The inter‐annual variability of southerly low‐level jets in North America

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135612/1/joc4708_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135612/2/joc4708.pd

Biological cohesion as the architect of bed movement under wave action

Funding for this project was provided by the National Key Research and Development Project, MOST, China (2018YFC0407506), the National Natural Science Foundation of China (51620105005), and the China Postdoctoral Science Foundation (2020M680580). D. M. Paterson acknowledges NERC funding (NE/N016009/1).Cohesive extracellular polymeric substances (EPS) generated by microorganisms abundant on Earth are regarded as bed “stabilizers” increasing the erosion threshold in sedimentary systems. However, most observations of this phenomenon have been taken under steady flow conditions. In contrast, we present how EPS affect the bed movement under wave action, showing a destabilization of the system. We demonstrate a complex behavior of the bio‐sedimentary deposits, which encompasses liquefaction, mass motion, varying bed formations and erosion, depending on the amount of EPS present. Small quantities of EPS induce higher mobility of the sediments, liquefying an otherwise stable bed. Bed with larger quantities of EPS undergoes a synchronized mechanical oscillation. Our analysis clarifies how biological cohesion can potentially put coastal wetlands at risk by increasing their vulnerability to waves. These findings lead to a revised understanding of the different roles played by microbial life, and their importance as mediators of seabed mobility.Publisher PDFPeer reviewe

C1GalT1 expression reciprocally controls tumour cell-cell and tumour-macrophage interactions mediated by galectin-3 and MGL with double impact on cancer development and progression.

Although most cell membrane proteins are modified by glycosylation, our understanding of the role and actions of protein glycosylation is still very limited. β1,3galactosyltransferase (C1GalT1) is a key glycosyltransferase that controls the biosynthesis of the Core 1 structure of O-linked mucin type glycans and is overexpressed by many common types of epithelial cancers. This study reports that suppression of C1GalT1 expression in human colon cancer cells caused substantial changes of protein glycosylation of cell membrane proteins, many of which were ligands of the galactoside-binding galectin-3 and the macrophage galactose-type lectin (MGL). This led to significant reduction of cancer cell proliferation, adhesion, migration and the ability of tumour cells to form colonies. Crucially, C1GalT1 suppression significantly reduced galectin-3-mediated tumour cell-cell interaction and galectin-3-promoted tumour cell activities. In the meantime, C1GalT1 suppression substantially increased MGL-mediated macrophage-tumour cell interaction and macrophage-tumour cell phagocytosis and cytokine secretion. C1GalT1-expressing cancer cells implanted in chick embryos resulted in the formation of significantly bigger tumours than C1GalT1-suppressed cells and the presence of galectin-3 increased tumour growth of C1GalT1-expressing but not C1GalT1-suppressed cells. More MGL-expressing macrophages and dendritic cells were seen to be attracted to the tumour microenvironment in ME C1galt1-/-/Erb mice than in C1galt1f/f /Erb mice. These results indicate that expression of C1GalT1 by tumour cells reciprocally controls tumour cell-cell and tumour-macrophage interactions mediated by galectin-3 and MGL with double impact on cancer development and progression. C1GalT1 overexpression in epithelial cancers therefore may represent a fundamental mechanism in cancer promotion and in reduction of immune response/surveillance in cancer progression

