Statistical Prediction of the South China Sea Surface Height Anomaly

Based on the simple ocean data assimilation (SODA) data, this study analyzes and forecasts the monthly sea surface height anomaly (SSHA) averaged over South China Sea (SCS). The approach to perform the analysis is a time series decomposition method, which decomposes monthly SSHAs in SCS to the following three parts: interannual, seasonal, and residual terms. Analysis results demonstrate that the SODA SSHA time series are significantly correlated to the AVISO SSHA time series in SCS. To investigate the predictability of SCS SSHA, an exponential smoothing approach and an autoregressive integrated moving average approach are first used to fit the interannual and residual terms of SCS SSHA while keeping the seasonal part invariant. Then, an array of forecast experiments with the start time spanning from June 1977 to June 2007 is performed based on the prediction model which integrates the above two models and the time-independent seasonal term. Results indicate that the valid forecast time of SCS SSHA of the statistical model is about 7 months, and the predictability of SCS SSHA in Spring and Autumn is stronger than that in Summer and Winter. In addition, the prediction skill of SCS SSHA has remarkable decadal variability, with better phase forecast in 1997-2007

MRI features of breast lymphoma: preliminary experience in seven cases

PURPOSEWe aimed to evaluate the imaging features of breast lymphoma using magnetic resonance imaging (MRI). METHODSThis retrospective study consisted of seven patients with pathologically confirmed breast lymphoma. The breast lymphomas were primary in six patients and secondary in one patient. All patients underwent preoperative dynamic contrast-enhanced MRI and one underwent additional diffusion-weighted imaging (DWI) with a b value of 600 s/mm2. Morphologic characteristics, enhancement features, and apparent diffusion coefficient (ADC) values were reviewed.RESULTSOn MRI, three patients presented with a single mass, one with two masses, two with multiple masses, and one with a single mass and a contralateral focal enhancement. The MRI features of the eight biopsied masses in seven patients were analyzed. On MRI, the margins were irregular in six masses (75%) and spiculated in two (25%). Seven masses (87.5%) displayed homogeneous internal enhancement, while one (12.5%) showed rim enhancement. Seven masses (87.5%) showed a washout pattern and one (12.5%) showed a plateau pattern. The penetrating vessel sign was found in two masses (25%). One patient with two masses underwent DWI. Both masses showed hyperintense signal on DWI with ADC values of 0.867×10-3 mm2/s and 0.732×10-3 mm2/s, respectively.CONCLUSIONBreast lymphoma commonly presents as a homogeneously enhancing mass with irregular margins and displays a washout curve pattern on dynamic MRI. A low ADC value may also indicate a possible diagnosis of breast lymphoma

Single cell atlas for 11 non-model mammals, reptiles and birds.

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs

Diagnostic values of magnetic resonance imaging in mammography detected BI-RADS≥4 category calcifications with negative ultrasound results

Objective: To assess the diagnostic value of magnetic resonance imaging (MRI) for diannosing suspicious calcification which were BI-RADS≥4 on mammography (MG) and negative on ultrasound. Methods: A total of 126 suspicious calcification lesions were included in the study. Those calcifications were classified as BI-RADS≥4 category and negative on ultrasound between January 2020 and December 2020, and lesions were examined with breast magnetic resonance imaging (MRI). Performance of MRI for diagnosing suspicious calcification was assessed based on biopsy pathologic results. Results: There was 100 benign lesions (79.37%), and 26 malignant lesions 26(20.63%). Benign calcifications were mostly presented as amorphous calcifications 68%（68/100）, while malignant calcifications were mostly presented as coarse heterogeneous calcifications 53.85%（14/26）. In malignant calcifications, 61.54% lesions were presented as mass enhancement, and mass enhancement were more common in malignant lesions than in benign lesions (61.54% vs 22.00%,P<0.001). 67.00% benign calcifications had no abnormal enhancement (P<0.001). Persistent time-signal intensity curve (TIC) suggested benign lesions (63.60% vs 11.54%, P<0.001) while wash-out curve suggested malignant lesions (73.08% vs 0%,P<0.001). The detection sensitivity of MG and MG+MRI for ultrasound-negative calcifications were 15.4% and 92.3% (P<0.001), with accuracy of 80.2%, 96.8% (P<0.001), respectively. The negative predictive value for MG and MRI+MG were 81.5% and 98.0% (P<0.001). Conclusions: MRI combined with MG has higher sensitivity, accuracy and negative predictive value than mammography alone in diagnosing suspicious calcifications of ultrasound negative results

Fully Automatic Analysis of Muscle B-Mode Ultrasound Images Based on the Deep Residual Shrinkage U-Net

