Gelsolin inhibits malignant phenotype of glioblastoma and is regulated by miR-654-5p and miR-450b-5p

13301甲第5149号博士（医学）金沢大学博士論文要旨Abstract 以下に掲載：Cancer Science 111(7) pp.2413-2422 2020. Wiley. 共著者：Jiakang Zhang, Takuya Furuta, Hemragul Sabit, Sho Tamai, Shabierjiang Jiapaer, Yu Dong, Masashi Kinoshita, Yasuo Uchida, Sumio Ohtsuki, Tetsuya Terasaki, Shiguang Zhao, Mitsutoshi Nakad

Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection

Current 3D object detection models follow a single dataset-specific training and testing paradigm, which often faces a serious detection accuracy drop when they are directly deployed in another dataset. In this paper, we study the task of training a unified 3D detector from multiple datasets. We observe that this appears to be a challenging task, which is mainly due to that these datasets present substantial data-level differences and taxonomy-level variations caused by different LiDAR types and data acquisition standards. Inspired by such observation, we present a Uni3D which leverages a simple data-level correction operation and a designed semantic-level coupling-and-recoupling module to alleviate the unavoidable data-level and taxonomy-level differences, respectively. Our method is simple and easily combined with many 3D object detection baselines such as PV-RCNN and Voxel-RCNN, enabling them to effectively learn from multiple off-the-shelf 3D datasets to obtain more discriminative and generalizable representations. Experiments are conducted on many dataset consolidation settings including Waymo-nuScenes, nuScenes-KITTI, Waymo-KITTI, and Waymo-nuScenes-KITTI consolidations. Their results demonstrate that Uni3D exceeds a series of individual detectors trained on a single dataset, with a 1.04x parameter increase over a selected baseline detector. We expect this work will inspire the research of 3D generalization since it will push the limits of perceptual performance.Comment: Accepted by CVPR-2023, and our code is available at https://github.com/PJLab-ADG/3DTran

AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset

It is a long-term vision for Autonomous Driving (AD) community that the perception models can learn from a large-scale point cloud dataset, to obtain unified representations that can achieve promising results on different tasks or benchmarks. Previous works mainly focus on the self-supervised pre-training pipeline, meaning that they perform the pre-training and fine-tuning on the same benchmark, which is difficult to attain the performance scalability and cross-dataset application for the pre-training checkpoint. In this paper, for the first time, we are committed to building a large-scale pre-training point-cloud dataset with diverse data distribution, and meanwhile learning generalizable representations from such a diverse pre-training dataset. We formulate the point-cloud pre-training task as a semi-supervised problem, which leverages the few-shot labeled and massive unlabeled point-cloud data to generate the unified backbone representations that can be directly applied to many baseline models and benchmarks, decoupling the AD-related pre-training process and downstream fine-tuning task. During the period of backbone pre-training, by enhancing the scene- and instance-level distribution diversity and exploiting the backbone's ability to learn from unknown instances, we achieve significant performance gains on a series of downstream perception benchmarks including Waymo, nuScenes, and KITTI, under different baseline models like PV-RCNN++, SECOND, CenterPoint.Comment: Code is available at: https://github.com/PJLab-ADG/3DTran

Mobile Cell-Free Massive MIMO: Challenges, Solutions, and Future Directions

Cell-free (CF) massive multiple-input multiple-output (MIMO) systems, which exploit many geographically distributed access points to coherently serve user equipments via spatial multiplexing on the same time-frequency resource, has become a vital component of the next-generation mobile communication networks. Theoretically, CF massive MIMO systems have many advantages, such as large capacity, great coverage, and high reliability, but several obstacles must be overcome. In this article, we study the paradigm of CF massive MIMO-aided mobile communications, including the main application scenarios and associated deployment architectures. Furthermore, we thoroughly investigate the challenges of CF massive MIMO-aided mobile communications. We then exploit a novel predictor antenna, hierarchical cancellation, rate-splitting and dynamic clustering system for CF massive MIMO. Finally, several important research directions regarding CF massive MIMO for mobile communications are presented to facilitate further investigation.Comment: 9 pages, 4 figures, 2 tables, accepted by IEEE Wireless Communications Magazin

Flexible-Position MIMO for Wireless Communications: Fundamentals, Challenges, and Future Directions

