Climate change determines the sign of productivity trends in US forests.

Forests are integral to the global land carbon sink, which has sequestered ~30% of anthropogenic carbon emissions over recent decades. The persistence of this sink depends on the balance of positive drivers that increase ecosystem carbon storage-e.g., CO2 fertilization-and negative drivers that decrease it-e.g., intensifying disturbances. The net response of forest productivity to these drivers is uncertain due to the challenge of separating their effects from background disturbance-regrowth dynamics. We fit non-linear models to US forest inventory data (113,806 plot remeasurements in non-plantation forests from ~1999 to 2020) to quantify productivity trends while accounting for stand age, tree mortality, and harvest. Productivity trends were generally positive in the eastern United States, where climate change has been mild, and negative in the western United States, where climate change has been more severe. Productivity declines in the western United States cannot be explained by increased mortality or harvest; these declines likely reflect adverse climate-change impacts on tree growth. In the eastern United States, where data were available to partition biomass change into age-dependent and age-independent components, forest maturation and increasing productivity (likely due, at least in part, to CO2 fertilization) contributed roughly equally to biomass carbon sinks. Thus, adverse effects of climate change appear to overwhelm any positive drivers in the water-limited forests of the western United States, whereas forest maturation and positive responses to age-independent drivers contribute to eastern US carbon sinks. The future land carbon balance of forests will likely depend on the geographic extent of drought and heat stress.

Adaptive nonequilibrium design of actin-based metamaterials: Fundamental and practical limits of control.

The adaptive and surprising emergent properties of biological materials self-assembled in far-from-equilibrium environments serve as an inspiration for efforts to design nanomaterials. In particular, controlling the conditions of self-assembly can modulate material properties, but there is no systematic understanding of either how to parameterize external control or how controllable a given material can be. Here, we demonstrate that branched actin networks can be encoded with metamaterial properties by dynamically controlling the applied force under which they grow and that the protocols can be selected using multi-task reinforcement learning. These actin networks have tunable responses over a large dynamic range depending on the chosen external protocol, providing a pathway to encoding "memory" within these structures. Interestingly, we obtain a bound that relates the dissipation rate and the rate of "encoding" that gives insight into the constraints on control-both physical and information theoretical. Taken together, these results emphasize the utility and necessity of nonequilibrium control for designing self-assembled nanostructures.

Adaptation of SARS-CoV-2 to ACE2H353K mice reveals new spike residues that drive mouse infection.

The Coronavirus Disease 2019 (COVID-19) pandemic continues to cause extraordinary loss of life and economic damage. Animal models of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection are needed to better understand disease pathogenesis and evaluate preventive measures and therapies. While mice are widely used to model human disease, mouse angiotensin converting enzyme 2 (ACE2) does not bind the ancestral SARS-CoV-2 spike protein to mediate viral entry. To overcome this limitation, we "humanized" mouse Ace2 using CRISPR gene editing to introduce a single amino acid substitution, H353K, predicted to facilitate S protein binding. While H353K knockin Ace2 (mACE2H353K) mice supported SARS-CoV-2 infection and replication, they exhibited minimal disease manifestations. Following 30 serial passages of ancestral SARS-CoV-2 in mACE2H353K mice, we generated and cloned a more virulent virus. A single isolate (SARS2MA-H353K) was prepared for detailed studies. In 7-11-month-old mACE2H353K mice, a 104 PFU inocula resulted in diffuse alveolar disease manifested as edema, hyaline membrane formation, and interstitial cellular infiltration/thickening. Unexpectedly, the mouse-adapted virus also infected standard BALB/c and C57BL/6 mice and caused severe disease. The mouse-adapted virus acquired five new missense mutations including two in spike (K417E, Q493K), one each in nsp4, nsp9, and M and a single nucleotide change in the 5' untranslated region. The Q493K spike mutation arose early in serial passage and is predicted to provide affinity-enhancing molecular interactions with mACE2 and further increase the stability and affinity to the receptor. This new model and mouse-adapted virus will be useful to evaluate COVID-19 disease and prophylactic and therapeutic interventions.IMPORTANCEWe developed a new mouse model with a humanized angiotensin converting enzyme 2 (ACE2) locus that preserves native regulatory elements. A single point mutation in mouse ACE2 (H353K) was sufficient to confer in vivo infection with ancestral severe acute respiratory syndrome-coronavirus-2 virus. Through in vivo serial passage, a virulent mouse-adapted strain was obtained. In aged mACE2H353K mice, the mouse-adapted strain caused diffuse alveolar disease. The mouse-adapted virus also infected standard BALB/c and C57BL/6 mice, causing severe disease. The mouse-adapted virus acquired five new missense mutations including two in spike (K417E, Q493K), one each in nsp4, nsp9, and M and a single nucleotide change in the 5' untranslated region. The Q493K spike mutation arose early in serial passage and is predicted to provide affinity-enhancing molecular interactions with mACE2 and further increase the stability and affinity to the receptor. This new model and mouse-adapted virus will be useful to evaluate COVID-19 disease and prophylactic and therapeutic interventions.

Threshold and Laser Conversion in Nanostructured-Resonator Parametric Oscillators.

We explore optical parametric oscillation (OPO) in nanophotonic resonators, enabling arbitrary, nonlinear phase matching and nearly lossless control of energy conversion. Such pristine OPO laser converters are determined by nonlinear light-matter interactions, making them both technologically flexible and broadly reconfigurable. We utilize a nanostructured inner-wall modulation in the resonator to achieve universal phase matching for OPO-laser conversion, but coherent backscattering also induces a counterpropagating pump laser. This depletes the intraresonator optical power in either direction, increasing the OPO threshold power and limiting laser-conversion efficiency, the ratio of optical power in target signal and idler frequencies to the pump. We develop an analytical model of this system that emphasizes an understanding of optimal laser-conversion and threshold behaviors, and we use the model to guide experiments with nanostructured-resonator OPO laser-conversion circuits, fully integrated on chip and unlimited by group-velocity dispersion. Our Letter demonstrates the fundamental connection between OPO laser-conversion efficiency and the resonator coupling rate, subject to the relative phase and power of counterpropagating pump fields. We achieve (40±4) mW of on-chip power, corresponding to (41±4)% conversion efficiency, and discover a path toward near-unity OPO laser-conversion efficiency.

Microscopic origin of tunable assembly forces in chiral active environments.

Across a variety of spatial scales, from nanoscale biological systems to micron-scale colloidal systems, equilibrium self-assembly is entirely dictated by-and therefore limited by-the thermodynamic properties of the constituent materials. In contrast, nonequilibrium materials, such as self-propelled active matter, expand the possibilities for driving the assemblies that are inaccessible in equilibrium conditions. Recently, a number of works have suggested that active matter drives or accelerates self-organization, but the emergent interactions that arise between solutes immersed in actively driven environments are complex and poorly understood. Here, we analyze and resolve two crucial questions concerning actively driven self-assembly: (i) how, mechanistically, do active environments drive self-assembly of passive solutes? (ii) Under which conditions is this assembly robust? We employ the framework of odd hydrodynamics to theoretically explain numerical and experimental observations that chiral active matter, i.e., particles driven with a directional torque, produces robust and long-ranged assembly forces. Together, these developments constitute an important step towards a comprehensive theoretical framework for controlling self-assembly in nonequilibrium environments.

Comparisons between dual-energy X-ray absorptiometry and bioimpedance devices for appendicular lean mass and muscle quality in Hispanic adults.

The purpose of this study was to compare single- and multi-frequency bioimpedance (BIA) devices against dual-energy X-ray absorptiometry (DXA) for appendicular lean mass (ALM) and muscle quality index (MQI) metrics in Hispanic adults. One hundred thirty-one Hispanic adults (18-55 years) participated in this study. ALM was measured with single-frequency bioimpedance analysis (SFBIA), multi-frequency bioimpedance analysis (MFBIA) and DXA. ALMTOTAL (left arm + right arm + left leg + right leg) and ALMARMS (left arm + right arm) were computed for all three devices. Handgrip strength (HGS) was measured using a dynamometer. The average HGS was used for all MQI models (highest left hand + highest right hand)/2. MQIARMS was defined as the ratio between HGS and ALMARMS. MQITOTAL was established as the ratio between HGS and ALMTOTAL. SFBIA and MFBIA had strong correlations with DXA for all ALM and MQI metrics (Lin's concordance correlation coefficient values ranged from 0·86 (MQIMFBIA-ARMS) to 0·97 (Arms LMSFBIA); all P < 0·001). Equivalence testing varied between methods (e.g. SFBIA v. DXA) when examining the different metrics (i.e. ALMTOTAL, ALMARMS, MQITOTAL and MQIARMS). MQIARMS was the only metric that did not differ from the line of identity and had no proportional bias when comparing all the devices against each other. The current study findings demonstrate good overall agreement between SFBIA, MFBIA and DXA for ALMTOTAL and ALMARMS in a Hispanic population. However, SFBIA and MFBIA have better agreement with DXA when used to compute MQIARMS than MQITOTAL.

Primary care usage at the end of life: a retrospective cohort study of cancer patients using linked primary and hospital care data.

PURPOSE: Health service use is most intensive in the final year of a person's life, with 80% of this expenditure occurring in hospital. Close involvement of primary care services has been promoted to enhance quality end-of-life care that is appropriate to the needs of patients. However, the relationship between primary care involvement and patients' use of hospital care is not well described. This study aims to examine primary care use in the last year of life for cancer patients and its relationship to hospital usage. METHODS: Retrospective cohort study in Victoria, Australia, using linked routine care data from primary care, hospital and death certificates. Patients were included who died related to cancer between 2008 and 2017. RESULTS: A total of 758 patients were included, of whom 88% (n = 667) visited primary care during the last 6 months (median 9.1 consultations). In the last month of life, 45% of patients were prescribed opioids, and 3% had imaging requested. Patients who received home visits (13%) or anticipatory medications (15%) had less than half the median bed days in the last 3 months (4 vs 9 days, p < 0.001, 5 vs 10 days, p = 0.001) and 1 month of life (0 vs 2 days, p = 0.002, 0 vs 3 days, p < 0.001), and reduced emergency department presentations (32% vs 46%, p = 0.006, 31% vs 47% p < 0.001) in the final month. CONCLUSION: This study identifies two important primary care processes-home visits and anticipatory medication-associated with reduced hospital usage and intervention at the end of life.

Computing equilibrium free energies through a nonequilibrium quench.

Many methods to accelerate sampling of molecular configurations are based on the idea that temperature can be used to accelerate rare transitions. These methods typically compute equilibrium properties at a target temperature using reweighting or through Monte Carlo exchanges between replicas at higher temperatures. A recent paper [G. M. Rotskoff and E. Vanden-Eijnden, Phys. Rev. Lett. 122, 150602 (2019)] demonstrated that accurate equilibrium densities of states can also be computed through a nonequilibrium "quench" process, where sampling is performed at a higher temperature to encourage rapid mixing and then quenched to lower energy states with dissipative dynamics. Here, we provide an implementation of the quench dynamics in LAMMPS and evaluate a new formulation of nonequilibrium estimators for the computation of partition functions or free energy surfaces (FESs) of molecular systems. We show that the method is exact for a minimal model of N-independent harmonic springs and use these analytical results to develop heuristics for the amount of quenching required to obtain accurate sampling. We then test the quench approach on alanine dipeptide, where we show that it gives an FES that is accurate near the most stable configurations using the quench approach but disagrees with a reference umbrella sampling calculation in high FE regions. We then show that combining quenching with umbrella sampling allows the efficient calculation of the free energy in all regions. Moreover, by using this combined scheme, we obtain the FES across a range of temperatures at no additional cost, making it much more efficient than standard umbrella sampling if this information is required. Finally, we discuss how this approach can be extended to solute tempering and demonstrate that it is highly accurate for the case of solvated alanine dipeptide without any additional modifications.

Controlled Stiffness of Direct-Write, Near-Field Electrospun Gelatin Fibers Generates Differences in Tenocyte Morphology and Gene Expression.

