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Social robots are becoming increasingly prevalent in everyday life and sex robots are a sub-category of especially high public interest and controversy. Starting from the concept of the otaku, a term from Japanese youth culture that describes secluded persons with a high affinity for fictional manga characters, we examine individual differences behind sex robot appeal (anime and manga fandom, interest in Japanese culture, preference for indoor activities, shyness). In an online-experiment, 261 participants read one out of three randomly assigned descriptions of future technologies (sex robot, nursing robot, genetically modified organism) and reported on their overall evaluation, eeriness, and contact/purchase intentions. Higher anime and manga fandom was associated with higher appeal for all three future technologies. For our male subsample, sex robots and GMOs stood out as shyness yielded a particularly strong relationship to contact/purchase intentions for these new technologies.
Strategies in Times of Pandemic Crisis — Retailers and Regional Resilience in Würzburg, Germany
(2021)
Research on the COVID-19 crisis and its implications on regional resilience is still in its infancy. To understand resilience on its aggregate level it is important to identify (non)resilient actions of individual actors who comprise regions. As the retail sector among others represents an important factor in an urban regions recovery, we focus on the resilience of (textile) retailers within the city of Würzburg in Germany to the COVID-19 pandemic. To address the identified research gap, this paper applies the concept of resilience. Firstly, conducting expert interviews, the individual (textile) retailers’ level and their strategies in coping with the crisis is considered. Secondly, conducting a contextual analysis of the German city of Würzburg, we wish to contribute to the discussion of how the resilience of a region is influenced inter alia by actors. Our study finds three main strategies on the individual level, with retailers: (1) intending to “bounce back” to a pre-crisis state, (2) reorganising existing practices, as well as (3) closing stores and winding up business. As at the time of research, no conclusions regarding long-term impacts and resilience are possible, the results are limited. Nevertheless, detailed analysis of retailers’ strategies contributes to a better understanding of regional resilience.
The Bateman functions and the allied Havelock functions were introduced as solutions of some problems in hydrodynamics about ninety years ago, but after a period of one or two decades they were practically neglected. In handbooks, the Bateman function is only mentioned as a particular case of the confluent hypergeometric function. In order to revive our knowledge on these functions, their basic properties (recurrence functional and differential relations, series, integrals and the Laplace transforms) are presented. Some new results are also included. Special attention is directed to the Bateman and Havelock functions with integer orders, to generalizations of these functions and to the Bateman-integral function known in the literature.
Understanding the causal relationship between genotype and phenotype is a major objective in biology. The main interest is in understanding trait architecture and identifying loci contributing to the respective traits. Genome-wide association mapping (GWAS) is one tool to elucidate these relationships and has been successfully used in many different species. However, most studies concentrate on marginal marker effects and ignore epistatic and gene-environment interactions. These interactions are problematic to account for, but are likely to make major contributions to many phenotypes that are not regulated by independent genetic effects, but by more sophisticated gene-regulatory networks. Further complication arises from the fact that these networks vary in different natural accessions. However, understanding the differences of gene regulatory networks and gene-gene interactions is crucial to conceive trait architecture and predict phenotypes.
The basic subject of this study – using data from the Arabidopsis 1001 Genomes Project – is the analysis of pre-mature stop codons. These have been incurred in nearly one-third of the ~ 30k genes. A gene-gene interaction network of the co-occurrence of stop codons has been built and the over and under representation of different pairs has been statistically analyzed. To further classify the significant over and under- represented gene-gene interactions in terms of molecular function of the encoded proteins, gene ontology terms (GO-SLIM) have been applied. Furthermore, co- expression analysis specifies gene clusters that co-occur over different genetic and phenotypic backgrounds. To link these patterns to evolutionary constrains, spatial location of the respective alleles have been analyzed as well. The latter shows clear patterns for certain gene pairs that indicate differential selection.
Introduction: Several common alleles have been shown to be associated with breast and/or ovarian cancer risk for BRCA1 and BRCA2 mutation carriers. Recent genome-wide association studies of breast cancer have identified eight additional breast cancer susceptibility loci: rs1011970 (9p21, CDKN2A/B), rs10995190 (ZNF365), rs704010 (ZMIZ1), rs2380205 (10p15), rs614367 (11q13), rs1292011 (12q24), rs10771399 (12p11 near PTHLH) and rs865686 (9q31.2).
Methods: To evaluate whether these single nucleotide polymorphisms (SNPs) are associated with breast cancer risk for BRCA1 and BRCA2 carriers, we genotyped these SNPs in 12,599 BRCA1 and 7,132 BRCA2 mutation carriers and analysed the associations with breast cancer risk within a retrospective likelihood framework.
Results: Only SNP rs10771399 near PTHLH was associated with breast cancer risk for BRCA1 mutation carriers (per-allele hazard ratio (HR) = 0.87, 95% CI: 0.81 to 0.94, P-trend = 3 x 10\(^{-4}\)). The association was restricted to mutations proven or predicted to lead to absence of protein expression (HR = 0.82, 95% CI: 0.74 to 0.90, P-trend = 3.1 x 10\(^{-5}\), P-difference = 0.03). Four SNPs were associated with the risk of breast cancer for BRCA2 mutation carriers: rs10995190, P-trend = 0.015; rs1011970, P-trend = 0.048; rs865686, 2df P = 0.007; rs1292011 2df P = 0.03. rs10771399 (PTHLH) was predominantly associated with estrogen receptor (ER)-negative breast cancer for BRCA1 mutation carriers (HR = 0.81, 95% CI: 0.74 to 0.90, P-trend = 4 x 10\(^{-5}\)) and there was marginal evidence of association with ER- negative breast cancer for BRCA2 mutation carriers (HR = 0.78, 95% CI: 0.62 to 1.00, P-trend = 0.049).
Conclusions: The present findings, in combination with previously identified modifiers of risk, will ultimately lead to more accurate risk prediction and an improved understanding of the disease etiology in BRCA1 and BRCA2 mutation carriers.
Olfactory circuits change structurally and physiologically during development and adult life. This allows insects to respond to olfactory cues in an appropriate and adaptive way according to their physiological and behavioral state, and to adapt to their specific abiotic and biotic natural environment. We highlight here findings on olfactory plasticity and modulation in various model and non-model insects with an emphasis on moths and social Hymenoptera. Different categories of plasticity occur in the olfactory systems of insects. One type relates to the reproductive or feeding state, as well as to adult age. Another type of plasticity is context-dependent and includes influences of the immediate sensory and abiotic environment, but also environmental conditions during postembryonic development, periods of adult behavioral maturation, and short- and long-term sensory experience. Finally, plasticity in olfactory circuits is linked to associative learning and memory formation. The vast majority of the available literature summarized here deals with plasticity in primary and secondary olfactory brain centers, but also peripheral modulation is treated. The described molecular, physiological, and structural neuronal changes occur under the influence of neuromodulators such as biogenic amines, neuropeptides, and hormones, but the mechanisms through which they act are only beginning to be analyzed.
Cyclic adenosine monophosphate (cAMP), the ubiquitous second messenger produced upon stimulation of GPCRs which couple to the stimulatory GS protein, orchestrates an array of physiological processes including cardiac function, neuronal plasticity, immune responses, cellular proliferation and apoptosis. By interacting with various effector proteins, among others protein kinase A (PKA) and exchange proteins directly activated by cAMP (Epac), it triggers signaling cascades for the cellular response. Although the functional outcomes of GSPCR-activation are very diverse depending on the extracellular stimulus, they are all mediated exclusively by this single second messenger. Thus, the question arises how specificity in such responses may be attained. A hypothesis to explain signaling specificity is that cellular signaling architecture, and thus precise operation of cAMP in space and time would appear to be essential to achieve signaling specificity. Compartments with elevated cAMP levels would allow specific signal relay from receptors to effectors within a micro- or nanometer range, setting the molecular basis for signaling specificity. Although the paradigm of signaling compartmentation gains continuous recognition and is thoroughly being investigated, the molecular composition of such compartments and how they are maintained remains to be elucidated. In addition, such compartments would require very restricted diffusion of cAMP, but all direct measurements have indicated that it can diffuse in cells almost freely.
