Disaster Site Structure Analysis: Examining Effective Remote Sensing Techniques in Blue Tarpaulin Inspection

This thesis aimed to evaluate three methods of analyzing blue roofing tarpaulin (tarp) placed on homes in post natural disaster zones with remote sensing techniques by assessing the different methods- image segmentation, machine learning (ML), and supervised classification. One can determine which is the most efficient and accurate way of detecting blue tarps. The concept here was that using the most efficient and accurate way to locate blue tarps can aid federal, state, and local emergency management (EM) operations and homeowners. In the wake of a natural disaster such as a tornado, hurricane, thunderstorm, or similar weather events, roofs are the most likely to be damaged (Esri Events., 2019). Severe roof damage needs to be mitigated as fast as possible: which in the United States is often done at no cost by the Federal Emergency Management Agency (FEMA). This research aimed to find the most efficient and accurate way of detecting blue tarps with three different remote sensing practices. The first method, image segmentation, separates parts of a whole image into smaller areas or categories that correspond to distinct items or parts of objects. Each pixel in a remotely sensed image is then classified into categories set by the user. A successful segmentation will result when pixels in the same category have comparable multivariate, grayscale values and form a linked area, whereas nearby pixels in other categories have distinct values. Machine Learning, ML, a second method, is a technique that processes data depending on many layers for feature v identification and pattern recognition. ArcGIS Pro mapping software processes data with ML classification methods to classify remote sensing imagery. Deep learning models may be used to recognize objects, classify images, and in this example, classify pixels. The resultant model definition file or deep learning software package is used to run the inference geoprocessing tools to extract particular item positions, categorize or label the objects, or classify the pixels in the picture. Finally, supervised classification is based on a system in which a user picks sample pixel in an image that are indicative of certain classes and then tells image-processing software to categorize the other pixels in the picture using these training sites as references. To group pixels together, the user also specifies the limits for how similar they must be. The number of classifications into which the image is categorized is likewise determined by the user. The importance of tracking blue roofs is multifaceted. Structures with roof damage from natural disasters face many immediate dangers, such as further water and wind damage. These communities are at a critical moment as responding to the damage efficiently and effectively should occur in the immediate aftermath of a disaster. In part due to strategies such as FEMA and the United States Army Corps of Engineers’ (USACE) Operation Blue Roof, most often blue tarpaulins are installed on structures to prevent further damage caused by wind and rain. From a Unmanned Arial Vehicles (UAV) perspective, these blue tarps stand out amid the downed trees, devastated infrastructure, and other debris that will populate the area. Understanding that recovery can be one of the most important stages of Emergency Management, testing techniques vi for speed, accuracy, and effectiveness will assist in creating more effective Emergency Management (EM) specialists

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Surveillance of Vermont wildlife in 2021–2022 reveals no detected SARS-CoV-2 viral RNA

Abstract Previous studies have documented natural infections of SARS-CoV-2 in various domestic and wild animals. More recently, studies have been published noting the susceptibility of members of the Cervidae family, and infections in both wild and captive cervid populations. In this study, we investigated the presence of SARS-CoV-2 in mammalian wildlife within the state of Vermont. 739 nasal or throat samples were collected from wildlife throughout the state during the 2021 and 2022 harvest season. Data was collected from red and gray foxes (Vulpes vulples and Urocyon cineroargentus, respectively), fishers (Martes pennati), river otters (Lutra canadensis), coyotes (Canis lantrans), bobcats (Lynx rufus rufus), black bears (Ursus americanus), and white-tailed deer (Odocoileus virginianus). Samples were tested for the presence of SARS-CoV-2 via quantitative RT-qPCR using the CDC N1/N2 primer set and/or the WHO-E gene primer set. Surprisingly, we initially detected a number of N1 and/or N2 positive samples with high cycle threshold values, though after conducting environmental swabbing of the laboratory and verifying with a second independent primer set (WHO-E) and PCR without reverse transcriptase, we showed that these were false positives due to plasmid contamination from a construct expressing the N gene in the general laboratory environment. Our final results indicate that no sampled wildlife were positive for SARS-CoV-2 RNA, and highlight the importance of physically separate locations for the processing of samples for surveillance and experiments that require the use of plasmid DNA containing the target RNA sequence. These negative findings are surprising, given that most published North America studies have found SARS-CoV-2 within their deer populations. The absence of SARS-CoV-2 RNA in populations sampled here may provide insights in to the various environmental and anthropogenic factors that reduce spillover and spread in North American’s wildlife populations