Molecular follow-up of disease progression and interferon therapy in chronic myelocytic leukemia

We previously reported that the abl promoter (Pa) undergoes de novo DNA methylation in the course of chronic myelocytic leukemia (CML). The clinical implications of this finding are the subject of the present study in which samples of CML patients, including a group treated with interferon alpha (IFNalpha) were surveyed. The methylation status of the abl promoter was monitored by polymerase chain reaction (PCR) amplification of the Pa region after digestion with several site-methylation sensitive restriction enzymes. Some 74% of the DNA samples from blood and marrow drawn in the chronic phase were nonmethylated, similar to control samples from non-CML patients. The remaining 26% were partially methylated in the abl Pa region. The latter samples were derived from patients who were indistinguishable from the others on the basis of clinical presentation. Methylated samples were mostly derived from patients known to have a disease of longer duration (26 months v 7.5 months, P = .01). Samples of 30 IFNalpha-treated patients were sequentially analyzed in the course of treatment. Fifteen patients with no evidence of Pa methylation before treatment remained methylation-free. The remainder, who displayed Pa methylation before treatment, reverted to the methylation-free status. The outcome is attributed to IFNalpha therapy, as the Pa methylation status was not reversed in any of the patients treated with hydroxyurea. Methylation of the abl promoter indicates a disease of long-standing, most likely associated with a higher probability of imminent blastic transformation. It appears to predict the outcome of IFNalpha therapy far better than the cytogenetic response.     

You said it before and you'll say it again: Expectations of consistency in communication.

Repeated reference creates strong expectations in addressees that a speaker will continue to use the same expression for the same object. The authors investigate the root reason for these expectations by comparing a cooperativeness-based account (Grice, 1975) with a simpler consistency-based account. In two eye-tracking experiments, the authors investigated the expectations underlying the effect of precedents on comprehension. The authors show that listeners expect speakers to be consistent in their use of expressions even when these expectations cannot be motivated by the assumption of cooperativeness. The authors conclude that though this phenomenon seems to be motivated by cooperativeness, listeners' expectation that speakers be consistent in their use of expressions is governed by a general expectation of consistency. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Modeling the Combining Site of the Human Asialoglycoprotein Receptor

A model for the carbohydrate recognition domain (CRD) and combining site of the human asialoglycoprotein (ASGP) receptor has been computed on the basis of the close sequence homology with the mannose-binding lectin (MBP), whose three-dimensional structure in complex with a ligand has been determined by crystallographic methods (Weis, W.I.; Drickamer, K.; Hendrickson, W.A. Nature 1992, 360: 127). Within the limitations of modeling methods, the model is compatible with data on ligand binding of the family C-type lectins, of which the MBP and the ASGP receptor are members. The model derived can serve as a guide for designing site-directed mutagenesis experiments in order to further elucidate the origins of specificity of the ASGP receptor toward galactose. In particular, the model focuses attention on the possible role of position 207 (MBP sequence numbering) in promoting galactose binding.     

Guaranteeing the Diversity of Number Generators

A major problem in using iterative number generators of the form x_i=f(x_i−1) is that they can enter unexpectedly short cycles. This is hard to analyze when the generator is designed, hard to detect in real time when the generator is used, and can have devastating cryptanalytic implications. In this paper we define a measure of security, called sequence diversity, which generalizes the notion of cycle-length for noniterative generators. We then introduce the class of counter-assisted generators and show how to turn any iterative generator (even a bad one designed or seeded by an adversary) into a counter-assisted generator with a provably high diversity, without reducing the quality of generators which are already cryptographically strong.     

Finite-element analysis for magnetic resonance image artifact evaluation

The static magnetic field of a magnetic resonance imaging scanner can be distorted by the presence of materials, perturbing the spatial encoding process in magnetic resonance imaging and often resulting in image artifacts. The relationship between the image artifact size and magnetic susceptibility of the material specimen is of interest to engineers for the design of devices that are to be compatible with the imaging volume of the scanner. In this study, a finite-element method was used to simulate the distorted magnetic field of samples with different susceptibilities. With the knowledge of the external- and self- magnetic field interactions, a Lorentz correction was applied to compute the magnetic field deviation. The simulated results were then validated by the corresponding experimental magnetic resonance images.     

Sleeved co-clustering of lagged data

The paper focuses on mining clusters that are characterized by a lagged relationship between the data objects. We call such clusters lagged co-clusters. A lagged co-cluster of a matrix is a submatrix determined by a subset of rows and their corresponding lag over a subset of columns. Extracting such subsets may reveal an underlying governing regulatory mechanism. Such a regulatory mechanism is quite common in real-life settings. It appears in a variety of fields: meteorology, seismic activity, stock market behavior, neuronal brain activity, river flow, and navigation, but a limited list of examples. Mining such lagged co-clusters not only helps in understanding the relationship between objects in the domain, but assists in forecasting their future behavior. For most interesting variants of this problem, finding an optimal lagged co-cluster is NP-complete problem. We present a polynomial-time Monte-Carlo algorithm for mining lagged co-clusters. We prove that, with fixed probability, the algorithm mines a lagged co-cluster which encompasses the optimal lagged co-cluster by a maximum 2 ratio columns overhead and completely no rows overhead. Moreover, the algorithm handles noise, anti-correlations, missing values, and overlapping patterns. The algorithm is extensively evaluated using both artificial and real-world test environments. The first enable the evaluation of specific, isolated properties of the algorithm. The latter (river flow and topographic data) enable the evaluation of the algorithm to efficiently mine relevant and coherent lagged co-clusters in environments that are temporal, i.e., time reading data and non-temporal.     

Self‐Assembling,Bioinspired Wax Crystalline Surfaces with Time‐Dependent Wettability

One of the most fascinating properties of materials in nature is the superhydrophobic and self‐cleaning capabilities of different plant surfaces. This is usually achieved by the hydrophobic cuticles that are made of cutin and contain wax crystals both within them and on their surfaces. Here, bioinspired n‐hexatriacontane wax films are deposited via thermal evaporation and it is shown that the surface evolves in time via self‐assembly. This leads to a dramatic change in the wetting properties with a transition from hydrophobic to superhydrophobic characteristics, which takes place within several days at room temperature. This phenomenon is investigated and strain‐induced recrystallization is proposed to be the mechanism for it. This work could become the basis for the inspiration and production of tuned, time‐dependant, temperature‐sensitive, variable‐wettability surfaces.     

From data to knowledge: The interaction between data management systems in educational institutions and the delivery of quality education

No school is an island; it is a part of a continuum or a pipeline of institutions which together form an educational pipeline through which groups of students pass. To turn a body of data into useful information for knowledge-based decision-making at any level, data must be collected, organised, analysed and reflected upon. The purpose of this paper is to discuss how schools and other educational institutions can not only collect better data but learn how to transform that data so that the information held within can be effectively shared among all stakeholders. This process will help to ensure that the school and the entire education system provide a more seamless and effective educational pipeline for students, and ultimately improve the quality of education delivered in the country as a whole.