727 research outputs found
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Benchmarking Mechanical Properties of 3D printed elastomeric Microstructures [data]
No full textThe characterization of mechanical properties in soft 3D printed materials at the microscale remains a significant challenge due to the lack of standardized methodologies. To address this issue, a microscale nanoindentation protocol for elastomeric 3D printed microstructures is developed, optimized, and benchmarked. Herein, a conospherical indenter tip (r = 10.26 µm), a modified trapezoidal displacement profile with lift-off segments to capture adhesion interactions, and the nano-Johnson-Kendall-Roberts model for data analysis are employed. The protocol is optimized and verified using four newly developed PDMS-based inks for two-photon 3D laser printing. The results are compared to a state-of-the-art literature protocol that uses a Berkovich tip and the Oliver-Pharr model. It is shown that adhesion forces play a significant role in mechanical properties overestimation, showing differences of up to 80% between the different protocols. This study highlights the importance of carefully selecting characterization protocol to yield comparable results between studies. By providing a standardized protocol, it paves the way for straightforward and accurate characterization of mechanical properties in soft 3D printed materials at the microscale
A role of pupil-linked arousal, cingulo-insular cortex, and intralaminar thalamus for auditory near-threshold perception [Research Data]
No full textAbstract:
The perception of near-threshold tones varies strongly across trials, likely because of fluctuations in sustained attention or arousal. We used parallel fMRI and pupillometry to study the role of attention networks for the detection of near-threshold tones in three phases: (1) passive listening, (2) active detection of salient tones, and (3) active detection of near-threshold tones. Results confirmed previous findings from MEG that auditory cortex activity and pupil-dilation responses for near-threshold tones were only observed when task-relevant, stronger for hit trials, but also present for miss trials. We then sought which attention-related areas show a similar response pattern, and found it in the insular cortex, anterior midcingulate cortex, and inferior precentral sulcus. Moreover, activity in the insula was already stronger for hit than miss trials in the pre-stimulus interval. Activity for hit trials was also observed in a number of subcortical nuclei, including thalamus, periaqueductal gray, locus coeruleus, and the colliculi. Like insula, activity in the intralaminar nuclei of the thalamus additionally showed activity for miss trials and stronger activity for hit trials in the baseline. Finally, BOLD activity correlated to spontaneous pupil fluctuations was evaluated and revealed biphasic activation and deactivation in a widespread cortical network, with a maximum 1 s and minimum 6 s after pupil dilation. The cortical networks included insula, anterior midcingulate cortex, retro-splenial, and sensory cortex. Overall these data identify the cingulo-insular network and the intralaminar thalamic nuclei as potential sources of fluctuations in auditory cortex activity in the context of near-threshold tone detection.
Dataset:
The data set includes single subject data (sMRI, fMRI and eyetracking) and group averages for both fMRI and the pupil dilation response. Analysis scripts are also included. Further details are provided in the readme file
Gold-Catalyzed Access to Pyrrolo- and (Aryl)Indolo-Fused Phenazines [data]
No full textNovel fused phenazines were synthesized through a combination of gold-catalyzed hydroamination and cascade cyclization reactions towards azaacenes. In total, 30 new compounds were synthesized and investigated with respect to their structural and optoelectronic properties. In solution, these targets exhibit strong green to red emission, with quantum yields of up to 60 %. The emission wavelength can be finely adjusted by the choice of solvent due to their solvatochromic behavior, as demonstrated with a representative example
Data for the PhD thesis "Modeling Lexical Fields for Translation: a Corpus-Based Study of Armenian, German, and English Culinary Verbs"
No full textThis dataset contains in high resolution all graphical visualizations of data analysis provided in my doctoral dissertation. The graphs are organized according to chapters and subchapters and titeled respectively. Additionally, this dataset provides all dataframes (German, English, and Armenian) in XLSX format of the manual semantic annotation based on which the graphs are generated. Among presented graphical visualizations are (Multiple) Correspondence Analysis (MCA vs. CA), Mosaic-Plots, Conditional Infererence Trees (CIT), and Context-Conditional Correlations Graphs (CCCG)
Fast, slow and reverse polymorph transformations in thin films of a 5,10-dihydroindolo[3,2-b]indole derivative [data]
No full textUnderlying data for figures in the publication "Fast, slow and reverse polymorph transformations in thin films of a 5,10-dihydroindolo[3,2-b]indole derivative
Nano-FFA: Ink Formulation and Process Optimization in Multiphoton 3D Laser Printing Using Full Factorial Analysis [data]
No full textMultiphoton 3D laser printing (MPLP) offers a unique combination of sub-micron resolution, geometrical freedom, and property variability. While this technique opens an extensive parameter space to develop new materials, it poses a significant challenge to disentangle and optimize the interrelated effects of chemical composition, process parameters, and resulting material properties. In this context, data analysis through full factorial analysis (FFA) can serve as a crucial tool for the systematic examination of how multiple factors interact and influence the final material properties of 3D printed microstructures, resulting in the identification of key parameters. In this work we propose a three-step approach, called ‘nano-FFA’, that involves: (1) evaluation of the printability of selected inks via scanning electron microscopy (SEM); (2) characterization of 3D printed structures using nanoindentation and vibrational spectroscopy; and (3) identification of interactions between ink formulation and printing parameters via FFA. Three scenarios have been investigated using the three-step nano-FFA approach: Scenario I focuses on the effect of the photoinitiator concentration. Scenario II examines the influence of different photoinitiator species and Scenario III evaluates the effect of the crosslinker. Across all scenarios, a significant interaction is observed between ink composition—i.e. photoinitiator concentration, photoinitiator type, and crosslinker—and the laser power (LP) printing parameter. This finding demonstrates that the properties of the final structures can be tailored by precisely selecting these two factors. The results of this study highlight the value of integrating statistical data analysis methods, such as FFA, into 3D printing material optimization toolboxes. Implementation of this new nano-FFA approach can provide a practical method for streamlining ink formulation and process optimization in MPLP, allowing rational ink development over a wide range of applications
