Global Calibration Research Articles

A global, continuous calibration curvature strategy for bending sensors of soft fingers

PurposeThe aim of the paper is to propose a global, automated and continuous curvature calibration strategy for bending sensors, which is used for the angle feedback control of soft fingers.Design/methodology/approachIn this work, the proposed curvature calibration strategy for bending sensors is based on the constant curvature bending properties of soft fingers. The strategy is to install the bending sensor on the soft finger and use the laser distance sensor to assist calibration, then calculate the relationship between the curvature and the voltage of the bending sensor through geometric conversion. In addition, this work also develops a full set of standard calibration systems and collection procedures for the bending sensor curvature calibration and uses machine learning algorithms to fit the collected data.FindingsFirst, compared with the traditional calibration methods, the proposed curvature calibration strategy can achieve constant curvature measurement with the advantages of better continuity. Second, using the sensor data obtained by the proposed calibration method as the feedback signal for the soft finger bending angle control, the control effect is better than that of the traditional method.Originality/valueThis work proposes and verifies a global, automated and continuous curvature calibration strategy for bending sensors and is used for the angle feedback control of soft fingers. In addition, this work also develops a full set of standard calibration systems and collection procedures, which can be applied to a variety of flexible bending sensors with a good adaptability.

A Globe Calibration Method for Optical Multisensor in 3D Complex Surface Measurement System

There are few existing omnipotent sensors that handle a complex surface inspection task in an accurate and effective way. The prevailing solution is integrating multiple sensors and taking advantage of their strengths. One key task is the extrinsic parameter calibration (global calibration) of the multiple sensors before measurement. This paper proposes a method of optimal extrinsic calibration for a structured light sensor (SLS) and conoscopic holography sensor (CHS). In adopting this method, a common planar calibration board is placed with different poses in front of the multisensory system, and the extrinsic calibration problem is solved through a three-dimensional reconstruction of the calibration board and using geometric constraints of the views from the SLS and CHS. This calibration method, which uses only the plane calibration board, is simple. Physical experiments demonstrate that the proposed method is robust and accurate in the calibration of multiple inhomogeneous optical sensors for the measurement of a complex surface.

Linear Laser Scanning Measurement Method Tracking by a Binocular Vision

The 3D scanning of a freeform structure relies on the laser probe and the localization system. The localization system, determining the effect of the point cloud reconstruction, will generate positioning errors when the laser probe works in complex paths with a fast speed. To reduce the errors, in this paper, a linear laser scanning measurement method is proposed based on binocular vision calibration. A simple and effective eight-point positioning marker attached to the scanner is proposed to complete the positioning and tracking procedure. Based on this, the method of marked point detection based on image moment and the principle of global coordinate system calibration are introduced in detail. According to the invariance principle of space distance, the corresponding points matching method between different coordinate systems is designed. The experimental results show that the binocular vision system can complete localization under different light intensities and complex environments, and that the repeated translation error of the binocular vision system is less than 0.22 mm, while the rotation error is less than 0.15°. The repeated error of the measurement system is less than 0.36 mm, which can meet the requirements of the 3D shape measurement of the complex workpiece.

Inferring Suspended Sediment Carbon Content and Particle Size at High Frequency From the Optical Response of a Submerged Spectrometer

AbstractReal time monitoring of suspended sediment (SS) carbon content and particle size information is essential to understand SS transport mechanisms and processes. However, limited in situ methods are available, and manual and unattended sampling makes high frequency observation challenging. Based on evidence that optical measurements of SS concentration are dependent on particle size and composition, we hypothesize that SS carbon content and mean particle size can simultaneously be inferred from the absorbance data measured with a submerged spectrometer at high frequency. With the aim to predict both parameters, we investigate if global calibrations can be obtained and used instead of site‐dependent local calibrations. To test this, we created a laboratory data set using a tank setup, and an in situ field data set. Sediment samples varying in composition were collected and used in a tank setup to obtain global (all sites grouped together) calibrations between absorbance, particle size and carbon content. Two sites were instrumented to collect in situ field data, to generate local (site‐specific) calibrations and to validate the global calibrations obtained in the laboratory. A procedure to account for the effect of SS concentration when using the global calibration to predict is also presented. Our findings indicate that global calibrations can accurately predict from in situ absorbance readings, with a median absolute error of 17.3 μm, versus 10.5 μm for a local calibration. Local calibrations are indispensable to reliably predict carbon content, with a median absolute error of 1.3%.

Universal calibration for a ring camera array based on a rotational target.

Panoramic dynamic and static measurements of objects in the application of vision measurement are difficult due to the constraints of a camera field of view and multicamera calibration technology. This paper proposes a universal global calibration method for ring multicamera systems based on rotating target and multi-view vision technology. This method uses a rotating target to establish the relationship between ring multi-camera arrays, retrieves the coordinates of the target corners from the fields of view of different cameras, and combines them with the rotation angle to complete the coordinate unification of the system. The unification of coordinates is unaffected by the overlapping fields of view between cameras, and the number of cameras can be configured arbitrarily. The calibration accuracy, validity, and precision of the proposed method are verified through reprojection error, dynamic tensile test, and 3D reconstruction.

Comparison of several strategies for the deployment of a multivariate regression model on several handheld NIR instruments. Application to the quality control of medicines

Chemometrics applied to spectroscopic measurements such as near-infrared are gaining more and more importance for quality control of pharmaceutical products. Handheld near-infrared devices show great promise as a medicines quality screening technique for post-marketing surveillance. These devices are able to detect substandard and falsified medicines in pharmaceutical supply chains and enable rapid action before these medicines reach patients. The instrumental and environmental changes, expected or not, can adversely affect the analytical performances of prediction models developed for routine applications. Based on a previous study, PLS prediction models were developed and validated on three similar handheld NIR transmission spectrophotometers of the same model and from same company. These models have shown to be effective in analyzing metformin tablet samples, but significant spectral differences between handheld systems complicated their deployment for routine analysis. In this study, different strategies have been applied and compared to correct the instrumental variations, including global modelling (GM) and calibration transfer methods (Direct Standardization, DS; Spectral Space Transformation, SST and Slope/Bias correction, SBC), considering the RMSEP and the accuracy profile as assessment criteria. The transfer methods showed good capabilities to maintain the predictive performances comparable to that of the global modelling approach, except for a remaining slight bias. This approach is interesting since very few standardization samples are required to develop an adequate transfer model. GM, SST and SBC were able to correct/handle drifts in the spectral responses of different handheld instruments and thus may help to avoid the need for a long, laborious, and costly full recalibration process due to inter-instrument variations.

Research on the global calibration of the multi-vision line structured light measurement system based on auxiliary camera

<p indent="0mm">To solve the problem of the absence of a common field of view between the cameras in the existing multi-vision line structured light measurement system, which leads to high requirements for calibration tools and complicated global-calibration process, a global-calibration method for the multi-vision line structured light measurement system based on an auxiliary camera is introduced to achieve a large-field-of-view measurement. First, an auxiliary camera is used as an intermediary to acquire an approximately one-half overlapped field of view with the line structured light sensor, which can constitute a binocular subsystem. Then, the spatial geometric relationship between the auxiliary camera and two adjacent line structured light sensors is established based on the different pose images of the calibration plate. Second, the position relationship between the two adjacent line structured light sensors without a common field of view is solved using coordinate transformation. Finally, the transformation matrix from a local to a global coordinate system is solved one at a time to complete the global calibration process. The requirement in terms of calibration tools of the proposed calibration method is low. Further, it does require the use of high-precision measuring equipment and complex targets but only requires a simple chessboard target to complete the global calibration of a multi-vision line structured light measurement system. Meanwhile, the calibration process can achieve high calibration accuracy. Meanwhile, the measurement experiment demonstrates that the global calibration error of this method is <sc>0.067 mm,</sc> which effectively improves the measurement accuracy and can guarantee the required accuracy for the integration of “measurement and machining” of large components using robots.

A global soil spectral calibration library and estimation service

There is growing global interest in the potential for soil reflectance spectroscopy to fill an urgent need for more data on soil properties for improved decision-making on soil security at local to global scales. This is driven by the capability of soil spectroscopy to estimate a wide range of soil properties from a rapid, inexpensive, and highly reproducible measurement using only light. However, several obstacles are preventing wider adoption of soil spectroscopy. The biggest obstacles are the large variation in the soil analytical methods and operating procedures used in different laboratories, poor reproducibility of analyses within and amongst laboratories and a lack of soil physical archives. In addition, adoption is hindered by the expense and complexity of building soil spectral libraries and calibration models. The Global Soil Spectral Calibration Library and Estimation Service is proposed to overcome these obstacles by providing a freely available estimation service based on an open, high quality and diverse spectral calibration library and the extensive soil archives of the Kellogg Soil Survey Laboratory (KSSL) of the Natural Resources Conservation Service of the United States Department of Agriculture (USDA). The initiative is supported by the Global Soil Laboratory Network (GLOSOLAN) of the Global Soil Partnership and the Soil Spectroscopy for Global Good network, which provide additional support through dissemination of standards, capacity development and research. This service is a global public good which stands to benefit soil assessments globally, but especially developing countries where soil data and resources for conventional soil analyses are most limited.

Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling

Abstract. This paper presents an efficient strategy for the Bayesian calibration of parameters of aerodynamic wind turbine models. The strategy relies on constructing a surrogate model (based on adaptive polynomial chaos expansions), which is used to perform both parameter selection using global sensitivity analysis and parameter calibration with Bayesian inference. The effectiveness of this approach is shown in two test cases: calibration of airfoil polars based on the measurements from the DANAERO MW experiments and calibration of five yaw model parameters based on measurements on the New MEXICO turbine in yawed conditions. In both cases, the calibrated models yield results much closer to the measurement data, and in addition they are equipped with an estimate of the uncertainty in the predictions.

Prediction of soil carbon and nitrogen contents using visible and near infrared diffuse reflectance spectroscopy in varying salt-affected soils in Sine Saloum (Senegal)

Soil organic carbon (C) and nitrogen (N) contents have an essential role in soil fertility, but they may be affected by salinity, which is especially responsible for land degradation in arid and semiarid regions. The objective of this work was to study the ability of visible and near infrared diffuse reflectance spectroscopy (VNIRS) to predict soil C and N contents and electrical conductivity (EC, a proxy for soil salinity) in variably salt-affected topsoils of the Sine Saloum region (Senegal). Different calibration procedures and spectral pretreatments were compared, and variable log-transformation usefulness was evaluated for prediction optimization.Predictions involved three calibration procedures: global partial least squares regression (PLSR), which used all calibration samples similarly; locally weighted (local) PLSR, with target samples predicted individually by giving higher weight to closest calibration spectra; and global PLSR per salinity class, after spectral discrimination of these classes. Predictions were performed with possible spectrum pretreatments (e.g., derivatization) and variable decimal log-transformation.The study was performed on 311topsoil samples (0–25 cm depth), either unsalted to slightly salty (Salt-, EC ≤ 2mScm−1; 262samples) or medium to highly salty (Salt+, EC > 2mScm−1; 49samples). Soil salinity was accurately discriminated using spectra: in validation, 100% and 95% of Salt- and Salt+ samples were correctly assigned on average, respectively. Best C and N content predictions were achieved after log-transformation using calibration by class (R2VAL = 0.87) and local calibration (R2VAL = 0.77), respectively; best EC prediction was achieved without log-transformation using global calibration (R2VAL = 0.90). This suggested C and N content predictions were affected by salinity; logC and logN distributions were almost symmetrical, hence log-transformation usefulness, while logEC distribution was very asymmetrical. No pretreatment yielded systematically good predictions; nevertheless, first-order derivative using 31-point gap often yielded good predictions, and second-order derivatives poor results.

