Changes in hydrodynamics and nutrient load of the coastal bay induced by Typhoon Talim (2023)

Typhoons can greatly alter the hydrodynamic and nutrient supply in coastal oceans. However, due to the complex conditions of typhoons, such as their intensity, even slight changes may cause substantial changes in hydrodynamics and nutrient supply, which needs to be better understood. In this study, we conducted two cruises before and after Typhoon Talim (2023) to quantitatively investigate changes in hydrodynamics and nutrient supply in Zhanjiang Bay using dual water isotopes. Before the typhoon, strong stratification occurred in the bay. However, the strong external force of the typhoon destroyed the stratification and substantially changed the water mixing in the bay after the typhoon. In the upper bay, massive freshwater input remarkably decreased the salinity during the post-typhoon period (freshwater increased by 18%). In contrast, the salinity variation in the lower bay was minimal, mainly due to massive seawater intrusion from the outer bay induced by the typhoon; the seawater mixed with freshwater columns from the upper bay, forming a strong ocean front. The intensity of ocean fronts induced by typhoons directly depended on the typhoon intensity landing in Zhanjiang Bay, as stronger typhoons will cause more intrusion of high-salinity seawater from the outer bay. Due to the formation of the ocean front, freshwater and terrestrial nutrients from the upper bay are prevented from being transported downwards, resulting in a large amount of accumulated pollutants within the bay. By contrast, due to the impact of high-salinity seawater intrusion, the contribution of seawater from the outer bay has increased, thereby diluting the nutrients in the lower bay. This study provides a new insight into the responses of coastal marine eco-environment systems to typhoons

A Construction of Butterfly Pedigree of Sports Law

This paper ventures to start from the two dimensions of sport’s specific property and law’s basic property, and then construct the butterfly pedigree of Sports Law. It tries to clarify the various relationships of Sports Law, and judge the rationality of the viewpoints of various theoretic schools in this circle .And it also tries to tamp a foundation for concepts of Sports Law and provide a comparatively complete theoretic framework for analysis of the history and temporary development

Remote sensing estimation of δ15NPN in the Zhanjiang Bay using Sentinel-3 OLCI data based on machine learning algorithm

The particulate nitrogen (PN) isotopic composition (δ15NPN) plays an important role in quantifying the contribution rate of particulate organic matter sources and indicating water environmental pollution. Estimation of δ15NPN from satellite images can provide significant spatiotemporal continuous data for nitrogen cycling and ecological environment governance. Here, in order to fully understand spatiotemporal dynamic of δ15NPN, we have developed a machine learning algorithm for retrieving δ15NPN. This is a successful case of combining nitrogen isotopes and remote sensing technology. Based on the field observation data of Zhanjiang Bay in May and September 2016, three machine learning retrieval models (Back Propagation Neural Network, Random Forest and Multiple Linear Regression) were constructed using optical indicators composed of in situ remote sensing reflectance as input variable and δ15NPN as output variable. Through comparative analysis, it was found that the Back Propagation Neural Network (BPNN) model had the better retrieval performance. The BPNN model was applied to the quasi-synchronous Ocean and Land Color Imager (OLCI) data onboard Sentinel-3. The determination coefficient (R2), root mean square error (RMSE) and mean absolute percentage error (MAPE) of satellite-ground matching point data based on the BPNN model were 0.63, 1.63‰, and 20.10%, respectively. From the satellite retrieval results, it can be inferred that the retrieval value of δ15NPN had good consistency with the measured value of δ15NPN. In addition, independent datasets were used to validate the BPNN model, which showed good accuracy in δ15NPN retrieval, indicating that an effective model for retrieving δ15NPN has been built based on machine learning algorithm. However, to enhance machine learning algorithm performance, we need to strengthen the information collection covering diverse coastal water bodies and optimize the input variables of optical indicators. This study provides important technical support for large-scale and long-term understanding of the biogeochemical processes of particulate organic matter, as well as a new management strategy for water quality and environmental monitoring

Response of nutrients and the surface phytoplankton community to ice melting in the central Arctic Ocean

During the fourth Chinese National Arctic Research Expedition cruise in summer 2010, a time-series observation was carried out to examine the response of nutrients and phytoplankton community in the ice-water interface to the ice melting ice in the central Arctic Ocean. Phosphate and silicate in the ice-water interface were rich relative to dissolved inorganic nitrogen (DIN), based on the Redfield ratio (16N:1P:16Si), suggesting that DIN was the potential limiting nutrient. DIN concentrations in the sea ice were about 3-4 times that in the surface seawater, indicating that melting ice delivered DIN to the surface water. Pigment analysis showed that fucoxanthin and chlorophyll a contribute to carotenoids and chlorophylls in particles. The mean concentrations of chlorophyll c, diatoxanthin, diadinoxanthin and fucoxanthin from 15 August to 18 August were 6 μg * m(-3), 22 μg * m(-3), 73 μg * m(-3) and 922 μg * m(-3), respectively, suggesting that diatoms dominated in the phytoplankton community composition. Furthermore, a notable enhancement in fucoxanthin and chlorophyll a during a large-scale ice melting was likely attributed to senescent diatoms released from the bottom sea-ice as well as phytoplankton diatoms growth in the water column due to the input of nutrients (i.e., DIN) and reducing light limitation from melting ice. Temporal distribution patterns of prasinoxanthin and lutein differed from fucoxanthin, indicating that the response of green algae and diatoms to ice melting were different