Psychoneuroendocrine Associations with Momentary Pelvic Pain in Endometriosis [data]
No full textAbstract: Endometriosis is a gynecological condition which often causes chronic or recurrent pelvic pain (CPP). The disease can thereby impose a significant burden on affected individuals and their romantic relationships. Existing research highlights the substantial influence of stress, social support, and hormonal factors on pain experience, but data from daily life is scarce. This ecological momentary assessment (EMA) study aimed to explore the association of stress, partners’ social support styles, cortisol, and oxytocin in daily life with pain experiences among women with CPP (N = 66) across 7 days, resulting in a dataset with up to 2100 data points per variable across multiple measures. Stress was positively correlated with pain ratings both within and between individuals, while no significant associations were observed between salivary cortisol or oxytocin levels and pain ratings. Distracting as well as solicitous social support was positively related to higher pain ratings on a between-person level but showed no or slightly negative associations with pain on a within-person level. These findings suggest that both stress and social support can adversely impact pain experience in endometriosis. This knowledge is essential for developing comprehensive interventions: While stress management can be beneficial, the role of social support is more intricate, requiring tailored guidance for close others and their support behavior
The ‘most beautiful place’ where ‘it’s not possible to live’: a qualitative study of Relational Well-being in an area of climate disaster risk, Bangladesh [data]
No full textPurpose: Climate change is the greatest global health threat of the 21st century, but little is known about well-being in climate vulnerable populations. We investigate how well-being is shaped and influenced in such a population, where unique and common stressors draw on human well-being.
Methods: We present findings from 60 semi-structured in-depth interviews from an area of climate disaster risk in Bangladesh. We inductively analyzed our data following a Reflexive Thematic Analysis approach and then deductively applied a Relational Well-being (RWB) framework to organize refined themes around the importance of relationships for well-being as personal, societal, and environmental drivers.
Results: We found that well-being was influenced negatively by factors such as financial worries, forced migration, social pressure, and natural disasters. Well-being was influenced positively by factors such as financial satisfaction, voluntary migration, social support, and place attachment.
Conclusions: Using relational well-being as a conceptual lens allowed us to explore the dynamism and complexity of influencing factors on well-being that were partly specific to the local context and partly rooted in wider societal and global structures. Policies which aim to improve the well-being of climate vulnerable populations should consider relational well-being as a conceptual tool to leverage locally available informal resources, such as supporting reciprocal relationships with place and people
Machine learning models incorporating somatic and mental comorbidities for prolonged length-of-stay prediction in a maximum care university hospital [code]
No full textAbstract
Background: Knowledge about the influencing factors on hospital in-patient length-of-stay is integral for optimizing care and resource planning. Many existing studies on prolonged length-of-stay prediction choose a single threshold for the number of days that classifies the length-of-stay as prolonged. The analyses are based on either very heterogeneous or specific cohorts. Most studies take somatic comorbidities into account, while only a few incorporate mental comorbidities.
Objectives: (I) After which timeframe does the number of days of inpatient treatment indicate a prolonged length-of-stay if the threshold for outliers is computed department-wise in a maximum care internal medicine university hospital? (II) How accurately can machine learning models predict prolonged length-of-stay in internal medicine patients? (III) Which mental and somatic comorbidities have the strongest influence on length-of-stay prediction?
Methods: N = 28,536 internal medicine cases treated as inpatients at the German University Hospital in Heidelberg in the years 2017 to 2019 comprised the study population. For each of six internal medicine departments, the threshold for prolonged length-of-stay was computed based on median absolute deviation. Department-wise machine learning models for prolonged length-of-stay classification (Random Forest, XGBoost, LightGBM, Logistic Regression) were built on 80% train data employing cross-validation and the Optuna framework for hyperparameter optimization. Model performance was assessed on 20% test data mainly by Area under the Receiver Operator Curve (AUROC). The models incorporated features derived from demographics and mental as well as somatic comorbidities.
Results: Length-of-stay was classified as prolonged if the number of days at the hospital equaled or exceeded 9 (Cardiology), 10 (General and Psychosomatics, Gastroenterology, Medical Oncology), 11 (Endocrinology) or 26 (Hematology). With AUROC = 0.89 the random forest for the Department of Hematology had the highest predictive power, the random forest for the Department of General and Psychosomatic with AUROC = 0.72 the lowest. The variables with strongest influence on prediction comprised the number of somatic comorbidities, the age at diagnosis, mental and somatic comorbidity subgroups. Among the mental comorbidities, stress-related adjustment disorder was the most prominent factor.
Conclusions: Consideration of department-level factors is recommended for prolonged length-of-stay prediction models. Mental as well as somatic comorbidities were among the most relevant factors for the prediction of prolonged length-of-stay and require adequate treatment and reimbursement opportunities
Sensitivity analysis in psychological and medical research [data]
No full textThis dataset contains all data and R code necessary to follow the tutorial "Sensitivity analysis in psychological and medical research: A tutorial using multiple imputation in a longitudinal case study".
People interested in learning sensitivity analysis with multiple imputation are free to download the data and R code and follow along