Prediction of Cognitive States During Flight Simulation Using Multimodal Psychophysiological Sensing

The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training case. These results support the goal of demonstrating simultaneous real-time classification of multiple states using multiple sensing modalities in high fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents

The \u3cem\u3eChlamydomonas\u3c/em\u3e Genome Reveals the Evolution of Key Animal and Plant Functions

Chlamydomonas reinhardtii is a unicellular green alga whose lineage diverged from land plants over 1 billion years ago. It is a model system for studying chloroplast-based photosynthesis, as well as the structure, assembly, and function of eukaryotic flagella (cilia), which were inherited from the common ancestor of plants and animals, but lost in land plants. We sequenced the ∼120-megabase nuclear genome of Chlamydomonas and performed comparative phylogenomic analyses, identifying genes encoding uncharacterized proteins that are likely associated with the function and biogenesis of chloroplasts or eukaryotic flagella. Analyses of the Chlamydomonas genome advance our understanding of the ancestral eukaryotic cell, reveal previously unknown genes associated with photosynthetic and flagellar functions, and establish links between ciliopathy and the composition and function of flagella

The Genome of Nectria haematococca: Contribution of Supernumerary Chromosomes to Gene Expansion

The ascomycetous fungus Nectria haematococca, (asexual name Fusarium solani), is a member of a group of >50 species known as the “Fusarium solani species complex”. Members of this complex have diverse biological properties including the ability to cause disease on >100 genera of plants and opportunistic infections in humans. The current research analyzed the most extensively studied member of this complex, N. haematococca mating population VI (MPVI). Several genes controlling the ability of individual isolates of this species to colonize specific habitats are located on supernumerary chromosomes. Optical mapping revealed that the sequenced isolate has 17 chromosomes ranging from 530 kb to 6.52 Mb and that the physical size of the genome, 54.43 Mb, and the number of predicted genes, 15,707, are among the largest reported for ascomycetes. Two classes of genes have contributed to gene expansion: specific genes that are not found in other fungi including its closest sequenced relative, Fusarium graminearum; and genes that commonly occur as single copies in other fungi but are present as multiple copies in N. haematococca MPVI. Some of these additional genes appear to have resulted from gene duplication events, while others may have been acquired through horizontal gene transfer. The supernumerary nature of three chromosomes, 14, 15, and 17, was confirmed by their absence in pulsed field gel electrophoresis experiments of some isolates and by demonstrating that these isolates lacked chromosome-specific sequences found on the ends of these chromosomes. These supernumerary chromosomes contain more repeat sequences, are enriched in unique and duplicated genes, and have a lower G+C content in comparison to the other chromosomes. Although the origin(s) of the extra genes and the supernumerary chromosomes is not known, the gene expansion and its large genome size are consistent with this species' diverse range of habitats. Furthermore, the presence of unique genes on supernumerary chromosomes might account for individual isolates having different environmental niches