ObjectiveTo identify nailfold videocapillaroscopic features and other clinical risk factors for new digital ulcers (DUs) during a 6‐month period in patients with systemic sclerosis (SSc).MethodsIn this multicenter, prospective, observational cohort study, the videoCAPillaroscopy (CAP) study, we evaluated 623 patients with SSc from 59 centers (14 countries). Patients were stratified into 2 groups: a DU history group and a no DU history group. At enrollment, patients underwent detailed nailfold videocapillaroscopic evaluation and assessment of demographic characteristics, DU status, and clinical and SSc characteristics. Risk factors for developing new DUs were assessed using univariable and multivariable logistic regression (MLR) analyses.ResultsOf the 468 patients in the DU history group (mean ± SD age 54.0 ± 13.7 years), 79.5% were female, 59.8% had limited cutaneous SSc, and 22% developed a new DU during follow‐up. The strongest risk factors for new DUs identified by MLR in the DU history group included the mean number of capillaries per millimeter in the middle finger of the dominant hand, the number of DUs (categorized as 0, 1, 2, or ≥3), and the presence of critical digital ischemia. The receiver operating characteristic (ROC) of the area under the curve (AUC) of the final MLR model was 0.738 (95% confidence interval [95% CI] 0.681–0.795). Internal validation through bootstrap generated a ROC AUC of 0.633 (95% CI 0.510–0.756).ConclusionThis international prospective study, which included detailed nailfold videocapillaroscopic evaluation and extensive clinical characterization of patients with SSc, identified the mean number of capillaries per millimeter in the middle finger of the dominant hand, the number of DUs at enrollment, and the presence of critical digital ischemia at enrollment as risk factors for the development of new DUs.

The problem of adjusting the entries of a large matrix to satisfy prior consistency requirements occurs in economics, urban planning, statistics, demography, and stochastic modeling; these problems are called Matrix Balancing Problems. We describe five applications of matrix balancing and compare the algorithmic and computational performance of balancing procedures that represent the two primary approaches for matrix balancing—matrix scaling and nonlinear optimization. The algorithms we study are the RAS algorithm, a diagonal similarity scaling algorithm, and a truncated Newton algorithm for network optimization. We present results from computational experiments with large-scale problems based on producing consistent estimates of Social Accounting Matrices for developing countries.

A weighted directed graph G IS a triple (V, A. g) where (V. A) IS a directed graph and g is a n arbitrary real-valued function defined on the arc set A. Let G be a strongly-connected, simple weighted directed graph. We say th a t G is max-balanced if fo r every nontrivial ~ubset of th e vertices W, the maxImum weight over arcs leavin g W equals th e maximum weIght over arcs e ntering W. We show that there ex ists a (up to an addItIve con~tant) un iq ue potential p, for (E V such that (V, A, g") IS max-b alanced where g/: = P" + g o-PI for a = (U , I) EA. We describe an O(1V 1 2 IAI) algorithm for computlJ1g P using an a lgorithm for computing the tnaxmwm cycle-mean of C. Fmally. we apply our principal res ult to th e similarity scaling of nonnegatIve matrices.

Strict patient selection on the basis of additional symptoms or signs is the key to increasing the yield of capsule endoscopy in patients with chronic abdominal pain. Inflammation seemed to be the additional sign with the highest value.

An application of neural network modeling is described for generating hypotheses about the relationships between response properties of neurons and information processing in the auditory system. The goal is to study response properties that are useful for extracting sound localization information from directionally selective spectral filtering provided by the pinna. For studying sound localization based on spectral cues provided by the pinna, a feedforward neural network model with a guaranteed level of fault tolerance is introduced. Fault tolerance and uniform fault tolerance in a neural network are formally defined and a method is described to ensure that the estimated network exhibits fault tolerance. The problem of estimating weights for such a network is formulated as a large-scale nonlinear optimization problem. Numerical experiments indicate that solutions with uniform fault tolerance exist for the pattern recognition problem considered. Solutions derived by introducing fault tolerance constraints have better generalization properties than solutions obtained via unconstrained back-propagation.