A Machine‐Learning‐Based Model for Water Quality in Coastal Waters, Taking Dissolved Oxygen and Hypoxia in Chesapeake Bay as an Example

Hypoxia is a big concern in coastal waters as it affects ecosystem health, fishery yield, and marine water resources. Accurately modeling coastal hypoxia is still very challenging even with the most advanced numerical models. A data‐driven model for coastal water quality is proposed in this study and is applied to predict the temporal‐spatial variations of dissolved oxygen (DO) and hypoxic condition in Chesapeake Bay, the largest estuary in the United States with mean summer hypoxic zone extending about 150 km along its main axis. The proposed model has three major components including empirical orthogonal functions analysis, automatic selection of forcing transformation, and neural network training. It first uses empirical orthogonal functions to extract the principal components, then applies neural network to train models for the temporal variations of principal components, and finally reconstructs the three‐dimensional temporal‐spatial variations of the DO. Using the first 75% of the 32‐year (1985–2016) data set for training, the model shows good performance for the testing period (the remaining 25% data set). Selection of forcings for the first mode points to the dominant role of streamflow in controlling interannual variability of bay‐wide DO condition. Different from previous empirical models, the approach is able to simulate three‐dimensional variations of water quality variables and it does not use in situ measured water quality variables but only external forcings as model inputs. Even though the approach is used for the hypoxia problem in Chesapeake Bay, the methodology is readily applicable to other coastal systems that are systematically monitored

When 3D Bounding-Box Meets SAM: Point Cloud Instance Segmentation with Weak-and-Noisy Supervision

Learning from bounding-boxes annotations has shown great potential in weakly-supervised 3D point cloud instance segmentation. However, we observed that existing methods would suffer severe performance degradation with perturbed bounding box annotations. To tackle this issue, we propose a complementary image prompt-induced weakly-supervised point cloud instance segmentation (CIP-WPIS) method. CIP-WPIS leverages pretrained knowledge embedded in the 2D foundation model SAM and 3D geometric prior to achieve accurate point-wise instance labels from the bounding box annotations. Specifically, CP-WPIS first selects image views in which 3D candidate points of an instance are fully visible. Then, we generate complementary background and foreground prompts from projections to obtain SAM 2D instance mask predictions. According to these, we assign the confidence values to points indicating the likelihood of points belonging to the instance. Furthermore, we utilize 3D geometric homogeneity provided by superpoints to decide the final instance label assignments. In this fashion, we achieve high-quality 3D point-wise instance labels. Extensive experiments on both Scannet-v2 and S3DIS benchmarks demonstrate that our method is robust against noisy 3D bounding-box annotations and achieves state-of-the-art performance

TRPV4, TRPC1, and TRPP2 assemble to form a flow-sensitive heteromeric channel

Transient receptor potential (TRP) channels, a superfamily of ion channels, can be divided into 7 subfamilies, including TRPV, TRPC, TRPP, and 4 others. Functional TRP channels are tetrameric complexes consisting of 4 pore-forming subunits. The purpose of this study was to explore the heteromerization of TRP subunits crossing different TRP subfamilies. Two-step coimmunoprecipitation (co-IP) and fluorescence resonance energy transfer (FRET) were used to determine the interaction of the different TRP subunits. Patch-clamp and cytosolic Ca2+ measurements were used to determine the functional role of the ion channels in flow conditions. The analysis demonstrated the formation of a heteromeric TRPV4-C1-P2 complex in primary cultured rat mesenteric artery endothelial cells (MAECs) and HEK293 cells that were cotransfected with TRPV4, TRPC1, and TRPP2. In functional experiments, pore-dead mutants for each of these 3 TRP isoforms nearly abolished the flow-induced cation currents and Ca2+ increase, suggesting that all 3 TRPs contribute to the ion permeation pore of the channels. We identified the first heteromeric TRP channels composed of subunits from 3 different TRP subfamilies. Functionally, this heteromeric TRPV4- C1-P2 channel mediates the flow-induced Ca2+ increase in native vascular endothelial cells.-Du, J., Ma, X., Shen, B., Huang, Y., Birnbaumer, L., Yao, X. TRPV4, TRPC1, and TRPP2 assemble to form a flowsensitive heteromeric channel.Fil: Du, Juan. Chinese University Of Hong Kong; Hong Kong. Anhui Medical University; ChinaFil: Ma, Xin. Chinese University Of Hong Kong; Hong KongFil: Shen, Bing. Chinese University Of Hong Kong; Hong Kong. Anhui Medical University; ChinaFil: Huang, Yu. Chinese University Of Hong Kong; Hong KongFil: Birnbaumer, Lutz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. National Institutes of Health; Estados UnidosFil: Yao, Xiaoqiang. Chinese University Of Hong Kong; Hong Kon

Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution

Magnetic resonance imaging (MRI) is a valuable clinical tool for displaying anatomical structures and aiding in accurate diagnosis. Medical image super-resolution (SR) reconstruction using deep learning techniques can enhance lesion analysis and assist doctors in improving diagnostic efficiency and accuracy. However, existing deep learning-based SR methods predominantly rely on convolutional neural networks (CNNs), which inherently limit the expressive capabilities of these models and therefore make it challenging to discover potential relationships between different image features. To overcome this limitation, we propose an A-network that utilizes multiple convolution operator feature extraction modules (MCO) for extracting image features using multiple convolution operators. These extracted features are passed through multiple sets of cross-feature extraction modules (MSC) to highlight key features through inter-channel feature interactions, enabling subsequent feature learning. An attention-based sparse graph neural network module is incorporated to establish relationships between pixel features, learning which adjacent pixels have the greatest impact on determining the features to be filled. To evaluate our model's effectiveness, we conducted experiments using different models on data generated from multiple datasets with different degradation multiples, and the experimental results show that our method is a significant improvement over the current state-of-the-art methods.Comment: 12 pages, 6 figure

Comparative pharmacokinetic study of five flavonoids in normal rats and rats with gastric ulcer following oral administration of Mongolian medicine, Shudage - 4 by UPLC – ESI – MS/MS

Purpose: To develop a simple, rapid and sensitive ultra-performance liquid chromatography - electrospray ionization-mass spectrometry (UPLC–ESI–MS/MS) method was developed and fully validated for the simultaneous determination of galangin, kaempferide, galangin-3-methylether, kaempferol and quercetin in rat plasma after oral administration of Mongolian Medicine, Shudage-4 extracts. Methods: The galangin, kaempferide, galangin-3-methylether, kaempferol and quercetin were separated on a C18 column using 0.1 % formic acid at a flow rate of 0.4 mL / min and detected by a mass spectrometer in negative-ion mode with selected reaction monitoring (SRM) mode. Plasma samples were processed with a simple deproteinization technique using ethyl acetate and acetonitrile. Following the protein precipitation, the plasma samples were evaporated under gentle stream of nitrogen and analyzed by above method. Naringin was used as an internal standard (IS). Method validation was performed according to the Chinese Food and Drug Administration guidelines. Results: A good linearity (r2 ≥ 0.9990) was showed by the UPLC – ESI – MS / MS method, the low limits of quantification for galangin, kaempferide, galangin-3-methylether, kaempferol and quercetin were 229.8, 78.8, 32.0, 123.7 and 137.8 ng / mL, respectively. The results of inter-day and intra-day precisions met the experimental requirement (< 7.8 %). The matrix effect and recovery efficiency of the five analytes were more than 72.9 and 88.7 % respectively. The stability of the analytes were satisfactory. The UPLC – ESI – MS / MS method has been used for the five analytes’ pharmacokinetics study successfully after gastrointestinal route of the Mongolian Medicine Shudage-4. The pharmacokinetic parameters showed significant differences (P < 0.05) between the normal and gastric ulcer groups. The metabolism and transport of the five analytes in gastric ulcer rates were faster than in normal rats after administration of Shudage - 4 extract. Double-peak phenomenon appeared in galangin, galangin – 3 - methylether and quercetin. Conclusion: The results suggest that the metabolism and transport of Mongolian Medicine Shudage-4 in gastric ulcer rats is faster than in normal rats and may be enriched and acted on at the lesion site. Keywords: UPLC – ESI – MS / MS; Mongolian medicine; Shudage - 4; pharmacokinetics; gastric ulce

Application of Weibull model for survival of patients with gastric cancer

<p>Abstract</p> <p>Background</p> <p>Researchers in the medical sciences prefer employing Cox model for survival analysis. In some cases, however, parametric methods can provide more accurate estimates. In this study, we used Weibull model to analyze the prognostic factors in patients with gastric cancer and compared with Cox.</p> <p>Methods</p> <p>We retrospectively studied 1715 patients with gastric cancer. Age at diagnosis, gender, family history, past medical history, tumor location, tumor size, eradicative degree of surgery, depth of tumor invasion, combined evisceration, pathologic stage, histologic grade and lymph node status were chosen as potential prognostic factors. Weibull and Cox model were performed with hazard rate and Akaike Information Criterion (AIC) to compare the efficiency of models.</p> <p>Results</p> <p>The results from both Weibull and Cox indicated that patients with the past history of having gastric cancer had the risk of death increased significantly followed by poorly differentiated or moderately differentiated in histologic grade. Eradicative degree of surgery, pathologic stage, depth of tumor invasion and tumor location were also identified as independent prognostic factors found significant. Age was significant only in Weibull model.</p> <p>Conclusion</p> <p>From the results of multivariate analysis, the data strongly supported the Weibull can elicit more precise results as an alternative to Cox based on AIC.</p