2021-1-28, Schleusener, Johannes, Guo, Shuxia, Darvin, Maxim E., Thiede, Gisela, Chernavskaia, Olga, Knorr, Florian, Lademann, Jürgen, Popp, Jürgen, Bocklitz, Thomas W.

Psoriasis is considered a widespread dermatological disease that can strongly affect the quality of life. Currently, the treatment is continued until the skin surface appears clinically healed. However, lesions appearing normal may contain modifications in deeper layers. To terminate the treatment too early can highly increase the risk of relapses. Therefore, techniques are needed for a better knowledge of the treatment process, especially to detect the lesion modifications in deeper layers. In this study, we developed a fiber-based SORS-SERDS system in combination with machine learning algorithms to non-invasively determine the treatment efficiency of psoriasis. The system was designed to acquire Raman spectra from three different depths into the skin, which provide rich information about the skin modifications in deeper layers. This way, it is expected to prevent the occurrence of relapses in case of a too short treatment. The method was verified with a study of 24 patients upon their two visits: the data is acquired at the beginning of a standard treatment (visit 1) and four months afterwards (visit 2). A mean sensitivity of ≥85% was achieved to distinguish psoriasis from normal skin at visit 1. At visit 2, where the patients were healed according to the clinical appearance, the mean sensitivity was ≈65%.

2021, Schu, Moritz, Terriac, Emmanuel, Koch, Marcus, Paschke, Stephan, Lautenschläger, Franziska, Flormann, Daniel A.D.

The cellular cortex is an approximately 200-nm-thick actin network that lies just beneath the cell membrane. It is responsible for the mechanical properties of cells, and as such, it is involved in many cellular processes, including cell migration and cellular interactions with the environment. To develop a clear view of this dense structure, high-resolution imaging is essential. As one such technique, electron microscopy, involves complex sample preparation procedures. The final drying of these samples has significant influence on potential artifacts, like cell shrinkage and the formation of artifactual holes in the actin cortex. In this study, we compared the three most used final sample drying procedures: critical-point drying (CPD), CPD with lens tissue (CPD-LT), and hexamethyldisilazane drying. We show that both hexamethyldisilazane and CPD-LT lead to fewer artifactual mesh holes within the actin cortex than CPD. Moreover, CPD-LT leads to significant reduction in cell height compared to hexamethyldisilazane and CPD. We conclude that the final drying procedure should be chosen according to the reduction in cell height, and so CPD-LT, or according to the spatial separation of the single layers of the actin cortex, and so hexamethyldisilazane.

2024, Fedorov, Pavel, Soldatov, Ivan, Neu, Volker, Schäfer, Rudolf, Schmidt, Oliver G., Karnaushenko, Daniil

Modification of the magnetic properties under the induced strain and curvature is a promising avenue to build three-dimensional magnetic devices, based on the domain wall motion. So far, most of the studies with 3D magnetic structures were performed in the helixes and nanowires, mainly with stationary domain walls. In this study, we demonstrate the impact of 3D geometry, strain and curvature on the current-induced domain wall motion and spin-orbital torque efficiency in the heterostructure, realized via a self-assembly rolling technique on a polymeric platform. We introduce a complete 3D memory unit with write, read and store functionality, all based on the field-free domain wall motion. Additionally, we conducted a comparative analysis between 2D and 3D structures, particularly addressing the influence of heat during the electric current pulse sequences. Finally, we demonstrated a remarkable increase of 30% in spin-torque efficiency in 3D configuration.

2022, Kemp, Luke, Xu, Chi, Depledge, Joanna, Ebi, Kristie L., Gibbins, Goodwin, Kohler, Timothy A., Rockström, Johan, Scheffer, Marten, Schellnhuber, Hans Joachim, Steffen, Will, Lenton, Timothy M.

2020, Bakalis, Serafim, Valdramidis, Vasilis P., Argyropoulos, Dimitrios, Ahrne, Lilia, Chen, Jianshe, Cullen, P.J., Cummins, Enda, Datta, Ashim K., Emmanouilidis, Christos, Foster, Tim, Fryer, Peter J., Gouseti, Ourania, Hospido, Almudena, Knoerzer, Kai, LeBail, Alain, Marangoni, Alejandro G., Rao, Pingfan, Schlüter, Oliver K., Taoukis, Petros, Xanthakis, Epameinondas, Van Impe, Jan F.M.

[no abstract available]

2022, Senft, Matthias, Stahl, Ulrike, Svoboda, Nikolai

To meet the future challenges and foster integrated and holistic research approaches in agricultural sciences, new and sustainable methods in research data management (RDM) are needed. The involvement of scientific users is a critical success factor for their development. We conducted an online survey in 2020 among different user groups in agricultural sciences about their RDM practices and needs. In total, the questionnaire contained 52 questions on information about produced and (re-)used data, data quality aspects, information about the use of standards, publication practices and legal aspects of agricultural research data, the current situation in RDM in regards to awareness, consulting and curricula as well as needs of the agricultural community in respect to future developments. We received 196 (partially) completed questionnaires from data providers, data users, infrastructure and information service providers. In addition to the diversity in the research data landscape of agricultural sciences in Germany, the study reveals challenges, deficits and uncertainties in handling research data in agricultural sciences standing in the way of access and efficient reuse of valuable research data. However, the study also suggests and discusses potential solutions to enhance data publications, facilitate and secure data re-use, ensure data quality and develop services (i.e. training, support and bundling services). Therefore, our research article provides the basis for the development of common RDM, future infrastructures and services needed to foster the cultural change in handling research data across agricultural sciences in Germany and beyond.

2020, Liu, Zhu, Ciais, Philippe, Deng, Zhu, Lei, Ruixue, Davis, Steven J., Feng, Sha, Zheng, Bo, Cui, Duo, Dou, Xinyu, Zhu, Biqing, Guo, Rui, Ke, Piyu, Sun, Taochun, Lu, Chenxi, He, Pan, Wang, Yuan, Yue, Xu, Wang, Yilong, Lei, Yadong, Zhou, Hao, Cai, Zhaonan, Wu, Yuhui, Guo, Runtao, Han, Tingxuan, Xue, Jinjun, Boucher, Olivier, Boucher, Eulalie, Chevallier, Frédéric, Tanaka, Katsumasa, Wei, Yiming, Zhong, Haiwang, Kang, Chongqing, Zhang, Ning, Chen, Bin, Xi, Fengming, Liu, Miaomiao, Bréon, François-Marie, Lu, Yonglong, Zhang, Qiang, Guan, Dabo, Gong, Peng, Kammen, Daniel M., He, Kebin, Schellnhuber, Hans Joachim

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (−1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic’s effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially.

2021, Yang, Tingxiang, Chettri, Avinash, Radwan, Basseem, Matuszyk, Ewelina, Baranska, Malgorzata, Dietzek, Benjamin

Small molecules are frequently used as dyes, labels and markers to visualize and probe biophysical processes within cells. However, very little is generally known about the light-driven excited-state reactivity of such systems when placed in cells. Here an experimental approach to study ps time-resolved excited state dynamics of a benchmark molecular marker, astaxanthin, in live human cells is introduced. © The Royal Society of Chemistry 2021.

2023, Cortacero, Kévin, McKenzie, Brienne, Müller, Sabina, Khazen, Roxana, Lafouresse, Fanny, Corsaut, Gaëlle, Van Acker, Nathalie, Frenois, François-Xavier, Lamant, Laurence, Meyer, Nicolas, Vergier, Béatrice, Wilson, Dennis G., Luga, Hervé, Staufer, Oskar, Dustin, Michael L., Valitutti, Salvatore, Cussat-Blanc, Sylvain

An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting “black box” models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches.

2021, Nittala, Aditya Shekhar, Karrenbauer, Andreas, Khan, Arshad, Kraus, Tobias, Steimle, Jürgen

Electro-physiological sensing devices are becoming increasingly common in diverse applications. However, designing such sensors in compact form factors and for high-quality signal acquisition is a challenging task even for experts, is typically done using heuristics, and requires extensive training. Our work proposes a computational approach for designing multi-modal electro-physiological sensors. By employing an optimization-based approach alongside an integrated predictive model for multiple modalities, compact sensors can be created which offer an optimal trade-off between high signal quality and small device size. The task is assisted by a graphical tool that allows to easily specify design preferences and to visually analyze the generated designs in real-time, enabling designer-in-the-loop optimization. Experimental results show high quantitative agreement between the prediction of the optimizer and experimentally collected physiological data. They demonstrate that generated designs can achieve an optimal balance between the size of the sensor and its signal acquisition capability, outperforming expert generated solutions.