Finite element simulations of crack propagation in functionally graded materials under flexural loading

Cracks in stepped and continuously graded material specimens under flexural loading were investigated via finite element analysis. Calculation of mechanical energy release rates and propagation angles with crack-opening displacement correlation and the local symmetry (K_II = 0) criterion, respectively, provided results most efficiently and accurately, as compared with compliance and J-integral approaches and other deflection criteria. A routine was developed for automatic crack extension and remeshing, enabling simulation of incremental crack propagation. Effects of gradient profile and crack geometry on crack-tip stresses and crack propagation path are examined, and implications of these for optimal design of graded components against failure by fast fracture are discussed.     

Measurement methods for LCoS displays

Abstract— Two measurement methods to characterize uniformity and the absolute cell gap of LCoS images will be reviewed. These are simple to use in a production environment and have allowed major improvements in manufacture to be achieved. For the purpose of this paper, the liquid‐crystal mode used is the 45° normally black mode.     

Dennett Explained?: A Critical Study of Dahlbom's Dennett and His Critics

Minds and Machines -     

Slicing Software for Model Construction

Applying finite-state verification techniques (e.g., model checking) to software requires that program source code be translated to a finite-state transition system that safely models program behavior. Automatically checking such a transition system for a correctness property is typically very costly, thus it is necessary to reduce the size of the transition system as much as possible. In fact, it is often the case that much of a program's source code is irrelevant for verifying a given correctness property.In this paper, we apply program slicing techniques to remove automatically such irrelevant code and thus reduce the size of the corresponding transition system models. We give a simple extension of the classical slicing definition, and prove its safety with respect to model checking of linear temporal logic (LTL) formulae. We discuss how this slicing strategy fits into a general methodology for deriving effective software models using abstraction-based program specialization.     

Identifying cell-to-cell variability in internalization using flow cytometry

Biological heterogeneity is a primary contributor to the variation observed in experiments that probe dynamical processes, such as the internalization of material by cells. Given that internalization is a critical process by which many therapeutics and viruses reach their intracellular site of action, quantifying cell-to-cell variability in internalization is of high biological interest. Yet, it is common for studies of internalization to neglect cell-to-cell variability. We develop a simple mathematical model of internalization that captures the dynamical behaviour, cell-to-cell variation, and extrinsic noise introduced by flow cytometry. We calibrate our model through a novel distribution-matching approximate Bayesian computation algorithm to flow cytometry data of internalization of anti-transferrin receptor antibody in a human B-cell lymphoblastoid cell line. This approach provides information relating to the region of the parameter space, and consequentially the nature of cell-to-cell variability, that produces model realizations consistent with the experimental data. Given that our approach is agnostic to sample size and signal-to-noise ratio, our modelling framework is broadly applicable to identify biological variability in single-cell data from internalization assays and similar experiments that probe cellular dynamical processes.     

Direct sampling of chemical weapons in water by photoionization mass spectrometry

The vulnerability of water supplies to toxic contamination calls for fast and effective means for screening water samples for multiple threats. We describe the use of photoionization (PI) mass spectrometry (MS) for high-speed, high-throughput screening and molecular identification of chemical weapons (CW) threats and other hazardous compounds. The screening technology can detect a wide range of compounds at subacute concentrations with no sample preparation and a sampling cycle time of approximately 45 s. The technology was tested with CW agents VX, GA, GB, GD, GF, HD, HN1, and HN3, in addition to riot agents and precursors. All are sensitively detected and give simple PI mass spectra dominated by the parent ion. The target application of the PI MS method is as a routine, real-time early warning system for CW agents and other hazardous compounds in air and in water. In this work, we also present comprehensive measurements for water analysis and report on the system detection limits, linearity, quantitation accuracy, and false positive (FP) and false negative rates for concentrations at subacute levels. The latter data are presented in the form of receiver operating characteristic curves of the form of detection probability P(D) versus FP probability P(FP). These measurements were made using the CW surrogate compounds, DMMP, DEMP, DEEP, and DIMP. Method detection limits (3sigma) obtained using a capillary injection method yielded 1, 6, 3, and 2 ng/mL, respectively. These results were obtained using 1-microL injections of water samples without any preparation, corresponding to mass detection limits of 1, 6, 3, and 2 pg, respectively. The linear range was about 3-4 decades and the dynamic range about 4-5 decades. The relative standard deviations were generally <10% at CW subacute concentrations levels.     

Tumor-Localized Administration of α-GalCer to Recruit Invariant Natural Killer T Cells and Enhance Their Antitumor Activity against Solid Tumors

Invariant natural killer T (iNKT) cells have the capacity to mount potent anti-tumor reactivity and have therefore become a focus in the development of cell-based immunotherapy. iNKT cells attack tumor cells using multiple mechanisms with a high efficacy; however, their clinical application has been limited because of their low numbers in cancer patients and difficulties in infiltrating solid tumors. In this study, we aimed to overcome these critical limitations by using α-GalCer, a synthetic glycolipid ligand specifically activating iNKT cells, to recruit iNKT to solid tumors. By adoptively transferring human iNKT cells into tumor-bearing humanized NSG mice and administering a single dose of tumor-localized α-GalCer, we demonstrated the rapid recruitment of human iNKT cells into solid tumors in as little as one day and a significantly enhanced tumor killing ability. Using firefly luciferase-labeled iNKT cells, we monitored the tissue biodistribution and pharmacokinetics/pharmacodynamics (PK/PD) of human iNKT cells in tumor-bearing NSG mice. Collectively, these preclinical studies demonstrate the promise of an αGC-driven iNKT cell-based immunotherapy to target solid tumors with higher efficacy and precision.     

Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models

Timely forecasts of the emergence, re-emergence and elimination of human infectious diseases allow for proactive, rather than reactive, decisions that save lives. Recent theory suggests that a generic feature of dynamical systems approaching a tipping point—early warning signals (EWS) due to critical slowing down (CSD)—can anticipate disease emergence and elimination. Empirical studies documenting CSD in observed disease dynamics are scarce, but such demonstration of concept is essential to the further development of model-independent outbreak detection systems. Here, we use fitted, mechanistic models of measles transmission in four cities in Niger to detect CSD through statistical EWS. We find that several EWS accurately anticipate measles re-emergence and elimination, suggesting that CSD should be detectable before disease transmission systems cross key tipping points. These findings support the idea that statistical signals based on CSD, coupled with decision-support algorithms and expert judgement, could provide the basis for early warning systems of disease outbreaks.