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With the arrival of modern internet era, large public networks of various types have come to existence to benefit the society as a whole and several research areas such as sociology, economics and geography in particular. However, the societal and research benefits of these networks have also given rise to potentially significant privacy issues in the sense that malicious entities may violate the privacy of the users of such a network by analyzing the network and deliberately using such privacy violations for deleterious purposes. Such considerations have given rise to a new active research area that deals with the quantification of privacy of users in large networks and the corresponding investigation of computational complexity issues of computing such quantified privacy measures. In this paper, we formalize three natural problems related to such a privacy measure for large networks and provide non-trivial theoretical computational complexity results for solving these problems. Our results show the first two problems can be solved efficiently, whereas the third problem is provably hard to solve within a logarithmic approximation factor. Furthermore, we also provide computational complexity results for the case when the privacy requirement of the network is severely restricted, including an efficient logarithmic approximation. [ABSTRACT FROM AUTHOR]

Objective To report entry criteria and clinical features of patients with newly diagnosed and relapsing giant cell arteritis (GCA) enrolled in a randomized trial of tocilizumab, an interleukin-6 receptor-alpha inhibitor. Methods Newly diagnosed GCA was defined as diagnosis ≤6 weeks before baseline. Relapsing GCA was defined as diagnosis >6 weeks before baseline with ≥2 consecutive weeks of prednisone ≥40 mg/day. All patients had active GCA within 6 weeks of baseline. All statistical results are exploratory. Results Of 251 patients, 119 (47%) had newly diagnosed and 132 (53%) had relapsing GCA. Mean age was 69 years in both subsets; 75% were women. Relapsing patients were heavier [difference in means (95% CI): women, 4.18 kg (0.49–7.87, P = 0.027); men, 8.25 kg (1.42–15.09, P = 0.019)] and had higher mean body mass index [difference in means (95% CI): women, 1.72 kg/m 2 (0.44–2.99, P = 0.009); men, 2.85 kg/m 2 (0.3 2 –5.37, P = 0.028)]. Relapsers had higher baseline prevalence of depression (16% vs. 4%) and osteopenia/osteoporosis (33% vs. 23%, P = 0.002 and P = 0.062, respectively). At diagnosis, 67% had new-onset headaches; 34% had mouth pain/jaw claudication. One-fifth had polymyalgia rheumatica symptoms but no cranial manifestations; 62% had positive temporal artery biopsy findings; 37% were enrolled on the basis of cross-sectional imaging study findings. Conclusions Demographics of the GiACTA population reflect the epidemiologic profile of GCA. Baseline comorbidities associated with glucocorticoids were more prevalent among relapsing patients than among those with newly diagnosed disease, highlighting the need for new GCA treatment options. More than one-third of patients were enrolled based on large-vessel imaging. [ABSTRACT FROM AUTHOR]

Virtual-system coupled adaptive umbrella sampling (VAUS) enhances sampling along a reaction coordinate by using a virtual degree of freedom. However, VAUS and regular adaptive umbrella sampling (AUS) methods are yet computationally expensive. To decrease the computational burden further, improvements of VAUS for all-atom explicit solvent simulation are presented here. The improvements include probability distribution calculation by a Markov approximation; parameterization of biasing forces by iterative polynomial fitting; and force scaling. These when applied to study Ala-pentapeptide dimerization in explicit solvent showed advantage over regular AUS. By using improved VAUS larger biological systems are amenable. [ABSTRACT FROM AUTHOR]

Abstract: A useful approach to the mathematical analysis of large-scale biological networks is based upon their decompositions into monotone dynamical systems. This paper deals with two computational problems associated to finding decompositions which are optimal in an appropriate sense. In graph-theoretic language, the problems can be recast in terms of maximal sign-consistent subgraphs. The theoretical results include polynomial-time approximation algorithms as well as constant-ratio inapproximability results. One of the algorithms, which has a worst-case guarantee of 87.9% from optimality, is based on the semidefinite programming relaxation approach of Goemans–Williamson [Goemans, M., Williamson, D., 1995. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. J. ACM 42 (6), 1115–1145]. The algorithm was implemented and tested on a Drosophila segmentation network and an Epidermal Growth Factor Receptor pathway model, and it was found to perform close to optimally. [Copyright &y& Elsevier]

