Search

An early genetic diagnosis can guide the time-sensitive treatment and care of individuals with genetic epilepsies. However, identification of a genetic cause often occurs long after onset of these disorders. Here, we aimed to identify early clinical features suggestive of genetic diagnoses in individuals with epilepsy by systematic large-scale analysis of clinical information from full-text patient notes in the electronic medical records (EMR).From the EMR of 32,112 individuals with childhood epilepsy, we retrieved 4,572,783 clinical notes spanning 203,369 total patient-years. A subcohort of 1,925 individuals had a known or presumed genetic epilepsy with 738 genetic diagnoses spanning 271 genes. We employed a customized natural language processing (NLP) pipeline to extract 89 million time-stamped standardized clinical annotations from free text of the retrieved clinical notes. Our analyses identified 47,641 clinical associations with a genetic cause at distinct ages prior to diagnosis. Notable among these associations were:SCN1Awith status epilepticus between 9 and 12 months of age (PSTXBP1with muscular hypotonia between 6 and 9 months (P=3.4×10−4, 95% CI=3.08-102);SCN2Awith autism between 1.5 and 1.75 years (PDEPDC5with focal-onset seizure between 5.75 and 6 years (PIQSEC2with myoclonic seizure between 2.75 and 3 years (P=2.5×10−4, 95% CI=11.3-1.15×104). We also identified associations between clinical terms and gene groups. Variants in ion channel gating mechanisms were associated with myoclonus between 3 and 6 months of age (PPKCNT1with migrating focal seizures from at 0 to 1.75 years (P15). A neurodevelopmental abnormality presenting between 6 and 9 months of age was strongly associated with an individual having any genetic diagnosis (PPP=5.5×10−3, 95% CI=1.23-3.35).In summary, we identified key clinical features that precede genetic diagnosis, leveraging EMR data at scale from a large cohort of individuals with genetic epilepsies. Our findings demonstrate that automated EMR analysis may assist clinical decision making, leading to earlier diagnosis and more precise prognostication and treatment of genetic epilepsies in the precision medicine era.

The Human Phenotype Ontology (HPO) is a dictionary of more than 15,000 clinical phenotypic terms with defined semantic relationships, developed to standardize their representation for phenotypic analysis. Over the last decade, the HPO has been used to accelerate the implementation of precision medicine into clinical practice. In addition, recent research in representation learning, specifically in graph embedding, has led to notable progress in automated prediction via learned features. Here, we present a novel approach to phenotype representation by incorporating phenotypic frequencies based on 53 million full-text health care notes from more than 1.5 million individuals. We demonstrate the efficacy of our proposed phenotype embedding technique by comparing our work to existing phenotypic similarity-measuring methods. Using phenotype frequencies in our embedding technique, we are able to identify phenotypic similarities that surpass the current computational models. In addition, we show that our embedding technique aligns with domain experts’ judgment at a level that exceeds their agreement. We show that our proposed technique efficiently represents complex and multidimensional phenotypes in HPO format, which can then be used as input for various downstream tasks that require deep phenotyping, including patient similarity analyses and disease trajectory prediction.

In this paper we propose an algorithm for retrieving an arbitrary watershed boundary from a 2D Flow Direction Grid. The proposed algorithm and associated data model provides geometric speed increases in watershed boundary retrieval while keeping storage constraints linear in comparison to existing techniques. The algorithm called Watershed Marching Algorithm (WMA) relies on an existing data structure, the modified nested set model, originally described by Celko and applied to hydrodynamic models by Haag and Shokoufandeh in 2017. In contrast to existing algorithms that scale proportionally to the area of the underlying region, the complexity of the WMA algorithm is proportional to the boundary length. Results for a group of tested watersheds (n = 14,718) in the ≈ 36,000 km2 Delaware River Watershed show a reduction of between 0 and 99% in computational complexity using a 30 m DEM vs. existing techniques.

In this manuscript, we describe a computational model to delineate watershed boundaries for simple geometries (points, lines or polygons) where maximum water flow distance can be constrained by a user-defined variable. This method generalizes earlier research to delineate watershed boundaries using a marching algorithm. Our proposed method allows users to delineate watersheds for a number of use-cases including evaluating best management practices (BMP's), measuring water impacts to municipal, city and parcel boundaries and others. We describe computational complexity and space costs and compare them experimentally to standard techniques.