Global validation and hybrid calibration of CAMS and MERRA-2 PM2.5 reanalysis products based on OpenAQ platform

It is highly valuable to obtain high-quality PM2.5 concentration worldwide for continuous monitoring of global air pollution. Recently, global reanalysis products of PM2.5 have come into the view. However, most studies focus on the validation and calibration of a single product regionally, few studies expand to a global scale and integrate multiple products. With the help of global open-source data provided by the OpenAQ platform, we propose a hybrid calibration method aimed to improve the accuracy of CAMSRA and the MERRA-2 PM2.5 products. In the study, the accuracy of the two datasets are assessed on multi-time scales at first. Secondly, we try to use some machine learning models to correct the deviation of the original products alone and then further explore the possibility of the hybrid calibration. Global-scale validation results show that CAMSRA products are generally overestimated (daily R = 0.6), and MERRA-2 products are underestimated (daily R = 0.3), which supports our hybrid calibration method to an extent. Using the Extremely Randomized Tree (ERT) to implement the separate calibration scheme, two products show different degrees of accuracy improvement, to be specific, R increases by 0.19 and 0.43 for daily CAMSRA and MERRA-2 products, respectively. Compared with the separate calibration modeling, the hybrid method performs better, with R reaching up to 0.81. RMSE is only 14.94 μg/m³, which has a decrease of 60.99% and 64.42% to two abovementioned original products. The obtained daily PM2.5 maps have higher quality with no data gaps, which can be a promising data source of air pollution monitoring and health research. This dataset is published in GeoTIFF format at https://doi.org/10.5281/zenodo.5168102.

Health Care Leaders’ Perspectives on the Maryland All-Payer Model

Since 2014, all hospitals in Maryland have operated under an all-payer global budget system. Hospital global budgets have gained renewed attention as a strategy for constraining cost growth, improving patient outcomes, and preserving health care access in rural and underserved communities. Lessons from the implementation of the Maryland All-Payer Model (MDAPM) may have implications for policy makers, payers, and hospitals in other settings seeking to adopt global budgets or other value-based payment models. To examine perspectives on the implementation of the MDAPM among health care leaders who participated in its design and execution. This qualitative study with semistructured telephone interviews was conducted from November 1, 2019, to February 11, 2020. The purposive sample of Maryland health care leaders represents diverse stakeholder groups, including hospitals, state government and regulatory agencies, the federal government, and payers. Key high-level themes were extracted from interviews using qualitative content analysis, with barriers and facilitators to implementation specified within each theme. A total of 20 interviews were conducted with hospital leaders (n = 6), state regulators (n = 4), federal regulators (n = 4), payer representatives (n = 3), and state leaders (n = 3). Key themes were labeled as (1) expectations (setting bold yet achievable goals), (2) autonomy (allowing hospitals to follow individual strategies within MDAPM parameters), (3) communication (encouraging early and ongoing communication between stakeholders), (4) actionable data (sharing useful hospital and patient-level data between stakeholders), (5) global budget calibration (anticipating technical challenges when negotiating budgets for individual hospitals), and (6) shared commitment to change (harnessing collective motivation for system change). Together, these themes suggest that implementing the payment model followed an evolving and collaborative process that requires stakeholder communication, data to guide decisions, and commitment to operating within the new payment system. The implementation of hospital global budgets in the state of Maryland offers generalizable lessons that can inform the evolution and expansion of this approach to value-based payment in other states and settings.

Near infrared reflectance spectroscopy as a tool to predict non-starch polysaccharide composition and starch digestibility profiles in common monogastric cereal feed ingredients

Carbohydrates are the main components of cereal grain ingredients and are comprised predominantly of starch which varies in the rate and extent of digestibility, and non-starch polysaccharides (NSP) from plant cell walls. These components are traditionally measured by chemical methods but there is interest in developing more rapid, cost effective predictions by use of near infrared reflectance spectroscopy (NIRS) technology. In this study, around 400 samples of wheat, corn, barley, wheat bran, oats, rye, corn germ, and sorghum were collected over several years to develop global NIR calibrations. Chemical analysis was performed for NSP, including details on solubility and individual constituent sugars, as well as separate determinations of cellulose and lignin. Total starch content and starch digestibility were characterized by the method of Englyst as rapidly (RDS) and slowly (SDS) digestible starch, and resistant starch (RS) determined as total starch content minus RDS and SDS. The same samples were scanned (400 to 2500 nm, every 0.5 nm) by two NIRS machines: a Foss model DS2500 and a Bruker Tango FT-NIR (Fourier transform near-infrared spectroscopy). The NIRS calibrations were developed by using the combined set of different cereal grains. The modified Partial Least Squares Regression method was employed to establish correlations between the reference chemical analysis and the collected NIRS spectra. The correlation between the NIRS and reference data for total and insoluble NSP and the main component sugars (arabinose, xylose, and glucose) were robust (R2 = >0.97) with a 1-VR > 0.96 and RPD values > 5.3. Similarly, the NIRS predicted lignin (R2 = 0.94; 1-VR = 0.93) and to a lesser extent cellulose (R2 = 0.70; 1-VR = 0.98) but still highly correlated with the reference data. The predicted total starch content was excellent (R2 = 0.99; 1-VR = 0.99). The NIRS prediction for RDS and SDS were lower in linearity (R2 = 0.81 and R2 = 0.97, respectively) but more than sufficient to provide an estimate of the starch digestion profile for many of the raw materials evaluated. In addition, the robust NIRS predictions for total starch content, RDS and SDS could then provide information to calculate the RS fraction of the ingredients. In conclusion, compared with the reference chemical data for this large set of cereal grains the global NIR calibrations developed provide a good prediction of NSP, monosaccharide sugars, lignin, cellulose and total starch content in cereal grains, and for the first time an NIR relationship with starch digestibility fractions in the grains has been shown.

Implementation Strategy and Spatiotemporal Extensibility of Multipredictor Ensemble Model for Water Quality Parameter Retrieval With Multispectral Remote Sensing Data

In previous studies, empirical models have been widely used to map water quality parameters for inland waters using remotely sensed imagery. Due to the complex optical properties of inland waters, empirical models often have limited performances and they cannot be extended or reused across space and time. To overcome the limitations of traditional empirical models, this article further explores the ensemble method for water quality parameter retrieval. Based on the Sentinel-2 multispectral images and extensive coincident <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> water quality data, we examined different implementation strategies and evaluated the extensibility of a multipredictor ensemble model for retrieving chlorophyll-a (Chl-a) across space and time. Our analysis shows that the multipredictor ensemble model can improve the Chl-a prediction accuracy by 46% compared with the best traditional empirical model. Our experiments suggest that the selection of heterogeneous component models improves the performance of the ensemble model and the new iterative <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -fold calibration approach results in a considerably better ensemble model than the global calibration approach. To evaluate the spatial extensibility, our ensemble model calibrated for Harsha Lake was applied to Caesar Creek Lake and Brookville Lake. This ensemble model calibrated with the data sets acquired on October 7, 2016 was also applied to Sentinel-2 images of Harsha Lake acquired on October 2, 2017 and August 23, 2018, to evaluate its extensibility for different years. Our evaluation results show that the ensemble model can be transferred and reused across space and time to provide reliable and accurate Chl-a estimates.

A combined calibration method of a mobile robotic measurement system for large-sized components

Product quality control of large-sized components with complex supports requires automatic and accurate 3D shape measurement. For accurate and efficient measurements of key local features (KLFs) on the mounting surface of multiple supports, we propose a flexible measurement method combining a laser tracker, a 3D scanner, and a mobile robot system. As for improving the overall measurement accuracy, we also introduce a calibration method based on a multidimensional combined collaboration target (MCCT) to establish correlation constraints between global and local calibration. Specifically, a global calibration method based on multidimensional geometric constraints provides a global measurement field. Additionally, a local calibration method derived from combination optimization model achieves accurate calibration of extrinsic parameters. We constructed the corresponding mobile robotic measurement system (MRMS) and conducted experiments. The experimental results demonstrate that the maximum and mean calibration errors are reduced from 0.045/0.0261 mm to 0.0231/0.0122 mm within 7 m, which features high accuracy.

