Ordering the braid groups

We give an explicit geometric argument that Artin's braid group $B_n$ is right-orderable. The construction is elementary, natural, and leads to a new, effectively computable, canonical form for braids which we call left-consistent canonical form. The left-consistent form of a braid which is positive (respectively negative) in our order has consistently positive (respectively negative) exponent in the smallest braid generator which occurs. It follows that our ordering is identical to that of Dehornoy, constructed by very different means, and we recover Dehornoy's main theorem that any braid can be put into such a form using either positive or negative exponent in the smallest generator but not both. Our definition of order is strongly connected with Mosher's normal form and this leads to an algorithm to decide whether a given braid is positive, trivial, or negative which is quadratic in the length of the braid word.Comment: 24 pages, 10 figure

Demonstration that the Neurospora crassa mutation un-4 is a single nucleotide change in the tim16 gene encoding a subunit of the mitochondrial inner membrane translocase

The Neurospora crassa temperature sensitive mutation known as un-4 has been shown by a map-based complementation approach to be a single nucleotide change in the open reading frame of the mitochondrial inner membrane translocase subunit tim16 (NCU05515)

Fantastic growth as the FGSC turns 50

Comparative Medicine - OneHealth and Comparative Medicine Poster SessionFounded in 1960, the Fungal Genetics Stock Center enters it's fiftieth year of operation during a period of tremendous growth. The collection has more than doubled since moving to UMKC in 2004 and has added new materials that reach out beyond it's traditional constituency. Among these are deletion sets for Neurospora, Cryptococcus and Candida as well as molecular genetic tools for working with industrial fungi, model organisms, and plant and human pathogens. With distribution growing every year, the FGSC sends materials to scientists in over 35 countries every year; approximately half of our orders are from within the US. In addition to being part of an NIH funded multi-institution Functional Genomics Program for Neurospora, the FGSC is involved in cutting edge genomics research with collaborators at the US DOE Joint Genome Institute. The FGSC and its staff are actively involved in national and international societies and ad hoc working groups fostering the development of collection resources in the US and around the world

Identification of the Neurospora crassa mutation un-10 as a point mutation in a gene encoding eukaryotic translation initiation factor 3, subunit B.

The Neurospora crassa temperature-sensitive mutant known as un-10 has been shown by a map-based complementation approach to be a single nucleotide change in the open reading frame of the eukaryotic translation initiation factor 3b (NCU02208.3)

Met Receptor Inhibitor SU11274 Localizes in the Endoplasmic Reticulum

We discovered that SU11274, a class I c-Met inhibitor, fluoresces when excited by 488 nm laser light and showed rapid specific accumulation in distinct subcellular compartments. Given that SU11274 reduces cancer cell viability, we exploited these newly identified spectral properties to determine SU11274 intracellular distribution and accumulation in human pancreatic cancer cells. The aim of the studies reported here was to identify organelle(s) to which SU11274 is trafficked. We conclude that SU11274 rapidly and predominantly accumulates in the endoplasmic reticulum

More Than Spikes: On the Added Value of Non-linear Intracranial EEG Analysis for Surgery Planning in Temporal Lobe Epilepsy.

Epilepsy surgery can be a very effective therapy in medication refractory patients. During patient evaluation intracranial EEG is analyzed by clinical experts to identify the brain tissue generating epileptiform events. Quantitative EEG analysis increasingly complements this approach in research settings, but not yet in clinical routine. We investigate the correspondence between epileptiform events and a specific quantitative EEG marker. We analyzed 99 preictal epochs of multichannel intracranial EEG of 40 patients with mixed etiologies. Time and channel of occurrence of epileptiform events (spikes, slow waves, sharp waves, fast oscillations) were annotated by a human expert and non-linear excess interrelations were calculated as a quantitative EEG marker. We assessed whether the visually identified preictal events predicted channels that belonged to the seizure onset zone, that were later resected or that showed strong non-linear interrelations. We also investigated whether the seizure onset zone or the resection were predicted by channels with strong non-linear interrelations. In patients with temporal lobe epilepsy (32 of 40), epileptic spikes and the seizure onset zone predicted the resected brain tissue much better in patients with favorable seizure control after surgery than in unfavorable outcomes. Beyond that, our analysis did not reveal any significant associations with epileptiform EEG events. Specifically, none of the epileptiform event types did predict non-linear interrelations. In contrast, channels with strong non-linear excess EEG interrelations predicted the resected channels better in patients with temporal lobe epilepsy and favorable outcome. Also in the small number of patients with seizure onset in the frontal and parietal lobes, no association between epileptiform events and channels with strong non-linear excess EEG interrelations was detectable. In contrast to patients with temporal seizure onset, EEG channels with strong non-linear excess interrelations did neither predict the seizure onset zone nor the resection of these patients or allow separation between patients with favorable and unfavorable seizure control. Our study indicates that non-linear excess EEG interrelations are not strictly associated with epileptiform events, which are one key concept of current clinical EEG assessment. Rather, they may provide information relevant for surgery planning in temporal lobe epilepsy. Our study suggests to incorporate quantitative EEG analysis in the workup of clinical cases. We make the EEG epochs and expert annotations publicly available in anonymized form to foster similar analyses for other quantitative EEG methods

Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning.

Motivation: Brain morphometry from magnetic resonance imaging (MRI) is a promising neuroimaging biomarker for the non-invasive diagnosis and monitoring of neurodegenerative and neurological disorders. Current tools for brain morphometry often come with a high computational burden, making them hard to use in clinical routine, where time is often an issue. We propose a deep learning-based approach to predict the volumes of anatomically delineated subcortical regions of interest (ROI), and mean thicknesses and curvatures of cortical parcellations directly from T1-weighted MRI. Advantages are the timely availability of results while maintaining a clinically relevant accuracy. Materials and Methods: An anonymized dataset of 574 subjects (443 healthy controls and 131 patients with epilepsy) was used for the supervised training of a convolutional neural network (CNN). A silver-standard ground truth was generated with FreeSurfer 6.0. Results: The CNN predicts a total of 165 morphometric measures directly from raw MR images. Analysis of the results using intraclass correlation coefficients showed, in general, good correlation with FreeSurfer generated ground truth data, with some of the regions nearly reaching human inter-rater performance (ICC > 0.75). Cortical thicknesses predicted by the CNN showed cross-sectional annual age-related gray matter atrophy rates both globally (thickness change of -0.004 mm/year) and regionally in agreement with the literature. A statistical test to dichotomize patients with epilepsy from healthy controls revealed similar effect sizes for structures affecting all subtypes as reported in a large-scale epilepsy study. Conclusions: We demonstrate the general feasibility of using deep learning to estimate human brain morphometry directly from T1-weighted MRI within seconds. A comparison of the results to other publications shows accuracies of comparable magnitudes for the subcortical volumes and cortical thicknesses