Droughts impede water balance recovery from fires in the Western United States.

A steady rise in fires in the Western United States, coincident with intensifying droughts, imparts substantial modifications to the underlying vegetation, hydrology and overall ecosystem. Drought can compound the ecosystem disturbance caused by fire, although how these compound effects on hydrologic and ecosystem recovery vary among ecosystems is poorly understood. Here we use remote sensing-derived high-resolution evapotranspiration (ET) estimates from before and after 1,514 fires to show that ecoregions dominated by grasslands and shrublands are more susceptible to drought, which amplifies fire-induced ET decline and, subsequently, shifts water flux partitioning. In contrast, severely burned forests recover from fire slowly or incompletely, but are less sensitive to dry extremes. We conclude that moisture limitation caused by droughts influences the dynamics of water balance recovery in post-fire years. This finding explains why moderate to extreme droughts aggravate impacts on the water balance in non-forested vegetation, while moisture accessed by deeper roots in forests helps meet evaporative demands unless severe burns disrupt internal tree structure and deplete fuel load availability. Our results highlight the dominant control of drought on altering the resilience of vegetation to fires, with critical implications for terrestrial ecosystem stability in the face of anthropogenic climate change in the West.

Fracking pond restoration in the western Rio Grande Plains, Texas: Stockpiled topsoils may not be necessary, but seeding helps.

Soil disturbances that accompany energy development can damage local habitats. Prior to oil and gas extraction, it is commonly recommended that topsoil stockpiles be created to aid future restoration. Our study area, a retired fracking pond in the western Rio Grande Plains, Texas, was restored in 2017 with stockpiled topsoil that was collected in 2013. We segregated the existing stockpile into three layers that were ∼1.5 m in thickness and distributed these layers, along with a non-amended control surface (consisting of former subsoil that made up the perimeter of the fracking pond), in strips over the restoration area. Each of the four surfaces was seeded with a mixture of (1) 13 native grasses, (2) 13 native grasses plus an annual warm-season grass cover crop, or (3) non-seeded. We monitored plant density and species composition two through five years post-restoration. The non-amended control surface had higher seeded grass density during the final 2 sampling periods; stockpiled surfaces seldom differed from each other. Previous year's competing plant density had little effect on restoration success. Providing supplemental seed initially increased seeded plant density but benefits diminished over time; adding a cover crop was not advantageous. Changes in community composition over time were similar on stockpile surfaces but more variable than observed on the control surface. Results suggest that stockpiling topsoil may not be necessary, but that supplemental seeding was beneficial, to restoration success.

Effects of meteorological and land surface modeling uncertainty on errors in winegrape ET calculated with SIMS.

Characterization of model errors is important when applying satellite-driven evapotranspiration (ET) models to water resource management problems. This study examines how uncertainty in meteorological forcing data and land surface modeling propagate through to errors in final ET data calculated using the Satellite Irrigation Management Support (SIMS) model, a computationally efficient ET model driven with satellite surface reflectance values. The model is applied to three instrumented winegrape vineyards over the 2017-2020 time period and the spatial and temporal variation in errors are analyzed. We illustrate how meteorological data inputs can introduce biases that vary in space and at seasonal timescales, but that can persist from year to year. We also observe that errors in SIMS estimates of land surface conductance can have a particularly strong dependence on time of year. Overall, meteorological inputs introduced RMSE of 0.33-0.65 mm/day (7-27%) across sites, while SIMS introduced RMSE of 0.55-0.83 mm/day (19-24%). The relative error contribution from meteorological inputs versus SIMS varied across sites; errors from SIMS were larger at one site, errors from meteorological inputs were larger at a second site, and the error contributions were of equal magnitude at the third site. The similar magnitude of error contributions is significant given that many satellite-driven ET models differ in their approaches to estimating land surface conductance, but often rely on similar or identical meteorological forcing data. The finding is particularly notable given that SIMS makes assumptions about the land surface (no soil evaporation or plant water stress) that do not always hold in practice. The results of this study show that improving SIMS by eliminating these assumptions would result in meteorological inputs dominating the error budget of the model on the whole. This finding underscores the need for further work on characterizing spatial uncertainty in the meteorological forcing of ET. Supplementary Information: The online version contains supplementary material available at 10.1007/s00271-022-00808-9.

Surgical incidents and their impact on operating theatre staff: qualitative study.

BACKGROUND: Surgical incidents can have significant effects on both patients and health professionals, including emotional distress and depression. The aim of this study was to explore the personal and professional impacts of surgical incidents on operating theatre staff. METHODS: Face-to-face semistructured interviews were conducted with a range of different healthcare professionals working in operating theatres, including surgeons and anaesthetists, operating department practitioners, and theatre nurses, and across different surgical specialties at five different hospitals. All interviews were audio recorded, transcribed verbatim, and analysed using an inductive thematic approach, which involved reading and re-reading the transcripts, assigning preliminary codes, and searching for patterns and themes within the codes, with the aid of NVivo 12 software. These emerging themes were discussed with the wider research team to gain their input. RESULTS: Some 45 interviews were conducted, generally lasting between 30 and 75 min. Three overarching themes emerged: personal and professional impact; impact of the investigation process; and positive consequences or impact. Participants recalled experiencing negative emotions following surgical incidents that depended on the severity of the incident, patient outcomes, and the support that staff received. A culture of blame, inadequate support, and lack of a clear and transparent investigative process appeared to worsen impact. CONCLUSION: The study indicated that more support is needed for operating theatre staff involved in surgical incidents. Greater transparency and better information during the investigation of such incidents for staff are still needed.

A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation.

BACKGROUND: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. OBJECTIVE: In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. METHODS: For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. RESULTS: Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. CONCLUSIONS: Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.

A Survey of Dense Low Energy Ions in Earth's Outer Magnetosphere: Relation to Solar Wind Dynamic Pressure, IMF, and Magnetospheric Activity.

The properties of cold, dense, low energy ( < 150 eV) ions within Earth's magnetosphere between 6 and 14 R E distance are examined using data sampled by Time History of Events and Macroscale Interactions during Substorms spacecraft during a new low-energy plasma mode that operated from June 2016 to July 2017. These ions are a persistent feature of the magnetosphere during enhanced solar wind dynamic pressure and/or magnetospheric activity. These ions have densities ranging from 0.5 to tens of c m - 3 , with a mean of ∼ 1 c m - 3 and temperatures of a few to tens of eV, with a mean of ∼ 13 eV. These yield cold to hot ion density and temperature ratios that are 4.4 and 4 × 1 0 - 3 , respectively. Comparisons reveal that the cold ion densities are positively correlated with solar wind dynamic pressure. These ions are organizable, according to their pitch-angle distribution, as being transverse/convection dominated (interpreted as plume plasma) or magnetic field-aligned (FAL) (uni- or bi-directional characteristic of ion outflow or cloak plasma). Transverse ions preferentially occur in the prenoon to dusk sectors during sustained active magnetospheric conditions driven by enhanced solar wind dynamic pressure under southward B z and westward B y IMF orientations. Transverse ion velocities (reaching several tens of km/s) have a westward directed tendency with a slight radially outward preference. In contrast FAL ions preferentially occur from morning to noon during northward IMF orientations, enhanced solar wind dynamic pressure, and quiet magnetospheric conditions within several hours after moderate to strong activity. The FAL ions also have bulk velocities ≲ 30 km/s, with an eastward and radially outward tendency.

Probing Depth-Dependent Transition-Metal Redox of Lithium Nickel, Manganese, and Cobalt Oxides in Li-Ion Batteries.

Layered lithium nickel, manganese, and cobalt oxides (NMC) are among the most promising commercial positive electrodes in the past decades. Understanding the detailed surface and bulk redox processes of Ni-rich NMC can provide useful insights into material design options to boost reversible capacity and cycle life. Both hard X-ray absorption (XAS) of metal K-edges and soft XAS of metal L-edges collected from charged LiNi0.6Mn0.2Co0.2O2 (NMC622) and LiNi0.8Mn0.1Co0.1O2 (NMC811) showed that the charge capacity up to removing ∼0.7 Li/f.u. was accompanied with Ni oxidation in bulk and near the surface (up to 100 nm). Of significance to note is that nickel oxidation is primarily responsible for the charge capacity of NMC622 and 811 up to similar lithium removal (∼0.7 Li/f.u.) albeit charged to different potentials, beyond which was followed by Ni reduction near the surface (up to 100 nm) due to oxygen release and electrolyte parasitic reactions. This observation points toward several new strategies to enhance reversible redox capacities of Ni-rich and/or Co-free electrodes for high-energy Li-ion batteries.