Abstract: In this paper we investigate the computational complexity of a combinatorial problem that arises in the reverse engineering of protein and gene networks. Our contributions are as follows: [ ] We abstract a combinatorial version of the problem and observe that this is “equivalent” to the set multicover problem when the “coverage” factor k is a function of the number of elements n of the universe. An important special case for our application is the case in which . [ ] We observe that the standard greedy algorithm produces an approximation ratio of even if k is “large” i.e for some constant . [ ] Let denote the maximum number of elements in any given set in our set multicover problem. Then, we show that a non-trivial analysis of a simple randomized polynomial-time approximation algorithm for this problem yields an expected approximation ratio that is an increasing function of . The behavior of is roughly as follows: it is about when is at least about , and for smaller values of it decreases towards 1 as a linear function of with . Our randomized algorithm is a cascade of a deterministic and a randomized rounding step parameterized by a quantity followed by a greedy solution for the remaining problem. We also comment about the impossibility of a significantly faster convergence of towards 1 for any polynomial-time approximation algorithm. [Copyright &y& Elsevier]

Abstract: In this paper we investigate the inverse protein folding (IPF) problem under the Canonical model on 3D and 2D lattices [W.E. Hart, On the computational complexity of sequence design problems, Proceedings of the First Annual International Conference on Computational Molecular Biology 1997, pp. 128–136; E.I. Shakhnovich, A.M. Gutin, Engineering of stable and fast-folding sequences of model proteins, Proc. Natl. Acad. Sci. 90 (1993) 7195–7199]. In this problem, we are given a contact graph of a protein sequence that is embeddable in a 3D (respectively, 2D) lattice and an integer . The goal is to find an induced subgraph of G of at most K vertices with the maximum number of edges. In this paper, we prove the following results: [•] An earlier proof of NP-completeness of the IPF problem on 3D lattices [W.E. Hart, On the computational complexity of sequence design problems, Proceedings of the First Annual International Conference on Computational Molecular Biology 1997, pp. 128–136] is based on the NP-completeness of the IPF problem on the 2D lattices. However, the reduction was not correct and we show that the IPF problem for 2D lattices can be solved in time. But, we show that the IPF problem on 3D lattices is indeed NP-complete by a providing a different reduction from a different NP-complete problem. [•] We design a polynomial-time approximation scheme for the IPF problem on 3D lattices using the shifted slice-and-dice approach in [P. Berman, B. DasGupta, S. Muthukrishnan, Approximation algorithms for MAX-MIN tiling, J. Algorithms 47(2) (2003) 122–134; D. Hochbaum, Approximation Algorithms for NP-hard Problems, PWS Publishing Company, MA, 1997; D.S. Hochbaum, W. Mass, Approximation schemes for covering and packing problems in image processing and VLSI, J. ACM 32(1) (1985) 130–136], thereby improving the previous best polynomial-time approximation algorithm which had a performance ratio of [W.E. Hart, On the computational complexity of sequence design problems, Proceedings of the First Annual International Conference on Computational Molecular Biology 1997, pp. 128–136]. [Copyright &y& Elsevier]

Abstract: In this paper we investigate the problem of searching for a hidden target in a bounded region of the plane using an autonomous robot which is only able to use local sensory information. The problem is naturally formulated in the continuous domain but the solution proposed is based on an aggregation/refinement approach in which the continuous search space is partitioned into a finite collection of regions on which we define a discrete search problem. A solution to the original problem is obtained through a refinement procedure that lifts the discrete path into a continuous one. We show that the discrete optimization is computationally difficult (NP-hard) but there are computationally efficient approximation algorithms for solving it. The resulting solution to the continuous problem is in general not optimal but one can construct bounds to gauge the cost penalty incurred due to (i) the discretization of the problem and (ii) the attempt to approximately solve the NP-hard problem in polynomial time. Numerical simulations show that the algorithms proposed behave significantly better than naive approaches such as a random walk or a greedy algorithm. [Copyright &y& Elsevier]

