2016, Deliano, Matthias, Tabelow, Karsten, König, Reinhard, Polzehl, Jörg

Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. Thereby, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors in the analysis of single-subject data as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from an avoidance learning experiment with rodents, these methods revealed performance changes occurring at multiple time scales within and across training sessions which were otherwise obscured in the conventional analysis. Our work shows that the proper assessment of the behavioral dynamics of learning at high temporal resolution can shed new light on specific learning processes, and, thus, allows to refine existing learning concepts. It further disambiguates the interpretation of neurophysiological signal changes recorded during training in relation to learning.

2016, Kluge, Susanne, Bekeschus, Sander, Bender, Claudia, Benkhai, Hicham, Sckell, Axel, Below, Harald, Stope, Matthias B., Kramer, Axel, Yousfi, Mohammed

Objective: So-called cold physical plasmas for biomedical applications generate reactive oxygen and nitrogen species and the latter can trigger DNA damage at high concentrations. Therefore, the mutagenic risks of a certified atmospheric pressure argon plasma jet (kINPen MED) and its predecessor model (kINPen 09) were assessed. Methods: Inner egg membranes of fertilized chicken eggs received a single treatment with either the kINPen 09 (1.5, 2.0, or 2.5 min) or the kINPen MED (3, 4, 5, or 10 min). After three days of incubation, blood smears (panoptic May-Grünwald-Giemsa stain) were performed, and 1000 erythrocytes per egg were evaluated for the presence of polychromatic and normochromic nuclear staining as well as nuclear aberrations and binucleated cells (hen’s egg test for micronuclei induction, HET-MN). At the same time, the embryo mortality was documented. For each experiment, positive controls (cyclophosphamide and methotrexate) and negative controls (NaCl-solution, argon gas) were included. Additionally, the antioxidant potential of the blood plasma was assessed by ascorbic acid oxidation assay after treatment. Results: For both plasma sources, there was no evidence of genotoxicity, although at the longest plasma exposure time of 10 min the mortality of the embryos exceeded 40%. The antioxidant potential in the egg’s blood plasma was not significantly reduced immediately (p = 0.32) or 1 h (p = 0.19) post exposure to cold plasma. Conclusion: The longest plasma treatment time with the kINPen MED was 5–10 fold above the recommended limit for treatment of chronic wounds in clinics. We did not find mutagenic effects for any plasma treatment time using the either kINPen 09 or kINPen MED. The data provided with the current study seem to confirm the lack of a genotoxic potential suggesting that a veterinary or clinical application of these argon plasma jets does not pose mutagenic risks.

2021, Strobbia, Pietro, Cupil-Garcia, Vanessa, Crawford, Bridget M., Fales, Andrew M., Pfefer, T. Joshua, Liu, Yang, Maiwald, Martin, Sumpf, Bernd, Vo-Dinh, Tuan

For the majority of cancer patients, surgery is the primary method of treatment. In these cases, accurately removing the entire tumor without harming surrounding tissue is critical; however, due to the lack of intraoperative imaging techniques, surgeons rely on visual and physical inspection to identify tumors. Surface-enhanced Raman scattering (SERS) is emerging as a non-invasive optical alternative for intraoperative tumor identification, with high accuracy and stability. However, Raman detection requires dark rooms to work, which is not consistent with surgical settings. Methods: Herein, we used SERS nanoprobes combined with shifted-excitation Raman difference spectroscopy (SERDS) detection, to accurately detect tumors in xenograft murine model. Results: We demonstrate for the first time the use of SERDS for in vivo tumor detection in a murine model under ambient light conditions. We compare traditional Raman detection with SERDS, showing that our method can improve sensitivity and accuracy for this task. Conclusion: Our results show that this method can be used to improve the accuracy and robustness of in vivo Raman/SERS biomedical application, aiding the process of clinical translation of these technologies. © The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.

