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The Southern Ocean is a complex system yet is sparsely sampled in both space and time. These factors raise questions about the confidence in present sampling strategies and associated machine learning (ML) reconstructions. Previous studies have not yielded a clear understanding of the origin of uncertainties and biases for the reconstructions of the partial pressure of carbon dioxide (pCO2) at the surface ocean (pCO2ocean). We examine these questions through a series of semi-idealized observing system simulation experiments (OSSEs) using a high-resolution (± 10 km) coupled physical and biogeochemical model (NEMO-PISCES, Nucleus for European Modelling of the Ocean, Pelagic Interactions Scheme for Carbon and Ecosystem Studies). Here we choose 1 year of the model sub-domain of 10∘ of latitude (40–50∘ S) by 20∘ of longitude (10∘ W–10∘ E). This domain is crossed by the sub-Antarctic front and thus includes both the sub-Antarctic zone and the polar frontal zone in the south-east Atlantic Ocean, which are the two most sampled sub-regions of the Southern Ocean. We show that while this sub-domain is small relative to the Southern Ocean scales, it is representative of the scales of variability we aim to examine. The OSSEs simulated the observational scales of pCO2ocean in ways that are comparable to existing ocean CO2 observing platforms (ships, Wave Gliders, carbon floats, Saildrones) in terms of their temporal sampling scales and not necessarily their spatial ones. The pCO2 reconstructions were carried out using a two-member ensemble approach that consisted of two machine learning (ML) methods, (1) the feed-forward neural network and (2) the gradient boosting machines. The baseline data were from the ship-based simulations mimicking ship-based observations from the Surface Ocean CO2 Atlas (SOCAT). For each of the sampling-scale scenarios, we applied the two-member ensemble method to reconstruct the full sub-domain pCO2ocean. The reconstruction skill was then assessed through a statistical comparison of reconstructed pCO2ocean and the model domain mean. The analysis shows that uncertainties and biases for pCO2ocean reconstructions are very sensitive to both the spatial and the temporal scales of pCO2 sampling in the model domain. The four key findings from our investigation are as follows: (1) improving ML-based pCO2 reconstructions in the Southern Ocean requires simultaneous high-resolution observations (<3 d) of the seasonal cycle of the meridional gradients of pCO2ocean; (2) Saildrones stand out as the optimal platforms to simultaneously address these requirements; (3) Wave Gliders with hourly/daily resolution in pseudo-mooring mode improve on carbon floats (10 d period), which suggests that sampling aliases from the 10 d sampling period might have a greater negative impact on their uncertainties, biases, and reconstruction means; and (4) the present seasonal sampling biases (towards summer) in SOCAT data in the Southern Ocean may be behind a significant winter bias in the reconstructed seasonal cycle of pCO2ocean.

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the first time, an approach is shown to reconcile the difference in our ELUC estimate with the one from national greenhouse gas inventories, supporting the assessment of collective countries' climate progress. For the year 2020, EFOS declined by 5.4 % relative to 2019, with fossil emissions at 9.5 ± 0.5 GtC yr−1 (9.3 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 0.9 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission of 10.2 ± 0.8 GtC yr−1 (37.4 ± 2.9 GtCO2). Also, for 2020, GATM was 5.0 ± 0.2 GtC yr−1 (2.4 ± 0.1 ppm yr−1), SOCEAN was 3.0 ± 0.4 GtC yr−1, and SLAND was 2.9 ± 1 GtC yr−1, with a BIM of −0.8 GtC yr−1. The global atmospheric CO2 concentration averaged over 2020 reached 412.45 ± 0.1 ppm. Preliminary data for 2021 suggest a rebound in EFOS relative to 2020 of +4.8 % (4.2 % to 5.4 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2020, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and datasets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this dataset (Friedlingstein et al., 2020, 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2021 (Friedlingstein et al., 2021).

