Data and Knowledge Co-driving for Cancer Subtype Classification on Multi-Scale Histopathological Slides

Artificial intelligence-enabled histopathological data analysis has become a valuable assistant to the pathologist. However, existing models lack representation and inference abilities compared with those of pathologists, especially in cancer subtype diagnosis, which is unconvincing in clinical practice. For instance, pathologists typically observe the lesions of a slide from global to local, and then can give a diagnosis based on their knowledge and experience. In this paper, we propose a Data and Knowledge Co-driving (D&K) model to replicate the process of cancer subtype classification on a histopathological slide like a pathologist. Specifically, in the data-driven module, the bagging mechanism in ensemble learning is leveraged to integrate the histological features from various bags extracted by the embedding representation unit. Furthermore, a knowledge-driven module is established based on the Gestalt principle in psychology to build the three-dimensional (3D) expert knowledge space and map histological features into this space for metric. Then, the diagnosis can be made according to the Euclidean distance between them. Extensive experimental results on both public and in-house datasets demonstrate that the D&K model has a high performance and credible results compared with the state-of-the-art methods for diagnosing histopathological subtypes. Code: https://github.com/Dennis-YB/Data-and-Knowledge-Co-driving-for-Cancer-Subtypes-Classificatio

HCV 6a Prevalence in Guangdong Province Had the Origin from Vietnam and Recent Dissemination to Other Regions of China: Phylogeographic Analyses

Recently in China, HCV 6a infection has shown a fast increase among patients and blood donors, possibly due to IDU linked transmission.We recruited 210 drug users in Shanwei city, Guangdong province. Among them, HCV RNA was detected in 150 (71.4%), both E1 and NS5B genes were sequenced in 136, and 6a genotyped in 70. Of the 6a sequences, most were grouped into three clusters while 23% represent emerging strains. For coalescent analysis, additional 6a sequences were determined among 21 blood donors from Vietnam, 22 donors from 12 provinces of China, and 36 IDUs from Liuzhou City in Guangxi Province. Phylogeographic analyses indicated that Vietnam could be the origin of 6a in China. The Guangxi Province, which borders Vietnam, could be the first region to accept 6a for circulation. Migration from Yunnan, which also borders Vietnam, might be equally important, but it was only detected among IDUs in limited regions. From Guangxi, 6a could have further spread to Guangdong, Yunnan, Hainan, and Hubei provinces. However, evidence showed that only in Guangdong has 6a become a local epidemic, making Guangdong the second source region to disseminate 6a to the other 12 provinces. With a rate of 2.737×10⁻³ (95% CI: 1.792×10⁻³ to 3.745×10⁻³), a Bayesian Skyline Plot was portrayed. It revealed an exponential 6a growth during 1994-1998, while before and after 1994-1998 slow 6a growths were maintained. Concurrently, 1994-1998 corresponded to a period when contaminated blood transfusion was common, which caused many people being infected with HIV and HCV, until the Chinese government outlawed the use of paid blood donations in 1998.With an origin from Vietnam, 6a has become a local epidemic in Guangdong Province, where an increasing prevalence has subsequently led to 6a spread to many other regions of China

LaSalle stationary oscillation theorem for affine periodic dynamic systems on time scales

Abstract In this paper, for affine periodic systems on time scales, we establish LaSalle stationary oscillation theorem to obtain the existence and asymptotic stability of affine periodic solutions on time scales. As applications, we present the existence and asymptotic stability of affine periodic solutions on time scales via Lyapunov’s method

Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application

Robotic vision-based crack detection in concrete bridges is an essential task to preserve these assets and their safety. The conventional human visual inspection method is time consuming and cost inefficient. In this paper, we propose a robust algorithm to detect cracks in a pixel-wise manner from real concrete surface images. In practice, crack detection remains challenging in the following aspects: (1) detection performance is disturbed by noises and clutters of environment; and (2) the requirement of high pixel-wise accuracy is difficult to obtain. To address these limitations, three steps are considered in the proposed scheme. First, a local pattern predictor (LPP) is constructed using convolutional neural networks (CNN), which can extract discriminative features of images. Second, each pixel is efficiently classified into crack categories or non-crack categories by LPP, using as context a patch centered on the pixel. Lastly, the output of CNN—i.e., confidence map—is post-processed to obtain the crack areas. We evaluate the proposed algorithm on samples captured from several concrete bridges. The experimental results demonstrate the good performance of the proposed method

The Study of the Effect of the Digital Economy on the Low-Carbon Transformation of Urban Economies under Public Attention

Using the panel data of 274 cities from 2011 to 2019, this article studied the impact of the digital economy on the low-carbon transformation and space overflow effect on the city’s low-carbon transformation and used the panel threshold model to calculate the regulatory effect of public attention. Studies found the following: (1) The digital economy, the urban low-carbon transformation level, and public attention show regional differences, manifesting as the distribution of "point clusters" and "decreasing from east to west", and as far as the speed of change, after the year 2015, the annual increase of various indicators increased. (2) The role of digital economy development and urban low-carbon transformation levels have a "U" relationship. At present, most cities across the country have passed the point of inflection, which presents a significant promotion effect. However, in a comparison of regional coefficients, the Eastern region > Western region > Midlands. (3) The development of the digital economy has a significant “siphon effect” on the impact of urban low-carbon transformation. (4) With the increase in public attention in the region, the positive promotion of the digital economy on the urban low-carbon transformation has gradually increased

Study on the Growth of Holes in Cold Spraying via Numerical Simulation and Experimental Methods

Cold spraying is a promising method for rapid prototyping due to its high deposition efficiency and high-quality bonding characteristic. However, many researchers have noticed that holes cannot be replenished and will grow larger and larger once formed, which will significantly decrease the deposition efficiency. No work has yet been done on this problem. In this paper, a computational simulation method was used to investigate the origins of these holes and the reasons for their growth. A thick copper coating was deposited around the pre-drilled, micro-size holes using a cold spraying method on copper substrate to verify the simulation results. The results indicate that the deposition efficiency inside the hole decreases as the hole become deeper and narrower. The repellant force between the particles perpendicular to the impaction direction will lead to porosity if the particles are too close. There is a much lower flattening ratio for successive particles if they are too close at the same location, because the momentum energy contributes to the former particle’s deformation. There is a high probability that the above two phenomena, resulting from high powder-feeding rate, will form the original hole, which will grow larger and larger once it is formed. It is very important to control the powder feeding rate, but the upper limit is yet to be determined by further simulation and experimental investigation

PPA-Net: Pyramid Pooling Attention Network for Multi-Scale Ship Detection in SAR Images

In light of recent advances in deep learning and Synthetic Aperture Radar (SAR) technology, there has been a growing adoption of ship detection models that are based on deep learning methodologies. However, the efficiency of SAR ship detection models is significantly impacted by complex backgrounds, noise, and multi-scale ships (the number of pixels occupied by ships in SAR images varies significantly). To address the aforementioned issues, this research proposes a Pyramid Pooling Attention Network (PPA-Net) for SAR multi-scale ship detection. Firstly, a Pyramid Pooled Attention Module (PPAM) is designed to alleviate the influence of background noise on ship detection while its parallel component favors the processing of multiple ship sizes. Different from the previous attention module, the PPAM module can better suppress the background noise in SAR images because it considers the saliency of ships in SAR images. Secondly, an Adaptive Feature Balancing Module (AFBM) is developed, which can automatically balance the conflict between ship semantic information and location information. Finally, the detection capabilities of the ship detection model for multi-scale ships are further improved by introducing the Atrous Spatial Pyramid Pooling (ASPP) module. This innovative module enhances the detection model’s ability to detect ships of varying scales by extracting features from multiple scales using atrous convolutions and spatial pyramid pooling. PPA-Net achieved detection accuracies of 95.19% and 89.27% on the High-Resolution SAR Images Dataset (HRSID) and the SAR Ship Detection Dataset (SSDD), respectively. The experimental results demonstrate that PPA-Net outperforms other ship detection models

Highly Selective Recognition of Pyrophosphate by a Novel Coumarin-Iron (III) Complex and the Application in Living Cells

In this paper, a novel NL-Fe3+ ensemble was designed as a fluorescent chemosensor for highly selective detection of pyrophosphate (PPi) in DMSO/H2O (2:8/v:v, pH = 7.2) solution and living cells. NL showed a strong affinity for Fe3+ and was accompanied by obvious fluorescence quenching. Upon the addition of PPi to the generated NL-Fe3+ ensemble, the fluorescence and absorption spectra were recovered completely. Spectroscopic investigation showed that the interference provoked by common anions such as adenosine-triphosphate (ATP), adenosine diphosphate (ADP), and phosphates (Pi) can be ignored. The detection limit of NL-Fe3+ to PPi was calculated to be 1.45 × 10−8 M. Intracellular imaging showed that NL-Fe3+ has good membrane permeability and could be used for the detection of PPi in living cells. A B3LYP/6-31G(d,p) basis set was used to optimize NL and NL-Fe3+ complex