Detailed numerical modeling of pyrolysis in a heterogeneous packed bed using XDEM

in Journal of Analytical and Applied Pyrolysis (2014), 106

Detailing the optimality of photosynthesis in cyanobacteria through systems biology analysis.

in Proceedings of the National Academy of Sciences of the United States of America (2012), 109(7), 2678-2683

Photosynthesis has recently gained considerable attention for its potential role in the development of renewable energy sources. Optimizing photosynthetic organisms for biomass or biofuel production will ... [more ▼]

Photosynthesis has recently gained considerable attention for its potential role in the development of renewable energy sources. Optimizing photosynthetic organisms for biomass or biofuel production will therefore require a systems understanding of photosynthetic processes. We reconstructed a high-quality genome-scale metabolic network for Synechocystis sp. PCC6803 that describes key photosynthetic processes in mechanistic detail. We performed an exhaustive in silico analysis of the reconstructed photosynthetic process under different light and inorganic carbon (Ci) conditions as well as under genetic perturbations. Our key results include the following. (i) We identified two main states of the photosynthetic apparatus: a Ci-limited state and a light-limited state. (ii) We discovered nine alternative electron flow pathways that assist the photosynthetic linear electron flow in optimizing the photosynthesis performance. (iii) A high degree of cooperativity between alternative pathways was found to be critical for optimal autotrophic metabolism. Although pathways with high photosynthetic yield exist for optimizing growth under suboptimal light conditions, pathways with low photosynthetic yield guarantee optimal growth under excessive light or Ci limitation. (iv) Photorespiration was found to be essential for the optimal photosynthetic process, clarifying its role in high-light acclimation. Finally, (v) an extremely high photosynthetic robustness drives the optimal autotrophic metabolism at the expense of metabolic versatility and robustness. The results and modeling approach presented here may promote a better understanding of the photosynthetic process. They can also guide bioengineering projects toward optimal biofuel production in photosynthetic organisms. [less ▲]

Details im Webdesign: Klein, aber fein

Article for general public (2017)

Web designers are used to look at the "big things" in design: grids, responsive layouts, typographic systems… However, looking at small details that enhance the user experience is also worthwhile, like ... [more ▼]

Web designers are used to look at the "big things" in design: grids, responsive layouts, typographic systems… However, looking at small details that enhance the user experience is also worthwhile, like micro interactions, micro copy, typographic details. [less ▲]

Detecting an exciton crystal by statistical means

in Physica B. Condensed Matter (2014), 441

We investigate an ensemble of excitons in a coupled quantum well excited via an applied laser field. Using an effective disordered quantum Ising model, we perform a numerical simulation of the ... [more ▼]

We investigate an ensemble of excitons in a coupled quantum well excited via an applied laser field. Using an effective disordered quantum Ising model, we perform a numerical simulation of the experimental procedure and calculate the probability distribution function P(M) to create M excitons as well as their correlation function. It shows clear evidence of the existence of two phases corresponding to a liquid and a crystal phase. We demonstrate that not only the correlation function but also the distribution P(M) is very well suited to monitor this transition. [less ▲]

Detecting and manipulating Majorana bound states in nanowires

Presentation (2015, June)

Detecting and predicting outages in mobile networks with log data.

in IEEE International Conference on Communications, ICC 2017 (2017, May)

Modern cellular networks are complex systems offering a wide range of services and present challenges in detecting anomalous events when they do occur. The networks are engineered for high reliability and ... [more ▼]

Modern cellular networks are complex systems offering a wide range of services and present challenges in detecting anomalous events when they do occur. The networks are engineered for high reliability and, hence, the data from these networks is predominantly normal with a small proportion being anomalous. From an operations perspective, it is important to detect these anomalies in a timely manner, to correct vulnerabilities in the network and preclude the occurrence of major failure events. The objective of our work is anomaly detection in cellular networks in near real-time to improve network performance and reliability. We use performance data from a 4G LTE network to develop a methodology for anomaly detection in such networks. Two rigorous prediction models are proposed: a non-parametric approach (Chi-Square test), and a parametric one (Gaussian Mixture Models). These models are trained to detect differences between distributions to classify a target distribution as belonging to a normal period or abnormal period with high accuracy. We discuss the merits between the approaches and show that both provide a more nuanced view of the network than simple thresh- olds of success/failure used by operators in production networks today. [less ▲]

Detecting coached feigning using the test of Memory Malingering (TOMM) and the structured inventory of Malingered Symptomatology (SIMS)

in Journal of Clinical Psychology (2011), 67(9), 850-855

Undergraduate students were administered the Test of Memory Malingering (TOMM) and the Structured Inventory of the Malingered Symptomatology (SIMS) and asked to respond honestly, or instructed to feign ... [more ▼]

Undergraduate students were administered the Test of Memory Malingering (TOMM) and the Structured Inventory of the Malingered Symptomatology (SIMS) and asked to respond honestly, or instructed to feign cognitive dysfunction due to head injury. Before both instruments were administered, symptom-coached feigners were provided with some information about brain injury, while feigners who received a mix of symptom-coaching and test-coaching were given the same information plus advice on how to defeat symptom validity tests. Results show that, although the accuracy of both instruments appears to be somewhat reduced by a mix of symptom coaching and test coaching, the TOMM and SIMS are relatively resistant to different kinds of coaching. [less ▲]

Detecting Crosstalk Modules of Combined Networks: the Case for the NF-kappaB and p53

in Proceedings of IEEE International Conference on Bioscience, Biochemistry and Bioinformatics (2011)

Detecting disease genes based on semi-supervised learning and protein--protein interaction networks

in Artificial Intelligence in Medicine (2012), 54(1), 63--71

Objective Predicting or prioritizing the human genes that cause disease, or “disease genes”, is one of the emerging tasks in biomedicine informatics. Research on network-based approach to this problem is ... [more ▼]

Objective Predicting or prioritizing the human genes that cause disease, or “disease genes”, is one of the emerging tasks in biomedicine informatics. Research on network-based approach to this problem is carried out upon the key assumption of “the network-neighbour of a disease gene is likely to cause the same or a similar disease”, and mostly employs data regarding well-known disease genes, using supervised learning methods. This work aims to find an effective method to exploit the disease gene neighbourhood and the integration of several useful omics data sources, which potentially enhance disease gene predictions. Methods We have presented a novel method to effectively predict disease genes by exploiting, in the semi-supervised learning (SSL) scheme, data regarding both disease genes and disease gene neighbours via protein–protein interaction network. Multiple proteomic and genomic data were integrated from six biological databases, including Universal Protein Resource, Interologous Interaction Database, Reactome, Gene Ontology, Pfam, and InterDom, and a gene expression dataset. Results By employing a 10 times stratified 10-fold cross validation, the SSL method performs better than the k-nearest neighbour method and the support vector machines method in terms of sensitivity of 85%, specificity of 79%, precision of 81%, accuracy of 82%, and a balanced F-function of 83%. The other comparative experimental evaluations demonstrate advantages of the proposed method given a small amount of labeled data with accuracy of 78%. We have applied the proposed method to detect 572 putative disease genes, which are biologically validated by some indirect ways. Conclusion Semi-supervised learning improved ability to study disease genes, especially a specific disease when the known disease genes (as labeled data) are very often limited. In addition to the computational improvement, the analysis of predicted disease proteins indicates that the findings are beneficial in deciphering the pathogenic mechanisms. [less ▲]

Detecting Electricity Theft

Poster (2016)