Air–sea flux of carbon dioxide (CO2) is a critical component of the global carbon cycle and the climate system with the ocean removing about a quarter of the CO2 emitted into the atmosphere by human activities over the last decade. A common approach to estimate this net flux of CO2 across the air–sea interface is the use of surface ocean CO2 observations and the computation of the flux through a bulk parameterization approach. Yet, the details for how this is done in order to arrive at a global ocean CO2 uptake estimate vary greatly, enhancing the spread of estimates. Here we introduce the ensemble data product, SeaFlux (Gregor and Fay, 2021, https://doi.org/10.5281/zenodo.5482547​​​​​​​, https://github.com/luke-gregor/pySeaFlux, last access: 9 September 2021​​​​​​​); this resource enables users to harmonize an ensemble of products that interpolate surface ocean CO2 observations to near-global coverage with a common methodology to fill in missing areas in the products. Further, the dataset provides the inputs to calculate fluxes in a consistent manner. Utilizing six global observation-based mapping products (CMEMS-FFNN, CSIR-ML6, JENA-MLS, JMA-MLR, MPI-SOMFFN, NIES-FNN), the SeaFlux ensemble approach adjusts for methodological inconsistencies in flux calculations. We address differences in spatial coverage of the surface ocean CO2 between the mapping products, which ultimately yields an increase in CO2 uptake of up to 17 % for some products. Fluxes are calculated using three wind products (CCMPv2, ERA5, and JRA55). Application of a scaled gas exchange coefficient has a greater impact on the resulting flux than solely the choice of wind product. With these adjustments, we present an ensemble of global surface ocean pCO2 and air–sea carbon flux estimates. This work aims to support the community effort to perform model–data intercomparisons which will help to identify missing fluxes as we strive to close the global carbon budget.

Ocean acidification has profoundly altered the ocean's carbonate chemistry since preindustrial times, with potentially serious consequences for marine life. Yet, no long-term, global observation-based data set exists that allows us to study changes in ocean acidification for all carbonate system parameters over the last few decades. Here, we fill this gap and present a methodologically consistent global data set of all relevant surface ocean parameters, i.e., dissolved inorganic carbon (DIC), total alkalinity (TA), partial pressure of CO2 (pCO2), pH, and the saturation state with respect to mineral CaCO3 (Ω) at a monthly resolution over the period 1985 through 2018 at a spatial resolution of 1∘×1∘. This data set, named OceanSODA-ETHZ, was created by extrapolating in time and space the surface ocean observations of pCO2 (from the Surface Ocean CO2 Atlas, SOCAT) and total alkalinity (TA; from the Global Ocean Data Analysis Project, GLODAP) using the newly developed Geospatial Random Cluster Ensemble Regression (GRaCER) method (code available at https://doi.org/10.5281/zenodo.4455354, Gregor, 2021). This method is based on a two-step (cluster-regression) approach but extends it by considering an ensemble of such cluster regressions, leading to improved robustness. Surface ocean DIC, pH, and Ω were then computed from the globally mapped pCO2 and TA using the thermodynamic equations of the carbonate system. For the open ocean, the cluster-regression method estimates pCO2 and TA with global near-zero biases and root mean squared errors of 12 µatm and 13 µmol kg−1, respectively. Taking into account also the measurement and representation errors, the total uncertainty increases to 14 µatm and 21 µmol kg−1, respectively. We assess the fidelity of the computed parameters by comparing them to direct observations from GLODAP, finding surface ocean pH and DIC global biases of near zero, as well as root mean squared errors of 0.023 and 16 µmol kg−1, respectively. These uncertainties are very comparable to those expected by propagating the total uncertainty from pCO2 and TA through the thermodynamic computations, indicating a robust and conservative assessment of the uncertainties. We illustrate the potential of this new data set by analyzing the climatological mean seasonal cycles of the different parameters of the surface ocean carbonate system, highlighting their commonalities and differences. Further, this data set provides a novel constraint on the global- and basin-scale trends in ocean acidification for all parameters. Concretely, we find for the period 1990 through 2018 global mean trends of 8.6 ± 0.1 µmol kg−1 per decade for DIC, −0.016 ± 0.000 per decade for pH, 16.5 ± 0.1 µatm per decade for pCO2, and −0.07 ± 0.00 per decade for Ω. The OceanSODA-ETHZ data can be downloaded from https://doi.org/10.25921/m5wx-ja34 (Gregor and Gruber, 2020).

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2010–2019), EFOS was 9.6 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.4 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.6 ± 0.7 GtC yr−1. For the same decade, GATM was 5.1 ± 0.02 GtC yr−1 (2.4 ± 0.01 ppm yr−1), SOCEAN 2.5 ±  0.6 GtC yr−1, and SLAND 3.4 ± 0.9 GtC yr−1, with a budget imbalance BIM of −0.1 GtC yr−1 indicating a near balance between estimated sources and sinks over the last decade. For the year 2019 alone, the growth in EFOS was only about 0.1 % with fossil emissions increasing to 9.9 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.7 ± 0.5 GtC yr−1 when cement carbonation sink is included), and ELUC was 1.8 ± 0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5 ± 0.9 GtC yr−1 (42.2 ± 3.3 GtCO2). Also for 2019, GATM was 5.4 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.6 ± 0.6 GtC yr−1, and SLAND was 3.1 ± 1.2 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 409.85 ± 0.1 ppm averaged over 2019. Preliminary data for 2020, accounting for the COVID-19-induced changes in emissions, suggest a decrease in EFOS relative to 2019 of about −7 % (median estimate) based on individual estimates from four studies of −6 %, −7 %, −7 % (−3 % to −11 %), and −13 %. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2019, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. Comparison of estimates from diverse approaches and observations shows (1) no consensus in the mean and trend in land-use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent discrepancy between the different methods for the ocean sink outside the tropics, particularly in the Southern Ocean. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Friedlingstein et al., 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2020 (Friedlingstein et al., 2020).

Over the last decade, advanced statistical inference and machine learning have been used to fill the gaps in sparse surface ocean CO2 measurements (Rödenbeck et al., 2015). The estimates from these methods have been used to constrain seasonal, interannual and decadal variability in sea–air CO2 fluxes and the drivers of these changes (Landschützer et al., 2015, 2016; Gregor et al., 2018). However, it is also becoming clear that these methods are converging towards a common bias and root mean square error (RMSE) boundary: “the wall”, which suggests that pCO2 estimates are now limited by both data gaps and scale-sensitive observations. Here, we analyse this problem by introducing a new gap-filling method, an ensemble average of six machine-learning models (CSIR-ML6 version 2019a, Council for Scientific and Industrial Research – Machine Learning ensemble with Six members), where each model is constructed with a two-step clustering-regression approach. The ensemble average is then statistically compared to well-established methods. The ensemble average, CSIR-ML6, has an RMSE of 17.16 µatm and bias of 0.89 µatm when compared to a test dataset kept separate from training procedures. However, when validating our estimates with independent datasets, we find that our method improves only incrementally on other gap-filling methods. We investigate the differences between the methods to understand the extent of the limitations of gap-filling estimates of pCO2. We show that disagreement between methods in the South Atlantic, southeastern Pacific and parts of the Southern Ocean is too large to interpret the interannual variability with confidence. We conclude that improvements in surface ocean pCO2 estimates will likely be incremental with the optimisation of gap-filling methods by (1) the inclusion of additional clustering and regression variables (e.g. eddy kinetic energy), (2) increasing the sampling resolution and (3) successfully incorporating pCO2 estimates from alternate platforms (e.g. floats, gliders) into existing machine-learning approaches.