Low-dose dobutamine cardiovascular magnetic resonance segmental strain study of early phase of intramyocardial hemorrhage rats

BACKGROUND: This study investigates the segmental myocardial strain of the early phase of intramyocardial hemorrhage (IMH) caused by reperfused myocardial infarction (MI) in rats by low-dose dobutamine (LDD) cardiovascular magnetic resonance (CMR) feature-tracking. METHODS: Nine sham rats and nine rats with 60-min myocardial ischemia followed by 48-h reperfusion were investigated using CMR, including T2*-mapping sequence and fast imaging with steady-state precession (FISP)-cine sequence. Another FISP-cine sequence was acquired after 2 min of dobutamine injection; the MI, IMH, and Non-MI (NMI) areas were identified. The values of peak radial strains (PRS) and peak circumferential strains (PCS) of the MI, IMH and NMI segments were acquired. The efficiency of PRS and PCS (EPRS and EPCS, respectively) were calculated on the basis of the time of every single heartbeat. RESULTS: The PRS, PCS, EPRS, and EPCS of the sham group increased after LDD injection. However, the PRS, PCS, EPRS, and EPCS of the IMH segment did not increase. Moreover, the PRS and PCS of the MI and NMI segments did not increase, but the EPRS and EPCS of these segments increased. The PRS, PCS, EPRS, and EPCS of the IMH segment were lower than those of the MI and NMI segments before and after LDD injection, but without a significant difference between MI segment and NMI segment before and after LDD injection. CONCLUSIONS: LDD could help assess dysfunctions in segments with IMH, especially using the efficiency of strain. IMH was a crucial factor that decreased segmental movement and reserved function

Myocardial infarction size as an independent predictor of intramyocardial haemorrhage in acute reperfused myocardial ischaemic rats

BACKGROUND: In previous studies, haemorrhage occurred only with large infarct sizes, and studies found a moderate correlation between the extent of necrosis and haemorrhage, but the extent of infarction size in these studies was limited. This study aimed to find the correlations between intramyocardial haemorrhage (IMH), myocardial infarction (MI), and myocardial oedema (ME) from small to large sizes of MI in a 7.0-T MR scanner. METHODS: Different sizes of myocardial infarction were induced by occluding different sections of the proximal left anterior descending coronary artery (1-3 mm under the left auricle). T2*-mapping, T2-mapping and late gadolinium enhancement (LGE) sequences were performed on a 7.0 T MR system at Days 2 and 7. T2*- and T2-maps were calculated using custom-made software. All areas were expressed as a percentage of the entire myocardial tissue of the left ventricle. The rats were divided into two groups based on the T2* results and pathological findings; MI with IMH was referred to as the + IMH group, while MI without IMH was referred to as the -IMH group. RESULTS: The final experimental sample consisted of 25 rats in the + IMH group and 10 rats in the -IMH group. For the + IMH group on Day 2, there was a significant positive correlation between IMH size and MI size (r = 0.677, P \u3c 0.01) and a positive correlation between IMH size and ME size (r = 0.552, P \u3c 0.01). On Day 7, there was a significant positive correlation between IMH size and MI size (r = 0.711, P \u3c 0.01), while no correlation was found between IMH size and ME size (r = 0.429, P = 0.097). The MI sizes of the + IMH group were larger than those of the -IMH group (P \u3c 0.01). CONCLUSIONS: Infarction size prior to reperfusion is a critical factor in determining IMH size in rats

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

Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor

BackgroundEssential tremor (ET) is one of the most common movement disorders. Histogram analysis based on brain intrinsic activity imaging is a promising way to identify ET patients from healthy controls (HCs) and further explore the spontaneous brain activity change mechanisms and build the potential diagnostic biomarker in ET patients.MethodsThe histogram features based on the Resting-state functional magnetic resonance imaging (Rs-fMRI) data were extracted from 133 ET patients and 135 well-matched HCs as the input features. Then, a two-sample t-test, the mutual information, and the least absolute shrinkage and selection operator methods were applied to reduce the feature dimensionality. Support vector machine (SVM), logistic regression (LR), random forest (RF), and k-nearest neighbor (KNN) were used to differentiate ET and HCs, and classification performance of the established models was evaluated by the mean area under the curve (AUC). Moreover, correlation analysis was carried out between the selected histogram features and clinical tremor characteristics.ResultsEach classifier achieved a good classification performance in training and testing sets. The mean accuracy and area under the curve (AUC) of SVM, LR, RF, and KNN in the testing set were 92.62%, 0.948; 92.01%, 0.942; 93.88%, 0.941; and 92.27%, 0.939, respectively. The most power-discriminative features were mainly located in the cerebello-thalamo-motor and non-motor cortical pathways. Correlation analysis showed that there were two histogram features negatively and one positively correlated with tremor severity.ConclusionOur findings demonstrated that the histogram analysis of the amplitude of low-frequency fluctuation (ALFF) images with multiple machine learning algorithms could identify ET patients from HCs and help to understand the spontaneous brain activity pathogenesis mechanisms in ET patients