Correcting satellite precipitation data and assimilating satellite-derived soil moisture data to generate ensemble hydrological forecasts within the HBV rainfall-runoff model

An implementation of bias correction and data assimilation using the ensemble Kalman filter (EnKF) as a procedure, dynamically coupled with the conceptual rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning (HBV) model, was assessed for the hydrological modeling of seasonal hydrographs. The enhanced HBV model generated ensemble hydrographs and an average stream-flow simulation. The proposed approach was developed to examine the possibility of using data (e.g., precipitation and soil moisture) from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Support to Operational Hydrology and Water Management (H-SAF), and to explore its usefulness in improving model updating and forecasting. Data from the Sola mountain catchment in southern Poland between 1 January 2008 and 31 July 2014 were used to calibrate the HBV model, while data from 1 August 2014 to 30 April 2015 were used for validation. A bias correction algorithm for a distribution-derived transformation method was developed by exploring generalized exponential (GE) theoretical distributions, along with gamma (GA) and Weibull (WE) distributions for the different data used in this study. When using the ensemble Kalman filter, the stochastically-generated ensemble of the model states generally induced bias in the estimation of non-linear hydrologic processes, thus influencing the accuracy of the Kalman analysis. In order to reduce the bias produced by the assimilation procedure, a post-processing bias correction (BC) procedure was coupled with the ensemble Kalman filter (EnKF), resulting in an ensemble Kalman filter with bias correction (EnKF-BC). The EnKF-BC, dynamically coupled with the HBV model for the assimilation of the satellite soil moisture observations, improved the accuracy of the simulated hydrographs significantly in the summer season, whereas, a positive effect from bias corrected (BC) satellite precipitation, as forcing data, was observed in the winter. Ensemble forecasts generated from the assimilation procedure are shown to be less uncertain. In future studies, the EnKF-BC algorithm proposed in the current study could be applied to a diverse array of practical forecasting problems (e.g., an operational assimilation of snowpack and snow water equivalent in forecasting models

Monitoring Achilles tendon healing progress in ultrasound imaging with convolutional neural networks

Achilles tendon rupture is a debilitating injury, which is typically treated with surgical repair and long-term rehabilitation. The recovery, however, is protracted and often incomplete. Diagnosis, as well as healing progress assessment, are largely based on ultrasound and magnetic resonance imaging. In this paper, we propose an automatic method based on deep learning for analysis of Achilles tendon condition and estimation of its healing progress on ultrasound images. We develop custom convolutional neural networks for classification and regression on healing score and feature extraction. Our models are trained and validated on an acquired dataset of over 250.000 sagittal and over 450.000 axial ultrasound slices. The obtained estimates show a high correlation with the assessment of expert radiologists, with respect to all key parameters describing healing progress. We also observe that parameters associated with i.a. intratendinous healing processes are better modeled with sagittal slices. We prove that ultrasound imaging is quantitatively useful for clinical assessment of Achilles tendon healing process and should be viewed as complementary to magnetic resonance imaging.Comment: Paper accepted to MICCAI'19 SUSI worksho

Doxorubicin–transferrin conjugate alters mitochondrial homeostasis and energy metabolism in human breast cancer cells

Doxorubicin (DOX) is considered one of the most powerful chemotherapeutic agents but its clinical use has several limitations, including cardiomyopathy and cellular resistance to the drug. By using transferrin (Tf) as a drug carrier, however, the adverse effects of doxorubicin as well as drug resistance can be reduced. The main objective of this study was to determine the exact nature and extent to which mitochondrial function is influenced by DOX–Tf conjugate treatment, specifically in human breast adenocarcinoma cells. We assessed the potential of DOX–Tf conjugate as a drug delivery system, monitoring its cytotoxicity using the MTT assay and ATP measurements. Moreover, we measured the alterations of mitochondrial function and oxidative stress markers. The effect of DOX–Tf was the most pronounced in MDA-MB-231, triple-negative breast cancer cells, whereas non-cancer endothelial HUVEC-ST cells were more resistant to DOX–Tf conjugate than to free DOX treatment. A different sensitivity of two investigate breast cancer cell lines corresponded to the functionality of their cellular antioxidant systems and expression of estrogen receptors. Our data also revealed that conjugate treatment mediated free radical generation and altered the mitochondrial bioenergetics in breast cancer cells.This work was supported in part by Grant No. B1511000001026.02 of the University of Lodz, Poland

GnRH-Agonist Cycles versus Combined Pretreatment with Oral Contraceptive Pills in Long Protocol GnRH-Agonist Cycles: A Randomised Controlled Trial

The strategy of in vitro fertilization (IVF) procedures relies on the increasing pregnancy rate and decreasing the risk of premature ovulation and ovarian hyperstimulation syndrome. They are also designed to avoid weekend oocyte retrievals. Combined oral contraceptive (OC) pills are among the medicines used to accomplish these objectives. Alternatively, estradiol can be used instead of OC to obtain similar results. The aim of our study was to compare the differences in pregnancy rates (PRs), implantation rates, and miscarriage rates between a short agonist protocol with estradiol priming and a long protocol with combined OC. Of the 298 women who participated in this study, 134 achieved clinical pregnancies (45.0%). A higher PR (58.4%, = 80, compared to 40.3%, = 54) was achieved in the long protocol after OC pretreatment group. The implantation rate was also higher for this group (37.8% versus 28.0%; = 0.03). The miscarriage rate was 15.0% ( = 12) for the long protocol after OC pretreatment group and 20.4% ( = 11) for the short agonist group ( = 0.81). The short agonist protocol required a 5.7% lower human menopausal gonadotropin (hMG) dosage than the long protocol but surprisingly the number of oocytes retrieved was also smaller

Multi-component low and high entropy metallic coatings synthesized by pulsed magnetron sputtering

This paper presents the findings of the synthesis of multicomponent (Al, W, Ni, Ti, Nb) alloy coatings from mosaic targets. For the study, a pulsed magnetron sputtering method was employed under different plasma generation conditions: modulation frequency (10 Hz and 1000 Hz), and power (600 W and 1000 W). The processes achieved two types of alloy coatings, high entropy and classical alloys. After the deposition processes, scanning electron microscopy, X-ray diffraction, and energy-dispersive X-ray spectroscopy techniques were employed to find the morphology, thickness, and chemical and phase compositions of the coatings. Nanohardness and its related parameters, namely H3.Er2, H.E, and 1.Er2H ratios, were measured. An annealing treatment was performed to estimate the stability range for the selected coatings. The results indicated the formation of as-deposited coatings exhibiting an amorphous structure as a single-phase solid solution. The process parameters had an influence on the resulting morphology-a dense and homogenous as well as a columnar morphology, was obtained. The study compared the properties of high-entropy alloy (HEA) coatings and classical alloy coatings concerning their structure and chemical and phase composition. It was found that the change of frequency modulation and the post-annealing process contributed to the increase in the hardness of the material in the case of HEA coatings

Modeling andsimulationofspeedselectiononleftventricular assist devices

The control problem for LVADs is to set pump speed such that cardiac output and pressure perfusion are within acceptable physiological ranges. However, current technology of LVADs cannot provide for a closed-loop control scheme that can make adjustments based on the patient\u27s level of activity. In this context, the SensorART Speed Selection Module (SSM) integrates various hardware and software components in order to improve the quality of the patients\u27 treatment and the workflow of the specialists. It enables specialists to better understand the patient-device interactions, and improve their knowledge. The SensorART SSM includes two tools of the Specialist Decision Support System (SDSS); namely the Suction Detection Tool and the Speed Selection Tool. A VAD Heart Simulation Platform (VHSP) is also part of the system. The VHSP enables specialists to simulate the behavior of a patient?s circulatory system, using different LVAD types and functional parameters. The SDSS is a web-based application that offers specialists with a plethora of tools for monitoring, designing the best therapy plan, analyzing data, extracting new knowledge and making informative decisions. In this paper, two of these tools, the Suction Detection Tool and Speed Selection Tool are presented. The former allows the analysis of the simulations sessions from the VHSP and the identification of issues related to suction phenomenon with high accuracy 93%. The latter provides the specialists with a powerful support in their attempt to effectively plan the treatment strategy. It allows them to draw conclusions about the most appropriate pump speed settings. Preliminary assessments connecting the Suction Detection Tool to the VHSP are presented in this paper