精品须知

我的工作真悠闲；我只需坐在那里，微笑着看人们把一台台计算机拿来，我可以玩一整天，然后他们还付我报酬。噢，当然，我也不能光高兴，也得写些东西，或者把其他人写的东西看看，编辑一下；也可能要对某个新出现的软件包硬件概念费心琢磨一番，但比四处奔波谋生强多了。在最近从我桌面上通过的无究无尽产品中，确实有一些吸引了我的注意力。     

Steps to a successful creation of a corporate threat management plan

One of the problems of computer security is deciding on how much security is necessary for proper control of system and network assets. This gets down to the concept of threat assessment or, more specifically, what do you have and who would want it? While it sounds relatively simple to state, it's not that easy to assess corporate network threats unless you approach things in a structured manner. In this article, the basics of threat assessment are discussed with some thought placed on how to identify who wants to take what you have or hurt you in some way.     

EXTENSION OF THE RELATIONAL DATABASE AND ITS ALGEBRA WITH ROUGH SET TECHNIQUES

This paper describes a database model based on the original rough sets theory. Its rough relations permit the representation of a rough set of tuples not definable in terms of the elementary classes, except through use of lower and upper approximations. The rough relational database model also incorporates indiscernibility in the representation and in all the operators of the rough relational algebra. This indiscernibility is based strictly on equivalence classes which must be defined for every attribute domain. There are several obvious applications for which the rough relational database model can more accurately model an enterprise than does the standard relational model. These include systems involving ambiguous, imprecise, or uncertain data. Retrieval over mismatched domains caused by the merging of one or more applications can be facilitated by the use of indiscernibility, and naive system users can achieve greater recall with the rough relational database. In addition, applications inherently “rough” could be more easily implemented and maintained in the rough relational database.     

Networking,Storytelling and Knowledge Production in First-Year Writing

As writing teachers, we seek to validate the knowledge that students bring to the classroom while introducing and cultivating new methods, strategies, techniques and technologies that may serve them now and in the future. In this article we describe an approach we take that integrates curricular and extracurricular co-apprenticeship strategies using public writing models in conjunction with social networking tools. With publication as a final goal, we urge students to move from expressing personal problems to addressing social issues, using a private, non-commercial learning network NoDiff <http://NoDiff.com> as a safe zone for skills development, knowledge production and social inquiry. In taking this approach, we resist dichotomies that place professional skills training in strict opposition to critical pedagogy. We argue that many of the so-called “extracurricular” composing activities that most of today's students engage in lay the groundwork for both professional and civic participation. Acknowledging that we as composition teachers are also beginners in the world of 21^st century communication, we emphasize both (1) the continual transference of skills and knowledge among teachers and learners; and (2) the importance of providing what is often lacking in skills-based ‘new literacy’ training, namely a critical pedagogical approach to learning as social engagement and critical intervention.     

The HaLoop approach to large-scale iterative data analysis

The growing demand for large-scale data mining and data analysis applications has led both industry and academia to design new types of highly scalable data-intensive computing platforms. MapReduce has enjoyed particular success. However, MapReduce lacks built-in support for iterative programs, which arise naturally in many applications including data mining, web ranking, graph analysis, and model fitting. This paper (This is an extended version of the VLDB 2010 paper “HaLoop: Efficient Iterative Data Processing on Large Clusters” PVLDB 3(1):285–296, 2010.) presents HaLoop, a modified version of the Hadoop MapReduce framework, that is designed to serve these applications. HaLoop allows iterative applications to be assembled from existing Hadoop programs without modification, and significantly improves their efficiency by providing inter-iteration caching mechanisms and a loop-aware scheduler to exploit these caches. HaLoop retains the fault-tolerance properties of MapReduce through automatic cache recovery and task re-execution. We evaluated HaLoop on a variety of real applications and real datasets. Compared with Hadoop, on average, HaLoop improved runtimes by a factor of 1.85 and shuffled only 4 % as much data between mappers and reducers in the applications that we tested.     

Biflavonoid-Induced Disruption of Hydrogen Bonds Leads to Amyloid-β Disaggregation

Deposition of amyloid β (Aβ) fibrils in the brain is a key pathologic hallmark of Alzheimer’s disease. A class of polyphenolic biflavonoids is known to have anti-amyloidogenic effects by inhibiting aggregation of Aβ and promoting disaggregation of Aβ fibrils. In the present study, we further sought to investigate the structural basis of the Aβ disaggregating activity of biflavonoids and their interactions at the atomic level. A thioflavin T (ThT) fluorescence assay revealed that amentoflavone-type biflavonoids promote disaggregation of Aβ fibrils with varying potency due to specific structural differences. The computational analysis herein provides the first atomistic details for the mechanism of Aβ disaggregation by biflavonoids. Molecular docking analysis showed that biflavonoids preferentially bind to the aromatic-rich, partially ordered N-termini of Aβ fibril via the π–π interactions. Moreover, docking scores correlate well with the ThT EC₅₀ values. Molecular dynamic simulations revealed that biflavonoids decrease the content of β-sheet in Aβ fibril in a structure-dependent manner. Hydrogen bond analysis further supported that the substitution of hydroxyl groups capable of hydrogen bond formation at two positions on the biflavonoid scaffold leads to significantly disaggregation of Aβ fibrils. Taken together, our data indicate that biflavonoids promote disaggregation of Aβ fibrils due to their ability to disrupt the fibril structure, suggesting biflavonoids as a lead class of compounds to develop a therapeutic agent for Alzheimer’s disease.     

Advances in Applying Computer-Aided Drug Design for Neurodegenerative Diseases

Neurodegenerative diseases (NDs) including Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and Huntington’s disease are incurable and affect millions of people worldwide. The development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools.