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

Identification of a Novel Testis-specific Gene in Mice and Its Potential Roles in Spermatogenesis

Aim Identification of a novel gene in mouse testis and its relation to spermatogenesis. Methods Genes expressed during different developmental stages of the mouse testis were screened by DNA microarray. The results of chip analysis were authenticated by reverse transcription-polymerase chain reaction (RT-PCR) technique, as well as the tissue distribution of the selected genes. The characteristics of the selected genes were analyzed by bioinformatics tools. Results A novel gene, TSC77, was identified and located at the mouse chromosome 2G1. The full cDNA length of TSC77 was 2280 bp, with a 2046 bp open reading frame encoding a 681 amino acids protein with a predicted molecular weight of 77.17 kDa. The results of subcellular localization of GFP-TSC77 fusion protein indicated TSC77 protein was located in the nucleus of Cos-7 cells. The analysis of multiple amino acid sequence alignment showed that TSC77 protein was highly homologous with the human CAI40813 (C2orf26 gene, 76%), and rat XP_230651 (77%). Three putative domains including nicotine amide dinucleotide phosphate (NAD(P))-nitrit

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

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

DNA self-assembly nanoflower reverse P-glycoprotein mediated drug resistance in chronic myelogenous leukemia therapy

Introduction: Chronic myelogenous leukemia (CML) is a clonal myeloproliferative disorder caused by the BCR-ABL chimeric tyrosine kinase. Vincristine (VCR) is widely used in leukemia therapy but is hindered by multidrug resistance (MDR).Methods: We prepared DNA nanoflower via self-assembly for the delivery of VCR and P-glycoprotein small interfering RNA (P-gp siRNA).Results and Discussion: The as-prepared nanoflower had a floriform shape with high loading efficiency of VCR (80%). Furthermore, the nanoflower could deliver VCR and P-gp siRNA into MDR CML cells and induce potent cytotoxicity both in vitro and in vivo, thus overcoming MDR of CML. Overall, this nanoflower is a promising tool for resistant CML therapy