Detecting changes in land use and land cover (LULC) from space has long been the main goal of satellite remote sensing (RS), yet the existing and available algorithms for cloud classification are not reliable enough to attain this goal in an automated fashion. Clouds are very strong optical signals that dominate the results of change detection if they are not removed completely from imagery. As various architectures of deep learning (DL) have been proposed and advanced quickly, their potential in perceptual tasks has been widely accepted and successfully applied to many fields. A comprehensive survey of DL in RS has been reviewed, and the RS community has been suggested to be leading researchers in DL. Based on deep residual learning, semantic image segmentation, and the concept of atrous convolution, we propose a new DL architecture, named CloudNet, with an enhanced capability of feature extraction for classifying cloud and haze from Sentinel-2 imagery, with the intention of supporting automatic change detection in LULC. To ensure the quality of the training dataset, scene classification maps of Taiwan processed by Sen2cor were visually examined and edited, resulting in a total of 12,769 sub-images with a standard size of 224 × 224 pixels, cut from the Sen2cor-corrected images and compiled in a trainset. The data augmentation technique enabled CloudNet to have stable cirrus identification capability without extensive training data. Compared to the traditional method and other DL methods, CloudNet had higher accuracy in cloud and haze classification, as well as better performance in cirrus cloud recognition. CloudNet will be incorporated into the Open Access Satellite Image Service to facilitate change detection by using Sentinel-2 imagery on a regular and automatic basis.

Hepatobiliary cancer is the third leading cause of cancer death worldwide. Appropriate markers for early diagnosis, monitoring of disease progression, and prediction of postsurgical outcome are still lacking. As the majority of circulating N ‐glycoproteins are originated from the hepatobiliary system, we sought to explore new markers by assessing the dynamics of N ‐glycoproteome in plasma samples from patients with hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), or combined HCC and CCA (cHCC‐CCA). Using a mass spectrometry‐based quantitative proteomic approach, we found that 57 of 5358 identified plasma proteins were differentially expressed in hepatobiliary cancers. The levels of four essential proteins, including complement C3 and apolipoprotein C‐III in HCC, galectin‐3‐binding protein in CCA, and 72 kDa inositol polyphosphate 5‐phosphatase in cHCC‐CCA, were highly correlated with tumor stage, tumor grade, recurrence‐free survival, and overall survival. Postproteomic site‐specific N ‐glycan analyses showed that human complement C3 bears high‐mannose and hybrid glycoforms rather than complex glycoforms at Asn85. The abundance of complement C3 with mannose‐5 or mannose‐6 glycoform at Asn85 was associated with HCC tumor grade. Furthermore, stepwise Cox regression analyses revealed that HCC patients with a hybrid glycoform at Asn85 of complement C3 had a lower postsurgery tumor recurrence rate or mortality rate than those with a low amount of complement C3 protein. In conclusion, our data show that particular plasma N ‐glycoproteins with specific N ‐glycan compositions could be potential noninvasive markers to evaluate oncological status and prognosis of hepatobiliary cancers.

Background Preeclampsia remains a serious disorder that puts at risk the lives of perinatal mothers and infants worldwide. This study assessed potential pathogenic mechanisms underlying preeclampsia by investigating differentially expressed proteins (DEPs) in the serum of patients with early-onset preeclampsia (EOPE) and late-onset preeclampsia (LOPE) compared with healthy pregnant women. Methods Blood samples were collected from four women with EOPE, four women with LOPE, and eight women with normal pregnancies, with four women providing control samples for each preeclampsia group. Serum proteins were identified by isobaric tags for relative and absolute quantitation combined with liquid chromatography–tandem mass spectrometry. Serum proteins with differences in their levels compared with control groups of at least 1.2 fold-changes and that were also statistically significantly different between the groups at P < 0.05 were further analyzed. Bioinformatics analyses, including gene ontology and Kyoto Encyclopedia of Genes and Genomes signaling pathway analyses, were used to determine the key proteins and signaling pathways associated with the development of PE and to determine those DEPs that differed between women with EOPE and those with LOPE. Key protein identified by mass spectrometry was verified by enzyme linked immunosorbent assay (ELISA). Results Compared with serum samples from healthy pregnant women, those from women with EOPE displayed 70 proteins that were differentially expressed with significance. Among them, 51 proteins were significantly upregulated and 19 proteins were significantly downregulated. In serum samples from women with LOPE, 24 DEPs were identified , with 10 proteins significantly upregulated and 14 proteins significantly downregulated compared with healthy pregnant women. Bioinformatics analyses indicated that DEPs in both the EOPE and LOPE groups were associated with abnormalities in the activation of the coagulation cascade and complement system as well as with lipid metabolism. In addition, 19 DEPs in the EOPE group were closely related to placental development or invasion of tumor cells. Downregulationof pregnancy-specific beta-1-glycoprotein 9 (PSG9) in the LOPE group was confirmed by ELISA. Conclusion The pathogenesis of EOPE and LOPE appeared to be associated with coagulation cascade activation, lipid metabolism, and complement activation. However, the pathogenesis of EOPE also involved processes associated with greater placental injury. This study provided several new proteins in the serum which may be valuable for clinical diagnosis of EOPE and LOPE, and offered potential mechanisms underpinning the development of these disorders.

No abstract

Scale-invariant Feature Transform (SIFT) is an algorithm to find local features in images. SIFT uses Differenceof-Gaussian (DoG) to locate candidate keypoints and performs a detailed fit to locate keypoints, then orientations are added to keypoints and keypoint descriptor is generated for each keypoint. Iris recognition is one of the most reliable biometric authentications. In this paper, we propose a reliable method of iris recognition by applying SIFT. It includes segmentation, matching and evaluation. Other than the conventional method, Normalizing and encoding are removed since SIFT is rotation-invariant and scale-invariant. Our proposed method is tested on CASIA and self-obtained images. Experiments show the proposed method is fast and accurate.