NK cells can recognize different forms of class I MHC

NK recognition and lysis of targets are mediated by activation receptor(s) whose effects may be over-ridden by inhibitory receptors recognizing class I MHC on the target. Incubation of normal lymphoblasts with a peptide that can bind to their class I MHC renders them sensitive to lysis by syngeneic NK cells. By binding to class I MHC, the peptide alters or masks the target structure recognized by an inhibitory NK receptor(s). This target structure is most likely an "empty" dimer of class I heavy chain and beta2m as opposed to a "full" class I trimer formed by binding of specific peptide that is recognized by CTL.     

Side-effect localization for lazy, purely functional languages via aspects

Many side-effecting programming activities, such as profiling and tracing, can be formulated as crosscutting concerns and be framed as side-effecting aspects in the aspect-oriented programming paradigm. The benefit gained from this separation of concerns is particularly evident in purely functional programming, as adding such aspects using techniques such as monadification will generally lead to crosscutting changes. This paper presents an approach to provide side-effecting aspects for lazy purely functional languages in a user transparent fashion. We propose a simple yet direct state manipulation construct for developing side-effecting aspects and devise a systematic monadification scheme to translate the woven code to monadic style purely functional code. Furthermore, we present a static and dynamic semantics of the aspect programs and reason about the correctness of our monadification scheme with respect to them.     

A two-asset stochastic model for long-term portfolio selection

In mean–variance (M–V) analysis, an investor with a holding period [0,T] operates in a two-dimensional space—one is the mean and the other is the variance. At time 0, he/she evaluates alternative portfolios based on their means and variances, and holds a combination of the market portfolio (e.g., an index fund) and the risk-free asset to maximize his/her expected utility at time T. In our continuous-time model, we operate in a three-dimensional space—the first is the spot rate, the second is the expected return on the risky asset (e.g., an index fund), and the third is time. At various times over [0,T], we determine, for each combination of the spot rate and expected return, the optimum fractions invested in the risky and risk-free assets to maximize our expected utility at time T. Hence, unlike those static M–V models, our dynamic model allows investors to trade at any time in response to changes in the market conditions and the length of their holding period. Our results show that (1) the optimum fraction y*(t) in the risky asset increases as the expected return increases but decreases as the spot rate increases; (2) y*(t) decreases as the holding period shortens; and (3) y*(t) decreases as the risk aversion parameter-γ is larger.     

A kernel‐based core growing clustering method

In this paper, a novel clustering method in the kernel space is proposed. It effectively integrates several existing algorithms to become an iterative clustering scheme, which can handle clusters with arbitrary shapes. In our proposed approach, a reasonable initial core for each of the cluster is estimated. This allows us to adopt a cluster growing technique, and the growing cores offer partial hints on the cluster association. Consequently, the methods used for classification, such as support vector machines (SVMs), can be useful in our approach. To obtain initial clusters effectively, the notion of the incomplete Cholesky decomposition is adopted so that the fuzzy c‐means (FCM) can be used to partition the data in a kernel defined‐like space. Then a one‐class and a multiclass soft margin SVMs are adopted to detect the data within the main distributions (the cores) of the clusters and to repartition the data into new clusters iteratively. The structure of the data set is explored by pruning the data in the low‐density region of the clusters. Then data are gradually added back to the main distributions to assure exact cluster boundaries. Unlike the ordinary SVM algorithm, whose performance relies heavily on the kernel parameters given by the user, the parameters are estimated from the data set naturally in our approach. The experimental evaluations on two synthetic data sets and four University of California Irvine real data benchmarks indicate that the proposed algorithms outperform several popular clustering algorithms, such as FCM, support vector clustering (SVC), hierarchical clustering (HC), self‐organizing maps (SOM), and non‐Euclidean norm fuzzy c‐means (NEFCM). © 2009 Wiley Periodicals, Inc.4     

Flow patterns in gas-assisted injection molding process in a channel

The flow field of a long bubble steadily expelling a viscous fluid confined by two closely located parallel plates is examined. In order to investigate the influence of bubble size on the flow field, a theoretical bubble profile is used to replace the complicated procedure for computing simultaneously the interface between the gas surface and fluid flows. The present study showed the two typical flow patterns and also a third flow pattern of the stagnation point moving in the region of the bubble tip front during transformation of the two typical flow patterns. The vorticity patterns are also drawn for various bubble profiles and are examined for their effect on the flow. The velocity field is also presented from two different viewpoints and the phenomena is examined. The stagnation point located on the center line between the bubble tip to the upstream is only found in the small range of in a channel, where λ is ratio of the bubble width to the distance between two parallel plates.     