Tendinopathy is a leading cause of mobility issues. Currently, the cell-matrix interactions involved in the development of tendinopathy are not fully understood. In vitro tendon models provide a unique tool for addressing this knowledge gap as they permit fine control over biochemical, micromechanical, and structural aspects of the local environment to explore cell-matrix interactions. In this study, direct-write, near-field electrospinning of gelatin solution was implemented to fabricate micron-scale fibrous scaffolds that mimic native collagen fiber size and orientation. The stiffness of these fibrous scaffolds was found to be controllable between 1 MPa and 8 MPa using different crosslinking methods (EDC, DHT, DHT+EDC) or through altering the duration of crosslinking with EDC (1 h to 24 h). EDC crosslinking provided the greatest fiber stability, surviving up to 3 weeks in vitro. Differences in stiffness resulted in phenotypic changes for equine tenocytes with low stiffness fibers (∼1 MPa) promoting an elongated nuclear aspect ratio while those on high stiffness fibers (∼8 MPa) were rounded. High stiffness fibers resulted in the upregulation of matrix metalloproteinase (MMPs) and proteoglycans (possible indicators for tendinopathy) relative to low stiffness fibers. These results demonstrate the feasibility of direct-written gelatin scaffolds as tendon in vitro models and provide evidence that matrix mechanical properties may be crucial factors in cell-matrix interactions during tendinopathy formation.

Propeller Flap Reconstruction Following Pilonidal Cyst Excision: A Single-Center Experience With Same-Day Discharge.

INTRODUCTION: Reconstruction following pilonidal cyst resection must balance risk of recurrence, healing time, and resumption of functional routine. Propeller flaps provide a reliable and effective reconstructive option. This study highlights our experience with propeller flap reconstruction following pilonidal cyst resection and demonstrates the efficacy of same-day discharge. METHODS: A single-institution retrospective chart review was performed for propeller flap reconstructions completed from March 2018 to July 2022. Patient demographics, pilonidal cyst details, operative details, and postoperative outcomes were collected. Primary outcomes included flap survival, flap complications, and pilonidal disease recurrence. RESULTS: Twenty-eight outpatient propeller flap reconstructions following pilonidal cyst resections were identified in 26 patients, with two patients receiving a second propeller flap due to recurrence. Most patients were male (n = 15, 57.7%) with a mean age at time of index operation of 25.5 ± 5.8 years and mean body mass index of 26.5 ± 4.1 kg/m2. Mean symptom duration prior to index surgery was 39.3 months. Mean skin defect size following resection was 28.3 ± 15.3 cm2, with a mean flap size of 44.7 ± 35.5 cm2. Flap survival was 100% (n = 28), with five flaps (17.9%) experiencing minor wound complications and one patient (3.8%) requiring return to the operating room. Mean time to functional improvement was 24.0 ± 22.8 days. Pilonidal disease recurrence occurred in three patients (11.5%). Mean follow-up was 4.1 ± 5.4 months. CONCLUSIONS: Propeller flaps provide a successful and reliable reconstructive option for pilonidal disease defects. Because patients in our cohort experienced favorable outcomes and functional improvement, we advocate for same-day discharge in order to reduce hospital and patient burden.

Tracking Body Composition Over a Competitive Season in Elite Soccer Players Using Laboratory- and Field-Based Assessment Methods.