In this work, we present the identification and characterize of a cAMP signaling compartment at a GSPCR. We created a Förster resonance energy transfer (FRET)-based receptor-sensor conjugate, allowing us to study cAMP dynamics in direct vicinity of the human glucagone-like peptide 1 receptor (hGLP1R). Additional targeting of analogous sensors to the plasma membrane and the cytosol enables assessment of cAMP dynamics in different subcellular regions. We compare both basal and stimulated cAMP levels and study cAMP crosstalk of different receptors. With the design of novel receptor nanorulers up to 60nm in length, which allow mapping cAMP levels in nanometer distance from the hGLP1R, we identify a cAMP nanodomain surrounding it. Further, we show that phosphodiesterases (PDEs), the only enzymes known to degrade cAMP, are decisive in constraining cAMP diffusion into the cytosol thereby maintaining a cAMP gradient. Following the discovery of this nanodomain, we sought to investigate whether downstream effectors such as PKA are present and active within the domain, additionally studying the role of A-kinase anchoring proteins (AKAPs) in targeting PKA to the receptor compartment. We demonstrate that GLP1-produced cAMP signals translate into local nanodomain-restricted PKA phosphorylation and determine that AKAP-tethering is essential for nanodomain PKA.
Taken together, our results provide evidence for the existence of a dynamic, receptor associated cAMP nanodomain and give prospect for which key proteins are likely to be involved in its formation. These conditions would allow cAMP to exert its function in a spatially and temporally restricted manner, setting the basis for a cell to achieve signaling specificity. Understanding the molecular mechanism of cAMP signaling would allow modulation and thus regulation of GPCR signaling, taking advantage of it for pharmacological treatment.
Part 1 of this work describes the development of accurate physically grounded force fields for
intermolecular Cation-π interactions based on SAPT energy decomposition analysis.
The presented results demonstrate the benefits of the used DFT-SAPT method to describe non-bonding
interactions. First of all, this method is able to reproduce the high level CCSD(T) energy values
but using much less computational time. Second it provides the possibility to separate the total
intermolecular interaction energy into several physically meaningful contributions. The relative
contributions of the dimers investigated can be seen in Fig. 6.16. In Tab. 6.3 the percentage
contribution of the attractive energy parts to the stabilization energy is shown. The polarization
energy is important for the NH+...C6H6 interaction, whereas it becomes less crucial
considering other dimers. The dispersion energy contribution is large in the case of
the C6H6...H2O dimers, whereas it is relatively less important for the NH+...C6H6
interaction. The electrostatic energy contributes a large amount of stabilizing energy
in all considered dimer interactions. ...
The bis(N-heterocyclic carbene)(diphenylacetylene)palladium complex Pd(ITMe)\(_2\)(PhCCPh)] (ITMe=1,3,4,5-tetramethylimidazol-2-ylidene) acts as a highly active pre-catalyst in the diboration and silaboration of azobenzenes to synthesize a series of novel functionalized hydrazines. The reactions proceed using commercially available diboranes and silaboranes under mild reaction conditions.
The Niger Delta belongs to the largest swamp and mangrove forests in the world hosting many endemic and endangered species. Therefore, its conservation should be of highest priority. However, the Niger Delta is confronted with overexploitation, deforestation and pollution to a large extent. In particular, oil spills threaten the biodiversity, ecosystem services, and local people. Remote sensing can support the detection of spills and their potential impact when accessibility on site is difficult. We tested different vegetation indices to assess the impact of oil spills on the land cover as well as to detect accumulations (hotspots) of oil spills. We further identified which species, land cover types, and protected areas could be threatened in the Niger Delta due to oil spills. The results showed that the Enhanced Vegetation Index, the Normalized Difference Vegetation Index, and the Soil Adjusted Vegetation Index were more sensitive to the effects of oil spills on different vegetation cover than other tested vegetation indices. Forest cover was the most affected land-cover type and oil spills also occurred in protected areas. Threatened species are inhabiting the Niger Delta Swamp Forest and the Central African Mangroves that were mainly affected by oil spills and, therefore, strong conservation measures are needed even though security issues hamper the monitoring and control.
