dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Analysis We compared the total number of detections yearly from 2017 to 2020, but also extracted monthly and daily marine activity to highlight the effect of the lockdown. We were able to statistically compare mean daily observations between 2020 and different periods during 2017-2019 using the mean comparison t-test implemented with the "t. test()" R function after a log transformation of our data in order to obtain a normal distribution. Since discriminating recreational and fishing activities is impossible directly through SAR imagery, it was assumed that the recreational activities are much lower during the morning hours (i. e. , before 8AM) and outside of the summer season. By looking at the difference between seasonal but also morning and evening detections, we assessed the extent to which leisure activities were affected by the Covid-19 restrictions.
In order to compare the 2020 activity to a control period, we used the mean yearly and monthly detections of the 2017-2019 considered as a baseline. In addition, ship concentration around coastal and touristic areas is an indicator of ship activity and presence of leisure boats. To compare the differences in ship clusters between 2020 and prior years, we used the "kde2d()" R function from the "MASS" package to calculate spatial kernel density of the detections (from 0 to 1). In order to limit false positives, we removed 32,231 detections identified by SUMO as being ships with a length under 15 meters. Twenty-seven images were removed when filtering out outliers (for which the number of detections was higher than 95% of observed values per image). We manually removed 34 images submitted to high radio frequency interference (RFI) by examining all high detection images by a human observer.
, 2015; Santamaria et al. , 2017; Grover et al. , 2018; Aiello et al. , 2019). This method allowed us to obtain an extensive dataset, thanks to the Sentinel-1 project that is fully operational since late 2016. SAR imagery can provide data archives and accessibility while not being affected by cloud cover nor sunlight and not needing voluntary actions on board to detect ship locations. These advantages make SAR a good alternative to other methods. Nevertheless, high wind conditions can cause small waves on the water surface that can result in false positive detections. It is important to take this into account and be careful with unusual numbers of detections in certain images. Another limitation comes from the use of Sentinel-1 SAR images which have a 10m resolution per pixel. With this resolution, our detection method cannot consider ships smaller than 15 meters, which represent a large part of recreational boats and many artisanal fisheries vessels.
Materials The imputed genotypes (reference panel: HRC r1. 1 , Supplementary Table  S1 )  [22] [23] [24] [25]. There were 12,084 women and 17,818 men in discovery dataset; and 6,366 women and 10,515 men in replication dataset. 60. 4% of the lung cancer patients were male in the discovery study compared with 64. 0% in replication study. 81. 6% of individuals in both discovery and validation studies were ever-smokers. Stringent quality controls were applied on SNPs and SNPs selected for the analysis had to qualify by two criteria: 1, had imputation information score > = 0. 7 in discovery data; 2, had information score > 0. 2 in each of the eight studies and sample-size weighted information score > = 0. 7 in replication data. About ~ 20,000,000, out of 39,000,000 imputed SNPs, with information score > = 0.
The SNPs with case-control gene-sex interaction p-value < 0. 05 and same direction of interaction effect in both discovery and replication datasets were reported as signi cant ndings. For the signi cant variants with low allele frequency (MAF < 0. 05), we further validated the signals using rth logistic regression, a method designed for rare variants association test to reduce small-sample bias in regular logistic regression (Fig. 1 )  26. The statistical analysis was adjusted for smoking status (ever-and never-smokers) and the rst 5 principal components. The analysis was conducted in overall lung cancer as well as adenocarcinoma (No. Cases = 9,791) and squamous (No. Cases = 6,107) lung cancer.
Eqtl Analysis Genotype dosage and gene expression rpkm (Reads Per Kilobase Million) data from lung tissue were downloaded from GTEx (phs000424. GTEx. v7. p2). There were 377 individuals with European ancestry available for the analysis, including 244 men and 133 women. Average rpkm for the gene was used if there were duplicated samples. Individuals with rpkm < 0. 25 were removed from the analysis. For imputed variants, we applied a best guess algorithm to assign most likely genotypes to each individual to transform the dosage into genotype. For variants with minor allele frequency (MAF) >= 0. 1, we adopted an additive model: dosage values <=0. 2 were coded as 0; dosage between 0. 4-0. 6 were coded as 1; and dosage >=0. 8 was coded as 1. For variants with MAF < 0. 1, we adopted a dominant model: dosage values <=0.
Functional annotation analysis Two public functional annotation tools, CADD and RegulomeDB were applied for functional inference. CADD was designed to predict functional variants by integrating diverse information from wide range of function categories  16. It computed a score inferring the functional ranking of the variants which is helpful for ne mapping. RegulomeDB is a tool designed to predict regulatory DNA elements in the Human genome based on information from GEO, ENCODE and public literature  17. It adopted SURF (Score of Uni ed Regulatory Features) model, trained on data from massively parallel reporter assays, to predict the functional variants in enhancer and promoter elements. RegulomeDB provides a probability score for the variant being transcription factor binding site (TFBS), promotor or DNase hypersensitivity, etc.
PROMO, an online tool to perform computational-based searches for gene expression regulatory sequence motif based on TFBS database, was used to search for the functional motif with sequence containing the candidate variants as input  18. SNPs with case-only joint p-value < 1x10 - 5 were selected for annotation analysis. eQTL displayed the information from GTEx Portal. The annotation information for rs208908 was bolded. 1, scaled CADD score by expressing the rank in order of magnitude terms; 2, RegulomeDB probability score is ranging from 0 to 1, with 1 being most likely to be a regulatory variant.
Results Novel signals from genome-wide gene-sex interaction analysis In the case-only analysis stage, a total of 19,943,415 10,359,674 SNPs were tested in the discovery and replication studies, respectively. Figure  2a  displays the Manhattan plot of joint case-only analysis of gene-sex interactions in lung cancer. No in ated type I error rate was detected (lambda = 0. 98). There were 22 SNPs with a joint case-only p-value < 5x10 - 8 in meta-analysis and three of them, rs17662871, rs79942605 and rs208908 had case-only p-value < 0. 1 in the replication study. Those SNPs were submitted to case-control interaction analysis using pooled discovery and replication data including 24,223 lung cancer patients and 22,560 controls. All of these SNPs had interaction p-value < 0. 05 in case-control analysis (Supplementary Table  S2 ).
Strati ed Analysis Of Lung Cancer Risk At cant Snps To explore how sex in genetic risk in lung cancer, we strati ed lung cancer risk analysis in men and women using discovery and replication combined dataset. We observed very distinct lung cancer risk-patterns between men and women at the three identi ed variants. For example, rs208908, had a protective effect for lung cancer in women with OR of 0. 81 (p = 1. 17x10 - 3 ) but had no signi cant effect in males (OR = 1. 03, p = 0. 59) (Fig. 2c ). Similarly, SNP rs17662871 had a protective effect for lung cancer in women with OR of 0. 86 (p = 1. 44x10 - 2 ). We further strati ed the analysis by smoking status (ever-. Vs. never-smokers) and the risk effect did not vary by smoking status for these two SNPs. rs79942605, on the other hand, had an increased risk for lung cancer in women (OR = 1.
Acknowledgment The  Genotype-Tissue Expression (GTEx) Project  was supported by the  Common Fund  of the O ce of the Director of the  National Institutes of Health , and by  NCI ,  NHGRI ,  NHLBI ,  NIDA ,  NIMH , and  NINDS. The data used for the analyses described in this manuscript were obtained from: dbGaP accession number phs000424. v7. p2. We thank the grants  U19-CA203654  ( NIH) ,  RR170048  ( Cancer Prevention Research Institute of Texas (CPRIT)) ,  RR160097T  (CPRIT), RR180061 (CPRIT),  U01 CA243483-02  ( Genetic Epidemiology of Lung Cancer Consortium (GELCC) ), X01HG007491,  Department of Health and Human Services  contracts  HHSN26820100007C ,  HHSN268201700012C ,  75N92020C00001 , and  1R21CA235464-01A1  ( NIH ) for support. CARET is funded by the  National Cancer Institute, National Institutes of Health  through grants  U01-CA063673 ,  UM1-CA167462 , and  U01-CA167462.
