Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness

Despite the common use of anesthetics to modulate consciousness in the clinic, brain-based monitoring of consciousness is uncommon. We com-bined electroencephalographic measurement of brain activity with deep neural networks to automatically discriminate anesthetic states induced by propofol. Our results with leave-one-participant-out-cross-validation show that convolutional neural networks significantly outperform multilayer perceptrons in discrimination accuracy when working with raw time series. Perceptrons achieved comparable accuracy when provided with power spec-tral densities. These findings highlight the potential of deep convolutional networks for completely automatic extraction of useful spatio-temporo-spectral features from human EEG

Post-operative breast cancer patients diagnosed with skeletal metastasis without bone pain had fewer skeletal-related events and deaths than those with bone pain

<p>Abstract</p> <p>Background</p> <p>Skeletal metastases are often accompanied by bone pain. To investigate the clinical meaning of bone pain associated with skeletal metastasis in breast cancer patients after surgery, we explored whether the presence of bone pain was due to skeletal-related events (SREs) or survival (cause specific death, CSD), retrospectively.</p> <p>Methods</p> <p>Consecutive breast cancer patients undergoing surgery between 1988 and 1998 were examined for signs of skeletal metastasis until December 2006. Patients who were diagnosed as having skeletal metastasis were the subjects of this study. Bone scans were performed annually for 5, 7 or 10 years; they were also conducted if skeletal metastasis was suspected. Data concerning bone pain and tumor markers at the time of skeletal metastasis diagnosis, and data relating to various factors including tumors, lymph nodes and hormone receptors at the time of surgery, were investigated. The relationships between factors such as bone pain, SRE and CSD were analyzed using the Kaplan-Meier method and Cox's analysis.</p> <p>Results</p> <p>Skeletal metastasis occurred in 668 patients but the pain status of two patients was unknown, therefore 666 patients were included in the study. At the time of skeletal metastasis diagnosis 270 patients complained of pain; however, 396 patients did not. Analysis of data using Cox's and Kaplan-Meier methods demonstrated that patients without pain had fewer SREs and better survival rates than those with pain. Hazard ratios regarding SRE (base = patients without pain) were 2.331 in univariate analysis and 2.243 in multivariate analysis. Hazard ratios regarding CSD (base = patients without pain) were 1.441 in univariate analysis and 1.535 in multivariate analysis. Similar results were obtained when analyses were carried out using the date of surgery as the starting point.</p> <p>Conclusion</p> <p>Bone pain at diagnosis of skeletal metastasis was an indicator of increased SRE and CSD. However, these data did not support recommendations of follow-up bone surveys in breast cancer patients.</p

Genome fluctuations in cyanobacteria reflect evolutionary, developmental and adaptive traits

<p>Abstract</p> <p>Background</p> <p>Cyanobacteria belong to an ancient group of photosynthetic prokaryotes with pronounced variations in their cellular differentiation strategies, physiological capacities and choice of habitat. Sequencing efforts have shown that genomes within this phylum are equally diverse in terms of size and protein-coding capacity. To increase our understanding of genomic changes in the lineage, the genomes of 58 contemporary cyanobacteria were analysed for shared and unique orthologs.</p> <p>Results</p> <p>A total of 404 protein families, present in all cyanobacterial genomes, were identified. Two of these are unique to the phylum, corresponding to an AbrB family transcriptional regulator and a gene that escapes functional annotation although its genomic neighbourhood is conserved among the organisms examined. The evolution of cyanobacterial genome sizes involves a mix of gains and losses in the clade encompassing complex cyanobacteria, while a single event of reduction is evident in a clade dominated by unicellular cyanobacteria. Genome sizes and gene family copy numbers evolve at a higher rate in the former clade, and multi-copy genes were predominant in large genomes. Orthologs unique to cyanobacteria exhibiting specific characteristics, such as filament formation, heterocyst differentiation, diazotrophy and symbiotic competence, were also identified. An ancestral character reconstruction suggests that the most recent common ancestor of cyanobacteria had a genome size of approx. 4.5 Mbp and 1678 to 3291 protein-coding genes, 4%-6% of which are unique to cyanobacteria today.</p> <p>Conclusions</p> <p>The different rates of genome-size evolution and multi-copy gene abundance suggest two routes of genome development in the history of cyanobacteria. The expansion strategy is driven by gene-family enlargment and generates a broad adaptive potential; while the genome streamlining strategy imposes adaptations to highly specific niches, also reflected in their different functional capacities. A few genomes display extreme proliferation of non-coding nucleotides which is likely to be the result of initial expansion of genomes/gene copy number to gain adaptive potential, followed by a shift to a life-style in a highly specific niche (e.g. symbiosis). This transition results in redundancy of genes and gene families, leading to an increase in junk DNA and eventually to gene loss. A few orthologs can be correlated with specific phenotypes in cyanobacteria, such as filament formation and symbiotic competence; these constitute exciting exploratory targets.</p