004 Datenverarbeitung; Informatik
Refine
Has Fulltext
- yes (29)
Is part of the Bibliography
- yes (29)
Year of publication
Document Type
- Journal article (27)
- Preprint (2)
Language
- English (29)
Keywords
- database (3)
- resistance (3)
- cosmology (2)
- engineering (2)
- evolution (2)
- genetics (2)
- 26S RDNA Data (1)
- ACKR4 (1)
- AI (1)
- AKT (1)
- Analysis (1)
- Barcodes (1)
- Biology (1)
- Boolean function (1)
- Boolean tree (1)
- CD4+T cells (1)
- CD8+T cells (1)
- CD95 (1)
- CETCH cycle (1)
- CO2-sequestration (1)
- COVID-19 (1)
- Caenorhabditis elegans (1)
- Colonial volvocales chlorophyta (1)
- Computer software (1)
- DNA (1)
- DNA storage (1)
- Dasycladales chlorophyta (1)
- E8 symmetry (1)
- FLIMbee (1)
- HGPS (1)
- HHblits (1)
- Hurwitz theorem (1)
- I-tasser (1)
- ICEP (1)
- IGFBP2 (1)
- III secretion (1)
- ImageJ (1)
- IronChip Evaluation Package (1)
- Java 3D (1)
- LC-MS/MS (1)
- Land plants (1)
- Lee Smolin (1)
- Measurement (1)
- Microarray (1)
- Molecular systematics (1)
- Mycoplasma (1)
- Neuromuscular junctions (1)
- Nuclear RDNA (1)
- Profile distances (1)
- Programmierbare logische Anordnung (1)
- RBCL Gene-sequences (1)
- RNA sequencing (1)
- SARS-CoV-2 (1)
- SMLM (1)
- Secondary structure (1)
- Septins (1)
- Software product lines (1)
- Synapses (1)
- Synaptic vesicles (1)
- V-antigen (1)
- Variability (1)
- Vesicles (1)
- Visualisierung (1)
- WH2 domain (1)
- WNT (1)
- Yersinia enterocolitica (1)
- Yolk protein (1)
- Zebrafish (1)
- actin nucleation (1)
- adaptation (1)
- aging (1)
- alignment (1)
- apixaban (1)
- arabidopsis thaliana (1)
- arabidpsis thaliana (1)
- behavior (1)
- binary decision diagram (1)
- biofuel (1)
- bioinformatics (1)
- biomanufacturing (1)
- bit (1)
- brain (1)
- caenorhabditis elegans (1)
- carboxylation (1)
- caspase-3 (1)
- cell membranes (1)
- cerebral ischemia (1)
- cisplatin (1)
- classification (1)
- colony-stimulating factor (1)
- combination therapy (1)
- comparative sequence analysis (1)
- complex traits (1)
- compressed sensing (1)
- computational (1)
- connector (1)
- corticotropin-releasing hormone (1)
- crosstalk (1)
- crystal growth (1)
- crystallization (1)
- cytokine profiling (1)
- dSTORM (1)
- design (1)
- diagnostic accuracy (1)
- differentiation (1)
- direct oral anticoagulants (1)
- direct thrombin inhibitor (1)
- disease (1)
- disruption project (1)
- drug (1)
- drug-minded protein (1)
- electrolytes (1)
- elementary mode analysis (1)
- elementary modes (1)
- emergent time (1)
- enzyme (1)
- expression (1)
- expression signature (1)
- factor XA inhibitor (1)
- flies (1)
- framework (1)
- functional analysis (1)
- gamma (1)
- genes (1)
- genetic regulatory network (1)
- hepatotoxicity (1)
- heuristics (1)
- histidine kinase (1)
- homology modeling (1)
- hypotonic (1)
- hypotonic solutions (1)
- immunity (1)
- inflation (1)
- inhibitor (1)
- intermediate host (1)
- internal transcribed spacer 2 (1)
- interpolation (1)
- isotonic (1)
- life-span regulation (1)
- light-gated proteins (1)
- lymphotoxicity (1)
- malaria (1)
- markers (1)
- membrane proteins (1)
- memory immune responses (1)
- metabolic flux (1)
- metabolic modeling (1)
- metabolism (1)
- metastasis (1)
- methylene blue (1)
- mice (1)
- microbes (1)
- modules (1)
- molecular systematics (1)
- mouse (1)
- mutation (1)
- nanocellulose (1)
- natural variation (1)
- networks (1)
- nonhuman-primates (1)
- omics (1)
- organogenesis (1)
- origin (1)
- oxidative stress (1)
- painful (1)
- pangolin (1)
- pathway (1)
- performance liquid chromatography (1)
- permeability (1)
- pestis infection (1)
- phase space (1)
- phase transition (1)
- photorespiration (1)
- phylogenetic tree (1)
- phylogeny (1)
- pneumonic plague (1)
- progeria (1)
- promoter (1)
- protein (1)
- protein chip (1)
- protein-interaction networks (1)
- pseudomas-syringae (1)
- quantification (1)
- qubit (1)
- receptor (1)
- recombinant protein rVE (1)
- recombination (1)
- response regulator (1)
- ribosomal RNA (1)
- richtersius coronifer (1)
- secondary structure (1)
- sensor (1)
- sequence alignment (1)
- serum (1)
- set (1)
- shootin-1 (1)
- single-electron transistors (1)
- spire (1)
- stability (1)
- stable state (1)
- sun exposure (1)
- superoxide-dismutase (1)
- survival (1)
- synthetic biology (1)
- synthetic pathways (1)
- temperature (1)
- tolerance (1)
- tonicity (1)
- transcription (1)
- transmission (1)
- vaccine (1)
- validation (1)
- vitellogenin (1)
- water stress (1)
Institute
- Theodor-Boveri-Institut für Biowissenschaften (29) (remove)
EU-Project number / Contract (GA) number
- 835102) (1)
The phase space for the standard model of the basic four forces for n quanta includes all possible ensemble combinations of their quantum states m, a total of n**m states. Neighbor states reach according to transition possibilities (S-matrix) with emergent time from entropic ensemble gradients.
We replace the “big bang” by a condensation event (interacting qubits become decoherent) and inflation by a crystallization event – the crystal unit cell guarantees same symmetries everywhere. Interacting qubits solidify and form a rapidly growing domain where the n**m states become separated ensemble states, rising long-range forces stop ultimately further growth. After that very early events, standard cosmology with the hot fireball model takes over. Our theory agrees well with lack of inflation traces in cosmic background measurements, large-scale structure of voids and filaments, supercluster formation, galaxy formation, dominance of matter and life-friendliness.
We prove qubit interactions to be 1,2,4 or 8 dimensional (agrees with E8 symmetry of our universe). Repulsive forces at ultrashort distances result from quantization, long-range forces limit crystal growth. Crystals come and go in the qubit ocean. This selects for the ability to lay seeds for new crystals, for self-organization and life-friendliness.
We give energy estimates for free qubits vs bound qubits, misplacements in the qubit crystal and entropy increase during qubit decoherence / crystal formation. Scalar fields for color interaction and gravity derive from the permeating qubit-interaction field. Hence, vacuum energy gets low only inside the qubit crystal. Condensed mathematics may advantageously model free / bound qubits in phase space.
Background: Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set. Results: Here we present a platform-independent framework based on Java and Java 3D for accelerated rendering of biological images. Our framework is seamlessly integrated into ImageJ, a free image processing package with a vast collection of community-developed biological image analysis tools. Our framework enriches the ImageJ software libraries with methods that greatly reduce the complexity of developing image analysis tools in an interactive 3D visualization environment. In particular, we provide high-level access to volume rendering, volume editing, surface extraction, and image annotation. The ability to rely on a library that removes the low-level details enables concentrating software development efforts on the algorithm implementation parts. Conclusions: Our framework enables biomedical image software development to be built with 3D visualization capabilities with very little effort. We offer the source code and convenient binary packages along with extensive documentation at http://3dviewer.neurofly.de.
Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single genes classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single genes classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single genes classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single genes sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single genes classifiers for predicting outcome in breast cancer.