OR-022 Effects of aerobic exercise on the hemodynamics and structure of the common carotid artery in obese adolescents

Objective With the population of obese adolescents increases dramatically, a series of cardiovascular diseases, especially atherosclerotic, are triggered by obese which seriously threatens the life and health of teenagers. The aim of this study is to investigate the effects of aerobic exercise intervention on the hemodynamics and structure of the common carotid artery in obese adolescents. Methods  Forty obese adolescents (18 ± 2years) were randomly assigned into the experimental group (EG; n = 20) and control group (CG; n = 20). EG undertook 12 weeks of aerobic exercise training (AET), CG had not any exercise intervention. The carotid artery of both CG and EG were examined and compared. Carotid artery responses were assessed in both groups. Color doppler ultrasound was used to determine the tube diameter and axial flow of the common carotid before and after exercise intervention. The heart rate, systolic and diastolic blood pressure were simultaneously measured on the left brachial artery by a sphygmomanometer. Results Compared with CG, there were improvements of EG in peripheral resistance (22.90±6.70 VS 29.58±8.71. p＜0.01) and Systolic blood pressure (123.57±7.36 VS 130.25±6.79. p＜0.05) were verified after AET, except diastolic blood pressure. Following AET, blood flow velocity (0.28±0.05 VS 0.21±0.05. p＜0.01) and wall shear stress (6.25±0.90 VS 4.97±1.54. p＜0.05) increased prominently, which were also significant differences only in EG. In contrast, the vascular diameter demonstrated consistently upper compared with CG, but no differences between EG and CG. Conclusions  Regular aerobic exercise lasting 12 weeks could effectively change the dynamic parameters of the common carotid artery in obese adolescents, but no changes in arterial diameter. These findings indicated that 12 weeks of aerobic exercise can induce some changes of the common carotid artery blood flow within the circulation function in a short time. But the changing in common carotid arteries structure is needed after a long-term blood flow to the stimulation

DAMO-YOLO : A Report on Real-Time Object Detection Design

In this report, we present a fast and accurate object detection method dubbed DAMO-YOLO, which achieves higher performance than the state-of-the-art YOLO series. DAMO-YOLO is extended from YOLO with some new technologies, including Neural Architecture Search (NAS), efficient Reparameterized Generalized-FPN (RepGFPN), a lightweight head with AlignedOTA label assignment, and distillation enhancement. In particular, we use MAE-NAS, a method guided by the principle of maximum entropy, to search our detection backbone under the constraints of low latency and high performance, producing ResNet/CSP-like structures with spatial pyramid pooling and focus modules. In the design of necks and heads, we follow the rule of ``large neck, small head''.We import Generalized-FPN with accelerated queen-fusion to build the detector neck and upgrade its CSPNet with efficient layer aggregation networks (ELAN) and reparameterization. Then we investigate how detector head size affects detection performance and find that a heavy neck with only one task projection layer would yield better results.In addition, AlignedOTA is proposed to solve the misalignment problem in label assignment. And a distillation schema is introduced to improve performance to a higher level. Based on these new techs, we build a suite of models at various scales to meet the needs of different scenarios. For general industry requirements, we propose DAMO-YOLO-T/S/M/L. They can achieve 43.6/47.7/50.2/51.9 mAPs on COCO with the latency of 2.78/3.83/5.62/7.95 ms on T4 GPUs respectively. Additionally, for edge devices with limited computing power, we have also proposed DAMO-YOLO-Ns/Nm/Nl lightweight models. They can achieve 32.3/38.2/40.5 mAPs on COCO with the latency of 4.08/5.05/6.69 ms on X86-CPU. Our proposed general and lightweight models have outperformed other YOLO series models in their respective application scenarios.Comment: Project Website: https://github.com/tinyvision/damo-yol

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations. Most existing methods are bottom-up approaches that try to group pixels into regions based on their visual cues or certain predefined rules. As a result, it is difficult for these bottom-up approaches to generate fine-grained semantic segmentation when coming to complicated scenes with multiple objects and some objects sharing similar visual appearance. In contrast, we propose the first top-down unsupervised semantic segmentation framework for fine-grained segmentation in extremely complicated scenarios. Specifically, we first obtain rich high-level structured semantic concept information from large-scale vision data in a self-supervised learning manner, and use such information as a prior to discover potential semantic categories presented in target datasets. Secondly, the discovered high-level semantic categories are mapped to low-level pixel features by calculating the class activate map (CAM) with respect to certain discovered semantic representation. Lastly, the obtained CAMs serve as pseudo labels to train the segmentation module and produce the final semantic segmentation. Experimental results on multiple semantic segmentation benchmarks show that our top-down unsupervised segmentation is robust to both object-centric and scene-centric datasets under different semantic granularity levels, and outperforms all the current state-of-the-art bottom-up methods. Our code is available at \url{https://github.com/damo-cv/TransFGU}.Comment: Accepted by ECCV 2022, Oral, open-source

MiR-708 promotes steroid-induced osteonecrosis of femoral head, suppresses osteogenic differentiation by targeting SMAD3

Steroid-induced osteonecrosis of femoral head (ONFH) is a serious complication of glucocorticoid (GC) use. We investigated the differential expression of miRs in the mesenchymal stem cells (MSCs) of patients with ONFH, and aimed to explain the relationship between GC use and the development of MSC dysfunction in ONFH. Cells were collected from bone marrow of patients with ONFH. Samples were assigned to either GCs Group or Control Group at 1:1 matched with control. We then used miRNA microarray analysis and real-time PCR to identify the differentially expressed miRs. We also induced normal MSCs with GCs to verify the differential expression above. Subsequently, we selected some of the miRs for further studies, including miRNA target and pathway prediction, and functional analysis. We discovered that miR-708 was upregulated in ONFH patients and GC-treated MSCs. SMAD3 was identified as a direct target gene of miR-708, and functional analysis demonstrated that miR-708 could markedly suppress osteogenic differentiation and adipogenesis differentiation of MSCs. Inhibition of miR-708 rescued the suppressive effect of GC on osteonecrosis. Therefore, we determined that GC use resulted in overexpression of miR-708 in MSCs, and thus, targeting miR-708 may serve as a novel therapeutic biomarker for the prevention and treatment of ONFH

Ferroptosis-related gene HIC1 in the prediction of the prognosis and immunotherapeutic efficacy with immunological activity

BackgroundHypermethylated in Cancer 1 (HIC1) was originally confirmed as a tumor suppressor and has been found to be hypermethylated in human cancers. Although growing evidence has supported the critical roles of HIC1 in cancer initiation and development, its roles in tumor immune microenvironment and immunotherapy are still unclear, and no comprehensive pan-cancer analysis of HIC1 has been conducted.MethodsHIC1 expression in pan-cancer, and differential HIC1 expression between tumor and normal samples were investigated. Immunohistochemistry (IHC) was employed to validate HIC1 expression in different cancers by our clinical cohorts, including lung cancer, sarcoma (SARC), breast cancer, and kidney renal clear cell carcinoma (KIRC). The prognostic value of HIC1 was illustrated by Kaplan-Meier curves and univariate Cox analysis, followed by the genetic alteration analysis of HIC1 in pan-cancer. Gene Set Enrichment Analysis (GSEA) was conducted to illustrate the signaling pathways and biological functions of HIC1. The correlations between HIC1 and tumor mutation burden (TMB), microsatellite instability (MSI), and the immunotherapy efficacy of PD-1/PD-L1 inhibitors were analyzed by Spearman correlation analysis. Drug sensitivity analysis of HIC1 was performed by extracting data from the CellMiner™ database.ResultsHIC1 expression was abnormally expressed in most cancers, and remarkable associations between HIC1 expression and prognostic outcomes of patients in pan-cancer were detected. HIC1 was significantly correlated with T cells, macrophages, and mast cell infiltration in different cancers. Moreover, GSEA revealed that HIC1 was significantly involved in immune-related biological functions and signaling pathways. There was a close relationship of HIC1 with TMB and MSI in different cancers. Furthermore, the most exciting finding was that HIC1 expression was significantly correlated with the response to PD-1/PD-L1 inhibitors in cancer treatment. We also found that HIC1 was significantly correlated with the sensitivity of several anti-cancer drugs, such as axitinib, batracylin, and nelarabine. Finally, our clinical cohorts further validated the expression pattern of HIC1 in cancers.ConclusionsOur investigation provided an integrative understanding of the clinicopathological significance and functional roles of HIC1 in pan-cancer. Our findings suggested that HIC1 can function as a potential biomarker for predicting the prognosis, immunotherapy efficacy, and drug sensitivity with immunological activity in cancers

Online Three Dimensional Liquid Chromatography/Mass Spectrometry Method for the Separation of Complex Samples

In this work, a novel online three dimensional liquid chromatography (3D-LC) system was first developed by effectively coupling of preseparation and comprehensive 2D-LC using a stop-flow interface, aiming at improving the separation of complex samples. The sample was separated into two or several fractions through the first dimensional separation, and then each fraction was transferred in an orderly way into the following comprehensive 2D-LC part for further analysis. More optimal conditions could be operated in the second and third dimensions according to the properties of each fraction. Thus, the resolution of the 3D-LC system was substantially improved. Analysis of soybean extract was taken as a proof-of-principle to demonstrate the powerful separation of the established 3D-LC system. The amide column was selected as the first dimension column. Weakly polar metabolites (such as lipids, aglycones, etc.) and polar metabolites (such as glycosides, etc.) were separated into different fractions. Fluorophenyl and C18 columns were used in the second and third dimensions of the 3D-LC system for further separation, respectively. There were 83 flavonoids characterized in the soybean extract, including many difficult to separate isomers and low-abundance flavonoids; in total, they were nearly 30% more than those identified in the comparative comprehensive 2D-LC approach. In conclusion, this 3D-LC system is flexible in construction and applicable to complex sample analysis

Online Measurement of Sodium Nitrite Based on Near-Infrared Spectroscopy

In this study, a method was developed for the rapid online measurement of sodium nitrite solutions using near-infrared spectroscopy. A series of standard solutions of sodium nitrite at different concentrations were prepared, and the samples were measured in cuvettes and flow cells. Following the preprocessing of raw spectra and band selection, partial least squares were used to establish a prediction model, and the coefficient of determination (R2) of the validation set and the root mean square error of prediction (RMSEP) of the model were 0.9989 and 0.0338. The results demonstrate that the established model can meet the demands of online measurement and perform the rapid, nondestructive detection of sodium nitrite solutions, which provides some basis for the automated formulation of feedstock in spent fuel reprocessing