Coastal Aquaculture Extraction Using GF-3 Fully Polarimetric SAR Imagery: A Framework Integrating UNet++ with Marker-Controlled Watershed Segmentation

Coastal aquaculture monitoring is vital for sustainable offshore aquaculture management. However, the dense distribution and various sizes of aquacultures make it challenging to accurately extract the boundaries of aquaculture ponds. In this study, we develop a novel combined framework that integrates UNet++ with a marker-controlled watershed segmentation strategy to facilitate aquaculture boundary extraction from fully polarimetric GaoFen-3 SAR imagery. First, four polarimetric decomposition algorithms were applied to extract 13 polarimetric scattering features. Together with the nine other polarisation and texture features, a total of 22 polarimetric features were then extracted, among which four were optimised according to the separability index. Subsequently, to reduce the “adhesion” phenomenon and separate adjacent and even adhering ponds into individual aquaculture units, two UNet++ subnetworks were utilised to construct the marker and foreground functions, the results of which were then used in the marker-controlled watershed algorithm to obtain refined aquaculture results. A multiclass segmentation strategy that divides the intermediate markers into three categories (aquaculture, background and dikes) was applied to the marker function. In addition, a boundary patch refinement postprocessing strategy was applied to the two subnetworks to extract and repair the complex/error-prone boundaries of the aquaculture ponds, followed by a morphological operation that was conducted for label augmentation. An experimental investigation performed to extract individual aquacultures in the Yancheng Coastal Wetlands indicated that the crucial features for aquacultures are Shannon entropy (SE), the intensity component of SE (SE_I) and the corresponding mean texture features (Mean_SE and Mean_SE_I). When the optimal features were introduced, our proposed method performed better than standard UNet++ in aquaculture extraction, achieving improvements of 1.8%, 3.2%, 21.7% and 12.1% in F1, IoU, MR and insF1, respectively. The experimental results indicate that the proposed method can handle the adhesion of both adjacent objects and unclear boundaries effectively and capture clear and refined aquaculture boundaries

Adversarial Robustness of Deep Code Comment Generation

Deep neural networks (DNNs) have shown remarkable performance in a variety of domains such as computer vision, speech recognition, or natural language processing. Recently they also have been applied to various software engineering tasks, typically involving processing source code. DNNs are well-known to be vulnerable to adversarial examples, i.e., fabricated inputs that could lead to various misbehaviors of the DNN model while being perceived as benign by humans. In this paper, we focus on the code comment generation task in software engineering and study the robustness issue of the DNNs when they are applied to this task. We propose ACCENT, an identifier substitution approach to craft adversarial code snippets, which are syntactically correct and semantically close to the original code snippet, but may mislead the DNNs to produce completely irrelevant code comments. In order to improve the robustness, ACCENT also incorporates a novel training method, which can be applied to existing code comment generation models. We conduct comprehensive experiments to evaluate our approach by attacking the mainstream encoder-decoder architectures on two large-scale publicly available datasets. The results show that ACCENT efficiently produces stable attacks with functionality-preserving adversarial examples, and the generated examples have better transferability compared with baselines. We also confirm, via experiments, the effectiveness in improving model robustness with our training method

Cis-regulatory functions of overlapping HIF-1alpha/E-box/AP-1-like sequences of CD164

<p>Abstract</p> <p>Background</p> <p>CD164 (also known as MGC-24v or endolyn) is a sialomucin which has been suggested to participate in regulating the proliferation, cell adhesion and differentiation of hematopoietic stem and progenitor cells. CD164 is also involved in the development of cancer. The functions of cis-regulatory elements of CD164 remain relatively unknown.</p> <p>Methods</p> <p>In this study, we investigated the function of cis-regulatory elements within the promoter of CD164. We fused the 5'-flanking region of CD164 to a luciferase reporter vector. The minimal promoter region was confirmed by luciferase reporter assay. Using <it>in silico </it>analysis, we found the presence of one HIF-1alpha (HIF-1A) motif (5_-RCGTG-3_) overlapping E-box (CACGTG) and two AP-1-like binding sites (CGCTGTCCC, GTCTGTTG), one of which is also overlapped with HIF-1alpha sequence. Dual-luciferase assay was performed to examine the transcriptional activity of AP-1 and HIF-1alpha of CD164 promoter. Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) was performed to measure CD164 expression. Chromatin Immunoprecipitation was used to confirm the binding of HIF-1alpha and CD164.</p> <p>Results</p> <p>Co-transfection of c-jun, HIF-1alpha and minimal promoter region construct demonstrated that c-jun and HIF-1alpha bound the CD164 promoter and promoted CD164 expression. Hypoxia treatment also led to the up-regulation of CD164 expression. The mutation of overlapping sequences resulted in the reduced expression of CD164 induced by HIF-1alpha. Chromatin Immunoprecipitation demonstrated that the HIF-1alpha bound the minimal promoter region.</p> <p>Conclusions</p> <p>Determination of the optimal promoter region and transcription factors governing CD164 expression is useful in understanding CD164 functions. These results suggest that cis-regulatory elements of CD164 overlapping HIF-1alpha/E-box/AP-1-like sequences may play important regulatory roles.</p

A Systematic Study of Dysregulated MicroRNA in Type 2 Diabetes Mellitus.

MicroRNAs (miRNAs) are small noncoding RNAs that modulate the cellular transcriptome at the post-transcriptional level. miRNA plays important roles in different disease manifestation, including type 2 diabetes mellitus (T2DM). Many studies have characterized the changes of miRNAs in T2DM, a complex systematic disease; however, few studies have integrated these findings and explored the functional effects of the dysregulated miRNAs identified. To investigate the involvement of miRNAs in T2DM, we obtained and analyzed all relevant studies published prior to 18 October 2016 from various literature databases. From 59 independent studies that met the inclusion criteria, we identified 158 dysregulated miRNAs in seven different major sample types. To understand the functional impact of these deregulated miRNAs, we performed targets prediction and pathway enrichment analysis. Results from our analysis suggested that the altered miRNAs are involved in the core processes associated with T2DM, such as carbohydrate and lipid metabolisms, insulin signaling pathway and the adipocytokine signaling pathway. This systematic survey of dysregulated miRNAs provides molecular insights on the effect of deregulated miRNAs in different tissues during the development of diabetes. Some of these miRNAs and their mRNA targets may have diagnostic and/or therapeutic utilities in T2DM

Automatic identification and analysis of cells using digital holographic microscopy and Sobel segmentation

Counting and analyzing of blood cells, as well as their subcellular structures, are indispensable for understanding biological processes, studying cell functions, and diagnosing diseases. In this paper, we combine digital holographic microscopy with cell segmentation guided by the Sobel operator using Dice coefficients for automatic threshold selection and aimed to automatic counting and analysis of blood cells in flow and different kinds of cells in the static state. We demonstrate the proposed method with automatic counting and analyzing rat red blood cells (RBCS) flowing in a microfluidic device, extracting quickly and accurately the size, concentration, and dry mass of the sample in a label-free manner. The proposed technique was also demonstrated for automatic segmentation of different cell types, such as COS7 and Siha. This method can help us in blood inspection, providing pathological information in disease diagnosis and treatment