Coordinated regulation of the lysosomal and autophagic systems ensures basal catabolism and normal cell physiology, and failure of either system causes disease. Here we describe an epigenetic rheostat orchestrated by c-MYC and histone deacetylases that inhibits lysosomal and autophagic biogenesis by concomitantly repressing the expression of the transcription factors MiT/TFE and FOXH1, and that of lysosomal and autophagy genes. Inhibition of histone deacetylases abates c-MYC binding to the promoters of lysosomal and autophagy genes, granting promoter occupancy to the MiT/TFE members, TFEB and TFE3, and/or the autophagy regulator FOXH1. In pluripotent stem cells and cancer, suppression of lysosomal and autophagic function is directly downstream of c-MYC overexpression and may represent a hallmark of malignant transformation. We propose that, by determining the fate of these catabolic systems, this hierarchical switch regulates the adaptive response of cells to pathological and physiological cues that could be exploited therapeutically.
Purpose
Artificial neural networks show promising performance in automatic segmentation of cardiac MRI. However, training requires large amounts of annotated data and generalization to different vendors, field strengths, sequence parameters, and pathologies is limited. Transfer learning addresses this challenge, but specific recommendations regarding type and amount of data required is lacking. In this study, we assess data requirements for transfer learning to experimental cardiac MRI at 7T where the segmentation task can be challenging. In addition, we provide guidelines, tools, and annotated data to enable transfer learning approaches by other researchers and clinicians.
Methods
A publicly available segmentation model was used to annotate a publicly available data set. This labeled data set was subsequently used to train a neural network for segmentation of left ventricle and myocardium in cardiac cine MRI. The network is used as starting point for transfer learning to 7T cine data of healthy volunteers (n = 22; 7873 images) by updating the pre-trained weights. Structured and random data subsets of different sizes were used to systematically assess data requirements for successful transfer learning.
Results
Inconsistencies in the publically available data set were corrected, labels created, and a neural network trained. On 7T cardiac cine images the model pre-trained on public imaging data, acquired at 1.5T and 3T, achieved DICE\(_{LV}\) = 0.835 and DICE\(_{MY}\) = 0.670. Transfer learning using 7T cine data and ImageNet weight initialization improved model performance to DICE\(_{LV}\) = 0.900 and DICE\(_{MY}\) = 0.791. Using only end-systolic and end-diastolic images reduced training data by 90%, with no negative impact on segmentation performance (DICE\(_{LV}\) = 0.908, DICE\(_{MY}\) = 0.805).
Conclusions
This work demonstrates and quantifies the benefits of transfer learning for cardiac cine image segmentation. We provide practical guidelines for researchers planning transfer learning projects in cardiac MRI and make data, models, and code publicly available.
New experimental methods have drastically accelerated the pace and quantity at which biological data is generated. High-throughput DNA sequencing is one of the pivotal new technologies. It offers a number of novel applications in various fields of biology, including ecology, evolution, and genomics. However, together with those opportunities many new challenges arise. Specialized algorithms and software are required to cope with the amount of data, often requiring substantial training in bioinformatic methods. Another way to make those data accessible to non-bioinformaticians is the development of programs with intuitive user interfaces.
In my thesis I developed analyses and programs to tackle current problems with high-throughput data in biology. In the field of ecology this covers the establishment of the bioinformatic workflow for pollen DNA meta-barcoding. Furthermore, I developed an application that facilitates the analysis of ecological communities in the context of their traits. Information from multiple public databases have been aggregated and can now be mapped automatically to existing community tables for interactive inspection. In evolution the new data are used to reconstruct phylogenetic trees from multiple genes. I developed the tool bcgTree to automate this process for bacteria. Many plant genomes have been sequenced in current years. Sequencing reads of those projects also contain data from the chloroplasts. The tool chloroExtractor supports the targeted extraction and analysis of the chloroplast genome. To compare the structure of multiple genomes specialized software is required for calculation and visualization of the relationships. I developed AliTV to address this. In contrast to existing programs for this task it allows interactive adjustments of produced graphics. Thus, facilitating the discovery of biologically relevant information. Another application I developed helps to analyze transcriptomes even if no reference genome is present. This is achieved by aggregating the different pieces of information, like functional annotation and expression level, for each transcript in a web platform. Scientists can then search, filter, subset, and visualize the transcriptome.
Together the methods and tools expedite insights into biological systems that were not possible before.
RNA sequencing (RNA-seq) has become a powerful tool to understand molecular mechanisms and/or developmental programs. It provides a fast, reliable and cost-effective method to access sets of expressed elements in a qualitative and quantitative manner. Especially for non-model organisms and in absence of a reference genome, RNA-seq data is used to reconstruct and quantify transcriptomes at the same time. Even SNPs, InDels, and alternative splicing events are predicted directly from the data without having a reference genome at hand. A key challenge, especially for non-computational personnal, is the management of the resulting datasets, consisting of different data types and formats. Here, we present TBro, a flexible de novo transcriptome browser, tackling this challenge. TBro aggregates sequences, their annotation, expression levels as well as differential testing results. It provides an easy-to-use interface to mine the aggregated data and generate publication-ready visualizations. Additionally, it supports users with an intuitive cart system, that helps collecting and analysing biological meaningful sets of transcripts. TBro’s modular architecture allows easy extension of its functionalities in the future. Especially, the integration of new data types such as proteomic quantifications or array-based gene expression data is straightforward. Thus, TBro is a fully featured yet flexible transcriptome browser that supports approaching complex biological questions and enhances collaboration of numerous researchers.
Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI
(2021)
Background
Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance.
Results
We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model.
Conclusions
Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.
LIM and SH3 protein 1 (LASP1) is a nucleocytoplasmic scaffolding protein. LASP1 interacts with various cytoskeletal proteins via its domain structure and is known to participate in physiological processes of cells. In the present study, a detailed investigation of the expression pattern of LASP1 protein in normal skin, melanocytic nevi and melanoma was carried out and the melanocyte–specific function of LASP1 was analyzed. LASP1 protein was identified in stratum basale of skin epidermis and a very high level was detected in nevi, the benign tumor of melanocyte. In the highly proliferative basal cells, an additional distinct nuclear localization of the protein was noted. In different tumor entities, an elevated LASP1 expression and nuclear localization, correlated positively with malignancy and tumor grade. However, LASP1 level was determined to be very low in melanoma and even reduced in metastases. Melanoma is distinguished as the first tumor tested to date – that displayed an absence of elevated LASP1 expression. In addition no significant relation was observed between LASP1 protein expression and clinicopathological parameters in melanoma.
The epidermal melanin unit of skin comprises of melanocytes and keratinocytes. Melanocytes are specialized cells that synthesize the photo protective coloring pigment, melanin inside unique organelles called melanosomes. The presence of LASP1 in melanocytes is reported for the first time through this study and the existence was confirmed by immunoblotting analysis in cultured normal human epidermal melanocyte (NHEM) and in melanoma cell lines, along with the immunohistostaining imaging in normal skin and in melanocytic nevi. LASP1 depletion in MaMel2 cells revealed a moderate increase in the intracellular melanin level independently of de novo melanogenesis, pointing to a partial hindrance in melanin release. Immunofluorescence images of NHEM and MaMel2 cells visualized co-localization of LASP1 with dynamin and tyrosinase concomitant with melanosomes at the dendrite tips of the cells. Melanosome isolation experiments by sucrose density gradient centrifugation clearly demonstrated the presence of LASP1 and the melanosome specific markers tyrosinase and TRP1 in late stage melanosomes.
The study identified LASP1 and dynamin as novel binding partners in melanocytes and provides first evidence for the existence of LASP1 and dynamin (a protein well–known for its involvement in vesicle formation and budding) in melanosomes. Co-localization of LASP1 and dynamin along the dendrites and at the tips of the melanocytes indicates a potential participation of the two proteins in the membrane vesicle fission at the plasma membrane.
In summary, a possible involvement of LASP1 in the actin–dynamin mediated membrane fission and exocytosis of melanin laden melanosome vesicles into the extracellular matrix is suggested.
We report a giant thermal shift of 2.1 MHz/K related to the excited-state zero-field splitting in the silicon vacancy centers in 4H silicon carbide. It is obtained from the indirect observation of the optically detected magnetic resonance in the excited state using the ground state as an ancilla. Alternatively, relative variations of the zero-field splitting for small temperature differences can be detected without application of radiofrequency fields, by simply monitoring the photoluminescence intensity in the vicinity of the level anticrossing. This effect results in an all-optical thermometry technique with temperature sensitivity of 100 mK/Hz\(^{1/2}\) for a detection volume of approximately 10\(^{−6}\) mm\(^3\). In contrast, the zero-field splitting in the ground state does not reveal detectable temperature shift. Using these properties, an integrated magnetic field and temperature sensor can be implemented on the same center.
Using own survey data and interviews, this study analyzes how businesses in Lower Franconia (Unterfranken) are entangled with China. Starting with a bird's-eye-view of the current situation, the study goes on to provide valuable insights from five specific industries. The study shows that a majority of the analyzed firms have some sort of ties to China, be it through Chinese customers, import/export activities, or else.
Background: Sorbents have been shown to adsorb iodinated radiocontrast media. Objective: In this study we describe a simple method to compare various sorbents in terms of capacity to adsorb radiocontrast media. Methods: Iodixanol solution was injected into columns filled with three types of sorbent at filtration velocities of increasing magnitude. Two variables of interest – contrast removal rate and matched iodine retention (MIR) – were calculated to measure the adsorption efficiency and the mass of contrast iodine adsorbed versus sorbent used, respectively. Results: The highest contrast removal and MIR for Porapak Q, CST 401 and Amberlite XAD4 were 41, 38 and 16% (p = 0.22 and 0.0005 for comparisons between Porapak Q-CST 401 and CST 401-Amberlite XAD4) and 0.060, 0.055 and 0.024, respectively (p = 0.18 and 0.0008). Extrapolation to a clinical scenario may suggest that removal of 8 ml iodixanol could be achieved by masses of sorbents of 43, 47 and 107 g, respectively. Conclusion: In this study we set a benchmark for comparing the radiocontrast-adsorbing efficiency of polymer sorbents during first-pass experiments, using a readily available methodology.
While beneficial sponge-microbe associations have received much attention in recent years, less effort has been undertaken to investigate the interactions of sponges with potentially pathogenic microorganisms. Thus, the aim of this study was to examine two selected Caribbean disease conditions, termed “Sponge Orange Band” and “Sponge White Patch”, via ecological and molecular methods. Sponge Orange Band (SOB) disease affects the prominent Caribbean barrel sponge Xestospongia muta that is counted among the high-microbial-abundance (HMA) sponges, whereas Sponge White Patch (SWP) disease affects the abundant rope sponge Amphimedon compressa that belongs to the low-microbial-abundance (LMA) sponges. I have documented for both Caribbean sponge diseases a disease progression going along with massive tissue destruction as well as loss of the characteristic microbial signatures. Even though new bacteria were shown to colonize the bleached areas, the infection trials revealed in both cases no indication for the involvement of a microbial pathogen as an etiologic agent of disease leaving us still in the dark about the cause of Sponge Orange Band as well as Sponge White Patch disease.