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In order to help journalists investigate inside large audiovisual archives, as maintained by news broadcast agencies, the multimedia data must be indexed by text-based search engies. By automatically creating a transcript through automatic speech recognition (ASR), the spoken word becomes accessible to text search, and queries for keywords are made possible. But stil, important contextual information like the identity of the speaker is not captured. Especially when gathering original footage in the political domain, the identity of the speaker can be the most important query constraint, although this name may not be prominent in the words spoken. It is thus desireable to have this information provided explicitely to the search engine. To provide this information, the archive must be an alyzed by automatic Speaker Identification (SID). While this research topic has seen substantial gains in accuracy and robustness over last years, it has not yet established itself as a helpful, large-scale tool outside the research community. This thesis sets out to establish a workflow to provide automatic speaker identification. Its application is to help journalists searching on speeches given in the German parliament (Bundestag). This is a contribution to the News-Stream 3.0 project, a BMBF funded research project that addresses accessibility of various data sources for journalists.
DNA Sequencing
(2011)
The glomerulosclerosis gene Mpv17 encodes a peroxisomal protein producing reactive oxygen species
(1994)
Interactive Distributed Rendering of 3D Scenes on Multiple Xbox 360 Systems and Personal Computers
(2012)
Transient up-regulation of P2 receptors influence differentiation of human mesenchymal stem cells
(2012)
RNA is one of the most important molecules in living organisms. One of its main functions is to regulate gene expression. This involves binding to and forming a joint structure with a messenger RNA. An RNAs functions is determined by its sequence and the structure it folds into. Accordingly, the prediction of individual as well as joint structures is an important area of research. In this thesis a method for the prediction of RNA-RNA joint structure using their minimum free energy (mfe) structures was developed. It is able to extensively explore the joint structural landscape of two interacting RNAs by taking advantage of the locality of changes in the RNAs structures as well as natural and energetic constraints. The method predicts the mfe joint structure as well as alternative stable joint structures while also computing non-optimal folding pathways from the unbound individual mfe structures to the predicted joint structures. It is shown how an enumeration approach is used which is able to deal with the enormous search space as well as to avoid any cyclic behaviour. The method is evaluated using two standard datasets of known interacting RNAs and shows good results.
MOTIVATION: The genome projects produce a wealth of protein sequences. Theoretical methods to predict possible structures and functions are needed for screening purposes, large-scale comparisons and in-depth analysis to identify worthwhile targets for further experimental research. Sequence-structure alignment is a basic tool for the identification of model folds for protein sequences and the construction of crude structural models. Empirical contact potentials (potentials of mean force) are used to optimize and evaluate such alignments. RESULTS: We propose new scoring schemes based on a contact definition derived from Voronoi decompositions of the three-dimensional coordinates of protein structures. We demonstrate that Voronoi potentials are superior to pure distance-based contact potentials with respect to recognition rate and significance for native folds. Moreover, the scoring scheme has the potential to provide a reasonable balance of detail and ion such that it is also useful for the recognition of distantly related (both homologous and non-homologous) proteins. This is demonstrated here on a set of structural alignments showing much better correspondence of native and model scores for the Voronoi potentials as compared to conventional distance-based potentials.
Scientific or statistical research has long been the domain of dedicated programming languages such as R, SPSS or SAS. A few years other competitors entered the arena, among them Python with its powerful SciPy package. The following article introduces SciPy by applying a small subset of its functionality to a well-known dataset.
Exposure to microgravity conditions causes cardiovascular deconditioning in astronauts during spaceflight. Until now, no specific drugs are available for countermeasure, since the underlying mechanism is largely unknown. Endothelial cells (ECs) and smooth muscle cells (SMCs) play key roles in various vascular functions, many of which are regulated by purinergic 2 (P2) receptors. However, their function in ECs and SMCs under microgravity conditions is still unclear. In this study, primary ECs and SMCs were isolated from bovine aorta and verified with specific markers. We show for the first time that the P2 receptor expression pattern is altered in ECs and SMCs after 24 h exposure to simulated microgravity using a clinostat. However, conditioned medium compensates this change in specific P2 receptors, for example, P2X7. Notably, P2 receptors such as P2X7 might be the important players during the paracrine interaction. Additionally, ECs and SMCs secreted different cytokines under simulated microgravity, leading into a pathogenic proliferation and migration. In conclusion, our data indicate P2 receptors might be important players responding to gravity changes in ECs and SMCs. Since some artificial P2 receptor ligands are applied as drugs, it is reasonable to assume that they might be promising candidates against cardiovascular deconditioning in the future.
Human mesenchymal stem cells (hMSCs) are considered a promising cell source for regenerative medicine, because they have the potential to differentiate into a variety of lineages among which the mesoderm-derived lineages such adipo- or osteogenesis are investigated best. Human MSCs can be harvested in reasonable to large amounts from several parts of the patient’s body and due to this possible autologous origin, allorecognition can be avoided. In addition, even in allogenic origin-derived donor cells, hMSCs generate a local immunosuppressive microenvironment, causing only a weak immune reaction. There is an increasing need for bone replacement in patients from all ages, due to a variety of reasons such as a new recreational behavior in young adults or age-related diseases. Adipogenic differentiation is another interesting lineage, because fat tissue is considered to be a major factor triggering atherosclerosis that ultimately leads to cardiovascular diseases, the main cause of death in industrialized countries. However, understanding the differentiation process in detail is obligatory to achieve a tight control of the process for future clinical applications to avoid undesired side effects. In this review, the current findings for adipo- and osteo-differentiation are summarized together with a brief statement on first clinical trials.
Background: Human mesenchymal stem cells (hMSCs) have shown their multipotential including differentiating towards endothelial and smooth muscle cell lineages, which triggers a new interest for using hMSCs as a putative source for cardiovascular regenerative medicine. Our recent publication has shown for the first time that purinergic 2 receptors are key players during hMSC differentiation towards adipocytes and osteoblasts. Purinergic 2 receptors play an important role in cardiovascular function when they bind to extracellular nucleotides. In this study, the possible functional role of purinergic 2 receptors during MSC endothelial and smooth muscle differentiation was investigated. Methods and Results: Human MSCs were isolated from liposuction materials. Then, endothelial and smooth muscle-like cells were differentiated and characterized by specific markers via Reverse Transcriptase-PCR (RT-PCR), Western blot and immunochemical stainings. Interestingly, some purinergic 2 receptor subtypes were found to be differently regulated during these specific lineage commitments: P2Y4 and P2Y14 were involved in the early stage commitment while P2Y1 was the key player in controlling MSC differentiation towards either endothelial or smooth muscle cells. The administration of natural and artificial purinergic 2 receptor agonists and antagonists had a direct influence on these differentiations. Moreover, a feedback loop via exogenous extracellular nucleotides on these particular differentiations was shown by apyrase digest. Conclusions: Purinergic 2 receptors play a crucial role during the differentiation towards endothelial and smooth muscle cell lineages. Some highly selective and potent artificial purinergic 2 ligands can control hMSC differentiation, which might improve the use of adult stem cells in cardiovascular tissue engineering in the future.
During space missions astronauts suffer from cardiovascular deconditioning, when they are exposed to microgravity conditions. Until now, no specific drugs are available for effective countermeasures, since the underlying mechanism is not completely understood. Endothelial cells (ECs) and smooth muscle cells (SMCs) play crucial roles in a variety of cardiovascular functions, many of which are regulated via P2 receptors. However, their function in ECs and SMCs under microgravity condition is still unknown. In this study, ECs and SMCs were isolated from bovine aorta and differentiated from human mesenchymal stem cells (hMSCs), respectively. Subsequently, the cells were verified based on specific markers. An altered P2 receptor expression pattern was detected during the commitment of hMSC towards ECs and SMCs. The administration of natural and artificial P2 receptor agonists and antagonists directly affected the differentiation process. By using EC growth medium as conditioned medium, a vessel cell model was created to culture SMCs and vice versa. Within this study, we were able to show for the first time that the expression of some P2 receptors were altered in ECs and SMCs grown for 24h under simulated microgravity conditions. On the other hand, in some P2 receptor expressions such as P2X7 conditioned medium compensated this change.
In conclusion, our data show that P2 receptors play an important functional role in hMSC differentiation towards ECs and SMCs. Since some P2 receptor artificial ligands are already used as drugs for patients with cardiovascular diseases, it is reasonable to assume that in the future they might be promising candidates for treating cardiovascular deconditioning.