Determining the extrinsic parameters of a network of Large-Volume Metrology sensors of different types

Large-Volume Metrology (LVM) instruments – such as laser trackers, photogrammetric systems, rotary-laser automatic theodolites, etc. – generally include several sensors, which measure the distances and/or angles subtended by some targets. This measurements, combined with the spatial position/orientation of sensors (i.e., the so-called extrinsic parameters), can be used to locate targets in the measurement volume. Extrinsic parameters of sensors are generally determined through dedicated sensor calibration methods, which are based on repeated measurements of specific artefacts.The combined use of multiple LVM instruments enables exploitation of available equipment but may require multiple instrument-dedicated sensor calibrations, which inevitably increase set-up time/cost.This document presents a novel calibration method – called global calibration – which allows the extrinsic parameters of all sensors to be determined in a single process. The proposed method uses a special artefact – i.e., a hand-held probe with assorted types of targets and inertial sensors – and includes a data-acquisition stage, in which the probe is repositioned in different areas of the measurement volume, followed by a data-processing stage, in which an ad hoc mathematical/statistical model is used to determine the extrinsic parameters of sensors. Additionally, the proposed method includes the formulation of a system of linearized equations, which are weighed considering the uncertainty of input variables.

Influence of environmental variability and Emiliania huxleyi ecotypes on alkenone-derived temperature reconstructions in the subantarctic Southern Ocean

Long-chain unsaturated alkenones produced by haptophyte algae are widely used as paleotemperature indicators. The unsaturation relationship to temperature is linear at mid-latitudes, however, non-linear responses detected in subpolar regions of both hemispheres have suggested complicating factors in these environments. To assess the influence of biotic and abiotic factors in alkenone production and preservation in the Subantarctic Zone, alkenone fluxes were quantified in three vertically-moored sediment traps deployed at the SOTS observatory (140°E, 47°S) during a year. Alkenone fluxes were compared with coccolithophore assemblages, satellite measurements and surface-water properties obtained by sensors at SOTS. Alkenone-based temperature reconstructions generally mirrored the seasonal variations of SSTs, except for late winter when significant deviations were observed (3–10 °C). Annual flux-weighted averages in the 3800 m trap returned alkenone-derived temperatures ~1.5 °C warmer than those derived from the 1000 m trap, a distortion attributed to surface production and signal preservation during its transit through the water column. Notably, changes in the relative abundance of E. huxleyi var. huxleyi were positively correlated with temperature deviations between the alkenone-derived temperatures and in situ SSTs (r = 0.6 and 0.7 at 1000 and 2000 m, respectively), while E. huxleyi var. aurorae, displayed an opposite trend. Our results suggest that E. huxleyi var. aurorae produces a higher proportion of C37:3 relative to C37:2 compared to its counterparts. Therefore, the dominance of var. aurorae south of the Subtropical Front could be at least partially responsible for the less accurate alkenone-based SST reconstructions in the Southern Ocean using global calibrations. However, the observed correlations were largely influenced by the samples collected during winter, a period characterized by low particle fluxes and slow sinking rates. Thus, it is likely that other factors such as selective degradation of the most unsaturated alkenones could also account for the deviations of the alkenone paleothermometer.

FROG: A global machine-learning temperature calibration for branched GDGTs in soils and peats

Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are a family of bacterial lipids which have emerged over time as robust temperature and pH paleoproxies in continental settings. Nevertheless, it was previously shown that other parameters than temperature and pH, such as soil moisture, thermal regime or vegetation can also influence the relative distribution of brGDGTs in soils and peats. This can explain a large part of the residual scatter in the global brGDGT calibrations with mean annual air temperature (MAAT) and pH in these settings. Despite improvements in brGDGT analytical methods and development of refined models, the root-mean-square error (RMSE) associated with global calibrations between brGDGT distribution and MAAT in soils and peats remains high (∼5 °C). The aim of the present study was to develop a new global terrestrial brGDGT temperature calibration from a worldwide extended dataset (i.e. 775 soil and peat samples, i.e. 112 samples added to the previously available global calibration) using a machine learning algorithm. Statistical analyses highlighted five clusters with different effects of potential confounding factors in addition to MAAT on the relative abundances of brGDGTs. The results also revealed the limitations of using a single index and a simple linear regression model to capture the response of brGDGTs to temperature changes. A new improved calibration based on a random forest algorithm was thus proposed, the so-called random Forest Regression for PaleOMAAT using brGDGTs (FROG). This multi-factorial and non-parametric model allows to overcome the use of a single index, and to be more representative of the environmental complexity by taking into account the non-linear relationships between MAAT and the relative abundances of the individual brGDGTs. The FROG model represents a refined brGDGT temperature calibration (R2 = 0.8; RMSE = 4.01 °C) for soils and peats, more robust and accurate than previous global soil calibrations while being proposed on an extended dataset. This novel improved calibration was further applied and validated on two paleo archives covering the last 110 kyr and the Pliocene, respectively.

Soil pH and aridity influence distributions of branched tetraether lipids in grassland soils along an aridity transect

Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are membrane lipids of certain soil bacteria, and their relative distributions are used as a proxy for air temperature and soil pH. While temperature is recorded by the degree of methylation, soil pH is reflected by the amount of internal cyclization and the relative abundance of 6-methyl isomers. Since the exact producers of brGDGTs remain enigmatic, the mechanisms underlying their empirical relationships with temperature and soil pH, and thus the reliability of brGDGT-based paleorecords, are not well understood, especially in arid regions where mean annual precipitation (MAP) is less than 500 mm. Here, we evaluate the influence of soil pH and aridity on brGDGT distributions in grassland soils along an aridity transect (MAP = 173–415 mm) in Inner Mongolia. While the absolute and fractional abundance of 6-methyl brGDGTs increases with increasing soil pH and aridity, following the trend in the global surface soil calibration dataset, the degree of cyclization does not. This indicates that in arid regions, soil pH reconstructions based on the relative contribution of 6-methyl brGDGTs are likely more reliable than those based on the degree of cyclization. Furthermore, 5- and 6-methyl brGDGTs respond differently to aridity, supporting prior suggestions that the distribution of brGDGTs could be the result of changes in bacterial community composition instead of the direct physiological alteration of molecular structures by the source organisms. Analysis of the bacterial community composition in the same soil transect indicates that the relative abundance of Acidobacteria, the phylum hosting potential brGDGT source-organisms, shows a poor relationship with aridity. Instead, Verrucomicrobia (r2 = 0.70, p < 0.01), and its subclass Spartobacteria (r2 = 0.70, p < 0.01) in particular, show a significant negative correlation with aridity, resembling that of 5-methyl brGDGTs. Similarly, Actinobacteria are positively correlated with aridity (r2 = 0.59, p < 0.01), following the same trend as that of 6-methyl brGDGTs. The ability of certain cultures of Verrucomicrobia and Actinobacteria to produce iso-C15:0 fatty acids that could serve as building blocks for brGDGTs hints that Verrucomicrobia and Actinobacteria could possibly produce brGDGTs in arid soils.

Circulating miR-184 is a potential predictive biomarker of cardiac damage in Anderson\u2013Fabry disease

Enzyme replacement therapy (ERT) is a mainstay of treatment for Anderson–Fabry disease (AFD), a pathology with negative effects on the heart and kidneys. However, no reliable biomarkers are available to monitor its efficacy. Therefore, we tested a panel of four microRNAs linked with cardiac and renal damage in order to identify a novel biomarker associated with AFD and modulated by ERT. To this end, 60 patients with a definite diagnosis of AFD and on chronic ERT, and 29 age- and sex-matched healthy individuals, were enrolled by two Italian university hospitals. Only miR-184 met both conditions: its level discriminated untreated AFD patients from healthy individuals (c-statistic = 0.7522), and it was upregulated upon ERT (P < 0.001). On multivariable analysis, miR-184 was independently and inversely associated with a higher risk of cardiac damage (odds ratio = 0.86; 95% confidence interval [CI] = 0.76–0.98; P = 0.026). Adding miR-184 to a comprehensive clinical model improved the prediction of cardiac damage in terms of global model fit, calibration, discrimination, and classification accuracy (continuous net reclassification improvement = 0.917, P < 0.001; integrated discrimination improvement [IDI] = 0.105, P = 0.017; relative IDI = 0.221, 95% CI = 0.002–0.356). Thus, miR-184 is a circulating biomarker of AFD that changes after ERT. Assessment of its level in plasma could be clinically valuable in improving the prediction of cardiac damage in AFD patients.