Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes

Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development. However, this task is challenging due to the use of thick-slice scans in clinically-acquired fetal brain data. To address this issue, we propose to leverage high-quality isotropic fetal brain MR volumes (and also their corresponding annotations) as guidance for segmentation of thick-slice scans. Due to existence of significant domain gap between high-quality isotropic volume (i.e., source data) and thick-slice scans (i.e., target data), we employ a domain adaptation technique to achieve the associated knowledge transfer (from high-quality volumes to thick-slice scans). Specifically, we first register the available high-quality isotropic fetal brain MR volumes across different gestational weeks to construct longitudinally-complete source data. To capture domain-invariant information, we then perform Fourier decomposition to extract image content and style codes. Finally, we propose a novel Cycle-Consistent Domain Adaptation Network (C2DA-Net) to efficiently transfer the knowledge learned from high-quality isotropic volumes for accurate tissue segmentation of thick-slice scans. Our C2DA-Net can fully utilize a small set of annotated isotropic volumes to guide tissue segmentation on unannotated thick-slice scans. Extensive experiments on a large-scale dataset of 372 clinically acquired thick-slice MR scans demonstrate that our C2DA-Net achieves much better performance than cutting-edge methods quantitatively and qualitatively.Comment: 10 pages, 9 figures, 5 tables, Fetal MRI, Brain tissue segmentation, Unsupervised domain adaptation, Cycle-consistenc

Precision Medicine: Role of Biomarkers in Early Prediction and Diagnosis of Alzheimer’s Disease

Alzheimer’s disease (AD), the most common form of dementia in the aged people, is a chronic and irreversible neurodegenerative disorder. Early prediction, intervention, and objective diagnosis are very critical in AD. In this chapter, we will introduce the current progress in the prediction and diagnosis of AD, including recent development in diagnostic criteria, genetic testing, neuroimaging techniques, and neurochemical assays. Focus will be on some new applied methods with more specific examples, that is, cerebrospinal fluid (CSF) and blood proteins and peptides, which might serve as biomarkers for the diagnosis of AD. We will also discuss biomarker-based diagnostic strategies and their practical application

Gentopia: A Collaborative Platform for Tool-Augmented LLMs

Augmented Language Models (ALMs) empower large language models with the ability to use tools, transforming them into intelligent agents for real-world interactions. However, most existing frameworks for ALMs, to varying degrees, are deficient in the following critical features: flexible customization, collaborative democratization, and holistic evaluation. We present gentopia, an ALM framework enabling flexible customization of agents through simple configurations, seamlessly integrating various language models, task formats, prompting modules, and plugins into a unified paradigm. Furthermore, we establish gentpool, a public platform enabling the registration and sharing of user-customized agents. Agents registered in gentpool are composable such that they can be assembled together for agent collaboration, advancing the democratization of artificial intelligence. To ensure high-quality agents, gentbench, an integral component of gentpool, is designed to thoroughly evaluate user-customized agents across diverse aspects such as safety, robustness, efficiency, etc. We release gentopia on Github and will continuously move forward

Progressive Research in the Molecular Mechanisms of Chronic Fluorosis

Long-term excessive intake of fluoride (F) leads to chronic fluorosis, resulting in dental fluorosis and skeletal fluorosis. Chronic exposure to high doses of fluoride can also cause damage to soft tissues, especially when it passes through the blood-brain, blood-testis, and blood-placenta barrier, causing damage to the corresponding tissues. Fluorosis has become a public health problem in some countries or regions around the world. Understanding the pathogenesis of fluorosis is very important. Although the exact mechanism of fluorosis has not been fully elucidated, various mechanisms of fluoride-induced toxicity have been proposed. In this chapter, we will introduce the research progress of the mechanism of fluorosis, focusing on dental fluorosis, skeletal fluorosis, nervous and reproductive system toxicity, and influential factors related to fluoride toxicity (i.e., genetic background, co-exposure with other element). In addition, the application of proteomics and metabolomics in the study of the pathogenesis of fluorosis is also introduced. Currently, there is still no specific treatment for fluorosis. However, since fluorosis is caused by excessive intake of fluoride, avoiding excessive fluoride intake is the critical measure to prevent the disease. In endemic regions, health education and supplement diet with vitamins C, D and E, and calcium and antioxidant compounds are important

