ALBERT

Description: Production of massive DNA sequence data sets is transforming phylogenetic inference, but best practices for analyzing such data sets are not well established. One uncertainty is robustness to missing data, particularly in coalescent frameworks. To understand the effects of increasing matrix size and loci at the cost of increasing missing data, we produced a 90 taxon, 2.2 megabase, 4,800 locus sequence matrix of landfowl using target capture of ultraconserved elements. We then compared phylogenies estimated with concatenated maximum likelihood, quartet-based methods executed on concatenated matrices and gene tree reconciliation methods, across five thresholds of missing data. Results of maximum likelihood and quartet analyses were similar, well resolved, and demonstrated increasing support with increasing matrix size and sparseness. Conversely, gene tree reconciliation produced unexpected relationships when we included all informative loci, with certain taxa placed toward the root compared with other approaches. Inspection of these taxa identified a prevalence of short average contigs, which potentially biased gene tree inference and caused erroneous results in gene tree reconciliation. This suggests that the more problematic missing data in gene tree–based analyses are partial sequences rather than entire missing sequences from locus alignments. Limiting gene tree reconciliation to the most informative loci solved this problem, producing well-supported topologies congruent with concatenation and quartet methods. Collectively, our analyses provide a well-resolved phylogeny of landfowl, including strong support for previously problematic relationships such as those among junglefowl ( Gallus ), and clarify the position of two enigmatic galliform genera ( Lerwa , Melanoperdix ) not sampled in previous molecular phylogenetic studies.

Description: Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. Neuroprosthetic devices are designed to restore lost function and could be used to form an electronic 'neural bypass' to circumvent disconnected pathways in the nervous system. It has previously been shown that intracortically recorded signals can be decoded to extract information related to motion, allowing non-human primates and paralysed humans to control computers and robotic arms through imagined movements. In non-human primates, these types of signal have also been used to drive activation of chemically paralysed arm muscles. Here we show that intracortically recorded signals can be linked in real-time to muscle activation to restore movement in a paralysed human. We used a chronically implanted intracortical microelectrode array to record multiunit activity from the motor cortex in a study participant with quadriplegia from cervical spinal cord injury. We applied machine-learning algorithms to decode the neuronal activity and control activation of the participant's forearm muscles through a custom-built high-resolution neuromuscular electrical stimulation system. The system provided isolated finger movements and the participant achieved continuous cortical control of six different wrist and hand motions. Furthermore, he was able to use the system to complete functional tasks relevant to daily living. Clinical assessment showed that, when using the system, his motor impairment improved from the fifth to the sixth cervical (C5-C6) to the seventh cervical to first thoracic (C7-T1) level unilaterally, conferring on him the critical abilities to grasp, manipulate, and release objects. This is the first demonstration to our knowledge of successful control of muscle activation using intracortically recorded signals in a paralysed human. These results have significant implications in advancing neuroprosthetic technology for people worldwide living with the effects of paralysis.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Bouton, Chad E -- Shaikhouni, Ammar -- Annetta, Nicholas V -- Bockbrader, Marcia A -- Friedenberg, David A -- Nielson, Dylan M -- Sharma, Gaurav -- Sederberg, Per B -- Glenn, Bradley C -- Mysiw, W Jerry -- Morgan, Austin G -- Deogaonkar, Milind -- Rezai, Ali R -- England -- Nature. 2016 Apr 13;533(7602):247-50. doi: 10.1038/nature17435.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio 43201, USA. ; Center for Neuromodulation, The Ohio State University, Columbus, Ohio 43210, USA. ; Department of Neurological Surgery, The Ohio State University, Columbus, Ohio 43210, USA. ; Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, Ohio 43210, USA. ; Advanced Analytics and Health Research, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio 43201, USA. ; Department of Psychology, The Ohio State University, Columbus, Ohio 43210, USA. ; Energy Systems, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio 43201, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/27074513" target="_blank"〉PubMed〈/a〉