Reduced global plant respiration due to the acclimation of leaf dark respiration coupled with photosynthesis.

Leaf dark respiration (Rd ) acclimates to environmental changes. However, the magnitude, controls and time scales of acclimation remain unclear and are inconsistently treated in ecosystem models. We hypothesized that Rd and Rubisco carboxylation capacity (Vcmax ) at 25°C (Rd,25 , Vcmax,25 ) are coordinated so that Rd,25 variations support Vcmax,25 at a level allowing full light use, with Vcmax,25 reflecting daytime conditions (for photosynthesis), and Rd,25 /Vcmax,25 reflecting night-time conditions (for starch degradation and sucrose export). We tested this hypothesis temporally using a 5-yr warming experiment, and spatially using an extensive field-measurement data set. We compared the results to three published alternatives: Rd,25 declines linearly with daily average prior temperature; Rd at average prior night temperatures tends towards a constant value; and Rd,25 /Vcmax,25 is constant. Our hypothesis accounted for more variation in observed Rd,25 over time (R2 = 0.74) and space (R2 = 0.68) than the alternatives. Night-time temperature dominated the seasonal time-course of Rd , with an apparent response time scale of c. 2 wk. Vcmax dominated the spatial patterns. Our acclimation hypothesis results in a smaller increase in global Rd in response to rising CO2 and warming than is projected by the two of three alternative hypotheses, and by current models.

Novel naphthoquinone-1H-1,2,3-triazole hybrids: Design, synthesis and evaluation as inductors of ROS-mediated apoptosis in the MCF-7 cells.

The search for novel anticancer drugs is essential to expand treatment options, overcome drug resistance, reduce toxicity, promote innovation, and tackle the economic impact. The importance of these studies lies in their contribution to advancing cancer research and enhancing patient outcomes in the battle against cancer. Here, we developed new asymmetric hybrids containing two different naphthoquinones linked by a 1,2,3-1H-triazole nucleus, which are potential new drugs for cancer treatment. The antitumor activity of the novel compounds was tested using the breast cancer cell lines MCF-7 and MDA-MB-231, using the non-cancer cell line MCF10A as control. Our results showed that two out of twenty-two substances tested presented potential antitumor activity against the breast cancer cell lines. These potential drugs, named here 12g and 12h were effective in reducing cell viability and promoting cell death of the tumor cell lines, exhibiting minimal effects on the control cell line. The mechanism of action of the novel drugs was assessed revealing that both drugs increased reactive oxygen species production with consequent activation of the AMPK pathway. Therefore, we concluded that 12g and 12h are novel AMPK activators presenting selective antitumor effects.

Transmission of Mycobacterium tuberculosis to Healthcare Personnel Resulting From Contaminated Bone Graft Material, United States, June 2021-August 2022.

A nationwide tuberculosis outbreak linked to a viable bone allograft product contaminated with Mycobacterium tuberculosis was identified in June 2021. Our subsequent investigation identified 73 healthcare personnel with new latent tuberculosis infection following exposure to the contaminated product, product recipients, surgical instruments, or medical waste.

Optimality principles explaining divergent responses of alpine vegetation to environmental change.

Recent increases in vegetation greenness over much of the world reflect increasing CO2 globally and warming in cold areas. However, the strength of the response to both CO2 and warming in those areas appears to be declining for unclear reasons, contributing to large uncertainties in predicting how vegetation will respond to future global changes. Here, we investigated the changes of satellite-observed peak season absorbed photosynthetically active radiation (Fmax ) on the Tibetan Plateau between 1982 and 2016. Although climate trends are similar across the Plateau, we identified robust divergent responses (a greening of 0.31 ± 0.14% year-1 in drier regions and a browning of 0.12 ± 0.08% year-1 in wetter regions). Using an eco-evolutionary optimality (EEO) concept of plant acclimation/adaptation, we propose a parsimonious modelling framework that quantitatively explains these changes in terms of water and energy limitations. Our model captured the variations in Fmax with a correlation coefficient (r) of .76 and a root mean squared error of .12 and predicted the divergent trends of greening (0.32 ± 0.19% year-1 ) and browning (0.07 ± 0.06% year-1 ). We also predicted the observed reduced sensitivities of Fmax to precipitation and temperature. The model allows us to explain these changes: Enhanced growing season cumulative radiation has opposite effects on water use and energy uptake. Increased precipitation has an overwhelmingly positive effect in drier regions, whereas warming reduces Fmax in wetter regions by increasing the cost of building and maintaining leaf area. Rising CO2 stimulates vegetation growth by enhancing water-use efficiency, but its effect on photosynthesis saturates. The large decrease in the sensitivity of vegetation to climate reflects a shift from water to energy limitation. Our study demonstrates the potential of EEO approaches to reveal the mechanisms underlying recent trends in vegetation greenness and provides further insight into the response of alpine ecosystems to ongoing climate change.

Using Machine Learning Techniques and National Tuberculosis Surveillance Data to Predict Excess Growth in Genotyped Tuberculosis Clusters.

The early identification of clusters of persons with tuberculosis (TB) that will grow to become outbreaks creates an opportunity for intervention in preventing future TB cases. We used surveillance data (2009-2018) from the United States, statistically derived definitions of unexpected growth, and machine-learning techniques to predict which clusters of genotype-matched TB cases are most likely to continue accumulating cases above expected growth within a 1-year follow-up period. We developed a model to predict which clusters are likely to grow on a training and testing data set that was generalizable to a validation data set. Our model showed that characteristics of clusters were more important than the social, demographic, and clinical characteristics of the patients in those clusters. For instance, the time between cases before unexpected growth was identified as the most important of our predictors. A faster accumulation of cases increased the probability of excess growth being predicted during the follow-up period. We have demonstrated that combining the characteristics of clusters and cases with machine learning can add to existing tools to help prioritize which clusters may benefit most from public health interventions. For example, consideration of an entire cluster, not only an individual patient, may assist in interrupting ongoing transmission.

Use of whole genome analysis to identify shared genomic variants across breeds in canine mitral valve disease.

Familial mitral valve prolapse in human beings has been associated with several genetic variants; however, in most cases, a known variant has not been identified. Dogs also have a naturally occurring form of familial mitral valve disease (MMVD) with similarities to the human disease. A shared genetic background and clinical phenotype of this disease in some dog breeds has indicated that the disease may share a common genetic cause. We evaluated DNA from 50 affected dogs from five different dog breeds in a whole genome sequencing approach to identify shared variants across and within breeds that could be associated with MMVD. No single causative genetic mutation was found from the 50 dogs with MMVD. Ten variants were identified in 37/50 dogs around and within the MED13L gene. These variants were no longer associated with MMVD when evaluated with a larger cohort including both affected and unaffected dogs. No high/moderate impact variants were identified in 10/10 miniature poodles, one was identified in 10/10 Yorkshire Terriers and 10/10 dachshunds, respectively, 14 were identified in 10/10 Miniature schnauzers, and 19 in 10/10 CKCS. Only one of these could be associated with the cardiac valve (Chr12:36801705, COL12A1; CKCS) but when evaluated in an additional 100 affected CKCS the variant was only identified in 84/100 affected dogs, perhaps indicating genetic heterogeneity in this disease. Our findings indicate that development of MMVD in the dog may be related to a combination of genetic and environmental factors that impact specific molecular pathways rather than a single shared genetic variant across or within breeds.

Coordination of plant hydraulic and photosynthetic traits: confronting optimality theory with field measurements.

Close coupling between water loss and carbon dioxide uptake requires coordination of plant hydraulics and photosynthesis. However, there is still limited information on the quantitative relationships between hydraulic and photosynthetic traits. We propose a basis for these relationships based on optimality theory, and test its predictions by analysis of measurements on 107 species from 11 sites, distributed along a nearly 3000-m elevation gradient. Hydraulic and leaf economic traits were less plastic, and more closely associated with phylogeny, than photosynthetic traits. The two sets of traits were linked by the sapwood to leaf area ratio (Huber value, vH ). The observed coordination between vH and sapwood hydraulic conductivity (KS ) and photosynthetic capacity (Vcmax ) conformed to the proposed quantitative theory. Substantial hydraulic diversity was related to the trade-off between KS and vH . Leaf drought tolerance (inferred from turgor loss point, -Ψtlp ) increased with wood density, but the trade-off between hydraulic efficiency (KS ) and -Ψtlp was weak. Plant trait effects on vH were dominated by variation in KS , while effects of environment were dominated by variation in temperature. This research unifies hydraulics, photosynthesis and the leaf economics spectrum in a common theoretical framework, and suggests a route towards the integration of photosynthesis and hydraulics in land-surface models.

Eco-evolutionary optimality as a means to improve vegetation and land-surface models.

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.

Disparities in COVID-19 Vaccination Coverage Among Health Care Personnel Working in Long-Term Care Facilities, by Job Category, National Healthcare Safety Network - United States, March 2021.