This paper deals with the static equilibrium formulation of mechanical systems, including rigid and compliant elements, and the determination of their final equilibrium configuration under known loads. Statically determinate as well as statically indeterminate systems are analysed. Relative coordinates are used as generalised coordinates. The system is modeled using homogeneous transformation matrices and loop closure equations. In the first phase of analysis (assembly), initial correct configuration of the system is obtained, from approximate input provided by the user. For the solution of the equilibrium problem, a potential function is used. Its minimisation, subject to rigid constraints yields the equilibrium configuration. The approach is implemented in a program and tested on a number of planar and spatial mechanical systems. The software program is intended to be used as a design aid for engineers. [Copyright &y& Elsevier]

This paper addresses the problem of singularity-free path-planning for parallel manipulators. Unlike in serial manipulators, where there are only boundary singularities, parallel manipulators also possess singular configurations within the workspace where the manipulators are uncontrollable. Therefore, it is imperative that the generated paths are singularity-free. In this paper, we use a variational approach for planning singularity-free paths for parallel manipulators, based on a Lagrangian incorporating both a kinetic energy term which keeps the path short, and a potential energy term which ensures that the obtained path is singularity-free and the actuator lengths remain within their prescribed limits. The approach is applied to several parallel manipulators to evaluate the performance of this method. [Copyright &y& Elsevier]

Giant cell arteritis is the most common vasculitis in Caucasians. The aetiology of this disease remains uncertain. This article reviews some of the recent work in epidemiology and pathology in this field, with particular regard to the immunohistochemical findings in temporal artery biopsy specimens. The possible correlation between clinical features and biopsy specimen histology is discussed, and a model of pathogenesis is presented. [Copyright &y& Elsevier]

Atomic Force Microscopy (AFM) is an experimental technique to study structure-function relationship of biomolecules. AFM provides images of biomolecules at nanometer resolution. High-speed AFM experiments produce a series of images following dynamics of biomolecules. To further understand biomolecular functions, information on three-dimensional (3D) structures is beneficial. We aim to recover 3D information from an AFM image by computational modeling. The AFM image includes only low-resolution representation of a molecule; therefore we represent the structures by a coarse grained model (Gaussian mixture model). Using Monte-Carlo sampling, candidate models are generated to increase similarity between AFM images simulated from the models and target AFM image. The algorithm was tested on two proteins to model their conformational transitions. Using a simulated AFM image as reference, the algorithm can produce a low-resolution 3D model of the target molecule. Effect of molecular orientations captured in AFM images on the 3D modeling performance was also examined and it is shown that similar accuracy can be obtained for many orientations. The proposed algorithm can generate 3D low-resolution protein models, from which conformational transitions observed in AFM images can be interpreted in more detail. High-speed AFM experiments allow us to directly observe biomolecules in action, which provides insights on biomolecular function through dynamics. However, as only partial structural information can be obtained from AFM data, this new AFM based hybrid modeling method would be useful to retrieve 3D information of the entire biomolecule. • High-speed AFM experiments relate protein structure to function via dynamics. • Proposed a method for modeling of protein 3D structures from AFM image. • Gaussian mixture model for low-resolution representation of molecules. • Modeling of conformational transitions of EF2 and Cas9 proteins from AFM images. • The method can be valuable in AFM study for protein conformational analyses. [ABSTRACT FROM AUTHOR]

