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To achieve diesel engine ultra-low nitrogen oxide emission, light-off selective catalyst reduction (LO-SCR) has been suggested for better performance with lower exhaust temperature. An electric heater upstream of the exhaust aftertreatment system was applied to significantly decrease the NOx emission at a low exhaust temperature. With a 7.2 kW electric heater coupled with LO-SCR, the NOx emission during 200~500 s of the world harmonized transient cycle (WHTC) decreased from 282.6 ppm to 61.5 ppm, which is a decrease of 45%. Application of an upstream diesel oxidation catalyst (DOC) decreased the NOx emission by 63% at the same interval at the cost of worse cold-start performance. The urea input was also adjusted to avoid NOx emission during the latter part of the WHTC. [ABSTRACT FROM AUTHOR]

In order to better manage demand response resources of user side and reduce short-term load forecasting error, a short-term load forecasting method considering demand response in virtual power plant mode is proposed. Firstly, the demand response mechanism of the virtual power plant is analyzed. Taking the maximum profit of the virtual power plant as the goal, considering the user’s energy consumption habits, self built photovoltaic, energy storage behavior and thermal electric coupling, the optimization model is established for each type of demand response resources. The CPLEX solver is called to solve the mixed integer linear programming problem after the model transformation, and the sub signals of each resource participating in the demand response are obtained. Then, based on this model, a long-term and short-term memory network model considering demand response signals is established to predict load power iteratively. At the same time, the long-term and short-term memory network model considering the demand response signals effectively makes up for the shortcomings of the traditional forecasting model without considering the demand response, and is more accurate in predicting the future trend of load change.

Gut-associated lymphoid tissue (GALT) carcinoma, also termed dome-type carcinoma, is an infrequent distinctive subtype of colorectal adenocarcinoma and only 18 cases have been reported in the English medical literature. These tumors have unique clinicopathological features and are considered to have a low malignant potential with favorable prognosis. Herein, we described a case of a 49-year-old male with intermittent hematochezia for 2 years. Colonoscopy revealed a sessile broad-based polyp of approximately 20 mm × 17 mm in the sigmoid colon 260 mm away from the anus, with a slightly hyperemic surface. Histologically, this lesion showed typical GALT carcinoma. The patient was followed up for one and a half year and he did not experience any discomfort, such as abdominal pain or hematochezia, and no tumor recurrence occurred. Moreover, we reviewed the literature, summarized the clinicopathological features of GALT carcinoma, and highlighted its pathological differential diagnosis to further explore this infrequent type of colorectal adenocarcinoma. [ABSTRACT FROM AUTHOR]

Most existing image deblurring methods are based on the estimation of blur kernels and end-to-end learning of the mapping relationship between blurred and sharp images. However, since different real-world blurred images typically have completely different blurring patterns, the performance of these methods in real image deblurring tasks is limited without explicitly modeling blurring as degradation representations. In this paper, we propose IDR 2 ENet , which is the Implicit Degradation Representations and Reblur Estimation Network, for real image deblurring. IDR 2 ENet consists of a degradation estimation process, a reblurring process, and a deblurring process. The degradation estimation process takes the real blurred image as input and outputs the implicit degradation representations estimated on it, which are used as the inputs of both reblurring and deblurring processes to better estimate the features of the blurred image. The experimental results show that whether compared with traditional or deep-learning-based deblurring algorithms, IDR 2 ENet achieves stable and efficient deblurring results on real blurred images. [ABSTRACT FROM AUTHOR]

Social relationships refer to the connections that exist between people and indicate how people interact in society. The effective recognition of social relationships is conducive to further understanding human behavioral patterns and thus can be vital for more complex social intelligent systems, such as interactive robots and health self-management systems. The existing works about social relation recognition (SRR) focus on extracting features on different scales but lack a comprehensive mechanism to orchestrate various features which show different degrees of importance. In this paper, we propose a new SRR framework, namely Multi-level Transformer-Based Social Relation Recognition (MT-SRR), for better orchestrating features on different scales. Specifically, a vision transformer (ViT) is firstly employed as a feature extraction module for its advantage in exploiting global features. An intra-relation transformer (Intra-TRM) is then introduced to dynamically fuse the extracted features to generate more rational social relation representations. Next, an inter-relation transformer (Inter-TRM) is adopted to further enhance the social relation representations by attentionally utilizing the logical constraints among relationships. In addition, a new margin related to inter-class similarity and a sample number are added to alleviate the challenges of a data imbalance. Extensive experiments demonstrate that MT-SRR can better fuse features on different scales as well as ameliorate the bad effect caused by a data imbalance. The results on the benchmark datasets show that our proposed model outperforms the state-of-the-art methods with significant improvement. [ABSTRACT FROM AUTHOR]