Cytokine-induced killer cells (CIK) in combination with dendritic cells (DCs) have shown favorable outcomes in renal cell carcinoma (RCC), yet some patients exhibit recurrence or no response to this therapy. In a broader perspective, enhancing the antitumor response of DC-CIK cells may help to address this issue. Considering this, herein, we investigated the effect of anti-CD40 and anti-CTLA-4 antibodies on the antitumor response of DC-CIK cells against RCC cell lines. Our analysis showed that, a) anti-CD40 antibody (G28.5) increased the CD3+CD56+ effector cells of CIK cells by promoting the maturation and activation of DCs, b) G28.5 also increased CTLA-4 expression in CIK cells via DCs, but the increase could be hindered by the CTLA-4 inhibitor (ipilimumab), c) adding ipilimumab was also able to significantly increase the proportion of CD3+CD56+ cells in DC-CIK cells, d) anti-CD40 antibodies predominated over anti-CTLA-4 antibodies for cytotoxicity, apoptotic effect and IFN-g secretion of DC-CIK cells against RCC cells, e) after ipilimumab treatment, the population of Tregs in CIK cells remained unaffected, but ipilimumab combined with G28.5 significantly reduced the expression of CD28 in CIK cells. Taken together, we suggest that the agonistic anti-CD40 antibody rather than CTLA-4 inhibitor may improve the antitumor response of DC-CIK cells, particularly in RCC. In addition, we pointed towards the yet to be known contribution of CD28 in the crosstalk between anti-CTLA-4 and CIK cells.
Cancer is a complex disease where resistance to therapies and relapses often pose a serious clinical challenge. The scenario is even more complicated when the cancer type itself is heterogeneous in nature, e.g., lymphoma, a cancer of the lymphocytes which constitutes more than 70 different subtypes. Indeed, the treatment options continue to expand in lymphomas. Herein, we provide insights into lymphoma-specific clinical trials based on cytokine-induced killer (CIK) cell therapy and other pre-clinical lymphoma models where CIK cells have been used along with other synergetic tumor-targeting immune modules to improve their therapeutic potential. From a broader perspective, we will highlight that CIK cell therapy has potential, and in this rapidly evolving landscape of cancer therapies its optimization (as a personalized therapeutic approach) will be beneficial in lymphomas.
Graph drawing with spring embedders employs a V x V computation phase over the graph's vertex set to compute repulsive forces. Here, the efficacy of forces diminishes with distance: a vertex can effectively only influence other vertices in a certain radius around its position. Therefore, the algorithm lends itself to an implementation using search data structures to reduce the runtime complexity. NVIDIA RT cores implement hierarchical tree traversal in hardware. We show how to map the problem of finding graph layouts with force-directed methods to a ray tracing problem that can subsequently be implemented with dedicated ray tracing hardware. With that, we observe speedups of 4x to 13x over a CUDA software implementation.
We describe a systematic approach for rendering time-varying simulation data produced by exa-scale simulations, using GPU workstations. The data sets we focus on use adaptive mesh refinement (AMR) to overcome memory bandwidth limitations by representing interesting regions in space with high detail. Particularly, our focus is on data sets where the AMR hierarchy is fixed and does not change over time. Our study is motivated by the NASA Exajet, a large computational fluid dynamics simulation of a civilian cargo aircraft that consists of 423 simulation time steps, each storing 2.5 GB of data per scalar field, amounting to a total of 4 TB. We present strategies for rendering this time series data set with smooth animation and at interactive rates using current generation GPUs. We start with an unoptimized baseline and step by step extend that to support fast streaming updates. Our approach demonstrates how to push current visualization workstations and modern visualization APIs to their limits to achieve interactive visualization of exa-scale time series data sets.
Modern GPUs come with dedicated hardware to perform ray/triangle intersections and bounding volume hierarchy (BVH) traversal. While the primary use case for this hardware is photorealistic 3D computer graphics, with careful algorithm design scientists can also use this special-purpose hardware to accelerate general-purpose computations such as point containment queries. This article explains the principles behind these techniques and their application to vector field visualization of large simulation data using particle tracing.
