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Abstract The Aids to Management are a product of the Global Campaign against Headache, a worldwide programme of action conducted in official relations with the World Health Organization. Developed in partnership with the European Headache Federation, they update the first edition published 11 years ago. The common headache disorders (migraine, tension-type headache and medication-overuse headache) are major causes of ill health. They should be managed in primary care, firstly because their management is generally not difficult, and secondly because they are so common. These Aids to Management, with the European principles of management of headache disorders in primary care as the core of their content, combine educational materials with practical management aids. They are supplemented by translation protocols, to ensure that translations are unchanged in meaning from the English-language originals. The Aids to Management may be individually downloaded and, as is the case for all products of the Global Campaign against Headache, are available without restriction for non-commercial use.

Abstract In joint initiatives, the European Headache Federation and Lifting The Burden have described a model of structured headache services (with their basis in primary care), defined service quality in this context, and developed practical methods for its evaluation. Here, in a continuation of the service quality evaluation programme, we set out ten suggested role- and performance-defining standards for specialized headache centres operating as an integral component of these services. Verifiable criteria for evaluation accompany each standard. The purposes are five-fold: (i) to inspire and promote, or stimulate the establishment of, specialized headache centres as centres of excellence; (ii) to define the role of such centres within optimally structured and organized national headache services; (iii) to set out criteria by which such centres may be recognized as exemplary in their fulfilment of this role; (iv) to provide the basis for, and to initiate and motivate, collaboration and networking between such centres both nationally and internationally; (v) ultimately to improve the delivery and quality of health care for headache.

This study investigates whether a water deficit index (WDI) based on imagery from unmanned aerial vehicles (UAVs) can provide accurate crop water stress maps at different growth stages of barley and in differing weather situations. Data from both the early and late growing season are included to investigate whether the WDI has the unique potential to be applicable both when the land surface is partly composed of bare soil and when crops on the land surface are senescing. The WDI differs from the more commonly applied crop water stress index (CWSI) in that it uses both a spectral vegetation index (VI), to determine the degree of surface greenness, and the composite land surface temperature (LST) (not solely canopy temperature).Lightweight thermal and RGB (red–green–blue) cameras were mounted on a UAV on three occasions during the growing season 2014, and provided composite LST and color images, respectively. From the LST, maps of surface-air temperature differences were computed. From the color images, the normalized green–red difference index (NGRDI), constituting the indicator of surface greenness, was computed. Advantages of the WDI as an irrigation map, as compared with simpler maps of the surface-air temperature difference, are discussed, and the suitability of the NGRDI is assessed. Final WDI maps had a spatial resolution of 0.25 m.It was found that the UAV-based WDI is in agreement with measured stress values from an eddy covariance system. Further, the WDI is especially valuable in the late growing season because at this stage the remote sensing data represent crop water availability to a greater extent than they do in the early growing season, and because the WDI accounts for areas of ripe crops that no longer have the same need for irrigation. WDI maps can potentially serve as water stress maps, showing the farmer where irrigation is needed to ensure healthy growing plants, during entire growing season.

Abstract Background Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) proteins are expressed on the surface of infected erythrocytes, mediating parasite sequestration in the vasculature. PfEMP1 is a major target of protective antibodies, but the features of the antibody response are poorly defined. Methods In Malawian children with cerebral or uncomplicated malaria, we characterized the antibody response to 39 recombinant PfEMP1 Duffy binding like (DBL) domains or cysteine-rich interdomain regions (CIDRs) in detail, including measures of antibody classes, subclasses, and engagement with Fcγ receptors and complement. Using elastic net regularized logistic regression, we identified a combination of seven antibody targets and Fc features that best distinguished between children with cerebral and uncomplicated malaria. To confirm the role of the selected targets and Fc features, we measured antibody-dependent neutrophil and THP-1 cell phagocytosis of intercellular adhesion molecule-1 (ICAM-1) and endothelial protein C (EPCR) co-binding infected erythrocytes. Results The selected features distinguished between children with cerebral and uncomplicated malaria with 87% accuracy (median, 80–96% interquartile range) and included antibody to well-characterized DBLβ3 domains and a less well-characterized CIDRγ12 domain. The abilities of antibodies to engage C1q and FcγRIIIb, rather than levels of IgG, correlated with protection. In line with a role of FcγRIIIb binding antibodies to DBLβ3 domains, antibody-dependent neutrophil phagocytosis of ICAM-1 and EPCR co-binding IE was higher in uncomplicated malaria (15% median, 8–38% interquartile range) compared to cerebral malaria (7%, 30–15%, p

Abstract Improvements of magnesium ionic conducting materials are important for the development of novel magnesium solid-state batteries. Here we investigate the effect of nanoconfinement of one of the most conductive magnesium electrolytes − the (Mg(BH4)2·NH3) x (Mg(BH4)2·2NH3)1−x composite. The synthesis of nanoconfined Mg(BH4)2·1.47NH3 in a mesoporous silica scaffold (SBA-15, pore size 5.8 nm) is achieved through melt infiltration. Various degrees of pore filling are investigated, ranging from 100 to 300 %. Solid-state 11B nuclear magnetic resonance analysis confirms the successful stabilization of the highly dynamic eutectic molten state of Mg(BH4)2·1.47NH3 through nanoconfinement. The confined sample exhibits notable thermal stability, maintaining integrity up to approximately 100 °C, and the eutectic molten composite does not recrystallize within five months after synthesis, upon storing at room temperature. Notably, among the confined samples, 200 % pore filling displays promising Mg2+ ionic conductivity within the range of 9.1 × 10−6 to 2.7 × 10−4 S cm−1 and a low activation energy of 0.69 eV at temperatures from 32 to 80 °C. Furthermore, the compound was found to have a low electronic conductivity of 7.93 × 10−11 S cm−1 at 70 °C resulting in an ionic transport number close to unity.

Estimating evaporation is important when managing water resources and cultivating crops. Evaporation can be estimated using land surface heat flux models and remotely sensed land surface temperatures (LST), which have recently become obtainable in very high resolution using lightweight thermal cameras and Unmanned Aerial Vehicles (UAVs). In this study a thermal camera was mounted on a UAV and applied into the field of heat fluxes and hydrology by concatenating thermal images into mosaics of LST and using these as input for the two-source energy balance (TSEB) modelling scheme. Thermal images are obtained with a fixed-wing UAV overflying a barley field in western Denmark during the growing season of 2014 and a spatial resolution of 0.20 m is obtained in final LST mosaics. Two models are used: the original TSEB model (TSEB-PT) and a dual-temperature-difference (DTD) model. In contrast to the TSEB-PT model, the DTD model accounts for the bias that is likely present in remotely sensed LST. TSEB-PT and DTD have already been well tested, however only during sunny weather conditions and with satellite images serving as thermal input. The aim of this study is to assess whether a lightweight thermal camera mounted on a UAV is able to provide data of sufficient quality to constitute as model input and thus attain accurate and high spatial and temporal resolution surface energy heat fluxes, with special focus on latent heat flux (evaporation). Furthermore, this study evaluates the performance of the TSEB scheme during cloudy and overcast weather conditions, which is feasible due to the low data retrieval altitude (due to low UAV flying altitude) compared to satellite thermal data that are only available during clear-sky conditions. TSEB-PT and DTD fluxes are compared and validated against eddy covariance measurements and the comparison shows that both TSEB-PT and DTD simulations are in good agreement with eddy covariance measurements, with DTD obtaining the best results. The DTD model provides results comparable to studies estimating evaporation with similar experimental setups, but with LST retrieved from satellites instead of a UAV. Further, systematic irrigation patterns on the barley field provide confidence in the veracity of the spatially distributed evaporation revealed by model output maps. Lastly, this study outlines and discusses the thermal UAV image processing that results in mosaics suited for model input. This study shows that the UAV platform and the lightweight thermal camera provide high spatial and temporal resolution data valid for model input and for other potential applications requiring high-resolution and consistent LST.

In this study we evaluate a methodology for disaggregating land surface energy fluxes estimated with the Two-Source Energy Balance (TSEB)-based Dual-Temperature Difference (DTD) model which uses day and night polar orbiting satellite observations of land surface temperature (LST) as a remotely sensed input. The DTD model is run with MODIS input data at a spatial resolution of around 1 km while the disaggregation uses Landsat observations to produce fluxes at a nominal spatial resolution of 30 m. The higher-resolution modelled fluxes can be directly compared against eddy covariance (EC)-based flux tower measurements to ensure more accurate model validation and also provide a better visualization of the fluxes' spatial patterns in heterogeneous areas allowing for development of, for example, more efficient irrigation practices. The disaggregation technique is evaluated in an area covered by the Danish Hydrological Observatory (HOBE), in the west of the Jutland peninsula, and the modelled fluxes are compared against measurements from two flux towers: the first one in a heterogeneous agricultural landscape and the second one in a homogeneous conifer plantation. The results indicate that the coarse-resolution DTD fluxes disaggregated at Landsat scale have greatly improved accuracy as compared to high-resolution fluxes derived directly with Landsat data without the disaggregation. At the agricultural site the disaggregated fluxes display small bias and very high correlation (r ≈ 0.95) with EC-based measurements, while at the plantation site the results are encouraging but still with significant errors. In addition, we introduce a~modification to the DTD model by replacing the "parallel" configuration of the resistances to sensible heat exchange by the "series" configuration. The latter takes into account the in-canopy air temperature and substantially improves the accuracy of the DTD model.

Abstract Culture-based microbial natural product discovery strategies fail to realize the extraordinary biosynthetic potential detected across earth’s microbiomes. Here we introduce Small Molecule In situ Resin Capture (SMIRC), a culture-independent method to obtain natural products directly from the environments in which they are produced. We use SMIRC to capture numerous compounds including two new carbon skeletons that were characterized using NMR and contain structural features that are, to the best of our knowledge, unprecedented among natural products. Applications across diverse marine habitats reveal biome-specific metabolomic signatures and levels of chemical diversity in concordance with sequence-based predictions. Expanded deployments, in situ cultivation, and metagenomics facilitate compound discovery, enhance yields, and link compounds to candidate producing organisms, although microbial community complexity creates challenges for the later. This compound-first approach to natural product discovery provides access to poorly explored chemical space and has implications for drug discovery and the detection of chemically mediated biotic interactions.

The atmospheric greenhouse gas (GHG) budget of a restored wetland in western Denmark was established for the years 2009–2011 from eddy covariance measurements of carbon dioxide (CO2) and methane (CH4) fluxes. The water table in the wetland, which was restored in 2002, was unregulated, and the vegetation height was limited through occasional grazing by cattle and grass cutting. The annual net CO2 uptake varied between 195 and 983 g m−2 and the annual net CH4 release varied between 11 and 17 g m−2. In all three years the wetland was a carbon sink and removed between 42 and 259 g C m−2 from the atmosphere. However, in terms of the full annual GHG budget (assuming that 1 g CH4 is equivalent to 25 g CO2 with respect to the greenhouse effect over a time horizon of 100 years) the wetland was a sink in 2009, a source in 2010 and neutral in 2011. Complementary observations of meteorological factors and management activities were used to explain the large inter-annual variations in the full atmospheric GHG budget of the wetland. The largest impact on the annual GHG fluxes, eventually defining their sign, came from site management through changes in grazing duration and animal stocking density. These changes accounted for half of the observed variability in the CO2 fluxes and about two thirds of the variability in CH4 fluxes. An unusually long period of snow cover in 2010 had the second largest effect on the annual CO2 flux, whose interannual variability was larger than that of the CH4 flux. Since integrated CO2 and CH4 flux data from restored wetlands are still very rare, it is concluded that more long-term flux measurements are needed to quantify the effects of ecosystem disturbance, in terms of management activities and exceptional weather patterns, on the atmospheric GHG budget more accurately.

Abstract Worldwide, trees are colonizing rangelands with high conservation value. The introduction of trees into grasslands and shrublands causes large‐scale changes in ecosystem structure and function, which have cascading impacts on ecosystem services, biodiversity, and agricultural economies. Satellites are increasingly being used to track tree cover at continental to global scales, but these methods can only provide reliable estimates of change over recent decades. Given the slow pace of tree cover expansion, remote sensing techniques that can extend this historical record provide critical insights into the magnitude of environmental change. Here, we estimate conifer expansion in rangelands of the northern Great Plains, United States, North America, using historical aerial imagery from the mid‐20th century and modern aerial imagery. We analyzed 19.3 million hectares of rangelands in Montana, USA, using a convolutional neural network (U‐Net architecture) and cloud computing to detect tree features and tree cover change. Our bias‐corrected results estimate 3.0 ± 0.2 million hectares of conifer tree cover expansion in Montana rangelands, which accounts for 15.4% of the total study area. Overall accuracy was >91%, but the producer's accuracy was lower than the user's accuracy (0.60 vs. 0.88) for areas of tree cover expansion. Nonetheless, the omission errors were not spatially clustered, suggesting that the method is reliable for identifying the regions of Montana where substantial tree expansion has occurred. Using the model results in conjunction with historical and modern imagery allows for effective communication of the scale of tree expansion while overcoming the recency effect caused by shifting environmental baselines.

Abstract Coral reefs represent some of the most biodiverse ecosystems in the world but are currently undergoing large‐scale degradation due to anthropogenic stressors. Such degradation usually begins with coral bleaching, and if the stress condition is inflicted for too long may eventually result in loss of structural complexity (or “flattening”) of the reef, dramatically changing habitat availability for reef‐associated fauna. Despite having been linked to important ecosystem functions, cryptobenthic organisms are often overlooked in ecological monitoring programs, and their microhabitat dependencies are poorly understood. Here, we combined collection‐based biodiversity monitoring techniques with five different environmental DNA (eDNA) sampling techniques (Reef water, sediment, crevice water, slurp gun, and bulk sediment) to survey cryptobenthic fishes and annelids on a Maldivian fringing coral reef. Collectively, 176 fish and 140 annelid taxa were detected with eDNA across 14 surveyed sites, more than doubling the reported annelid taxa in the region with 88 new occurrences. Water filtered near the reef structure revealed the highest species richness out of the five eDNA sampling techniques tested. Furthermore, we found correlations between fish species richness and topographic complexity for both collection‐ and eDNA‐based techniques. This suggests that detection by eDNA may be linked to site‐specific predictors and reveal community differences across small spatial scales (tens of meters). We also report that reef flattening (going from structural complex to less complex sites) can cause a 50% reduction in fish diversity and that cryptobenthic fish species richness was highly associated with branching corals. In contrast, annelid communities showed no clear correlations with environmental predictors, but co‐amplification of non‐target, non‐annelid taxa may have distorted such correlations if present. This suggest that the predictive powers of eDNA for environmental gradients may be dependent on the targeted taxa.

Volatile organic compound (VOC) emissions and subsequent oxidation contribute to the formation of secondary pollutants and poor air quality in general. As more VOCs at lower mixing ratios have become the target of air quality investigations, their quantification has been aided by technological advancements in proton-transfer-reaction time-of-flight mass spectrometry (PTR-TOF-MS). However, such quantification requires appropriate instrument background measurements and calibrations, particularly for VOCs without calibration standards. This study utilized a Vocus PTR-TOF-MS coupled with a gas chromatograph for real-time and speciated measurements of ambient VOCs in Boulder, Colorado, during spring 2021. The aim of these measurements was to understand and characterize instrument response and temporal variability as to inform the quantification of a broader range of detected VOCs. Fast, frequent calibrations were made every 2 h in addition to daily multipoint calibrations. Sensitivities derived from the fast calibrations were 5 ± 6 % (average and 1 standard deviation) lower than those derived from the multipoint calibrations due to an offset between the calibrations and instrument background measurement. This offset was caused, in part, by incomplete mixing of the standard with diluent. These fast calibrations were used in place of a normalization correction to account for variability in instrument response and accounted for non-constant reactor conditions caused by a gradual obstruction of the sample inlet. One symptom of these non-constant conditions was a trend in fragmentation, although the greatest observed variability was 6 % (1 relative standard deviation) for isoprene. A PTR Data Toolkit (PTR-DT) was developed to assess instrument performance and rapidly estimate the sensitivities of VOCs which could not be directly calibrated on the timescale of the fast calibrations using the measured sensitivities of standards, molecular properties, and simple reaction kinetics. Through this toolkit, the standards' sensitivities were recreated within 1 ± 8 % of the measured values. Three clean-air sources were compared: a hydrocarbon trap, zero-grade air and ultra-high purity nitrogen, and a catalytic zero-air generator. The catalytic zero-air generator yielded the lowest instrument background signals for the majority of ions, followed by the hydrocarbon trap. Depending on the ionization efficiency, product ion fragmentation, ion transmission, and instrument background, standards' limits of detection (5 s measurement integration) derived from the catalytic zero-air generator and the fast calibration sensitivities ranged from 2 ppbv (methanol) to 1 pptv (decamethylcyclopentasiloxane; D5 siloxane) with most standards having detection limits below 20 pptv. Finally, applications of measurements with low detection limits are considered for a few low-signal species including sub-parts-per-trillion by volume (pptv) enhancements of icosanal (and isomers; 1 min average) in a plume of cooking emissions, and sub-parts-per-trillion by volume enhancements in dimethyl disulfide in plumes containing other organosulfur compounds. Additionally, chromatograms of hexamethylcyclotrisiloxane, octamethylcyclotetrasiloxane, and decamethylcyclopentasiloxane (D3, D4, and D5 siloxanes, respectively), combined with high sensitivity, suggest that online measurements can reasonably be associated with the individual isomers.

Green spaces and blue spaces in cities provide a wealth of benefits to the urban social–ecological system. Unfortunately, urban development fragments natural habitats, reducing connectivity and biodiversity. Urban green–blue infrastructure (UGI) networks can mitigate these effects by providing ecological corridors that enhance habitat connectivity. This study examined UGI connectivity for two indicator species in a rapidly developing city in the southern United States. We mapped and analyzed UGI at a high resolution (0.6 m) across the entire city, with a focus on semi-natural areas in private land and residential neighborhoods. Integrating graph theory and a gravity model, we assessed structural UGI networks and ranked them based on their ability to support functional connectivity. Most of the potential habitat corridors we mapped in this project traversed private lands, including 58% of the priority habitat for the Golden-cheeked Warbler and 69% of the priority habitat for the Rio Grande Wild Turkey. Riparian zones and other areas with dense tree cover were critical linkages in these habitat corridors. Our findings illustrate the important role that private semi-natural areas play in UGI, habitat connectivity, and essential ecosystem services.

