Transformative Deep Neural Network Approaches in Kidney Ultrasound Segmentation: Empirical Validation with an Annotated Dataset.

Kidney ultrasound (US) images are primarily employed for diagnosing different renal diseases. Among them, one is renal localization and detection, which can be carried out by segmenting the kidney US images. However, kidney segmentation from US images is challenging due to low contrast, speckle noise, fluid, variations in kidney shape, and modality artifacts. Moreover, well-annotated US datasets for renal segmentation and detection are scarce. This study aims to build a novel, well-annotated dataset containing 44,880 US images. In addition, we propose a novel training scheme that utilizes the encoder and decoder parts of a state-of-the-art segmentation algorithm. In the pre-processing step, pixel intensity normalization improves contrast and facilitates model convergence. The modified encoder-decoder architecture improves pyramid-shaped hole pooling, cascaded multiple-hole convolutions, and batch normalization. The pre-processing step gradually reconstructs spatial information, including the capture of complete object boundaries, and the post-processing module with a concave curvature reduces the false positive rate of the results. We present benchmark findings to validate the quality of the proposed training scheme and dataset. We applied six evaluation metrics and several baseline segmentation approaches to our novel kidney US dataset. Among the evaluated models, DeepLabv3+ performed well and achieved the highest dice, Hausdorff distance 95, accuracy, specificity, average symmetric surface distance, and recall scores of 89.76%, 9.91, 98.14%, 98.83%, 3.03, and 90.68%, respectively. The proposed training strategy aids state-of-the-art segmentation models, resulting in better-segmented predictions. Furthermore, the large, well-annotated kidney US public dataset will serve as a valuable baseline source for future medical image analysis research.

Study on the effect of digital economy development on carbon emissions: evidence from 30 provinces in China.

Currently, China is moving towards the era of the digital economy, which is gradually becoming a new engine of high-quality development. In the "double carbon" strategy context, the digital economy is characterized by low carbon emissions and high permeability, making it essential for carbon emission reduction. There needs to be more empirical research on the digital economy and carbon emissions. Given this, this study empirically examines the impact of digital economy development on carbon emissions intensity and its mechanisms in a multidimensional way based on the panel data of 30 provincial-level administrative regions in China from 2011 to 2019, utilizing a fixed-effects model, a mediated-effects model, a spatial Durbin model, and other methods. The study results show that (1) the digital economy can significantly reduce carbon emissions intensity. (2) The digital economy can indirectly affect the intensity of carbon emissions through industrial structure, energy structure, and environmental regulation. (3) The development of the local digital economy has a positive spillover effect on the carbon emissions intensity of neighbouring places. However, the overall effect is negative. This paper reveals some new features of the digital economy and carbon emissions intensity, which provides a reference for advancing the country's construction and realizing China's "double carbon" goal.

Effect of Waiting Time for Radioactive Iodine Therapy on Outcome in N1 Stage Papillary Thyroid Cancer.

CONTEXT: The waiting time for radioactive iodine therapy (WRAIT) after total thyroidectomy (TT) in patients with papillary thyroid cancer (PTC) and lymph node metastases (N1) has not been sufficiently investigated for risk of adverse outcomes. OBJECTIVE: This work aimed to estimate the effect of WRAIT on the outcomes of disease persistence and recurrence among patients with N1 PTC and investigate factors predictive of delayed radioactive iodine therapy (RAIT). METHODS: This retrospective cohort study was conducted in a university hospital. A total of 909 patients with N1 PTC were referred for RAIT between 2014 and 2018. WRAIT is the duration between TT and initial RAIT. The optimal WRAIT threshold determined using recursive partitioning analysis was used to define early and delayed RAIT. The primary end point was tumor persistence/recurrence. We compared the outcomes of patients with early and delayed RAIT using inverse probability weighting based on the propensity score. RESULTS: The WRAIT threshold that optimally differentiated worse long-term remission/excellent response outcomes was greater than 88 days (51% of our cohort; n = 464). WRAIT exceeding 88 days was associated with an augmented risk of disease persistence/recurrence (odds ratio, 2.47; 95% CI, 1.60-3.82) after adjustment. Predictors of delayed RAIT included residence in lower-income areas, reoperation before the initial RAIT, TT at a nonuniversity-affiliated hospital, multifocality, extrathyroidal extension, N1b disease, and pre-RAIT-stimulated thyroglobulin level less than 1 ng/mL. CONCLUSION: Delayed RAIT beyond 88 days after TT in patients with N1 PTC independently increased the risk of disease persistence/recurrence. Evaluation of the predictive determinants of prolonged WRAIT may help target at-risk patients and facilitate interventions.

