Wpływ tributylocyny na przyjmowanie pokarmu i ekspresję neuropeptydów w mózgu szczurów

Introduction: Tributyltin (TBT) is a largely diffused environmental pollutant. Several studies have demonstrated that TBT is involved in the development of obesity. However, few studies addressing the effects of TBT on the brain neuropeptides involved in appetite and body weight homeostasis have been published.Material and methods: Experiments were carried out on female and male Sprague-Dawley rats. Animals were exposed to TBT (0.5 μg/kg body weight) for 54 days. The hepatic triglyceride and total cholesterol were determined using commercial enzyme kits. The NPY, AgRP, POMC and CART mRNA expression in brains were quantified by real-time PCR.Results: TBT exposure resulted in significant increases in the hepatic total cholesterol and triglyceride concentration of both male and female rats. Interestingly, increases in body weight and fat mass were only found in the TBT-treated male rats. TBT exposure also led to a significant increase in food intake by the female rats, while no change was observed in the male rats. Moreover, the neuropeptides expression was different between males and females after TBT exposure. TBT induced brain NPY expression in the female rats, and depressed brain POMC, AgRP and CART expression in the males.Conclusions: TBT can increase food intake in female rats, which is associated with the disturbance of NPY in brains. TBT had sex-different effects on brain NPY, AgRP, POMC and CART mRNA expression, which indicates a complex neuroendocrine mechanism of TBT. (Endokrynol Pol 2014; 65 (6): 485–490)Wstęp: Tributylocyna (TBT) jest powszechnie występującym w środowisku zanieczyszczeniem. Prowadzone dotychczas badania wykazały, że obecność TBT może mieć związek z rozwojem otyłości. Niewiele jest jednak doniesień na temat wpływu TBT na układ neuropeptydów w mózgowiu regulujących łaknienie i utrzymanie masy ciała. Materiał i metody: Doświadczenia przeprowadzono na szczurach obu płci szczepu Sprague-Dawley. Zwierzętom podawano przez 54 dni TBT w dawce 0,5 μg/kg masy ciała. Stężenie triglicerydów i całkowite stężenie cholesterolu w wątrobie oznaczano przy użyciu komercyjnych zestawów analitycznych. Obecność mRNA NPY, AgRP, POMC i CART w mózgach szczurów oznaczano metodą PCR w czasie rzeczywistym (real time-PCR).Wyniki: Ekspozycja na TBT powodowała istotne zwiększenie całkowitego stężenia cholesterolu i trójglicerydów w wątrobie zarówno samców, jak i samic szczura. Co ciekawe, zwiększenie masy ciała i masy tkanki tłuszczowej odnotowano jedynie u samców, którym podawano TBT. Stwierdzono także istotne zwiększenie ilości pokarmu przyjmowanego przez samice, natomiast nie obserwowano takich zmian u samców. Ponadto, odnotowano różnice w ekspresji neuropeptydów w mózgowiu samic i samców szczura, którym podawano TBT. Ekspozycja na TBT nasilała ekspresję NPY w mózgach samic, ale równocześnie zmniejszała ekspresję POMC, AgRP i CART w mózgach samców szczura.Wnioski: Ekspozycja na TBT może zwiększać ilość pokarmu spożywanego przez samice szczura, co wiąże się z zaburzeniem układu NPY w mózgowiu. Trybutylocyna wywiera odmienny wpływ na ekspresję mRNA NPY, AgRP, POMC i CART w mózgach samców i samic szczura, co wskazuje na istnienie złożonego mechanizmu działania tej substancji na układ neuroendokrynny. (Endokrynol Pol 2014; 65 (6): 485–490

