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Körperhaltung und Muskelspannung beeinflussen den Klang der Stimme. Aber gibt es auch einen Zusammenhang zwischen der motorischen Kontrolle der Nacken-, Gesichts- und Kieferregion und der Stimme? Die Pilotstudie mit 12 Sängerinnen ging dieser Frage nach und zeigt: Es ist sinnvoll, die motorische Kontrolle zu testen, wenn Patient*innen mit Stimmproblemen zur Physiotherapie kommen.
In modern times, closed-loop control systems (CLCSs) play a prominent role in a wide application range, from production machinery via automated vehicles to robots. CLCSs actively manipulate the actual values of a process to match predetermined setpoints, typically in real time and with remarkable precision. However, the development, modeling, tuning, and optimization of CLCSs barely exploit the potential of artificial intelligence (AI). This paper explores novel opportunities and research directions in CLCS engineering, presenting potential designs and methodologies incorporating AI. Combining these opportunities and directions makes it evident that employing AI in developing and implementing CLCSs is indeed feasible. Integrating AI into CLCS development or AI directly within CLCSs can lead to a significant improvement in stakeholder confidence. Integrating AI in CLCSs raises the question: How can AI in CLCSs be trusted so that its promising capabilities can be used safely? One does not trust AI in CLCSs due to its unknowable nature caused by its extensive set of parameters that defy complete testing. Consequently, developers working on AI-based CLCSs must be able to rate the impact of the trainable parameters on the system accurately. By following this path, this paper highlights two key aspects as essential research directions towards safe AI-based CLCSs: (I) the identification and elimination of unproductive layers in artificial neural networks (ANNs) for reducing the number of trainable parameters without influencing the overall outcome, and (II) the utilization of the solution space of an ANN to define the safety-critical scenarios of an AI-based CLCS.
Hyperhydricity (HH) is one of the most important physiological disorders that negatively affects various plant tissue culture techniques. The objective of this study was to characterize optical features to allow an automated detection of HH. For this purpose, HH was induced in two plant species, apple and Arabidopsis thaliana, and the severity was quantified based on visual scoring and determination of apoplastic liquid volume. The comparison between the HH score and the apoplastic liquid volume revealed a significant correlation, but different response dynamics. Corresponding leaf reflectance spectra were collected and different approaches of spectral analyses were evaluated for their ability to identify HH-specific wavelengths. Statistical analysis of raw spectra showed significantly lower reflection of hyperhydric leaves in the VIS, NIR and SWIR region. Application of the continuum removal hull method to raw spectra identified HH-specific absorption features over time and major absorption peaks at 980 nm, 1150 nm, 1400 nm, 1520 nm, 1780 nm and 1930 nm for the various conducted experiments. Machine learning (ML) model spot checking specified the support vector machine to be most suited for classification of hyperhydric explants, with a test accuracy of 85% outperforming traditional classification via vegetation index with 63% test accuracy and the other ML models tested. Investigations on the predictor importance revealed 1950 nm, 1445 nm in SWIR region and 415 nm in the VIS region to be most important for classification. The validity of the developed spectral classifier was tested on an available hyperspectral image acquisition in the SWIR-region.
Dairy farming has been the subject of public debate on animal welfare for a number of years now. Animal welfare discussions on dairy farming often include the demand for more nature connectedness in this area. This study focuses on the divergent perspectives of consumers and scientists on the importance of more nature connectedness for animal welfare strategies in German dairy farming. Within Europe, Germany is the main producer of cow’s milk and an important industry in many rural areas in Germany is dairy farming. The insights presented are based on qualitative interviews with dairy farming and livestock researchers from Germany and Austria. A key finding of this study is that we need to look more closely at the actual content of nature claims in animal welfare debates. The scientists interviewed tend to see idealized conditions in animal welfare discussions with images of nature which in fact seldom lead to improved conditions in dairy farming and, even then, only to a limited extent. The scientists interviewed rate calls for more nature connectedness in dairy farming from the nonagricultural public as anti-modern, complexity-reducing, and normative. Nevertheless, some of the scientists interviewed did have valuable insights into the nonagricultural public’s criticism of dairy farming practices. These scientists argued, however, that animal welfare needs to differentiate between nature connectedness and the innate needs of cattle when it comes to animal welfare strategies. An important conclusion of the study is that more discussion formats are needed to promote the exchange of ideas between different social groups attempting to understand animal welfare in dairy farming.
Background
The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable.
Results
An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed.
Conclusion
The technical realization of “Phenomenon” allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time.
Klimabäume scheinen in der Mitte der Gesellschaft angekommen zu sein. Zahlreiche Empfehlungen finden sich in Onlinemedien und stellen die fünf oder zehn besten Baumarten vor. Dabei gibt es Schnittmengen. Interessanterweise enthält das Sortiment weitgehend bekannte und auch heute schon verwendete Arten. Es steht eine breite Palette an zukunftsfähigen Klimabäumen zur Verfügung, die teilweise noch gar nicht beachtet werden. In zwei Teilen sollen bekannte und weniger bekannte Klimabäume bezüglich ihrer Verwendbarkeit im urbanen Raum, ihrer Herkunft und ihres Ausbreitungspotenzials diskutiert werden.
Primary Liver Cancers : Connecting the Dots of Cellular Studies and Epidemiology with Metabolomics
(2023)
Liver cancers are rising worldwide. Between molecular and epidemiological studies, a research gap has emerged which might be amenable to the technique of metabolomics. This review investigates the current understanding of liver cancer’s trends, etiology and its correlates with existing literature for hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA) and hepatoblastoma (HB). Among additional factors, the literature reports dysfunction in the tricarboxylic acid metabolism, primarily for HB and HCC, and point mutations and signaling for CCA. All cases require further investigation of upstream and downstream events. All liver cancers reported dysfunction in the WNT/β-catenin and P13K/AKT/mTOR pathways as well as changes in FGFR. Metabolites of IHD1, IDH2, miRNA, purine, Q10, lipids, phosphatidylcholine, phosphatidylethanolamine, acylcarnitine, 2-HG and propionyl-CoA emerged as crucial and there was an attempt to elucidate the WNT/β-catenin and P13K/AKT/mTOR pathways metabolomically.
Aims and Objectives:
Preventive home visits are a low-threshold counselling and support approach. They have been reported to achieve heterogeneous effects. However, preventive home visits have the potential to reduce the risk of becoming dependent on long-term care. The aim of this study is to investigate the effect of preventive home visits as a nursing intervention on health-related quality of life of older people in a longitudinal survey and to develop recommendations for which target groups preventive home visits have the highest benefit. The sample consisted of 75 people, aged between 65 and 85, who were able to understand and speak German, had not yet been eligible for benefits from the long-term care insurance and lived in the municipality under study.
Methodological Design and Justification:
A quantitative longitudinal study in order to investigate the effects of preventive home visits.
Ethical Issues and Approval:
There were no ethical concerns. Accordingly, ethical approval was granted.
Research Methods, Results and Conclusions:
The health-related quality of life was recorded four times between 01/2017 and 08/2020 with the Short-Form- Health- Survey- 12 and analysed using descriptive statistics. Results reveal that the physical health status cannot be easily influenced over a short period of time. The main effect, however, is that preventive home visits have a significant positive effect on the mental health status. The main topics during the home visits were mobility, nutrition and social participation. Increased knowledge and motivation for preventive behaviour extended the autonomy of older people. Accordingly, preventive home visits can support a self-determined life in a familiar environment. The results of the present study show that preventive home visits as a nursing intervention in rural areas are successful. In Germany, preventive home visits have not yet been implemented on a regular basis. In order to do so, a general definition of the concept is needed. Preventive home visits should be officially included in the regular health care services in Germany.
While developing traffic-based cognitive enhancement technology (CET), such as bike accident prevention systems, it can be challenging to test and evaluate them properly. After all, the real-world scenario could endanger the subjects’ health and safety. Therefore, a simulator is needed, preferably one that is realistic yet low cost. This paper introduces a way to use the video game Grand Theft Auto V (GTA V) and its sophisticated traffic system as a base to create such a simulator, allowing for the safe and realistic testing of dangerous traffic situations involving cyclists, cars, and trucks. The open world of GTA V, which can be explored on foot and via various vehicles, serves as an immersive stand-in for the real world. Custom modification scripts of the game give the researchers control over the experiment scenario and the output data to be evaluated. An off-the-shelf bicycle equipped with three sensors serves as a realistic input device for the subject’s movement direction and speed. The simulator was used to test two early-stage CET concepts enabling cyclists to sense dangerous traffic situations, such as trucks approaching from behind the cyclist. Thus, this paper also presents the user evaluation of the cycling simulator and the CET used by the subjects to sense dangerous traffic situations. With the knowledge of the first iteration of the user-centered design (UCD) process, this paper concludes by naming improvements for the cycling simulator and discussing further research directions for CET that enable users to sense dangerous situations better.
Semi-natural grasslands (SNGs) are an essential part of European cultural landscapes. They are an important habitat for many animal and plant species and offer a variety of ecological functions. Diverse plant communities have evolved over time depending on environmental and management factors in grasslands. These different plant communities offer multiple ecosystem services and also have an effect on the forage value of fodder for domestic livestock. However, with increasing intensification in agriculture and the loss of SNGs, the biodiversity of grasslands continues to decline. In this paper, we present a method to spatially classify plant communities in grasslands in order to identify and map plant communities and weed species that occur in a semi-natural meadow. For this, high-resolution multispectral remote sensing data were captured by an unmanned aerial vehicle (UAV) in regular intervals and classified by a convolutional neural network (CNN). As the study area, a heterogeneous semi-natural hay meadow with first- and second-growth vegetation was chosen. Botanical relevés of fixed plots were used as ground truth and independent test data. Accuracies up to 88% on these independent test data were achieved, showing the great potential of the usage of CNNs for plant community mapping in high-resolution UAV data for ecological and agricultural applications.