The space of tropically collinear points is shellable

The space T_{d,n} of n tropically collinear points in a fixed tropical projective space TP^{d-1} is equivalent to the tropicalization of the determinantal variety of matrices of rank at most 2, which consists of real d x n matrices of tropical or Kapranov rank at most 2, modulo projective equivalence of columns. We show that it is equal to the image of the moduli space M_{0,n}(TP^{d-1},1) of n-marked tropical lines in TP^{d-1} under the evaluation map. Thus we derive a natural simplicial fan structure for T_{d,n} using a simplicial fan structure of M_{0,n}(TP^{d-1},1) which coincides with that of the space of phylogenetic trees on d+n taxa. The space of phylogenetic trees has been shown to be shellable by Trappmann and Ziegler. Using a similar method, we show that T_{d,n} is shellable with our simplicial fan structure and compute the homology of the link of the origin. The shellability of T_{d,n} has been conjectured by Develin in 2005.Comment: final version, minor revision, 15 page

MicroRNAs in non-small cell lung cancer: Gene regulation, impact on cancer cellular processes, and therapeutic potential.

Lung cancer remains the most lethal cancer among men and women in the United States and worldwide. The majority of lung cancer cases are classified as non-small cell lung cancer (NSCLC). Developing new therapeutics on the basis of better understanding of NSCLC biology is critical to improve the treatment of NSCLC. MicroRNAs (miRNAs or miRs) are a superfamily of genome-derived, small noncoding RNAs that govern posttranscriptional gene expression in cells. Functional miRNAs are commonly dysregulated in NSCLC, caused by genomic deletion, methylation, or altered processing, which may lead to the changes of many cancer-related pathways and processes, such as growth and death signaling, metabolism, angiogenesis, cell cycle, and epithelial to mesenchymal transition, as well as sensitivity to current therapies. With the understanding of miRNA biology in NSCLC, there are growing interests in developing new therapeutic strategies, namely restoration of tumor suppressive miRNAs and inhibition of tumor promotive miRNAs, to combat against NSCLC. In this article, we provide an overview on the molecular features of NSCLC and current treatment options with a focus on pharmacotherapy and personalized medicine. By illustrating the roles of miRNAs in the control of NSCLC tumorigenesis and progression, we highlight the latest efforts in assessing miRNA-based therapies in animal models and discuss some critical challenges in developing RNA therapeutics

The ciliary GTPase Arl13b regulates cell migration and cell cycle progression

Acknowledgments We acknowledge Prof. Tamara Caspary from Emory University for kindly providing the cell lines, Linda Duncan from the University of Aberdeen Ian Fraser Cytometry Center for help with flow cytometry. MP was funded by the Scottish Universities Life Science Alliance (SULSA) and the University of Aberdeen. Funding This work was supported by grants from British Council China (Sino-UK higher Education for PhD studies) to YD and CM, The Carnegie Trust for the Universities of Scotland (70190) and The NHS Grampian Endowment Funds (14/09) to BL, and National Natural Science Foundation of China (31528011) to BL and YD.Peer reviewedPostprin

Dietary bioactive lipid compounds rich in menthol alter Interactions among members of ruminal microbiota in sheep

This study aimed to investigate the effects of two practically relevant doses of menthol-rich plant bioactive lipid compounds (PBLC) on fermentation, microbial community composition, and their interactions in sheep rumen. Twenty-four growing Suffolk sheep were divided into three treatments and were fed hay ad libitum plus 600 g/d of concentrate containing no PBLC (Control) or PBLC at low dose (80 mg/d; PBLC-L) or high dose (160 mg/d; PBLC-H). After 4 weeks on the diets, samples of ruminal digesta were collected and analyzed for short-chain fatty acid (SCFA), ammonia, and microbiota; microbiota being analyzed in the solid and the liquid digesta fractions separately. Ruminal SCFA and ammonia concentrations were not affected by the PBLC treatments. The microbiota in the solid fraction was more diverse than that in the liquid fraction, and the relative abundance of most taxa differed between these two fractions. In the solid fraction, phylogenetic diversity increased linearly with increased PBLC doses, whereas evenness (lowest in PBLC-L) and Simpson diversity index (greatest in PBLC-H) changed quadratically. In the liquid fraction, however, the PBLC supplementation did not affect any of the microbial diversity measurements. Among phyla, Chloroflexi (highest in PBLC-L) and unclassified_bacteria (lowest in PBLC-L) were altered quadratically by PBLC. Lachnospiraceae, Bacteroidaceae (increased linearly), BS11 (increased in PBLC-L), Christensenellaceae (decreased in PBLC treatments), and Porphyromonadaceae (increased in PBLC treatments) were affected at the family level. Among genera, Butyrivibrio increased linearly in the solid fraction, YRC22 increased linearly in the liquid fraction, whereas Paludibacter increased and BF311 increased linearly with increasing doses of PBLC in both fractions. The PBLC treatments also lowered methanogens within the classes Thermoplasmata and Euryarchaeota. Correlation network analysis revealed positive and negative correlations among many microbial taxa. Differential network analysis showed that PBLC supplementation changed the correlation between some microbial taxa and SCFA. The majority of the predicted functional features were different between the solid and the liquid digesta fractions, whereas the PBLC treatments altered few of the predicted functional gene categories. Overall, dietary PBLC treatments had little influence on the ruminal fermentation and microbiota but affected the associations among some microbial taxa and SCFA

SemARFlow: Injecting Semantics into Unsupervised Optical Flow Estimation for Autonomous Driving

Unsupervised optical flow estimation is especially hard near occlusions and motion boundaries and in low-texture regions. We show that additional information such as semantics and domain knowledge can help better constrain this problem. We introduce SemARFlow, an unsupervised optical flow network designed for autonomous driving data that takes estimated semantic segmentation masks as additional inputs. This additional information is injected into the encoder and into a learned upsampler that refines the flow output. In addition, a simple yet effective semantic augmentation module provides self-supervision when learning flow and its boundaries for vehicles, poles, and sky. Together, these injections of semantic information improve the KITTI-2015 optical flow test error rate from 11.80% to 8.38%. We also show visible improvements around object boundaries as well as a greater ability to generalize across datasets. Code is available at https://github.com/duke-vision/semantic-unsup-flow-release.Comment: Accepted by ICCV-2023; Code is available at https://github.com/duke-vision/semantic-unsup-flow-releas

Unsupervised Flow Refinement near Motion Boundaries

Unsupervised optical flow estimators based on deep learning have attracted increasing attention due to the cost and difficulty of annotating for ground truth. Although performance measured by average End-Point Error (EPE) has improved over the years, flow estimates are still poorer along motion boundaries (MBs), where the flow is not smooth, as is typically assumed, and where features computed by neural networks are contaminated by multiple motions. To improve flow in the unsupervised settings, we design a framework that detects MBs by analyzing visual changes along boundary candidates and replaces motions close to detections with motions farther away. Our proposed algorithm detects boundaries more accurately than a baseline method with the same inputs and can improve estimates from any flow predictor without additional training