Lithium-enriched graphite anode surfaces investigated using nuclear reaction analysis.

Non-destructive Li nuclear reaction analysis techniques were used to profile the Li distribution at the surface of graphitic Li-ion battery anodes. These techniques show that Li concentrations are elevated within 300 nm of the anode surface, even in fully delithiated states. The surface region, which includes the solid electrolyte interphase, contains at least 60% of the total Li irreversibly lost during formation and cycling.

Accurately Differentiating COVID-19, Other Viral Infection, and Healthy Individuals Using Multimodal Features via Late Fusion Learning

Effectively identifying COVID-19 patients using non-PCR clinical data is critical for the optimal clinical outcomes. Currently, there is a lack of comprehensive understanding of various biomedical features and appropriate technical approaches to accurately detecting COVID-19 patients. In this study, we recruited 214 confirmed COVID-19 patients in non-severe (NS) and 148 in severe (S) clinical type, 198 non-infected healthy (H) participants and 129 non-COVID viral pneumonia (V) patients. The participants clinical information (23 features), lab testing results (10 features), and thoracic CT scans upon admission were acquired as three input feature modalities. To enable late fusion of multimodality data, we developed a deep learning model to extract a 10-feature high-level representation of the CT scans. Exploratory analyses showed substantial differences of all features among the four classes. Three machine learning models (k-nearest neighbor kNN, random forest RF, and support vector machine SVM) were developed based on the 43 features combined from all three modalities to differentiate four classes (NS, S, V, and H) at once. All three models had high accuracy to differentiate the overall four classes (95.4%-97.7%) and each individual class (90.6%-99.9%). Multimodal features provided substantial performance gain from using any single feature modality. Compared to existing binary classification benchmarks often focusing on single feature modality, this study provided a novel and effective breakthrough for clinical applications. Findings and the analytical workflow can be used as clinical decision support for current COVID-19 and other clinical applications with high-dimensional multimodal biomedical features. One sentence summaryWe trained and validated late fusion deep learning-machine learning models to predict non-severe COVID-19, severe COVID-19, non-COVID viral infection, and healthy classes from clinical, lab testing, and CT scan features extracted from convolutional neural network and achieved predictive accuracy of > 96% to differentiate all four classes at once based on a large dataset of 689 participants.

What is the 'spectral diet' of humans?

Our visual perception of the world - seeing form and colour or navigating the environment - depends on the interaction of light and matter in the environment. Light also has a more fundamental role in regulating rhythms in physiology and behaviour, as well as in the acute secretion of hormones such as melatonin and changes in alertness, where light exposure at short-time, medium-time and long-time scales has different effects on these visual and non-visual functions. Yet patterns of light exposure in the real world are inherently messy: we move in and out of buildings and are therefore exposed to mixtures of artificial and natural light, and the physical makeup of our environment can also drastically alter the spectral composition and spatial distribution of the emitted light. In spatial vision, the examination of natural image statistics has proven to be an important driver in research. Here, we expand this concept to the spectral domain and develop the concept of the 'spectral diet' of humans.

Correlating structure and transport behavior in Li+ and O2 containing pyrrolidinium ionic liquids.

Ionic liquids are a unique class of materials with several potential applications in electrochemical energy storage. When used in electrolytes, these highly coordinating solvents can influence device performance through their high viscosities and strong solvation behaviors. In this work, we explore the effects of pyrrolidinium cation structure and Li+ concentration on transport processes in ionic liquid electrolytes. We present correlated experimental measurements and molecular simulations of Li+ mobility and O2 diffusivity, and connect these results to dynamic molecular structural information and device performance. In the context of Li-O2/Li-air battery chemistries, we find that Li+ mobility is largely influenced by Li+-anion coordination, but that both Li+ and O2 diffusion may be affected by variations of the pyrrolidinium cation and Li+ concentration.

