Unique Spatial Patterns Research Articles

Strong seasonality and unsuspected diversity of haptophytes explored by metabarcoding analysis in the Chinese seas

The haptophytes, are essential components of the marine pico- and nano-plankton but little is known about their diversity and abundance due to the small size. In this study, the taxonomic composition, geographical distribution, and seasonal variation of the haptophytes in the Bohai Sea, the Yellow Sea, and the East China Sea were investigated using DNA metabarcoding in April and October of 2021. A total of 623 and 3756 haptophyte amplicon sequence variants (ASVs) were obtained in spring and autumn, respectively. All currently described or detected haptophyte orders were retrieved, including several deep-branching novel environmental lineages with relative high abundance. The predominant groups were Chrysochromulina, Clade HAP 2-3-4-5, Phaeocystis, and Prymnesium in spring, and Chrysochromulina, Phaeocystis, and Emiliania/Gephyrocapsa compelx in autumn. The richness and diversity showed seasonal variation, with higher alpha diversity occurred in autumn than that of spring. Different haptophyte taxa exhibited unique spatial distribution patterns and water temperature was significantly correlated with the observed community dissimilarities and was the most influential driving factor in both seasons. Our results highlight the high hidden diversity and seasonal variations of haptophytes in the Chinese seas.

Community Group Purchasing of Next-Day Delivery: Bridging the Last Mile Delivery for Urban Residents during COVID-19

The rapid development of new retail and the impact of COVID-19 have catalyzed the blowout growth of community group purchasing. The emergence of community group purchasing collection and delivery points (CGPCDPs) has become a new way to solve the “last mile” problem of new retail delivery. Based on the point of interest (POI) data of CGPCDPs of Nansha District, Guangzhou City, this study advances our understanding by identifying unique operational models, service targets, and spatial distribution patterns of CGPCDPs, which differ significantly from traditional pick-up points (PPs). The conclusions are as follows: (1) Most CGPCDPs depend on wholesale and retail shops, and their main service targets are urban and rural communities, followed by industrial areas. (2) The distribution of CGPCDPs has apparent spatial differentiation. At the macro scale, it shows the characteristics of “central agglomeration and peripheral dispersion”. It is distributed along the “northwest-southeast” direction and presents a “dual-core multi-center” pattern. At the meso–micro scale, different built environments in developed areas of cities, villages in the city (ChengZhongCun), and rural areas show distinct distribution patterns. (3) The main influencing factors of their spatial distribution are population density, construction land, house price, supporting place, residence density, urban community, and road proximity.

Quantitative analysis of spatial gene in traditional villages: a case study of Korean traditional villages in Northeast China

ABSTRACT Traditional villages possess distinct regional cultural traits and serve as a concrete manifestation and inheritance of traditional culture. In today’s era, where the preservation of traditional villages is emphasized, the study of Korean traditional villages in Northeast China, with their ethnic culture and unique spatial patterns, becomes significantly important. In this paper, we adopt the spatial gene perspective and establish a framework for analyzing spatial genes in traditional villages. Based on frequency analysis of qualitative data obtained from NVivo 14, 16 spatial gene entries were identified. Village spatial gene entries were quantified using ArcGIS, FRAGSTATS and other software. Additionally, 47 spatial gene fragments were extracted using the natural breaks method. Spatial gene sequences were mined using Spearman correlation analysis, and spatial gene maps were created. The study results indicate that: (1) the primary genes of traditional Korean villages include close to mountain, close to water, globular boundary, low integration road, etc. (2) There are significant correlations between spatial genes such as distance from mountains, mountain coverage and road integration, water coverage and boundary shape index. (3) Plain, mountain, and hill-type villages differ in characteristics such as road linear density, water coverage, and courtyard type.

Mass Spectrometry Imaging Reveals Region-Specific Lipid Alterations in the Mouse Brain in Response to Efavirenz Treatment.