StyHighNet: Semi-Supervised Learning Height Estimation from a Single Aerial Image via Unified Style Transferring

Recovering height information from a single aerial image is a key problem in the fields of computer vision and remote sensing. At present, supervised learning methods have achieved impressive results, but, due to domain bias, the trained model cannot be directly applied to a new scene. In this paper, we propose a novel semi-supervised framework, StyHighNet, for accurately estimating the height of a single aerial image in a new city that requires only a small number of labeled data. The core is to transfer multi-source images to a unified style, making the unlabeled data provide the appearance distribution as additional supervision signals. The framework mainly contains three sub-networks: (1) the style transferring sub-network maps multi-source images into unified style distribution maps (USDMs); (2) the height regression sub-network, with the function of predicting the height maps from USDMs; and (3) the style discrimination sub-network, used to distinguish the sources of USDMs. Among them, the style transferring sub-network shoulders dual responsibilities: On the one hand, it needs to compute USDMs with obvious characteristics, so that the height regression sub-network can accurately estimate the height maps. On the other hand, it is necessary that the USDMs have consistent distribution to confuse the style discrimination sub-network, so as to achieve the goal of domain adaptation. Unlike previous methods, our style distribution function is learned unsupervised, thus it is of greater flexibility and better accuracy. Furthermore, when the style discrimination sub-network is shielded, this framework can also be used for supervised learning. We performed qualitatively and quantitative evaluations on two sets of public data, Vaihingen and Potsdam. Experiments show that the framework achieved superior performance in both supervised and semi-supervised learning modes

The China Digital Museum Project

The China Digital Museum Project is a collaborative project between the Chinese Ministry of Education, Hewlett-Packard Company and several Chinese universities, including Beijing Normal University and Beihang University. Many universities in China have one or more museums. In order to improve access to the artefacts in these museums, these universities are undertaking the digitisation of those artefacts. The principal aim of this project is to provide these universities with infrastructure based on DSpace to store, manage, preserve and disseminate the digitised versions of the artefacts. In the final phase of the project, there will be around 100 university museums with digital artefacts stored in federated DSpace installations. In fulfilling this requirement, we need to address many problems associated with managing distributed digital asset management, including persistent identifiers, metadata standardisation, deployment processes and management, and content and metadata replication. Another challenge that this project faces is how to consolidate the very diverse digital assets each university museum manages so that users can navigate the entire collection by subject. Hence we aim to create "virtual museums", formed by arrangements of digital assets by subject, regardless of physical location. In this presentation we will describe the project and our current progress

The Role of Protected Areas in Mitigating Vegetation Disturbances on the Qinghai-Tibetan Plateau

Long-term vegetation dynamics with satellite observations can provide valuable insights into natural variation in ecosystems and quantify disturbances associated with external pressures. Monitoring vegetation dynamics within protected areas (PAs) is essential, given their crucial role in protecting biodiversity and maintaining ecosystem integrity. In this study, using the normalized difference vegetation index (NDVI) and Breaks For Additive Seasonal and Trend (BFAST)model, we detected vegetation dynamics especially abrupt changes inside nature reserves (NRs, the primary type of PAs) on the Qinghai-Tibetan Plateau from 2000 to 2020. We then applied the matching approach and postmatching regression to evaluate the effect of NRs on natural vegetation with average NDVI, NDVI slope, and the number of abrupt changes. Our results showed that 78.97% of the vegetation within NRs exhibited greening trends. In addition, 29.15% of the area inside of the NRs experienced 1 or more abrupt changes, with the major change type interrupted greening (15.96%), followed by greening to browning (6.27%) and browning to greening (4.00%). The NRs significantly reduced the frequency of disturbances, and older NRs also showed a higher value of average NDVI compared to those in matched unprotected areas. Postregression models indicated that vegetation in newer NRs tended to be more vulnerable to disturbances and stricter NR management could benefit vegetation enhancement. Our analysis offers a new approach to vegetation dynamic monitoring that considers short-term disturbances. The findings of this work can help better understand effectiveness of PAs on ecosystem protection and offer practical guidance to future PAs management