Giant cell arteritis is the most common systemic vasculitis in the elderly and is a potentially life-threatening ophthalmic emergency that can result in irreversible blindness. Blindness is most commonly associated with acute onset, irreversible arteritic ischemic optic neuropathy. Without treatment, second eye involvement may occur, resulting in bilateral blindness. Patients with established visual loss are treated with high-dose steroids and generally undergo a temporal artery biopsy to confirm their diagnosis. A significant number of patients are, however, referred for urgent ophthalmology assessment from concerns about "incipient" arteritic ischemic optic neuropathy. Before visual loss, patients may experience a range of ocular symptoms related to ischemia. This generally leads to treatment with high-dose systemic steroid and an urgent request for a temporal artery biopsy. Temporal artery biopsy is considered as the standard investigation for confirmatory diagnosis. It is generally arranged as soon as possible, although it is often not carried out for several days, and there may also be delays in histopathological reporting. It is often perceived that the patient is "safe" while on corticosteroids, in that they are being treated to avoid visual loss. What is not acknowledged, however, is that, if patients do not have giant cell arteritis and are being treated "just in case," they will often require a tapering of oral steroids over several weeks, exposing them to unnecessary and significant side effects. In the rheumatology setting, vascular ultrasound has emerged as a safe and reliable alternative to temporal artery biopsy as a point of care diagnostic tool in the management of giant cell arteritis. Given an experienced sonographer and optimal equipment, a rapid diagnosis can be established in a fast-track clinic setting, taking into consideration clinical assessment, scoring, and ultrasound findings. A huge advantage of ultrasound is that it provides immediate information that can be used to inform treatment decisions. We explore the evidence that supports the incorporation of vascular ultrasound into the ophthalmology repertoire to make a more efficient diagnosis that is cost-effective and associated with better patient outcomes, including a potential reduction in loss of sight and avoidance of unnecessary long-term steroid treatment by early exclusion of mimics. [ABSTRACT FROM AUTHOR]

In this paper, a new approach to path planning in static configuration space is presented. This also includes the problem of finding a path in the presence of obstacles. The method proceeds in two phases: a domain mapping phase and a query phase. In the domain mapping, the region is mapped onto a chosen convex region. For implementation in 2-D domain, the mapping has been shown onto convex hulls for the sake of better quality of mapping. However, for 3-D cases, for simplicity, the target convex region has been taken as a sphere. An obstacle in the non-convex region is shrunk to a point. Thus, there are two regions, one is the given region and the other is the corresponding mapped region. In the query phase, given any two points in the original region, corresponding positions of the two points in the mapped region are found out from the mapping already established. In the mapped (convex) region, these two points are connected by a straight line. Now, inverting the entire path back to the original region, we can get the actual path. The path obtained by this method is optimal and is away from the boundary of the region. An algorithm for the above mentioned problem has been implemented and tested on various 2-D and 3-D non-convex regions. Results exhibit a strong potential of the method for path planning. [Copyright &y& Elsevier]

A letter to the editor is presented in response to the article "Improved renal function in diabetic patients with acute gout treated with anakinra" by G. Balasubramaniam, M. Almond and B. Dasgupta in the previous issue.

Abstract: This paper proposes a path planner for serial manipulators with a large number of degrees of freedom, working in cluttered workspaces. Based on the variational principles, this approach involves formulating the path planning problem as constrained minimization of a functional representing the total joint movement over the complete path. We use modified boundary conditions at both ends of the trajectory to find more suitable start and end configurations. The concept of monotonic optimality is introduced in order to optimize the manipulator paths between the resulting end configurations. For obstacle avoidance, volume and proximity based penalizing schemes are developed and used. The presented planner uses a global approach to search for feasible paths and at the same time involves no pre-processing task. A variety of test cases have been presented to establish the efficacy of the presented scheme in providing good quality paths. The extent of advantage accruing out of the measures of free end-configurations and monotonic optimality are also analyzed quantitatively. [Copyright &y& Elsevier]

Abstract: The problem of motif identification in protein sequences has been studied for many years in the literature. Current popular algorithms of motif identification in protein sequences face two difficulties, high computational cost and the possibility of insertions and deletions. In this paper, we provide a new strategy that solve the problem more efficiently. We develop a self-organizing neural network structure with multiple levels of subnetworks to make an intelligent classification of the subsequences obtained from protein sequences. We maintain a low computational complexity through the use of this multi-level structure so that the classification of each subsequence is performed with respect to a small subspace of the whole input space. The new definition of pairwise distance between motif patterns provided in this paper can deal with up to two insertions/deletions allowed in a motif, while other existing algorithm can only deal with one insertion or deletion. We also maintain a high reliability using our self-organizing neural network since it will grow as needed to make sure all input patterns are considered and are given the same amount of attention. Simulation results show that our algorithm significantly outperforms existing algorithms in both accuracy and reliability aspects. [Copyright &y& Elsevier]