Study on the application of percutaneous closed pleural brushing combined with cell block technique in the diagnosis of malignant pleural effusion.

INTRODUCTION: This study was to investigate the diagnostic value of percutaneous closed pleural brushing (CPBR) followed by cell block technique for malignant pleural effusion (MPE) and the predictive efficacy of pleural fluid carcinoembryonic antigen (CEA) for epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma patients with MPE. METHODS: All patients underwent closed pleural biopsy (CPB) and CPBR followed by cell block examination. MPE-positive diagnostic rates between the two methods were compared. Univariate and multivariate analyses were performed to determine factors influencing the EGFR mutations. Receiver operating characteristic (ROC) curve was used to analyze the predictive efficacy of pleural fluid CEA for EGFR mutations. RESULTS: The cumulative positive diagnostic rates for MPE after single and twice CPBR followed by cell block examination were 80.5% and 89.0%, higher than CPB (45.7%, 54.3%) (P < 0.001). Univariate analysis showed that EGFR mutation was associated with pleural fluid and serum CEA (P < 0.05). Multivariate analysis showed that pleural fluid CEA was an independent risk factor for predicting EGFR mutation (P < 0.001). The area under the curve (AUC) of pleural fluid CEA for EGFR mutation prediction was 0.774, higher than serum CEA (P = 0.043), but no difference with the combined test (P > 0.05). CONCLUSION: Compared with CPB, CPBR followed by the cell block technique can significantly increase the positive diagnostic rate of suspected MPE. CEA testing of pleural fluid after CPBR has a high predictive efficacy for EGFR mutation in lung adenocarcinoma patients with MPE, implying pleural fluid extracted for cell block after CPBR may be an ideal specimen for genetic testing.

New urbanization and carbon emissions intensity reduction: Mechanisms and spatial spillover effects.

The advancement of new urbanization policy (NU) and the attainment of double carbon targets play pivotal roles in facilitating high-quality economic development in China. This paper conducts a comprehensive analysis of the mechanism and spatial spillover effects of NU on carbon emission intensity reduction (CEIR), building upon an examination of the nature of NU and the principles of urban carbon pollution control. The research employs a multi-period difference-in-difference model (DID) to explore the causal relationship between NU and CEIR, using panel data from 278 prefecture-level cities spanning the period of 2006 to 2020. Empirical results demonstrate that the implementation of NU resulted in an 8.4 % reduction in carbon emission intensity (CEI). Furthermore, the analysis of the transmission mechanism reveals that NU stimulates green technology innovation and facilitates the development of industrial agglomeration, thus achieving CEIR. The decomposition of the spatial Durbin model indicates significant spatial spillover effects in the effectiveness of NU, signifying its positive impacts not only within the region but also in generating benefits for surrounding areas. Moreover, the dynamic heterogeneity results indicate that entrepreneurial vitality and urbanization rate exhibit dynamic effects on the policy's CEIR effect, both displaying nonlinear enhancement curves. Based on this, the policy implications of this paper include: The government should enhance regional coordinated governance to address carbon emissions pollution in alignment with China's NU. This can be accomplished by effectively harnessing the driving role of green innovation and industrial agglomeration. Additionally, the local government can actively create an entrepreneurial atmosphere and expedite the urbanization process in order to support NU in the implementation and achievement of CEIR.

Artificial intelligence, industrial structure optimization, and CO2 emissions.

The carbon-reducing effects of artificial intelligence (AI) will be a critical means of achieving carbon peak and carbon neutrality in China. However, in order to efficiently harness the power of AI, the relationship between AI and carbon reduction needs to be fully understood. In this study, we systematically investigated the impacts and mechanisms of action of AI on CO2 emissions by constructing econometric models using dynamic panel data from 30 provinces in mainland China from 2006 to 2019. The empirical results show that AI significantly reduces CO2 emissions. Further mediation effect tests found that in the western region, there are mediation effects of the quantity and quality of industrial structure advancedization and industrial structure ecology, while the mediation effect of industrial structure rationalization is not significant. In the eastern and central regions, the mediating effect of the quantity of industrial structure advanced is not significant, while the mediating effect of the quality of industrial structure advanced, industrial structure rationalization, and industrial structure ecology all exist. Our work provides evidence to support that AI reduces CO2 emissions in various regions of China. This can help regions formulate appropriate policies to promote the synergistic development of AI and the "dual-carbon" goal.

