Перинатология. Настоящее и будущее

На основании обобщения многолетнего собственного опыта автора, данных литературы и результатов работы Московского центра планирования семьи и репродукции освещены актуальные проблемы перинатологии от пренатального периода до постнатальной охраны здоровья плода. Сделано заключение о необходимости и возможности создания стандартов в области лечебных мероприятий и тактики ведения родов при осложнениях.Basing on the generalization of many−year experience as well as the data of the literature and results of the work of Moscow Center for Family Planning and Reproduction, the author features the urgent issues of perinatology from prenatal period to postnatal health protection. The author concludes about the necessity and possibility to create the standards in the field of therapeutic measures and management tactics in complicated delivery

The Optical Trapezoid Model: A Novel Approach to Remote Sensing of Soil Moisture Applied to Sentinel-2 and Landsat-8 Observations

The “trapezoid” or “triangle” model constitutes the most popular approach to remote sensing (RS) of surface soil moisture based on coupled thermal (i.e., land surface temperature) and optical RS observations. The model, hereinafter referred to as Thermal-Optical TRAapezoid Model (TOTRAM), is based on interpretation of the pixel distribution within the land surface temperature - vegetation index (LST-VI) space. TOTRAM suffers from two inherent limitations. It is not applicable to satellites that do not provide thermal data (e.g., Sentinel-2) and it requires parameterization for each individual observation date. To overcome these restrictions we propose a novel OPtical TRApezoid Model (OPTRAM), which is based on the linear physical relationship between soil moisture and shortwave infrared transformed reflectance (STR) and is parameterized based on the pixel distribution within the STR-VI space. The OPTRAM-based surface soil moisture estimates derived from Sentinel-2 and Landsat-8 observations for the Walnut Gulch and Little Washita watersheds were compared with ground truth soil moisture data. Results indicate that the prediction accuracies of OPTRAM and TOTRAM are comparable, with OPTRAM only requiring observations in the optical electromagnetic frequency domain. The volumetric moisture content estimation errors of both models were below 0.04 cm3 cm− 3 with local calibration and about 0.04–0.05 cm3 cm− 3 without calibration. We also demonstrate that OPTRAM only requires a single universal parameterization for a given location, which is a significant advancement that opens a new avenue for remote sensing of soil moisture

Major Interaction between Warfarin and Na Valproate: A Case Report

Abstract: Warfarin is the most commonly used oral anticoagulant drug in clinical practice with extreme inter and intra-individual variation in pharmacokinetic properties. Na Valproate, a broad spectrum anticonvulsant agent, is best known for its enzyme inhibition properties and also displacement of protein binding sites. Interaction between Warfarin and psychotropic drugs including Valproate are important and perhaps under recognized. In this report, we present a 48 year old female patient with chief complaints of abdominal pain, tea-color urine, blurred vision and headache. She had been suffering from “migraine headache” for 15 years that was relatively well controlled with Na Valproate 200mg twice daily. She was experienced a deep vein thrombosis (DVT) following oral contraceptive. For management of DVT, she was received Warfarin 5mg/day which was increased to 7.5 mg /day after 2 weeks. Three days after this increment of dose, her Prothrombin Time (PT) rose to 35.3 seconds (three times of normal value) and evidences of bleeding including hematuria and hematemesis were observed. Based on the history and laboratory findings, “Warfarin toxicity” was the first impression and she was treated with fresh frozen plasma and vitamin K with a well recovery. This experience emphasizes the clinical significant interaction between Warfarin and Na Valproate, which may take place even with the usual doses of each agent.

Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning

Root zone soil moisture (RZSM) estimation and monitoring based on high spatial resolution remote sensing information such as obtained with an Unmanned Aerial System (UAS) is of significant interest for field-scale precision irrigation management, particularly in water-limited regions of the world. To date, there is no accurate and widely accepted model that relies on UAS optical surface reflectance observations for RZSM estimation at high spatial resolution. This study is aimed at the development of a new approach for RZSM estimation based on the fusion of high spatial resolution optical reflectance UAS observations with physical and hydraulic soil information integrated into Automated Machine Learning (AutoML). The H2O AutoML platform includes a number of advanced machine learning algorithms that efficiently perform feature selection and automatically identify complex relationships between inputs and outputs. Twelve models combining UAS optical observations with various soil properties were developed in a hierarchical manner and fed into AutoML to estimate surface, near-surface, and root zone soil moisture. The addition of independently measured surface and near-surface soil moisture information to the hierarchical models to improve RZSM estimation was investigated. The accuracy of soil moisture estimates was evaluated based on a comparison with Time Domain Reflectometry (TDR) sensors that were deployed to monitor surface, near-surface and root zone soil moisture dynamics. The obtained results indicate that the consideration of physical and hydraulic soil properties together with UAS optical observations improves soil moisture estimation, especially for the root zone with a RMSE of about 0.04 cm cm . Accurate RZSM estimates were obtained when measured surface and near-surface soil moisture data was added to the hierarchical models, yielding RMSE values below 0.02 cm cm and R and NSE values above 0.90. The generated high spatial resolution RZSM maps clearly capture the spatial variability of soil moisture at the field scale. The presented framework can aid farm scale precision irrigation management via improving the crop water use efficiency and reducing the risk of groundwater contamination

Short- and mid-term forecasts of actual evapotranspiration with deep learning

Evapotranspiration is a key component of the hydrologic cycle. Accurate short-, medium-, and long-term forecasts of actual evapotranspiration (ETa) are crucial not only for quantifying the impacts of climate change on the water and energy balance, but also for real-time estimation of crop water demand and irrigation water allocation in agriculture. Despite considerable advances in satellite remote sensing technology and the availability of long ground-measured and remotely sensed ETa timeseries, real-time ETa forecasts are deficient. Applying a state-of-the-art deep learning (DL) approach, Long Short-Term Memory (LSTM) models were employed to nowcast (real-time) and forecast (ahead of time) ETa based on (1) major meteorological and ground-measured (i.e., soil moisture) input variables and (2) long ETa timeseries from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard of the NASA Aqua satellite. The conventional LSTM and convolutional LSTM (ConvLSTM) DL models were evaluated for seven distinct climatic zones across the contiguous United States. The employed LSTM and ConvLSTM models were trained and evaluated with data from the National Climate Assessment-Land Data Assimilation System (NCA-LDAS) and with MODIS/Aqua Net Evapotranspiration MYD16A2 product data. The obtained results indicate that when major atmospheric and soil moisture input variables are used for the conventional LSTM models, they yield accurate daily ETa forecasts for short (1, 3, and 7 days) and medium (30 days) time scales, with normalized root mean squared errors (NRMSE) and Nash-Sutcliffe efficiencies (NSE) of less than 10% and greater than 0.77, respectively. At the watershed scale, the univariate ConvLSTM models yielded accurate weekly spatiotemporal ETa forecasts (mean NRMSE less than 6.4% and NSE greater than 0.66) with higher computational efficiency for various climatic conditions. The employed models enable precise forecasts of both the current and future states of ETa, which is crucial for understanding the impact of climate change on rapidly depleting water resources

Towards Retrieving Soil Hydraulic Properties by Hyperspectral Remote Sensing

Soil spectroscopy is very attractive for retrieving soil hydraulic properties in subsurface hydrology. Spectral signatures at three spectral resolutions were used to retrieve soil hydraulic parameters and evaluated by HYPRES and Rosetta pedotransfer functions. Results indicated that the performance of estimations depends on the type of hydraulic parameter as well as the spectral resolution of the inputs

Mapping soil moisture with the OPtical TRApezoid Model (OPTRAM) based on long-term MODIS observations

The Optical TRApezoid Model (OPTRAM) has recently been proposed for estimation of soil moisture using only optical remote sensing data. The model relies on a physical linear relationship between the soil moisture content and shortwave infrared transformed reflectance (SIR) and can be parameterized universally (i.e., a single calibration for a given area) based on the pixel distribution within the STR-Normalized Difference Vegetation Index (NDVI) trapezoidal space. The main motivation for this study was to evaluate how the universal parameterization of OPTRAM works for long periods of time (e.g., several decades). This is especially relevant for uncovering the soil moisture and agricultural drought history in response to climate change in different regions. In this study, MODIS satellite observations from 2001 to 2017 were acquired and used for the analysis. Cosmic ray neutron (CRN) soil moisture data, collected with the Cosmic-ray Soil Moisture Observing System (COSMOS) at five different sites in the U.S. covering diverse climates, soil types, and land covers, were applied for evaluation of the MODIS-OPTRAM-based soil moisture estimates. The OPTRAM soil moisture estimates were further compared to the Soil Moisture Active and Passive (SMAP) (L-band), the Soil Moisture Ocean Salinity (SMOS) (L band), and the Advanced AScatterometer (ASCAT) (C-band) soil moisture retrievals. OPTRAM soil moisture data were also analyzed for potential monitoring of agricultural drought through comparison of the OPTRAM-based Soil Water Deficit Index (OPTRAM-SWDI) with the widely-applied Crop Moisture Index (CMI). Evaluation results indicate that OPTRAM-based soil moisture estimates provide overall unbiased RMSE and R between 0.050 and 0.085 cm(3) cm(-3) and 0.10 to 0.70, respectively, for all investigated sites. The performance of OPTRAM is comparable with the ASCAT retrievals, but slightly less accurate than SMAP and SMOS. OPTRAM and the three microvave satellites captured CRN soil moisture temporal dynamics very well for all five investigated sites. A close agreement was observed between the OPTRAM-SWDI and CMI drought indices for most selected sites. In conclusion, OPTRAM can estimate temporal soil moisture dynamics with reasonable accuracy for a range of climatic conditions (semi-arid to humid), soil types, and land covers, and can potentially be applied for agricultural drought monitoring.National Science Foundation (NSF) [1521469]; US National Science Foundation [ATM-0838491]24 month embargo; published online: 25 April 2018This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

A Comparative Study of Multiple Approaches for Predicting the Soil–Water Retention Curve: Hyperspectral Information vs. Basic Soil Properties

Information about the soil–water retention curve is necessary for modeling water flow and solute transport processes in soils. Soil spectroscopy in the visible, near-infrared, and shortwave infrared (Vis-NIR-SWIR) range has been widely used as a rapid, cost-effective and nondestructive technique to predict soil properties. However, less attention has been paid to predict soil hydraulic properties using soil spectral data. In this paper, spectral reflectances of soil samples from the Zanjanrood watershed, Iran, were measured in the Vis-NIR-SWIR ranges (350–2500 nm). Stepwise multiple linear regression coupled with the bootstrap method was used to construct predictive models and to estimate the soil–water retention curve. We developed point and parametric transfer functions based on the van Genuchten (VG) and Brooks-Corey (BC) soil hydraulic models. Three different types of transfer functions were developed: (i) spectral transfer functions (STFs) that relate VG/BC hydraulic parameters to spectral reflectance values, (ii) pedotransfer function (PTFs) that use basic soil data as input, and (iii) PTFs that consider spectral data and basic soil properties, further referred to as spectral pedotransfer functions (SPTFs). We also derived and evaluated point transfer functions which estimate soil–water contents at specific matric potentials. The point STFs and SPTFs were found to be accurate at low and intermediate water contents (R2 > 0.50 and root mean squared error [RMSE] < 0.018 cm3 cm−3), while the point PTFs performed better close to saturation. The parametric STFs and SPTFs of both the VG and BC models performed similarly to parametric PTFs in estimating the retention curve. The best predictions of soil–water contents were obtained for all the three transfer functions when the VG and BC retention models were fitted to the retention points estimated by the point transfer functions. Overall, our findings indicate that spectral data can provide useful information to predict soil—water contents and the soil–water retention curve. However, there is a need to extend and validate the derived transfer functions to other soils and regions

A comparative study of multiple approaches for predicting the soil-water retention curve: Hyperspectral information vs. basic soil properties

Information about the soil-water retention curve is necessary for modeling water flow and solute transport processes in soils. Soil spectroscopy in the visible, nearinfrared, and shortwave infrared (Vis-NIR-SWIR) range has been widely used as a rapid, cost-effective and nondestructive technique to predict soil properties. However, less attention has been paid to predict soil hydraulic properties using soil spectral data. In this paper, spectral reflectances of soil samples from the Zanjanrood watershed, Iran, were measured in the Vis-NIR-SWIR ranges (350-2500 nm). Stepwise multiple linear regression coupled with the bootstrap method was used to construct predictive models and to estimate the soil-water retention curve. We developed point and parametric transfer functions based on the van Genuchten (VG) and Brooks-Corey (BC) soil hydraulic models. Three different types of transfer functions were developed: (i) spectral transfer functions (STFs) that relate VG/BC hydraulic parameters to spectral reflectance values, (ii) pedotransfer function (PTFs) that use basic soil data as input, and (iii) PTFs that consider spectral data and basic soil properties, further referred to as spectral pedotransfer functions (SPTFs). We also derived and evaluated point transfer functions which estimate soil-water contents at specific matric potentials. The point STFs and SPTFs were found to be accurate at low and intermediate water contents (R2 > 0.50 and root mean squared error [RMSE] <0.018 cm3 cm-3), while the point PTFs performed better close to saturation. The parametric STFs and SPTFs of both the VG and BC models performed similarly to parametric PTFs in estimating the retention curve. The best predictions of soil-water contents were obtained for all the three transfer functions when the VG and BC retention models were fitted to the retention points estimated by the point transfer functions. Overall, our findings indicate that spectral data can provide useful information to predict soil-water contents and the soil-water retention curve. However, there is a need to extend and validate the derived transfer functions to other soils and regions