2016, Thamm, Kristina, Graupner, Sylvi, Werner, Carsten, Huttner, Wieland B., Corbeil, Denis, Nabi, Ivan R

The pentaspan membrane glycoprotein prominin-1 (CD133) is widely used in medicine as a cell surface marker of stem and cancer stem cells. It has opened new avenues in stem cell-based regenerative therapy and oncology. This molecule is largely used with human samples or the mouse model, and consequently most biological tools including antibodies are directed against human and murine prominin-1. Although the general structure of prominin-1 including its membrane topology is conserved throughout the animal kingdom, its primary sequence is poorly conserved. Thus, it is unclear if anti-human and -mouse prominin-1 antibodies cross-react with their orthologs in other species, especially dog. Answering this issue is imperative in light of the growing number of studies using canine prominin-1 as an antigenic marker. Here, we address this issue by cloning the canine prominin-1 and use its overexpression as a green fluorescent protein fusion protein in Madin-Darby canine kidney cells to determine its immunoreactivity with antibodies against human or mouse prominin-1. We used immunocytochemistry, flow cytometry and immunoblotting techniques and surprisingly found no cross-species immunoreactivity. These results raise some caution in data interpretation when anti-prominin-1 antibodies are used in interspecies studies.

2018-11-1, Galán, Elena, Llonch, Pol, Villagrá, Arantxa, Levit, Harel, Pinto, Severino, del Prado, Agustín

Introduction Projected temperature rise in the upcoming years due to climate change has increased interest in studying the effects of heat stress in dairy cows. Environmental indices are commonly used for detecting heat stress, but have been used mainly in studies focused on the productivity-related effects of heat stress. The welfare approach involves identifying physiological and behavioural measurements so as to start heat stress mitigation protocols before the appearance of impending severe health or production issues. Therefore, there is growing interest in studying the effects of heat stress on welfare. This systematic review seeks to summarise the animal-based responses to heat stress (physiological and behavioural, excluding productivity) that have been used in scientific literature. Methods Using systematic review guidelines set by PRISMA, research articles were identified, screened and summarised based on inclusion criteria for physiology and behaviour, excluding productivity, for animal-based resilience indicators. 129 published articles were reviewed to determine which animal-based indicators for heat stress were most frequently used in dairy cows. Results The articles considered report at least 212 different animal-based indicators that can be aggregated into body temperature, feeding, physiological response, resting, drinking, grazing and pasture-related behaviour, reactions to heat management and others. The most common physiological animal-based indicators are rectal temperature, respiration rate and dry matter intake, while the most common behavioural indicators are time spent lying, standing and feeding. Conclusion Although body temperature and respiration rate are the animal-based indicators most frequently used to assess heat stress in dairy cattle, when choosing an animal-based indicator for detecting heat stress using scientific literature to establish thresholds, characteristics that influence the scale of the response and the definition of heat stress must be taken into account, e.g. breed, lactation stage, milk yield, system type, climate region, bedding type, diet and cooling management strategies. © 2018 Galan∗E.∗Elena et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

2016, Schirrmann, Michael, Joschko, Monika, Gebbers, Robin, Kramer, Eckart, Zörner, Mirjam, Barkusky, Dietmar, Timmer, Jens

Background: Earthworms are important for maintaining soil ecosystem functioning and serve as indicators of soil fertility. However, detection of earthworms is time-consuming, which hinders the assessment of earthworm abundances with high sampling density over entire fields. Recent developments of mobile terrestrial sensor platforms for proximal soil sensing (PSS) provided new tools for collecting dense spatial information of soils using various sensing principles. Yet, the potential of PSS for assessing earthworm habitats is largely unexplored. This study investigates whether PSS data contribute to the spatial prediction of earthworm abundances in species distribution models of agricultural soils. Methodology/Principal Findings: Proximal soil sensing data, e.g., soil electrical conductivity (EC), pH, and near infrared absorbance (NIR), were collected in real-time in a field with two management strategies (reduced tillage / conventional tillage) and sandy to loam soils. PSS was related to observations from a long-term (11 years) earthworm observation study conducted at 42 plots. Earthworms were sampled from 0.5 x 0.5 x 0.2 m³ soil blocks and identified to species level. Sensor data were highly correlated with earthworm abundances observed in reduced tillage but less correlated with earthworm abundances observed in conventional tillage. This may indicate that management influences the sensor-earthworm relationship. Generalized additive models and state-space models showed that modelling based on data fusion from EC, pH, and NIR sensors produced better results than modelling without sensor data or data from just a single sensor. Regarding the individual earthworm species, particular sensor combinations were more appropriate than others due to the different habitat requirements of the earthworms. Earthworm species with soil-specific habitat preferences were spatially predicted with higher accuracy by PSS than more ubiquitous species. Conclusions/Significance: Our findings suggest that PSS contributes to the spatial modelling of earthworm abundances at field scale and that it will support species distribution modelling in the attempt to understand the soil-earthworm relationships in agroecosystems.

2021, Phillips, Helen R. P., Bach, Elizabeth M., Bartz, Marie L. C., Bennett, Joanne M., Beugnon, Rémy, Briones, Maria J. I., Brown, George G., Ferlian, Olga, Gongalsky, Konstantin B., Guerra, Carlos A., König-Ries, Birgitta, López-Hernández, Danilo, Loss, Scott R., Marichal, Raphael, Matula, Radim, Minamiya, Yukio, Moos, Jan Hendrik, Moreno, Gerardo, Morón-Ríos, Alejandro, Motohiro, Hasegawa, Muys, Bart, Krebs, Julia J., Neirynck, Johan, Norgrove, Lindsey, Novo, Marta, Nuutinen, Visa, Nuzzo, Victoria, Mujeeb Rahman, P., Pansu, Johan, Paudel, Shishir, Pérès, Guénola, Pérez-Camacho, Lorenzo, Orgiazzi, Alberto, Ponge, Jean-François, Prietzel, Jörg, Rapoport, Irina B., Rashid, Muhammad Imtiaz, Rebollo, Salvador, Rodríguez, Miguel Á., Roth, Alexander M., Rousseau, Guillaume X., Rozen, Anna, Sayad, Ehsan, Ramirez, Kelly S., van Schaik, Loes, Scharenbroch, Bryant, Schirrmann, Michael, Schmidt, Olaf, Schröder, Boris, Seeber, Julia, Shashkov, Maxim P., Singh, Jaswinder, Smith, Sandy M., Steinwandter, Michael, Russell, David J., Szlavecz, Katalin, Talavera, José Antonio, Trigo, Dolores, Tsukamoto, Jiro, Uribe-López, Sheila, de Valença, Anne W., Virto, Iñigo, Wackett, Adrian A., Warren, Matthew W., Webster, Emily R., Schwarz, Benjamin, Wehr, Nathaniel H., Whalen, Joann K., Wironen, Michael B., Wolters, Volkmar, Wu, Pengfei, Zenkova, Irina V., Zhang, Weixin, Cameron, Erin K., Eisenhauer, Nico, Wall, Diana H., Brose, Ulrich, Decaëns, Thibaud, Lavelle, Patrick, Loreau, Michel, Mathieu, Jérôme, Mulder, Christian, van der Putten, Wim H., Rillig, Matthias C., Thakur, Madhav P., de Vries, Franciska T., Wardle, David A., Ammer, Christian, Ammer, Sabine, Arai, Miwa, Ayuke, Fredrick O., Baker, Geoff H., Baretta, Dilmar, Barkusky, Dietmar, Beauséjour, Robin, Bedano, Jose C., Birkhofer, Klaus, Blanchart, Eric, Blossey, Bernd, Bolger, Thomas, Bradley, Robert L., Brossard, Michel, Burtis, James C., Capowiez, Yvan, Cavagnaro, Timothy R., Choi, Amy, Clause, Julia, Cluzeau, Daniel, Coors, Anja, Crotty, Felicity V., Crumsey, Jasmine M., Dávalos, Andrea, Cosín, Darío J. Díaz, Dobson, Annise M., Domínguez, Anahí, Duhour, Andrés Esteban, van Eekeren, Nick, Emmerling, Christoph, Falco, Liliana B., Fernández, Rosa, Fonte, Steven J., Fragoso, Carlos, Franco, André L. C., Fusilero, Abegail, Geraskina, Anna P., Gholami, Shaieste, González, Grizelle, Gundale, Michael J., López, Mónica Gutiérrez, Hackenberger, Branimir K., Hackenberger, Davorka K., Hernández, Luis M., Hirth, Jeff R., Hishi, Takuo, Holdsworth, Andrew R., Holmstrup, Martin, Hopfensperger, Kristine N., Lwanga, Esperanza Huerta, Huhta, Veikko, Hurisso, Tunsisa T., Iannone, Basil V., Iordache, Madalina, Irmler, Ulrich, Ivask, Mari, Jesús, Juan B., Johnson-Maynard, Jodi L., Joschko, Monika, Kaneko, Nobuhiro, Kanianska, Radoslava, Keith, Aidan M., Kernecker, Maria L., Koné, Armand W., Kooch, Yahya, Kukkonen, Sanna T., Lalthanzara, H., Lammel, Daniel R., Lebedev, Iurii M., Le Cadre, Edith, Lincoln, Noa K.

Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change.