Efavirenz (EFV) is a commonly used drug to treat human immunodeficiency virus infection and is known to exert adverse effects on the brain. Although it is known that EFV is associated with abnormal plasma lipid levels, the changes in the spatial localization of individual lipid molecules in brain tissue following EFV treatment are yet to be explored. In this study, we employed a matrix-assisted laser desorption/ionization mass spectrometry imaging approach to determine region-specific lipid alterations in mouse brains following EFV treatment. We detected unique spatial localization patterns of phosphatidylcholine (PC), sphingomyelin (SM), ceramide phosphoinositol (PI-Cer), and hexosylceramide (HexCer) molecules in the mouse brain. Interestingly, PC(32:0), PC(38:5), and SM(36:1;O2) showed high abundance in the hippocampus region, whereas PI-Cer(38:8) exhibited low abundance in the hippocampus region of the EFV-treated mouse brains. Additionally, we observed low abundance of PC(38:6), PC(40:6), and PI-Cer(40:3) in the thalamus region of the EFV-treated mouse brains. Furthermore, SM(40:1;O2), SM(42:2;O2), SM(42:1;O2), SM(43:2;O2), and SM(43:1;O2) exhibited their accumulation in the corpus callosum region of the EFV-treated mouse brains as compared to controls. However, HexCer(42:1;O3) exhibited depletion in the corpus callosum region in response to EFV treatment. To characterize the expression patterns of proteins, including lipid metabolizing enzymes, in response to EFV treatment, mass spectrometry-based proteomics was utilized. From these, the expression levels of 12 brain proteins were found to be significantly decreased following EFV treatment. Taken together, these multiomics data provide important insights into the effects of EFV on brain lipid metabolism.

HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics

Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higher F1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${F}_{1}$$\\end{document} scores (average F1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${F}_{1}$$\\end{document} Score=0.948), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average AUC = 0.792) than other comparative methods without prespecifying spatial patterns. Furthermore, by clustering SVGs, we uncover two distinct tumor spatial domains characterized by unique spatial expression patterns, spatial-temporal locations, and biological functions in human colorectal cancer data, unraveling the complexity of tumors.

The application of PTR-MS and non-targeted analysis to characterize VOCs emitted from a plastic recycling facility fire.

On April 11th, 2023, the My Way Trading (MWT) recycling facility in Richmond, Indiana caught fire, mandating the evacuation of local residents and necessitating the U.S. Environmental Protection Agency (EPA) to conduct air monitoring. The EPA detected elevated levels of plastic combustion-related air pollutants, including hydrogen cyanide and benzene. We aimed to identify these and other volatile organic compounds (VOCs) present as well as to identify the potential hazard of each compound for various human health effects. To identify the VOCs, we conducted air monitoring at sites within and bordering the evacuation zone using proton transfer reaction mass spectrometry (PTR-MS) and non-targeted analysis (NTA). To facilitate risk assessment of the emitted VOCs, we used the EPA Hazard Comparison Dashboard. We identified 46 VOCs, within and outside the evacuation zone, with average detection levels above local background levels measured in Middletown, OH. Levels of hydrogen cyanide and 4 other VOCs were at least 1.8-fold higher near the incidence site in comparison to background levels and displayed unique temporal and spatial patterns. The 46 VOCs identified had the highest hazardous potential for eye and skin irritation, with approximately 45% and 39%, respectively, of the VOCs classified as high and very high hazards for these endpoints. Notably, all detected VOC levels were below the hazard thresholds set for single VOC exposures; however, hazard thresholds for exposure to VOC mixtures are currently unclear. This study serves as a proof-of-concept that PTR-MS coupled with NTA can facilitate rapid identification and hazard assessment of VOCs emitted following anthropogenic disasters. Furthermore, it demonstrates that this approach may augment future disaster responses to quantify additional VOCs present in complex combustion mixtures.

Abstract 7547: Enhancers as targets for improvements in immunotherapy

Abstract Role of epigenetic elements in mediating tumor immune interaction remains poorly understood. We previously identified enrichment of active enhancer states on a set of genomic loci in non-responders to immune checkpoint blockade (ICB) therapy. We noted that the activated non-responder enhancers marked a group of key regulators of in melanoma cells that are known to mediate resistance to ICB therapy and several checkpoint receptors in T cells. Through spatial epigenomics (Spatial ATAC and CUT&Tag) approaches, we identified the tumor-immune niches harboring unique gene regulatory elements and transcription factors that could mediate melanoma-T cell interaction. Further, we aimed to identify functional epigenetic elements through custom CRISPRi screening of enhancers in patient tumor-derived melanoma and T cells. CRISPRi approaches identified novel functional enhancers that mediate T cell mediated killing. For example, we identified distal c-MET enhancers as important contributors to T cell mediated killing. In T cells, we identified enhancer hubs that target multiple genes. These hubs could be targeted to activate interferon gamma, reduce T cell exhaustion, and improve T cell mediated killing of melanoma cells. Such enhancer hubs could serve as targets in the ex vivo setting to improve adoptive T cell therapy. Overall, our results suggest that enhancers with unique spatial patterns in melanoma tumors contribute to the response to immunotherapy and could be novel targets for therapy. Citation Format: Anand Singh, Veena Kochat, Emre Arslan, Suresh Satpati, Wentao Deng, Sandeep Yadav, Efthalia Bataki, Cassian Yee, Kunal Rai. Enhancers as targets for improvements in immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7547.

Land cover changes in grassland landscapes: combining enhanced Landsat data composition, LandTrendr, and machine learning classification in google earth engine with MLP-ANN scenario forecasting

ABSTRACT Understanding grassland habitat dynamics in space and time is crucial for evaluating the effectiveness of protection measures and developing sustainable management practices, specifically within the Natura 2000 network and in light of the European Biodiversity Strategy. Land cover maps, derived from remote sensing data, are essential for understanding long-term changes in vegetation cover and land use and assessing the impact of land use changes on grassland ecosystems. In this study, we conducted a 20-year land cover analysis of grassland landscapes in Umbria, Italy, using Random Forest classifications of Landsat data in Google Earth Engine. Our analysis was based on the years 2000, 2010, and 2020. We integrated harmonic modeling, Gray-Level Co-occurrence Matrix (GLCM) textural analysis, statistical image and gradient analysis, and other spectral and Digital Terrain Model (DTM)-derived indices to enhance the classification capabilities. The LandTrendr (LT) algorithm was used in GEE to collect ground control points in no-change areas automatically. We used a method based on Multilayer Perceptron-Artificial Neural Networks (MLP-ANNs) to forecast 2040 land cover. Our land cover classifications and the scenario model validation achieved an overall accuracy of over 90%. However, the classification of shrublands proved challenging due to their mixed composition and unique spatial patterns, resulting in lower accuracies. Feature importance analysis demonstrated the value of the enhanced map composition, and applying the LandTrendr algorithm simplified the diachronic land use and land cover (LULC) classification and change analysis by supporting automatic training data collection. Results support the interpretation of grassland dynamics in Umbria over the past two decades and identify areas affected by encroachment from shrubs, woody plants, or those with reduced green biomass. The forecasting method along with the selection of spatial drivers to predict land cover change, demonstrated high efficiency compared to other studies. A specific analysis was developed to identify areas where conservation measures related to the Natura 2000 network have been more or less effective in preserving grasslands. Overall, the research provides a scientific foundation for a methodology helpful in informing policy decisions and defining spatially explicit management strategies to enhance grassland conservation inside and outside Natura 2000 areas.

Wavelet Analysis of a Sentinel-2 Time Series to Detect Land Use Changes in Agriculture in the Vega Alta of the Guadalquivir River: Cantillana Case Study (Seville)

Historically, the Vega Alta of the Guadalquivir River (southern Spain) has been an anthropized space. Over time, the dominance of latifundia agriculture has evolved towards more intensive citrus-based agriculture. In this study, wavelet algorithms applied to Sentinel-2 time series were used to determine both the expansion of citrus plantations and the level of intensification of these plantations within the municipality of Cantillana. Sentinel-2 provides comprehensive global coverage from March 2017 to the present. Our study applied a 90% power wavelet transformation for the creation of a wavelet-smoothed time series for four years of Sentinel-2 NDVI data. Based on the data, it can be stated that within our research region covering 5000 hectares of agricultural land, over a span of four years (2017 to 2020), more than 980 hectares of native vegetation and pasture were transformed into citrus orchards, giving rise, at the end of 2020, to a total area of 3250 ha. Analyzing unique spatial patterns within a wavelet-smoothed time series data is very useful for land management, as it allows land use changes to be controlled. For this reason, it becomes feasible to assess the reliability of the wavelet method using both remote sensing and GIS tools.

Tropical Cyclone Fullness and Outer Region Size Growth: The Role of Spatial Distribution of Very Deep Convection

AbstractThis paper investigates the relationship between the growth of the 34‐kt wind radius (R34) of tropical cyclones (TCs) and their fullness using best‐track data from 2001 to 2020. The storms were categorized into four groups based on a fullness scale: FS1 (fullness ≤ 0.4), FS2 (0.4 < fullness ≤ 0.6), FS3 (0.6 < fullness ≤ 0.8), and FS4 (fullness > 0.8). These groups exhibit unique spatial patterns of very deep convective clouds with infrared brightness temperatures <208 K. The mean R34 growth rates in 24 hr decrease from FS1 to FS4, which is linked to the higher coverage of very deep convection around R34 in storms with lower fullness. This study demonstrates that TC fullness can characterize the spatial distribution of deep convection in storms and serve as a representation of the growth of TC outer region size. These results have implications for understanding the mechanisms behind TC outer size growth.

Characterization of Rice-Magnaporthe oryzae Interactions by Hyperspectral Imaging.

Hyperspectral imaging has the potential to detect, characterize, and quantify plant diseases objectively and nondestructively to improve phenotyping in breeding for disease resistance. In this study, leaf spectral reflectance characteristics of five rice genotypes diseased with blast caused by three Magnaporthe oryzae isolates differing in virulence were compared with visual disease ratings under greenhouse conditions. Spectral information (140 wavebands, range 450 to 850 nm) of infected leaves was recorded with a hyperspectral imaging microscope at 3, 5, and 7 days postinoculation to examine differences in symptom phenotypes and to characterize the compatibility of host-pathogen interactions. Depending on the rice genotype × M. oryzae genotype interaction, blast symptoms varied from tiny necrosis to enlarged lesions with symptom subareas differing in tissue coloration and indicated gene-for-gene-specific interactions. The blast symptom types were differentiated based on their spectral characteristics in the visible/near-infrared range. Symptom-specific spectral signatures and differences in the composition of leaf blast symptom type(s) resulted in unique spectral and spatial patterns of the rice × M. oryzae interactions based on the size, shape, and color of the symptom subareas. Spectral angle mapper classification of spectra enabled (i) discrimination between healthy (green) and diseased tissue of rice genotypes, (ii) classification and quantification of different blast symptom subareas, and (iii) grading of the host-pathogen compatibility (low - intermediate - high). Hyperspectral imaging was more sensitive to small changes in disease resistance than visual disease assessments and enabled the characterization of various types of resistance/susceptibility reactions of tissue subjected to M. oryzae infection.

Intracellular Dynamics-Resolved Label-Free Scattering Reveals Real-Time Metabolism of Single Bacteria.

Nanoscopic investigation of bacterial cells is essential to reveal their physiological status, impacting all cellular functions. Currently, this requires labeled probes or targeted staining procedures. Herein, we report a new bacterial feature, intracellular dynamics-resolved Rayleigh scattering (IDRS), that visualizes spatiotemporal cytoplasmic transitions in unlabeled bacteria and characterizes their real-time physiological status in 10 s. From single-bacterium IDRS signals, we discovered unique spatial patterns and their multiple transitions in Gram-negative and Gram-positive bacteria. The magnitude of IDRS signal variation highly correlated with the metabolic status of bacteria, differentiating persistent subpopulations. This is also the first report demonstrating distinct real-time metabolic conditions of unlabeled drug-resistant bacteria that are exposed to different doses of antibiotics. Our strategy opens up a way to simultaneously trace in situ metabolic and antibiotic resistance statuses, which can be applied in single-cell level control of bacterial metabolism and efficacy with a heterogeneous nature.

Leveraging species richness and ecological condition indices to guide systematic conservation planning

The global crises of biodiversity loss and climate change are interconnected in root cause and solutions. Targeted land conservation has emerged as a leading strategy to protect vulnerable species and buffer climate impacts, however, consistent methods to assess biodiversity and prioritize areas for protection have not yet been established. Recent landscape-scale planning initiatives in California present an opportunity to conserve biodiversity, but to enhance their effectiveness, assessment approaches should move beyond commonly used measures of terrestrial species richness. In this study, we compile publicly available datasets and explore how distinct biodiversity conservation indices - including indicators of terrestrial and aquatic species richness and of biotic and physical ecosystem condition - are represented in watersheds of the northern Sierra Nevada mountain region of California (n = 253). We also evaluate the extent to which the existing protected area network covers watersheds that support high species richness and intact ecosystems. Terrestrial and aquatic species richness showed unique spatial patterns (Spearman R = 0.27), with highest richness of aquatic species in the low-elevation watersheds of the study area and highest richness of terrestrial species in mid- and high-elevation watersheds. Watersheds with the highest ecosystem condition were concentrated in upper-elevations and were poorly correlated with those with the highest species richness (Spearman R = −0.34). We found that 28% of watersheds in the study area are conserved by the existing protected area network. Protected watersheds had higher ecosystem condition (mean rank-normalized score = 0.71) than unprotected areas (0.42), but species richness was generally lower (0.33 in protected versus 0.57 in unprotected watersheds). We illustrate how the complementary measures of species richness and ecosystem condition can be used to guide strategies for landscape-scale ecosystem management, including prioritization of watersheds for targeted protection, restoration, monitoring, and multi-benefit management. Though designed for California, application of these indices to guide conservation planning, design monitoring networks, and implement landscape-scale management interventions provides a model for other regions of the world.

The philosophy of "makdanakan" in the community settlement of Bontang Kuala Urban Village, Bontang City

The Bontang Kuala urban village was originally a settlement of the Bajau tribe, which has a unique culture and tradition based on the local wisdom values in it. This study aimed to determine the concept that serves as the bottom-line for the unique spatial patterns of settlements formed by using a qualitative inductive method with a phenomenological approach. Study results showed that the formation of residential spaces for the people of Bontang Kuala urban village is based on the “makdanakan” philosophy. This philosophy gives both physical and non-physical influences in the formation of residential spaces.

Determinants of functional synaptic connectivity among amygdala-projecting prefrontal cortical neurons in male mice

The medial prefrontal cortex (mPFC) mediates a variety of complex cognitive functions via its vast and diverse connections with cortical and subcortical structures. Understanding the patterns of synaptic connectivity that comprise the mPFC local network is crucial for deciphering how this circuit processes information and relays it to downstream structures. To elucidate the synaptic organization of the mPFC, we developed a high-throughput optogenetic method for mapping large-scale functional synaptic connectivity in acute brain slices. We show that in male mice, mPFC neurons that project to the basolateral amygdala (BLA) display unique spatial patterns of local-circuit synaptic connectivity, which distinguish them from the general mPFC cell population. When considering synaptic connections between pairs of mPFC neurons, the intrinsic properties of the postsynaptic cell and the anatomical positions of both cells jointly account for ~7.5% of the variation in the probability of connection. Moreover, anatomical distance and laminar position explain most of this fraction in variation. Our findings reveal the factors determining connectivity in the mPFC and delineate the architecture of synaptic connections in the BLA-projecting subnetwork.

Effects of social context manipulation on dorsal and ventral hippocampal neuronal responses.

The hippocampus is critical for contextual memory and has recently been implicated in various kinds of social memory. Traditionally, studies of hippocampal context coding have manipulated elements of the background environment, such as the shape and color of the apparatus. These manipulations produce large shifts in the spatial firing patterns, a phenomenon known as remapping. These findings suggest that the hippocampus encodes and differentiates contexts by generating unique spatial firing patterns for each environment a subject encounters. However, we do not know whether the hippocampus encodes social contexts defined by the presence of particular conspecifics. We examined this by exposing rats to a series of manipulations of the social context, including the presence of familiar male, unfamiliar male and female conspecifics, in order to determine whether remapping is a plausible mechanism for encoding socially-defined contexts. Because the dorsal and ventral regions of the hippocampus are thought to play different roles in spatial and social cognition, we recorded neurons in both regions. Surprisingly, we found little evidence of remapping in response to manipulation of the social context in either the dorsal or ventral hippocampus, although we saw typical remapping in response to changing the background color. This result suggests that remapping is not the primary mechanism for encoding different social contexts. However, we found that a subset of hippocampal neurons fired selectively near the cages that contained the conspecifics, and these responses were most prevalent in the ventral hippocampus. We also found a striking increase in the spatial information content of ventral hippocampal firing patterns. These results indicate that the ventral hippocampus is sensitive to changes in the social context and neurons that respond selectively near the conspecific cages could play an important, if not fully understood role in encoding the conjunction of conspecifics, their location and the environment.

A shape-attention Pivot-Net for identifying central pivot irrigation systems from satellite images using a cloud computing platform: an application in the contiguous US

ABSTRACT Forty percent of global food production relies upon irrigation, which accounts for 70% of total global freshwater use. Thus, the mapping of cropland irrigation plays a significant role in agricultural water management and estimating food production. However, current spaceborne irrigated cropland mapping is highly reliant upon its spectral behavior, which often has high uncertainty and lacks information about the method of irrigation. Deep learning (DL) allows for the classification of irrigated cropland according to unique spatial patterns, such as the central pivot irrigation system (CPIS). But convolutional neural networks (CNNs) are usually biased toward color and texture features, a spatial transferable and accurate CPIS identification model is lacking owing to previous model seldom involves the round shapes of CPIS, which is usually key to distinguishing CPIS. To address this lack, we proposed a shape attention neural network by integrating spatial-attention gate, residual block, and multi-task learning, Pivot-Net, to incorporate shape information identify CPIS in satellite imagery. Specifically, we employed CPIS in Kansas to train our model using Sentinel-2 and Landsat-8 optical images. We found that Pivot-Net is superior to seven state-of-the-art semantic segmentation models on second-stage validation. We also evaluated the performance of Pivot-Net at three validation sites, which had an average F1-score and mean IOU of 90.68% and 90.45%, respectively, which further demonstrated the high accuracy of the proposed model. Moreover, to show that the proposed Pivot-Net can map CPIS at the country scale, we generated the first CPIS map at 30 m for the contiguous US using a cloud computing platform and our Pivot-Net model. The total CPIS area for the contiguous US was 61,094 km2 in 2018, which comprised 26.22% of all irrigated lands. Our results can be accessed at https://tianfyou.users.earthengine.app/view/cpisus. Therefore, the proposed shape-attention Pivot-Net demonstrates the ability to classify CPIS at large spatial scales and are feasible to map CPIS at national scales.

Grassland productivity responds unexpectedly to dynamic light and soil water environments induced by photovoltaic arrays

AbstractAgrivoltaic (AV) systems are designed to coproduce photovoltaic (PV) energy on lands simultaneously supporting agriculture (food/forage production). PV infrastructure in agroecosystems alters resources critical for plant growth, and water‐limited agroecosystems such as grasslands are likely to be particularly sensitive to the unique spatial and temporal patterns of incident sunlight and soil water inherent within AV systems. However, the impact of resource alteration on forage production, the primary ecosystem service from managed grasslands, is poorly resolved. Here, we evaluated seasonal patterns of soil moisture (SM) and diurnal variation in incident sunlight (photosynthetic photon flux density [PPFD]) in a single‐axis‐tracking AV system established in a formerly managed semiarid C3 grassland in Colorado. Our goals were to (1) quantify dynamic patterns of PPFD and SM within a 1.2 MW PV array in a perennial grassland, and (2) determine how aboveground net primary production (ANPP) and photosynthetic parameters responded to the resource patterns created by the PV array. We hypothesized that spatial variability in ANPP would be strongly related to SM patterns, typical of most grasslands. We measured significant reductions in ANPP directly beneath PV panels, where SM and PPFD were both low. However, in locations with significantly increased SM from the shedding and redistribution of precipitation by PV panels, ANPP was not increased. Instead, ANPP was greatest in locations where plants were shaded in the afternoon but received high levels of PPFD in the morning hours, when air temperatures and vapor pressure deficits were relatively low. Thus, contrary to expectations, we found relatively weak relationships between SM and ANPP despite significant spatial variability in both. Further, there was little evidence that light‐saturated photosynthesis (Asat) and quantum yield of CO2 assimilation (ϕCO2) differed for plants growing directly beneath (lowest PPFD) versus between (highest PPFD) PV panels. Overall, the AV system established in this semiarid managed grassland did not alter patterns of ANPP in ways predictable from past studies of controls of ANPP in open grasslands. However, our results suggest that the diurnal timing of low versus high periods of PPFD incident on plants is an important determinant of productivity patterns in grasslands.

Variations of Subgrid-Scale Turbulent Fluxes in the Dry Convective Boundary Layer at Gray Zone Resolutions

Abstract In the numerical gray zone of the convective boundary layer (CBL), the horizontal resolution is comparable to the size of organized convective circulation. As turbulence becomes partially resolved, gridscale variations of the subgrid-scale (SGS) turbulent fluxes become significant compared to the mean. Previously, such variations have often been ignored in scale-adaptive planetary boundary layer (PBL) schemes developed for the gray zone. This study investigates these variations with respect to height and resolution based on large-eddy simulations. It is found that SGS fluxes exhibit maximum variability at the center of the gray zone, where the resolved and the SGS mean fluxes are approximately equal. A simple analytical model is used to associate such characteristic variations to the nonlinear interactions of the dominant energy-containing mode of CBL turbulence. Examination of the horizontal distribution of the SGS fluxes reveals their preferential location over the updraft edges surrounding the core. A priori analysis further suggests the ability of a scale-similarity closure to reproduce the unique spatial patterns of the SGS fluxes at gray zone resolutions. Four scale-adaptive PBL schemes are evaluated focusing on their representations of the modeled SGS flux variability. Their shared shortcomings as a result of their gradient diffusion–based formulation are exposed. This study suggests that a mixed model consisting of a scale-adaptive PBL scheme to represent the mean, and a scale-similarity component to account for gridscale variability to be advantageous for the gray zone. Significance Statement As Gresho and Lee’s famous quote on numerical schemes goes, “Don’t suppress the wiggles. They’re telling you something!” In the numerical gray zone, where the grid spacing is comparable to the characteristic length scale of the flow, turbulence is partially resolved. The “wiggles” (or gridscale variability) reflecting the resolved heterogeneity of turbulent fluxes is an outstanding feature of the gray zone. However, they are often overlooked as error bars to the mean. This study uncovers the significance of these error bars, and characterizes and explains their variations with height and grid spacing. A suitable model that captures such variations is investigated to help build better planetary boundary layer schemes.

Adolescent alcohol use is linked to disruptions in age-appropriate cortical thinning: an unsupervised machine learning approach.

Cortical thickness changes dramatically during development and is associated with adolescent drinking. However, previous findings have been inconsistent and limited by region-of-interest approaches that are underpowered because they do not conform to the underlying spatially heterogeneous effects of alcohol. In this study, adolescents (n = 657; 12-22 years at baseline) from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study who endorsed little to no alcohol use at baseline were assessed with structural magnetic resonance imaging and followed longitudinally at four yearly intervals. Seven unique spatial patterns of covarying cortical thickness were obtained from the baseline scans by applying an unsupervised machine learning method called non-negative matrix factorization (NMF). The cortical thickness maps of all participants' longitudinal scans were projected onto vertex-level cortical patterns to obtain participant-specific coefficients for each pattern. Linear mixed-effects models were fit to each pattern to investigate longitudinal effects of alcohol consumption on cortical thickness. We found in six NMF-derived cortical thickness patterns, the longitudinal rate of decline in no/low drinkers was similar for all age cohorts. Among moderate drinkers the decline was faster in the younger adolescent cohort and slower in the older cohort. Among heavy drinkers the decline was fastest in the younger cohort and slowest in the older cohort. The findings suggested that unsupervised machine learning successfully delineated spatially coordinated patterns of vertex-level cortical thickness variation that are unconstrained by neuroanatomical features. Age-appropriate cortical thinning is more rapid in younger adolescent drinkers and slower in older adolescent drinkers, an effect that is strongest among heavy drinkers.