A stochastical model for periodic domain structuring in ferroelectric crystals

A stochastical description is applied in order to understand how ferroelectric structures can be formed. The predictions are compared with experimental data of the so-called electrical fixing: Domains are patterned in photorefractive lithium niobate crystals by the combination of light-induced space-charge fields with externally applied electrical fields. In terms of our stochastical model the probability for domain nucleation is modulated according to the sum of external and internal fields. The model describes the shape of the domain pattern as well as the effective degree of modulation

Location based context awareness through tag-cloud visualizations

People often only see what that they already know or explicitly look for. Thus, while mobile users might be interested in the historic background of their surrounding, its current significance or its relation to specific events, they are likely to miss places of interest if they are not explicitly pointed out. Location based services (LBS) like mobile tourist guides offer a potential technological solution, but the production cost of multimedia content is prohibitive in many cases, limiting the coverage of such services to major tourist areas. To provide mobile users with information on the spatial context of a location or a route we present an approach that gathers context information from freely available sources like Wikipedia and creates visualizations of this data that provide users with the necessary cues to increase awareness of their spatial context. In our approach we first gather geo-referenced information that is located close to a point or route. In the second step this data is filtered to extract key-words that characterize the environment. These are then rendered as a tag-cloud in the third step. By skimming the tag-clouds a user gets a good impression of the characteristic features of an environment and in essence performs a further filtering step. The user can interactively adjust the level of detail of the visualization or follow up on individual key-words to adjust the presentation to his interests. By combining web 2.0 technologies and public data sources with filtering and visualization techniques we exploit the browsing capability of humans to provide a service that increases location awareness at arbitrary locations. The approach makes it easy to author an additional text and it can incorporate the ever increasing amount of available geo-referenced information

Entwicklung eines Systems zur Untersuchung der antimikrobiellen Eigenschaften von Implantatmaterialien zur Prävention von Implantat-assoziierten Staphylokokkeninfektionen

In dieser Arbeit gelang es, das Wachstum zweier Staphylokokkenstämme, S. aureus ATCC 25923 und S. epidermidis RP62A, auf Titanoberflächen mit und ohne Einfluss von Kupfersulfat zu charakterisieren. Die erhobenen Erkenntnisse wurden verwendet, um den Effekt zweier Verfahren zur Oberflächenfunktionalisierung auf das Staphylokokkenwachstum zu untersuchen. Darüber hinaus wurde die fluoreszenzmikroskopische Darstellung des Biofilmwachstums mittels Fluorochrom-produzierender Staphylokokken optimiert

Identification of differentially expressed genes in cutaneous squamous cell carcinoma by microarray expression profiling

BACKGROUND: Carcinogenesis is a multi-step process indicated by several genes up- or down-regulated during tumor progression. This study examined and identified differentially expressed genes in cutaneous squamous cell carcinoma (SCC). RESULTS: Three different biopsies of 5 immunosuppressed organ-transplanted recipients each normal skin (all were pooled), actinic keratosis (AK) (two were pooled), and invasive SCC and additionally 5 normal skin tissues from immunocompetent patients were analyzed. Thus, total RNA of 15 specimens were used for hybridization with Affymetrix HG-U133A microarray technology containing 22,283 genes. Data analyses were performed by prediction analysis of microarrays using nearest shrunken centroids with the threshold 3.5 and ANOVA analysis was independently performed in order to identify differentially expressed genes (p < 0.05). Verification of 13 up- or down-regulated genes was performed by quantitative real-time reverse transcription (RT)-PCR and genes were additionally confirmed by sequencing. Broad coherent patterns in normal skin vs. AK and SCC were observed for 118 genes. CONCLUSION: The majority of identified differentially expressed genes in cutaneous SCC were previously not described

Formation of extrachromosomal circular DNA from long terminal repeats of retrotransposons in <i>Saccharomyces cerevisiae</i>

Extrachromosomal circular DNA (eccDNA) derived from chromosomal Ty retrotransposons in yeast can be generated in multiple ways. Ty eccDNA can arise from the circularization of extrachromosomal linear DNA during the transpositional life cycle of retrotransposons, or from circularization of genomic Ty DNA. Circularization may happen through nonhomologous end-joining (NHEJ) of long terminal repeats (LTRs) flanking Ty elements, by Ty autointegration, or by LTR–LTR recombination. By performing an in-depth investigation of sequence reads stemming from Ty eccDNAs obtained from populations of Saccharomyces cerevisiae S288c, we find that eccDNAs predominantly correspond to full-length Ty1 elements. Analyses of sequence junctions reveal no signs of NHEJ or autointegration events. We detect recombination junctions that are consistent with yeast Ty eccDNAs being generated through recombination events within the genome. This opens the possibility that retrotransposable elements could move around in the genome without an RNA intermediate directly through DNA circularization

Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus

Deep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techniques to distinguish between normal and abnormal samples. This method limits the type of anomalies that can be classified as the anomalies need to be present in the training data. Further, many data points from normal and anomaly class are needed for the model to achieve satisfactory classification performance. However, experienced clinicians can segregate between normal samples (healthy maxillary sinus) and anomalous samples (anomalous maxillary sinus) after looking at a few normal samples. We mimic the clinicians ability by learning the distribution of healthy maxillary sinuses using a 3D convolutional auto-encoder (cAE) and its variant, a 3D variational autoencoder (VAE) architecture and evaluate cAE and VAE for this task. Concretely, we pose the paranasal anomaly detection as an unsupervised anomaly detection problem. Thereby, we are able to reduce the labelling effort of the clinicians as we only use healthy samples during training. Additionally, we can classify any type of anomaly that differs from the training distribution. We train our 3D cAE and VAE to learn a latent representation of healthy maxillary sinus volumes using L1 reconstruction loss. During inference, we use the reconstruction error to classify between normal and anomalous maxillary sinuses. We extract sub-volumes from larger head and neck MRIs and analyse the effect of different fields of view on the detection performance. Finally, we report which anomalies are easiest and hardest to classify using our approach. Our results demonstrate the feasibility of unsupervised detection of paranasal anomalies from MRIs with an AUPRC of 85% and 80% for cAE and VAE, respectively

Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus

Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these anomalies, especially when working with limited datasets. Additionally, current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. These challenges necessitate the need for further research and development in this area. In this study, we investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary sinuses (MS) and MS with polyps or cysts. The task of accurately identifying the relevant MS volume within larger head and neck Magnetic Resonance Imaging (MRI) scans can be difficult, but we develop a straightforward strategy to tackle this challenge. Our end-to-end solution includes the use of a novel sampling technique that not only effectively localizes the relevant MS volume, but also increases the size of the training dataset and improves classification results. Additionally, we employ a multiple instance ensemble prediction method to further boost classification performance. Finally, we identify the optimal size of MS volumes to achieve the highest possible classification performance on our dataset. With our multiple instance ensemble prediction strategy and sampling strategy, our 3D CNNs achieve an F1 of 0.85 whereas without it, they achieve an F1 of 0.70. We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy alongside a novel ensembling strategy that proves to be beneficial for paranasal anomaly classification in the MS

Deep sequencing reveals differential expression of microRNAs in favorable versus unfavorable neuroblastoma

Small non-coding RNAs, in particular microRNAs(miRNAs), regulate fine-tuning of gene expression and can act as oncogenes or tumor suppressor genes. Differential miRNA expression has been reported to be of functional relevance for tumor biology. Using next-generation sequencing, the unbiased and absolute quantification of the small RNA transcriptome is now feasible. Neuroblastoma(NB) is an embryonal tumor with highly variable clinical course. We analyzed the small RNA transcriptomes of five favorable and five unfavorable NBs using SOLiD next-generation sequencing, generating a total of >188 000 000 reads. MiRNA expression profiles obtained by deep sequencing correlated well with real-time PCR data. Cluster analysis differentiated between favorable and unfavorable NBs, and the miRNA transcriptomes of these two groups were significantly different. Oncogenic miRNAs of the miR17-92 cluster and the miR-181 family were overexpressed in unfavorable NBs. In contrast, the putative tumor suppressive microRNAs, miR-542-5p and miR-628, were expressed in favorable NBs and virtually absent in unfavorable NBs. In-depth sequence analysis revealed extensive post-transcriptional miRNA editing. Of 13 identified novel miRNAs, three were further analyzed, and expression could be confirmed in a cohort of 70 NBs

CyTOF mass cytometry reveals phenotypically distinct human blood neutrophil populations differentially correlated with melanoma stage

BACKGROUND: Understanding neutrophil heterogeneity and its relationship to disease progression has become a recent focus of cancer research. Indeed, several studies have identified neutrophil subpopulations associated with protumoral or antitumoral functions. However, this work has been hindered by a lack of widely accepted markers with which to define neutrophil subpopulations. METHODS: To identify markers of neutrophil heterogeneity in cancer, we used single-cell cytometry by time-of-flight (CyTOF) coupled with high-dimensional analysis on blood samples from treatment-naïve patients with melanoma. RESULTS: Our efforts allowed us to identify seven blood neutrophil clusters, including two previously identified individual populations. Interrogation of these neutrophil subpopulations revealed a positive trend between specific clusters and disease stage. Finally, we recapitulated these seven blood neutrophil populations via flow cytometry and found that they exhibited diverse capacities for phagocytosis and reactive oxygen species production in vitro. CONCLUSIONS: Our data provide a refined consensus on neutrophil heterogeneity markers, enabling a prospective functional evaluation in patients with solid tumors.</p