The parameters of muscle ultrasound images reflect the function and state of muscles. They are of great significance to the diagnosis of muscle diseases. Because manual labeling is time-consuming and laborious, the automatic labeling of muscle ultrasound image parameters has become a research topic. In recent years, there have been many methods that apply image processing and deep learning to automatically analyze muscle ultrasound images. However, these methods have limitations, such as being non-automatic, not applicable to images with complex noise, and only being able to measure a single parameter. This paper proposes a fully automatic muscle ultrasound image analysis method based on image segmentation to solve these problems. This method is based on the Deep Residual Shrinkage U-Net(RS-Unet) to accurately segment ultrasound images. Compared with the existing methods, the accuracy of our method shows a great improvement. The mean differences of pennation angle, fascicle length and muscle thickness are about 0.09°, 0.4 mm and 0.63 mm, respectively. Experimental results show that the proposed method realizes the accurate measurement of muscle parameters and exhibits stability and robustness

Fully Automatic Analysis of Muscle B-Mode Ultrasound Images Based on the Deep Residual Shrinkage U-Net

The parameters of muscle ultrasound images reflect the function and state of muscles. They are of great significance to the diagnosis of muscle diseases. Because manual labeling is time-consuming and laborious, the automatic labeling of muscle ultrasound image parameters has become a research topic. In recent years, there have been many methods that apply image processing and deep learning to automatically analyze muscle ultrasound images. However, these methods have limitations, such as being non-automatic, not applicable to images with complex noise, and only being able to measure a single parameter. This paper proposes a fully automatic muscle ultrasound image analysis method based on image segmentation to solve these problems. This method is based on the Deep Residual Shrinkage U-Net(RS-Unet) to accurately segment ultrasound images. Compared with the existing methods, the accuracy of our method shows a great improvement. The mean differences of pennation angle, fascicle length and muscle thickness are about 0.09°, 0.4 mm and 0.63 mm, respectively. Experimental results show that the proposed method realizes the accurate measurement of muscle parameters and exhibits stability and robustness

Statistical Prediction of the South China Sea Surface Height Anomaly

Based on the simple ocean data assimilation (SODA) data, this study analyzes and forecasts the monthly sea surface height anomaly (SSHA) averaged over South China Sea (SCS). The approach to perform the analysis is a time series decomposition method, which decomposes monthly SSHAs in SCS to the following three parts: interannual, seasonal, and residual terms. Analysis results demonstrate that the SODA SSHA time series are significantly correlated to the AVISO SSHA time series in SCS. To investigate the predictability of SCS SSHA, an exponential smoothing approach and an autoregressive integrated moving average approach are first used to fit the interannual and residual terms of SCS SSHA while keeping the seasonal part invariant. Then, an array of forecast experiments with the start time spanning from June 1977 to June 2007 is performed based on the prediction model which integrates the above two models and the time-independent seasonal term. Results indicate that the valid forecast time of SCS SSHA of the statistical model is about 7 months, and the predictability of SCS SSHA in Spring and Autumn is stronger than that in Summer and Winter. In addition, the prediction skill of SCS SSHA has remarkable decadal variability, with better phase forecast in 1997–2007

Organization and Unconventional Integration of the Mating-Type Loci in <i>Morchella</i> Species

True morels (Morchella spp.) are a group of delicious fungi in high demand worldwide, and some species of morels have been successfully cultivated in recent years. To better understand the sexual reproductive mechanisms of these fungi, we characterized the structure of the mating-type loci from ten morel species, and seven of them were obtained using long-range PCR amplification. Among the studied species, eight were heterothallic, two were homothallic, and four types of composition were observed in the MAT loci. In three of the five black morel species, the MAT1-1-1, MAT1-1-10, and MAT1-1-11 genes were in the MAT1-1 idiomorph, and only the MAT1-2-1 gene was in the MAT1-2 idiomorph, while an integration event occurred in the other two species and resulted in the importation of the MAT1-1-11 gene into the MAT1-2 idiomorph and survival as a truncated fragment in the MAT1-1 idiomorph. However, the MAT1-1-11 gene was not available in the four yellow morels and one blushing morel species. M. rufobrunnea, a representative species of the earliest diverging branch of true morels, along with another yellow morel Mes-15, were confirmed to be homothallic, and the MAT1-1-1, MAT1-1-10, and MAT1-2-1 genes were arranged in a tandem array. Therefore, we hypothesized that homothallism should be the ancestral reproductive state in Morchella. RT-PCR analyses revealed that four mating genes could be constitutively expressed, while the MAT1-1-10 gene underwent alternative splicing to produce different splice variants