Midsagittal tissue bridges are associated with walking ability in incomplete spinal cord injury: A magnetic resonance imaging case series.

Context: Following spinal cord injury (SCI), early prediction of future walking ability is difficult, due to factors such as spinal shock, sedation, impending surgery, and secondary long bone fracture. Accurate, objective biomarkers used in the acute stage of SCI would inform individualized patient management and enhance both patient/family expectations and treatment outcomes. Using magnetic resonance imaging (MRI) and specifically a midsagittal T2-weighted image, the amount of tissue bridging (measured as spared spinal cord tissue) shows potential to serve as such a biomarker. Ten participants with incomplete SCI received MRI of the spinal cord. Using the midsagittal T2-weighted image, anterior and posterior tissue bridges were calculated as the distance from cerebrospinal fluid to the damage. Then, the midsagittal tissue bridge ratio was calculated as the sum of anterior and posterior tissue bridges divided by the spinal cord diameter. Each participant also performed a 6-minute walk test, where the total distance walked was measured within six minutes.Findings: The midsagittal tissue bridge ratio measure demonstrated a high level of inter-rater reliability (ICC = 0.90). Midsagittal tissue bridge ratios were significantly related to distance walked in six minutes (R = 0.68, P = 0.03).Conclusion/clinical relevance: We uniquely demonstrated that midsagittal tissue bridge ratios were correlated walking ability. These preliminary findings suggest potential for this measure to be considered a prognostic biomarker of residual walking ability following SCI.

Massive computational acceleration by using neural networks to emulate mechanism-based biological models.

For many biological applications, exploration of the massive parametric space of a mechanism-based model can impose a prohibitive computational demand. To overcome this limitation, we present a framework to improve computational efficiency by orders of magnitude. The key concept is to train a neural network using a limited number of simulations generated by a mechanistic model. This number is small enough such that the simulations can be completed in a short time frame but large enough to enable reliable training. The trained neural network can then be used to explore a much larger parametric space. We demonstrate this notion by training neural networks to predict pattern formation and stochastic gene expression. We further demonstrate that using an ensemble of neural networks enables the self-contained evaluation of the quality of each prediction. Our work can be a platform for fast parametric space screening of biological models with user defined objectives.

Establishing the inter-rater reliability of spinal cord damage manual measurement using magnetic resonance imaging.

Study design: Retrospective study. Objectives: To establish the inter-rater reliability in the quantitative evaluation of spinal cord damage following cervical incomplete spinal cord injury (SCI) utilizing magnetic resonance imaging (MRI). MRI was used to perform manual measurements of the cranial and caudal boundaries of edema, edema length, midsagittal tissue bridge ratio, axial damage ratio, and edema volume in 10 participants with cervical incomplete SCI. Setting: Academic university setting. Methods: Structural MRIs of 10 participants with SCI were collected from Northwestern University's Neuromuscular Imaging and Research Lab. All manual measures were performed using OsiriX (Pixmeo Sarl, Geneva, Switzerland). Intraclass correlation coefficients (ICC) were used to determine inter-rater reliability across seven raters of varying experience. Results: High-to-excellent inter-rater reliability was found for all measures. ICC values for cranial/caudal levels of involvement, edema length, midsagittal tissue bridge ratio, axial damage ratio, and edema volume were 0.99, 0.98, 0.90, 0.84, and 0.93, respectively. Conclusions: Manual MRI measures of spinal cord damage are reliable between raters. Researchers and clinicians may confidently utilize manual MRI measures to quantify cord damage. Future research to predict functional recovery following SCI and better inform clinical management is warranted.

Small sets of interacting proteins suggest functional linkage mechanisms via Bayesian analogical reasoning.

MOTIVATION: Proteins and protein complexes coordinate their activity to execute cellular functions. In a number of experimental settings, including synthetic genetic arrays, genetic perturbations and RNAi screens, scientists identify a small set of protein interactions of interest. A working hypothesis is often that these interactions are the observable phenotypes of some functional process, which is not directly observable. Confirmatory analysis requires finding other pairs of proteins whose interaction may be additional phenotypical evidence about the same functional process. Extant methods for finding additional protein interactions rely heavily on the information in the newly identified set of interactions. For instance, these methods leverage the attributes of the individual proteins directly, in a supervised setting, in order to find relevant protein pairs. A small set of protein interactions provides a small sample to train parameters of prediction methods, thus leading to low confidence. RESULTS: We develop RBSets, a computational approach to ranking protein interactions rooted in analogical reasoning; that is, the ability to learn and generalize relations between objects. Our approach is tailored to situations where the training set of protein interactions is small, and leverages the attributes of the individual proteins indirectly, in a Bayesian ranking setting that is perhaps closest to propensity scoring in mathematical psychology. We find that RBSets leads to good performance in identifying additional interactions starting from a small evidence set of interacting proteins, for which an underlying biological logic in terms of functional processes and signaling pathways can be established with some confidence. Our approach is scalable and can be applied to large databases with minimal computational overhead. Our results suggest that analogical reasoning within a Bayesian ranking problem is a promising new approach for real-time biological discovery. AVAILABILITY: Java code is available at: www.gatsby.ucl.ac.uk/~rbas. CONTACT: airoldi@fas.harvard.edu; kheller@mit.edu; ricardo@stats.ucl.ac.uk.

Sequence information for the splicing of human pre-mRNA identified by support vector machine classification.

Vertebrate pre-mRNA transcripts contain many sequences that resemble splice sites on the basis of agreement to the consensus,yet these more numerous false splice sites are usually completely ignored by the cellular splicing machinery. Even at the level of exon definition,pseudo exons defined by such false splices sites outnumber real exons by an order of magnitude. We used a support vector machine to discover sequence information that could be used to distinguish real exons from pseudo exons. This machine learning tool led to the definition of potential branch points,an extended polypyrimidine tract,and C-rich and TG-rich motifs in a region limited to 50 nt upstream of constitutively spliced exons. C-rich sequences were also found in a region extending to 80 nt downstream of exons,along with G-triplet motifs. In addition,it was shown that combinations of three bases within the splice donor consensus sequence were more effective than consensus values in distinguishing real from pseudo splice sites; two-way base combinations were optimal for distinguishing 3' splice sites. These data also suggest that interactions between two or more of these elements may contribute to exon recognition,and provide candidate sequences for assessment as intronic splicing enhancers.