Resolving and understanding the drivers of variability of CO2 in the Southern Ocean and its potential climate feedback is one of the major scientific challenges of the ocean-climate community. Here we use a regional approach on empirical estimates of pCO2 to understand the role that seasonal variability has in long-term CO2 changes in the Southern Ocean. Machine learning has become the preferred empirical modelling tool to interpolate time- and location-restricted ship measurements of pCO2. In this study we use an ensemble of three machine-learning products: support vector regression (SVR) and random forest regression (RFR) from Gregor et al. (2017), and the self-organising-map feed-forward neural network (SOM-FFN) method from Landschützer et al. (2016). The interpolated estimates of ΔpCO2 are separated into nine regions in the Southern Ocean defined by basin (Indian, Pacific, and Atlantic) and biomes (as defined by Fay and McKinley, 2014a). The regional approach shows that, while there is good agreement in the overall trend of the products, there are periods and regions where the confidence in estimated ΔpCO2 is low due to disagreement between the products. The regional breakdown of the data highlighted the seasonal decoupling of the modes for summer and winter interannual variability. Winter interannual variability had a longer mode of variability compared to summer, which varied on a 4–6-year timescale. We separate the analysis of the ΔpCO2 and its drivers into summer and winter. We find that understanding the variability of ΔpCO2 and its drivers on shorter timescales is critical to resolving the long-term variability of ΔpCO2. Results show that ΔpCO2 is rarely driven by thermodynamics during winter, but rather by mixing and stratification due to the stronger correlation of ΔpCO2 variability with mixed layer depth. Summer pCO2 variability is consistent with chlorophyll a variability, where higher concentrations of chlorophyll a correspond with lower pCO2 concentrations. In regions of low chlorophyll a concentrations, wind stress and sea surface temperature emerged as stronger drivers of ΔpCO2. In summary we propose that sub-decadal variability is explained by summer drivers, while winter variability contributes to the long-term changes associated with the SAM. This approach is a useful framework to assess the drivers of ΔpCO2 but would greatly benefit from improved estimates of ΔpCO2 and a longer time series.

The Southern Ocean accounts for 40 % of oceanic CO2 uptake, but the estimates are bound by large uncertainties due to a paucity in observations. Gap-filling empirical methods have been used to good effect to approximate pCO2 from satellite observable variables in other parts of the ocean, but many of these methods are not in agreement in the Southern Ocean. In this study we propose two additional methods that perform well in the Southern Ocean: support vector regression (SVR) and random forest regression (RFR). The methods are used to estimate ΔpCO2 in the Southern Ocean based on SOCAT v3, achieving similar trends to the SOM-FFN method by Landschützer et al. (2014). Results show that the SOM-FFN and RFR approaches have RMSEs of similar magnitude (14.84 and 16.45 µatm, where 1 atm = 101 325 Pa) where the SVR method has a larger RMSE (24.40 µatm). However, the larger errors for SVR and RFR are, in part, due to an increase in coastal observations from SOCAT v2 to v3, where the SOM-FFN method used v2 data. The success of both SOM-FFN and RFR depends on the ability to adapt to different modes of variability. The SOM-FFN achieves this by having independent regression models for each cluster, while this flexibility is intrinsic to the RFR method. Analyses of the estimates shows that the SVR and RFR's respective sensitivity and robustness to outliers define the outcome significantly. Further analyses on the methods were performed by using a synthetic dataset to assess the following: which method (RFR or SVR) has the best performance? What is the effect of using time, latitude and longitude as proxy variables on ΔpCO2? What is the impact of the sampling bias in the SOCAT v3 dataset on the estimates? We find that while RFR is indeed better than SVR, the ensemble of the two methods outperforms either one, due to complementary strengths and weaknesses of the methods. Results also show that for the RFR and SVR implementations, it is better to include coordinates as proxy variables as RMSE scores are lowered and the phasing of the seasonal cycle is more accurate. Lastly, we show that there is only a weak bias due to undersampling. The synthetic data provide a useful framework to test methods in regions of sparse data coverage and show potential as a useful tool to evaluate methods in future studies.

The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million fCO2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.6 million fCO2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water fCO2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water fCO2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) "living data" publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014). Individual data set files, included in the synthesis product, can be downloaded here: doi:10.1594/PANGAEA.849770. The gridded products are available here: doi:10.3334/CDIAC/OTG.SOCAT_V3_GRID.

The authors compare the cleaning effect of various root canal preparation techniques (hand as well as power driven preparation). Two hand techniques (circumferential filing and balanced force technique) and two power driven techniques (the rotary system Wizard CD Plus, Medin, CR, and oscillating NiTi instruments AET Endo-EZE, Ultradent, USA) were used. The preparation was provided under standard conditions (lubrication with the EDTA gel, rinsing with 2% NaOCl). After the preparation the specimen were split longitudinaly and observed in SEM using the three step scala of the cleanliness of the root canal walls. Part of the specimen was split transversaly and observed macroscopically with magnification 5x. The time of the preparation (circumferential filing and rotary preparation) was comapred using the clear resin blocks. The cleanliness of the root canal walls after the hand preparation techniques was comparable to that after the rotary preparation. The preparation using the oscillating instruments (AET Endo -EZE) showed more smear layer and debris in comparsion to the others methods. In all cases the unprepared areas were found. The preparation with the rotary instruments was less time consuming in compasrion to the circumferential filing.

Introduction: The effort to exclude metals from stomatological treatment follows to continuing effort for development of new materials or application of materials from other branches. The fiber-reinforced composites have been used in dental medicine since the end of 80ies. They are composed from glass fibers embedded in a resin matrix. For splinting the materials have been used in the form of strips which toughen during the application. Care report: The authors planned the magnetic resonance imaging of the head in the orthodontic patient for suspected neurological illness. In view of the character of the disease the physician was asked to finish the orthodontic examination as soon as possible and to dismantle the fixed orthodontic apparatus.As a retention device the authors made use of a strip from FRC material, which was not contraindicated for MR imaging in contrast to the conventional steel wire. Conclusion: With growing requirements to esthetic outcome the development of non-metal technologies has been increasing. The application of FRC materials for retention after orthodontic therapy has been gaining importance. In case of MR imaging of the head is the use of non-metal materials of cardinal importance.

In this study we investigate the effect of deep cryogenic treatment (DCT) on a high-alloyed ferrous alloy (HAFA) to understand the resulting chemical and physical changes to the HAFA's microstructure and properties. Using multiple materials analysis techniques, we uncover the fundamental chemical changes to the HAFA with DCT, and link changes in material properties to changes in the microstructure. The increased carbide nucleation with DCT is linked to greater solute mobility resulting from the modified stress state of the material and modified chemical bonding of the solutes with the matrix. In turn this provides the possibility of prior formation of nucleation points for primordial carbide formation that act as accelerators for both nucleation and evolution of carbides. These result in modified chemical and microstructural homogeneity of the material that fundamentally led to changes in the surface properties and applicability of HAFA in advanced wear, corrosion and fatigue resistance demanding conditions.

Catalytic reactions may exhibit oscillations in the reaction rate even at constant external parameters. Here, the authors observe and explain the coexistence of such oscillations and the steady states of catalytic activity in H2 oxidation on differently structured grains of a polycrystalline Rh foil.

Structural phase stability and homogeneity of the electrochemically synthesized Mn2V2O7 (MVO) on nanocarbon network structures are investigated. The cup-stacked multi-walled carbon nanotubes (CNTs) and electrolytic graphene oxide (eGO) are the networks exploited in present work. It is shown that co-electrodeposition of carbonaceous network and V2O5.nH2O followed by electro-insertion of Mn2+ cations results in the formation of intermixed β-Mn2V2O7 nano-structures. The morphology and surface chemistry of the synthesized nano-structures is studied via high-resolution electron transmission and scanning X-ray photo-emission microscopies as well as Raman spectroscopy. The synthesized MVO on carbon fiber surface in the absence of the nanocarbon network shows a non-uniform phase formation and uneven coating distribution. The nanocarbon networks assisted MVO demonstrate uniform phase formation and distribution. The dominant MVO structural phase product present in these samples differs with different type of carbonaceous networks. The possible effect of the catalytic activity of the carbonaceous network as well as their hydrophilicity on the final structural and phase formation is discussed. The present study establishes new possibilities on catalytic assisted metal alloy oxide deposition for advanced applications.

