The NGS Survey Control Map provides a map of the US which allows you to find and display geodetic survey control points stored in the database of the National Geodetic Survey and access the geodetic control data sheets associated with the points. Data sheets are in ASCII format and show precise latitude and longitude, orthometric heights, and gravity data for individual survey control points.

This dataset represents the geographic position of geodetic control as maintained by the National Geodetic Survey that have been placed in the field and used as control points by land surveyors. Includes some monuments within a 5 mile buffer from the County border. This file is not actively updated by County staff and was last compiled on December 9, 2014. More detailed information can be obtained from the NGS Datasheet Page (http://www.ngs.noaa.gov/cgi-bin/datasheet.prl).

Geodetic Control Points dataset current as of 2007. Effingham Geodetic NGS Monuments - benchmarks downloaded from http://www.ngs.noaa.gov/cgi-bin/datasheet.prl.

Highly pathogenic avian influenza is endemic and widespread in wild birds and is causing major outbreaks in poultry worldwide and in U.S. dairy cows, with several recent human cases, highlighting the need for reliable and rapid sequencing to track mutations that may facilitate viral replication in different hosts. SNP analysis is a useful molecular epidemiology tool to track outbreaks, but it requires accurate whole-genome sequencing (WGS) with sufficient read depth across all eight segments. In outbreak situations, where timely data is critical for controlling the spread of the virus, reducing sequencing preparation time while maintaining high-quality standards is particularly important. In this study, we optimized a custom barcoded primer strategy for influenza A whole-genome sequencing on the nanopore sequencing platform, combining the high performance of the Native Barcoding Kit with the prompt preparation time of the Rapid Barcoding Kit. Custom barcoded primers were designed to perform barcode attachment during RT-PCR amplification, eliminating the need for separate barcoding and clean-up steps, thus reducing library preparation time. We compared the performance of the custom barcoded primer method with the Native and Rapid barcoding kits in terms of read quality, read depth, and sequencing output. The results show that the custom barcoded primers provided performance comparable to the Native Barcoding Kit while reducing library preparation time by 2.3X compared to the Native kit and being only 15 minutes longer than the Rapid kit with better depth of sequencing. Additionally, the custom barcoded primer method was evaluated on a variety of clinical sample types. This approach offers a promising solution for influenza A sequencing, providing both high throughput and time efficiency, which significantly improves the time-to-result turnaround, making sequencing more accessible for real-time surveillance.

The primary purpose of this dataset is to provide VCRLTER researchers and students with a convenient and comprehensive set of historical NOS t-sheet shorelines spanning the full Virginia Eastern Shore in a single GIS data layer. From NOAA-NOS-NGS source metadata: "These shoreline data represent a vector conversion of a set of NOS raster shoreline manuscripts identified by t-sheet or tp-sheet numbers. These vector data were created by contractors for NOS who vectorized georeferenced raster shoreline manuscripts using Environmental Systems Research Institute, Inc. (ESRI)(r), ArcInfo's(r) ArcScan(r) software to create individual ArcInfo coverages. The individual coverages were ultimately edgematched within a surveyed project area and appended together. The NOAA NESDIS Environmental Data Rescue Program (EDRP) funded this project. The NOAA National Ocean Service, Coastal Services Center, developed the procedures used in this project and was responsible for project oversight. The project intent was to rescue valuable historical data and make it accessible and useful to the coastal mapping community. This process involved the conversion of original analog products to digital mapping products. This file is a further conversion of that product from a raster to a vector product that may be useful for Electronic Charting and Display Information Systems (ECDIS) and geographic information systems (GIS)." Original NOAA-NOS-NGS data were organized by project, with each project containing a single shapefile containing the historical shoreline features from multiple T-sheets based on surveys from roughly the same time period. There were 43 projects containing information from 208 T-sheets and TP-sheets that were found to cover the Eastern Shore of VA and southern MD and ranging in time from 1847 to 1978 (plus one set of shorelines from 2009 for the new Chincoteague bridge and the immediate surrounding area). VCRLTER staff combined these 43 shapefiles into a single shapefile with an added "PROJID" attribute to identify the source project. This shoreline dataset compliments and overlaps other VCRLTER shoreline datasets for the Virginia barrier islands that contain historical shorelines derived from a combination of sources, including: a subset of the included NOS t-sheets (digitized by VCRLTER researchers prior to availability in digital format from NOAA-NOS-NGS); NOAA coastal change maps; photointerpretation of aerial photos (from USGS, USACE, VITA-VGIN-VBMP, and others), and satellite imagery (from ETM+ Landsat 7 and IKONOS); and GPS surveys.

This dataset is an excel file that summarises information of patients that found potential causal variant(s) or VUS(s) incompatible with the clinical diagnosis. It includes patients' gender, symptom onset age, age at last follow-up, clinical presentation, provisional clinical diagnosis, prior genetic test and results, availability of the WES and WGS data, and WES and WGS of their parents.

The first sheet is the patients that found potential causal variants. The last three columns are the identified potential causal variants, gene of the variants, inheritance model, ACMG guideline classification of the variants.

The second sheet is the patients found VUS(s) incompatible with the clinical diagnosis. The last three columns are the identified VUS(s) incompatible with the clinical diagnosis, gene of the VUS(s), ACMG guideline classification of the VUS(s).

BackgroundColorectal cancer (CRC) is recognized as a multifactorial disease, where both genetic and environmental factors play critical roles in its development and progression. The identification of pathogenic germline variants has proven to be a valuable tool for early diagnosis, the implementation of surveillance strategies, and the identification of individuals at increased cancer risk. Next-generation sequencing (NGS) has facilitated comprehensive multigene analysis in both hereditary and sporadic cases of CRC.Patients and methodsIn this study, we analyzed 100 unselected Colombian patients with CRC to identify pathogenic (P) and likely pathogenic (LP) germline variants, classified according to the guidelines established by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP). Using the BoostDM artificial intelligence method, we were able to identify oncodriver germline variants with potential implications for disease progression. We assessed the model’s accuracy in predicting germline variants by comparing its results with the AlphaMissense pathogenicity prediction model. Additionally, a minigene assay was employed for the functional validation of intronic mutations.ResultsOur findings revealed that 12% of the patients carried pathogenic/likely pathogenic (P/LP) variants according to ACMG/AMP criteria. Using BoostDM, we identified oncodriver variants in 65% of the cases. These results highlight the significance of expanded multigene analysis and the integration of artificial intelligence in detecting germline variants associated with CRC. The average overall AUC values for the comparison between BoostDM and AlphaMissense were 0.788 for the entire BoostDM dataset and 0.803 for the genes within our panel, with individual gene AUC values ranging from 0.606 to 0.983. Functional validation through the minigene assay revealed the generation of aberrant transcripts, potentially linked to the molecular etiology of the disease.ConclusionOur study provided valuable insights into the prevalence and frequency of P/LP germline variants in unselected Colombian CRC patients through NGS. Integrating advanced genomic analysis and artificial intelligence has proven instrumental in enhancing variant detection beyond conventional methods. Our functional validation results provide insights into the potential pathogenicity of intronic variants. These findings underscore the necessity of a multifaceted approach to unravel the complex genetic landscape of CRC.

Images of National Coast & Geodetic Survey (now NOAA's National Geodetic Survey/NGS) tidal benchmarks which have been superseded by new markers or locations. Period of record is 1830-1984. Scanned under the Climate Database Modernization Program.

Peruvian maize exhibits abundant morphological diversity, with landraces cultivated from sea level (sl) up to 3,500 m above sl. Previous research based on morphological descriptors, defined at least 52 Peruvian maize races, but its genetic diversity and population structure remains largely unknown. Here, we used genotyping-by-sequencing (GBS) to obtain single nucleotide polymorphisms (SNPs) that allow inferring the genetic structure and diversity of 423 maize accessions from the genebank of Universidad Nacional Agraria la Molina (UNALM) and Universidad Nacional Autónoma de Tayacaja (UNAT). These accessions represent nine races and one sub-race, along with 15 open-pollinated lines (purple corn) and two yellow maize hybrids. It was possible to obtain 14,235 high-quality SNPs distributed along the 10 maize chromosomes of maize. Gene diversity ranged from 0.33 (sub-race Pachia) to 0.362 (race Ancashino), with race Cusco showing the lowest inbreeding coefficient (0.205) and Ancashino the highest (0.274) for the landraces. Population divergence (FST) was very low (mean = 0.017), thus depicting extensive interbreeding among Peruvian maize. A cluster containing maize landraces from Ancash, Apurímac, and Ayacucho exhibited the highest genetic variability. Population structure analysis indicated that these 423 distinct genotypes can be included in 10 groups, with some maize races clustering together. Peruvian maize races failed to be recovered as monophyletic; instead, our phylogenetic tree identified two clades corresponding to the groups of the classification of the races of Peruvian maize based on their chronological origin, that is, anciently derived or primary races and lately derived or secondary races. Additionally, these two clades are also congruent with the geographic origin of these maize races, reflecting their mixed evolutionary backgrounds and constant evolution. Peruvian maize germplasm needs further investigation with modern technologies to better use them massively in breeding programs that favor agriculture mainly in the South American highlands. We also expect this work will pave a path for establishing more accurate conservation strategies for this precious crop genetic resource.

The values for the City of Richardson GPS Control Network were established using Static data collection procedures during the month of February 2020. CP&Y, Inc., in conjunction with the City of Richardson set all 3-1/4 inch domed aluminum monuments throughout the City at designated key areas. All fieldwork and post processing were performed by CP&Y, Inc. Final data sheets were published after consultation with the City of Richardson. Horizontal State Plane Coordinates (SPC) are on the Lambert Projection System — NAD83 (CORS96) — Texas Coordinate System (Texas North Central Zone 4202). A position was derived using the National Geodetic Service (NGS) Online Positioning User Service (OPUS) position for Monument No. 116 (previously City of Richardson Monument H7) near the center of Richardson, as a base point. All other monuments within the City of Richardson were tied to a constellation of multiple observations (baselines) using 3 GPS receivers from this base point location. The vectors were assembled into a three-dimensional network least squares model using the Trimble Business Center® software. A minimally constrained adjustment proved the integrity of the vector data. The positions of four Continuously Operating Reference Stations (CORS) in the NGS network were included in the project as control and the vectors from each of these stations were used to develop coordinates for Monument No. 116. The resulting coordinate values for all stations matched the OPUS solution and separate vector data corrected from the Trimble RTKNet. The dual-frequency vectors were added to the project to obtain coordinates for all stations for publication. Vertical values are NAVD88 elevations and validated by the City of Richardson Control Network generated in 1990, OPUS, and Trimble RTKNet datums. The 1990 City of Richardson Control Network was previously adjusted to the Federal Emergency Management Agency (FEMA) with GPS Static observations on U.S. Coast & Geodetic Survey Monuments M923 and S923. All GPS derived elevations were computed using a Geoid Model (GEOID12B). In summary, CP&Y, Inc. provided the City of Richardson with a GPS Control Network based on the position of Monument No. 116 and its elevation verified from the City of Richardson 1990 Control Network. All data sheets will show State Plane Coordinates in both Geodetic and Grid formats. The horizontal data is referenced to NAD83 Texas North Central Zone- 4202, in both U.S. Survey feet and meters. Vertical data is referenced to NAVD88 elevations.

The values for the City of Richardson GPS Control Network were established using Static data collection procedures during the month of February 2020. CP&Y, Inc., in conjunction with the City of Richardson set all 3-1/4 inch domed aluminum monuments throughout the City at designated key areas. All fieldwork and post processing were performed by CP&Y, Inc. Final data sheets were published after consultation with the City of Richardson. Horizontal State Plane Coordinates (SPC) are on the Lambert Projection System — NAD83 (CORS96) — Texas Coordinate System (Texas North Central Zone 4202). A position was derived using the National Geodetic Service (NGS) Online Positioning User Service (OPUS) position for Monument No. 116 (previously City of Richardson Monument H7) near the center of Richardson, as a base point. All other monuments within the City of Richardson were tied to a constellation of multiple observations (baselines) using 3 GPS receivers from this base point location. The vectors were assembled into a three-dimensional network least squares model using the Trimble Business Center® software. A minimally constrained adjustment proved the integrity of the vector data. The positions of four Continuously Operating Reference Stations (CORS) in the NGS network were included in the project as control and the vectors from each of these stations were used to develop coordinates for Monument No. 116. The resulting coordinate values for all stations matched the OPUS solution and separate vector data corrected from the Trimble RTKNet. The dual-frequency vectors were added to the project to obtain coordinates for all stations for publication. Vertical values are NAVD88 elevations and validated by the City of Richardson Control Network generated in 1990, OPUS, and Trimble RTKNet datums. The 1990 City of Richardson Control Network was previously adjusted to the Federal Emergency Management Agency (FEMA) with GPS Static observations on U.S. Coast & Geodetic Survey Monuments M923 and S923. All GPS derived elevations were computed using a Geoid Model (GEOID12B). In summary, CP&Y, Inc. provided the City of Richardson with a GPS Control Network based on the position of Monument No. 116 and its elevation verified from the City of Richardson 1990 Control Network. All data sheets will show State Plane Coordinates in both Geodetic and Grid formats. The horizontal data is referenced to NAD83 Texas North Central Zone- 4202, in both U.S. Survey feet and meters. Vertical data is referenced to NAVD88 elevations.

Peruvian maize exhibits abundant morphological diversity, with landraces cultivated from sea level (sl) up to 3,500 m above sl. Previous research based on morphological descriptors, defined at least 52 Peruvian maize races, but its genetic diversity and population structure remains largely unknown. Here, we used genotyping-by-sequencing (GBS) to obtain single nucleotide polymorphisms (SNPs) that allow inferring the genetic structure and diversity of 423 maize accessions from the genebank of Universidad Nacional Agraria la Molina (UNALM) and Universidad Nacional Autónoma de Tayacaja (UNAT). These accessions represent nine races and one sub-race, along with 15 open-pollinated lines (purple corn) and two yellow maize hybrids. It was possible to obtain 14,235 high-quality SNPs distributed along the 10 maize chromosomes of maize. Gene diversity ranged from 0.33 (sub-race Pachia) to 0.362 (race Ancashino), with race Cusco showing the lowest inbreeding coefficient (0.205) and Ancashino the highest (0.274) for the landraces. Population divergence (FST) was very low (mean = 0.017), thus depicting extensive interbreeding among Peruvian maize. A cluster containing maize landraces from Ancash, Apurímac, and Ayacucho exhibited the highest genetic variability. Population structure analysis indicated that these 423 distinct genotypes can be included in 10 groups, with some maize races clustering together. Peruvian maize races failed to be recovered as monophyletic; instead, our phylogenetic tree identified two clades corresponding to the groups of the classification of the races of Peruvian maize based on their chronological origin, that is, anciently derived or primary races and lately derived or secondary races. Additionally, these two clades are also congruent with the geographic origin of these maize races, reflecting their mixed evolutionary backgrounds and constant evolution. Peruvian maize germplasm needs further investigation with modern technologies to better use them massively in breeding programs that favor agriculture mainly in the South American highlands. We also expect this work will pave a path for establishing more accurate conservation strategies for this precious crop genetic resource.

This is a revised supplementary file and it includes additional data sheets to explain the results in a better way.

IntroductionAcute mountain sickness (AMS) is a common altitude illness that occurs when individuals rapidly ascend to altitudes ≥2,500 m without proper acclimatization. Genetic and genomic factors can contribute to the development of AMS or predispose individuals to susceptibility. This study aimed to investigate differential gene regulation and biological pathways to diagnose AMS from high-altitude (HA; 4,300 m) blood samples and predict AMS-susceptible (AMS+) and AMS-resistant (AMS─) individuals from sea-level (SL; 50 m) blood samples.MethodsTwo independent cohorts were used to ensure the robustness of the findings. Blood samples were collected from participants at SL and HA. RNA sequencing was employed to profile gene expression. Differential expression analysis and pathway enrichment were performed to uncover transcriptomic signatures associated with AMS. Biomarker panels were developed for diagnostic and predictive purposes.ResultsAt HA, hemoglobin-related genes (HBA1, HBA2, and HBB) and phosphodiesterase 5A (PDE5A) emerged as key differentiators between AMS+ and AMS− individuals. The cAMP response element-binding protein (CREB) pathway exhibited contrasting regulatory patterns at SL and HA, reflecting potential adaptation mechanisms to hypoxic conditions. Diagnostic and predictive biomarker panels were proposed based on the identified transcriptomic signatures, demonstrating strong potential for distinguishing AMS+ from AMS− individuals.DiscussionThe findings highlight the importance of hemoglobin-related genes and the CREB pathway in AMS susceptibility and adaptation to hypoxia. The differential regulation of these pathways provides novel insights into the biological mechanisms underlying AMS. The proposed biomarker panels offer promising avenues for the early diagnosis and prediction of AMS risk, which could enhance preventive and therapeutic strategies.

BackgroundTargeted next-generation sequencing (tNGS) has become a trending tool in the field of infection diagnosis, but concerns are also raising about its performance compared with metagenomic next-generation sequencing (mNGS). This study aims to explore the clinical feasibility of a tNGS panel for respiratory tract infection diagnosis and compare it with mNGS in the same cohort of inpatients.Methods180 bronchoalveolar lavage fluid samples were collected and sent to two centers for mNGS and tNGS blinded tests, respectively. The concordance between pathogen reports of both methods and the clinical significance among samples with/without known etiology was further evaluated.ResultsOverall, both methods displayed high agreement on pathogen reports, as the average percent agreement reached 95.29%. But tNGS presented a slightly higher detection rate per species than mNGS (PWilcoxon=1.212e-05; standard mean difference = 0.2887091), as detection rates for 32 out of 48 species were higher than those of mNGS. Due to limitations of panel coverage, tNGS identified 28 fewer species than mNGS, among which only 3 were considered clinically relevant. In reference to composite reference standard, accuracy, sensitivity, and specificity combining both tNGS and mNGS reached 95.61%, 96.71%, and 95.68%, respectively, while positive prediction value (PPV) was low at 48.13%, which was caused by low agreement regarding opportunistic pathogens. tNGS and mNGS improved the etiology identification in 30.6% (55/180) and 33.9% (61/180) cases, respectively.ConclusionCollectively, tNGS presented a similar overall performance in pathogen identification compared to mNGS, but outperformed in some pathogens. This study also demonstrated that deployment of tNGS significantly improves etiology identification in routine practice and provides hints for clinical decisions. The low agreement between clinical diagnosis and NGS reports towards opportunistic pathogens implies that adjudication is essential for report interpretation. Finally, We proposed tNGS as a diagnosis option in clinical practice due to its cost-efficiency.

BackgroundMinimal residual disease (MRD) is a critical prognostic marker in acute lymphoblastic leukemia (ALL). The well studied and used MRD detection methods, multiparametric flow cytometry (MFC) and real-time quantitative polymerase chain reaction (qRT-PCR) for fusion genes and receptor gene rearrangements have significantly improved risk stratification, but have limitations in sensitivity and applicability. Next-generation sequencing (NGS) has emerged as a promising approach for MRD assessment, offering better sensitivity and the ability to track clonal evolution.ObjectivesThis systematic review evaluates the clinical utility and prognostic value of NGS for MRD detection in ALL, comparing its performance with conventional methods and exploring its potential role in therapeutic guidance.MethodsA comprehensive literature search was conducted across PubMed and Web of Science following PRISMA guidelines. Studies were included if they assessed MRD using NGS in ALL patients and provided data on sensitivity and prognostic value. Comparative analyses with MFC or qRT-PCR were considered. Data on end-of-induction MRD values, event-free survival (EFS), and overall survival (OS) were extracted.ResultsThirteen studies met the inclusion criteria. NGS demonstrated superior sensitivity in detecting MRD-positive cases compared to MFC in patients classified as MRD-negative. Higher correlation was observed in MRD-positive cases than in MRD-negative cases. NGS-based MRD stratification correlated strongly with clinical outcomes, with patients achieving NGS-MRD negativity exhibiting superior EFS and OS rates. Additionally, NGS was highly predictive of relapse following hematopoietic stem cell transplantation and CAR-T cell therapy. The IGH rearrangements as the primary marker in NGS panels has demonstrated good prognostic value in B-ALL.ConclusionNGS represents a transformative tool for MRD monitoring in ALL, offering enhanced sensitivity and prognostic accuracy. Challenges such as high costs, complex bioinformatics analysis and the need for standardization remain. While its integration into clinical practice holds significant promise, further research is needed to establish standardized protocols, cost-effectiveness, and its optimal role in treatment decision-making. The combination of NGS with MFC may provide complementary advantages.

PurposeDeveloping innovative precision and personalized cancer therapeutics is essential to enhance cancer survivability, particularly for prevalent cancer types such as colorectal cancer. This study aims to demonstrate various approaches for discovering new targets for precision therapies using artificial intelligence (AI) on a Polish cohort of colorectal cancer patients. MethodsWe analyzed 71 patients with histopathologically confirmed advanced resectional colorectal adenocarcinoma. Whole exome sequencing was performed on tumor and peripheral blood samples, while RNA sequencing (RNAseq) was conducted on tumor samples. We employed three approaches to identify potential targets for personalized and precision therapies. First, using our in-house neoantigen calling pipeline, ARDentify, combined with an AI-based model trained on immunopeptidomics mass spectrometry data (ARDisplay), we identified neoepitopes in the cohort. Second, based on recurrent mutations found in our patient cohort, we selected corresponding cancer cell lines and utilized knock-out gene dependency scores to identify synthetic lethality genes. Third, an AI-based model trained on cancer cell line data was employed to identify cell lines with genomic profiles similar to selected patients. Copy number variants and recurrent single nucleotide variants in these cell lines, along with gene dependency data, were used to find personalized synthetic lethality pairs. ResultsWe identified approximately 8,700 unique neoepitopes, but none were shared by more than two patients, indicating limited potential for shared neoantigenic targets across our cohort. Additionally, we identified three synthetic lethality pairs: the well-known APC-CTNNB1 and BRAF-DUSP4 pairs, along with the recently described APC-TCF7L2 pair, which could be significant for patients with APC and BRAF variants. Furthermore, by leveraging the identification of similar cancer cell lines, we uncovered a potential gene pair, VPS4A and VPS4B, with therapeutic implications. ConclusionOur study highlights three distinct approaches for identifying potential therapeutic targets in cancer patients. Each approach yielded valuable insights into our cohort, underscoring the relevance and utility of these methodologies in the development of precision and personalized cancer therapies. Importantly, we developed a novel AI model that aligns tumors with representative cell lines using RNAseq and methylation data. This model enables us to identify cell lines closely resembling patient tumors, facilitating accurate selection of models needed for in vitro validation.