The flexible-position multiple-input multiple-output (FLP-MIMO), such as fluid antennas and movable antennas, is a promising technology for future wireless communications. This is due to the fact that the positions of antennas at the transceiver and reflector can be dynamically optimized to achieve better channel conditions and, as such, can provide high spectral efficiency (SE) and energy efficiency (EE) gains with fewer antennas. In this article, we introduce the fundamentals of FLP-MIMO systems, including hardware design, structure design, and potential applications. We shall demonstrate that FLP-MIMO, using fewer flexible antennas, can match the channel hardening achieved by a large number of fixed antennas. We will then analyze the SE-EE relationship for FLP-MIMO and fixed-position MIMO. Furthermore, we will design the optimal trajectory of flexible antennas to maximize system sum SE or total EE at a fixed travel distance of each antenna. Finally, several important research directions regarding FLP-MIMO communications are presented to facilitate further investigation.Comment: 10 pages, 5 figures, 1 tables, accepted by IEEE Wireless Communications Magazin

UniDA3D: Unified Domain Adaptive 3D Semantic Segmentation Pipeline

State-of-the-art 3D semantic segmentation models are trained on off-the-shelf public benchmarks, but they will inevitably face the challenge of recognition accuracy drop when these well-trained models are deployed to a new domain. In this paper, we introduce a Unified Domain Adaptive 3D semantic segmentation pipeline (UniDA3D) to enhance the weak generalization ability, and bridge the point distribution gap between domains. Different from previous studies that only focus on a single adaptation task, UniDA3D can tackle several adaptation tasks in 3D segmentation field, by designing a unified source-and-target active sampling strategy, which selects a maximally-informative subset from both source and target domains for effective model adaptation. Besides, benefiting from the rise of multi-modal 2D-3D datasets, UniDA3D investigates the possibility of achieving a multi-modal sampling strategy, by developing a cross-modality feature interaction module that can extract a representative pair of image and point features to achieve a bi-directional image-point feature interaction for safe model adaptation. Experimentally, UniDA3D is verified to be effective in many adaptation tasks including: 1) unsupervised domain adaptation, 2) unsupervised few-shot domain adaptation; 3) active domain adaptation. Their results demonstrate that, by easily coupling UniDA3D with off-the-shelf 3D segmentation baselines, domain generalization ability of these baselines can be enhanced

Soil microorganisms and methane emissions in response to short-term warming field incubation in Svalbard

IntroductionGlobal warming is caused by greenhouse gases (GHGs). It has been found that the release of methane (CH4) from Arctic permafrost, soil, ocean, and sediment is closely related to microbial composition and soil factors resulting from warming over several months or years. However, it is unclear for how long continuous warming due to global warming affects the microbial composition and GHG release from soils along Arctic glacial meltwater rivers.MethodsIn this study, the soil upstream of the glacial meltwater river (GR) and the estuary (GR-0) in Svalbard, with strong soil heterogeneity, was subjected to short-term field incubation at 2°C (in situ temperature), 10°C, and 20°C. The incubation was carried out under anoxic conditions and lasted for few days. Bacterial composition and CH4 production potential were determined based on high-throughput sequencing and physiochemical property measurements.ResultsOur results showed no significant differences in bacterial 16S rRNA gene copy number, bacterial composition, and methanogenic potential, as measured by mcrA gene copy number and CH4 concentration, during a 7- and 13-day warming field incubation with increasing temperatures, respectively. The CH4 concentration at the GR site was higher than that at the GR-0 site, while the mcrA gene was lower at the GR site than that at the GR-0 site.DiscussionBased on the warming field incubation, our results indicate that short-term warming, which is measured in days, affects soil microbial composition and CH4 concentration less than the spatial scale, highlighting the importance of warming time in influencing CH4 release from soil. In summary, our research implied that microbial composition and CH4 emissions in soil warming do not increase in the first several days, but site specificity is more important. However, emissions will gradually increase first and then decrease as warming time increases over the long term. These results are important for understanding and exploring the GHG emission fluxes of high-latitude ecosystems under global warming

Investigation of acousto-optic interaction with momentum mismatching considering acoustic anisotropy in birefringent crystal

In this work, the momentum mismatching based on which the acousto-optic (AO) transfer function and diffraction efficiency was acquired, was calculated considering the properties of AO crystals in AO interactions in acousto-optic tunable filter (AOTF). Transfer functions were obtained using a 4f optical system combined with AOTF and compared with theoretical calculations. It demonstrated the influence of acoustic energy shift on the AO interaction which should be considered in the design of AOTF