Mantle heterogeneity caused by trapped water in the Southwest Basin of the South China Sea.

Water is the most common volatile component inside the Earth. A substantial amount of water can be carried down to the interior of the Earth by subducting plates. However, how the subducted water evolves after the subducting slab breaks off remains poorly understood. Here we use the data from a passive seismic experiment using ocean bottom seismometers (OBSs) together with the land stations to determine the high-resolution, three-dimensional seismic structure of the Southwest Sub-basin (SWSB) of the South China Sea (SCS). At depths below 40 km, the mantle shear velocity (Vsv) beneath the northern side of the SWSB is similar to that of the conventional oceanic pyrolite mantle, but roughly 3% shear-velocity reduction is found beneath the southern side of the SWSB. Results of thermal dynamic modeling reveal that the observed shear-velocity reduction could be explained by the presence of 150-300 ppm of water and 5-10% of lower continental crust. The inferred high-water content at the southern side of the SWSB is consistent with a model in which the Proto-SCS plate subducted southward prior to and during the formation of the SCS basin, releasing water into the upper mantle of the SWSB.

Phenotypic differences in the appearance of soft rice and its endosperm structural basis.

In view of the significant differences among genotypes in the appearance of soft rice, it is necessary to conduct research on the differences in the appearance quality of soft rice and their mechanisms. It can provide a theoretical basis for the selection and breeding of superior appearance varieties at a later stage. In order to clarify the differences in appearance phenotypes between different soft rice genotypes and structural basis of endosperm structures behind the differences, four soft rice varieties were selected in this study, including two varieties with good-appearance and two varieties with cloudy appearance. The differences in appearance phenotypes and endosperm structure in mature grains of soft rice with different appearance phenotypes were scientifically analyzed. The development process of their endosperm differences at the filling stage was investigated. The results show that the difference in the rice appearance of soft rice varieties mainly lay in the chalk-free seed transparency and chalkiness. These differences were caused by two completely different types of endosperm structure. Fewer and smaller starch grain cavities were responsible for higher chalk-free transparency of soft rice grains, denser starch granules arrangement caused lower chalkiness of soft rice grains. Ten days after flowering, the starch granules in the back and heart of good-appearance soft rice were already significantly fuller and more closely packed than those of cloudy soft rice. At the same time, the number and area of starch granule holes were significantly smaller than those of cloudy soft rice. This difference gradually increased until maturity. Therefore, based on appearance evaluation, soft rice with good-appearance should have higher transparency and lower chalkiness. The endosperm starch granules should be full and tightly arranged. The number of starch grain cavities and the area should be smaller. These differences develop in the early stages of grouting and gradually increase.

The number of metastatic lymph nodes optimizes staging in patients aged 55 years or older with papillary thyroid cancer.

Purpose: Current staging criteria for papillary thyroid cancer (PTC) do not include the number of metastatic lymph nodes (LNs), which is highly predictive of survival in multiple cancers. The LN metastasis burden is particularly relevant for older adults with thyroid cancer because of their poor prognosis. We examined a modified staging system for this population utilizing node number (Nn). Methods: Overall, 14,341 patients aged 55 years or older with stage I-IVB PTC were identified in the 2004-2015 Surveillance, Epidemiology and End Results database. Cox regression models were conducted to test the relationship between positive LN number and PTC-specific survival (PTCSS). Independent training/validation sets were used to derive and validate a new revised TNnM grouping. The 8th edition American Joint Committee on Cancer TNM staging system was compared with TNnM stage by calculating the 10-year PTCSS rates, Harrell's concordance index (C-index), and Akaike's information criterion (AIC). Results: An increase in number of LN metastases was identified as an independent, negative prognostic factor for PTCSS in multivariate analysis. 10-year PTCSS for stage I-IVB based on the AJCC 8th edition TNM were 98.83%, 93.49%, 71.21%, 72.95%, and 58.52%, respectively, while 10-year PTCSS for the corresponding stage in the TNnM were 98.59%, 92.2%, 83.26%, 75.24%, and 56.73%, respectively. The revised TNnM stage was superior, with a higher C-index and a lower AIC in both the training and validation cohorts. Conclusion: The TNnM staging system for PTC patients ≥ 55 years could be associated with improved outcomes. External validation studies of this system are warranted.

Weighted Error Entropy-Based Information Theoretic Learning for Robust Subspace Representation.

In most of the existing representation learning frameworks, the noise contaminating the data points is often assumed to be independent and identically distributed (i.i.d.), where the Gaussian distribution is often imposed. This assumption, though greatly simplifies the resulting representation problems, may not hold in many practical scenarios. For example, the noise in face representation is usually attributable to local variation, random occlusion, and unconstrained illumination, which is essentially structural, and hence, does not satisfy the i.i.d. property or the Gaussianity. In this article, we devise a generic noise model, referred to as independent and piecewise identically distributed (i.p.i.d.) model for robust presentation learning, where the statistical behavior of the underlying noise is characterized using a union of distributions. We demonstrate that our proposed i.p.i.d. model can better describe the complex noise encountered in practical scenarios and accommodate the traditional i.i.d. one as a special case. Assisted by the proposed noise model, we then develop a new information-theoretic learning framework for robust subspace representation through a novel minimum weighted error entropy criterion. Thanks to the superior modeling capability of the i.p.i.d. model, our proposed learning method achieves superior robustness against various types of noise. When applying our scheme to the subspace clustering and image recognition problems, we observe significant performance gains over the existing approaches.

Optimal Pre-Filtering for Improving Facebook Shared Images.

Online Social Networks (OSNs) have attracted a huge number of users, who store and share various images on a daily basis. As a well-known fact, most OSN platforms apply a series of lossy operations on the uploaded images, which could severely degrade the quality of the shared images, negatively affecting the user experiences. In this work, we consider the problem of significantly improving OSN-shared images through applying an optimal pre-filtering prior to image sharing, without any cooperation from the OSN platform itself. Facebook, as one of the most popular and representative OSNs, is chosen as the platform to present our designed pre-filtering strategy. We first treat Facebook as a black box, and thoroughly recover its mechanism of processing color images. Based on the precise knowledge on the image processing pipeline on Facebook, we design the pre-filter under an optimization framework, minimizing the end-to-end distortion between the shared image and the original one. Compared with the directly shared images, our proposed pre-filtering-then-sharing strategy brings significant improvements in terms of both quantitative and qualitative metrics. Extensive experimental results are provided to show the superiority of our proposed method. Finally, we discuss the strategy on how to extend our proposed technique to other OSN platforms.

[Exploration of anatomical location of Baliao points of adult female].

Fifteen morphologically and structurally complete sacrum specimens of normotrophic adult females were choosen. Distances between posterior sacral foramina and median sacral crest,and between the cores of adjacent posterior sacral foramina were measured. Then statistical analysis was done so as to provide objective anatomical evidence for the surface localization of Baliao points. The average distance between Shangliao (BL 31) and median sacral crest was (2.08 ± 0.19) cm; and the average distance between Ciliao (BL 32) and median sacral crest was (1.75 ± 0.12) cm; Zhongliao (BL 33), (1.59 ± 0.15) cm; Xialiao (BL 34), (1.56 ± 0.15) cm. And the distance of S1-S2 was (2.36 ± 0.31) cm averagely; S2-S3, (1.98 ± 0.23) cm; S3-S4, (1.71 ± 0.18) cm. It is considered that to locate Baliao points, Ciliao (BL 32) needs to be ascertained firstly.