Dense Pixel-to-Pixel Harmonization via Continuous Image Representation

High-resolution (HR) image harmonization is of great significance in real-world applications such as image synthesis and image editing. However, due to the high memory costs, existing dense pixel-to-pixel harmonization methods are mainly focusing on processing low-resolution (LR) images. Some recent works resort to combining with color-to-color transformations but are either limited to certain resolutions or heavily depend on hand-crafted image filters. In this work, we explore leveraging the implicit neural representation (INR) and propose a novel image Harmonization method based on Implicit neural Networks (HINet), which to the best of our knowledge, is the first dense pixel-to-pixel method applicable to HR images without any hand-crafted filter design. Inspired by the Retinex theory, we decouple the MLPs into two parts to respectively capture the content and environment of composite images. A Low-Resolution Image Prior (LRIP) network is designed to alleviate the Boundary Inconsistency problem, and we also propose new designs for the training and inference process. Extensive experiments have demonstrated the effectiveness of our method compared with state-of-the-art methods. Furthermore, some interesting and practical applications of the proposed method are explored. Our code is available at https://github.com/WindVChen/INR-Harmonization.Comment: Accepted by IEEE Transactions on Circuits and Systems for Video Technology (TCSVT

Graph Sampling-based Meta-Learning for Molecular Property Prediction

Molecular property is usually observed with a limited number of samples, and researchers have considered property prediction as a few-shot problem. One important fact that has been ignored by prior works is that each molecule can be recorded with several different properties simultaneously. To effectively utilize many-to-many correlations of molecules and properties, we propose a Graph Sampling-based Meta-learning (GS-Meta) framework for few-shot molecular property prediction. First, we construct a Molecule-Property relation Graph (MPG): molecule and properties are nodes, while property labels decide edges. Then, to utilize the topological information of MPG, we reformulate an episode in meta-learning as a subgraph of the MPG, containing a target property node, molecule nodes, and auxiliary property nodes. Third, as episodes in the form of subgraphs are no longer independent of each other, we propose to schedule the subgraph sampling process with a contrastive loss function, which considers the consistency and discrimination of subgraphs. Extensive experiments on 5 commonly-used benchmarks show GS-Meta consistently outperforms state-of-the-art methods by 5.71%-6.93% in ROC-AUC and verify the effectiveness of each proposed module. Our code is available at https://github.com/HICAI-ZJU/GS-Meta.Comment: Accepted by IJCAI 202

Differentiation in stem and leaf traits among sympatric lianas, scandent shrubs and trees in a subalpine cold temperate forest

The scandent shrub plant form is a variant of liana that has upright and self-supporting stems when young but later becomes a climber. We aimed to explore the associations of stem and leaf traits among sympatric lianas, scandent shrubs and trees, and the effects of growth form and leaf habit on variation in stem or leaf traits. We measured 16 functional traits related to stem xylem anatomy, leaf morphology and nutrient stoichiometry in eight liana, eight scandent shrub and 21 tree species co-occurring in a subalpine cold temperate forest at an elevation of 2600–3200 m in Southwest China. Overall, lianas, scandent shrubs and trees were ordered along a fast-slow continuum of stem and leaf functional traits, with some traits overlapping. We found a consistent pattern of lianas > scandent shrubs > trees for hydraulically weighted vessel diameter, maximum vessel diameter and theoretical hydraulic conductivity. Vessel density and sapwood density showed a pattern of lianas = scandent shrubs < trees, and lianas < scandent shrubs = trees, respectively. Lianas had significantly higher specific leaf area and lower carbon concentration than co-occurring trees, with scandent shrubs showing intermediate values that overlapped with lianas and trees. The differentiation among lianas, scandent shrubs and trees was mainly explained by variation in stem traits. Additionally, deciduous lianas were positioned at the fast end of the trait spectrum, and evergreen trees at the slow end of the spectrum. Our results showed for the first time clear differentiation in stem and leaf traits among sympatric liana, scandent shrub and tree species in a subalpine cold temperate forest. This work will contribute to understanding the mechanisms responsible for variation in ecological strategies of different growth forms of woody plants

Reconstruction of Cardiac Cine MRI under Free-breathing using Motion-guided Deformable Alignment and Multi-resolution Fusion

Objective: Cardiac cine magnetic resonance imaging (MRI) is one of the important means to assess cardiac functions and vascular abnormalities. However, due to cardiac beat, blood flow, or the patient's involuntary movement during the long acquisition, the reconstructed images are prone to motion artifacts that affect the clinical diagnosis. Therefore, accelerated cardiac cine MRI acquisition to achieve high-quality images is necessary for clinical practice. Approach: A novel end-to-end deep learning network is developed to improve cardiac cine MRI reconstruction under free breathing conditions. First, a U-Net is adopted to obtain the initial reconstructed images in k-space. Further to remove the motion artifacts, the Motion-Guided Deformable Alignment (MGDA) method with second-order bidirectional propagation is introduced to align the adjacent cine MRI frames by maximizing spatial-temporal information to alleviate motion artifacts. Finally, the Multi-Resolution Fusion (MRF) module is designed to correct the blur and artifacts generated from alignment operation and obtain the last high-quality reconstructed cardiac images. Main results: At an 8$\times$ acceleration rate, the numerical measurements on the ACDC dataset are SSIM of 78.40%$\pm$4.57%, PSNR of 30.46$\pm$1.22 dB, and NMSE of 0.0468$\pm$0.0075. On the ACMRI dataset, the results are SSIM of 87.65%$\pm$4.20%, PSNR of 30.04$\pm$1.18 dB, and NMSE of 0.0473$\pm$0.0072. Significance: The proposed method exhibits high-quality results with richer details and fewer artifacts for cardiac cine MRI reconstruction on different accelerations under free breathing conditions.Comment: 28 pages, 5 tables, 11 figure

Multimodal imaging features of primary pericardial synovial sarcoma: a case report

BackgroundPrimary pericardial synovial sarcoma is an extremely rare malignant tumor, and affected patients have a poor prognosis. Only a few cases have been reported in the literature.Case summaryA 34-year-old man was admitted to our hospital with chest tightness and a cough. An echocardiogram revealed a heterogeneous mass with a large pericardial effusion. Further computed tomography (CT) of the chest and cardiac magnetic resonance imaging (CMRI) demonstrated an irregular pericardial mass abutting the left atrium and left ventricle and invading the mediastinal structures. Pathology results showed that the tumor was a monophasic synovial sarcoma. The patient underwent chemotherapy and survived for 17 months.DiscussionMany cardiac tumors are clinically asymptomatic or nonspecific, and they are frequently detected or diagnosed at an advanced stage of the disease. Multimodal cardiac imaging facilitates the detection and assessment of cardiac tumors. In particular, CMRI is considered as a superior imaging tool, because it provides high tissue contrast and can detect invasion of the myocardium. We describe the clinical details and multimodal imaging features of a rare primary pericardial synovial sarcoma, hoping to provide guidance for the diagnosis of similar cases in the future

Continuous Cross-resolution Remote Sensing Image Change Detection

Most contemporary supervised Remote Sensing (RS) image Change Detection (CD) approaches are customized for equal-resolution bitemporal images. Real-world applications raise the need for cross-resolution change detection, aka, CD based on bitemporal images with different spatial resolutions. Given training samples of a fixed bitemporal resolution difference (ratio) between the high-resolution (HR) image and the low-resolution (LR) one, current cross-resolution methods may fit a certain ratio but lack adaptation to other resolution differences. Toward continuous cross-resolution CD, we propose scale-invariant learning to enforce the model consistently predicting HR results given synthesized samples of varying resolution differences. Concretely, we synthesize blurred versions of the HR image by random downsampled reconstructions to reduce the gap between HR and LR images. We introduce coordinate-based representations to decode per-pixel predictions by feeding the coordinate query and corresponding multi-level embedding features into an MLP that implicitly learns the shape of land cover changes, therefore benefiting recognizing blurred objects in the LR image. Moreover, considering that spatial resolution mainly affects the local textures, we apply local-window self-attention to align bitemporal features during the early stages of the encoder. Extensive experiments on two synthesized and one real-world different-resolution CD datasets verify the effectiveness of the proposed method. Our method significantly outperforms several vanilla CD methods and two cross-resolution CD methods on the three datasets both in in-distribution and out-of-distribution settings. The empirical results suggest that our method could yield relatively consistent HR change predictions regardless of varying bitemporal resolution ratios. Our code is available at \url{https://github.com/justchenhao/SILI_CD}.Comment: 21 pages, 11 figures. Accepted article by IEEE TGR