When the Artemis missions launch, NASA's Orion spacecraft (and crew as of the Artemis II mission) will be exposed to the deep space radiation environment beyond the protection of Earth's magnetosphere. Hence, it is essential to characterize the effects of space radiation, microgravity, and the combination thereof on cells and organisms, i.e., to quantify any correlations between the deep space radiation environment, genetic variation, and induced genetic changes in cells. To address this, the Artemis I mission will include the Peristaltic Laboratory for Automated Science with Multigenerations (PLASM) hardware containing the Deep Space Radiation Genomics (DSRG) experiment. The scientific aims of DSRG are (i) to identify the metabolic and genomic pathways in yeast affected by microgravity, space radiation, and their combination, and (ii) to differentiate between gravity and radiation exposure on single-gene deletion/overexpressing strains' ability to thrive in the spaceflight environment. Yeast is used as a model system because 70% of its essential genes have a human homolog, and over half of these homologs can functionally replace their human counterpart. As part of the experiment preparation towards spaceflight, an Experiment Verification Test (EVT) was performed at the Kennedy Space Center to verify that the experiment design, hardware, and approach to automated operations will enable achieving the scientific aims. For the EVT, fluidic systems were assembled, sterilized, loaded, and acceptance-tested, and subsequently integrated with the engineering parts to produce a flight-like PLASM unit. Each fluidic system consisted of (i) a Media Bag, (ii) four Culture Bags loaded with Saccharomyces cerevisiae (two with deletion series and the remaining two with overexpression series), and (iii) tubing and check valves. The EVT PLASM unit was put under a temperature profile replicating the anticipated different phases of flight, including handover to launch, spaceflight, and splashdown to handover back to the science team, for a 58-day period. At EVT completion, the rate of activation, cellular growth, RNA integrity, and sample contamination were interrogated. All of the experiment's success criteria were satisfied, encouraging our efforts to perform this investigation on Artemis I. This manuscript thus describes the process of spaceflight experiment design maturation with a focus on the EVT, its results, DSRG's preparation for its planned launch on Artemis I in 2022, and how the PLASM hardware can enable other scientific goals on future Artemis missions and/or the Lunar Orbital Platform – Gateway.
Error analysis in a high accuracy sampled-data velocity stabilising system using Volterra series
(2015)
Extremophiles are optimal models in experimentally addressing questions about the effects of cosmic radiation on biological systems. The resistance to high charge energy (HZE) particles, and helium (He) ions and iron (Fe) ions (LET at 2.2 and 200 keV/µm, respectively, until 1000 Gy), of spores from two thermophiles, Bacillushorneckiae SBP3 and Bacilluslicheniformis T14, and two psychrotolerants, Bacillus sp. A34 and A43, was investigated. Spores survived He irradiation better, whereas they were more sensitive to Fe irradiation (until 500 Gy), with spores from thermophiles being more resistant to irradiations than psychrotolerants. The survived spores showed different germination kinetics, depending on the type/dose of irradiation and the germinant used. After exposure to He 1000 Gy, D-glucose increased the lag time of thermophilic spores and induced germination of psychrotolerants, whereas L-alanine and L-valine increased the germination efficiency, except alanine for A43. FTIR spectra showed important modifications to the structural components of spores after Fe irradiation at 250 Gy, which could explain the block in spore germination, whereas minor changes were observed after He radiation that could be related to the increased permeability of the inner membranes and alterations of receptor complex structures. Our results give new insights on HZE resistance of extremophiles that are useful in different contexts, including astrobiology.
We present GEM-NI -- a graph-based generative-design tool that supports parallel exploration of alternative designs. Producing alternatives is a key feature of creative work, yet it is not strongly supported in most extant tools. GEM-NI enables various forms of exploration with alternatives such as parallel editing, recalling history, branching, merging, comparing, and Cartesian products of and for alternatives. Further, GEM-NI provides a modal graphical user interface and a design gallery, which both allow designers to control and manage their design exploration. We conducted an exploratory user study followed by in-depth one-on-one interviews with moderately and highly skills participants and obtained positive feedback for the system features, showing that GEM-NI supports creative design work well.
We present a new interface for interactive comparisons of more than two alternative documents in the context of a generative design system that uses generative data-flow networks defined via directed acyclic graphs. To better show differences between such networks, we emphasize added, deleted, (un)changed nodes and edges. We emphasize differences in the output as well as parameters using highlighting and enable post-hoc merging of the state of a parameter across a selected set of alternatives. To minimize visual clutter, we introduce new difference visualizations for selected nodes and alternatives using additive and subtractive encodings, which improve readability and keep visual clutter low. We analyzed similarities in networks from a set of alternative designs produced by architecture students and found that the number of similarities outweighs the differences, which motivates use of subtractive encoding. We ran a user study to evaluate the two main proposed difference visualization encodings and found that they are equally effective.