The choroid plexus (CP), located at the blood–brain interface, is partially responsible for maintaining the composition of cerebrospinal fluid. Epithelial cell clusters isolated from the CP secrete numerous biologically active molecules, and are neuroprotective when transplanted in animal models of Huntington's disease and stroke. The transcriptomic and proteomic profiles of CP may extend beyond CNS applications due to an abundance of trophic and regenerative factors, including vascular endothelial growth factor, transforming growth factor-β, and others. We used microarray to investigate the transcriptome of porcine CP epithelium, and then assessed the in vitro and in vivo regenerative capability of secreted CP products in cell monolayers and full-thickness cutaneous wounds. In vitro, CP reduced the void area of fibroblast and keratinocyte scratch cultures by 70% and 33%, respectively, compared to empty capsule controls, which reduced the area by only 35% and 6%, respectively. In vivo, after 10 days of topical application, CP conditioned medium lyophilate dispersed in antibiotic ointment produced a twofold improvement in incision tensile strength compared to ointment containing lyophilized control medium, and an increase in the regeneration of epidermal appendages from roughly 50–150 features per wound. Together, these data identify the CP as a source of secreted regenerative molecules to accelerate and improve the healing of superficial wounds and potentially other similar indications.

Abstract Polymers are promising candidates as solid‐state electrolytes due to their performance and processability, but fillers play a critical role in adjusting the polymer network structure and electrochemical, thermal, and mechanical properties. Most fillers studied so far are anisotropic, limiting the possibility of homogeneous ion transport. Here, applying metal‐organic framework (MOF) glass as an isotropic functional filler, solid‐state polyethylene oxide (PEO) electrolytes are prepared. Calorimetric and diffusion kinetics tests show that the MOF glass addition reduces the glass transition temperature of the polymer phase, improving the mobility of the polymer chains, and thereby facilitating lithium (Li) ion transport. By also incorporating the lithium salt and ionic liquid (IL), Li–Li symmetric cell tests of the PEO‐lithium salt‐MOF glass‐IL electrolyte reveal low overpotential, indicating low interfacial impedance. Simulations show that the isotropic structure of the MOF glass facilitates the wettability of the IL by enhancing interfacial interactions, leading to a less confined IL structure that promotes Li‐ion mobility. Finally, the obtained electrolyte is used to construct Li–lithium iron phosphate full batteries that feature high cycle stability and rate capability. This work therefore demonstrates how an isotropic functional filler can be used to enhance the electrochemical performance of solid‐state polymer electrolytes.

Abstract Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting.

Abstract Background The influence of diet on immune function and resistance to enteric infection and disease is becoming ever more established. Highly processed, refined diets can lead to inflammation and gut microbiome dysbiosis, whilst health-promoting dietary components such as phytonutrients and fermentable fibres are thought to promote a healthy microbiome and balanced mucosal immunity. Chicory (Cichorium intybus) is a leafy green vegetable rich in fibres and bioactive compounds that may promote gut health. Results Unexpectedly, we here show that incorporation of chicory into semisynthetic AIN93G diets renders mice susceptible to infection with enteric helminths. Mice fed a high level of chicory leaves (10% dry matter) had a more diverse gut microbiota, but a diminished type-2 immune response to infection with the intestinal roundworm Heligmosomoides polygyrus. Furthermore, the chicory-supplemented diet significantly increased burdens of the caecum-dwelling whipworm Trichuris muris, concomitant with a highly skewed type-1 immune environment in caecal tissue. The chicory-supplemented diet was rich in non-starch polysaccharides, particularly uronic acids (the monomeric constituents of pectin). In accordance, mice fed pectin-supplemented AIN93G diets had higher T. muris burdens and reduced IgE production and expression of genes involved in type-2 immunity. Importantly, treatment of pectin-fed mice with exogenous IL-25 restored type-2 responses and was sufficient to allow T. muris expulsion. Conclusions Collectively, our data suggest that increasing levels of fermentable, non-starch polysaccharides in refined diets compromises immunity to helminth infection in mice. This diet-infection interaction may inform new strategies for manipulating the gut environment to promote resistance to enteric parasites.

The mucosal chemokine CCL28 is a promising target for immunotherapy drug development due to its elevated expression level in epithelial cells and critical role in creating and maintaining an immunosuppressive tumor microenvironment. Using sulfotyrosine as a probe, NMR chemical shift mapping identified a potential receptor-binding hotspot on the human CCL28 surface. CCL28 was screened against 2,678 commercially available chemical fragments by 2D NMR, yielding thirteen verified hits. Computational docking predicted that two fragments could occupy adjoining subsites within the sulfotyrosine recognition cleft. Dual NMR titrations confirmed their ability to bind CCL28 simultaneously, thereby validating an initial fragment pair for linking and merging strategies to design high-potency CCL28 inhibitors.

Introduction: Insufficient prenatal nutrition can affect fetal development and lead to intrauterine growth restriction (IUGR). The aim of this study was to investigate hepatic transcriptional responses and innate immune function in piglets suffering from IUGR compared to normal-sized piglets at 3 days of age and explore whether the provision of an energy-rich supplement at birth could modulate these parameters.Methods: A total of 68 piglets were included in the study. Peripheral blood mononuclear cells were harvested for LPS stimulation, and organs were harvested post-mortem to quantify relative weights. Liver tissue was utilized for RNA sequencing coupled with gene-set enrichment analysis.Results: IUGR resulted in increased expression of genes such as PDK4 and substantial alterations in transcriptional pathways related to metabolic activity (e.g., citric acid and Krebs cycles), but these changes were equivalent in piglets given an energy-rich supplement or not. Transcriptomic analysis and serum biochemistry suggested altered glucose metabolism and a shift toward oxidation of fatty acids. IUGR piglets also exhibited suppression of genes related to innate immune function (e.g., CXCL12) and pathways related to cell proliferation (e.g., WNT and PDGF signaling). Moreover, they produced less IL-1β in response to LPS stimulation and had lower levels of blood eosinophils than normal-sized piglets.Discussion: Taken together, our results indicate that IUGR results in early-life alterations in metabolism and immunity that may not be easily restored by the provision of exogenous energy supplementation.