FetusMapV2: Enhanced Fetal Pose Estimation in 3D Ultrasound

Fetal pose estimation in 3D ultrasound (US) involves identifying a set of associated fetal anatomical landmarks. Its primary objective is to provide comprehensive information about the fetus through landmark connections, thus benefiting various critical applications, such as biometric measurements, plane localization, and fetal movement monitoring. However, accurately estimating the 3D fetal pose in US volume has several challenges, including poor image quality, limited GPU memory for tackling high dimensional data, symmetrical or ambiguous anatomical structures, and considerable variations in fetal poses. In this study, we propose a novel 3D fetal pose estimation framework (called FetusMapV2) to overcome the above challenges. Our contribution is three-fold. First, we propose a heuristic scheme that explores the complementary network structure-unconstrained and activation-unreserved GPU memory management approaches, which can enlarge the input image resolution for better results under limited GPU memory. Second, we design a novel Pair Loss to mitigate confusion caused by symmetrical and similar anatomical structures. It separates the hidden classification task from the landmark localization task and thus progressively eases model learning. Last, we propose a shape priors-based self-supervised learning by selecting the relatively stable landmarks to refine the pose online. Extensive experiments and diverse applications on a large-scale fetal US dataset including 1000 volumes with 22 landmarks per volume demonstrate that our method outperforms other strong competitors.Comment: 16 pages, 11 figures, accepted by Medical Image Analysis(2023

Segment Anything Model for Medical Images?

The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It designed a novel promotable segmentation task, ensuring zero-shot image segmentation using the pre-trained model via two main modes including automatic everything and manual prompt. SAM has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging due to the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. SAM has achieved impressive results on various natural image segmentation tasks. Meanwhile, zero-shot and efficient MIS can well reduce the annotation time and boost the development of medical image analysis. Hence, SAM seems to be a potential tool and its performance on large medical datasets should be further validated. We collected and sorted 52 open-source datasets, and build a large medical segmentation dataset with 16 modalities, 68 objects, and 553K slices. We conducted a comprehensive analysis of different SAM testing strategies on the so-called COSMOS 553K dataset. Extensive experiments validate that SAM performs better with manual hints like points and boxes for object perception in medical images, leading to better performance in prompt mode compared to everything mode. Additionally, SAM shows remarkable performance in some specific objects and modalities, but is imperfect or even totally fails in other situations. Finally, we analyze the influence of different factors (e.g., the Fourier-based boundary complexity and size of the segmented objects) on SAM's segmentation performance. Extensive experiments validate that SAM's zero-shot segmentation capability is not sufficient to ensure its direct application to the MIS.Comment: 23 pages, 14 figures, 12 table

The Chinese Open Science Network (COSN): Building an Open Science Community From Scratch

Open Science is becoming a mainstream scientific ideology in psychology and related fields. However, researchers, especially early-career researchers (ECRs) in developing countries, are facing significant hurdles in engaging in Open Science and moving it forward. In China, various societal and cultural factors discourage ECRs from participating in Open Science, such as the lack of dedicated communication channels and the norm of modesty. To make the voice of Open Science heard by Chinese-speaking ECRs and scholars at large, the Chinese Open Science Network (COSN) was initiated in 2016. With its core values being grassroots-oriented, diversity, and inclusivity, COSN has grown from a small Open Science interest group to a recognized network both in the Chinese-speaking research community and the international Open Science community. So far, COSN has organized three in-person workshops, 12 tutorials, 48 talks, and 55 journal club sessions and translated 15 Open Science-related articles and blogs from English to Chinese. Currently, the main social media account of COSN (i.e., the WeChat Official Account) has more than 23,000 subscribers, and more than 1,000 researchers/students actively participate in the discussions on Open Science. In this article, we share our experience in building such a network to encourage ECRs in developing countries to start their own Open Science initiatives and engage in the global Open Science movement. We foresee great collaborative efforts of COSN together with all other local and international networks to further accelerate the Open Science movement

Functional nanomaterials via colloidal assisted fabrication

Spherical colloidal nanoparticles have been popular building blocks for larger scale super structures due to their easy synthesis, unique while simple shape and relatively well understood interactions. Despite concentrated and lasting efforts in the scientific community to explore design phases of colloidal super structures and their applications, these little spheres continue to inspire the imaginations of people and creates new opportunities. In this document, a few research topics related to colloidal particles are presented. Their applications range from narrow band thermal emitters, extremely sensitive surface plasmon sensors to nanoparticles with unique geometries that might have important biological applications. Other than the scientific discoveries which will be described in details in the following chapters, the main purpose, I believe, is to fuel the inspirations of the readers. And I have the faith that the best is yet to come with these little spheres