Surface oxide formation under reaction conditions may change the catalytic activity of a catalyst. Here, the authors explore the effect of atomic structure of Rh surfaces on the surface oxide formation and its influence on catalytic activity in hydrogen oxidation, revealing a high transient activity.

Expertise in laparoscopic surgery is realized through both manual dexterity and efficient eye movement patterns, creating opportunities to use gaze information in the educational process. To better understand how expert gaze behaviors are acquired through deliberate practice of technical skills, three surgeons were assessed and five novices were trained and assessed in a 5-visit protocol on the Fundamentals of Laparoscopic Surgery peg transfer task. The task was adjusted to have a fixed action sequence to allow recordings of dwell durations based on pre-defined areas of interest (AOIs). Trained novices were shown to reach more than 98% (M = 98.62%, SD = 1.06%) of their behavioral learning plateaus, leading to equivalent behavioral performance to that of surgeons. Despite this equivalence in behavioral performance, surgeons continued to show significantly shorter dwell durations at visual targets of current actions and longer dwell durations at future steps in the action sequence than trained novices (ps ≤ .03, Cohen’s ds > 2). This study demonstrates that, while novices can train to match surgeons on behavioral performance, their gaze pattern is still less efficient than that of surgeons, motivating surgical training programs to involve eye tracking technology in their design and evaluation.

ABSTRACT The success of Mycobacterium tuberculosis as a human pathogen is due in part to its ability to survive stress conditions, such as hypoxia or nutrient deprivation, by entering nongrowing states. In these low-metabolism states, M. tuberculosis can tolerate antibiotics and develop genetically encoded antibiotic resistance, making its metabolic adaptation to stress crucial for survival. Numerous bacteria, including M. tuberculosis, have been shown to reduce their rates of mRNA degradation under growth limitation and stress. While the existence of this response appears to be conserved across species, the underlying bacterial mRNA stabilization mechanisms remain unknown. To better understand the biology of nongrowing mycobacteria, we sought to identify the mechanistic basis of mRNA stabilization in the nonpathogenic model Mycobacterium smegmatis. We found that mRNA half-life was responsive to energy stress, with carbon starvation and hypoxia causing global mRNA stabilization. This global stabilization was rapidly reversed when hypoxia-adapted cultures were reexposed to oxygen, even in the absence of new transcription. The stringent response and RNase levels did not explain mRNA stabilization, nor did transcript abundance. This led us to hypothesize that metabolic changes during growth cessation impact the activities of degradation proteins, increasing mRNA stability. Indeed, bedaquiline and isoniazid, two drugs with opposing effects on cellular energy status, had opposite effects on mRNA half-lives in growth-arrested cells. Taken together, our results indicate that mRNA stability in mycobacteria is not directly regulated by growth status but rather is dependent on the status of energy metabolism. IMPORTANCE The logistics of tuberculosis therapy are difficult, requiring multiple drugs for many months. Mycobacterium tuberculosis survives in part by entering nongrowing states in which it is metabolically less active and thus less susceptible to antibiotics. Basic knowledge on how M. tuberculosis survives during these low-metabolism states is incomplete, and we hypothesize that optimized energy resource management is important. Here, we report that slowed mRNA turnover is a common feature of mycobacteria under energy stress but is not dependent on the mechanisms that have generally been postulated in the literature. Finally, we found that mRNA stability and growth status can be decoupled by a drug that causes growth arrest but increases metabolic activity, indicating that mRNA stability responds to metabolic status rather than to growth rate per se. Our findings suggest a need to reorient studies of global mRNA stabilization to identify novel mechanisms that are presumably responsible.

