SuperpixelGraph: Semi-automatic generation of building footprint through semantic-sensitive superpixel and neural graph networks

Most urban applications necessitate building footprints in the form of concise vector graphics with sharp boundaries rather than pixel-wise raster images. This need contrasts with the majority of existing methods, which typically generate over-smoothed footprint polygons. Editing these automatically produced polygons can be inefficient, if not more time-consuming than manual digitization. This paper introduces a semi-automatic approach for building footprint extraction through semantically-sensitive superpixels and neural graph networks. Drawing inspiration from object-based classification techniques, we first learn to generate superpixels that are not only boundary-preserving but also semantically-sensitive. The superpixels respond exclusively to building boundaries rather than other natural objects, while simultaneously producing semantic segmentation of the buildings. These intermediate superpixel representations can be naturally considered as nodes within a graph. Consequently, graph neural networks are employed to model the global interactions among all superpixels and enhance the representativeness of node features for building segmentation. Classical approaches are utilized to extract and regularize boundaries for the vectorized building footprints. Utilizing minimal clicks and straightforward strokes, we efficiently accomplish accurate segmentation outcomes, eliminating the necessity for editing polygon vertices. Our proposed approach demonstrates superior precision and efficacy, as validated by experimental assessments on various public benchmark datasets. A significant improvement of 8% in AP50 was observed in vector graphics evaluation, surpassing established techniques. Additionally, we have devised an optimized and sophisticated pipeline for interactive editing, poised to further augment the overall quality of the results

Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances

To achieve state-of-the-art performance, one still needs to train NER models on large-scale, high-quality annotated data, an asset that is both costly and time-intensive to accumulate. In contrast, real-world applications often resort to massive low-quality labeled data through non-expert annotators via crowdsourcing and external knowledge bases via distant supervision as a cost-effective alternative. However, these annotation methods result in noisy labels, which in turn lead to a notable decline in performance. Hence, we propose to denoise the noisy NER data with guidance from a small set of clean instances. Along with the main NER model we train a discriminator model and use its outputs to recalibrate the sample weights. The discriminator is capable of detecting both span and category errors with different discriminative prompts. Results on public crowdsourcing and distant supervision datasets show that the proposed method can consistently improve performance with a small guidance set.Comment: 14 page

Gastric mixed neuroendocrine non-neuroendocrine neoplasms

The uncommon tumour known as gastric mixed neuroendocrine-non-neuroendocrine neoplasms (G-MiNENs) is made up of parts of neuroendocrine carcinoma and adenocarcinoma. The biological and clinical features are different from those of gastric adenocarcinoma. Their pathophysiology, diagnostic standards, and clinical behaviour have all been the subject of lengthy debates, and their nomenclature has undergone multiple changes. Its emergence has created new challenges in the classification and diagnosis of gastric tumours. This review will update information on the topic, covering molecular aspects, diagnostic criteria, treatment, and prognostic factor discovery. It will also provide a historical context that will aid in understanding the evolution of the idea and nomenclature of mixed gastric tumours. Additionally, it will provide the reader a thorough understanding of this difficult topic of cancer that is applicable to real-world situations

miR-221/222 promotes S-phase entry and cellular migration in control of basal-like breast cancer.

The miR-221/222 cluster has been demonstrated to function as oncomiR in human cancers. miR-221/222 promotes epithelial-to-mesenchymal transition (EMT) and confers tamoxifen resistance in breast cancer. However, the effects and mechanisms by which miR-221/222 regulates breast cancer aggressiveness remain unclear. Here we detected a much higher expression of miR-221/222 in highly invasive basal-like breast cancer (BLBC) cells than that in non-invasive luminal cells. A microRNA dataset from breast cancer patients indicated an elevated expression of miR-221/222 in BLBC subtype. S-phase entry of the cell cycle was associated with the induction of miR-221/222 expression. miRNA inhibitors specially targeting miR-221 or miR-222 both significantly suppressed cellular migration, invasion and G1/S transition of the cell cycle in BLBC cell types. Proteomic analysis demonstrated the down-regulation of two tumor suppressor genes, suppressor of cytokine signaling 1 (SOCS1) and cyclin-dependent kinase inhibit 1B (CDKN1B), by miR-221/222. This is the first report to reveal miR-221/222 regulation of G1/S transition of the cell cycle. These findings demonstrate that miR-221/222 contribute to the aggressiveness in control of BLBC

In Vivo Anti-Tumor Activity of Polypeptide HM-3 Modified by Different Polyethylene Glycols (PEG)

HM-3, designed by our laboratory, is a polypeptide composed of 18 amino acids. Pharmacodynamic studies in vivo and in vitro indicated that HM-3 could inhibit endothelial cell migration and angiogenesis, thereby inhibiting tumor growth. However, the half-life of HM-3 is short. In this study, we modified HM-3 with different polyethylene glycols (PEG) in order to reduce the plasma clearance rate, extend the half-life in the body, maintain a high concentration of HM-3 in the blood and increase the therapeutic efficiency. HM-3 was modified with four different types of PEG with different molecular weights (ALD-mPEG5k, ALD-mPEG10k, SC-mPEG10k and SC-mPEG20k), resulting in four modified products (ALD-mPEG5k-HM-3, ALD-mPEG10k-HM-3, SC-mPEG10k-HM-3 and SC-mPEG20k-HM-3, respectively). Anti-tumor activity of these four modified HM-3 was determined in BALB/c mice with Taxol as a positive control and normal saline as a negative control. Tumor weight inhibition rates of mice treated with Taxol, HM-3, ALD-mPEG5k-HM-3, ALD-mPEG10k-HM-3, SC-mPEG10k-HM-3 and SC-mPEG20k-HM-3 were 44.50%, 43.92%, 37.95%, 31.64%, 20.27% and 50.23%, respectively. Tumor inhibition rates in the Taxol, HM-3 and SC-mPEG20k-HM-3 groups were significantly higher than that in the negative control group. The efficiency of tumor inhibition in the SC-mPEG20k-HM-3 group (drug treatment frequency: once per two days) was better than that in the HM-3 group (drug treatment frequency: twice per day). In addition, tumor inhibition rate in the SC-mPEG20k-HM-3 group was higher than that in the taxol group. We conclude that SC-mPEG20k-HM-3 had a low plasma clearance rate and long half-life, resulting in high anti-tumor therapeutic efficacy in vivo. Therefore, SC-mPEG20k-HM-3 could be potentially developed as new anti-tumor drugs