Non-Faradaic Li+ Migration and Chemical Coordination across Solid-State Battery Interfaces.

Efficient and reversible charge transfer is essential to realizing high-performance solid-state batteries. Efforts to enhance charge transfer at critical electrode-electrolyte interfaces have proven successful, yet interfacial chemistry and its impact on cell function remains poorly understood. Using X-ray photoelectron spectroscopy combined with electrochemical techniques, we elucidate chemical coordination near the LiCoO2-LIPON interface, providing experimental validation of space-charge separation. Space-charge layers, defined by local enrichment and depletion of charges, have previously been theorized and modeled, but the unique chemistry of solid-state battery interfaces is now revealed. Here we highlight the non-Faradaic migration of Li+ ions from the electrode to the electrolyte, which reduces reversible cathodic capacity by ∼15%. Inserting a thin, ion-conducting LiNbO3 interlayer between the electrode and electrolyte, however, can reduce space-charge separation, mitigate the loss of Li+ from LiCoO2, and return cathodic capacity to its theoretical value. This work illustrates the importance of interfacial chemistry in understanding and improving solid-state batteries.

Exploring a wider range of Mg-Ca-Zn metallic glass as biocompatible alloys using combinatorial sputtering.

In order to bypass the limitation of bulk metallic glasses fabrication, we synthesized thin film metallic glasses to study the corrosion characteristics of a wide atomic% composition range, Mg(35.9-63%)Ca(4.1-21%)Zn(17.9-58.3%), in simulated body fluid. We highlight a clear relationship between Zn content and corrosion current such that Zn-medium metallic glasses exhibit minimum corrosion. In addition, we found higher Zn content leads to a poor in vitro cell viability. These results showcase the benefit of evaluating a larger alloy compositional space to probe the limits of corrosion resistance and prescreen for biocompatible applications.

Combined effect of toe out gait and high tibial osteotomy on knee adduction moment in patients with varus knee deformity.

BACKGROUND: Gait adaptations, including toe out gait, have been proposed as treatments for knee osteoarthritis. The clinical application of toe out gait, however, is unclear. This study aims to identify the changes in Knee adduction moment in varus knee deformity assessing toe out gait as an alternative to high tibial osteotomy, and if any change in dynamic loading persists post operatively, when anatomical alignment is restored. METHODS: Three-dimensional motion analysis was performed on 17 patients with medial compartment knee osteoarthritis and varus deformity prior to undergoing high tibial osteotomy, 13 patients were assessed post-operatively, and results compared to 13 healthy controls. FINDINGS: Pre-operatively, there was no significant difference between natural and toe out gait for measures of knee adduction moment. Post high tibial osteotomy, first (2.70 to 1.51% BW·h) and second peak (2.28 to 1.21% BW·h) knee adduction moment were significantly reduced, as was knee adduction angular impulse (1.00 to 0.52% BW·h·s), to a healthy level. Adopting toe out gait post-operatively reduced the second peak further to a level below that of healthy controls. INTERPRETATION: Increasing the foot progression angle from 20° (natural) to 30° in isolation did not significantly alter the knee adduction moment or angular impulse. This suggests that adopting a toe out gait, in isolation, in an already high natural foot progression angle, is not of benefit. Adopting toe out gait post-operatively, however, resulted in a further reduction in the second peak to below that of the healthy control cohort, however, this may increase lateral compartment load.

Mindboggling morphometry of human brains.

Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle's algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available.

Systematic review of psychological, emotional and behavioural impacts of surgical incidents on operating theatre staff.

BACKGROUND: Adverse surgical incidents affect both patients and health professionals. This study sought to explore the effect of surgical incidents on operating theatre staff and their subsequent behaviours. METHODS: Eligible studies were primary research or reviews that focused on the effect of incidents on operating theatre staff in primary, secondary or tertiary care settings. MEDLINE, Embase, CINALH and PsycINFO were searched. A data extraction form was used to capture pertinent information from included studies and the Critical Appraisal Skills Programme (CASP) tool to appraise their quality. PRISMA-P reporting guidelines were followed and the review is registered with PROSPERO. RESULTS: A total of 3918 articles were identified, with 667 duplicates removed and 3230 excluded at the title, abstract and full-text stages. Of 21 included articles, eight focused on the impact of surgical incidents on surgeons and anaesthetists. Only two involved theatre nurses and theatre technicians. Five key themes emerged: the emotional impact on health professionals, organization culture and support, individual coping strategies, learning from surgical complications and recommended changes to practice. CONCLUSION: Health professionals suffered emotional distress and often changed their behaviour following a surgical incident. Both organizations and individual clinicians can do a great deal to support staff in the aftermath of serious incidents.

