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Abstract Introduction In contrast with patients receiving therapy for retinal disease during clinical trials, those treated in routine clinical practice experience various challenges (including administrative, clinic, social, and patient-related factors) that can often result in high patient and clinic burden, and contribute to suboptimal visual outcomes. The objective of this study was to understand the challenges associated with clinical management of diabetic macular edema from the perspectives of patients, healthcare providers, and clinic staff, and identify opportunities to improve eye care for people with diabetes. Methods We conducted a survey of patients with diabetic macular edema, providers, and clinic staff in 78 clinics across 24 countries on six continents, representing a diverse range of individuals, healthcare systems, settings, and reimbursement models. Surveys comprised a series of single- and multiple-response questions completed anonymously. Data gathered included patient personal characteristics, challenges with appointment attendance, treatment experiences, and opportunities to improve support. Provider and clinic staff surveys asked similar questions about their perspectives; and clinic characteristics were also captured. Results Overall, 5681 surveys were gathered: 3752 from patients with diabetic macular edema, 680 from providers, and 1249 from clinic staff. Too many appointments, too short treatment intervals, difficulties in traveling to the clinic or arranging adequate support to travel, out-of-pocket costs, office/parking fees, and long waiting times were noted by all as contributing to increase the burden on the patient and caregiver. Patients generally desired more in-depth discussions with their provider, which would help with information exchange and better expectation-setting. Conclusions The wealth of systematic data generated by this global survey highlights the breadth and scale of challenges associated with the clinical management of patients with diabetic macular edema. Addressing the opportunities for improvement raised by patients, providers, and clinic staff could increase patient adherence to treatment, reduce appointment burden, and improve clinic capacity.

BackgroundWhole Eye Transplantation (WET) offers potential for vision restoration but is hindered by the complex challenge of immune rejection. Understanding and closely monitoring these immunological responses is crucial for advancing WET. This study delves into the timeline and nature of immune responses in a rodent model of WET without immunosuppression, aiming to elucidate a detailed picture of the immune landscape post-transplantation and establish innovative diagnostic and monitoring methods.MethodsWe employed a multi-faceted approach to analyze immune responses post-WET, including assessments of gross changes in corneal transparency, thickness, and skin condition. Histopathological examinations of both ocular and surrounding skin tissues provided insights into cellular changes, complemented by ocular RT-qPCR for molecular analysis. Serological analysis was employed to quantify cytokines, chemokines, and donor-specific antibodies, aiming to identify potential biomarkers correlating with WET rejection and to validate the presence of antibody-mediated rejection. These methodologies collectively contribute to the development of non-invasive diagnostic and monitoring strategies for WET.ResultsOur study revealed a rapid and acute immune response following WET, characterized by an early innate immune response dominated by complement involvement, and infiltration of neutrophils and monocytes by post-operative day (POD) 2. This was succeeded by an acute T-cell-mediated immune reaction, predominantly involving T helper 1 (Th1) cells and cytotoxic T lymphocytes (CTLs). The presence of donor specific antibody (DSA) and indications of pyroptosis in the early phases of rejection were observed. Notably, the early elevation of serum CXCL10 by POD4, coupled with ocular CD3+ cell infiltration, emerged as a potential early biomarker for WET rejection. Additionally, corneal transparency grading proved effective as a non-invasive monitoring tool.ConclusionThis study offers a first-time comprehensive exploration of immune responses in WET, unveiling rapid and complex rejection mechanisms. The identification of early biomarkers and the development of non-invasive monitoring techniques significantly advance our understanding of WET rejection. Additionally, these findings establish an essential baseline for future research in this evolving field.

Study region: Mulberry River, Arkansas, USA Study focus: Low alkalinity streams within siliciclastic dominated catchments like the Mulberry River in the south-central United States experience episodic acidification. Since 2009, reaches of the Mulberry River have been classified as impaired due to low pH. We hypothesize that if pH time series patterns are similar among tributaries, external watershed factors like seasonal weather may be related to low stream pH. We investigated pH variability in the Mulberry River watershed including changepoint detection analysis. Patterns of pH and changepoints were compared with streamflow variability and drought conditions using Palmer Drought Severity Index (PDSI). New hydrological insights for the region: Changes in pH were near synchronous and similar magnitude across the study area, despite observed increases in median tributary pH from east to west. From the comparison of pH time series and PDSI, low pH values tended to occur during more negative PDSI, i.e. drier periods. The specific mechanisms driving low pH variability remain elusive, but we infer low stream pH is influenced, in part, by seasonal weather variability leading to drier watershed conditions and lower pH. The spatial variability of median pH values from east to west, though, are more related to spatial variability in bedrock lithology. We hypothesize other acid-sensitive streams in the region likely share similar pH behavior and with low pH occurring during drier periods.

Abstract The harmful effects of mining waste on human and ecosystem health make reclamation of former mine sites an environmental management priority. However, field-based monitoring of reclamation requires significant investments of time, labor, and money. Remote sensing offers a less expensive alternative to field-based monitoring, but work is still needed to determine which metrics can be reliably estimated using remote sensing techniques. This study uses remote sensing to examine over 20 years of reclamation efforts at Tar Creek Superfund Site and to assess revegetation after site restoration. Using the Normalized Difference Vegetation Index (NDVI), we quantify key factors affecting vegetation recovery and stability after reclamation across 123 surface mining waste cleanup locations within Tar Creek. Leveraging long-term imagery from Landsat and high-resolution PlanetScope imagery, we combine time series analysis of vegetation regrowth and landcover change detection for a comprehensive picture of recovery at each site. Across all reclamation sites, the average recovery duration was 3.5 years, and average recovery rate was 0.1 NDVI year−1. After vegetative growth had plateaued, reclaimed sites had an average NDVI of 0.70. All reclamation areas were converted from majority barren landcover to vegetated landcover classes after reclamation, and vegetative stability at reclamation sites was high (70% of reclaimed area saw continuous vegetative cover from 2017 to 2023). These results demonstrate a strong potential for multi-method remote sensing techniques in tracking and explaining vegetation recovery after reclamation and represent a cost-effective approach for real-time monitoring of reclamation progress and outcomes.

Exponential population increases are threatening food security, particularly in mountainous areas. One potential solution is dual-use intercropped agroforestry systems such as olive (Olea europaea)–maize (Zea mays), which may mitigate risk by providing multiple market sources (oil and grain) for smallholder producers. Several studies have conducted integrated agroforestry land suitability analyses; however, few studies have used machine learning (ML) algorithms to evaluate multiple variables (i.e., soil physicochemical properties and climatic and topographic data) for the selection of suitable rainfed sites in mountainous terrain systems. The goal of this study is therefore to identify suitable land classes for an integrated olive–maize agroforestry system based on the Food and Agriculture Organization (FAO) land suitability assessment framework for 1757 km2 in Khyber Pakhtunkhwa province, Pakistan. Information on soil physical and chemical properties was obtained from 701 soil samples, along with climatic and topographic data. After determination of land suitability classes for an integrated olive–maize-crop agroforestry system, the region was then mapped through ML algorithms using random forest (RF) and support vector machine (SVM), as well as using traditional techniques of weighted overlay (WOL). Land suitability classes predicted by ML techniques varied greatly. For example, the S1 area (highly suitable) classified through RF was 9%↑ than that of SVM, and 8%↓ than that through WOL. The area of S2 (moderately suitable) classified through RF was 18%↑ than that of SWM and was 17%↓ than the area classified through WOL; similarly, the S3 (marginally suitable) class area via RF was 27%↓ than that of SVM, and 45%↓ than the area classified through WOL. Conversely, the area of N2 (permanently not suitable class) classified through RF and SVM was 6%↑ than the area classified through WOL. Model performance was assessed through overall accuracy and Kappa Index and indicated that RF performed better than SVM and WOL. Crop suitability limitations of the study area included high elevation, slope, pH, and large gravel content. Results can be used for sustainable intensification in mountainous rainfed regions by expanding intercrop agroforestry systems in developing nations to close yield gaps.

Digital soil mapping (DSM) relies on machine-learning and geostatistics to represent soil property observations across space. DSM techniques are powerful but often empirical, being limited to the quality and density of point samples. Water dynamics are closely related to soil variability, and the physics that govern water movement are well known. Hydrological properties can hence be simulated by physical models through space and time, unveiling key characteristics about soils. We propose the use of hydrologic models to map soils across the surface (2D), depth (1D), and time (1D)–which provides a 4D approach to digital soil mapping (4DSM). The Distributed Hydrology Soil Vegetation Model (DHSVM) was applied to a watershed currently under pasture. Moisture sensors and wells were installed at different depths in the watershed on summit, sideslope and toeslope positions to validate the model. DHSVM simulations of soil moisture distribution and depth to saturation were performed during the hydrological year (October 2008-September 2009). Clusters of similar pixels based on soil moisture values were determined using Dynamic Time Warping (DTW) to align temporal data and K-means. Clustering was performed both seasonally and for the entire year. Temporal patterns simulated by DHSVM matched measurements given by moisture sensors and wells. Seasonal clusters differed from the annual cluster. Distinct clusters were observed for each season and with depth, showing that spatiotemporal soil variability is lost when statically assessing soils. Spatiotemporal clusters corroborated field observations of fragipan occurrence not explicitly spatially mapped by Soil Survey Geographic Database (SSURGO). If a connection can be made between water and soils, static and dynamic soil variability can be predicted using physically based hydrologic models. Hydrologic models can benefit soil mapping by enabling reliable 4D simulation of water dynamics, which are fundamental to soil variability and soil classification and directly relate to biological, physical and chemical soil processes not captured by typical soil sampling protocols.

Alluvial plains are vexing landscapes for soil mapping and spatial soil property predictions. Alluvial sediments often exhibit unpredictable spatial variability from both fluvial and anthropogenic disturbance. The determination of optimal number of soil sampling points for capturing soil variability remains a persistent issue for mapping and monitoring soil conditions. Here, soil organic matter (SOM) and cation exchange capacity (CEC) were estimated in an alluvial plain from a dense array of 2145 soil samples over 250 ha. The primary goals were to i) use pixel-based statistics from time-series Sentinel-2 satellite reflectance data to estimate SOM and CEC; ii) evaluate the use of images with vegetation cover versus bare soil images; and, iii) investigate the optimal number of sampling points to map alluvial soils using different sampling strategies. The optimal sample density was 1 sample per 2.5 ha based on the overlapping of prediction distributions (OV > 0.9). Conditioned Latin Hypercube Sampling (cLHS) was the most efficient sampling strategy. Random grid sampling provided the least consistent results. The use of cLHS coupled with the mean and standard deviation bands was the optimal sampling strategy. Pixel-based statistics from readily available satellite imagery captured persistent soil-reflectance relationships that enabled the use of row crops as proxies to predict soil properties. The combined use of pixel-based statistics as inputs to cLHS can reduce the chances of oversampling and associated costs. Pixel-based statistics provided persistent spatial information that can enhance precision agriculture management, carbon mapping, and is likely applicable to other soil properties and remotely sensed products. Use of pixel-based statistics from Sentinel-2 and similar global-coverage imagery could provide readily-calculable inputs for improved soil mapping and more optimal site sample selections in other agricultural row crop areas, globally.

Soil nutrient content varies spatially across agricultural fields in hard-to-predict ways, particularly in floodplains with complex fluvial depositional history. Satellite reflectance data from the Sentinel-2 (S2) mission provides spatially continuous land reflectance data that can aid model development when used with point observations of nutrients. Reflectance from vegetation is assumed to obstruct land reflectance of bare soil, such that researchers have masked vegetation in models. We developed a routine for masking vegetation within Google Earth Engine (GEE) using Random Forest classification for iterative application to libraries of S2-images. Using gradient boosting, we then developed soil nutrient models for surface soils at a 250-ha agricultural site using S2 images. Soils were sampled at 2145 point locations to a 23-cm depth and analyzed for Ca, K, Mg, P, pH, S, and Zn. Results showed that masking vegetation improved model performance for models from subsets of the data (80% of samples used for model development, 20% validation), but full data sets did not require masking to achieve accuracy. Models of Ca, K, Mg, and S were successful (validation R2 > 0.60 to 0.96), but models for pH, P, and Zn failed. Bare soil composite images from S2 data are helpful in predicting soil fertility in low-relief floodplains.

Abstract Soil respiration is one of the main soil health indicators and is influenced by several factors in agricultural fields. Identifying key factors that control soil respiration is desirable for informed soil management decisions and for promoting and scaling up soil health. This study aimed to (i) quantify the relationships between potential soil respiration and selected soil properties, crops, and slope positions, and (ii) identify key factors controlling these relationships using a neural network model. Ninety soil samples from 0‐ to 5‐ and 5‐ to 20‐cm soil depth were collected from footslope, backslope, and summit in three fields planted with soybean (Glycine max L. Merr.), alfalfa (Medicago sativa L.), and corn (Zea mays L.). The model provided great accuracy (coefficient of determination: 0.96; root‐mean square error: 7.8; and mean absolute deviation: 3.8) and explained nearly 96% of variations in soil respiration across soil depth, crop, and slope positions. Soil depth, ammoniacal nitrogen (NH4‐N), crop types, slope position, and silt content were identified as the top five factors influencing potential soil respiration at the field level. Potential soil respiration was more sensitive to potassium, phosphorus, pH, cation exchange capacity, and mean weight diameter and less sensitive to NH4‐N, nitrate nitrogen, soil organic matter, and clay content. It increased with pH, electrical conductivity, mean weight diameter, potential nitrogen mineralization, and potassium, and it decreased with increasing silt content. Soil from 0 to 5 cm under soybean or at the summit slope position exhibited a higher respiration. Using a small dataset, this pilot study accurately predicted potential soil respiration in agricultural fields and identified key drivers controlling it. The results from this study highlight the complexity of using potential soil respiration as a standalone test for evaluating soil health. This does not diminish the usefulness of potential soil respiration as a soil health indicator to support agricultural management decisions and as a reference in future soil health studies. However, it emphasizes the importance of considering multiple factors when interpreting the significance of soil biological indicators for soil health assessments.

Abstract Minimal research has been conducted relating apparent electrical conductivity (ECa) surveys to plant and terrain properties in agroforestry systems. Objectives were to identify: (i) ECa–forage yield, –tree growth, and –terrain attribute relationships within ECa‐derived soil management zones (SMZs) and (ii) terrain attributes that drive ECa variability within a 20‐year‐old, 4.25‐ha, agroforestry system in the Ozark Highlands of northwest Arkansas. The average of 12 monthly perpendicular (PRP) and horizontal coplanar (HCP) ECa surveys (August 2020 to July 2021) and 14 terrain attributes were obtained. Tree diameter at breast height (DBH) and height (TH) measurements were made in December 2020 and March 2021, respectively, and forage yield samples were collected during Summer 2018 and 2019. Apparent EC‐tree property relationships were generally stronger within the whole site (averaged across tree property and ECa configuration, |r| = 0.38) than within the SMZs (averaged across tree property, ECa configuration, and SMZ, |r| = 0.27). The strength of the SMZs’ terrain‐attribute‐PRP‐ECa relationships were 9% to 205% greater than that for the whole site. In whole‐site, multi‐linear regressions, slope length and steepness factor (10.5%), mid‐slope (9.4%), and valley depth (7.2%) had the greatest influence (i.e., percent of total sum of squares) on PRP ECa variability, whereas valley depth (15.3%), wetness index (11.9%), and mid‐slope (11.2%) had the greatest influence on HCP ECa variability. Results show how ECa relates to plant (i.e., DBH, TH, and forage yield) and terrain data within SMZs in agroforestry systems with varying topography and could be used to precisely manage agroforestry systems.

BackgroundResponsive neurostimulation (RNS) is an implantable device for persons with medically refractory focal-onset epilepsy. We report a single-center experience for RNS outcomes with special focus on stereoelectroencephalography (sEEG) for seizure onset localization.MethodsWe performed retrospective review of patients with drug resistant focal epilepsy implanted with the RNS System for a minimum of six months between July 2014 and July 2019. Records were reviewed for demographic data, epilepsy duration, seizure frequency, number of prior antiepileptic drugs (AEDs), number of AEDs at RNS System implantation, prior epilepsy surgery or device use, previous seizure localization with sEEG, and RNS system information. Clinical response was defined as a 50% reduction in seizures. Differing response rates were calculated using Fisher Exact test.Results30 patients met inclusion criteria. Seventeen (57%) underwent previous sEEG. Average clinical follow up was 3.0 years. Overall response rate was 70%. Median seizure reduction was 74.5%. Response rate was 82.3% for patients with sEEG compared to 53.8% without (p = 0.08); 37.5% for prior epilepsy surgery compared to 81.8% without (p = 0.02); 70% for mesial temporal onset; 50% for previous vagal nerve stimulator compared to 77.3% without (p = 0.13).ConclusionOur response rates match or surpass outcome metrics of previous studies. Although limited by small study size, subpopulation analyses show positive response rates in patients with previous sEEG versus no sEEG and in temporal versus extratemporal pathology. Additional research is needed to evaluate efficacy of RNS in patients with previous epilepsy surgery, and utility of sEEG in this population.

Animal manure improves soil fertility and organic carbon, but long-term deposition may contribute to antibiotic resistance genes (ARGs) entering the soil-water environment. Additionally, long-term impacts of applying animal manure to soil on the soil-water microbiome, a crucial factor in soil health and fertility, are not well understood. The aim of this study is to assess: (1) impacts of long-term conservation practices on the distribution of ARGs and microbial dynamics in soil, and runoff; and (2) associations between bacterial taxa, heavy metals, soil health indicators, and ARGs in manures, soils, and surface runoff in a study following 15 years of continuous management. This management strategy consists of two conventional and three conservation systems, all receiving annual poultry litter. High throughput sequencing of the 16S ribosomal RNA was carried out on samples of cattle manure, poultry litter, soil, and runoff collected from each manureshed. In addition, four representative ARGs (intl1, sul1, ermB, and blactx-m-32) were quantified from manures, soil, and runoff using quantitative PCR. Results revealed that conventional practice increased soil ARGs, and microbial diversity compared to conservation systems. Further, ARGs were strongly correlated with each other in cattle manure and soil, but not in runoff. After 15-years of conservation practices, relationships existed between heavy metals and ARGs. In the soil, Cu, Fe and Mn were positively linked to intl1, sul1, and ermB, but trends varied in runoff. These findings were further supported by network analyses that indicated complex co-occurrence patterns between bacteria taxa, ARGs, and physicochemical parameters. Overall, this study provides system-level linkages of microbial communities, ARGs, and physicochemical conditions based on long-term conservation practices at the soil-water-animal nexus.

Long-term continuous deposits and well-preserved thick loess sections are natural archives for the reconstruction of global climate change. However, the loess sections are often discontinuous owing to soil erosions and accumulations. This study aimed at reconstructing long-term continuous paleoclimatic records by integrating the target sections of two typical loess sequences in northeast (NE) China. One section, the Dajiugang (DJG) section, was investigated and sampled, and the Sanbahuo (SBH) section was used as the target for integrating and reconstructing the paleoclimate. Sedimentary, pedogenesis, and luminescence dating analyses were conducted to analyze the potentials for reconstructing the paleoclimate by integrating two typical loess sections. The results indicated that the paleoclimate records derived from the DJG section can be used to reconstruct the continuous paleoclimate of NE China by integrating them with the SBH section. The paleoclimate information inferred from the DJG section could impute paleoclimate data from 71 to 11.7 ka, which were missing in the SBH section. The paleoclimate of NE China generally consisted of three periods from 71 to 11.7 ka: cold and dry from 29 to 11.7 ka, alternately cold and warm from 57 to 29 ka, and warm and wet to cold and dry from 71 to 57 ka. Integrating the target loess sections can contribute to reconstructing regional long-term continuous paleoclimate records where discontinuous loess sequences occurred.

Abstract Systems-level studies aimed at determining how soil properties are linked to plant production and ultimately animal response spatially are lacking. This study aims to identify if grazing pressure is linked to soil properties, terrain attributes, and above-ground plant accumulation and nutritive value in a silvopastoral (or integrated tree-livestock) system. Overall, cattle prefer grazing native grasses (2.81 vs. 1.24 h ha−1 AU−1) and udic (dry) landscape positions compared to aquic (wet) areas (2.07 vs. 1.60 h ha−1 AU−1). Greater grazing frequency occurs in udic soils with greater phosphorus and potassium contents and with accumulated forage with less lignin (P ≤ 0.05), which correspond to reduced elevation and greater tree height and diameter (shade) during summer mob grazing. Combining spatial monitoring technologies (both soil and animal) with forage allowance can optimize grazing systems management and sustainability spatially and temporally.

Abstract Background Liver cancer (Hepatocellular carcinoma; HCC) prevalence is increasing and with poor clinical outcome expected it means greater understanding of HCC aetiology is urgently required. This study explored a deep learning solution to detect biologically important features that distinguish prognostic subgroups. A novel architecture of an Artificial Neural Network (ANN) trained with a customised objective function (LRSC) was developed. The ANN should discover new data representations, to detect patient subgroups that are biologically homogenous (clustering loss) and similar in survival (survival loss) while removing noise from the data (reconstruction loss). The model was applied to TCGA-HCC multi-omics data and benchmarked against baseline models that only use a reconstruction objective function (BCE, MSE) for learning. With the baseline models, the new features are then filtered based on survival information and used for clustering patients. Different variants of the customised objective function, incorporating only reconstruction and clustering losses (LRC); and reconstruction and survival losses (LRS) were also evaluated. Robust features consistently detected were compared between models and validated in TCGA and LIRI-JP HCC cohorts. Results The combined loss (LRSC) discovered highly significant prognostic subgroups (P-value = 1.55E−77) with more accurate sample assignment (Silhouette scores: 0.59–0.7) compared to baseline models (0.18–0.3). All LRSC bottleneck features (N = 100) were significant for survival, compared to only 11–21 for baseline models. Prognostic subgroups were not explained by disease grade or risk factors. Instead LRSC identified robust features including 377 mRNAs, many of which were novel (61.27%) compared to those identified by the other losses. Some 75 mRNAs were prognostic in TCGA, while 29 were prognostic in LIRI-JP also. LRSC also identified 15 robust miRNAs including two novel (hsa-let-7g; hsa-mir-550a-1) and 328 methylation features with 71% being prognostic. Gene-enrichment and Functional Annotation Analysis identified seven pathways differentiating prognostic clusters. Conclusions Combining cluster and survival metrics with the reconstruction objective function facilitated superior prognostic subgroup identification. The hybrid model identified more homogeneous clusters that consequently were more biologically meaningful. The novel and prognostic robust features extracted provide additional information to improve our understanding of a complex disease to help reveal its aetiology. Moreover, the gene features identified may have clinical applications as therapeutic targets.

Abstract Knowledge of energy expenditure informs conservation managers for long term plans for endangered species health and habitat suitability. We measured field metabolic rate (FMR) of free-roaming giant pandas in large enclosures in a nature reserve using the doubly labeled water method. Giant pandas in zoo like enclosures had a similar FMR (14,182 kJ/day) to giant pandas in larger field enclosures (13,280 kJ/day). In winter, giant pandas raised their metabolic rates when living at − 2.4 °C (36,108 kJ/day) indicating that they were below their thermal neutral zone. The lower critical temperature for thermoregulation was about 8.0 °C and the upper critical temperature was about 28 °C. Giant panda FMRs were somewhat lower than active metabolic rates of sloth bears, lower than FMRs of grizzly bears and polar bears and 69 and 81% of predicted values based on a regression of FMR versus body mass of mammals. That is probably due to their lower levels of activity since other bears actively forage for food over a larger home range and pandas often sit in a patch of bamboo and eat bamboo for hours at a time. The low metabolic rates of giant pandas in summer, their inability to acquire fat stores to hibernate in winter, and their ability to raise their metabolic rate to thermoregulate in winter are energetic adaptations related to eating a diet composed almost exclusively of bamboo. Differences in FMR of giant pandas between our study and previous studies (one similar and one lower) appear to be due to differences in activity of the giant pandas in those studies.

To achieve reproduction, male solitary mammals need to locate females using chemical communication with high levels of precision. In the case of giant pandas, the total estrus period of females was usually 15 days each year, however, successful mating activity is finished within 3 days from respective home range. The mating pattern of giant pandas, where multiple males compete for each female requires females employ efficient systems to communicate their estrus phases. To verifying whether the scent secretions of giant pandas changes by gender and estrus progression, the microbiota and compounds in 29 anogenital gland samples from 14 individuals during estrus were analyzed by 16S rRNA sequencing and GC-MS. We show that the microbiota communities covary by gender with 4 particular compounds of scent secretions. Among 597 genera, 34 were identified as biomarkers that could be used to distinguish between different estrus phases. By bacterial-compounds co-analysis, 3 fatty ester acids and squalene compounds covaried with the development of estrus in the bacterial communities of female giant pandas. This study helps clarify how a large, solitary mammal expresses accurate information to improve the likelihood of successful reproduction by changing the composition of microbiota and odor compounds of anogenital glands during estrus.

Follicular dendritic cell sarcomas (FDCS) are rare tumors derived from non-migrating antigen-presenting cells. They are distinguished from other histiocytic and dendritic cells by their immunophenotype, which supports a mesenchymal, non-myeloid stem cell origin. A 63-year-old woman reported painless epigastric fullness for 20 years, and was eventually found to have a 16.0 cm mass indistinguishable from the pancreas and surrounding tissue. Initial needle biopsy showed a high-grade epithelioid malignant neoplasm. A Whipple resection was ultimately performed, and evaluation revealed a malignant epithelioid neoplasm with histologic and immunophenotypic features of FDCS. FDCS is a rare tumor that can involve virtually any nodal or extranodal site, and to our knowledge this is the first reported case of FDCS of the pancreas. This case report emphasizes that FDCS should be considered in the differential diagnosis of pancreatic neoplasms with focal cytokeratin positivity, so that appropriate IHC testing with FDC-associated markers can be used to confirm the diagnosis.

Summary: Background: Delayed diagnosis and treatment of sight threatening diabetic retinopathy (STDR) is a common cause of visual impairment in people with Type 2 diabetes. Therefore, systematic regular retinal screening is recommended, but global coverage of such services is challenging. We aimed to develop and validate predictive models for STDR to identify ‘at-risk’ population for retinal screening. Methods: Models were developed using datasets obtained from general practices in inner London, United Kingdom (UK) on adults with type 2 Diabetes during the period 2007–2017. Three models were developed using Cox regression and model performance was assessed using C statistic, calibration slope and observed to expected ratio measures. Models were externally validated in cohorts from Wales, UK and India. Findings: A total of 40,334 people were included in the model development phase of which 1427 (3·54%) people developed STDR. Age, gender, diabetes duration, antidiabetic medication history, glycated haemoglobin (HbA1c), and history of retinopathy were included as predictors in the Model 1, Model 2 excluded retinopathy status, and Model 3 further excluded HbA1c. All three models attained strong discrimination performance in the model development dataset with C statistics ranging from 0·778 to 0·832, and in the external validation datasets (C statistic 0·685 – 0·823) with calibration slopes closer to 1 following re-calibration of the baseline survival. Interpretation: We have developed new risk prediction equations to identify those at risk of STDR in people with type 2 diabetes in any resource-setting so that they can be screened and treated early. Future testing, and piloting is required before implementation. Funding: This study was funded by the GCRF UKRI (MR/P207881/1) and supported by the NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology.

Abstract Prediction models for population-based screening need, for global usage, to be resource-driven, involving predictors that are affordably resourced. Here, we report the development and validation of three resource-driven risk models to identify people with type 2 diabetes (T2DM) at risk of stage 3 CKD defined by a decline in estimated glomerular filtration rate (eGFR) to below 60 mL/min/1.73m2. The observational study cohort used for model development consisted of data from a primary care dataset of 20,510 multi-ethnic individuals with T2DM from London, UK (2007–2018). Discrimination and calibration of the resulting prediction models developed using cox regression were assessed using the c-statistic and calibration slope, respectively. Models were internally validated using tenfold cross-validation and externally validated on 13,346 primary care individuals from Wales, UK. The simplest model was simplified into a risk score to enable implementation in community-based medicine. The derived full model included demographic, laboratory parameters, medication-use, cardiovascular disease history (CVD) and sight threatening retinopathy status (STDR). Two less resource-intense models were developed by excluding CVD and STDR in the second model and HbA1c and HDL in the third model. All three 5-year risk models had good internal discrimination and calibration (optimism adjusted C-statistics were each 0.85 and calibration slopes 0.999–1.002). In Wales, models achieved excellent discrimination(c-statistics ranged 0.82–0.83). Calibration slopes at 5-years suggested models over-predicted risks, however were successfully updated to accommodate reduced incidence of stage 3 CKD in Wales, which improved their alignment with the observed rates in Wales (E/O ratios near to 1). The risk score demonstrated similar model performance compared to direct evaluation of the cox model. These resource-driven risk prediction models may enable universal screening for Stage 3 CKD to enable targeted early optimisation of risk factors for CKD.

Abstract Species distributions are one of the fundamental factors needed for understanding and conserving wildlife. While the IUCN Red List of Threatened Species is the primary applied reference for biodiversity conservation, limitations in data availability and analyses of the distributions of some species may limit accurate threat classification assessments and conservation recommendations. Improving the accuracy of species distributions in light of growing data and analytical methods is a key step to increasing the efficacy of the Red List. In this study, we reassessed the distribution of takin (Budorcas taxicolor tibetana), a large ungulate in Sichuan Province, southwest China, classified by the IUCN as vulnerable. Using species distribution models and reported habitat requirements, we updated the takin distribution map. Our updated distribution range in the study area (79,449 km2) was 61.31% of the current distribution range (CDR) on the IUCN Red List. This reduction was in large part due to the inclusion in the CDR of substantial areas of lowland plains, high human disturbance, and non‐forest habitat, which provide no suitable habitat for takin. According to our results, suitable takin habitat covered 18.97% of the CDR, suggesting a substantially overestimated distribution. However, there are high proportions of habitat (40%) still covered by the nature reserve network, indicating the importance of protected areas (PAs) in the conservation threaten species. We recommend that experts apply the basic approach presented herein to update the Red List distributions for more species to increase the accuracy of assessments and resulting conservation applications.

Species reintroductions and translocations are widely used management interventions to restore locally extinct or augment severely depleted species. In such projects, the human dimension issues that influence the success of these conservation interventions are encountered at five different stages of the project life cycle: (1) planning, (2) initiation, (3) implementation, (4) ending stage, and (5) post-exit. Overlooking or failing to consider the human dimension in any of these phases could jeopardise the conservation translocation project's success. When the human dimensions are included there is greater possibility of community involvement, peers' acceptance and support from various interest groups and avoidance of conflict situations. The Human-Wildlife Interactions Working Group (HWIWG) was formed in 2018 by members of the IUCN Conservation Translocation Specialist Group (CTSG). HWIWG has facilitated online discussions and workshops with practitioners, researchers and academics from across the globe, on a range of aspects of human-wildlife interactions in conservation translocations, as well as leading discussion sessions during international research conferences. These events have provided a rich source of material from which to draw a series of recommendations. In this paper we discuss findings from the HWIWG that illustrate how, in each of the five stages of the project life cycle, human-dimensions influenced conservation translocation projects. Our aim is to provide useful and multidimensional insights for those working in species' reintroductions and translocations.

Abstract Studies show a strong relationship between soil health and crop yield, but those relating soil health and grain quality are limited. We studied the relationship between soil health and grain protein and oil content from a corn (Zea mays L.) field in Texas. Protein and oil content data were collected in the field with a CropScan monitor. Soil health values were measured at 202 locations using the Haney Soil Health Tool. We first mapped protein and oil content using apparent electrical conductivity (ECa) and 14 terrain attributes as predictors, and we then quantified the relationship with data from sample locations. Soil health was positively correlated with protein and oil content, but the relationship was rather weak. Soil health accounted for up to 13% of the variability in protein (p

Abstract Tractor guidance (TG) improve environmental gains relative to nonprecision technologies; however, studies evaluating how tractor operator experience for nonguidance comparisons affect gains are nonexistent. This study explores spatial relationships of overlaps and gaps with operator experience level (0–1, 2–3, 6+ yr) during fertilizer and herbicide applications based on terrain attributes. Tractor paths recorded by global navigation satellite systems were used to create overlap polygons. Results illustrate operator experience level is critical for better efficiency gains estimation (for non‐TG comparisons). Operators with 6+ yr of experience reduced overlap by 7.7 and 20.6% compared with operators with 2–3 and 0–1 yr of experience, respectively. New operators had consistently higher overlap across all slope (1). A low interpersonal reliability value of 0.02–0.03 indicates operator experience is crucial to estimate TG efficiency gains and consistent drivers experience levels are needed when evaluating economic and environmental gains from TG.

Broad-base agricultural terraces can be difficult to delineate in flat landscapes, particularly when covered by crops, due to subtle changes in elevation over relatively wide distances. In northeastern Oklahoma, these terraces are usually less than half a meter high and 15 to 20 m wide. The objective of this research was to develop and test a technique for identifying and classifying terraces using computer vision applied to terrain derivatives calculated from digital elevation models at five sites. We tested 38 terrain-derivative grid combinations or sets that represented 19 terrain characteristics, calculated from elevation models after two Gaussian smoothing strategies to provide some degree of generalization and a removal of excess noise. The best subsets achieved a 98% classification accuracy (kappa 0.96) and consisted of derivatives representing hydrology, morphometry, and visibility categories. Inaccuracies occurred primarily at the edges of some of the study sites, where agricultural fields bordered incised drainage areas where changes in elevation were similar to those for the terraces. Further study will elucidate the relationships between terrace “borrow” and “deposition” areas in the terrace areas and their relationships to yield and salinity issues. This work seeks to automate terrace identification for digital soil mapping on terraced fields for the improved delivery of soil information for resource conservation and land use.

Abstract Preferential flow through soil macropores is a major cause of rapid transport of contaminants through soils to drainages systems. Knowledge of the extent of macropore flow in different soil systems is crucial for the effective implementation of nutrient management plans. The objective of this study was to determine the differences in preferential flow in soil columns characterized by varying contents of coarse fragments with depth and obtained from a regional, pasture‐based karst and a nonkarst landscape. Intact soil columns (40 × 100 cm; two replicates per treatment) were subjected to two Brˉ leaching experiments (BL1 and BL2) which included a pulse application of Brˉ tracer solution (3.02 and 1.15 g L−1 during BL1 and BL2, respectively), each followed by five successive irrigation events (5.67 mm min−1 for 2 min in 30‐min intervals). Each soil column rested on a stainless‐steel grid (consisting of 100 cells, each measuring 3.81 cm × 3.81 cm), which facilitated partitioning of the soil bottom and collection of drainage from each grid. Significant differences in the drainage losses of water and Brˉ through the 1‐m‐deep soil profiles were observed with karst‐derived stony soils draining approximately five times greater water and Brˉ, compared with nonkarst‐derived soil. These results suggest that soils with high fraction of coarse fragments are more susceptible to leaching losses than soils with less coarse fragments. Further studies under different flow regimes and encompassing different soil units may be beneficial in understanding the potential leaching risk in these areas and for implementing best management practices for landscape‐based nutrient management.

Topographic wetness index (TWI) is used as a proxy for soil moisture, but how well it performs across varying timescales and methods of calculation is not well understood. To assess the effectiveness of TWI, we examined spatial correlations between in situ soil volumetric water content (VWC) and TWI values over 5 years in soils at 42 locations in an agroforestry catena in Fayetteville, Arkansas, USA. We calculated TWI 546 ways using different flow algorithms and digital elevation model (DEM) preparations. We found that most TWI algorithms performed poorly on DEMs that were not first filtered or resampled, but DEM filtration and resampling (collectively called generalization) greatly improved the TWI performance. Seasonal variation of soil moisture influenced TWI performance which was best when conditions were not saturated and not dry. Pearson correlation coefficients between TWI and grand mean VWC for the 5-year measurement period ranged from 0.18 to 0.64 on generalized DEMs and 0.15 to 0.59 for on DEMs that were not generalized. These results aid management of crop fields with variable moisture characteristics.

Greater adoption and better management of spatially complex, conservation systems such as agroforestry (AF) are dependent on determining methods suitable for delineating in-field variability. However, no work has been conducted using repeated electromagnetic induction (EMI) or apparent electrical conductivity (ECa) surveys in AF systems within the Ozark Highlands of northwest Arkansas. As a result, objectives were to (i) evaluate spatiotemporal ECa variability; (ii) identify ECa-derived soil management zones (SMZs); (iii) establish correlations among ECa survey data and in situ, soil-sensor volumetric water content, sentential site soil-sample EC, and gravimetric water content and pH; and (iv) determine the optimum frequency at which ECa surveys could be conducted to capture temporal changes in field variability. Monthly ECa surveys were conducted between August 2020 and July 2021 at a 4.25 ha AF site in Fayetteville, Arkansas. The overall mean perpendicular geometry (PRP) and horizontal coplanar geometry (HCP) ECa ranged from 1.8 to 18.0 and 3.1 to 25.8 mS m−1, respectively, and the overall mean HCP ECa was 67% greater than the mean PRP ECa. The largest measured ECa values occurred within the local drainage way or areas of potential groundwater movement, and the smallest measured ECa values occurred within areas with decreased effective soil depth and increased coarse fragments. The PRP and HCP mean ECa, standard deviation (SD), and coefficient of variation (CV) were unaffected (p > 0.05) by either the weather or growing/non-growing season. K-means clustering delineated three precision SMZs that were reflective of areas with similar ECa and ECa variability. Results from this study provided valuable information regarding the application of ECa surveys to quantify small-scale changes in soil properties and delineate SMZs in highly variable AF systems.

One strategy to protect endangered species, and improve the quality of habitat to meet the needs of both the wildlife and human communities that rely on large protected areas (such as national park), is by zoning its management for multiple uses. We assessed whether multi-use zoning of the Giant Panda National Park (GPNP) could achieve the goal of protecting the panda population and its habitat, while also promoting community development. We integrate panda presence data, human disturbance distribution, and environmental factors to evaluate panda habitat suitability, the effectiveness of zoning designations, and the impact of human disturbance on panda habitat. Our results show that panda habitat suitability has been partially improved, as certain areas of increased in suitability or stabilized (maintained suitability) across the research region between 2001–2003 and 2011–2013, based on the 3rd and 4th national surveys of the giant panda. Most of the optimum and suitable habitat for panda were located in the core protection area of the GPNP. However, the optimum habitat in the general control area is often located at the junction with the core protection area, indicating a particular need to strengthen management in these areas. Meanwhile, the encounter rates of livestock grazing were highest in optimum habitat and unsuitable habitat. As such, particular attention needs to be paid to the pressures on wildlife caused by human disturbances, such as livestock grazing, in the general control area. Our analyses and these findings have important implications for giant panda conservation and GPNP construction.

Runoff from land-applied manure and poultry litter is one mechanism by which manure-borne bacteria are transported over large distances in the environment. There is a global concern that antimicrobial resistant (AMR) genes may be transmitted through the food chain from animal manures to soil to surface water. However, details are lacking on the ecology of AMR genes in water runoff as well as how conservation management practices may affect the runoff microbiome or minimize the movement of AMR genes. The aim of this study was to identify microbial community structure and diversity in water runoff following 14-years of poultry litter and cattle manure deposition and to evaluate the amount of AMR genes under five conventional and conservation pasture management strategies. Since 2004, all watersheds received annual poultry litter at a rate of 5.6 Mg ha−1 and were consistently managed. Surface runoff samples were collected from each watershed from 2018 to 2019, characterized using Illumina 16S rRNA gene amplicon sequencing and enumerated for four AMR-associated genes (ermB, sulI, intlI, and blactx-m-32) using quantitative PCR. Overall, long-term pasture management influenced water microbial community structure, with effects differing by year (p 0.05) from pasture management on the abundance of these genes, indicating both conventional and conservation practices have similar ecologies for these targets; however, there was a greater detection of sulI genes from runoff in continuously grazed systems in 2019, with hay being lowest in 2019. Results illustrate that the edge of field buffer strips may increase bacterial richness in water runoff, but these changes in richness do not greatly impact target AMR genes in the United States largest land-use category.

Introduction Studies of prevalence and the demographic profile of type 1 diabetes are challenging because of the relative rarity of the condition, however, these outcomes can be determined using routine healthcare data repositories. Understanding the epidemiology of type 1 diabetes allows for targeted interventions and care of this life-affecting condition. Objectives To describe the prevalence, incidence and demographics of persons with type 1 diabetes diagnosed in Wales, UK, using the Secure Anonymised Information Linkage (SAIL) Databank. Methods Data derived from primary and secondary care throughout Wales available in the SAIL Databank were used to identify people with type 1 diabetes to determine the prevalence and incidence of type 1 diabetes over a 10 year period (2008–18) and describe the demographic and clinical characteristics of this population by age, socioeconomic deprivation and settlement type. The seasonal variation in incidence rates was also examined. Results The prevalence of type 1 diabetes in 2018 was 0.32% in the whole population, being greater in men compared to women (0.35% vs 0.28% respectively); highest in those aged 15-29 years (0.52%) and living in the most socioeconomically deprived areas (0.38%). The incidence of type 1 diabetes over 10 years was 14.0 cases/100,000 people/year for the whole population of Wales. It was highest in children aged 0-14 years (33.6 cases/100,000 people/year) and areas of high socioeconomic deprivation (16.8 cases/100,000 people/year) and least in those aged 45-60 years (6.5 cases/100,000 people/year) and in areas of low socioeconomic deprivation (11.63 cases/100,000 people/year). A seasonal trend in the diagnoses of type 1 diabetes was observed with higher incidence in winter months. Conclusion This nation-wide retrospective epidemiological study using routine data revealed that the incidence of type 1 diabetes in Wales was greatest in those aged 0-14 years with a higher incidence and prevalence in the most deprived areas. These findings illustrate the need for health-related policies targeted at high deprivation areas to include type 1 diabetes in their remit.

Objectives To describe the frequency of symptoms compatible with SARS-CoV-2 infection in immunocompromised children and young people in the UK during the SARS-CoV-2 pandemic. To describe patient/parent anxiety regarding SARS-CoV-2 infection in this cohort.Design A prospective observational cohort study.Setting 46 centres across the UK between 16 March and 4 July 2020. A weekly online questionnaire based on the International Severe Acute Respiratory and emerging Infections Consortium-WHO Case Report Form was used to collect participant reported data on symptoms, test results, National Health Service attendance, hospital admission and impact on daily life.Participants 1490 immunocompromised children, defined as those requiring an annual influenza vaccination due to their underlying condition or medication.Main outcome measures Incidence of SARS-CoV-2-like symptoms and patient/parent anxiety score.Results Over 16 weeks during the first wave of the pandemic, no SARS-CoV-2 infection was diagnosed in this large immunocompromised paediatric cohort (median age 11 years, 54.4% female). 110 symptomatic participants underwent a test for SARS-CoV-2; all were negative. 922 (67.4%) participants reported at least one symptom consistent with suspected SARS-CoV-2 infection over the study period. 476 (34.8%) reported three or more symptoms. The most frequently reported symptoms included joint pain, fatigue, headache, nausea and muscle pain. SARS-CoV-2 testing during this period was performed on admitted patients only. 137 participants had their medication suspended or changed during the study period due to assumed COVID-19 disease risk. 62% reported high levels of anxiety (scores of 7–10 out of 10) at the start of the study, with anxiety levels remaining high throughout the study period.Conclusions Although symptoms related to SARS-CoV-2 infection in children were common, there were no positive tests in this large immunocompromised cohort. Symptom-based screening to facilitate early detection of SARS-CoV-2 infection may not be helpful in these individuals. Patient/parent anxiety about SARS-CoV-2 infection was high.Trial registration number NCT04382508.

Optimizing soil—crop—landscape occurrence is essential for sustainable intensification and food security, but little work has been done to evaluate these parameters on Tribal lands. The objective of this study was to develop first ever high-resolution crop suitability maps and compare two established crop suitability models for their ability to optimize soil resource management of the Quapaw Tribal lands. We built on previously developed continuous soil properties maps for 22,880 ha of Quapaw Tribal lands that used a digital elevation model and a fuzzy-logic based data mining approach to calculate and evaluate the Dideriksen and Storie crop suitability indices. Suitability index results were evaluated against observed yield (n ≥ 130,000) within the study area. Results showed that the observed yield was positively correlated with the Storie suitability index (Spearman rho = 0.16, p < 0.01), but not the Dideriksen index, suggesting the Storie index is more appropriate than the Dideriksen for modeling crop suitability in this area. Additionally, very little (

The Chaoyang profile represents a rare multi-period, continuous and complete sequence of aeolian paleo-deposits with a stable sedimentary origin and multi-stage paleoclimatic cycles. Benchmark profiles including soil types at different pedogenic stages can be used for the recognition and classification of paleosols and paleoclimate reconstruction. The loess–paleosol sequence benchmark profile (LBP) is also helpful in comparing the results of paleoenvironment reconstruction from different ecological regions. In this study, a loess–paleosol profile derived from thick loess in Chaoyang city of Liaoning province, Northeast China, was investigated as a well-preserved LBP that included various paleosol types. To determine the nature and origin of the Chaoyang profile, the geographic, stratigraphic and morphological characteristics were described in the field. Bulk samples from 42 horizons were collected for chemical and physical analysis, and sub-sampling of 946 samples at 2 cm intervals from the surface to the bottom were taken to measure grain size distributions and magnetic susceptibility. Results showed that the 19.85 m thick loess–paleosol profile had been continuously deposited since 423 ka BP. The upper part (0–195 cm), or UPP, was predominantly of aeolian loess deposition origin but was mixed with water-reworked materials from a nearby secondary loess source. The middle part (195–228 cm), or MIP, was also indirectly affected by the water-reworking process through the leaching of materials from the overlying UPP. The lower part (228–1985 cm), or LOP, was characterized by four reddish stratigraphic layers interbedded with five yellowish ones, indicating several types of paleosols developed under different ecological environments. The multi-stage paleoclimatic cycles as evidenced by morphological and physical characteristics as well as age dating and magnetic susceptibility correlated well with the Lingtai section and LR04 benthic δ18O. Because of these attributes, the Chaoyang profile can be deemed as a benchmark loess–paleosol profile for the recognition and classification of paleosols and paleoclimate reconstruction in Northeast China. The differences in morphological and physical properties between paleosols and loess suggest different soil fertility and agronomic properties and need further studies to assess their functionality with climate fluctuation.

Producers in Northwest Arkansas and globally need alternative management practices to ensure long-term sustainable and economical use of poultry litter, which is an abundant source of valuable carbon (C), nitrogen (N) and phosphorus (P). Project objectives were to measure the efficacy of conservation management practices (i.e., pasture aeration and subsurface litter incorporation) to reduce nutrient runoff compared to poultry litter surface applications from small watersheds under rainfed and grazed conditions. Watersheds (0.23 ha each) were assigned a treatment [pasture aeration, subsurface litter incorporation, or surface application of litter (positive control)] on a Leadvale (fine-silty, siliceous, thermic Typic Fragiudult) silt loam. Poultry litter was applied annually to each watershed from 2007-2012. Over the 4-yr study period, runoff loads of NO3–N, total nitrogen (TN), soluble reactive phosphorus (SRP), and total phosphorus (TP) varied per conservation practice (P ≤ 0.05). Specifically, average annual loads of NO3–N, TN, SRP, and TP loads were reduced 49, 42, 28, and 35% following pasture aeration and by 78, 72, 55, and 59% from subsurface applying poultry litter, relative to surface applications, respectively. Greatest annual N loads and runoff corresponded with surface poultry litter applications, followed by pasture aeration, with subsurface incorporation of poultry litter resulting in lowest (P ≤ 0.05) TN and NO3–N loads. Overall, subsurface incorporation of poultry litter and pasture aeration are two promising conservation practices for reducing non-point source pollution in watersheds with nutrient imbalances. Further work needs to be done on factors influencing the efficacy of these conservation practices under rainfed conditions, as well as the economic feasibility of these conservation agricultural practices.

Abstract The Soil Management Assessment Framework (SMAF) is a quantitative soil quality (SQ) evaluation tool that is widely applied to assess soil response to specific agricultural management practices over time. Considering the reported SQ benefits of agroforestry (AF) systems and the potential usefulness of SMAF, the objective of this study was to evaluate the effects of tree species (pecan [Carya illinoinensis (Wangenh.) K. Koch] and northern red oak [Quercus rubra L.]), soil fertility source (poultry litter [PL] and inorganic N fertilizer [control]), and soil depth (0–15 and 15–30 cm) on SMAF‐derived SQ indices after 17 yr of management at an AF site in northwest Arkansas. Averaged across soil depth, soil organic C scores under red oak with PL application had a lower score (0.48) than red oak fertilized with inorganic N (0.60) and pecan receiving long‐term PL applications (0.60), which did not differ from pecan with inorganic N fertilizer application (0.51). Averaged across soil depth, the soil quality index (SQI) for pecan receiving PL applications was 1.1 times greater than that under red oak receiving PL and soils under pecan receiving inorganic N fertilizer. Soil quality assessments use in AF are novel, as SMAF has not been used to identify soil health in these systems, although specific tree crop codes need to be developed in SMAF. Results of this study demonstrate that soils planted under various tree species respond dissimilarly to fertilizer sources and that management may improve overall SQ.

The persistence of antimicrobial resistant (AMR) genes in the soil-environment is a concern, yet practices that mitigate AMR are poorly understood, especially in grasslands. Animal manures are widely deposited on grasslands, which are the largest agricultural land-use in the United States. These nutrient-rich manures may contain AMR genes. The aim of this study was to enumerate AMR genes in grassland soils following 14-years of poultry litter and cattle manure deposition and evaluate if best management practices (rotationally grazed with a riparian (RBR) area and a fenced riparian buffer strip (RBS), which excluded cattle grazing and poultry litter applications) relative to standard pasture management (continuously grazed (CG) and hayed (H)) minimize the presence and amount of AMR genes. Quantitative PCR (Q-PCR) was performed to enumerate four AMR genes (ermB, sulI, intlI, and blactx-m-32) in soil, cattle manure, and poultry litter environments. Six soil samples were additionally subjected to metagenomic sequencing and resistance genes were identified from assembled sequences. Following 14-years of continuous management, ermB, sulI, and intlI genes in soil were greatest (P < 0.05) in samples collected under long-term continuous grazing (relative to conservation best management practices), under suggesting overgrazing and continuous cattle manure deposition may increase AMR gene presence. In general, AMR gene prevalence increased downslope, suggesting potential lateral movement and accumulation based on landscape position. Poultry litter had lower abundance of AMR genes (ermB, sulI, and intlI) relative to cattle manure. Long-term applications of poultry litter increased the abundance of sulI and intlI genes in soil (P

Objectives: Peripheral artery disease is a major cardiovascular disease affecting more than 200 million people globally and up to 4 times more frequent in the diabetic population. It can lead to lower extremity amputations or revascularisation and is associated with an increased risk of myocardial infarction, stroke and early mortality. This novel cross-sectional study aimed to explore the feasibility and acceptability of incorporating diabetic foot screening at routine diabetic retinopathy screening appointments. Methods: Participants underwent foot screening during the interval between pupil dilatation and retinal photography as part of the eye screening procedure. Lower limb arterial assessment included ankle brachial index, pulse volume waveform and protective light touch sensation. Results: Of 364 participants invited, 88% (n = 321) met the inclusion criteria. About 26.4% (n = 86) had asymptomatic peripheral artery disease and 3% (n = 10) had peripheral sensory neuropathy. Binary logistical regression analysis identified age (p

High-resolution maps of soil health measurements could help farmers finetune input resources and management practices for profit maximization. Within-field soil heath variations can be mapped using local topography and apparent electrical conductivity (ECa) as predictors. To address these issues, a study was conducted in Texas Blackland Prairie soils with the following objectives: (i) to assess and map within-field soil health variations using machine learning; (ii) to evaluate the usefulness of topography and ECa as soil health predictors; and (iii) to quantify the relationship between ECa and soil health index and use ECa to estimate soil health spatial distribution. We collected 218 topsoil (0–15 cm) samples following a 35 m × 35 m grid design and analyzed for one-day CO2, organic C, organic N, and soil health index (SHI) based on the Haney Soil Health Tool. A random forest model was applied to predict and map those properties on a 5 m × 5 m grid where ECa, and terrain attributes were used as predictors. Furthermore, the empirical relationship between SHI and ECa was established and mapped across the field. Results showed that the study area was variable in terms of one-day CO2, organic C, organic N, SHI, and ECa distribution. The ECa, wetness index, multiresolution valley bottom flatness, and topographic position index were among the top predictors of soil health measurements. The model was sufficiently robust to predict one day CO2, organic C, organic N (R2 between 0.24–0.90), and SHI (R2 between 0.47–0.90). Overall, we observed a moderate to strong spatial dependency of soil health measurements which could impact within-field yield variability. The study confirmed the applicability of easy to obtain ECa as a good predictor of SHI, and the predicted maps at high resolution which could be useful in site-specific management decisions within these types of soils.

Combining data into a centralized, searchable, and linked platform will provide a data exploration platform to agricultural stakeholders and researchers for better agricultural decision making, thus fully utilizing existing data and preventing redundant research. Such a data repository requires readiness to share data, knowledge, and skillsets and working with Big Data infrastructures. With the adoption of new technologies and increased data collection, agricultural workforces need to update their knowledge, skills, and abilities. The partnerships for data innovation (PDI) effort integrates agricultural data by efficiently capturing them from field, lab, and greenhouse studies using a variety of sensors, tools, and apps and provides a quick visualization and summary of statistics for real-time decision making. This paper aims to evaluate and provide examples of case studies currently using PDI and use its long-term continental US database (18 locations and 24 years) to test the cover crop and grazing effects on soil organic carbon (SOC) storage. The results show that legume and rye (Secale cereale L.) cover crops increased SOC storage by 36% and 50%, respectively, compared with oat (Avena sativa L.) and rye mixtures and low and high grazing intensities improving the upper SOC by 69–72% compared with a medium grazing intensity. This was likely due to legumes providing a more favorable substrate for SOC formation and high grazing intensity systems having continuous manure deposition. Overall, PDI can be used to democratize data regionally and nationally and therefore can address large-scale research questions aimed at addressing agricultural grand challenges.

Recent advances suggest that miniaturised mid-infrared (MIR) devices could replace more time-consuming, laboratory-based techniques for clinical diagnostics. This work uses Fourier transform infrared spectroscopy to show that the MIR complex refractive index of whole blood varies across a range of haematocrit. This indicates that the use of an evanescent measurement is not sufficient to optically exclude the cellular content of blood in the MIR, as previously assumed. Here, spectral refractive index data is presented in two ways. First, it is given as whole blood with varying haematocrit. Second, it is given as the percentage error that haematocrit introduces to plasma. The maximum error in the effective plasma refractive index due to the haematocrit of healthy adults was 0.25% for the real part n and 11% for the imaginary part k. This implies that calibration measurements of haematocrit can be used to account for errors introduced by the cellular content, enabling plasma spectra and analyte concentrations to be indirectly calculated from a whole blood sample. This methodological advance is of clinical importance as plasma concentration of analytes such as drugs can be determined using MIR without the preprocessing of whole blood.

Pseudophilothrips ichini is a recently approved biological control agent for the highly invasive Brazilian peppertree in Florida, USA. Prior to approval for field release in 2019, thrips colonies used for host specificity testing were produced and maintained in small cylinders to fit in restricted quarantine spaces. This next segment in the classical biological control pipeline is mass production and distribution of P. ichini. To accomplish this, we developed novel techniques to expand from small colony maintenance to large-scale production. We first quantified the productivity of the small cylinders, each containing a 3.8 L potted plant and producing an average of 368 thrips per generation. Given the amount of maintenance the cylinders required, we investigated larger cages to see if greater numbers of thrips could be produced with less effort. Acrylic boxes (81.5 × 39.5 × 39.5 cm) each contained two 3.8 L plants and produced an average of 679 thrips per generation. The final advancement was large, thrips-proof Lumite® screen cages (1.8 × 1.8 × 1.8 m) that each held six plants in 11.4 L pots and produced 13,864 thrips in as little as 5 wk. Screen cages and cylinders had the greatest thrips fold production, but screen cages required ten times less labor per thrips compared to either cylinders or boxes. The efficiency of these large screen cages ensured sustained mass production and field release capacity in Schinus-infested landscapes. The screen cage method is adapted and used by collaborators, and this will expand the literature on beneficial thrips mass rearing methods.