BackgroundSkeletal stem/progenitor cells (SSPCs) in the bone marrow can differentiate into osteoblasts or adipocytes in response to microenvironmental signalling input, including hormonal signalling. Glucocorticoids (GC) are corticosteroid hormones that promote adipogenic differentiation and are endogenously increased in patients with Cushing´s syndrome (CS). Here, we investigate bone marrow adiposity changes in response to endogenous or exogenous GC increases. For that, we characterize bone biopsies from patients with CS and post-menopausal women with glucocorticoid-induced osteoporosis (GC-O), compared to age-matched controls, including postmenopausal osteoporotic patients (PM-O).MethodsTransiliac crest bone biopsies from CS patients and healthy controls, and from postmenopausal women with GC-O and matched controls were analysed; an additional cohort included biopsies from women with PM-O. Plastic-embedded biopsies were sectioned for histomorphometric characterization and quantification of adipocytes. The fraction of adipocyte area per tissue (Ad.Ar/T.Ar) and marrow area (Ad.Ar/Ma.Ar), mean adipocyte profile area (Ad.Pf.Ar) and adipocyte profile density (N.Ad.Pf/Ma.Ar) were determined and correlated to steroid levels. Furthermore, the spatial distribution of adipocytes in relation to trabecular bone was characterized and correlations between bone marrow adiposity and bone remodeling parameters investigated.ResultsBiopsies from patients with CS and GC-O presented increased Ad.Ar/Ma.Ar, along with adipocyte hypertrophy and hyperplasia. In patients with CS, both Ad.Ar/Ma.Ar and Ad.Pf.Ar significantly correlated with serum cortisol levels. Spatial distribution analyses revealed that, in CS, the increase in Ad.Ar/Ma.Ar near to trabecular bone (100 µm) remained unchanged. In contrast, patients with GC-O only presented increased Ad.Ar/Ma.Ar and mean Ad.Pf.Ar>100 µm from trabecular bone surface, highlighting the differential effect of increased endogenous steroid accumulation. Finally, the Ad.Ar/Ma.Ar and Ad.Ar/T.Ar correlated with the canopy coverage above remodeling events.ConclusionIncreased cortisol production in patients with CS induces increased bone marrow adiposity, primarily mediated by adipocyte hypertrophy. This adiposity is particularly evident near trabecular bone surfaces, where hyperplasia also occurs. The differential pattern of adiposity in patients with CS and GC-O highlights that bone marrow adipocytes and their progenitors may respond differently in these two GC-mediated bone diseases.

ABSTRACTThe concept of deep learning has become a popular and well recognised term in contemporary educational literature and international political documents. Deep learning typically induces positive connotations and represents the learning strategy that educational institutions should adopt in order to assure a sustainable future in modern societies. However, a major challenge with deep learning is the fact that the concept is used in many different scientific fields with a variety of definitions, understandings and applications. Thus, there is an imbalance between the quantity of governing documents in education that endorse the use of deep learning as a main learning strategy and the amount of academic theory and research examining its definitional clarity and related unresolved questions. The aim of the present theoretical analysis is threefold. First, we examine the concept of deep learning in light of the three fundamental questions “why”, “what”, and “how” and make assessments concerning its status in contemporary literature. Second, we discuss the compatibility between the idea of deep learning and the concepts of educational inclusion and adapted instruction. Finally, we make several recommendations for future development and application of the term deep learning in educational contexts.

Current capabilities in genomic sequencing outpace functional interpretations. Our previous work showed that 3D protein structure calculations enhance mechanistic understanding of genetic variation in sequenced tumors and patients with rare diseases. The KRAS GTPase is among the critical genetic factors driving cancer and germline conditions. Because KRAS-altered tumors frequently harbor one of three classic hotspot mutations, nearly all studies have focused on these mutations, leaving significant functional ambiguity across the broader KRAS genomic landscape observed in cancer and non-cancer diseases. Herein, we extend structural bioinformatics with molecular simulations to study an expanded landscape of 86 KRAS mutations. We identify multiple coordinated changes strongly associated with experimentally established KRAS biophysical and biochemical properties. The patterns we observe span hotspot and non-hotspot alterations, which can all dysregulate Switch regions, producing mutation-restricted conformations with different effector binding propensities. We experimentally measured mutation thermostability and identified shared and distinct patterns with simulations. Our results indicate mutation-specific conformations, which show potential for future research into how these alterations reverberate into different molecular and cellular functions. The data we present is not predictable using current genomic tools, demonstrating the added functional information derived from molecular simulations for interpreting human genetic variation.

While rehabilitation robots present a much-needed solution to improving early mobilization therapy in demanding clinical settings, they also present new challenges and opportunities in patient monitoring. Aside from the fundamental challenge of quantifying a patient’s voluntary contribution during robot-led therapy motion, many sensors cannot be used in clinical settings due to time and space limitations. In this paper, we present and compare two metrics for monitoring a patient’s active participation in the motion. The two metrics, each derived from first principles, have the same biomechanical interpretability, i.e., active work by the patient during the robotic mobilization therapy, but are calculated in two different spaces (Cartesian vs. muscle space). Furthermore, the sensors used to quantify these two metrics are fully independent from each other and the associated measurements are unrelated. Specifically, the robot-based work metric utilizes robot-integrated force sensors, while the EMG-based work metric requires electrophysiological sensors. We then apply the two metrics to therapy performed using a clinically certified, commercially available robotic system and compare them against the specific instructions given to the healthy subjects as well as against each other. Both metric outputs qualitatively match the expected behavior of the healthy subjects. Additionally, strong correlations (median $R^{2}\gt 0.80$ ) are shown between the two metrics, not only for healthy subjects (n = 12) but also for patients (n = 2), providing solid evidence for their validity and translatability. Importantly, the robot-based work metric does not rely on any sensors outside of those integrated into the robot, thus making it ideal for application in clinical settings.

This study investigates the functional significance of assorted variants of uncertain significance (VUS) in euchromatic histone lysine methyltransferase 1 (EHMT1), which is critical for early development and normal physiology. EHMT1 mutations cause Kleefstra syndrome and are linked to various human cancers. However, accurate functional interpretations of these variants are yet to be made, limiting diagnoses and future research. To overcome this, we integrate conventional tools for variant calling with computational biophysics and biochemistry to conduct multi-layered mechanistic analyses of the SET catalytic domain of EHMT1, which is critical for this protein function. We use molecular mechanics and molecular dynamics (MD)-based metrics to analyze the SET domain structure and functional motions resulting from 97 Kleefstra syndrome missense variants within the domain. Our approach allows us to classify the variants in a mechanistic manner into SV (Structural Variant), DV (Dynamic Variant), SDV (Structural and Dynamic Variant), and VUS (Variant of Uncertain Significance). Our findings reveal that the damaging variants are mostly mapped around the active site, substrate binding site, and pre-SET regions. Overall, we report an improvement for this method over conventional tools for variant interpretation and simultaneously provide a molecular mechanism for variant dysfunction.

Abstract Ocular neovascular diseases including neovascular age-related macular degeneration (nvAMD) are widespread causes of blindness. Patients’ non-responsiveness to currently used biologics that target vascular endothelial growth factor (VEGF) poses an unmet need for novel therapies. Here, we identify protein arginine methyltransferase 5 (PRMT5) as a novel therapeutic target for nvAMD. PRMT5 is a well-known epigenetic enzyme. We previously showed that PRMT5 methylates and activates a proangiogenic and proinflammatory transcription factor, the nuclear factor kappa B (NF-κB), which has a master role in tumor progression, notably in pancreatic ductal adenocarcinoma and colorectal cancer. We identified a potent and specific small molecule inhibitor of PRMT5, PR5-LL-CM01, that dampens the methylation and activation of NF-κB. Here for the first time, we assessed the antiangiogenic activity of PR5-LL-CM01 in ocular cells. Immunostaining of human nvAMD sections revealed that PRMT5 is highly expressed in the retinal pigment epithelium (RPE)/choroid where neovascularization occurs, while mouse eyes with laser induced choroidal neovascularization (L-CNV) showed PRMT5 is overexpressed in the retinal ganglion cell layer and in the RPE/choroid. Importantly, inhibition of PRMT5 by PR5-LL-CM01 or shRNA knockdown of PRMT5 in human retinal endothelial cells (HRECs) and induced pluripotent stem cell (iPSC)-derived choroidal endothelial cells (iCEC2) reduced NF-κB activity and the expression of its target genes, such as tumor necrosis factor α (TNF-α) and VEGF-A. In addition to inhibiting angiogenic properties of proliferation and tube formation, PR5-LL-CM01 blocked cell cycle progression at G1/S-phase in a dose-dependent manner in these cells. Thus, we provide the first evidence that inhibition of PRMT5 impedes angiogenesis in ocular endothelial cells, suggesting PRMT5 as a potential therapeutic target to ameliorate ocular neovascularization.

To celebrate the 10th anniversary of the journal Inorganics, the “Inorganic Materials” section launched this Special Issue entitled “10th Anniversary of Inorganics: Inorganic Materials”, which collected 25 interesting papers (i [...]

About 40 trans-Neptunian binaries (TNBs) have fully determined orbits with about 10 others being solved except for breaking the mirror ambiguity. Despite decades of study, almost all TNBs have only ever been analyzed with a model that assumes perfect Keplerian motion (e.g., two point masses). In reality, all TNB systems are non-Keplerian due to nonspherical shapes, possible presence of undetected system components, and/or solar perturbations. In this work, we focus on identifying candidates for detectable non-Keplerian motion based on sample of 45 well-characterized binaries. We use MultiMoon , a non-Keplerian Bayesian inference tool, to analyze published relative astrometry allowing for nonspherical shapes of each TNB system’s primary. We first reproduce the results of previous Keplerian fitting efforts with MultiMoon , which serves as a comparison for the non-Keplerian fits and confirms that these fits are not biased by the assumption of a Keplerian orbit. We unambiguously detect non-Keplerian motion in eight TNB systems across a range of primary radii, mutual orbit separations, and system masses. As a proof of concept for non-Keplerian fitting, we perform detailed fits for (66652) Borasisi-Pabu, possibly revealing a J _2 ≈ 0.44, implying Borasisi (and/or Pabu) may be a contact binary or an unresolved compact binary. However, full confirmation of this result will require new observations. This work begins the next generation of TNB analyses that go beyond the point mass assumption to provide unique and valuable information on the physical properties of TNBs with implications for their formation and evolution.

Variable rate irrigation (VRI) is used to save water whilst maintaining crop yields in semiarid regions. A key problem is to be able to inexpensively determine spatial patterns in soil moisture so that VRI zones can be defined. In Southern Idaho, USA, the annual precipitation is low and most fall as winter snow. This research investigates whether snow melt patterns measured using freely available time-series Sentinel 2 imagery from Google Earth Engine can define useful VRI zones for two arable fields (Grace and Rexburg). The normalized difference snow index (NDSI) was computed for each 10 m pixel with snow for all winter images of the fields for 2018–2022. NDSI values were ranked within each image and average ranks were calculated for each month and over several years. The patterns of March NDSI were most similar to patterns in yield and soil moisture observed in previous years. Zones were determined using K-means classification of the mean ranks of March NDSI. Kruskal Wallis H tests showed consistent and significant differences between zones for key soil, plant, and topographic variables. For the Grace site, differences between zones were more consistent in their order of magnitude than VRI zones which were calculated using a labor-intensive method. For the Rexburg site, zones were shown to be better when based on snowmelt data from March 2018 to 2022 rather than just March 2019. It is important to base zones on several years of data because in some years there was no snow observed in the Grace field in March. In locations where the majority of soil moisture comes from snowmelt, basing VRI zones on several years of snowmelt patterns in March is a useful and inexpensive tool for deriving meaningful VRI zones. The code used to automatically extract suitable sentinel images and calculate the NDSI is included so that practitioners can use this approach in other locations.

ABSTRACT Microbial polyketide synthase (PKS) genes encode the biosynthesis of many biomedically or otherwise commercially important natural products. Despite extensive discovery efforts, metagenomic analyses suggest that only a small fraction of nature’s polyketide biosynthetic potential has been realized. Much of this potential originates from type I PKSs (T1PKSs), which can be further delineated based on their domain organization and the structural features of the compounds they encode. Notably, phylogenetic relationships among ketosynthase (KS) domains provide an effective method to classify the larger and more complex T1PKS genes in which they occur. Increased access to large metagenomic data sets from diverse habitats provides opportunities to assess T1PKS biosynthetic diversity and distributions through their smaller and more tractable KS domain sequences. Here, we used the web tool NaPDoS2 to detect and classify over 35,000 type I KS domains from 137 metagenomic data sets reported from eight diverse, globally distributed biomes. We found biome-specific separation with soils enriched in KSs from modular cis-acetyltransferase (AT) and hybrid cis-AT KSs relative to other biomes and marine sediments enriched in KSs associated with polyunsaturated fatty acid and enediyne biosynthesis. We linked the phylum Actinobacteria to soil-derived enediyne and cis-AT KSs while marine-derived KSs associated with enediyne and monomodular PKSs were linked to phyla from which the compounds produced by these biosynthetic enzymes have not been reported. These KSs were phylogenetically distinct from those associated with experimentally characterized PKSs suggesting they may be associated with novel structures or enzyme functions. Finally, we employed our metagenome-extracted KS domains to evaluate the PCR primers commonly used to amplify type I KSs and identified modifications that could increase the KS sequence diversity recovered from amplicon libraries. IMPORTANCE Polyketides are a crucial source of medicines, agrichemicals, and other commercial products. Advances in our understanding of polyketide biosynthesis, coupled with the increased availability of metagenomic sequence data, provide new opportunities to assess polyketide biosynthetic potential across biomes. Here, we used the web tool NaPDoS2 to assess type I polyketide synthase (PKS) diversity and distributions by detecting and classifying ketosynthase (KS) domains across 137 metagenomes. We show that biomes are differentially enriched in type I KS domains, providing a roadmap for future biodiscovery strategies. Furthermore, KS phylogenies reveal biome-specific clades that do not include biochemically characterized PKSs, highlighting the biosynthetic potential of poorly explored environments. The large metagenome-derived KS data set allowed us to identify regions of commonly used type I KS PCR primers that could be modified to capture a larger extent of environmental KS diversity. These results facilitate both the search for novel polyketides and our understanding of the biogeographical distribution of PKSs across Earth’s major biomes.

Hydrogen is heralded as a future global energy carrier [...]

Psoriasis is characterized by intense pruritus, with a subset of individuals with psoriasis experiencing thermal hypersensitivity. However, the pathophysiology of thermal hypersensitivity in psoriasis and other skin conditions remains enigmatic. Linoleic acid is an omega-6 fatty acid that is concentrated in the skin, and oxidation of linoleic acid into metabolites with multiple hydroxyl and epoxide functional groups has been shown to play a role in skin barrier function. Previously, we identified several linoleic acid‒derived mediators that were more concentrated in psoriatic lesions, but the role of these lipids in psoriasis remains unknown. In this study, we report that two such compounds—9,10-epoxy-13-hydroxy-octadecenoate and 9,10,13-trihydroxy-octadecenoate—are present as free fatty acids and induce nociceptive behavior in mice but not in rats. By chemically stabilizing 9,10-epoxy-13-hydroxy-octadecenoate and 9,10,13-trihydroxy-octadecenoate through the addition of methyl groups, we observed pain and hypersensitization in mice. The nociceptive responses suggest an involvement of the TRPA1 channel, whereas hypersensitive responses induced by these mediators may require both TRPA1 and TRPV1 channels. Furthermore, we showed that 9,10,13-trihydroxy-octadecenoate‒induced calcium transients in sensory neurons are mediated through the Gβγ subunit of an unidentified G-protein coupled receptor (GPCR). Overall, mechanistic insights from this study will guide the development of potential therapeutic targets for the treatment of pain and hypersensitivity.

Summary: Lyme neuroborreliosis (LNB), a tick-borne infection caused by spirochetes within the Borrelia burgdorferi sensu lato (s.L.) complex, is among the most prevalent bacterial central nervous system (CNS) infections in Europe and the US. Here we have screened a panel of low-passage B. burgdorferi s.l. isolates using a novel, human-derived 3D blood-brain barrier (BBB)-organoid model. We show that human-derived BBB-organoids support the entry of Borrelia spirochetes, leading to swelling of the organoids and a loss of their structural integrity. The use of the BBB-organoid model highlights the organotropism between B. burgdorferi s.l. genospecies and their ability to cross the BBB contributing to CNS infection.

Breastmilk is the optimal source of infant nutrition, with short-term and long-term health benefits. Some of these benefits are mediated by human milk oligosaccharides (HMOs), a unique group of carbohydrates representing the third most abundant solid component of human milk. We performed the first clinical study on infant formula supplemented with five different HMOs (5HMO-mix), comprising 2′-fucosyllactose, 3-fucosyllactose, lacto-N-tetraose, 3′-sialyllactose and 6′-sialyllactose at a natural total concentration of 5.75 g/L, and here report the analysis of the infant fecal microbiome. We found an increase in the relative abundance of bifidobacteria in the 5HMO-mix cohort compared with the formula-fed control, specifically affecting bifidobacteria that can produce aromatic lactic acids. 5HMO-mix influenced the microbial composition as early as Week 1, and the observed changes persisted to at least Week 16, including a relative decrease in species with opportunistic pathogenic strains down to the level observed in breastfed infants during the first 4 weeks. We further analyzed the functional potential of the microbiome and observed features shared between 5HMO-mix-supplemented and breastfed infants, such as a relative enrichment in mucus and tyrosine degradation, with the latter possibly being linked to the aromatic lactic acids. The 5HMO-mix supplement, therefore, shifts the infant fecal microbiome closer to that of breastfed infants.

Applying machine learning methods to geographic data provides insights into spatial patterns in the data as well as assists in interpreting and describing environments. This paper investigates the results of k-means clustering applied to 51 geospatial layers, selected and scaled for a model of outdoor acoustic environments, in the continental United States. Silhouette and elbow analyses were performed to identify an appropriate number of clusters (eight). Cluster maps are shown and the clusters are described, using correlations between the geospatial layers and clusters to identify distinguishing characteristics for each cluster. A subclustering analysis is presented in which each of the original eight clusters is further divided into two clusters. Because the clustering analysis used geospatial layers relevant to modeling outdoor acoustics, the geospatially distinct environments corresponding to the clusters may aid in characterizing acoustically distinct environments. Therefore, the clustering analysis can guide data collection for the problem of modeling outdoor acoustic environments by identifying poorly sampled regions of the feature space (i.e., clusters which are not well-represented in the training data).

Burn severity is commonly assessed using Burn Ratios and field measurements to provide land managers with estimates of the degree of burning in an area. However, less commonly studied is the ability of spectral indices and Burn Ratios to estimate field-measured fire effects. Past research has shown low correlations between fire effects and Landsat-derived Burn Ratios, but with the launch of the Sentinel-2 constellation, more spectral bands with finer spatial resolutions have become available. This paper explores the use of several red-edge-based indices and Burn Ratios alongside more ‘traditional’ spectral indices for predicting fire effects, measured from the Maple and Berry fires in Wyoming, USA. The fire effects include ash depth, char depth, post-fire dead lodgepole pine (Pinus contorta; PICO) density/stumps, mean basal diameter, cone density on dead post-fire trees, coarse wood percent cover/volume/mass, percent cover of ghost logs and initial regeneration of post-fire PICO/aspen density. All-possible-models regression was used to determine the best models for estimating each fire effect. Models with satisfactory R2 values were constructed for post-fire dead PICO stumps (0.663), coarse wood percent cover (0.691), coarse wood volume (0.833), coarse wood mass (0.838), ash depth (0.636) and percent cover of ghost logs (0.717). Red-edge-based indices were included in all of the satisfactory models, which shows that the red-edge bands may be useful for measuring fire effects.

Non-coding RNA (ncRNA) classes take over important housekeeping and regulatory functions and are quite heterogeneous in terms of length, sequence conservation and secondary structure. High-throughput sequencing reveals that the expressed novel ncRNAs and their classification are important to understand cell regulation and identify potential diagnostic and therapeutic biomarkers. To improve the classification of ncRNAs, we investigated different approaches of utilizing primary sequences and secondary structures as well as the late integration of both using machine learning models, including different neural network architectures. As input, we used the newest version of RNAcentral, focusing on six ncRNA classes, including lncRNA, rRNA, tRNA, miRNA, snRNA and snoRNA. The late integration of graph-encoded structural features and primary sequences in our MncR classifier achieved an overall accuracy of >97%, which could not be increased by more fine-grained subclassification. In comparison to the actual best-performing tool ncRDense, we had a minimal increase of 0.5% in all four overlapping ncRNA classes on a similar test set of sequences. In summary, MncR is not only more accurate than current ncRNA prediction tools but also allows the prediction of long ncRNA classes (lncRNAs, certain rRNAs) up to 12.000 nts and is trained on a more diverse ncRNA dataset retrieved from RNAcentral.

Irrigated turfgrass is a major crop in urban areas of the drought-stricken Western United States. A considerable proportion of irrigation water is wasted through the use of conventional sprinkler systems. While smart sprinkler systems have made progress in reducing temporal mis-applications, more research is needed to determine the most appropriate variables for accurately and cost-effectively determining spatial zones for irrigation application. This research uses data from ground and drone surveys of two large sports fields. Surveys were conducted pre-, within and towards the end of the irrigation season to determine spatial irrigation zones. Principal components analysis and k-means classification were used to develop zones using several variables individually and combined. The errors associated with uniform irrigation and different configurations of spatial zones are assessed to determine comparative improvements in irrigation efficiency afforded by spatial irrigation zones. A determination is also made as to whether the spatial zones can be temporally static or need to be re-determined periodically. Results suggest that zones based on spatial soil moisture surveys and simple observations of whether the grass felt wet or dry are better than those based on NDVI, other variables and several variables in combination. In addition, due to the temporal variations observed in spatial patterns, ideally zones should be re-evaluated periodically. However, a less labor-intensive solution is to determine temporally static zones based on patterns in soil moisture averaged from several surveys. Of particular importance are the spatial patterns observed prior to the start of the irrigation season as they reflect more temporally stable variation that relates to soil texture and topography rather than irrigation management.

This study examines the use of leaf area index (LAI) to inform variable-rate irrigation (VRI) for irrigated alfalfa (Medicago sativa). LAI is useful for predicting zone-specific evapotranspiration (ETc). One approach toward estimating LAI is to utilize the relationship between LAI and visible vegetation indices (VVIs) using unmanned aerial vehicle (UAV) imagery. This research has three objectives: (1) to measure and describe the within-field variation in LAI and canopy height for an irrigated alfalfa field, (2) to evaluate the relationships between the alfalfa LAI and various VVIs with and without field average canopy height, and (3) to use UAV images and field average canopy height to describe the within-field variation in LAI and the potential application to VRI. The study was conducted in 2021–2022 in Rexburg, Idaho. Over the course of the study, the measured LAI varied from 0.23 m2 m−2 to 11.28 m2 m−2 and canopy height varied from 6 cm to 65 cm. There was strong spatial clustering in the measured LAI but the spatial patterns were dynamic between dates. Among eleven VVIs evaluated, the four that combined green and red wavelengths but excluded blue wavelengths showed the most promise. For all VVIs, adding average canopy height to multiple linear regression improved LAI prediction. The regression model using the modified green–red vegetation index (MGRVI) and canopy height (R2 = 0.93) was applied to describe the spatial variation in the LAI among VRI zones. There were significant (p < 0.05) but not practical differences (

Understanding spatial and temporal dynamics of soil water within fields is critical for effective variable rate irrigation (VRI) management. The objectives of this study were to develop VRI zones, manage irrigation rates within VRI zones, and examine temporal differences in soil volumetric water content (VWC) from irrigation events via soil sensors across zones. Five irrigation zones were delineated after two years (2016 and 2017) of yield and evapotranspiration (ET) data collection. Soil sensors were placed within each zone to give real time data of VWC values and assist in irrigation decisions within a 23 ha field of winter wheat (Triticum aestivum ‘UI Magic’) near Grace, Idaho, USA (2019). Cumulative irrigation rates among zones ranged from 236 to 298 mm. Although a statistical comparison could not be made, the irrigation rates were 0.6 to 21% less than an estimated uniform grower standard practice (GSP) irrigation approach. Based on soil sensor data, crop water stress was avoided with VRI management in all but Zone 3. Thus, this simple approach to VRI zone delineation and VWC monitoring has the potential to reduce irrigation, such as this study, on average by 12% and should be evaluated in other site-years to assess its viability.