Background: Frail older adults have an increased risk of adverse health outcomes and premature death. They also exhibit altered gait characteristics in comparison with healthy individuals. Methods: In this study, we created a Fried's frailty phenotype (FFP) labelled casual walking video set of older adults based on the West China Health and Aging Trend study. A series of hyperparameters in machine vision models were evaluated for body key point extraction (AlphaPose), silhouette segmentation (Pose2Seg, DPose2Seg, and Mask R-CNN), gait feature extraction (Gaitset, LGaitset, and DGaitset), and feature classification (AlexNet and VGG16), and were highly optimised during analysis of gait sequences of the current dataset. Results: The area under the curve (AUC) of the receiver operating characteristic (ROC) at the physical frailty state identification task for AlexNet was 0.851 (0.827–0.8747) and 0.901 (0.878–0.920) in macro and micro, respectively, and was 0.855 (0.834–0.877) and 0.905 (0.886–0.925) for VGG16 in macro and micro, respectively. Furthermore, this study presents the machine vision method equipped with better predictive performance globally than age and grip strength, as well as than 4-m-walking-time in healthy and pre-frailty classifying. Conclusion: The gait analysis method in this article is unreported and provides promising original tool for frailty and pre-frailty screening with the characteristics of convenience, objectivity, rapidity, and non-contact. These methods can be extended to any gait-related disease identification processes, as well as in-home health monitoring. [ABSTRACT FROM AUTHOR]

To analyze the anthropogenic activities on water-environment conditions, sampling campaigns and isotope measurement for precipitation, river water and groundwater were conducted in Jinjiang River System, Chengdu, China for one year. The results show that runoff is the main factor affecting the isotopic composition in natural-state rivers, while in rivers affected by human activities, the isotopic composition varies greatly due to the influence of impermeable surface and municipal pipe networks on water evaporation and mixing. The warm-and-humid moisture source and "heat island" effect lead to high slope and intercept of local meteoric water line (LMWL), and the characteristics of surface water line (SWL) are closely related to land types. Isotope changes along the flow path indicate significant evaporation effect in wet season, while the operation of municipal pipe networks partially conceals the evaporation effect in dry season. The intensity of river evaporation is positively correlated with the urbanization level along the river. Isotope mixing model shows that precipitation and river water are the recharge sources of groundwater in rain and dry seasons, respectively, and the mixing ratios between Jiangan River and Fuhe River shows significant seasonal variation. The combination of isotopic analysis and pollutant data reveals that the seasonal water contribution is the main factor influencing the water quality. Risks for water safety and environmental health in Chengdu and cities along lower Minjiang River were assessed. [ABSTRACT FROM AUTHOR]

Underwater images suffer from severe color casts, low contrast and blurriness, which are caused by scattering and absorption when light propagates through water. However, existing deep learning methods treat the restoration process as a whole and do not fully consider the underwater physical distortion process. Thus, they cannot adequately tackle both absorption and scattering, leading to poor restoration results. To address this problem, we propose a novel two-stage network for underwater image restoration (UIR), which divides the restoration process into two parts viz. horizontal and vertical distortion restoration. In the first stage, a model-based network is proposed to handle horizontal distortion by directly embedding the underwater physical model into the network. The attenuation coefficient, as a feature representation in characterizing water type information, is first estimated to guide the accurate estimation of the parameters in the physical model. For the second stage, to tackle vertical distortion and reconstruct the clear underwater image, we put forth a novel attenuation coefficient prior attention block (ACPAB) to adaptively recalibrate the RGB channel-wise feature maps of the image suffering from the vertical distortion. Experiments on both synthetic dataset and real-world underwater images demonstrate that our method can effectively tackle scattering and absorption compared with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Ultrasonic extraction and nebulization in real-time/carbon fiber ionization mass spectrometry (UEN/CFI-MS) was developed to screen the synthetic drugs adulterated into herbal products such as antidiabetic drug, antihypertensive drug, and hypolipidemic drug. Recently, ambient ionization MS techniques have achieved great advance for rapid analysis of sample surface. However, direct analysis of the analytes inside samples remains a challenge due to a lack of effective online sample extraction procedures. Owing to disappointing desorption efficiency, analytes inside the sample suffer from low detecting sensitivity when applying ambient ionization MS techniques. In this study, online ultrasonic extraction combined with carbon fiber ionization was used for real-time extraction, nebulization and detection of the analytes inside samples. The ultrasonic atomizer could produce a high-frequency vibration to realize online extraction and nebulization of sample. Then, the produced sample droplets could be immediately ionized by the carbon fiber ionization mass spectrometry. UEN/CFI-MS has shown great compatibility to solvents and compounds with a wide range of polarity and has few limitations for the shape of sample. UEN/CFI-MS was successfully applied for the rapid screening of synthetic drugs adulterated into herbal products. Among 37 batches of herbal products, 1 batch of Chinese patent medicine and 6 batches of dietary supplements were detected to be adulterated with the synthetic chemicals without labeling. Image 1 • An ultrasonic extraction and nebulization in real-time/carbon fiber ionization mass spectrometry method was developed. • UEN/CFI-MS allows real-time extraction and detection of the analytes inside samples with a measurement cycle of 10 seconds. • The method shows advantages of high sensitivity, good repeatability and acceptable semi-quantification capability. • Rapid screening the synthetic drugs adulterated into herbal products has been performed. [ABSTRACT FROM AUTHOR]

Abstract: Biological tests demonstrated that the inactivation of Nosema bombycis (N. bombycis) spores by chlorine dioxide (ClO2) occurs very fast and is highly sensitive. The lowest effective inactivation dosage and time was 15mg/mL for 30min. The inactivation of spores was additionally verified by using double color fluorescence stain and spore germination testing. A series of biological changes, including a large number of substrates that were leaked out from the spores included proteins, DNA, polysaccharide, K+, and Ca2+, occurred a short time after N. bombycis spores were treated with ClO2. In addition, the lipid of spores was disrupted and ATPase activity was inhibited, which resulted in the destruction of the inner structure of the spores. [Copyright &y& Elsevier]

Deep neural networks have been successfully used in image processing and have benefitted image compression technology. On this basis, deeper and more extensive use of compression algorithms in remote sensing applications was investigated in our study. As a general residual block does not effectively utilize the connections between channels and spatial regions in the reconstruction process, an attention mechanism codec framework based on the channel-spatial attention residual block was introduced. Consequently, the network can account for the interdependence between channels and enhance specific spatial areas. Simultaneously, the feature importance map makes the network focus on valuable information, especially for small objects and remote sensing images with many tiny details, which is crucial to describe their contours. Introducing the attention mechanism commonly used in target detection can effectively recover the missing detailed information for remote sensing compressed images. Moreover, to utilize the underlying information of the various scene categories and characterize the feature differences between scenes in remote sensing images, an adaptive scene-aware module was introduced to enhance the feature representation in remote sensing images of different scenes. The experimental results show that the proposed method has superior subjective and objective effects in remote sensing image compression tasks. [ABSTRACT FROM AUTHOR]

The urban environment has a great impact on the wellbeing of citizens and it is of great significance to understand how citizens perceive and evaluate places in a large scale urban region and to provide scientific evidence to support human-centered urban planning with a better urban environment. Existing studies for assessing urban perception have primarily relied on low efficiency methods, which also result in low evaluation accuracy. Furthermore, there lacks a sophisticated understanding on how to correlate the urban perception with the built environment and other socio-economic data, which limits their applications in supporting urban planning. In this study, a new data-enabled intelligence framework for evaluating human perceptions of urban space is proposed. Specifically, a novel classification-then-regression strategy based on a deep convolutional neural network and a random-forest algorithm is proposed. The proposed approach has been applied to evaluate the perceptions of Beijing and Chengdu against six perceptual criteria. Meanwhile, multi-source data were employed to investigate the associations between human perceptions and the indicators for the built environment and socio-economic data including visual elements, facility attributes and socio-economic indicators. Experimental results show that the proposed framework can effectively evaluate urban perceptions. The associations between urban perceptions and the visual elements, facility attributes and a socio-economic dimension have also been identified, which can provide substantial inputs to guide the urban planning for a better urban space. [ABSTRACT FROM AUTHOR]

