Natural Protective Features Along New York\u27s Ocean Shoreline and Their Response to Extreme Events

Features providing natural protection against erosion and flooding are defined in the laws of New York State to include dunes, bluffs, and beaches. In addition, structural hazard areas are defined to be stretches of the coast where the long-term recession rate is greater than one foot per year. LiDAR data was used to identify NPFs and examine shoreline recession rates at 750 transects along the south shore of eastern Long Island. Meaningful combinations of NPFs included dunes in front of bluffs, dunes formed on top of bluffs and multiple dunes. Single dunes were the NPFs along 27.1% of the shoreline; multiple dunes comprising 20.7%. Bluffs were the NPF along 26.6% of the shoreline. Dunes in front of a bluff comprised 12.1% and dune on top of a bluff crest made up 12.9%. (The remaining 0.6% of the shoreline was identified as the beach). Dunes provide the first line of defense against extreme events, but in the face of a long-term rise in sea level, the excavation of the bluff face is likely to be the factor controlling shoreline retreat. Combination of NPFs do not necessarily equate to high (or low) resilience. Shoreline recession rates were calculated as a linear regression of high-water shorelines from 1983, 1999, 2003, 2010 and 2013. Calculated recession rates were biased by the occurrence of longshore sandwaves. These features were found to occur between 23% and 82% of the time. Spectra analysis shows a dominant wavelength of shoreline recession rate to be 1.5 km. The cause of sandwaves are debatable, but their presence can impact the calculation of recession rates. A shore-process model (CSHORE) using wave and surge data for a 12-day period based on Superstorm Sandy were used predicted that the beach profile would have lost an average volume of 68 m3/m but ranged up to 137 m3/m. Model results were about twice those observed using LiDAR data after Hurricane Sandy, but neither that event or earlier historical events seemed to permanently alter the response of the shoreline to later conditions. | 135 page

From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework

Textual adversarial attacks can discover models' weaknesses by adding semantic-preserved but misleading perturbations to the inputs. The long-lasting adversarial attack-and-defense arms race in Natural Language Processing (NLP) is algorithm-centric, providing valuable techniques for automatic robustness evaluation. However, the existing practice of robustness evaluation may exhibit issues of incomprehensive evaluation, impractical evaluation protocol, and invalid adversarial samples. In this paper, we aim to set up a unified automatic robustness evaluation framework, shifting towards model-centric evaluation to further exploit the advantages of adversarial attacks. To address the above challenges, we first determine robustness evaluation dimensions based on model capabilities and specify the reasonable algorithm to generate adversarial samples for each dimension. Then we establish the evaluation protocol, including evaluation settings and metrics, under realistic demands. Finally, we use the perturbation degree of adversarial samples to control the sample validity. We implement a toolkit RobTest that realizes our automatic robustness evaluation framework. In our experiments, we conduct a robustness evaluation of RoBERTa models to demonstrate the effectiveness of our evaluation framework, and further show the rationality of each component in the framework. The code will be made public at \url{https://github.com/thunlp/RobTest}.Comment: Accepted to Findings of ACL 202

Deciphering a mitochondria-related signature to supervise prognosis and immunotherapy in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is a major public health problem in humans. The imbalance of mitochondrial function has been discovered to be closely related to the development of cancer recently. However, the role of mitochondrial-related genes in HCC remains unclear.MethodsThe RNA-sequencing profiles and patient information of 365 samples were derived from the Cancer Genome Atlas (TCGA) dataset. The mitochondria-related prognostic model was established by univariate Cox regression analysis and LASSO Cox regression analysis. We further determined the differences in immunity and drug sensitivity between low- and high-risk groups. Validation data were obtained from the International Cancer Genome Consortium (ICGC) dataset of patients with HCC. The protein and mRNA expression of six mitochondria-related genes in tissues and cell lines was verified by immunohistochemistry and qRT-PCR.ResultsThe six mitochondria-related gene signature was constructed for better prognosis forecasting and immunity, based on which patients were divided into high-risk and low-risk groups. The ROC curve, nomogram, and calibration curve exhibited admirable clinical predictive performance of the model. The risk score was associated with clinicopathological characteristics and proved to be an independent prognostic factor in patients with HCC. The above results were verified in the ICGC validation cohort. Compared with normal tissues and cell lines, the protein and mRNA expression of six mitochondria-related genes was upregulated in HCC tissues and cell lines.ConclusionThe signature could be an independent factor that supervises the immunotherapy response of HCC patients and possess vital guidance value for clinical diagnosis and treatment

Identification of m6a-related signature genes in esophageal squamous cell carcinoma by machine learning method

Background: We aimed to construct and validate the esophageal squamous cell carcinoma (ESCC)-related m6A regulators by means of machine leaning.Methods: We used ESCC RNA-seq data of 66 pairs of ESCC from West China Hospital of Sichuan University and the transcriptome data extracted from The Cancer Genome Atlas (TCGA)-ESCA database to find out the ESCC-related m6A regulators, during which, two machine learning approaches: RF (Random Forest) and SVM (Support Vector Machine) were employed to construct the model of ESCC-related m6A regulators. Calibration curves, clinical decision curves, and clinical impact curves (CIC) were used to evaluate the predictive ability and best-effort ability of the model. Finally, western blot and immunohistochemistry staining were used to assess the expression of prognostic ESCC-related m6A regulators.Results: 2 m6A regulators (YTHDF1 and HNRNPC) were found to be significantly increased in ESCC tissues after screening out through RF machine learning methods from our RNA-seq data and TCGA-ESCA database, respectively, and overlapping the results of the two clusters. A prognostic signature, consisting of YTHDF1 and HNRNPC, was constructed based on our RNA-seq data and validated on TCGA-ESCA database, which can serve as an independent prognostic predictor. Experimental validation including the western and immunohistochemistry staining were further successfully confirmed the results of bioinformatics analysis.Conclusion: We constructed prognostic ESCC-related m6A regulators and validated the model in clinical ESCC cohort as well as in ESCC tissues, which provides reasonable evidence and valuable resources for prognostic stratification and the study of potential targets for ESCC

Preparation, anti-biofouling and drag-reduction properties of a biomimetic shark skin surface

Shark skin surfaces show non-smoothness characteristics due to the presence of a riblet structure. In this study, biomimetic shark skin was prepared by using the polydimethylsiloxane (PDMS)-embedded elastomeric stamping (PEES) method. Scanning electron microscopy (SEM) was used to examine the surface microstructure and fine structure of shark skin and biomimetic shark skin. To analyse the hydrophobic mechanism of the shark skin surface microstructure, the effect of biomimetic shark skin surface microstructure on surface wettability was evaluated by recording water contact angle. Additionally, protein adhesion experiments and anti-algae adhesion performance testing experiments were used to investigate and evaluate the anti-biofouling properties of the surface microstructure of biomimetic shark skin. The recorded values of the water contact angle of differently microstructured surfaces revealed that specific microstructures have certain effects on surface wettability. The anti-biofouling properties of the biomimetic shark skin surface with microstructures were superior to a smooth surface using the same polymers as substrates. Moreover, the air layer fixed on the surface of the biomimetic shark skin was found to play a key role in their antibiont adhesion property. An experiment into drag reduction was also conducted. Based on the experimental results, the microstructured surface of the prepared biomimetic shark skin played a significant role in reducing drag. The maximum of drag reduction rate is 12.5%, which is higher than the corresponding maximum drag reduction rate of membrane material with a smooth surface