Recommendations on measurement and analysis of results obtained on biological substances using isothermal titration calorimetry

Isothermal titration calorimetry (ITC) is widely used to determine the thermodynamics of biological interactions including protein-protein, small molecule- protein, protein-DNA, small molecule-DNA, and antigen-antibody interactions. An ITC measurement consists of monitoring the transfer of heat between an analyte solution in a sample vessel and a reference solution in a reference vessel upon injection of a small aliquot of titrant solution into the sample vessel at a fixed ITC operating temperature. A binding isotherm is generated from the heat-transferred- per-injection data and values for the binding constants, the apparent binding enthalpies, and the apparent ratio of the amount of titrant to analyte for the binding reaction are then determined from fits of a binding model, whether it is a single site, identical multi-site, or an interacting multi-site binding model, to the binding isotherm. Prior to the fitting procedure, corrections should be made for contributions from extraneous heat of mixing determined separately from injections of the titrant into just the dialysate/buffer solution. Ultra-high binding constants, which cannot be directly determined from an ITC measurement, can be determined by a displacement ITC method where injections of the tight-binding titrant into a solution of a weaker-binding titrant-analyte complex displaces the weaker-binding titrant from the complex. The Michaelis and catalytic constants can be determined for an enzyme reaction from injections of a substrate or enzyme titrant into an enzyme or substrate analyte solution. Several binding reactions are suggested to check the operating performance of the ITC. The reporting of ITC results must be specific with regard to the composition of the titrant and the analyte solutions, the temperature, and the model used in the analysis

imageseg: An R package for deep learning-based image segmentation

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2022 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological SocietyConvolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications and are particularly suited for image data. Image segmentation (the classification of all pixels in images) is one such application and can, for example, be used to assess forest structural metrics. While CNN-based image segmentation methods for such applications have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists. Here, we present R package imageseg which implements a CNN-based workflow for general purpose image segmentation using the U-Net and U-Net++ architectures in R. The workflow covers data (pre)processing, model training and predictions. We illustrate the utility of the package with image recognition models for two forest structural metrics: tree canopy density and understorey vegetation density. We trained the models using large and diverse training datasets from a variety of forest types and biomes, consisting of 2877 canopy images (both canopy cover and hemispherical canopy closure photographs) and 1285 understorey vegetation images. Overall segmentation accuracy of the models was high with a Dice score of 0.91 for the canopy model and 0.89 for the understorey vegetation model (assessed with 821 and 367 images respectively). The image segmentation models performed significantly better than commonly used thresholding methods and generalized well to data from study areas not included in training. This indicates robustness to variation in input images and good generalization strength across forest types and biomes. The package and its workflow allow simple yet powerful assessments of forest structural metrics using pretrained models. Furthermore, the package facilitates custom image segmentation with single or multiple classes and based on colour or grayscale images, for example, for applications in cell biology or for medical images. Our package is free, open source and available from CRAN. It will enable easier and faster implementation of deep learning-based image segmentation within R for ecological applications and beyond.publishedVersio

Urkunde und Übersetzung des Urkundentextes

Dieser Band dokumentiert Ansprachen und Reden, die anlässlich der Verleihung des Dr. theol. honoris causa durch den Fachbereich Evangelische Theologie an Professor Dr. mult. Walter Jens gehalten worden sind.This volume documents speeches and speeches given on the occasion of the award of the Dr. theol. honoris causa by the Department of Protestant Theology to Professor Dr. mult. Walter Jens have been held on June 3, 2005

Transplantation of Renal Allografts From Organ Donors Reactive for HCV Antibodies to HCV-Negative Recipients: Safety and Clinical Outcome

IntroductionBecause of the shortage of available organs for renal transplantation, strategies enabling the safe use of organs from donors with potential chronic infections such as hepatitis C are necessary. The aim of this study was to analyze the outcome of renal transplant donation from hepatitis C virus (HCV)-positive donors.MethodsBetween September 2002 and May 2007, 51 kidneys (34 donors) reactive for HCV antibodies were further evaluated. Six kidneys (5 donors) were transplanted to 6 recipients with known chronic HCV infection. The remaining 29 donors underwent extended virological testing. Nine donors were HCV RNA positive and thus not suitable for HCV-negative patients. Twenty donors (21 kidneys) did not have detectable HCV RNA copies and were transplanted into 21 HCV-negative recipients. Clinical outcomes focusing on safety, allograft function, and de novo HCV infection in the recipient were collected.ResultsThere were no de novo HCV infections detected in recipients who were HCV negative before transplantation. The extended virological donor screening did not have an impact on median cold ischemia time. Five-year graft survival was 75%.DiscussionOrgans from anti-HCV-reactive, nonviremic donors can be transplanted safely to HCV-negative recipients

Lung Surfactant Accelerates Skin Wound Healing : A Translational Study with a Randomized Clinical Phase I Study

Lung surfactants are used for reducing alveolar surface tension in preterm infants to ease breathing. Phospholipid films with surfactant proteins regulate the activity of alveolar macrophages and reduce inflammation. Aberrant skin wound healing is characterized by persistent inflammation. The aim of the study was to investigate if lung surfactant can promote wound healing. Preclinical wound models, e.g. cell scratch assays and full-thickness excisional wounds in mice, and a randomized, phase I clinical trial in healthy human volunteers using a suction blister model were used to study the effect of the commercially available bovine lung surfactant on skin wound repair. Lung surfactant increased migration of keratinocytes in a concentration-dependent manner with no effect on fibroblasts. Significantly reduced expression levels were found for pro-inflammatory and pro-fibrotic genes in murine wounds. Because of these beneficial effects in preclinical experiments, a clinical phase I study was initiated to monitor safety and tolerability of surfactant when applied topically onto human wounds and normal skin. No adverse effects were observed. Subepidermal wounds healed significantly faster with surfactant compared to control. Our study provides lung surfactant as a strong candidate for innovative treatment of chronic skin wounds and as additive for treatment of burn wounds to reduce inflammation and prevent excessive scarring. © 2020, The Author(s)

Influence of degree of deformation on welding pore reduction in high-carbon steels

Locally adapted properties within a machine component offer opportunities to increase the performance of a component by using high strenght materials where they are needed. The economic production of such hybrid components on the other hand represents a major challenge. The new tailored forming process chain, which is developed within the collaborative research center (CRC 1153) represents a possible solution to produce hybrid components. This is made possible by the use of pre-joined hybrid semi-finished products made from two different steel alloys, which are subsequently formed. The semi-finished products can be manufactured for example by means of deposition welding. Due to a thermal mechanical treatment, an overall higher component strength of the joining zone can be achieved. The deposition welding processes can be used to generate a cladding on a base material. During the welding, one of the most difficult tasks is to reduce the amount and size of pores in the joining zone. These pores can reduce the strength in the joining zone of the welded parts. However, additional pores can occur in the intermediate zone between the substrate and the cladding. In the presented study, the influence of the forming process on the closing of pores in the cladding and in the intermediate zone was investigated. Therefore, cylindrical specimen were extracted in longitudinal direction of the welding track by wire-cut eroding. These welding tracks are manufactured by plasma-transferred arc welding of AISI 52100 on a base plate made of AISI 1015. Further, specimens were prepared transversely, so that the base material, the intermediate layer, and the welded material are axially arranged in the specimen. The prepared specimen were checked for pores by means of scanning acoustic microscopy. Subsequently, an uniaxial compression test was carried out with various degrees of deformation and the two specimen designs were examined again for pores. A microstructure analysis was carried out after each step. The investigations show that there is a need for a minimum degree of deformation to reduce pores in the welded material. However, this required plastic strain cannot be achieved in the welded material of the hybrid specimen, which is a result of the homogeneous temperature distribution in the specimen. The homogeneous temperature distribution leads to different flow properties in the specimen, which means that the main plastic deformation is taking place in the base material. © 2021, The Author(s)

Prognostic Value of Urinary Calprotectin, NGAL and KIM-1 in Chronic Kidney Disease

Background/Aims: Urinary biomarkers like neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) do not only allow an early diagnosis of acute kidney injury, but also provide prognostic information in this setting. The present prospective study investigates, whether the urinary biomarkers NGAL, KIM-1 and calprotectin have prognostic information in chronic kidney disease (CKD) as well. Methods: Urinary calprotectin, NGAL and KIM-1 concentrations were assessed in a study population of 143 patients with stable CKD comprising diabetic and hypertensive nephropathy, glomerulonephritis/vasculitis, and autosomal dominant polycystic kidney disease. An eGFR fluctuation > 5ml/min/1.73m2 in the past 12 months was defined as an exclusion criterion in order to exclude cases with acute on chronic kidney injury. Renal function was monitored for a median follow-up of 37 months. Results: In the overall study population, all the three biomarkers failed to predict DeGFR and DACR from baseline to follow-up in linear regression analysis adjusted for age, gender, and baseline eGFR. Contrarily, baseline ACR was significantly associated with DeGFR (p< 0.001). In the subgroup of patients with vasculitis and glomerulonephritis, all the three biomarkers were significantly associated with DeGFR, with calprotectin having the highest regression coefficient. Conclusion: In contrast to the traditional biomarker “albuminuria”, neither the inflammatory biomarker calprotectin, nor the tubular biomarkers NGAL and KIM-1, provide robust prognostic information on the loss or renal function in a heterogeneous CKD population. All of them, however, are candidate prognostic biomarkers in primarily inflammatory renal diseases