In recent years, to save bandwidth and storage space, images are usually compressed to reduce data volume, which leads to the loss of image details and affects the super-resolution (SR) performance. SR of compressed images is a key technique for addressing this problem. We propose the compressed-image super-resolution using enhanced attention network (CISREAN). First, the task is divided into two subtasks: image decompression and SR. Each subprocess introduces an enhanced residual module (ERM) with an attention mechanism. The ERM consists of several wide-activation residual blocks (WARBs) and an attention unit called the cascading residual attention (CRA) block. A WARB achieves better reconstruction than traditional image processing with the same computational complexity, and the CRA extracts more useful information in feature mapping even with fewer channels. This makes the ERM light and effective. Next, initial features are selected for wider inception and less blocking by overlapping extractions from the compressed image during decompression by convolutional layers. After completing both subprocesses, an end-to-end network is trained; it reduces compression artifacts, performing SR simultaneously. Extensive experiments on JPEG images with different quality factors show that CISREAN provides state-of-the-art performance based on objective metrics and subjective visual quality. [ABSTRACT FROM AUTHOR]

Remote sensing images have been widely used in many applications. However, the resolution of the obtained remote sensing images may not meet the increasing demands for some applications. In general, the sparse representation-based super-resolution (SR) method is one of the most popular methods to solve this issue. However, traditional sparse representation SR methods do not fully exploit the complementary constraints of images. Therefore, they cannot accurately reconstruct the unknown HR images. To address this issue, we propose a novel adaptive joint constraint (AJC) based on sparse representation for the single remote sensing image SR. First, we construct a nonlocal constraint by using the nonlocal self-similarity. Second, we propose a local structure filter according to the local gradient of the image and then construct a local constraint. Next, the nonlocal and local constraints are introduced into the sparse representation-based SR framework. Finally, the parameters of the joint constraint model are selected adaptively according to the level of image noise. We utilize the alternate iteration algorithm to tackle the minimization problem in AJC. Experimental results show that the proposed method achieves good SR performance in preserving image details and significantly improves the objective evaluation indices. [ABSTRACT FROM AUTHOR]

Super-resolution (SR), which aims at recovering a high-resolution (HR) image from single or sequential low-resolution (LR) images, is a widely used technology in image processing. In the field of image SR, convolutional neural networks (CNNs) have attracted increasing attention because of their high-quality performance. However, most CNN-based methods treat each channel-wise feature equally, which lacks discriminative learning ability across feature channels. Furthermore, many methods neglect to fully use information of each convolutional layer. To resolve these problems, we propose a remote sensing image SR method named dense channel attention network (DCAN). In our DCAN, a sequence of residual dense channel attention blocks (RDCABs) is cascaded with a densely connected structure. In each RDCAB, we make full use of the information from all convolution layers via densely connected convolutional layers. In addition, RDCABs utilize the channel attention mechanism to adaptively recalibrate channel-wise feature responses by explicitly modeling the interdependencies between the channels. In addition, our DCAN can make full use of the hierarchical features by densely connecting each RDCAB. Finally, to further improve the SR performance, the proposed DCAN is learned in both the pixel and wavelet domains, and a fusion layer is used to fuse the outputs of these two domains. Extensive quantitative and qualitative evaluations verify the superiority of our proposed method over several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The development of rapid approaches to detect prognostic markers is significant in reducing the morbidity and mortality of cancer. In this paper, we describe a rapid and specific biosensing platform for target DNA (p53 gene as a model) detection based on reverse strand displacement amplification (R-SDA). When the p53 gene is added, multifuctional molecular beacon (MMB) is unfolded via the hybridization with p53 gene. With the assist of Klenow fragment (KF) and Nt.BbvCI (the nicking endonuclease), p53 gene recycling could be initiated and considerable amount of complementary sequences for the MMBs (Nicked fragments, NFs) could be formed, generating enhanced fluorescence signal. Using this amplification strategy, the proposed biosensor displays the detection limit of 1 nM and a wide linear range from 1 to 100 nM, even if only one type of probe is involved. Notably, remarkable detection specificity for single-base mismatched target p53 gene is achieved. Moreover, the described biosensor also exhibited the stability in real biological samples (human serum). The rapid detection strategy can be performed less than 30 min without harsh reaction conditions or expensive nanoparticles. This biosensor shows great potential for application in clinic assay, especially, for early cancer diagnosis. [ABSTRACT FROM AUTHOR]