O objetivo deste artigo é relatar um caso de orquiepididimite associado com infecção por Salmonella enterica subespécie diarizonae em carneiro da raça Santa Inês de quatro anos de idade, vasectomizado. Ao exame clínico reprodutivo, o animal mostrou aumento severo do conteúdo escrotal, sendo o testículo direito maior do que o esquerdo e a cauda do epidídimo direita maior do que a esquerda. A consistência testicular, avaliada em escala de 1 a 5, foi 5 para o testículo direito e 2,5 para o esquerdo; o órgão apresentava-se muito sensível ao toque. Na ultrassonografia foram observadas estruturas anecoicas/hipoecoicas circulares na cauda do epidídimo, sugestivas de abscessos; alguns pontos hiperecogênicos no parênquima testicular, sugerindo lesões de calcificação; e todo o testículo direito rodeado por imagem hipoecoica, indicativa de edema. Uma das estruturas da cauda do epidídimo direita foi puncionada, encontrando-se exsudato purulento, o qual foi enviado para exame microbiológico, sendo isolada e identificada Salmonella enterica subespécie diarizonae. O carneiro foi submetido a orquiepididectomia, e o órgão foi caracterizado macroscopicamente por adesões fibrosas entre as camadas escrotais, coexistência de abscessos epididimários e degeneração testicular. A Salmonella enterica subespécie diarizonae deve ser considerada no diagnóstico diferencial de infecção genital em ovinos.

We present some preliminary results on the mapping of coseismically-induced ground ruptures following the Aug. 24, 2016, Central Italy earthquake (Mw 6.0). The seismogenic source, as highlighted by InSAR and seismological data, ruptured across two adjacent structures: the Vettore and Laga faults. We collected field data on ground breaks along the whole deformed area and two different scenarios of on-fault coseismic displacement arise from these observations. To the north, along the Vettore fault, surface faulting can be mapped quite continuously along a well-defined fault strand while such features are almost absent to the south, along the Laga fault, where flysch-like marly units are present. A major lithological control, affects the surface expression of faulting, resulting in a complex deformation pattern.

In a clinical survey of caprine arthritis-encephalitis in 14 naturally infected herds in the State of São Paulo, Brazil, 17.1% (64/374) of seroreagents presented the articular form of the disease and 6.6% (17/249) of the seroreagent females presented the mammary form of the disease.Caprinos de 14 plantéis localizados no estado de São Paulo, naturalmente infectados pelo vírus da artrite-encefalite dos caprinos, foram clinicamente avaliados. Demonstrou-se que 17,1% (64/374) dos caprinos sororreagentes apresentavam a forma clínica articular da enfermidade e que 6,6% (17/249) das cabras sororreagentes apresentavam a forma mamária.

This article presents two experiences of application of a model-based fault identification method on real machines. The first case presented is an unbalance identification on a 320-MW turbogenerator unit operating in a fossil power plant. In the second case, concerning a machine of the same size but of a different manufacturer, the Low Pressure (LP) turbine was affected by a rub in the sealings and this time, the fault was modeled by local bows. The identification of the faults is performed by means of a model-based identification technique in frequency domain, suitably modified in order to take into account simultaneous faults. The theoretical background of the applied method is briefly illustrated and some considerations also are presented about the best choice of the rotating speed set of the run-down transient to be used for an effective identification and about the appropriate weighting of vibration measurements at the machine bearings.

This article presents two experiences of applying a model-based fault-identification method to real machines. The first case presented is an unbalance identification in a 320 MW turbogenerator unit operating in a fossil power plant. In the second case, concerning a machine of the same size but from a different manufacturer, the turbine has been affected by a rub in the sealings. This time, the fault is modeled by local bows. The identification of the faults is performed by means of a model-based identification technique in a frequency domain, suitably modified in order to take into account simultaneous faults. The theoretical background of the applied method is briefly illustrated and some considerations are also presented about the best choice of the rotating speed set of the run-down transient to be used for an effective identification and about the appropriate weighting of vibration measurements at the machine bearings.