Striatal dopamine D1-type receptor availability: no difference from control but association with cortical thickness in methamphetamine users.

Chronic methamphetamine use poses potentially devastating consequences for directly affected individuals and for society. Lower dopamine D2-type receptor availability has been observed in striata of methamphetamine users as compared with controls, but an analogous comparison of D1-type receptors has been conducted only on post-mortem material, with no differences in methamphetamine users from controls in the caudate nucleus and putamen and higher D1-receptor density in the nucleus accumbens. Released from neurons when methamphetamine is self-administered, dopamine binds to both D1- and D2-type receptors in the striatum, with downstream effects on cortical activity. Thus, both receptor subtypes may contribute to methamphetamine-induced alterations in cortical morphology and behavior. In this study, 21 methamphetamine-dependent subjects and 23 healthy controls participated in positron emission tomography and structural magnetic resonance imaging for assessment of striatal D1- and D2-type receptor availability and cortical gray-matter thickness, respectively. Although D2-type receptor availability (BPnd) was lower in the methamphetamine group, as shown previously, the groups did not differ in D1-type BPnd. In the methamphetamine group, mean cortical gray-matter thickness was negatively associated with cumulative methamphetamine use and craving for the drug. Striatal D1-type but not D2-type BPnd was negatively associated with global mean cortical gray-matter thickness in the methamphetamine group, but no association was found between gray-matter thickness and BPnd for either dopamine receptor subtype in the control group. These results suggest a role of striatal D1-type receptors in cortical adaptation to chronic methamphetamine use

Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge

In this paper we summarize the contributions of participants to the Sussex-Huawei Transportation-Locomotion (SHL) Recognition Challenge organized at the HASCA Workshop of UbiComp 2018. The SHL challenge is a machine learning and data science competition, which aims to recognize eight transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the inertial and pressure sensor data of a smartphone. We introduce the dataset used in the challenge and the protocol for the competition. We present a meta-analysis of the contributions from 19 submissions, their approaches, the software tools used, computational cost and the achieved results. Overall, two entries achieved F1 scores above 90%, eight with F1 scores between 80% and 90%, and nine between 50% and 80%

Optical Spectropolarimetry of SN 2002ap: High Velocity Asymmetric Explosion

We present spectropolarimetry of the Type Ic supernova SN 2002ap and give a preliminary analysis: the data were taken at two epochs, close to and one month later than the visual maximum (2002 February 8). In addition we present June 9 spectropolarimetry without analysis. The data show the development of linear polarization. Distinct polarization profiles were seen only in the O I \lambda 7773 multiplet/Ca II IR triplet absorption trough at maximum light and in the Ca II IR triplet absorption trough a month later, with the latter showing a peak polarization as high as ~2 %. The intrinsic polarization shows three clear position angles: 80 degs for the February continuum, 120 degs for the February line feature, and 150 degs for the March data. We conclude that there are multiple asymmetric components in the ejecta. We suggest that the supernova has a bulk asymmetry with an axial ratio projected on the sky that is different from 1 by of order 10 %. Furthermore, we suggest very speculatively that a high velocity ejecta component moving faster than ~0.115c (e.g., a jet) contributes to polarization in the February epoch.Comment: 7 pages, 3 figures, accepted for publication in the Astrophysical Journal (Letters

MITSuME--Multicolor Imaging Telescopes for Survey and Monstrous Explosions

Development of MITSuME is reported. Two 50-cm optical telescopes have been built at Akeno in Yamanashi prefecture and at Okayama Astrophysical Observatory (OAO) in Okayama prefecture. Three CCD cameras for simultaneous g'RcIc photometry are to be mounted on each focal plane, covering a wide FOV of about 30" x 30". The limiting magnitude at V is fainter than 18. In addition to these two optical telescopes, a 91-cm IR telescope with a 1 deg x 1 deg field of view is being built at OAO, which performs photometry in YJHK bands. These robotic telescopes can start the observation of counterparts of a GRB within a minute from an alert. We aim to obtain photometric redshifts exceeding 10 with these telescopes. The performance and the current construction status of the telescopes are presented.Comment: 4 pages, 3 figures, 4th Workshop on Gamma-Ray Burst in the Afterglow Era, Roma, October 18-22, 200

Efficacy of lamivudine for preventing hepatocellular carcinoma in chronic hepatitis B: A multicenter retrospective study of 2795 patients

ArticleHEPATOLOGY RESEARCH. 32(3): 173-184 (2005)journal articl

Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge 2019

In this paper we summarize the contributions of participants to the third Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge organized at the HASCAWorkshop of UbiComp/ISWC 2020. The goal of this machine learning/data science challenge is to recognize eight locomotion and transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the inertial sensor data of a smartphone in a user-independent manner with an unknown target phone position. The training data of a “train” user is available from smartphones placed at four body positions (Hand, Torso, Bag and Hips). The testing data originates from “test” users with a smartphone placed at one, but unknown, body position. We introduce the dataset used in the challenge and the protocol of the competition. We present a meta-analysis of the contributions from 15 submissions, their approaches, the software tools used, computational cost and the achieved results. Overall, one submission achieved F1 scores above 80%, three with F1 scores between 70% and 80%, seven between 50% and 70%, and four below 50%, with a latency of maximum of 5 seconds

Summary of the Sussex-Huawei locomotion-transportation recognition challenge 2020

In this paper we summarize the contributions of participants to the third Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge organized at the HASCAWorkshop of UbiComp/ISWC 2020. The goal of this machine learning/data science challenge is to recognize eight locomotion and transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the inertial sensor data of a smartphone in a user-independent manner with an unknown target phone position. The training data of a “train” user is available from smartphones placed at four body positions (Hand, Torso, Bag and Hips). The testing data originates from “test” users with a smartphone placed at one, but unknown, body position. We introduce the dataset used in the challenge and the protocol of the competition. We present a meta-analysis of the contributions from 15 submissions, their approaches, the software tools used, computational cost and the achieved results. Overall, one submission achieved F1 scores above 80%, three with F1 scores between 70% and 80%, seven between 50% and 70%, and four below 50%, with a latency of maximum of 5 seconds