Quantity or quality? Regional innovation policy and green technology innovation.

As a typical practice of regional innovation policy, national innovation demonstration zones (NIIDZ) plays an important role in empowering green technology innovation. Analyzing the internal logic between NIIDZ and green technology innovation is of great practical significance to enhance China's independent innovation capability and accelerate the ecological civilization construction. Based on the panel data of 280 cities from 2005 to 2020, we use the difference-in-difference model (DID) to evaluate the policy effect of NIIDZ on green technology innovation from the dual perspective of quality and quantity. The results show that NIIDZ effectively contributes to the quantity of green technology innovation (GTI) and the quality of green technology innovation (GTQ), especially GTI. The conclusion is still valid after robustness tests like PSM-DID and eliminating the pilot interference of innovative cities. The test of the intermediary mechanism shows that regional innovation policy can not only directly drive GTI and GTQ but also indirectly promote them through accelerating talent gather, driving industrial clusters, and increasing government financial support. Spatial heterogeneity analysis indicates that the effectiveness of regional innovation policy demonstrates a decreasing trend from east to west, and this positive effect is more significant in cities with a higher level of financial development. Our results provide important policy insights for the Chinese government to further stimulate the effectiveness of green innovation in the NIIDZ, accelerate the construction of an innovative country, and achieve high-quality green economic development.

Artificial intelligence-assisted ultrasound image analysis to discriminate early breast cancer in Chinese population: a retrospective, multicentre, cohort study.

Background: Early diagnosis of breast cancer has always been a difficult clinical challenge. We developed a deep-learning model EDL-BC to discriminate early breast cancer with ultrasound (US) benign findings. This study aimed to investigate how the EDL-BC model could help radiologists improve the detection rate of early breast cancer while reducing misdiagnosis. Methods: In this retrospective, multicentre cohort study, we developed an ensemble deep learning model called EDL-BC based on deep convolutional neural networks. The EDL-BC model was trained and internally validated on B-mode and color Doppler US image of 7955 lesions from 6795 patients between January 1, 2015 and December 31, 2021 in the First Affiliated Hospital of Army Medical University (SW), Chongqing, China. The model was assessed by internal and external validations, and outperformed radiologists. The model performance was validated in two independent external validation cohorts included 448 lesions from 391 patients between January 1 to December 31, 2021 in the Tangshan People's Hospital (TS), Chongqing, China, and 245 lesions from 235 patients between January 1 to December 31, 2021 in the Dazu People's Hospital (DZ), Chongqing, China. All lesions in the training and total validation cohort were US benign findings during screening and biopsy-confirmed malignant, benign, and benign with 3-year follow-up records. Six radiologists performed the clinical diagnostic performance of EDL-BC, and six radiologists independently reviewed the retrospective datasets on a web-based rating platform. Findings: The area under the receiver operating characteristic curve (AUC) of the internal validation cohort and two independent external validation cohorts for EDL-BC was 0.950 (95% confidence interval [CI]: 0.909-0.969), 0.956 (95% [CI]: 0.939-0.971), and 0.907 (95% [CI]: 0.877-0.938), respectively. The sensitivity values were 94.4% (95% [CI]: 72.7%-99.9%), 100% (95% [CI]: 69.2%-100%), and 80% (95% [CI]: 28.4%-99.5%), respectively, at 0.76. The AUC for accurate diagnosis of EDL-BC (0.945 [95% [CI]: 0.933-0.965]) and radiologists with artificial intelligence (AI) assistance (0.899 [95% [CI]: 0.883-0.913]) was significantly higher than that of the radiologists without AI assistance (0.716 [95% [CI]: 0.693-0.738]; p < 0.0001). Furthermore, there were no significant differences between the EDL-BC model and radiologists with AI assistance (p = 0.099). Interpretation: EDL-BC can identify subtle but informative elements on US images of breast lesions and can significantly improve radiologists' diagnostic performance for identifying patients with early breast cancer and benefiting the clinical practice. Funding: The National Key R&D Program of China.

An Efficient CRT Based Algorithm for Frequency Determination from Undersampled Real Waveform.