Comparison of autochthonous and allogeneic breast-tumour cells in tests for lymphocyte immunity to human tumours

Lymphocytes from 10 patients with breast carcinoma were seeded in autologous serum, on autochthonous tumour cells and allogeneic tissue-cultured breast tumour cell lines. In 4 patients, the anti-tumour cell cytotoxicity against at least one of 3 breast tumour cell lines differed significantly from that against autochthonous tumour cells. Further study of these 4 individuals (using their previously frozen lymphoid cells and sera) showed that these differences occurred because serum which decreased ("blocked") lymphocyte anti-tumour cytotoxicity when applied to one tumour cell line, could either have no effect or potentiate it when applied to another, without any consistent pattern vis-à-vis target-cell susceptibility to these different humoral effects.     

Effects of the heterogeneity of game complexity and user population in learning performance of business simulation games

Previous studies on business simulation games (BSGs) have concluded that improved performance may not be the primary benefit of using BSGs, due to mixed results between student performance and perceptions. Two relevant and insightful issues attract our attention, namely, the impacts of the heterogeneous student population and the different complexity levels of BSG software. To address these issues, the present study aims to understand the relationship between student profile/characteristics and performance in the classroom with BSG-facilitated learning. An in-depth case study is conducted on a general college course designed to teach three different complexities of BSGs to students enrolled in different majors. Four student profile factors are individually tested for differences in performance scores as evaluated by the teacher. Additionally, the influences of 11 student characteristics are assessed with regard their self-reported perceived learning performances. Regression analysis and ANOVA are used to investigate the impacts of heterogeneous users and game complexity on student performance. Based on the regression analyses of the data collected from 43 respondents who participated in the general course, the study concludes that knowledge and skill may influence the heterogeneous student population; moreover, student participation and tacit learning preference improve performance, and students with an auditory learning preference or high learning motivation may not perform well in classroom BSG learning. However, the low value of adjusted R square implies that more dimensions or variables are needed to increase the explaining power of the performance scores in the regression analyses. In contrast, heterogeneous BSG software with different complexity levels present different results. The current research contributes practical and incremental knowledge on the complexity of heterogeneous BSG software on performance scores and the perceived learning performance of heterogeneous student populations. With the research limitations acknowledged, a series of suggestions for teachers pertaining to appropriate applications of BSGs in classes is offered as well as recommendations to BSG providers. Nevertheless, in-depth analyses are required, preferably with larger student population samples, to further explore the insignificant relationship between student perceptions and attitude under nonlinear extended complexity.     

Effect of temperature on the absorption loss of chalcogenide glass fibers

The change in the absorption loss of IR-transmitting chalcogenide glass fibers in the temperature range of -90 degrees C /= 6 mum the change in loss was mainly due to multiphonon absorption. The change in loss for tellurium-based glass fibers increased significantly at T = 60 degrees C. The increase in the loss at short wavelengths (lambda /= 9 mum, multiphonon absorption dominated the loss spectrum.     

Synthesis of PbTe nanowire arrays using lithographically patterned nanowire electrodeposition

We describe the preparation by electrodeposition of arrays of lead telluride (PbTe) nanowires using the lithographically patterned nanowire electrodeposition (LPNE) method. PbTe nanowires had a rectangular cross-section with adjustable width and height ranging between 60-400 nm (w) and 20-100 nm (h). The characterization of these nanowire arrays using X-ray diffraction, transmission electron microscopy and electron diffraction, scanning electron microscopy, atomic force microscopy, and X-ray photoelectron spectroscopy (XPS) is reported. PbTe nanowires were electrodeposited using a cyclic electrodeposition-stripping technique that produced polycrystalline, stoichiometric, face-centered cubic PbTe with a mean grain diameter of 10-20 nm. These nanowires were more than 1 mm in length and two additional processing steps permitted their suspension across 25 microm air gaps microfabricated on these surfaces. The LPNE synthesis of lithographically patterned PbTe nanowires was carried out in unfiltered laboratory air. Nanowires with lengths of 70-100 microm showed an electrical resistivity comparable to bulk PbTe. XPS reveals that exposure of PbTe nanowires to air causes the formation on the nanowire surface of approximately one monolayer of a mixed lead oxide and tellurium oxide within a few minutes.