The  Harvard Lung Cancer Study  is funded by  NIH (NCI)  grant  U01CA209414  ( Boston Lung Cancer Survival Cohort). The Liverpool Lung Project is supported by  MPAD. Roy Castle Lung Foundation (UK). The EAGLE study was supported by the  intramural program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute ,  NIH. Joan E. The ReSoLuCENT study is funded by the  Weston Park Hospital Cancer Charity. Acknowledgement to the  George Isaac Family Fund for Cancer Research  for grant support. Bailey-Wilson  was supported by the  Intramural Program of the National Human Genome Research Institute, NIH. Chris Amos and Chao Cheng are Research Scholars of the Cancer Prevention Institute of Texas.
Therefore, such testing may fail to capture all security flaws and vulnerabilities due to the code complexity. On the other side, the black box testing technique, also known as behavioural testing, is in which the internal design or structure of the testing item is unknown to the tester. These tests can be functional or non-functional; however, most of the time, they are functional. For comparison, there is a well-defined standard implemented for assessment purposes: OWASP (Open Web Application Security Project)  [12]. This standard provides software developers and purchasers with a means to decide whether the tools in consideration for use should meet the software assurance requirements or not. OWASP addresses only the top 10 security risks every three years. It publishes the ranking of the ten most dangerous security vulnerabilities in the document "OWASP Top 10"  [13]  and enables the security team to protect the web application against the most important threats  [7].
Doupé et al. [22]  assessed ten tools which are Acunetix, AppScan [18], BurpSuite  [23] , Arachni, Hailstorm, NTOSpider, Paros, N-Stalker, Webinspect, and W3af for web application assessment. These tools were selected to consider a variety of open-source and commercial scanners. Lilan hu et al. [24]  presented an in-depth study of the existing security vulnerability detection technology, combined with the development process of machine learning security vulnerability detection technology, the requirements of the security vulnerability detection model are analyzed in detail, and a cross-site scripting security vulnerability detection model for web application is designed and implemented. Based on the existing network vulnerability detection technology and tools, the verification code identification function is added, which solves the problem that the data can be submitted to the server only by inputting the verification code.
The performance was evaluated using multiple evaluation metrics: precision, recall, Youden index, OWASP web benchmark evaluation, and the web application security scanner evaluation criteria. The experimental results show that, while the commercial scanners effectively detect security vulnerabilities, some open-source scanners (such as ZAP and Skipfish) can also be effective. The evaluation focused mainly on the selected web application assessment capabilities against XSS (Cross-Site Scripting), SQL injection, code injection, and broken access controls. Exposed vulnerabilities were categorized to enable the assessment process for seventeen different vulnerabilities. In addition, the authors developed their test application called WackoPicko  [29] , which is geared toward the assessment and evaluation process to test the scanner under realistic conditions.
Conclusively, their research accomplishes the goal of assessing the healthcare information system domain by a comprehensive assessment of databases, networks, applications, and infrastructure. Mohit et al. [35]  proposed a model for web vulnerability detection based on probabilistic classification. Their research mainly focused on the top four OWASP vulnerabilities: CSS, SQL injection, Remote code execution, and File inclusion. The results were based on improving performance parameters such as false alarm and accuracy for the detection of the four mentioned vulnerabilities. The scope of their work is very limited, which addresses only four mentioned vulnerabilities on OWASP's list. Gaurav et al. [36]  presented a survey for monitoring web services using the cloud. It provides an assessment system for penetration testing for vulnerability and malware detection.
It also demonstrates the ability to propose solutions that can mitigate these security vulnerabilities  [45]. Nessus is a commercial tool that works on Linux, MAC OS, and Windows OS. For this work, a cloud-based version of the Nessus vulnerability scanner was evaluated. The Nessus vulnerability scanner was not evaluated in any of the previous works mentioned. NetSparker: A web application security scanner is designed to discover and audit web application vulnerabilities such as SQL Injection and XSS possibilities. NetSparker is used by cybersecurity space professionals and is considered by many researchers  [19]. However, NetSparker is a commercial tool that works on Windows OS only. Older versions of NetSparker were evaluated in  [35]. APPSCAN: This tool provides centralized control with new advanced application scanning, remediation capabilities, enterprise application security status metrics, key regulatory compliance reporting, and seamless integration with AppScan Standard.
The software itself does not require any special installation; a standard PC will be fine, though a dedicated workstation or server would likely yield results more quickly. OWASP-ZAP: The OWASP Zed Attack Proxy (ZAP) is one of the most popular web application security testing tools. It is made available for free as an open-source project and is contributed to and maintained by OWASP  [47]. The OWASP is a vendor-neutral, non-profit group of volunteers dedicated to making web applications more secure. The OWASP ZAP tool can be used during web application development by web developers or experienced security experts during penetration testing to assess web applications for vulnerabilities. Wapiti: This is a generic command-line tool to automate the audit process of a web application  [48]. It is an open-source tool and has some current updates.
Therefore, there is a need to discover more vulnerabilities by using scripts and plugins in open-source tools. Results from proprietary tools such as Acunetix, APPSCAN, Nessus, NetSparker, and HP WebInspect were collected from different research studies. The results collection process aimed at a state-of-the-art comparison of these proprietary tools' performances on OWASP and NIST defined vulnerabilities. Open-source tools such as OWASP-ZAP, Burp Suite, Nikto, Arachni  [51] , and Wapiti  [48]  are tested on the DVWA framework. As a result, this framework is vulnerable to the following attacks: Brute Force Attack, Command Injection Attack, CSRF, File Inclusion, File Upload, Insecure Captcha, SQL injection, SQL Injection blind, CSS stored, CSS reflected, CSS-DOM, CSP (Content Security Policy) bypass, and Weak Session ID's.
Endpoint extraction can be conducted by searching the web application underscan's source code (including configuration files). Using this approach, a crawler can uncover hidden parameters of requests (e. g. , debugging parameters). However, this approach is only feasible when the source code of the web application under test is available. If it is not available, then the second approach comes into play. This approach consists of using the best available crawler to obtain additional URLs. Our survey assessed that Arachni provides better crawling performance compared to other open-source crawlers such as ZAP, W3af, and Wapiti. We used the second approach to develop the web application scanner toolkit. To check the crawling coverage, WIVET (Web Input Vector Extractor Teaser) version 4 was used, which is a benchmarking project specifically designed to assess crawling coverage.
e. , they have a better vulnerability identification capability in the proprietary tools category. Moreover, this survey provides compared results of different detected vulnerabilities. A comparative analysis will be performed by developing plugin scripts and algorithms for open-source web assessment tools. This approach will help to increase crawling coverage with better authentication features and helps to decrease false positives. In the future, other web application assessment tools can also be compared with these tools for a better false positive detection rate. Some of these tools can be customized in terms of their features and functions to detect web application vulnerabilities better. Open-source tools such as Nikto and Arachni use scripts and plugins for vulnerability detection. Therefore, more plugins and scripts are developed to detect more significant vulnerabilities.
Literature Search and Selection Criteria To obtain journal articles reporting hybrid performance, we performed a systematic literature research in Web of Science database using the key words "hybrid" and "fitness" with no restriction on publication year. Even though the term "fitness" refers to the breeding success of individuals, many ecological and evolutionary studies use this term when assessing differences for a very broad range of performance measures. Because these terms have a broad meaning and can be used in different contexts, we refined the research selecting Web of Science categories particularly relevant for evolutionary or ecological studies: ecology, evolutionary biology, genetics heredity, computer sciences interdisciplinary applications, multidisciplinary sciences, biology, zoology, entomology, marine freshwater biology, environmental sciences, fisheries, biodiversity conservation, forestry, behavioural sciences, ornithology, oceanography, water resources, physiology, reproductive biology, developmental biology.
We used the ImageJ software to extract data from the plots  (Schindelin et al. , 2015). 3. We only used comparisons of traits representing hybrid performance. Morphological and behavioural characters were considered when they could be interpreted in terms of performance (e. g. differences in body condition, growth rate, foraging ability). 4. We exclude studies about parasitism, which were analysed in a dedicated review  (Theodosopoulos et al. , 2019). The comparison between the performance of hybrids can be performed using different approaches, each of which has its own merit and limitations. In the mid-parent approach, the performance of hybrids is compared with the average value of parental lineages  (Atsumi et al. , 2021; Thompson et al. , 2021). This approach is used to test the null-hypothesis that hybrids have intermediate performance compared to the parental species.
We transformed the obtained mean and standard deviation in Fisher's z and its variance (z-var) and we extracted one effect size for each comparison between hybrid and mid-parent value. For separate-parent approach, we transformed the statistics values reported in studies (F, t, R 2 , χ 2 , means, and standard deviation of populations) in Fisher's z and its variance (z-var) using the compute. es package in R  (Del Re, 2013). When the statistic reported was Z-value, we directly calculated Fisher's z and its variance as: Fisher's z = Z/√(n-3) and z-var = 1/ (n-3)  (Hartung et al. , 2008). For one study, we converted d-value to Pearson's correlation coefficient r and then we extracted the Fisher's z and its variance using the compute. es package. For studies that did not report test statistics, we calculated the effect size from P-values.
Statistical Analyses For each comparison approach (mid-parent and separate-parent), we calculated the Rosenberg's fail-safe number to evaluate file-drawer bias. Rosenberg's fail-safe number establishes the studies that should be added to the meta-analysis to make the difference between observed and expected no longer significant and it estimates the strength of the results of sampling bias meta-analysis. We used Egger's regression test and Begg's rank test to evaluate the occurrence of publication bias in the dataset as procedure to implement the funnel plot  (Begg & Mazumdar, 1994; Egger et al. , 1997). Finally, we quantified heterogeneity using I 2  (Nakagawa & Santos, 2012).
A second binominal GLMM assessed whether the frequency of significant effect sizes was related to: relationships between parents, hybrid generations, alien populations in parental cross, laboratory or field study, hybrid identification method, and trait category as fixed factors. Also in this case, we included taxonomic genus, identity of the study and hybrid ID as random factors. Binomial GLMMs were run using the lme4 package in R; we used a likelihood-ratio test to assess the significance of fixed factors.
Meta-analysis We implemented meta-analysis and meta-regression approaches in a Bayesian framework using generalized linear mixed models (MCMCglmm package in R)  (Hadfield, 2010; Nakagawa & Santos, 2012). We fitted different mixed models with different aims. All MCMC models were run with 60,000 iterations, discarding the first 10,000 iterations as a burn-in and with a thinning interval of 24. We used the mev argument in the MCMCglmm function to consider 1/z-variance as a weight for the records  (Hadfield & Nakagawa, 2010).
Meta-regression: Factors Potentially Affecting Hybrid Performance To determine the factors related to the variation of hybrid performance compared to parents, we run two multivariable generalized linear mixed models one for each comparison approach. In these analyses, we used relationships between parents (intra or inter-specific) as an estimate of the divergence between parents, inasmuch there was available data for all the comparisons included. Contrary, TIMETREE database had a limited sample size for the divergence times. We used as dependent variable all the effect sizes of the comparisons and six parameters as fixed effects: relationships between parents, hybrid generations, invasive species cross, field vs lab studies, hybrid identification method, trait 1 3 category used for comparisons; taxonomic genus, identity of the study and hybrid ID were added as random factors.
g. insects have much faster generations than vertebrates). Unfortunately, information on generation time is too scanty to be tested in this study. It is known that different biological processes can affect hybrid performance and have been described quite well   (a)  discrimination between intraspecific or interspecific crosses using all the effect sizes, (b) discrimination between intraspecific or interspecific crosses using effect sizes of hybrids only genetically identified, (c) TIMETREE data (divergence in time) of parental lineages using all the effect sizes, (d) TIMETREE data (divergence in time) of parental lineages using effect sizes of hybrids only genetically identified relative to a specific cross in the literature (e. g. : Campbell & Meinke, 2010; Casas et al. , 2012). However, our metaanalysis did not identify a clear effect on these biological processes on hybrid performance, as only the divergence between parents affect hybrid performance.