Synthesis and characterization of fluorinated azadipyrromethene complexes as acceptors for organic photovoltaics.

Homoleptic zinc(II) complexes of di(phenylacetylene)azadipyrromethene (e.g., Zn(WS3)2) are potential non-fullerene electron acceptors for organic photovoltaics. To tune their properties, fluorination of Zn(WS3)2 at various positions was investigated. Three fluorinated azadipyrromethene-based ligands were synthesized with fluorine at the para-position of the proximal and distal phenyl groups, and at the pyrrolic phenylacetylene moieties. Additionally, a CF3 moiety was added to the pyrrolic phenyl positions to study the effects of a stronger electron withdrawing unit at that position. The four ligands were chelated with zinc(II) and BF2+ and the optical and electrochemical properties were studied. Fluorination had little effect on the optical properties of both the zinc(II) and BF2+ complexes, with λmax in solution around 755 nm and 785 nm, and high molar absorptivities of 100 × 103 M-1cm-1 and 50 × 103 M-1cm-1, respectively. Fluorination of Zn(WS3)2 raised the oxidation potentials by 0.04 V to 0.10 V, and the reduction potentials by 0.01 V to 0.10 V, depending on the position and type of substitution. The largest change was observed for fluorine substitution at the proximal phenyl groups and CF3 substitution at the pyrrolic phenylacetylene moieties. The later complexes are expected to be stronger electron acceptors than Zn(WS3)2, and may enable charge transfer from other conjugated polymer donors that have lower energy levels than poly(3-hexylthiophene) (P3HT).

Heme biomolecule as redox mediator and oxygen shuttle for efficient charging of lithium-oxygen batteries.

One of the greatest challenges with lithium-oxygen batteries involves identifying catalysts that facilitate the growth and evolution of cathode species on an oxygen electrode. Heterogeneous solid catalysts cannot adequately address the problematic overpotentials when the surfaces become passivated. However, there exists a class of biomolecules which have been designed by nature to guide complex solution-based oxygen chemistries. Here, we show that the heme molecule, a common porphyrin cofactor in blood, can function as a soluble redox catalyst and oxygen shuttle for efficient oxygen evolution in non-aqueous Li-O2 batteries. The heme's oxygen binding capability facilitates battery recharge by accepting and releasing dissociated oxygen species while benefiting charge transfer with the cathode. We reveal the chemical change of heme redox molecules where synergy exists with the electrolyte species. This study brings focus to the rational design of solution-based catalysts and suggests a sustainable cross-link between biomolecules and advanced energy storage.

A New Design Strategy for Observing Lithium Oxide Growth-Evolution Interactions Using Geometric Catalyst Positioning.

Understanding the catalyzed formation and evolution of lithium-oxide products in Li-O2 batteries is central to the development of next-generation energy storage technology. Catalytic sites, while effective in lowering reaction barriers, often become deactivated when placed on the surface of an oxygen electrode due to passivation by solid products. Here we investigate a mechanism for alleviating catalyst deactivation by dispersing Pd catalytic sites away from the oxygen electrode surface in a well-structured anodic aluminum oxide (AAO) porous membrane interlayer. We observe the cross-sectional product growth and evolution in Li-O2 cells by characterizing products that grow from the electrode surface. Morphological and structural details of the products in both catalyzed and uncatalyzed cells are investigated independently from the influence of the oxygen electrode. We find that the geometric decoration of catalysts far from the conductive electrode surface significantly improves the reaction reversibility by chemically facilitating the oxidation reaction through local coordination with PdO surfaces. The influence of the catalyst position on product composition is further verified by ex situ X-ray photoelectron spectroscopy and Raman spectroscopy in addition to morphological studies.