students cannot do, but they are not conscious of what they should do, while the latter is defined as what students cannot do despite their consciousness of what they should do. Through an eighth grade classroom episode, we argue that it is important in mathematics education research to focus not only on illusionary difficulties but also on the transition from illusionary to real difficulties. Researchers are encouraged to design a learning environment in which students become conscious of what they cannot do and to observe their mathematics learning in such an environment.The current paper is based on the first two authors’ poster presentation at the third research meeting of the Japan Society for Science Education in 2018. An earlier version of this manuscript for the poster presentation appeared in JSSE Research Report, 33(3), 161–166, and is retrievable from https:// doi. org/ 10. 14935/ jsser. 33.3_ 161, though it is only available in Japanese