Source attribution of poly- and perfluoroalkyl substances (PFASs) in surface waters from Rhode Island and the New York Metropolitan Area

Exposure to poly- and perfluoroalkyl substances (PFASs) has been associated with adverse health effects in humans and wildlife. Understanding pollution sources is essential for environmental regulation, but source attribution for PFASs has been confounded by limited information about industrial releases and rapid changes in chemical production. Here we use principal component analysis (PCA), hierarchical clustering, and geospatial analysis to understand source contributions to 14 PFASs measured across 37 sites in the northeastern United States in 2014. PFASs are significantly elevated in urban areas compared to rural sites except for perfluorobutanesulfonate, N-methyl perfluorooctanesulfonamidoacetic acid, perfluoroundecanate, and perfluorododecanate. The highest PFAS concentrations across sites were those of perfluorooctanate (PFOA, 56 ng L−1) and perfluorohexanesulfonate (PFHxS, 43 ng L−1), and perfluorooctanesulfonate (PFOS) levels are lower than earlier measurements of U.S. surface waters. PCA and cluster analysis indicate three main statistical groupings of PFASs. Geospatial analysis of watersheds reveals the first component/cluster originates from a mixture of contemporary point sources such as airports and textile mills. Atmospheric sources from the waste sector are consistent with the second component, and the metal smelting industry plausibly explains the third component. We find this source-attribution technique is effective for better understanding PFAS sources in urban areas

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

A voice recognition-based digital cognitive screener for dementia detection in the community: Development and validation study

IntroductionTo facilitate community-based dementia screening, we developed a voice recognition-based digital cognitive screener (digital cognitive screener, DCS). This proof-of-concept study aimed to investigate the reliability, validity as well as the feasibility of the DCS among community-dwelling older adults in China.MethodsEligible participants completed demographic, clinical, and the DCS. Diagnosis of mild cognitive impairment (MCI) and dementia was made based on the Montreal Cognitive Assessment (MoCA) (MCI: MoCA < 23, dementia: MoCA < 14). Time and venue for test administration were recorded and reported. Internal consistency, test-retest reliability and inter-rater reliability were examined. Receiver operating characteristic (ROC) analyses were conducted to examine the discriminate validity of the DCS in detecting MCI and dementia.ResultsA total of 103 participants completed all investigations and were included in the analysis. Administration time of the DCS was between 5.1–7.3 min. No significant difference (p > 0.05) in test scores or administration time was found between 2 assessment settings (polyclinic or community center). The DCS showed good internal consistency (Cronbach’s alpha = 0.73), test-retest reliability (Pearson r = 0.69, p < 0.001) and inter-rater reliability (ICC = 0.84). Area under the curves (AUCs) of the DCS were 0.95 (0.90, 0.99) and 0.77 (0.67, 086) for dementia and MCI detection, respectively. At the optimal cut-off (7/8), the DCS showed excellent sensitivity (100%) and good specificity (80%) for dementia detection.ConclusionThe DCS is a feasible, reliable and valid digital dementia screening tool for older adults. The applicability of the DCS in a larger-scale community-based screening stratified by age and education levels warrants further investigation

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

Detection of Poly- and Perfluoroalkyl Substances (PFASs) in U.S. Drinking Water

Spatial analysis of relationship between point sources in watershed and the national occurence data of PFASs in drinking wate

Sodium butyrate reduces ammonia production in the cecum of laying hens by regulating ammonia-producing bacteria

ABSTRACT: Sodium butyrate is a commonly used feed additive and can reduce ammonia (NH3) emissions from laying hens, but the mechanism of this effect is unknown. In this study, the sodium butyrate and cecal content of Lohmann pink laying hens were measured, and in vitro fermentation experiments and NH3-producing bacteria coculture experiments were carried out to explore the relationship between NH3 emissions and its associated microbiota metabolism. Sodium butyrate was found to significantly reduce NH3 emission from the cecal microbial fermentation of Lohmann pink laying hens (P < 0.05). The concentration of NO3−-N in the fermentation broth of the sodium butyrate-supplemented group increased significantly, and the concentration of NH4+-N decreased significantly (P < 0.05). Moreover, sodium butyrate significantly reduced the abundance of harmful bacteria and increased the abundance of beneficial bacteria in the cecum. The culturable NH3-producing bacteria consisted mainly of Escherichia and Shigella, such as Escherichia fergusonii, Escherichia marmotae and Shigella flexnerii. Among them, E. fergusonii had the highest potential for NH3 production. The coculture experiment showed that sodium butyrate can significantly downregulate the expression of the lpdA, sdaA, gcvP, gcvH and gcvT genes of E. fergusonii (P < 0.05), thus reducing the NH3 emission produced by the bacteria during metabolism. In general, sodium butyrate regulated NH3-producing bacteria to reduce NH3 production in the cecum of laying hens. These results are of great significance for NH3 emission reduction in the layer breeding industry and for future research

Hydrolysis-Induced Morphology Evolution of Linear and Bottlebrush Block Copolymers in Thin Films with Acid Vapor or Photoacid Generators

The self-assembly of high-χ low-N block copolymers (BCPs) can give patterns with sub-10 nm full pitch, serving as a promising alternative to photolithographic methods. In this work, we synthesized poly(solketal methacrylate)-block-polystyrene copolymers, PSM-b-PS, with various volume ratios of the two blocks. After hydrolysis of the PSM block into poly(glycerol monomethacrylate), PGM, the BCPs had both lamellar and cylindrical microdomain morphologies in the bulk phase and in thin films. In addition to our previously developed solid-state hydrolysis strategy involving trifluoroacetic acid vapor, we developed a new photoinduced solid-state hydrolysis using photoacid generators, PAGs, embedded within the polymer films. After exposure to UV followed by a postexposure baking or solvent vapor annealing, the BCPs transitioned from the disordered, phase-mixed state into laterally ordered cylindrical patterns. In comparison to linear BCPs that rely on a random copolymer layer to modify interfacial interactions with the substrate to promote an orientation of the microdomains normal to the interface, we found that the microdomains in bottlebrush multiblock copolymers oriented normal to the interface absent substrate modification due to the chain architecture