The Chinese Remainder Theorem (CRT) based frequency estimation has been widely studied during the past two decades. It enables one to estimate frequencies by sub-Nyquist sampling rates, which reduces the cost of hardware in a sensor network. Several studies have been done on the complex waveform; however, few works studied its applications in the real waveform case. Different from the complex waveform, existing CRT methods cannot be straightforwardly applied to handle a real waveform's spectrum due to the spurious peaks. To tackle the ambiguity problem, in this paper, we propose the first polynomial-time closed-form Robust CRT (RCRT) for the single-tone real waveform, which can be considered as a special case of RCRT for arbitrary two numbers. The time complexity of the proposed algorithm is O(L), where L is the number of samplers. Furthermore, our algorithm also matches the optimal error-tolerance bound.

Heterogeneous two-sided effects of different types of environmental regulations on carbon productivity in China.

In the context of globalization, the importance of enhancing carbon productivity is becoming increasingly evident. The study is a continuation of previous studies on the relationship between environmental regulations and carbon productivity. Based on a dataset of 30 provinces in China from 2006 to 2018, the paper decomposes the two-sided effects of command-and-control and market-based environmental regulations on carbon productivity. First, empirical research shows that the average positive effect of command-and-control environmental regulation on carbon productivity is 0.0158, which is much less than the average of negative effect of 0.0697, highlighting mainly the negative effect on carbon productivity. Conversely, the positive effect of market-based environmental regulation on carbon productivity is 0.0691, much greater than the negative effect of 0.0038, which highlights the obvious positive impact characteristics. Overall, the net effect of command-and-control environmental regulation on carbon productivity is -0.0541, and net effect of market-based environmental regulation on carbon productivity is 0.0653. Second, the negative impact of command-and-control environmental regulations on carbon productivity underwent a "back-to-N" change process in 2006-2018, while the driving effect of market-based environmental regulation on carbon productivity increased continuously during the 2006-2018 period. Third, most of the regions with high negative effects of command-and-control environmental regulation on carbon productivity tend to be resource-intensive and carbon-intensive, while the positive effects of market-based environmental regulation on carbon productivity have no obvious geographical agglomeration characteristics. Fourth, the continuous improvement of regional development conditions is clearly conducive to the continuous reduction of the negative effects of command-and-control environmental regulation on carbon productivity, while the higher positive effects of market-based environmental regulation on carbon productivity at this stage need to meet different regional condition intervals.

The two-sided effects of foreign direct investment on carbon emissions performance in China.

Climate change caused by rapid increases in greenhouse gas concentrations is now a global challenge. Foreign direct investment (FDI), as a key player in global economic growth, is a major contributor to carbon emissions. Based on panel data of 30 provinces in China collected from 2007 to 2018, this paper uses the two-tier stochastic frontier model to analyse the opposing two-sided effects of FDI on carbon emission performance and calculates their combined effects. The empirical study shows that FDI has both a promoting and an inhibiting effect on carbon emission performance, and the overall effect is characterized by less of an inhibiting effect than a promoting effect, resulting in the positive driving characteristic of the combined effect. The average inhibiting effect is 0.0402, and the average promoting effect is 0.1065, causing the comprehensive effect of FDI on carbon emission performance to have an average value of 0.0663. The empirical results also show that the promoting effects of FDI are greater than the inhibiting effects in most regions; however, after 2013, the level of the promoting effect declined overall. There are regional differences in the combined effects of FDI on carbon emission performance, and the driving effects present in the central and western regions of China are significantly lower than that in the eastern region. Based on the research conclusions, while promoting a "green" revolution in FDI utilization patterns, it is necessary to strengthen the interaction mechanism between FDI and low-carbon economic development in China and to enhance the driving effect of FDI on carbon emission performance in China.

Image-Based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era.

3D reconstruction is a longstanding ill-posed problem, which has been explored for decades by the computer vision, computer graphics, and machine learning communities. Since 2015, image-based 3D reconstruction using convolutional neural networks (CNN) has attracted increasing interest and demonstrated an impressive performance. Given this new era of rapid evolution, this article provides a comprehensive survey of the recent developments in this field. We focus on the works which use deep learning techniques to estimate the 3D shape of generic objects either from a single or multiple RGB images. We organize the literature based on the shape representations, the network architectures, and the training mechanisms they use. While this survey is intended for methods which reconstruct generic objects, we also review some of the recent works which focus on specific object classes such as human body shapes and faces. We provide an analysis and comparison of the performance of some key papers, summarize some of the open problems in this field, and discuss promising directions for future research.

The Profile of Timing Dialysis Initiation in Patients with End-stage Renal Disease in China: A Cohort Study.

BACKGROUND: Hemodialysis is the main approach for renal replacement therapy in patients with end-stage renal disease (ESRD) in China. The timing of dialysis initiation is one of the key factors influencing patient survival and prognosis. Over the past decade, the relationship between the timing of dialysis initiation and mortality has remained unclear in patients with ESRD in China. METHODS: Patients who commenced maintenance hemodialysis from 2009 to 2014 from 24 hemodialysis centers in Mainland China were enrolled in the study (n = 1,674). Patients were divided into 2 groups based on the year they started hemodialysis (patients who started hemodialysis from 2009 to 2011, and patients who started hemodialysis from 2012 to 2014). Analysis of the yearly change in the estimated glomerular filtration rate (eGFR) at the initiation of dialysis was performed for the 2 groups. Meanwhile, the patients were divided into 3 groups based on their eGFR at the initiation of dialysis (<4, 4-8, and >8 mL/min/1.73 m2). For these 3 groups, the relationship between the eGFR at the start of dialysis and mortality were analyzed. RESULTS: The average eGFRs were 5.68 and 5.94 mL/min/1.73 m2 for 2009-2011 and 2012-2014, respectively. Compared with the 2009-2011 group, the proportion of patients with diabetes in 2012-2014 increased from 26.7 to 37.7%. The prognosis of patients with different eGFRs at the start of dialysis was analyzed using Kaplan-Meier survival curves. After adjusting for confounding factors through a Cox regression model, no significant difference was demonstrated among the 3 groups (<4 mL/min/1.73 m2 was used as the reference, in comparison with 4-8 mL/min/1.73 m2 [p = 0.681] and >8 mL/min/1.73 m2 [p = 0.403]). CONCLUSION: In Mainland China, the eGFR at the start of dialysis did not change significantly over time from 2008 to 2014 and had no association with the mortality of patients with ESRD.

Speech Emotion Recognition with Heterogeneous Feature Unification of Deep Neural Network.

Automatic speech emotion recognition is a challenging task due to the gap between acoustic features and human emotions, which rely strongly on the discriminative acoustic features extracted for a given recognition task. We propose a novel deep neural architecture to extract the informative feature representations from the heterogeneous acoustic feature groups which may contain redundant and unrelated information leading to low emotion recognition performance in this work. After obtaining the informative features, a fusion network is trained to jointly learn the discriminative acoustic feature representation and a Support Vector Machine (SVM) is used as the final classifier for recognition task. Experimental results on the IEMOCAP dataset demonstrate that the proposed architecture improved the recognition performance, achieving accuracy of 64% compared to existing state-of-the-art approaches.

An Equation Based on Fuzzy Mathematics to Assess the Timing of Haemodialysis Initiation.

In order to develop an equation that integrates multiple clinical factors including signs and symptoms associated with uraemia to assess the initiation of dialysis, we conducted a retrospective cohort study including 25 haemodialysis centres in Mainland China. Patients with ESRD (n = 1281) who commenced haemodialysis from 2008 to 2011 were enrolled in the development cohort, whereas 504 patients who began haemodialysis between 2012 and 2013 were enrolled in the validation cohort comprised. An artificial neural network model was used to select variables, and a fuzzy neural network model was then constructed using factors affecting haemodialysis initiation as input variables and 3-year survival as the output variable. A logistic model was set up using the same variables. The equation's performance was compared with that of the logistic model and conventional eGFR-based assessment. The area under the bootstrap-corrected receiver-operating characteristic curve of the equation was 0.70, and that of two conventional eGFR-based assessments were 0.57 and 0.54. In conclusion, the new equation based on Fuzzy mathematics, covering laboratory and clinical variables, is more suitable for assessing the timing of dialysis initiation in a Chinese ESRD population than eGFR, and may be a helpful tool to quantitatively evaluate the initiation of haemodialysis.