ABSTRACT: Bongiovanni, T, Lacome, M, Rodriguez, C, and Tinsley, GM. Tracking body composition over a competitive season in elite soccer players using laboratory- and field-based assessment methods. J Strength Cond Res 38(3): e104-e115, 2024-The purpose of this study was to describe body composition changes in professional soccer players over the course of a competitive playing season and compare the ability of different assessment methods to detect changes. Twenty-one elite male soccer players (age: 23.7 ± 4.8 years; height: 185.0 ± 5.2 cm; body mass: 80.7 ± 5.5 kg; body fat: 12.8 ± 2.2%) playing for an Italian national second league (Serie B) championship team were assessed at 4 time points throughout a competitive season: T0 (mid-October), T1 (mid-December), T2 (mid-February), and T3 (end of April). Dual-energy x-ray absorptiometry (DXA), skinfolds (SKF), and bioelectrical impedance analysis were performed at each time point, and multiple SKF-based equations were applied. A modified 4-compartment (4C) model was also produced. Data were analyzed using repeated measures analysis of variance, relevant post hoc tests, and Pearson's correlations. Dual-energy x-ray absorptiometry, 4C, and the SKF-based equations of Reilly and Civar detected differences in fat-free mass (FFM) between time points, with the most differences observed for DXA. Fat-free mass increased from T0 values to a peak at T2, followed by a decrease by T3, although FFM values remained higher than T0. Fat-free mass gain was primarily driven by increases in the lower limbs. Fat-free mass changes between all methods were significantly correlated, with correlation coefficients of 0.70-0.97. No significant differences between time points were observed for absolute fat mass or body fat percentage, although significant correlations between several methods for change values were observed. Select laboratory and field methods can detect changes in FFM over the course of a season in elite, professional soccer athletes, with a more limited ability to detect changes in adiposity-related variables. For SKF in this population, the equation of Reilly is recommended.

Factors influencing patient selection of orthopaedic surgeons for total hip (THA) and total knee arthroplasty (TKA).

INTRODUCTION: The importance of identifying how patients choose their healthcare providers has grown with the prevalence of consumer-centric health insurance plans. There is currently a lack of studies exploring the factors associated with how patients select their hip and knee joint arthroplasty surgeons. The purpose of this study was to determine how patients find their arthroplasty providers and the relative importance of various arthroplasty surgeon characteristics. METHODS: An electronic mail survey was sent to 3522 patients who had visited our institution for an arthroplasty surgeon office visit between August 2022 and January 2023. The survey consisted of multiple-choice questions, which aimed to inquire about the patients' referral sources for their current arthroplasty surgeon. In addition, patients were requested to rate the significance of 22 surgeon-related factors, on a scale of 1 (Not Important At All) to 5 (Very Important), in choosing their arthroplasty surgeon. RESULTS: Of the 3522 patients that received the survey, 538 patients responded (15.3%). The most common referral sources were physician referral (50.2%), family/friend referral (27.7%), and self-guided research (24.5%). Of those that were referred by a physician, 54.4% of respondents were referred by another orthopaedic provider. Patients rated board certification (4.72 ± 0.65), in-network insurance status (4.66 ± 0.71), fellowship training (4.50 ± 0.81), bedside manner/personality (4.32 ± 0.86), and facility appearance (4.26 ± 0.81) as the five most important factors in picking an arthroplasty surgeon. Television (1.42 ± 0.83), print (1.50 ± 0.88), and online (1.58 ± 0.93) advertisements, along with social media presence (1.83 ± 1.08), and practice group size (2.97 ± 1.13) were rated as the five least important factors. CONCLUSION: Patients are most likely to select an arthroplasty surgeon based on referral from other physicians, namely orthopedic surgeons, in addition to board certification status, in-network insurance, and fellowship training. Overall, these findings highlight the importance of physician credentials and reputation within the orthopaedic community in order to attract and retain patients.

The effect of mephedrone on human neuroblastoma and astrocytoma cells.

Mephedrone is an illegal drug that is used recreationally. Few studies have been conducted to investigate the mechanisms by which mephedrone is harming cells. In this research, we investigated the effect of mephedrone using toxicology coupled with LC-MS/MS based metabolomics in the two CNS derived cell lines. Methods of assessment such as neutral red (NR) assay, dimethylthiazolyl diphenyltetrazolium bromide (MTT), lactose dehydrogenase (LDH) measurement, and morphology were performed to identify the effect on cell viability and to identify the best concentration to be used in a metabolomics study. A concentration of 100 µM of mephedrone was used in the metabolomic experiment because at this concentration mephedrone had induced several intracellular changes. Although there no clear indicators of cellular damage caused by mephedrone. In astrocytes there was a clear indication that cell membrane function might be impaired by depletion of ether lipids.

Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers.

Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models on a diverse set of high-dimensional sampling tasks. Unlike image generation or even the closely related problem of protein structure prediction, there are currently no data sources with sufficient breadth to parametrize generative models for conformational ensembles. To enable discovery, a fundamentally different approach to building generative models is required: models should be able to propose rare, albeit physical, conformations that may not arise in even the largest data sets. Here we introduce a modular strategy to generate conformations based on "backmapping" from a fixed protein backbone that (1) maintains conformational diversity of the side chains and (2) couples the side-chain fluctuations using global information about the protein conformation. Our model combines simple statistical models of side-chain conformations based on rotamer libraries with the now ubiquitous transformer architecture to sample with atomistic accuracy. Together, these ingredients provide a strategy for rapid data acquisition and hence a crucial ingredient for scalable physical simulation with generative neural networks.

Assessing Relative Linguistic Impairment With Model-Based Item Selection.

PURPOSE: A picture naming test is presented that reveals impairment to specific mechanisms involved in the naming process, using accuracy scores on curated item sets. A series of psychometric validation experiments are reported. METHOD: Using a computational model that enables estimation of item difficulty at the lexical and sublexical stages of word retrieval, two complimentary sets of items were constructed that challenge the respective psycholinguistic levels of representation. The difference in accuracy between these item sets yields the relative linguistic impairment (RLI) score. In a cohort of 91 people with chronic left-hemisphere stroke who enrolled in a clinical trial for anomia, we assessed psychometric properties of the RLI score and then used the new scale to make predictions about other language behaviors, lesion distributions, and functional activation during naming. RESULTS: RLI scores had adequate psychometric properties for clinical significance. RLI scores contained predictive information about spontaneous speech fluency, over and above accuracy. A dissociation was observed between performance on the RLI item sets and performance on the subtests of an independent language battery. Sublexical RLI was significantly associated with apraxia of speech and with lesions encompassing perisylvian regions, while lexical RLI was associated with lesions to deep white matter. The RLI construct was reflected in functional brain activity during naming, independent of overall accuracy, with a respective shift of activation between dorsal and ventral networks responsible for different aspects of word retrieval. CONCLUSION: The RLI assessment satisfies the psychometric requirements to serve as a useful clinical measure.

Improving wood carbon fractions for multiscale forest carbon estimation.

BACKGROUND: Wood carbon fractions (CFs)-the proportion of dry woody biomass comprised of elemental carbon (C)-are a key component of forest C estimation protocols and studies. Traditionally, a wood CF of 50% has been assumed in forest C estimation protocols, but recent studies have specifically quantified differences in wood CFs across several different forest biomes and taxonomic divisions, negating the need for generic wood CF assumptions. The Intergovernmental Panel on Climate Change (IPCC), in its 2006 "Guidelines for National Greenhouse Gas Inventories", published its own multitiered system of protocols for estimating forest C stocks, which included wood CFs that (1) were based on the best available literature (at the time) and (2) represented a significant improvement over the generic 50% wood CF assumption. However, a considerable number of new studies on wood CFs have been published since 2006, providing more accurate, robust, and spatially- and taxonomically- specific wood CFs for use in forest C estimation. MAIN TEXT: We argue that the IPCC's recommended wood CFs and those in many other forest C estimation models and protocols (1) differ substantially from, and are less robust than, wood CFs derived from recently published data-rich studies; and (2) may lead to nontrivial errors in forest C estimates, particularly for countries that rely heavily on Tier 1 forest C methods and protocols (e.g., countries of the Global South with large expanses of tropical forests). Based on previous studies on this topic, we propose an alternative set of refined wood CFs for use in multiscale forest C estimation, and propose a novel decision-making framework for integrating species- and location-specific wood CFs into forest C estimation models. CONCLUSION: The refined wood CFs that we present in this commentary may be used by the IPCC to update its recommended wood CFs for use in forest C estimation. Additionally, we propose a novel decision-making framework for integrating data-driven wood CFs into a wider suite of multitiered forest C estimation protocols, models, and studies.

Race and ethnicity moderate the associations between lifetime psilocybin use and past year hypertension.

Background: Hypertension is a major source of morbidity and mortality worldwide, particularly for racial and ethnic minorities who face higher rates of hypertension and worse health-related outcomes. Recent research has reported on protective associations between classic psychedelics and hypertension; however, there is a need to explore how race and ethnicity may moderate such associations. Methods: We used data from the National Survey on Drug Use and Health (2005-2014) to assess whether race and ethnicity moderate the associations between classic psychedelic use - specifically psilocybin - and past year hypertension. Results: Hispanic identity moderated the associations between psilocybin use and past year hypertension. Furthermore, individuals who used psilocybin and identified as Non-Hispanic White had reduced odds of hypertension (aOR: 0.83); however, these associations were not observed for any other racial or ethnic groups in our study for individuals who used psilocybin. Conclusion: Overall, our results demonstrate that the associations between psychedelics and hypertension may vary by race and ethnicity. Longitudinal studies and clinical trials can further advance this research and determine whether such differences exist in causal contexts. Project registration: https://osf.io/xsz2p/?view_only=0bf7b56749034c18abb2a3f8d3d4bc0b.

Application of the wildland fire emissions inventory system to estimate fire emissions on forest lands of the United States.

BACKGROUND: Forests are significant terrestrial biomes for carbon storage, and annual carbon accumulation of forest biomass contributes offsets affecting net greenhouse gases in the atmosphere. The immediate loss of stored carbon through fire on forest lands reduces the annual offsets provided by forests. As such, the United States reporting includes annual estimates of direct fire emissions in conjunction with the overall forest stock and change estimates as a part of national greenhouse gas inventories within the United Nations Framework Convention on Climate Change. Forest fire emissions reported for the United States, such as the 129 Tg CO2 reported for 2022, are based on the Wildland Fire Emissions Inventory System (WFEIS). Current WFEIS estimates are included in the Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2022 published in 2024 by the United States Environmental Protection Agency. Here, we describe WFEIS the fire emissions inventory system we used to address current information needs, and an analysis to confirm compatibility of carbon mass between estimated forest fire emissions and carbon in forest stocks. RESULTS: The summaries of emissions from forests are consistent with previous reports that show rates and interannual variability in emissions and forest land area burned are generally greater in recent years relative to the 1990s. Both emissions and interannual variability are greater in the western United States. The years with the highest CO2 emissions from forest fires on the 48 conterminous states plus Alaska were 2004, 2005, and 2015. In some years, Alaska emissions exceed those of the 48 conterminous states, such as in 2022, for example. Comparison of forest fire emission to forest carbon stocks indicate there is unlikely any serious disconnect between inventory and fire emissions estimates. CONCLUSIONS: The WFEIS system is a user-driven approach made available via a web browser. Model results are compatible with the scope and reporting needs of the annual national greenhouse gas inventories.

Evolving through multiple, co-existing pressures to change: a case study of self-organization in primary care during the COVID-19 pandemic in Canada.

BACKGROUND: Primary care is often described as slow to change. But conceptualized through complexity theory, primary care is continually changing in unpredictable, non-linear ways through self-organization processes. Self-organization has proven hard to study directly. We aimed to develop a methodology to study self-organization and describe how a primary care clinic self-organizes over time. METHODOLOGY: We completed a virtual case study of an urban primary care clinic from May-Nov 2021, applying methodological insights from actor-network theory to examine the complexity theory concept of self-organization. We chose to focus our attention on self-organization activities that alter organizational routines. Data included fieldnotes of observed team meetings, document collection, interviews with clinic members, and notes from brief weekly discussions to detect actions to change clinical and administrative routines. Adapting schema analysis, we described changes to different organizational routines chronologically, then explored intersecting changes. We sought feedback on results from the participating clinic. FINDINGS: Re-establishing equilibrium remained challenging well into the COVID-19 pandemic. The primary care clinic continued to self-organize in response to changing health policies, unintended consequences of earlier adaptations, staff changes, and clinical care initiatives. Physical space, technologies, external and internal policies, guidelines, and clinic members all influenced self-organization. Changing one created ripple effects, sometimes generating new, unanticipated problems. Member checking confirmed we captured most of the changes to organizational routines during the case study period. CONCLUSIONS: Through insights from actor-network theory, applied to studying actions taken that alter organizational routines, it is possible to operationalize the theoretical construct of self-organization. Our methodology illuminates the primary care clinic as a continually changing entity with co-existing and intersecting processes of self-organization in response to varied change pressures.