Hemoglobin as a source of iron overload in multiple sclerosis: does multiple sclerosis share risk factors with vascular disorders?

Although iron is known to be essential for the normal development and health of the central nervous system, abnormal iron deposits are found in and around multiple sclerosis (MS) lesions that themselves are closely associated with the cerebral vasculature. However, the origin of this excess iron is unknown, and it is not clear whether this is one of the primary causative events in the pathogenesis of MS, or simply another consequence of the long-lasting inflammatory conditions. Here, applying a systems biology approach, we propose an additional way for understanding the neurodegenerative component of the disease caused by chronic subclinical extravasation of hemoglobin, in combination with multiple other factors including, but not limited to, dysfunction of different cellular protective mechanisms against extracellular hemoglobin reactivity and oxidative stress. Moreover, such considerations could also shed light on and explain the higher susceptibility of MS patients to a wide range of cardiovascular disorders.     

Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions

Computational prediction of intrinsic disorder in protein sequences dates back to late 1970 and has flourished in the last two decades. We provide a brief historical overview, and we review over 30 recent predictors of disorder. We are the first to also cover predictors of molecular functions of disorder, including 13 methods that focus on disordered linkers and disordered protein–protein, protein–RNA, and protein–DNA binding regions. We overview their predictive models, usability, and predictive performance. We highlight newest methods and predictors that offer strong predictive performance measured based on recent comparative assessments. We conclude that the modern predictors are relatively accurate, enjoy widespread use, and many of them are fast. Their predictions are conveniently accessible to the end users, via web servers and databases that store pre-computed predictions for millions of proteins. However, research into methods that predict many not yet addressed functions of intrinsic disorder remains an outstanding challenge.     

Constancy and change: Flexible organization structures

Management organization structure is the most sensitive characteristic displayed by a business organization in the process of its adaptation to the changing environment. It is well-known, however, that excessive changeability of the structure makes the management system unstable and leads to less effective organizational behavior. The author believes that the reason lies in the fact that the structural changes almost always involve the management system's composition too. This shortcoming is rarely found in matrix management of goal-oriented programs. However, application of this type of management structure is limited as complexity of management increases disproportionately. Systems analysis of the problem situation leads to two new conclusions: (a) the variety of management organization structures required by business organizations to adapt to the changing environment can be extended if the organizational relations are regarded as part of organizational/economic symbiosis and their interrelated change; and (b) enhancement of the program component in the matrix structure makes it possible to design stable manage-management structures with a dynamic composition. Implementation of this concept in large-scale business systems brought about essentially new forms of production units' self-organization that became known as state production associations. It makes the large-scale business organizations' structures far less complex and increase their flexibility without disturbing the management systems' resilience. The newly developed principles of organizational design are universal.     

Structural basis for the selectivity of the external thioesterase of the surfactin synthetase

Non-ribosomal peptide synthetases (NRPS) and polyketide synthases (PKS) found in bacteria, fungi and plants use two different types of thioesterases for the production of highly active biological compounds. Type I thioesterases (TEI) catalyse the release step from the assembly line of the final product where it is transported from one reaction centre to the next as a thioester linked to a 4'-phosphopantetheine (4'-PP) cofactor that is covalently attached to thiolation (T) domains. The second enzyme involved in the synthesis of these secondary metabolites, the type II thioesterase (TEII), is a crucial repair enzyme for the regeneration of functional 4'-PP cofactors of holo-T domains of NRPS and PKS systems. Mispriming of 4'-PP cofactors by acetyl- and short-chain acyl-residues interrupts the biosynthetic system. This repair reaction is very important, because roughly 80% of CoA, the precursor of the 4'-PP cofactor, is acetylated in bacteria. Here we report the three-dimensional structure of a type II thioesterase from Bacillus subtilis free and in complex with a T domain. Comparison with structures of TEI enzymes shows the basis for substrate selectivity and the different modes of interaction of TEII and TEI enzymes with T domains. Furthermore, we show that the TEII enzyme exists in several conformations of which only one is selected on interaction with its native substrate, a modified holo-T domain.     

Optimal Variable Weighting for Ultrametric and Additive Trees and K-means Partitioning: Methods and Software

K -means partitioning. We also describe some new features and improvements to the algorithm proposed by De Soete. Monte Carlo simulations have been conducted using different error conditions. In all cases (i.e., ultrametric or additive trees, or K-means partitioning), the simulation results indicate that the optimal weighting procedure should be used for analyzing data containing noisy variables that do not contribute relevant information to the classification structure. However, if the data involve error-perturbed variables that are relevant to the classification or outliers, it seems better to cluster or partition the entities by using variables with equal weights. A new computer program, OVW, which is available to researchers as freeware, implements improved algorithms for optimal variable weighting for ultrametric and additive tree clustering, and includes a new algorithm for optimal variable weighting for K-means partitioning.     

Arnold问题集

本书是以当代著名俄罗斯（及前苏联）数学家Amold命名的数学研究问题的汇集，是按俄文原版（2000，莫斯科）英译的第二版平装本。Amold对当代数学发展有重要影响，他主持的莫斯科大学数学讨论班在国际数学界颇负盛名。     

A Comparison of SuperDARN-Derived Plasma Convection and DMSP Ion Drift Measurements

Recently several studies indicated possible deterioration of accuracy in the SuperDARN convection measurements at large distances （〉2 500 km）. In this study, the SuperDARN-derived convection was compared with ion drift measurements given by the DMSP satellites, focused on SuperDARN far-range measurements, to investigate the above-mentioned problem. Two different procedures of SuperDARN convection estimation were applied. An overall reasonable agreement between two observation systems were found though individual points in some passes show significant differences. Besides, a tendency is also clear that the SuperDARN-derived velocities were slightly smaller than those of DMSP ion drift measurements. Potential factors contributing to the velocity differences were explained.