Residents of long-term care facilities (LTCFs) and health care personnel (HCP) working in these facilities are at high risk for COVID-19-associated mortality. As of March 2021, deaths among LTCF residents and HCP have accounted for almost one third (approximately 182,000) of COVID-19-associated deaths in the United States (1). Accordingly, LTCF residents and HCP were prioritized for early receipt of COVID-19 vaccination and were targeted for on-site vaccination through the federal Pharmacy Partnership for Long-Term Care Program (2). In December 2020, CDC's National Healthcare Safety Network (NHSN) launched COVID-19 vaccination modules, which allow U.S. LTCFs to voluntarily submit weekly facility-level COVID-19 vaccination data.* CDC analyzed data submitted during March 1-April 4, 2021, to describe COVID-19 vaccination coverage among a convenience sample of HCP working in LTCFs, by job category, and compare HCP vaccination coverage rates with social vulnerability metrics of the surrounding community using zip code tabulation area (zip code area) estimates. Through April 4, 2021, a total of 300 LTCFs nationwide, representing approximately 1.8% of LTCFs enrolled in NHSN, reported that 22,825 (56.8%) of 40,212 HCP completed COVID-19 vaccination.† Vaccination coverage was highest among physicians and advanced practice providers (75.1%) and lowest among nurses (56.7%) and aides (45.6%). Among aides (including certified nursing assistants, nurse aides, medication aides, and medication assistants), coverage was lower in facilities located in zip code areas with higher social vulnerability (social and structural factors associated with adverse health outcomes), corresponding to vaccination disparities present in the wider community (3). Additional efforts are needed to improve LTCF immunization policies and practices, build confidence in COVID-19 vaccines, and promote COVID-19 vaccination. CDC and partners have prepared education and training resources to help educate HCP and promote COVID-19 vaccination coverage among LTCF staff members.§.

Shifting eye balance using monocularly directed attention in normal vision.

In binocular vision, even without conscious awareness of eye of origin, attention can be selectively biased toward one eye by presenting a visual stimulus uniquely to that eye. Monocularly directed visual cues can bias perceptual dominance, as shown by studies using discrete measures of percept changes in continuous-flash suppression. Here, we use binocular rivalry to determine whether eye-based visual cues can modulate eye balance using continuous percept reporting. Using a dual-task versus single-task paradigm, we investigated whether the attentional load of these cues differentially modulates eye balance. Furthermore, both color-based and motion-based cue stimuli, non-overlaid and peripheral to the rivalry grating stimuli, were used to determine whether shifts in eye balance were stimulus specific. Aligned to cue stimulus onset, time series of percept reports were constructed and averaged across trials and participants. Specifically, for the monocular attention conditions, we found a significant shift in eye balance toward the cued eye and a significant difference in the time taken to switch from the dominating percept, regardless of whether the attention stimuli is color based or motion based. Although we did not find a significant main effect of attentional load, we found a significant interaction effect between the attentionally cued eye and attentional load on the shift in eye balance, indicating an influence of monocular attention on the shift in eye balance.

Global ecosystems and fire: Multi-model assessment of fire-induced tree-cover and carbon storage reduction.

In this study, we use simulations from seven global vegetation models to provide the first multi-model estimate of fire impacts on global tree cover and the carbon cycle under current climate and anthropogenic land use conditions, averaged for the years 2001-2012. Fire globally reduces the tree covered area and vegetation carbon storage by 10%. Regionally, the effects are much stronger, up to 20% for certain latitudinal bands, and 17% in savanna regions. Global fire effects on total carbon storage and carbon turnover times are lower with the effect on gross primary productivity (GPP) close to 0. We find the strongest impacts of fire in savanna regions. Climatic conditions in regions with the highest burned area differ from regions with highest absolute fire impact, which are characterized by higher precipitation. Our estimates of fire-induced vegetation change are lower than previous studies. We attribute these differences to different definitions of vegetation change and effects of anthropogenic land use, which were not considered in previous studies and decreases the impact of fire on tree cover. Accounting for fires significantly improves the spatial patterns of simulated tree cover, which demonstrates the need to represent fire in dynamic vegetation models. Based upon comparisons between models and observations, process understanding and representation in models, we assess a higher confidence in the fire impact on tree cover and vegetation carbon compared to GPP, total carbon storage and turnover times. We have higher confidence in the spatial patterns compared to the global totals of the simulated fire impact. As we used an ensemble of state-of-the-art fire models, including effects of land use and the ensemble median or mean compares better to observational datasets than any individual model, we consider the here presented results to be the current best estimate of global fire effects on ecosystems.

Quantifying leaf-trait covariation and its controls across climates and biomes.

Plant functional ecology requires the quantification of trait variation and its controls. Field measurements on 483 species at 48 sites across China were used to analyse variation in leaf traits, and assess their predictability. Principal components analysis (PCA) was used to characterize trait variation, redundancy analysis (RDA) to reveal climate effects, and RDA with variance partitioning to estimate separate and overlapping effects of site, climate, life-form and family membership. Four orthogonal dimensions of total trait variation were identified: leaf area (LA), internal-to-ambient CO2 ratio (χ), leaf economics spectrum traits (specific leaf area (SLA) versus leaf dry matter content (LDMC) and nitrogen per area (Narea )), and photosynthetic capacities (Vcmax , Jmax at 25°C). LA and χ covaried with moisture index. Site, climate, life form and family together explained 70% of trait variance. Families accounted for 17%, and climate and families together 29%. LDMC and SLA showed the largest family effects. Independent life-form effects were small. Climate influences trait variation in part by selection for different life forms and families. Trait values derived from climate data via RDA showed substantial predictive power for trait values in the available global data sets. Systematic trait data collection across all climates and biomes is still necessary.

Multidecadal variability in Atlas cedar growth in Northwest Africa during the last 850 years: Implications for dieback and conservation of an endangered species.

Widespread forest dieback is a phenomenon of global concern that requires an improved understanding of the relationship between tree growth and climate to support conservation efforts. One priority for conservation is the Atlas cedar (Cedrus atlantica), an endangered species exhibiting dieback throughout its North African range. In this study, we evaluate the long-term context for recent dieback and develop a projection of future C. atlantica growth by exploring the periodic variability of its growth through time. First, we present a new C. atlantica tree-ring chronology (1150-2013 CE) from the Middle Atlas mountains, Morocco. We then compare the new chronology to existing C. atlantica chronologies in Morocco and use principal components analysis (PCA) to isolate the common periodic signal from the seven longest available records (PCA7, 1271-1984 CE) in the Middle and High Atlas portions of the C. atlantica range. PCA7 captures 55.7% of the variance and contains significant multidecadal (˜95 yr, ˜57 yr, ˜21 yr) periodic components, revealed through spectral and wavelet analyses. Parallel analyses of historical climate data (1901-2016 CE) suggests that the multidecadal growth signal originates primarily in growing season (spring and summer) precipitation variability, compounded by slow-changing components of summer and winter temperatures. Finally, we model the long-term growth patterns between 1271-1984 CE using a small number (three to four) of harmonic components, illustrating that suppressed growth since the 1970s - a factor implicated in the dieback of this species - is consistent with recurrent climatically-driven growth declines. Forward projection of this model suggests two climatically-favourable periods for growth in the 21st century that may enhance current conservation actions for the long-term survival of the C. atlantica in the Middle and High Atlas mountains.

Statistical Method to Detect Tuberculosis Outbreaks among Endemic Clusters in a Low-Incidence Setting.

We previously reported use of genotype surveillance data to predict outbreaks among incident tuberculosis clusters. We propose a method to detect possible outbreaks among endemic tuberculosis clusters. We detected 15 possible outbreaks, of which 10 had epidemiologic data or whole-genome sequencing results. Eight outbreaks were corroborated.

Frost and leaf-size gradients in forests: global patterns and experimental evidence.

Explanations of leaf size variation commonly focus on water availability, yet leaf size also varies with latitude and elevation in environments where water is not strongly limiting. We provide the first conclusive test of a prediction of leaf energy balance theory that may explain this pattern: large leaves are more vulnerable to night-time chilling, because their thick boundary layers impede convective exchange with the surrounding air. Seedlings of 15 New Zealand evergreens spanning 12-fold variation in leaf width were exposed to clear night skies, and leaf temperatures were measured with thermocouples. We then used a global dataset to assess several climate variables as predictors of leaf size in forest assemblages. Leaf minus air temperature was strongly correlated with leaf width, ranging from -0.9 to -3.2°C in the smallest- and largest-leaved species, respectively. Mean annual temperature and frost-free period were good predictors of evergreen angiosperm leaf size in forest assemblages, but no climate variable predicted deciduous leaf size. Although winter deciduousness makes large leaves possible in strongly seasonal climates, large-leaved evergreens are largely confined to frost-free climates because of their susceptibility to radiative cooling. Evergreen leaf size data can therefore be used to enhance vegetation models, and to infer palaeotemperatures from fossil leaf assemblages.

The China Plant Trait Database: toward a comprehensive regional compilation of functional traits for land plants.

