A deep learning approach to 3D segmentation of brain vasculature

The segmentation of blood-vessels is an important preprocessing step for the quantitative analysis of brain vasculature. We approach the segmentation task for two-photon brain angiograms using a fully convolutional 3D deep neural network.Published versio

Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational Imaging

Deep learning has transformed computational imaging, but traditional pixel-based representations limit their ability to capture continuous, multiscale details of objects. Here we introduce a novel Local Conditional Neural Fields (LCNF) framework, leveraging a continuous implicit neural representation to address this limitation. LCNF enables flexible object representation and facilitates the reconstruction of multiscale information. We demonstrate the capabilities of LCNF in solving the highly ill-posed inverse problem in Fourier ptychographic microscopy (FPM) with multiplexed measurements, achieving robust, scalable, and generalizable large-scale phase retrieval. Unlike traditional neural fields frameworks, LCNF incorporates a local conditional representation that promotes model generalization, learning multiscale information, and efficient processing of large-scale imaging data. By combining an encoder and a decoder conditioned on a learned latent vector, LCNF achieves versatile continuous-domain super-resolution image reconstruction. We demonstrate accurate reconstruction of wide field-of-view, high-resolution phase images using only a few multiplexed measurements. LCNF robustly captures the continuous object priors and eliminates various phase artifacts, even when it is trained on imperfect datasets. The framework exhibits strong generalization, reconstructing diverse objects even with limited training data. Furthermore, LCNF can be trained on a physics simulator using natural images and successfully applied to experimental measurements on biological samples. Our results highlight the potential of LCNF for solving large-scale inverse problems in computational imaging, with broad applicability in various deep-learning-based techniques

Analysis of monitoring results of food microbial pathogenic factors in Zhoushan City from 2017 to 2019

Objective To understand the contamination status of microorganisms and pathogenic factors in 19 kinds of food in Zhoushan City, so as to provide basic data for food safety risk monitoring and early warning. Methods A total of 1 246 food samples were collected from 2017 to 2019 according to the requirements of the national risk monitor manual of food contamination and harmful factors from 2017 to 2019. The samples were tested for food microbial pathogenic factors. Results A total of 243 pathogenic factors were detected and the total detection rate was 19.50% (243/1 246). The detection rate of Salmonella in raw meat was 41.67% (30/72), the detection rate of Vibrio parahaemolyticus in bivalve shellfish was 31.58% (48/152), the detection rate of Anisakid in fresh marine fish was 27.00% (27/100), and the detection rate of Staphylococcus aureus in cold-made pastry was 16.25% (13/80). The detection rate of microorganism and its pathogenic factors in bulk food was higher than that in pre-packaged food. The difference was statistically significant (χ2=92.333, P<0.05). In different sampling locations, the highest detection rate of pathogenic factors was found in farmers’ markets (32.54%, 150/461), followed by online stores (25.44%, 29/114) and small restaurants (23.88%, 16/67). Conclusion From 2017 to 2019, 19 types of food on sale in Zhoushan City were contaminated by microorganisms and pathogenic factors at varying degrees. The contamination of microorganisms and pathogenic factors in take-out meals, raw animal meat and bivalve shellfish products was relatively serious. It is suggested to strengthen hygiene supervision on these types of food to prevent the occurrence of foodborne diseases

PO-109 Resistance Training prevents Skeletal Muscle Atrophy Induced by hypoxia through regulating Akt-FoxO1 pathway

Objective Skeletal muscle atrophy induced by hypoxia on the plateau will lead to the decrease of muscle strength and the degeneration of athletic ability. Resistance training is an efficient method to stimulate the growth of muscle and improve protein synthesis. Akt-FoxO1 (Fork head box protein 1) pathway plays a significant role in the regulation of skeletal muscle protein degradation. However, it is not clear whether resistance training could prevent skeletal muscle atrophy induced by hypoxia and what is the regulation role of Akt-FoxO1 pathway. This study built a rat model that resistance training inhibited the skeletal muscle atrophy induced by hypoxia and explore the variation of Akt, FoxO1, Murf and Atrogin-1. Methods 40 male 8-week-old Sprague-Dawley (SD) rats were divided into 4 groups randomly: control group (C), resistance training group (R), hypoxia group (H) and hypoxia resistance training group (HR). H and HR group were placed into simulated 4000m altitude (12.4%, O2%) and R and HR group received ladder resistance training. Their incremental load is calculated by using average body weight. After 4 weeks intervention of hypoxia and resistance training, body composition, wet weight of skeletal muscle (soleus, musculus gastrocnemius,extensor digitorum longus&nbsp;and muscelus biceps brachii) and skeletal muscle cross-sectional area (CSA) were measured. The expression of Akt, FoxO1, Murf and Atrogin-1 were detected by Western blot and RT-PCR.Moreover,immunofluorescence technique was used to locate the phosphorylation of FoxO1.&nbsp; Results The lean body mass of HR group was significantly higher than H group (P&lt;0.05). The wet weight and CSA of muscelus biceps brachii in HR group were also higher than H group obviously (P&lt;0.05). The results of real-time fluorescence quantitative PCR and western blot showed that the expression of FoxO1 and MuRF of hypoxia group (H group) were significantly higher than control group. However after the intervention of resistance training, the expression of Akt was significantly up-regulate and FoxO1, MuRF were significantly down-regulate. Immunofluorescence technique&nbsp;was used to observe the location of FoxO1 phosphorylation and the expression out of nucleus. Conclusions Resistance training contribute to prevent the occurrence of skeletal muscle atrophy induced by hypoxia and the form of climbing ladder training can stimulate the hypertrophy of biceps in rats. The results revealed that FoxO1 phosphorylation out of nucleus became higher after resistance training. All above revealed that resistance training could inhibit skeletal muscle atrophy induced by hypoxia. Akt promoted FoxO1 phosphorylation may become the molecular mechanisms that resistance training can inhibit the atrophy of skeletal muscle induced by hypoxia

Bond-Selective Intensity Diffraction Tomography

Recovering molecular information remains a grand challenge in the widely used holographic and computational imaging technologies. To address this challenge, we developed a computational mid-infrared photothermal microscope, termed Bond-selective Intensity Diffraction Tomography (BS-IDT). Based on a low-cost brightfield microscope with an add-on pulsed light source, BS-IDT recovers both infrared spectra and bond-selective 3D refractive index maps from intensity-only measurements. High-fidelity infrared fingerprint spectra extraction is validated. Volumetric chemical imaging of biological cells is demonstrated at a speed of ~20 seconds per volume, with a lateral and axial resolution of ~350 nm and ~1.1 micron, respectively. BS-IDT's application potential is investigated by chemically quantifying lipids stored in cancer cells and volumetric chemical imaging on Caenorhabditis elegans with a large field of view (~100 micron X 100 micron)

Factors associated with the incidence and the expenditure of self-medication among middle-aged and older adults in China: A cross-sectional study

BackgroundWith the accelerated ageing of population and the growing prevalence of various chronic diseases in China, self-medication plays an increasingly important role in complementing the health care system due to its convenience and economy.ObjectiveThis study aimed to investigate the incidence of self-medication and the amount of self-medication expenditure among middle-aged and older adults in China, and to explore factors associated with them.MethodsA total of 10,841 respondents aged 45 years and older from the China Health and Retirement Longitudinal Study (CHARLS) wave 4 which conducted in 2018 were included as the sample of this study. The two-part model was adopted to identify the association between the incidence of self-medication and the amount of self-medication expenditure and specific factors, respectively.ResultsThe incidence of self-medication among Chinese middle-aged and older adults was 62.30%, and the average total and out-of-pocket (OOP) pharmaceutical expenditure of self-medication of the self-medicated individuals were 290.50 and 264.38 Chinese yuan (CNY) respectively. Participants who took traditional Chinese medicine (TCM), self-reported fair, and poor health status, suffered from one and multiple chronic diseases had strongly higher incidence of self-medication. Older age and multiple chronic diseases were strongly associated with higher expenditure of self-medication. Those who took TCM had more self-medication expenditure, while those who drank alcohol had less.ConclusionOur study demonstrated the great prevalence of self-medication among middle-aged and older adults in China and the large pharmaceutical expenditure that come with it, especially in the high-risk groups of self-medication identified in this paper. These findings enhanced our understanding of self-medication behaviors among Chinese middle-aged and older adults and may contribute to the formulation of targeted public health policy

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery