Ancillary Information Research Articles

APPLICATION OF NATURAL LANGUAGE PARSING FOR IDENTIFYING NON-SURVEYED BOUNDARIES TOWARDS ENHANCED SYSTEMATIC LAND TITLING: RESULTS FROM PRELIMINARY EXPERIMENT

The need for the adoption of systematic land titling (SLT) in Nigeria cannot be overemphasised. Nonetheless, the problems of speed and cost of geospatial data acquisition, as well as identification of non-surveyed boundaries, remain unresolved, impeding the effectiveness of SLT for non-surveyed boundaries. The integration of language into Artificial Intelligence (AI) has allowed Natural Language Parsing (NLP) to effectively serve as a tool for communication between humans and computer systems. This study presents preliminary results of testing a prototype application that utilises NLP to convert textual descriptions into graphic sketches as a tool towards the production of a-priori sketches that can aid SLT in non-surveyed boundaries. The study determines that NLP alone cannot be used to achieve the required accuracy in geospatial data for SLT; however, the study concludes that NLP can be integrated alongside other ancillary information to enhance SLT in peri-urban regions.

Variability of bone marrow biopsy reporting affects accuracy of diagnosis of myeloproliferative neoplasms: data from the ALLG MPN01 registry

The Philadelphia-negative myeloproliferative neoplasms (MPN) are a heterogeneous group of overlapping bone marrow disorders defined by characteristic peripheral blood counts and bone marrow morphological findings in conjunction with recurrent somatic mutations. The accurate diagnosis and subclassification of MPN relies upon careful reporting of bone marrow morphology combined with ancillary information in an integrated pathology report. This co-operative trial group study ALLG MPN01 (ANZCTR:12613000138785), led by the Australasian Leukaemia & Lymphoma Group (ALLG), aimed to describe the current approach to diagnosis of MPN in routine practice. Specifically, we assessed the frequency with which bone marrow biopsies were performed, and the adherence of reporting pathologists to recommendations contained in the revised 2016 WHO classification pertaining to MPN. We reviewed the diagnosis of 152 patients from eight institutions who were enrolled in a national MPN registry of the ALLG between 2010 and 2016. The ALLG MPN01 registry is now closed to recruitment. Key features were extracted from pathology reports provided to the registry. Bone marrow biopsies were performed in 112/152 cases (74%). The pathological information entered was concordant with the stated clinical diagnosis in 75/112 cases (67%). The main reasons for discordant results were incomplete descriptions of megakaryocyte topography and morphology, inconsistent grading of reticulin fibrosis, and failure to integrate the available morphological and ancillary clinicopathological information. In this retrospective audit, 26% of MPN patients did not undergo a diagnostic bone marrow biopsy. In those who did, the specific MPN subtype may not have been reported correctly in 33% of cases, as evidenced by inconsistent features reported or insufficient information to assess. A more standardised approach to bone marrow reporting is required to ensure accuracy of MPN diagnoses and consistent reporting to cancer registries and clinical trials.

New developments in the analysis of catch time series as the basis for fish stock assessments: The CMSY++ method

Following an introduction to the nature of fisheries catches and their information content, a new development of CMSY, a data-limited stock assessment method for fishes and invertebrates, is presented. This new version, CMSY++, overcomes several of the deficiencies of CMSY, which itself improved upon the “Catch-MSY” method published by S. Martell and R. Froese in 2013. The catch-only application of CMSY++ uses a Bayesian implementation of a modified Schaefer model, which also allows the fitting of abundance indices should such information be available. In the absence of historical catch time series and abundance indices, CMSY++ depends strongly on the provision of appropriate and informative priors for plausible ranges of initial and final stock depletion. An Artificial Neural Network (ANN) now assists in selecting objective priors for relative stock size based on patterns in 400 catch time series used for training. Regarding the cross-validation of the ANN predictions, of the 400 real stocks used in the training of ANN, 94% of final relative biomass (B/k) Bayesian (BSM) estimates were within the approximate 95% confidence limits of the respective CMSY++ estimate. Also, the equilibrium catch-biomass relations of the modified Schaefer model are compared with those of alternative surplus-production and age-structured models, suggesting that the latter two can be strongly biased towards underestimating the biomass required to sustain catches at low abundance. Numerous independent applications demonstrate how CMSY++ can incorporate, in addition to the required catch time series, both abundance data and a wide variety of ancillary information. We stress, however, the caveats and pitfalls of naively using the built-in prior options, which should instead be evaluated case-by-case and ideally be replaced by independent prior knowledge.

New developments in the analysis of catch time series as the basis for fish stock assessments: The CMSY++ method

Following an introduction to the nature of fisheries catches and their information content, a new development of CMSY, a data-limited stock assessment method for fishes and invertebrates, is presented. This new version, CMSY++, overcomes several of the deficiencies of CMSY, which itself improved upon the “Catch-MSY” method published by S. Martell and R. Froese in 2013. The catch-only application of CMSY++ uses a Bayesian implementation of a modified Schaefer model, which also allows the fitting of abundance indices should such information be available. In the absence of historical catch time series and abundance indices, CMSY++ depends strongly on the provision of appropriate and informative priors for plausible ranges of initial and final stock depletion. An Artificial Neural Network (ANN) now assists in selecting objective priors for relative stock size based on patterns in 400 catch time series used for training. Regarding the cross-validation of the ANN predictions, of the 400 real stocks used in the training of ANN, 94% of final relative biomass (B/k) Bayesian (BSM) estimates were within the approximate 95% confidence limits of the respective CMSY++ estimate. Also, the equilibrium catch-biomass relations of the modified Schaefer model are compared with those of alternative surplus-production and age-structured models, suggesting that the latter two can be strongly biased towards underestimating the biomass required to sustain catches at low abundance. Numerous independent applications demonstrate how CMSY++ can incorporate, in addition to the required catch time series, both abundance data and a wide variety of ancillary information. We stress, however, the caveats and pitfalls of naively using the built-in prior options, which should instead be evaluated case-by-case and ideally be replaced by independent prior knowledge.

Estimating daily surface downward shortwave radiation over rugged terrain without bright surface at 30 m on clear-sky days using CERES data

ABSTRACTIn this study, the authors propose a model, called the Daily Downward Shortwave Radiation Random Forest Model over Rugged Terrain (DSRMT), to accurately calculate the downward shortwave radiation over a terrain without bright surface on clear days at a daily scale (DSRdaily−rugged). It was built by using the random forest method based on the comprehensive samples from CERES4_SYN1deg_Ed4A within 17 typical mountainous regions. DSRMT could directly estimate DSRdaily-rugged from the instantaneous direct and diffuse solar radiation on a flat surface during 10:30–14:30hrs on each day by comparing with the terrain factors from a digital elevation model, broadband albedo from the Global Land Surface Satellite, and ancillary information. The in-situ validation results showed that it generally delivered superior performance in estimating DSRdaily-rugged at any time during 10:30–14:30hrs, especially at noon, yielding a validated root mean-squared error (RMSE) of 24.90–29.22 Wm−2 and mean absolute error (MAE) of 19.16–22.94 Wm−2, and the average weighted DSRdaily-rugged were usually more accurate with the RMSE and MAE of 21.63 and 17.14 Wm−2. Overall, DSRMT was found to deliver satisfactory performance because of its high accuracy, robustness, ease of implementation, and efficiency, so it has the strong potential to be widely used in practice.

Want to Describe and Share Biodiversity Inventory and Monitoring Data? The Humboldt Extension for Ecological Inventories Can Help!

Access to high-quality ecological data is critical to assessing and modeling biodiversity and its changes through space and time. The Darwin Core standard has proven to be immensely helpful in sharing species occurrence data (see Wieczorek et al. 2012, Global Biodiversity Information Facility, GBIF) and promoting biodiversity research following the FAIR principles of findability, accessibility, interoperability and reusability (Wilkinson et al. 2016). However, it is limited in its ability to fully accommodate inventory data (i.e., linked records of multiple taxa at a specific place and time). Information about the inventory processes is often either unreported or described in an unstructured manner, limiting its potential re-use for larger-scale analyses. Two key aspects that are not captured in a structured manner yet are: i) information about the species that were not detected during an inventory, and ii) ancillary information about sampling effort and completeness. Non-detections (i.e., reported counts of zero) potentially enable more accurate and precise estimates of distribution, abundance, and changes in abundance. This becomes possible when variation in effort is used to estimate the likelihood that a non-detection represents a true absence of that taxon during the inventory. Currently, ecological inventory data, when shared at all, are typically discoverable through dataset catalogs (e.g., governmental data repositories) and supplementary materials to publications. With few exceptions, indexing of such data with the detail and structure needed has not been attempted at broad temporal and spatial scales, despite the potentially high value resulting from making inventory data more readily accessible. To address these limitations in documenting inventory data using the Darwin Core, Guralnick et al. (2018) proposed the Humboldt Core. Subsequent discussions within the biodiversity standards community made it clear that greater integration could be achieved by creating an extension of the Darwin Core, rather than developing a new standard in isolation. Extension design work began in 2021 and progress has been reported by Brenton (2021) and Sica et al. (2022). Over the last year the Humboldt Extension Task Group has sought advice from data providers and aggregators and updated its vocabulary terms. A challenging aspect has been creating terminology for the parent-child relationships (see Properties of Hierarchical Events) needed to describe surveys that may be as simple as a collection of checklists (one level of hierarchy) or as complex as species records from traps within plots along transects across habitats over multiple years (at least four levels of hierarchy). The Task Group has committed to completing a User Guide for the Humboldt Extension. Group members who contributed to the Darwin Core (Darwin Core Task Group 2009) and the Vocabulary Maintenance Specification (Vocabulary Maintenance Specification Task Group 2017) have provided valuable expertise on term refinement and process. Through ratification of the Humboldt Extension as a Darwin Core Event extension, we expect to provide the community with a usable solution, tied to well-established data publication mechanisms, for sharing and using inventory data. This effort promises to overcome a key bottleneck in the sharing of critically important ecological data, enhancing data discoverability, interoperability and re-use while lowering reporting burden and data and metadata heterogeneity. Global data aggregation initiatives, such as GBIF, will benefit from this development as they develop their data models and the range of standards and extensions they support. We anticipate that the Humboldt Extension will be attractive both to data publishers and data users, by facilitating the representation and indexing of data in richer, more meaningful ways. Despite the data-intensive nature of fundamental ecological research and applied monitoring for management and policy, ecological data have remained as one of the FAIR data frontiers. We anticipate that the Humboldt Extension will address most data exchange needs of all professional communities involved.

SILVERRUSH. XIII. A Catalog of 20,567 Lyα Emitters at z = 2−7 Identified in the Full-depth Data of the Subaru/HSC-SSP and CHORUS Surveys

We present 20,567 Lyα emitters (LAEs) at z = 2.2 − 7.3 that are photometrically identified by the SILVERRUSH program in a large survey area up to 25 deg2 with deep images of five broadband filters (grizy) and seven narrowband filters targeting Lyα lines at z = 2.2, 3.3, 4.9, 5.7, 6.6, 7.0, and 7.3 taken by the Hyper Suprime-Cam Subaru Strategic Program and the Cosmic HydrOgen Reionization Unveiled with Subaru survey. We select secure >5σ sources showing narrowband color excesses via Lyα break screening, taking into account the spatial inhomogeneity of limiting magnitudes. After removing spurious sources by careful masking and visual inspection of coadded and multiepoch images obtained over the 7 yr of the surveys, we construct LAE samples consisting of 6995, 4641, 726, 6124, 2058, 18, and 5 LAEs at z = 2.2, 3.3, 4.9, 5.7, 6.6, 7.0, and 7.3, respectively, although the z = 7.3 candidates are tentative. Our LAE catalogs contain 289 spectroscopically confirmed LAEs at the expected redshifts from previous work. We demonstrate that the number counts of our LAEs are consistent with previous studies with similar LAE selection criteria. The LAE catalogs will be made public on our project webpage with detailed descriptions of the content and ancillary information about the masks and limiting magnitudes.