Plant functional traits provide information about adaptations to climate and environmental conditions, and can be used to explore the existence of alternative plant strategies within ecosystems. Trait data are also increasingly being used to provide parameter estimates for vegetation models. Here we present a new database of plant functional traits from China. Most global climate and vegetation types can be found in China, and thus the database is relevant for global modeling. The China Plant Trait Database contains information on morphometric, physical, chemical, and photosynthetic traits from 122 sites spanning the range from boreal to tropical, and from deserts and steppes through woodlands and forests, including montane vegetation. Data collection at each site was based either on sampling the dominant species or on a stratified sampling of each ecosystem layer. The database contains information on 1,215 unique species, though many species have been sampled at multiple sites. The original field identifications have been taxonomically standardized to the Flora of China. Similarly, derived photosynthetic traits, such as electron-transport and carboxylation capacities, were calculated using a standardized method. To facilitate trait-environment analyses, the database also contains detailed climate and vegetation information for each site. The data set is released under a Creative Commons BY license. When using the data set, we kindly request that you cite this article, recognizing the hard work that went into collecting the data and the authors' willingness to make it publicly available.

Underlying causes of Eurasian midcontinental aridity in simulations of mid-Holocene climate.

Climate model simulations uniformly show drier and warmer summers in the Eurasian midcontinent during the mid-Holocene, which is not consistent with paleoenvironmental observations. The simulated climate results from a reduction in the zonal temperature gradient, which weakens westerly flow and reduces moisture flux and precipitation in the midcontinent. As a result, sensible heating is favored over evaporation and latent heating, resulting in substantial surface-driven atmospheric warming. Thus, the discrepancy with the paleoenvironmental evidence arises initially from a problem in the simulated circulation and is exacerbated by feedback from the land surface. This region is also drier and warmer than indicated by observations in the preindustrial control simulations, and this bias arises in the same way: zonal flow and hence moisture flux into the midcontinent are too weak, and feedback from the land surface results in surface-driven warming. These analyses suggest the need to improve those aspects of climate models that affect the strength of westerly circulation.

Registered nurses' perceptions regarding nurse-led antiretroviral therapy initiation in Hhohho region, Swaziland.

BACKGROUND: Swaziland has the highest HIV prevalence globally. It faces a critical shortage of health workers for addressing the HIV pandemic. To curb this human resource challenge, Swaziland adopted a nurse-driven model for antiretroviral therapy delivery in line with the recommendations of the World Health Organization on task shifting. OBJECTIVE: The study explored the perceptions of registered nurses on the nurse-led antiretroviral therapy initiation programme in the Hhohho region of Swaziland (NARTIS). DESIGN: The study utilized a phenomenological design, specifically a phenomenographic design. SETTING: The study was conducted in ten health facilities in the Hhohho region of Swaziland. These facilities comprised eight clinics, a hospital and a health centre. PARTICIPANTS: These were registered nurses, trained and certified in the nurse-led antiretroviral therapy initiation programme. The nurses also had experience of working in a nurse-led antiretroviral therapy initiation programme. Eighteen (18) nurses were purposively selected and recruited to participate in the study. METHODS: Data were collected through open and deep individual interviews guided by a semi-structured interview schedule. The audio-recorded interviews were transcribed and analysed thematically using Sjöström and Dahlgren's approach to data analysis. RESULTS: Three major themes emerged from the study data: nurses' emotional reactions to the implementation of the NARTIS programme, and influences and overcoming barriers to the programme. CONCLUSIONS: The study findings have generated insights into this program which is useful for the provision of care to people living with HIV/AIDS in Swaziland. But nurses need support to ensure effective implementation. IMPLICATION FOR NURSING AND HEALTH POLICY: The study findings have implications for both the practice of the NARTIS programme and health policy development. The development of a health policy that alleviates the barriers to the NARTIS programme can enhance nurses' role and make care provision to people living with HIV/AIDS more effective.

Drought and resprouting plants.

Many species have the ability to resprout vegetatively after a substantial loss of biomass induced by environmental stress, including drought. Many of the regions characterised by ecosystems where resprouting is common are projected to experience more frequent and intense drought during the 21st Century. However, in assessments of ecosystem response to drought disturbance there has been scant consideration of the resilience and post-drought recovery of resprouting species. Systematic differences in hydraulic and allocation traits suggest that resprouting species are more resilient to drought-stress than nonresprouting species. Evidence suggests that ecosystems dominated by resprouters recover from disturbance more quickly than ecosystems dominated by nonresprouters. The ability of resprouters to avoid mortality and withstand drought, coupled with their ability to recover rapidly, suggests that the impact of increased drought stress in ecosystems dominated by these species may be small. The strategy of resprouting needs to be modelled explicitly to improve estimates of future climate-change impacts on the carbon cycle, but this will require several important knowledge gaps to be filled before resprouting can be properly implemented.