Recently, we discovered a cholinergic mechanism that inhibits the adenosine triphosphate (ATP)-dependent release of interleukin-1 beta (IL-1 beta) by human monocytes via nicotinic acetylcholine receptors (nAChRs) composed of alpha 7, alpha 9 and/or alpha 10 subunits. Furthermore, we identified phosphocholine (PC) and dipalmitoylphosphatidylcholine (DPPC) as novel nicotinic agonists that elicit metabotropic activity at monocytic nAChR. Interestingly, PC does not provoke ion channel responses at conventional nAChRs composed of subunits alpha 9 and alpha 10. The purpose of this study is to determine the composition of nAChRs necessary for nicotinic signaling in monocytic cells and to test the hypothesis that common metabolites of phosphatidylcholines, lysophosphatidylcholine (LPC) and glycerophosphocholine (G-PC), function as nAChR agonists. In peripheral blood mononuclear cells from nAChR gene-deficient mice, we demonstrated that inhibition of ATP-dependent release of IL-1 beta by acetylcholine (ACh), nicotine and PC depends on subunits alpha 7, alpha 9 and alpha 10. Using a panel of nAChR antagonists and siRNA technology, we confirmed the involvement of these subunits in the control of IL-1 beta release in the human monocytic cell line U937. Furthermore, we showed that LPC (C16:0) and G-PC efficiently inhibit ATP-dependent release of IL-1 beta. Of note, the inhibitory effects mediated by LPC and G-PC depend on nAChR subunits alpha 9 and alpha 10, but only to a small degree on alpha 7. In Xenopus laevis oocytes heterologously expressing different combinations of human alpha 7, alpha 9 or alpha 10 subunits, ACh induced canonical ion channel activity, whereas LPC, G-PC and PC did not. In conclusion, we demonstrate that canonical nicotinic agonists and PC elicit metabotropic nAChR activity in monocytes via interaction of nAChR subunits alpha 7, alpha 9 and alpha 10. For the metabotropic signaling of LPC and G-PC, nAChR subunits alpha 9 and alpha 10 are needed, whereas alpha 7 is virtually dispensable. Furthermore, molecules bearing a PC group in general seem to regulate immune functions without perturbing canonical ion channel functions of nAChR.
The increasing complexity of tasks that are required to be executed by robots demands higher reliability of robotic platforms. For this, it is crucial for robot developers to consider fault diagnosis. In this study, a general non-intrusive fault diagnosis system for robotic platforms is proposed. A mini-PC is non-intrusively attached to a robot that is used to detect and diagnose faults. The health data and diagnosis produced by the mini-PC is then standardized and transmitted to a remote-PC. A storage device is also attached to the mini-PC for data logging of health data in case of loss of communication with the remote-PC. In this study, a hybrid fault diagnosis method is compared to consistency-based diagnosis (CBD), and CBD is selected to be deployed on the system. The proposed system is modular and can be deployed on different robotic platforms with minimum setup.
This work presents the preliminary research towards developing an adaptive tool for fault detection and diagnosis of distributed robotic systems, using explainable machine learning methods. Autonomous robots are complex systems that require high reliability in order to operate in different environments. Even more so, when considering distributed robotic systems, the task of fault detection and diagnosis becomes exponentially difficult.
To diagnose systems, models representing the behaviour under investigation need to be developed, and with distributed robotic systems generating large amount of data, machine learning becomes an attractive method of modelling especially because of its high performance. However, with current day methods such as artificial neural networks (ANNs), the issue of explainability arises where learnt models lack the ability to give explainable reasons behind their decisions.
This paper presents current trends in methods for data collection from distributed systems, inductive logic programming (ILP); an explainable machine learning method, and fault detection and diagnosis.
Intention: Within the research project EnerSHelF (Energy-Self-Sufficiency for Health Facilities in Ghana), i. a. energy-meteorological and load-related measurement data are collected, for which an overview of the availability is to be presented on a poster.
Context: In Ghana, the total electricity consumed has almost doubled between 2008 and 2018 according to the Energy Commission of Ghana. This goes along with an unstable power grid, resulting in power outages whenever electricity consumption peaks. The blackouts called "dumsor" in Ghana, pose a severe burden to the healthcare sector. Innovative solutions are needed to reduce greenhouse gas emissions and improve energy and health access.
Orešković and Porsdam Mann draw a distinction between ‘fast’ and ‘slow’ science. Whereas the latter involves rigorous and laborious adherence to the scientific method, the former represents the reality that much scientific work faces time pressures which at times force shortcuts. The distinction can be seen to operate in contemporary research into the coronavirus pandemic: whereas the development of vaccines and treatments usually requires years of meticulous laboratory work and several more years of clinical testing, the many millions suffering from the disease need a treatment now. However, by taking too many safeguards off the treatment discovery and testing pipelines, or by refusing to act in accordance with scientific advice, governments risk sacrificing the public’s trust not only in the government’s scientific bona fides but in the scientific process itself. This is a heavy price to pay, argue Orešković and Porsdam Mann, and point to evidence indicating that the success of Germany and Japan in combating COVID-19 can be traced to public trust in science and government, as well as scientifically-informed and respectful national leadership.