Improvement of streamflow simulation by combining physically hydrological model with deep learning methods in data-scarce glacial river basin

Robust streamflow simulation at glacial basins is essential for the improvement of water sustainability assessment, water security evaluation, and water resource management under the rapidly changing climate. Therefore, we proposed a hybrid modelling framework to link the SWAT+ model considering glacial hydrological processes (GSWAT+) with Gated Recurrent Unit (GRU) neural networks to improve the model simulations and to establish a framework for the robust simulation and forecast of high and low flows in glacial river basins, which could be further used for the explorations of extreme hydrological events under a warming climate. The performance of different models (GSWAT+, GRU, and GRU-GSWAT+, respectively) were thoroughly investigated based on numerical experiments for two data-scarce glacial watersheds in Northwest China. The results suggested that the hybrid model (GRU-GSWAT+) outperformed both the individual deep learning (DL) model (GRU) and the conventional hydrological model (GSWAT+) in terms of simulation and prediction accuracy. Notably, the proposed hybrid model considerably enhanced the simulations of low and high flows that the conventional GSWAT+ failed to capture. Furthermore, utilizing suitable data integration (DI) schemes on feature and target sequences can substantially help to strengthen model stability and representativeness for monthly and annual streamflow sequences. Specifically, introducing one order differential method and decomposition approach, such as ensemble empirical signal decomposition (EEMD) and complete EEMD with adaptive noise (CEEMDAN), into feature and target sequences enriched the learnable ancillary information, which consequently strengthened the predictive performance of the proposed model. Overall, the proposed hybrid model with the suitable DI scheme has the potential to significantly enhance the accuracy of streamflow simulation in data-scarce glacial river basins. This hybrid model not only upheld the fundamental physical principles from the GSWAT+ model, but also considerably mitigated the accumulated bias errors, which caused by the shortage of climate data and inadequate hydrological principles, by using DL based model and DI schemes.

Habitability Studies of a Replica Fremont-style Pithouse

A full-sized Fremont-style pithouse was reconstructed to explore habitability. The results are: (1) the pithouse dampens daily and seasonal temperature fluctuations; (2) a wood and leather access closure was thermally more effective than a stone slab; (3) computational fluid dynamics (CFD) modeling shows the superstructure affects interior ventilation; (4) a floor-level vent is necessary for adequate ventilation and CFD modeling indicates its position relative to prevailing wind is important; (5) once reached, the thermal comfort level of 16.5°C can be maintained by body heat alone. Ancillary information acquired during construction and subsequent destruction of the pithouse: (6) the estimated time required by a Fremont community to construct a pithouse is roughly 538 person-hours; (7) expediting the burning of the structure required an animal fat accelerant and ventilation holes cut into the superstructure; and (8) nearly a half-hour of burning occurred before there was an abrupt catastrophic roof collapse.

Urban and Peri-Urban Vegetation Monitoring Using Satellite MODIS NDVI Time Series, Singular Spectrum Analysis, and Fisher–Shannon Statistical Method

The purpose of this work was to evaluate the potential of Singular Spectrum Analysis (SSA) and the Fisher–Shannon method to analyse NDVI MODIS time series and to capture and estimate inner vegetation anomalies in forest covers. In particular, the Fisher–Shannon method allows to calculate two quantities, the Fisher Information Measure (FIM) and the Shannon entropy power (SEP), which are used to characterise the complexity of a time series in terms of organisation/disorder. Pilot sites located both in urban (Milano, Torino, and Roma) and peri-urban areas (Appia Park, Castel Porziano, and Castel Volturno) were selected. Among the six sites, Roma, Castel Porziano, and Castel Volturno are affected by the parasite Toumeyella parvicornis. The time series was analysed using the products available in Google Earth Engine. To explore and characterise long-term vegetation dynamics, the time series was analysed using a multistep processing chain based on the (i) normalisation of the satellite time series, (ii) removal of seasonality and any other periodical cycles using SSA, (iii) analysis of the de-trended data using the Fisher–Shannon statistical method, and (iv) validation through comparison with independent data and ancillary information. Our findings point out to a clear discrimination between healthy and unhealthy sites, being the first (Milano, Torino, Appia) characterised by a larger FIM (lower SEP) and the second (Roma, Castel Porziano, Castel Volturno) by a lower FIM (larger SEP). The results of the investigations showed that the use of the SSA and Fisher–Shannon statistical methods coupled with the NDVI time series of the MODIS satellite made it possible to effectively identify and characterise subtle but physically significant signals veiled by seasonality and annual cycles.

An Illustration of FY-3E GNOS-R for Global Soil Moisture Monitoring.

An effective soil moisture retrieval method for FY-3E (Fengyun-3E) GNOS-R (GNSS occultation sounder II-reflectometry) is developed in this paper. Here, the LAGRS model, which is totally oriented for GNOS-R, is employed to estimate vegetation and surface roughness effects on surface reflectivity. Since the LAGRS (land surface GNSS reflection simulator) model is a space-borne GNSS-R (GNSS reflectometry) simulator based on the microwave radiative transfer equation model, the method presented in this paper takes more consideration on the physical scattering properties for retrieval. Ancillary information from SMAP (soil moisture active passive) such as the vegetation water content and the roughness coefficient are investigated for the final algorithm's development. At first, the SR (surface reflectivity) data calculated from GNOS-R is calculated and then calibrated, and then the vegetation roughness factor is achieved and used to eliminate the effects on both factors. After receiving the Fresnel reflectivity, the corresponding soil moisture estimated from this method is retrieved. The results demonstrate good consistency between soil moisture derived from GNOS-R data and SMAP soil moisture, with a correlation coefficient of 0.9599 and a root mean square error of 0.0483 cm3/cm3. This method succeeds in providing soil moisture on a global scale and is based on the previously developed physical LAGRS model. In this way, the great potential of GNOS-R for soil moisture estimation is presented.

Impact of incidental visualizations on primary tasks

Incidental visualizations are meant to be seen at-a-glance, on-the-go, and during short exposure times. They will always appear side-by-side with an ongoing primary task while providing ancillary information relevant to those tasks. They differ from glanceable visualizations because looking at them is never their major focus, and they differ from ambient visualizations because they are not embedded in the environment, but appear when needed. However, unlike glanceable and ambient visualizations that have been studied in the past, incidental visualizations have yet to be explored in-depth. In particular, it is still not clear what is their impact on the users’ performance of primary tasks. Therefore, we conducted an empirical online between-subjects user study where participants had to play a maze game as their primary task. Their goal was to complete several mazes as quickly as possible to maximize their score. This game was chosen to be a cognitively demanding task, bound to be significantly affected if incidental visualizations have a meaningful impact. At the same time, they had to answer a question that appeared while playing, regarding the path followed so far. Then, for half the participants, an incidental visualization was shown for a short period while playing, containing information useful for answering the question. We analyzed various metrics to understand how the maze performance was impacted by the incidental visualization. Additionally, we aimed to understand if working memory would influence how the maze was played and how visualizations were perceived. We concluded that incidental visualizations of the type used in this study do not disrupt people while they played the maze as their primary task. Furthermore, our results strongly suggested that the information conveyed by the visualization improved their performance in answering the question. Finally, working memory had no impact on the participants’ results.

Accuracy of Artificial Intelligence in Estimating Best-Corrected Visual Acuity From Fundus Photographs in Eyes With Diabetic Macular Edema

Best-corrected visual acuity (BCVA) is a measure used to manage diabetic macular edema (DME), sometimes suggesting development of DME or consideration of initiating, repeating, withholding, or resuming treatment with anti-vascular endothelial growth factor. Using artificial intelligence (AI) to estimate BCVA from fundus images could help clinicians manage DME by reducing the personnel needed for refraction, the time presently required for assessing BCVA, or even the number of office visits if imaged remotely. To evaluate the potential application of AI techniques for estimating BCVA from fundus photographs with and without ancillary information. Deidentified color fundus images taken after dilation were used post hoc to train AI systems to perform regression from image to BCVA and to evaluate resultant estimation errors. Participants were patients enrolled in the VISTA randomized clinical trial through 148 weeks wherein the study eye was treated with aflibercept or laser. The data from study participants included macular images, clinical information, and BCVA scores by trained examiners following protocol refraction and VA measurement on Early Treatment Diabetic Retinopathy Study (ETDRS) charts. Primary outcome was regression evaluated by mean absolute error (MAE); the secondary outcome included percentage of predictions within 10 letters, computed over the entire cohort as well as over subsets categorized by baseline BCVA, determined from baseline through the 148-week visit. Analysis included 7185 macular color fundus images of the study and fellow eyes from 459 participants. Overall, the mean (SD) age was 62.2 (9.8) years, and 250 (54.5%) were male. The baseline BCVA score for the study eyes ranged from 73 to 24 letters (approximate Snellen equivalent 20/40 to 20/320). Using ResNet50 architecture, the MAE for the testing set (n = 641 images) was 9.66 (95% CI, 9.05-10.28); 33% of the values (95% CI, 30%-37%) were within 0 to 5 letters and 28% (95% CI, 25%-32%) within 6 to 10 letters. For BCVA of 100 letters or less but more than 80 letters (20/10 to 20/25, n = 161) and 80 letters or less but more than 55 letters (20/32 to 20/80, n = 309), the MAE was 8.84 letters (95% CI, 7.88-9.81) and 7.91 letters (95% CI, 7.28-8.53), respectively. This investigation suggests AI can estimate BCVA directly from fundus photographs in patients with DME, without refraction or subjective visual acuity measurements, often within 1 to 2 lines on an ETDRS chart, supporting this AI concept if additional improvements in estimates can be achieved.

An Item Response Theory Model for Incorporating Response Times in Forced-Choice Measures

Forced-choice (FC) measures have been widely used in many personality or attitude tests as an alternative to rating scales, which employ comparative rather than absolute judgments. Several response biases, such as social desirability, response styles, and acquiescence bias, can be reduced effectively. Another type of data linked with comparative judgments is response time (RT), which contains potential information concerning respondents’ decision-making process. It would be challenging but exciting to combine RT into FC measures better to reveal respondents’ behaviors or preferences in personality measurement. Given this situation, this study aims to propose a new item response theory (IRT) model that incorporates RT into FC measures to improve personality assessment. Simulation studies show that the proposed model can effectively improve the estimation accuracy of personality traits with the ancillary information contained in RT. Also, an application on a real data set reveals that the proposed model estimates similar but different parameter values compared with the conventional Thurstonian IRT model. The RT information can explain these differences.

Retrieval of aerosol single scattering albedo using joint satellite and surface visibility measurements

Aerosol single scattering albedo (SSA) measuring the ratio of scattering to extinction is a critical parameter in determining aerosol radiative effect. However, the retrieval of SSA from satellite platforms is limited because most existing passive satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) only provide the measurements of reflected solar radiation at the top of the atmosphere (TOA), which are only capable of retrieving aerosol optical depth (AOD) on the basis of assumed SSA. In this study, we develop a machine learning based algorithm to retrieve SSA using joint surface visibility and satellite radiation measurements. With ancillary information such as meteorological condition, surface visibility can be regarded as a proxy of column AOD. By combining MODIS measured TOA apparent reflectance with ground visibility as well as other auxiliary parameters, we retrieve SSA at over 6000 visibility stations worldwide. The validation results during the period from 2010 to 2019 show high consistency with AERONET retrieved SSA, with daily, monthly, and seasonal retrievals all exhibiting high correlations of 0.68, 0.96, and 0.97, respectively. Daily, monthly, and seasonal RMSE values are 0.044, 0.012, and 0.011, respectively. The mean absolute bias (MAB) of daily retrievals is 0.031 and 62% of the samples fall within the uncertainty interval of ±0.03. Monthly and seasonal MAB values decrease to below 0.01. Over 97% of the monthly and seasonal retrievals are within the error envelope of ±0.03. Our work generates a global aerosol SSA dataset with extensive coverage over land, which can be used for validating and improving climate models and the estimation of aerosol radiative forcing.

Historical pesticide applications for the treatment of eastern spruce budworm infestations in New Brunswick.

Pesticides have been used in Canada since 1945 as part of large-scale aerial spray applications to control insect pests on forested lands. Some of the pesticides used historically were efficacious, nonselective, persistent, and have led to serious impacts on the environment. A well known, and extensively documented example is the large-scale aerial spray programs in New Brunswick, Canada. From 1952 to 1993, 97% of the 6.2 million ha of the forested lands of New Brunswick were treated with at least one application of one insecticide, the majority of which were applied to control outbreaks of eastern spruce budworm (Choristoneura fumiferana). The most well known insecticide was dichlorodiphenyltrichloroethane (DDT), applied from 1952 to 1968, which still persists in treated soils and adjacent water bodies, and caused the individual and cumulative ecosystem effects that can still be measured today. The insecticides that replaced DDT were nonpersistent and unlikely to be found today. However, during the years of application some of the insecticides were likely to have impacted local ecosystems to some degree. To aid future studies on the efficacy and environmental impact of these insecticides we created a digital spatial data set of known pesticide application in New Brunswick forestry from 1952 to 1993. The data set includes active ingredient, formulation, application rate, tank mix, aircraft type, and other ancillary information. The current version of the data is available on the New Brunswick Department of Natural Resources and Energy Development, GIS Open Data Page and in the supplemental material. Use of the data set for academic and educational purposes is encouraged, provided that both this data paper and the data source are properly cited; the Government of New Brunswick should be acknowledged as the data source (Open Government License http://www.snb.ca/e/2000/data-E.html).

Analysis and Processing of the COSMO-SkyMed Second Generation Images of the 2022 Marche (Central Italy) Flood

The use of SAR data for flood mapping is well established. However, the problem of the missed detection of rapidly evolving floods remains. Recently, the Italian Space Agency deployed the COSMO-SkyMed Second Generation (CSG) constellation, with an on-demand capability that makes it possible to reduce the number of missed floods. However, for on-demand SAR acquisitions, pre-flood images are generally not available, and change-detection methods, commonly adopted for flood mapping using SAR, cannot be applied. This study focused on the high-resolution CSG images of a flood that occurred in central Italy. An accurate analysis of the radar responses of the different targets included in the scenes observed by GSG was performed. Then, a methodology to detect floods using high-resolution single SAR images was developed. The methodology was based on image segmentation and fuzzy logic. Image segmentation allowed us to analyze homogeneous areas in the CSG images. Fuzzy logic was used to integrate the SAR data with ancillary information that was useful when change-detection methods could not be applied. A comparison with the maps produced by the Copernicus Emergency Service, using high-resolution optical images, demonstrated the reliability of the methodology.

Cephalochordate Hemocytes: First Demonstration for Asymmetron lucayanum (Bahamas Lancelet) Plus Augmented Description for Branchiostoma floridae (Florida Amphioxus).

AbstractWithin phylum Chordata, the subphylum Cephalochordata (amphioxus and lancelets) has figured large in considerations of the evolutionary origin of the vertebrates. To date, these discussions have been predominantly based on knowledge of a single cephalochordate genus (Branchiostoma), almost to the exclusion of the other two genera (Asymmetron and Epigonichthys). This uneven pattern is illustrated by cephalochordate hematology, until now known entirely from work done on Branchiostoma. The main part of the present study is to describe hemocytes in the dorsal aorta of a species of Asymmetron by serial block-face scanning electron microscopy. This technique, which demonstrates three-dimensional fine structure, showed that the hemocytes have a relatively uniform morphology characterized by an oval shape and scanty cytoplasm. Ancillary information is also included for Branchiostoma hemocytes, known from previous studies to have relatively abundant cytoplasm; our serial block-face scanning electron microscopy provides more comprehensive views of the highly variable shapes of these cells, which typically extend one or several pseudopodium-like protrusions. The marked difference in hemocyte morphology found between Asymmetron and Branchiostoma was unexpected and directs attention to investigating comparable cells in the genus Epigonichthys. A broader knowledge of the hemocytes in all three cephalochordate genera would provide more balanced insights into the evolution of vertebrate hematopoiesis.

Beyond the US-SOMO-AF database: a new website for hydrodynamic, structural, and circular dichroism calculations on user-supplied structures.

At the 25th International Analytical Ultracentrifugation Workshop and Symposium, we described the recent implementation of the UltraScan SOlution MOdeler AlphaFold (US-SOMO-AF) database, containing hydrodynamic, structural, CD calculations, and other ancillary information, performed on the entire AF v2 database of predicted protein structures, containing more than 1,000,000 entries. The scope of the US-SOMO-AF database was that of providing direct access to pre-calculated physicochemical parameters for rapid assessment against their experimentally determined counterparts to test the compatibility in solution of predicted AlphaFold structures. In the meantime, the AlphaFold consortium has extended its database of predicted structures to an astonishing > 200 million entries, making it quite impractical for their coverage in the US-SOMO-AF database. Therefore, we have created the US-SOMO-Web site, allowing the rapid calculations of all the properties, as present in the US-SOMO-AF database, on user-supplied PDB and mmCIF structures, as well as allowing direct processing of the latest AlphaFold models. Major features on the website are described, along with current limitations and potential future developments.

The Hi-GLASS all-wave daily net radiation product: Algorithm and product validation

The surface net radiation (Rn) represents the balance of the radiative budget on the land surface and drives many physical and biological processes. An accurate and long-term product for global daily coverage of Rn at a high spatial resolution is needed for a variety of applications at regional and local scales. This study proposes two algorithms, called the downward shortwave radiation (DSR)-based algorithm and the top-of-atmosphere (TOA)-based algorithm, to estimate Rn by using Landsat data. The DSR-based algorithm consists of three conditional models, and was developed based on the analysis of the relationship between Rn and shortwave radiation as well as ancillary information from ground measurements and various datasets. The TOA-based algorithm was developed by linking Rn to TOA observations from Landsat sensors and ancillary information. The two algorithms were developed by using the random forest method. The results of their validation against ground measurements showed that the DSR-based algorithm outperformed the TOA-based algorithm in terms of accuracy, with a determination coefficient (R2) of 0.93, root-mean-squared error (RMSE) of 17.58 Wm−2, and bias of −4.27 Wm−2. It was stable under various conditions. We then applied the DSR-based algorithm to generate a product of the global daily Rn, called the High-resolution (Hi)- Global LAnd Surface Satellite (GLASS), from 2013 to 2018 at a spatial resolution of 30 m under a clear sky based on remotely sensed products, including the DSR from GLASS, the normalized difference vegetation index (NDVI) obtained from Landsat, surface broadband albedo from Hi-GLASS, and meteorological factors based on reanalysis data from MERRA2. Following its validation using in-situ observations from 2013 to 2018, the overall accuracy of the daily Rn acquired by Hi-GLASS under clear sky was found to be satisfactory, with a value of R2 of 0.90 and an RMSE of 25.03 Wm−2. Moreover, compared with the daily Rn obtained from the GLASS product at a spatial resolution of 5 km, that obtained by Hi-GLASS can better characterize the surface by providing more details and capturing the variations in the measurements, especially large and small values. However, due to limitations of the available datasets and the algorithm, the data on Rn for most regions lacked information on cloudy skies and areas at high latitudes. This information thus cannot be provided by Hi-GLASS yet. Moreover, the influence of the topography on values of Rn has not been thoroughly considered. Nonetheless, values of Rn under clear sky obtained from Hi-GLASS offer promise for use in a wide range of areas, and efforts are underway to improve this product.