Hudson Canyon is the largest shelf-sourced canyon system off the east coast of the United States, and hosts a productive ecosystem that supports key fisheries. Here we report the results of a multi-year interdisciplinary study of the geological, geochemical, and physical oceanographic features and processes in the canyon that underpin that ecosystem. High-resolution multi-beam bathymetric and backscatter data show that the contrasting morphology of the two perpendicularly oriented branches at the head of the Hudson Canyon is indicative of different states of geomorphological activity and sediment transport. Tightly spaced ridges and gullies extend perpendicularly towards the canyon axis from the canyon walls. Numerous depressions are found at the base of the canyon walls or along the canyon axis at depths from 300 m to 600 m. Elevated concentrations of dissolved methane in the water column, where the highest density of depressions occur, suggests that methane is actively venting there. The topography and reflective floors of circular depressions in canyon walls and their association with methane maxima suggest that these represent active methane gas release-collapse pockmarks with carbonate floors. Patterns of irregular, low-relief, reflective depressions on the canyon floor may also represent methane release points, either as gas release or cold-seep features. The presence of methane maxima in a region of strong advective currents suggests continuous and substantial methane supply. Hydrographic observations in the canyon show that multiple layers of distinct inter-leaved shelf (cold, fresh) and slope (warm, salty) water masses occupy the head of the canyon during the summer. Their interactions with the canyon and with each other produce shifting fronts, internal waves, and strong currents that are influenced by canyon topography. Strong tidal currents with along-canyon-axis flow shear help to drive the advection, dispersion and mixing of dissolved materials in the water column that likely help support the rich canyon ecosystem.

With the increasing appreciation that sea grass habitats are in global decline, there is a great need to be able to efficiently and effectively assess and characterize the status and trends of sea grass in our coastal ecosystems. This paper examines the utility of remotely sensed vs. in situ plot-based monitoring using the Barnegat Bay-Little Egg Harbor (BB-LEH), New Jersey, USA estuarine system as a case study. Eelgrass (Zostera marina) is the dominant species, while widgeon grass (Ruppia maritima) is also common in lower salinity regions of the BB-LEH. Aerial imagery collected during the months of July and August 2009 was interpreted and mapped using object based image analysis techniques, similar to techniques used in the 2003 mapping survey of this system. Boat-based in situ monitoring data were collected concurrently with the aerial photography to assist the image interpretation and for an independent accuracy assessment. We compared the remotely-sensed mapping of sea grass cover change (in 2003 vs. 2009) vs. in situ plot-based monitoring conducted from 2004 through 2009. Comparison of the remotely-sensed vs. the in situ plot-change analysis suggests that the two methodologies had broadly similarly results, with the percent area showing declines in sea grass cover greater than those that exhibited increases. In conclusion, the two studies provide corroborating evidence that sea grass has declined in percent cover in the BB-LEH system during the decade of the 2000’s. While remotely-sensed surveys provide synoptic information for a “big picture” view on sea grass distribution, site specific in situ sampling is required to determine other aspects of sea grass status, e.g. above vs. below-ground biomass, blade length, shoot density, epiphytic loading, etc. Either method alone gives an incomplete picture. As demonstrated in this study, to fully characterize the spatial extent, health, and density of sea grass meadows across the entire estuary, combining remote sensing surveys concomitantly with comprehensive in situ assessment provides the most robust approach.

Increasing development of coastal watersheds has altered groundwater and surface runoff patterns, and led to the progressive eutrophication of freshwater tributaries and adjacent coastal waters. Effective and properly engineered stormwater management systems represent one of the most important water resource protection strategies to counter this trend. Geospatial tools are needed to place existing and proposed development and stormwater management system (SWMS) infrastructure into a watershed context. A Geographic Information System (GIS) inventory of the existing SWMS infrastructure is a critical first step. To be most effective, this geospatial inventory, complemented by decision support tools, needs to be user friendly and readily available to a wide audience of target groups. To address this issue, we developed the StormWater Management Planning Tool (SWMPT) as a suite of on-line interactive mapping and query tools to facilitate access to data for display, assessment and analysis purposes needed for s...

Aim Spatial evolutionary and ecological vicariance analysis (SEEVA) is a simple analytical method that evaluates environmental or ecological divergence associated with evolutionary splits. It integrates evolutionary hypotheses, phylogenetic data, and spatial, temporal, environmental and geographical information to elucidate patterns. Using a phylogeny of Prepusa Mart. and Senaea Taub. (Angiospermae: Gentianaceae), SEEVA is used to describe the radiation and ecological patterns of this basal gentian group across south-eastern Brazil. Location Latin America, global. Methods Environmental data for 151 geolocated botanical collections, associated with specimens from seven species, were compiled with ARCGIS, and were matched with geolocated base layers of eight climatological variables, as well as one each of geological, soil type, elevational and vegetation variables. Sister groups were defined on the basis of the six nested nodes that defined the phylogenetic tree of these two genera. A (0, 1)-scaled divergence index (D) was defined and tested for each of 12 environmental and for each of the six phylogenetic nodes, by means of contingency analyses. We contrast divergence indices of nested clades, allopatric and sympatric sister clades. Results The level of ecological divergence between sister clades/species, defined in terms of D measures, was substantial for five of six nodes, with 21 of 72 environmental comparisons having D > 0.75. Soil types and geological age of bedrock were strongly divergent only for basal nodes in the phylogeny, by contrast with temperature and precipitation, which exhibited strong divergence at all nodes. There has been strong divergence and progressive occupation of wetter and colder habitats throughout the history of Prepusa. Nodes separating allopatric sister clades exhibited larger niche divergence than did those separating sympatric sister clades. Main conclusions SEEVA provides a multi-source, direct analysis method for correlating field collections, phylogenetic hypotheses, species distributions and georeferenced environmental data. Using SEEVA, it was possible to quantify and test the divergence between sister lineages, illustrating both niche conservatism and ecological specialization. SEEVA permits elucidation of historical and ecological vicariance for evolutionary lineages, and is amenable to wide application, taxonomically, geographically and ecologically.

The pains Macrocarpaea (Griseb.) Gilg (Gentianaceae. Helieae) is among the largest woody genera of tropical gentians, with most of its species occurring in the wet mountainous forest., of the Andes. Phylogenetic and dispersal-vicariance analyses (DIVA) of 57 of the 105 currently recognized species in the genus. Using two data sets front nuclear DNA (ITS and 5S-NTS sequences) and morphology. show it single origin of the Andean species front all ancestral distribution that includes southeastern Brazil. Within the Andes. species divide into two major clades: (1) northern species front the cordilleras of northern Ecuador, Colombia. and Venezuela; and (2) southern species of the Andean Amotape-Huancabamba Zone in Ecuador and Peru as well as the Andes of central and southern Peru and Bolivia. The Amotape-Huancabamba zone is supported as the ancestral area for Macrocarpaea within the Andes. There are repeated speciation patterns within the Andes, and three Mesoamerican species derive front the northern clade, as is the single sampled species front the Guayana Shield. The position of the subclade of the three Caribbean Species is less certain. but it currently nests among Andean species. An Atlantic coastal Brazilian clade is placed its sister group to all other Macrocarpaea, providing further support for all ancestral refuge in southeastern Brazil for the Helieae. The biogeographic analysis showed that local speciation is more common than long-distance dispersal, and allopatric speciation is more common than sympatric speciation. Using detailed, georeferenced herbarium collection data. patterns in environmental characteristics between clades and sister species were analyzed with Spatial Evolutionary and Ecological Vicariance, Analysis (SEEVA), utilizing geographic information system (GIS) and statistical methods. Sister clades and taxa were evaluated for statistical significance in variables such as annual rainfall and temperature, elevation, temperature and rainfall seasonality, geological bedrock age, and soil type to evaluate ecological vicariance between sister group. The results indicate that there are no general patterns for each variable, but that there are significant divergences in ecological niches between both larger sister groups and sister species, and ecological niche conservation was also observed when subsequent nodes in the phylogeny were compared.

A benthic habitat assessment study conducted in the Barnegat Bay-Little Egg Harbor Estuary from June to November 2006 reveals that boat-based videographic imaging is comparable to in situ diver observations for determining the occurrence and percent cover of seagrasses in actively growing beds. A Seaviewer Sea Drop camera and recorder unit deployed along 12 transects in the estuary generated 331 images of seagrass habitat during the study period. A comparison of video still images with data derived from diver observations indicates consistent results in terms of the presence/absence and percent cover of seagrass on the estuarine floor throughout the seagrass growing season. Plots of the percent cover of seagrass recorded by the camera system vs. in situ diver observations reveal a high correlation for the June–July, August–September, and October–November sampling periods (R2 = 0.936, 0823, and 0.894, respectively) as well as for the entire June–November sampling period (R2 = 0.888). A kappa stati...

A detailed submerged aquatic vegetation (SAV) study was conducted in Little Egg Harbor (39°35′N, 74°14′W), New Jersey, a lagoonal estuary located within the boundaries of the Jacques Cousteau National Estuarine Reserve, to assess the demographic characteristics and spatial habitat changes of Zostera marina beds over an annual growing period and to determine the species composition, relative abundance, and potential impacts of benthic macroalgae on seagrass habitat in the system. Two disjunct seagrass beds in Little Egg Harbor, covering an area of ∼1700 ha, were sampled at 10 equally spaced points along six, east–west-trending transects in spring, summer, and fall (June–November) of 2004. During this period, 180 seagrass samples were collected at 60 transect sites, together with an array of water quality measurements. Results of this investigation indicate that both aboveground and belowground biomass of seagrass peaked during June–July and declined significantly into the fall months. Mean abovegr...

The purpose of this study was to map the areal extent and density of submerged aquatic vegetation, principally the seagrasses, Zostera marina and Ruppia maritima, as part of ongoing monitoring for the Barnegat Bay, New Jersey National Estuary Program. We examine the utility of multiscale image segmentation/object-oriented image classification using the eCognition software to map seagrass across our 36,000 ha study area. The multi-scale image segmentation/ object oriented classification approach closely mirrored our conceptual model of the spatial structure of the seagrass habitats and successfully extracted the features of ecological interest. The agreement between the mapped results and the original field reference was 68 percent (Kappa � 56.5 percent) for the four category map and 83 percent (Kappa � 63.1 percent) for the presence/absence map; the agreement between the mapped results and the independent reference data was 71 percent (Kappa � 43.0 percent) for a simple presence/absence map. While the aerial digital camera imagery employed in this study had the advantage of flexible acquisition, suitable image scale, fast processing return time, and comparatively low cost, it had inconsistent radiometric response from image to image. This inconsistency made it difficult to develop a rule-based classification that was universally applicable across the 14 individual image mosaics. However, within the individual scene mosaics, using the eCognition software in a “manual classification” mode provided a flexible and time effective approach to mapping seagrass habitats.

A 3 year study (2000–2002) in Barnegat Bay-Little Egg Harbor (BB/LEH), New Jersey (USA), was conducted by the New Jersey Department of Environmental Protection, Division of Science Research and Technology (DSRT) in cooperation with several partners to assess brown tide blooms in coastal waters in NJ. Water samples were collected by boat and helicopter at coastal stations from 2000 to 2002 along with field measurements. Aureococcus anophagefferens were enumerated and associated environmental factors were analyzed. A. anophagefferens abundances were classified using the Brown Tide Bloom Index and mapped, along with salinity and temperature parameters, to their geo-referenced location using the ArcView GIS. The highest A. anophagefferens abundances (>106 cells ml−1), including category 3 blooms (≥200,000 cells ml−1) and category 2 blooms (≥35,000 to ≤200,000 cells ml−1), recurred during each of the 3 years of sampling and covered significant geographic areas of the estuary, especially in Little Egg Harbor. While category 3 blooms were generally associated with warmer water temperatures (>16 °C) and higher salinity (>25–26 ppt), these factors were not sufficient alone to explain the timing or distribution of A. anophagefferens blooms. There was no significant relationship between brown tide abundances and dissolved organic nitrogen measured in 2002 but this was consistent with other studies. Extended drought conditions, with corresponding low freshwater inputs and elevated bay water salinities, occurring during this time were conducive to blooms. A. anophagefferens abundances were well above the reported levels that have been reported to cause negative impacts on shellfish. It was shown that over 50% of the submerged aquatic vegetation (SAV) habitat located in Barnegat Bay/Little Egg Harbor was categorized as having a high frequency of category 2 or 3 blooms for all 3 years.

The benthic macrofaunal community of the Mullica River–Great Bay estuary in the Jacques Cousteau National Estuarine Research Reserve is characterized by both small-scale and large-scale distribution patterns that can be correlated to gradients in physicochemical parameters. Sediment composition is a major factor regulating the local distribution of benthic assemblages in the estuary, with the amount of silt-clay being particularly important. For example, one suite of benthic fauna (i.e., Ampelisca verrilli, Ensis directus, Haustorius arenarius, Pygospio elegans, and Oxyurostylis smithi) reported in the estuary has been found only in sediments with less than 20% silt-clay, whereas another (i.e., Acteocina canaliculata, Lumbrineris tenuis, Maldinopsis elongata, Tellina agilis, Turbonilla sp., and Unciola irrorata) has been observed only in sediments with more than 38% silt-clay. Other physicochemical factors (e.g., organic carbon content of the sediments, dissolved oxygen levels, bottom currents, a...

Side-scan sonar imaging conducted in Great Bay, New Jersey, during August 2003 reveals local variations in acoustic backscatter from the seabed that can be related to small-, medium-, and large-scale bedforms. Analysis of 600-kilohertz side-scan sonar data collected at a System-Wide Monitoring Program (SWMP) survey site of the Jacques Cousteau National Estuarine Research Reserve shows a relationship between backscatter intensity, bathymetry, and changing seabed morphology. Side-scan sonar records obtained at the survey site in Great Bay via deployment of an autonomous underwater vehicle (Remote Environmental Monitoring UnitS) provide in-depth characterization of an array of complex and multiscaled bedforms and associated benthic habitats. Bedforms documented within the swath map region by side-scan sonar imaging include constructive transverse forms, notably ripples, dunes, and sand waves. These bedforms are valuable for assessing potentially important habitats for benthic invertebrate and demers...

Large-scale fuel assessments are useful for developing policy aimed at mitigating wildfires in the wildland–urban interface (WUI), while finer-scale characterisation is necessary for maximising the effectiveness of fuel reduction treatments and directing suppression activities. We developed and tested an objective, consistent approach for characterising hazardous fuels in the WUI at the scale of individual structures by integrating aerial photography, airborne laser scanning and cadastral datasets into a hazard assessment framework. This methodology is appropriate for informing zoning policy questions, targeting presuppression planning and fuel reduction treatments, and assisting in prioritising structure defence during suppression operations. Our results show increased variability in fuel loads with decreasing analysis unit area, indicating that fine-scale differences exist that may be omitted owing to spatial averaging when using a coarser, grid-based approach. Analyses using a local parcel database indicate that approximately 75% of the structures in this study have ownership of less than 50% of the 30 m buffer around their building, illustrating the complexity of multiple ownerships when attempting to manage fuels in the WUI. Our results suggest that our remote-sensing approach could augment, and potentially improve, ground-based survey approaches in the WUI.

Onion maggots have reduced green onion and leek production in southern New Jersey for at least the last 80 years. Growers routinely apply soil insecticides at planting and spray for larvae and adult flies during the season. Two monitoring methods are available for determining adult fly activity. New York researchers have demonstrated that cone traps can be used. Two traps are placed near onion fields and checked for adult flies twice per week to determine peak fly emergence. Ontario researchers use yellow sticky cards for monitoring onion maggot flies in the onion fields. Three 10 × 15-cm cards are placed on each side of the field and are checked twice per week. An experiment was conducted in New Jersey to determine which system is more reliable and easier for consultants and growers to use. Two cone traps were placed at the edge of one onion field and yellow cards were placed in another field on four farms. The traps were checked twice per week from 29 Mar. to 22 Oct. Both monitoring methods tracked the adult flights, but the average number of flies captured was higher on the sticky cards. Ease of use is important if either system will be used as a monitoring tool. Sticky cards are more difficult to maintain since they must be replaced at least every 2 weeks. Since fields are irrigated or cultivated every week in southern New Jersey, the cards become covered with soil, reducing effectiveness. Also, it is more difficult to determine male and female flies on sticky cards.