A new path planning strategy integrating improved ACO and DWA algorithms for mobile robots in dynamic environments

This article is concerned with the path planning of mobile robots in dynamic environments. A new path planning strategy is proposed by integrating the improved ant colony optimization (ACO) and dynamic window approach (DWA) algorithms. An improved ACO is developed to produce a globally optimal path for mobile robots in static environments. Through improvements in the initialization of pheromones, heuristic function, and updating of pheromones, the improved ACO can lead to a shorter path with fewer turning points in fewer iterations. Based on the globally optimal path, a modified DWA is presented for the path planning of mobile robots in dynamic environments. By deleting the redundant nodes, optimizing the initial orientation, and improving the evaluation function, the modified DWA can result in a more efficient path for mobile robots to avoid moving obstacles. Some simulations are conducted in different environments, which confirm the effectiveness and superiority of the proposed path planning algorithms

Controllable distant interactions at bound state in the continuum

Distant interactions at arbitrary locations and their dynamic control are fundamentally important for realizing large-scale photonic and quantum circuits. Conventional approaches suffer from short coupling distance, poor controllability, fixed locations and low wavelength uniformity, significantly restricting the scalability of photonic and quantum networks. Here, we exploit the intrinsic advantages of optical bound state in the continuum (BIC) and demonstrate an all-in-one solution for dynamically controllable long-range interactions. BIC metasurface can support a series of finite-sized quasi-BIC microlasers at arbitrary locations. The quasi-BICs microlasers have the same wavelength and are inherently connected through BIC waveguide. Consequently, the coupling distances in experiment increase significantly from subwavelength to tens of micrometers. Such long-range interaction in BIC metasurface enables scaling to two-dimensional architectures and ultrafast control of internal laser actions, e.g., non-Hermitian zero-mode lasing and enhanced optical gain. This research shall facilitate the advancement of scalable and reconfigurable photonic networks.Comment: 12 pages, 4 figure

Reduced expression of tissue factor pathway inhibitor-2 contributes to apoptosis and angiogenesis in cervical cancer

<p>Abstract</p> <p>Background</p> <p>Tissue factor pathway inhibitor-2 (TFPI-2) is an extracellular matrix associated broad-spectrum Kunitz-type serine proteinase inhibitor. Recently, down regulation of TFPI-2 was suggested to be involved in tumor invasion and metastasis in some cancers.</p> <p>Methods</p> <p>This study involved 12 normal cervical squamous epithelia, 48 cervical intraepithelial neoplasia (CIN), and 68 cervical cancer. The expression of TFPI-2, Ki-67 and vascular endothelial growth factor (VEGF) were investigated by immunohistochemistry staining. The apoptolic index(AI) was determined with an in situ end-labeling assay(TUNEL). And the marker of CD34 staining was used as an indicator of microvessel density (MVD).</p> <p>Results</p> <p>TFPI-2 expression has a decreasing trend with the progression of cervical cancer and was significantly correlated with FIGO stage, lymph node metastasis and HPV infection. In addition, there were significant positive correlations between the grading of TFPI-2 expression and AI(P = 0.004). In contrast, the expression of TFPI-2 and VEGF or MVD was negatively correlated (both p < 0.001). However, we did not establish any significant correlation between Ki-67 and TFPI-2 expression in cervical cancer.</p> <p>Conclusions</p> <p>The results suggested that the expression of TFPI-2 had a decreasing trend with tumor progression of cervical cancer. There was a close association between the expression of TFPI-2 and tumor cell apoptosis and angiogenesis in patients with cervical cancer. TFPI-2 may play an inhibitive role during the development of cervical cancer.</p

TFPI-2 is a putative tumor suppressor gene frequently inactivated by promoter hypermethylation in nasopharyngeal carcinoma

<p>Abstract</p> <p>Background</p> <p>Epigenetic silencing of tumor suppressor genes play important roles in NPC tumorgenesis. Tissue factor pathway inhibitor-2 (TFPI-2), is a protease inhibitor. Recently, <it>TFPI-2 </it>was suggested to be a tumor suppressor gene involved in tumorigenesis and metastasis in some cancers. In this study, we investigated whether <it>TFPI-2 </it>was inactivated epigenetically in nasopharyngeal carcinoma (NPC).</p> <p>Methods</p> <p>Transcriptional expression levels of <it>TFPI-2 </it>was evaluated by RT-PCR. Methylation status were investigated by methylation specific PCR and bisulfate genomic sequencing. The role of <it>TFPI-2 </it>as a tumor suppressor gene in NPC was addressed by re-introducing <it>TFPI-2 </it>expression into the NPC cell line CNE2.</p> <p>Results</p> <p><it>TFPI-2 </it>mRNA transcription was inactivated in NPC cell lines. <it>TFPI-2 </it>was aberrantly methylated in 66.7% (4/6) NPC cell lines and 88.6% (62/70) of NPC primary tumors, but not in normal nasopharyngeal epithelia. <it>TFPI-2 </it>expression could be restored in NPC cells after demethylation treatment. Ectopic expression of TFPI-2 in NPC cells induced apoptosis and inhibited cell proliferation, colony formation and cell migration.</p> <p>Conclusions</p> <p>Epigenetic inactivation of <it>TFPI-2 </it>by promoter hypermethylation is a frequent and tumor specific event in NPC. <it>TFPI-2 </it>might be considering as a putative tumor suppressor gene in NPC.</p

Determining PTEN Functional Status by Network Component Deduced Transcription Factor Activities

PTEN-controlled PI3K-AKT-mTOR pathway represents one of the most deregulated signaling pathways in human cancers. With many small molecule inhibitors that target PI3K-AKT-mTOR pathway being exploited clinically, sensitive and reliable ways of stratifying patients according to their PTEN functional status and determining treatment outcomes are urgently needed. Heterogeneous loss of PTEN is commonly associated with human cancers and yet PTEN can also be regulated on epigenetic, transcriptional or post-translational levels, which makes the use of simple protein or gene expression-based analyses in determining PTEN status less accurate. In this study, we used network component analysis to identify 20 transcription factors (TFs) whose activities deduced from their target gene expressions were immediately altered upon the re-expression of PTEN in a PTEN-inducible system. Interestingly, PTEN controls the activities (TFA) rather than the expression levels of majority of these TFs and these PTEN-controlled TFAs are substantially altered in prostate cancer mouse models. Importantly, the activities of these TFs can be used to predict PTEN status in human prostate, breast and brain tumor samples with enhanced reliability when compared to straightforward IHC-based or expression-based analysis. Furthermore, our analysis indicates that unique sets of PTEN-controlled TFAs significantly contribute to specific tumor types. Together, our findings reveal that TFAs may be used as “signatures” for predicting PTEN functional status and elucidate the transcriptional architectures underlying human cancers caused by PTEN loss

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe