Spatial variations in aromatic hydrocarbon emission in a dust-rich galaxy.

Dust grains absorb half of the radiation emitted by stars throughout the history of the universe, re-emitting this energy at infrared wavelengths1-3. Polycyclic aromatic hydrocarbons (PAHs) are large organic molecules that trace millimetre-size dust grains and regulate the cooling of interstellar gas within galaxies4,5. Observations of PAH features in very distant galaxies have been difficult owing to the limited sensitivity and wavelength coverage of previous infrared telescopes6,7. Here we present James Webb Space Telescope observations that detect the 3.3 µm PAH feature in a galaxy observed less than 1.5 billion years after the Big Bang. The high equivalent width of the PAH feature indicates that star formation, rather than black hole accretion, dominates infrared emission throughout the galaxy. The light from PAH molecules, hot dust and large dust grains and stars are spatially distinct from one another, leading to order-of-magnitude variations in PAH equivalent width and ratio of PAH to total infrared luminosity across the galaxy. The spatial variations we observe suggest either a physical offset between PAHs and large dust grains or wide variations in the local ultraviolet radiation field. Our observations demonstrate that differences in emission from PAH molecules and large dust grains are a complex result of localized processes within early galaxies.

A Generic Model to Estimate Wheat LAI over Growing Season Regardless of the Soil-Type Background.

It is valuable to develop a generic model that can accurately estimate the leaf area index (LAI) of wheat from unmanned aerial vehicle-based multispectral data for diverse soil backgrounds without any ground calibration. To achieve this objective, 2 strategies were investigated to improve our existing random forest regression (RFR) model, which was trained with simulations from a radiative transfer model (PROSAIL). The 2 strategies consisted of (a) broadening the reflectance domain of soil background to generate training data and (b) finding an appropriate set of indicators (band reflectance and/or vegetation indices) as inputs of the RFR model. The RFR models were tested in diverse soils representing varying soil types in Australia. Simulation analysis indicated that adopting both strategies resulted in a generic model that can provide accurate estimation for wheat LAI and is resistant to changes in soil background. From validation on 2 years of field trials, this model achieved high prediction accuracy for LAI over the entire crop cycle (LAI up to 7 m2 m-2) (root mean square error (RMSE): 0.23 to 0.89 m2 m-2), including for sparse canopy (LAI less than 0.3 m2 m-2) grown on different soil types (RMSE: 0.02 to 0.25 m2 m-2). The model reliably captured the seasonal pattern of LAI dynamics for different treatments in terms of genotypes, plant densities, and water-nitrogen managements (correlation coefficient: 0.82 to 0.98). With appropriate adaptations, this framework can be adjusted to any type of sensors to estimate various traits for various species (including but not limited to LAI of wheat) in associated disciplines, e.g., crop breeding, precision agriculture, etc.

VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation.

Applying deep learning to images of cropping systems provides new knowledge and insights in research and commercial applications. Semantic segmentation or pixel-wise classification, of RGB images acquired at the ground level, into vegetation and background is a critical step in the estimation of several canopy traits. Current state of the art methodologies based on convolutional neural networks (CNNs) are trained on datasets acquired under controlled or indoor environments. These models are unable to generalize to real-world images and hence need to be fine-tuned using new labelled datasets. This motivated the creation of the VegAnn - Vegetation Annotation - dataset, a collection of 3775 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions. We anticipate that VegAnn will help improving segmentation algorithm performances, facilitate benchmarking and promote large-scale crop vegetation segmentation research.

Unsupervised Plot-Scale LAI Phenotyping via UAV-Based Imaging, Modelling, and Machine Learning.

High-throughput phenotyping has become the frontier to accelerate breeding through linking genetics to crop growth estimation, which requires accurate estimation of leaf area index (LAI). This study developed a hybrid method to train the random forest regression (RFR) models with synthetic datasets generated by a radiative transfer model to estimate LAI from UAV-based multispectral images. The RFR models were evaluated on both (i) subsets from the synthetic datasets and (ii) observed data from two field experiments (i.e., Exp16, Exp19). Given the parameter ranges and soil reflectance are well calibrated in synthetic training data, RFR models can accurately predict LAI from canopy reflectance captured in field conditions, with systematic overestimation for LAI<2 due to background effect, which can be addressed by applying background correction on original reflectance map based on vegetation-background classification. Overall, RFR models achieved accurate LAI prediction from background-corrected reflectance for Exp16 (correlation coefficient (r) of 0.95, determination coefficient (R 2) of 0.90~0.91, root mean squared error (RMSE) of 0.36~0.40 m2 m-2, relative root mean squared error (RRMSE) of 25~28%) and less accurate for Exp19 (r =0.80~0.83, R 2 = 0.63~0.69, RMSE of 0.84~0.86 m2 m-2, RRMSE of 30~31%). Additionally, RFR models correctly captured spatiotemporal variation of observed LAI as well as identified variations for different growing stages and treatments in terms of genotypes and management practices (i.e., planting density, irrigation, and fertilization) for two experiments. The developed hybrid method allows rapid, accurate, nondestructive phenotyping of the dynamics of LAI during vegetative growth to facilitate assessments of growth rate including in breeding program assessments.

Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning.

A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. To address this problem, this research investigated a conceptual framework by integrating a crop growth model with a radiative transfer model to introduce biological constraints in a synthetic training dataset. In addition to the comparison of two datasets without and with biological constraints, we also investigated the effects of observation geometry, retrieval method, and wavelength range on estimation accuracy of four crop traits (leaf area index, leaf chlorophyll content, leaf dry matter, and leaf water content) of wheat. The theoretical analysis demonstrated potential advantages of adding biological constraints in synthetic training datasets as well as the capability of deep learning. Additionally, the predictive models were validated on real unmanned aerial vehicle-based multispectral images collected from wheat plots contrasting in canopy structure. The predictive model trained over a synthetic dataset with biological constraints enabled the prediction of leaf water content from using wavelengths in the visible to near infrared range based on the correlations between crop traits. Our findings presented the potential of the proposed conceptual framework in simultaneously retrieving multiple crop traits from canopy reflectance for applications in precision agriculture and plant breeding.

Phenological optimization of late reproductive phase for raising wheat yield potential in irrigated mega-environments.

Increasing grain number through fine-tuning duration of the late reproductive phase (LRP; terminal spikelet to anthesis) without altering anthesis time has been proposed as a genetic strategy to increase yield potential (YP) of wheat. Here we conducted a modelling analysis to evaluate the potential of fine-tuning LRP in raising YP in irrigated mega-environments. Using the known optimal anthesis and sowing date of current elite benchmark genotypes, we applied a gene-based phenology model for long-term simulations of phenological stages and yield-related variables of all potential germplasm with the same duration to anthesis as the benchmark genotypes. These diverse genotypes had the same duration to anthesis but varying LRP duration. Lengthening LRP increased YP and harvest index by increasing grain number to some extent and an excessively long LRP reduced YP due to reduced time for canopy construction for high biomass production of pre-anthesis phase. The current elite genotypes could have their LRP extended for higher YP in most sites. Genotypes with a ratio of the duration of LRP to pre-anthesis phase of about 0.42 ensured high yields (≥95% of YP) with their optimal sowing and anthesis dates. Optimization of intermediate growth stages could be further evaluated in breeding programmes to improve YP.

A wiring diagram to integrate physiological traits of wheat yield potential.

As crop yields are pushed closer to biophysical limits, achieving yield gains becomes increasingly challenging and will require more insight into deterministic pathways to yields. Here, we propose a wiring diagram as a platform to illustrate the interrelationships of the physiological traits that impact wheat yield potential and to serve as a decision support tool for crop scientists. The wiring diagram is based on the premise that crop yield is a function of photosynthesis (source), the investment of assimilates into reproductive organs (sinks) and the underlying processes that enable expression of both. By illustrating these linkages as coded wires, the wiring diagram can show connections among traits that may not have been apparent, and can inform new research hypotheses and guide crosses designed to accumulate beneficial traits and alleles in breeding. The wiring diagram can also serve to create an ever-richer common point of reference for refining crop models in the future.

Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery.

In plant breeding, unmanned aerial vehicles (UAVs) carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping (HTP) to aid the interpretation of genotype and environment effects on morphological, biochemical, and physiological traits. A key constraint remains the reduced resolution and quality extracted from "stitched" mosaics generated from UAV missions across large areas. This can be addressed by generating high-quality reflectance data from a single nadir image per plot. In this study, a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications. Sequential steps involved (i) imagery calibration, (ii) spectral band alignment, (iii) backward calculation, (iv) plot segmentation, and (v) application. Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot. Using a derived nadir image of each plot, the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads, respectively. Furthermore, the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours (i.e., red and white). Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches.

Using a gene-based phenology model to identify optimal flowering periods of spring wheat in irrigated mega-environments.

To maximize the grain yield of spring wheat, flowering needs to coincide with the optimal flowering period (OFP) by minimizing frost and heat stress on reproductive development. This global study conducted a comprehensive modelling analysis of genotype, environment, and management to identify the OFPs for sites in irrigated mega-environments of spring wheat where the crop matures during a period of increasing temperatures. We used a gene-based phenology model to conduct long-term simulation analysis with parameterized genotypes to identify OFPs and optimal sowing dates for sites in irrigated mega-environments, considering the impacts of frost and heat stress on yield. The validation results showed that the gene-based model accurately predicted wheat heading dates across global wheat environments. The long-term simulations indicated that frost and heat stress significantly advanced or delayed OFPs and shrank the durations of OFPs in irrigated mega-environments when compared with OFPs where the model excluded frost and heat stress impacts. The simulation results (incorporating frost and heat penalties on yield) also showed that earlier flowering generally resulted in higher yields, and early sowing dates and/or early flowering genotypes were suggested to achieve early flowering. These results provided an interpretation of the regulation of wheat flowering to the OFP by the selection of sowing date and cultivar to achieve higher yields in irrigated mega-environments.

Scaling up high-throughput phenotyping for abiotic stress selection in the field.

KEY MESSAGE: High-throughput phenotyping (HTP) is in its infancy for deployment in large-scale breeding programmes. With the ability to measure correlated traits associated with physiological ideotypes, in-field phenotyping methods are available for screening of abiotic stress responses. As cropping environments become more hostile and unpredictable due to the effects of climate change, the need to characterise variability across spatial and temporal scales will become increasingly important. The sensor technologies that have enabled HTP from macroscopic through to satellite sensors may also be utilised here to complement spatial characterisation using envirotyping, which can improve estimations of genotypic performance across environments by better accounting for variation at the plot, trial and inter-trial levels. Climate change is leading to increased variation at all physical and temporal scales in the cropping environment. Maintaining yield stability under circumstances with greater levels of abiotic stress while capitalising upon yield potential in good years, requires approaches to plant breeding that target the physiological limitations to crop performance in specific environments. This requires dynamic modelling of conditions within target populations of environments, GxExM predictions, clustering of environments so breeding trajectories can be defined, and the development of screens that enable selection for genetic gain to occur. High-throughput phenotyping (HTP), combined with related technologies used for envirotyping, can help to address these challenges. Non-destructive analysis of the morphological, biochemical and physiological qualities of plant canopies using HTP has great potential to complement whole-genome selection, which is becoming increasingly common in breeding programmes. A range of novel analytic techniques, such as machine learning and deep learning, combined with a widening range of sensors, allow rapid assessment of large breeding populations that are repeatable and objective. Secondary traits underlying radiation use efficiency and water use efficiency can be screened with HTP for selection at the early stages of a breeding programme. HTP and envirotyping technologies can also characterise spatial variability at trial and within-plot levels, which can be used to correct for spatial variations that confound measurements of genotypic values. This review explores HTP for abiotic stress selection through a physiological trait lens and additionally investigates the use of envirotyping and EC to characterise spatial variability at all physical scales in METs.

Fluorescent angiography used as a tool to guide angiosome-directed endovascular therapy for diabetic foot ulcers.

Angiosome-directed endovascular therapy for the treatment of chronic limb-threatening ischemia (CLTI) remains controversial owing to the overlap of wound angiosomes. Angiographic grading of success has limitations and translesional pressure assessments are seldom performed in the infrapopliteal vessels. Objective criteria to determine revascularization success in tibiopedal vessels have not been well described. Quantifying perfusion to a wound bed after establishing direct or indirect (via collateral) flow after revascularization is an important component for treating CLTI patients yet is seldom performed. We report the use of fluorescent angiography to quantitatively examine perfusion of a diabetic foot ulcer before and after angiosome-directed endovascular therapy.

Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops.

Ground coverage (GC) allows monitoring of crop growth and development and is normally estimated as the ratio of vegetation to the total pixels from nadir images captured by visible-spectrum (RGB) cameras. The accuracy of estimated GC can be significantly impacted by the effect of 'mixed pixels', which is related to the spatial resolution of the imagery as determined by flight altitude, camera resolution and crop characteristics (fine vs coarse textures). In this study, a two-step machine learning method was developed to improve the accuracy of GC of wheat (Triticum aestivum L.) estimated from coarse-resolution RGB images captured by an unmanned aerial vehicle (UAV) at higher altitudes. The classification tree-based per-pixel segmentation (PPS) method was first used to segment fine-resolution reference images into vegetation and background pixels. The reference and their segmented images were degraded to the target coarse spatial resolution. These degraded images were then used to generate a training dataset for a regression tree-based model to establish the sub-pixel classification (SPC) method. The newly proposed method (i.e. PPS-SPC) was evaluated with six synthetic and four real UAV image sets (SISs and RISs, respectively) with different spatial resolutions. Overall, the results demonstrated that the PPS-SPC method obtained higher accuracy of GC in both SISs and RISs comparing to PPS method, with root mean squared errors (RMSE) of less than 6% and relative RMSE (RRMSE) of less than 11% for SISs, and RMSE of less than 5% and RRMSE of less than 35% for RISs. The proposed PPS-SPC method can be potentially applied in plant breeding and precision agriculture to balance accuracy requirement and UAV flight height in the limited battery life and operation time.

Characteristics and outcomes of patients with peripheral artery disease undergoing endovascular revascularization: A community hospital perspective.

BACKGROUND: There are limited data on outcomes for patients with peripheral artery disease undergoing endovascular revascularization by multi-disciplinary teams in a community hospital setting. METHODS: From January 2015 through December 2015, we assembled a multi-disciplinary program comprised of cardiologists, surgeons, radiologists, nurses, and administrative staff for managing patients with peripheral artery disease undergoing endovascular revascularization. Demographic, procedural, and outcomes data were collected with use of a template from the Society for Vascular Surgery Vascular Quality Initiative database. We compared characteristics and outcomes of patients with intermittent claudication and critical limb ischemia. We used Kaplan-Meier methods to estimate the rate of overall survival and freedom from rehospitalization between groups. RESULTS: After excluding patients with acute limb ischemia (n = 5), peripheral intervention to the upper extremity (n = 6), or abdominal aorta (n = 11), there were 82 patients in the study cohort; 45 had intermittent claudication and 37 had critical limb ischemia. Baseline and procedural characteristics were similar between groups, although critical limb ischemia patients were more likely to have hyperlipidemia (75.7% vs. 53.3%, P = .42). Procedural success was achieved in 91.3% of cases. Actionable access site bleeding occurred in 2.4% of patients. High rates of aspirin (91.5%) and statin (87.8%) were noted at discharge. After two years of post endovascular revascularization, survival was 57.5% for critical limb ischemia patients and 94.4% for intermittent claudication patients (P < .001). Freedom from rehospitalization was 32.7% for critical limb ischemia patients and 83.5% for intermittent claudication patients (P < .001). CONCLUSIONS: We found that favorable outcomes may be achieved with a multi-disciplinary peripheral artery disease program at community hospitals. The incorporation of quality improvement practices may further help to develop standardized and regionalized approaches to care delivery for patients with peripheral artery disease.

Postoperative outcomes in thoracic outlet decompression for acute versus chronic venous thoracic outlet syndrome.

OBJECTIVE: Venous thoracic outlet syndrome (VTOS) is a rare disorder that occurs in young athletes and working adults. There are multiple published reports demonstrating excellent outcomes with thoracic outlet (TO) decompression surgery when patients present acutely (within 2 weeks of symptom onset). Our objective was to assess outcomes after decompression surgery in patients with acute, subacute, chronic, and secondary VTOS. Additionally, we sought to identify risk factors for persistence of symptoms following operative decompression. METHODS: A retrospective chart review was performed for all patients who underwent operative decompression for VTOS at the University of Pittsburgh Medical Center from 2013 to 2017. We examined baseline characteristics, comorbidities, presenting symptoms, interventions performed, and postoperative clinical outcomes. Patients were characterized as acute, subacute, or chronic based on onset of symptoms and presentation to our surgeons (acute <2 weeks, subacute 2 weeks to 3 months, and chronic >3 months). Our outcomes of interest were return to baseline functional status as defined by resumption of sports activity or occupation and axillosubclavian vein patency. RESULTS: A total of 51 operative decompressions were performed in 48 patients for VTOS. There were 23 operations (45%) performed on patients who presented acutely, 7 (14%) in the subacute group, and 21 (41%) surgeries in patients with chronic symptoms. Of these 51 operations, 4 (7.8%) were deemed unsuccessful-two surgeries were in the acute group, one in the subacute, and one in the chronic group. The 30-day morbidity after 51 first rib resections included no pneumothoraces, no lymphatic leaks, two surgical site hematomas with associated hemothorax in one patient, two surgical site infections, and only two unplanned returns to the operating room for hematoma evacuation and superficial wound infection washout. In terms of preoperative vein patency, those who presented acutely were more likely to have an occluded axillosubclavian vein (P = .029). The Fisher's exact was 0.540, indicating that the proportion of patients returning to baseline functional status were similar when comparing acute presenters with those who present late. A multivariate Cox proportional hazards model was attempted; however, a small sample size greatly limited the power of the study and prohibited identification of risk factors for surgical failure. CONCLUSIONS: Patients with acute and chronic VTOS resumed their preintervention sports activity or vocation after TO decompression in more than 90% of cases with a low incidence of adverse events. Based on our study results, patients with chronic VTOS benefit as much from TO decompression as those with acute VTOS.

Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods.

The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, genotypic differences, development stages, and head orientation makes wheat head detection a challenge for computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse, and well-labelled dataset of wheat images, called the Global Wheat Head Detection (GWHD) dataset. It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles, and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection.

Endovascular approach to arterial branches mimicking a type II endoleak after popliteal artery aneurysm exclusion and bypass.

The management of popliteal artery aneurysms (PAAs) has continued to evolve. Open surgical treatment remains an excellent option. Aneurysm exclusion with saphenous vein bypass through a medial incision remains a preferred approach. After PAA exclusion, however, a possibility remains of sac expansion from geniculate arterial branches. This can mimic a type II endoleak occurring after endovascular aortic aneurysm repair. In the present report, we have described an endovascular technique used to treat an enlarging PAA after exclusion and bypass.

CT Three-Dimensional (3D) Modeling Maintains Fluoroscopy Time and Contrast Volume to a Minimum in the Endovascular Treatment of Great Vessel Injury from Iatrogenic Trauma.

CT angiography with multislice detector has become the preferred method for assessment of hemodynamically stable patients suspected of great vessel injury from iatrogenic and blunt trauma. The CT images obtained can be transformed into a three-dimensional (3D) model using the software within minutes. This allows the clinician to evaluate the injury and the injury's proximity to other vital structures for operative planning. The 3D modeling provides geometric calibration of the c-arm or image intensifier in regard to optimal cranial/caudal and oblique angles to evaluate and treat the vessel injury. We describe a case of a 28-year-old female undergoing a cesarian section and hysterectomy for placenta percreta, who sustained a right subclavian artery injury (presumed wire injury) from inadvertent right common carotid artery cannulation during placement of a right internal jugular 8 French resuscitative central line. A 3D model was created from the CT angiography to locate the small pseudoaneurysm of the right subclavian artery. The optimal projection was obtained using the 3D reconstructive software to visualize the injury and its relation to the right vertebral artery ostium for endovascular planning. This preoperative maneuver allowed for limited radiation exposure and contrast volume used to treat the injury.

Masson tumor (intravascular papillary endothelial hyperplasia) arising in a superficial temporal artery aneurysm.

Masson tumor (intravascular papillary endothelial hyperplasia) is a rare proliferation of endothelial cells within the wall of a vessel, often thought to represent an aberrant resolution of a thrombosis. We describe the unique case of a 75-year-old man who presented to the clinic with a tender, spontaneous aneurysmal dilation of his left superficial temporal artery (STA). Only 8% of all STA aneurysms are believed to be spontaneous true aneurysms, with the majority being post-traumatic pseudoaneurysms. After successful surgical resection, pathologic examination demonstrated a Masson tumor within an STA aneurysm. This paper describes a case in which both rare entities were discovered and briefly outlines the diagnostic and therapeutic modalities available.

Functional popliteal artery entrapment syndrome: A review of diagnostic and management approaches.

Functional popliteal artery entrapment syndrome (FPAES) presents a challenge for clinicians in the field today. The relative rarity of the disease coupled with its affliction primarily in young, athletic individuals makes it a difficult condition to diagnose. Treatment modalities have been limited to surgical intervention, with more novel methods being developed over the last several years. We present a case report and review the diagnostic and management approaches for FPAES, shedding light on proposed future interventions.