The parameter evaluation of reservoir rocks can help us to identify components and calculate the permeability and other parameters, and it plays an important role in the petroleum industry. Until now, computed tomography (CT) has remained an irreplaceable way to acquire the microstructure of reservoir rocks. During the evaluation and analysis, large samples and high-resolution images are required in order to obtain accurate results. Owing to the inherent limitations of CT, however, a large field of view results in low-resolution images, and high-resolution images entail a smaller field of view. Our method is a promising solution to these data collection limitations. In this study, a framework for sparse representation-based 3D volumetric super-resolution is proposed to enhance the resolution of 3D voxel images of reservoirs scanned with CT. A single reservoir structure and its downgraded model are divided into a large number of 3D cubes of voxel pairs and these cube pairs are used to calculate two overcomplete dictionaries and the sparse-representation coefficients in order to estimate the high frequency component. Future more, to better result, a new feature extract method with combine BM4D together with Laplacian filter are introduced. In addition, we conducted a visual evaluation of the method, and used the PSNR and FSIM to evaluate it qualitatively. [ABSTRACT FROM AUTHOR]

Recently, learning-based algorithms have shown impressive performance in underwater image enhancement. Most of them resort to training on synthetic data and obtain outstanding performance. However, these deep methods ignore the significant domain gap between the synthetic and real data (i.e., inter-domain gap), and thus the models trained on synthetic data often fail to generalize well to real-world underwater scenarios. Moreover, the complex and changeable underwater environment also causes a great distribution gap among the real data itself (i.e., intra-domain gap). However, almost no research focuses on this problem and thus their techniques often produce visually unpleasing artifacts and color distortions on various real images. Motivated by these observations, we propose a novel Two-phase Underwater Domain Adaptation network (TUDA) to simultaneously minimize the inter-domain and intra-domain gap. Concretely, in the first phase, a new triple-alignment network is designed, including a translation part for enhancing realism of input images, followed by a task-oriented enhancement part. With performing image-level, feature-level and output-level adaptation in these two parts through jointly adversarial learning, the network can better build invariance across domains and thus bridging the inter-domain gap. In the second phase, an easy-hard classification of real data according to the assessed quality of enhanced images is performed, in which a new rank-based underwater quality assessment method is embedded. By leveraging implicit quality information learned from rankings, this method can more accurately assess the perceptual quality of enhanced images. Using pseudo labels from the easy part, an easy-hard adaptation technique is then conducted to effectively decrease the intra-domain gap between easy and hard samples. Extensive experimental results demonstrate that the proposed TUDA is significantly superior to existing works in terms of both visual quality and quantitative metrics.

Colorectal cancer (CRC) is the leading contributor to cancer-relevant deaths worldwide with severe incidence and mortality. An extensive body of evidence has demonstrated that lncRNA plays a critical role in the oncogenicity of CRC. Despite the oncogenic function of FAM201A in esophageal squamous cell cancer and non-small-cell lung cancer, the potential of FAM201A in CRC progression remains unknown. FAM201A expression level was significantly enhanced in CRC cells compared with normal cells. Further, functional experiments illustrated that knockdown of FAM201A restrained cell growth, stemness and promoted chemoresistance of CRC cells. By exploring molecular mechanism of FAM201A, we found that FAM201A acted as a sponge of miR-3163. More importantly, oncogene MACC1 was confirmed to be a direct target of miR-3163 and FAM201A modulated MACC1 expression level via competing for miR-3163. Subsequently, we testified that FAM201A exerted its role in the tumorigenesis and development of CRC through targeting miR-3163/MACC1. Animal assay certified that FAM201A expedited CRC cell growth in vivo. In conclusion, our study was the first to unveil that FAM201A promoted cell proliferation and CSC characteristics in CRC via regulation of the miR-3163/MACC1 axis, which provided clues for the clinical treatment of patients with this disease., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Inc.)

Reconstructing a three-dimensional (3D) structure from a single two-dimensional training image (TI) is a challenging issue. Multiple-point statistics (MPS) is an effective method to solve this problem. However, in the traditional MPS method, errors occur while statistical features of reconstruction, such as porosity, connectivity, and structural properties, deviate from those of TI. Due to the MPS reconstruction mechanism that the voxel being reconstructed is dependent on the reconstructed voxel, it may cause error accumulation during simulations, which can easily lead to a significant difference between the real 3D structure and the reconstructed result. To reduce error accumulation and improve morphological similarity, an improved MPS method based on porosity matching is proposed. In the reconstruction, we search the matching pattern in the TI directly. Meanwhile, a multigrid approach is also applied to capture the large-scale structures of the TI. To demonstrate its superiority over the traditional MPS method, our method is tested on different sandstone samples from many aspects, including accuracy, stability, generalization, and flow characteristics. Experimental results show that the reconstruction results by the improved MPS method effectively match the CT sandstone samples in correlation functions, local porosity distribution, morphological parameters, and permeability.