EL2 deep donor state in semi-insulating GaAs revealed by frequency dependent positron mobility measurements

Positron mobility measurements carried out on semi-insulating GaAs, using the Doppler shift in annihilation radiation technique, show a sharp transition from a high mobility value ∼120 cm2 V-1 s-1 to a lower value ∼45 V-1 s-1 just below room temperature. The temperature of the transition is found to be dependent on the frequency of the applied AC bias. We show that this effect is an artifact due to the thermal ionization of the EL2 deep donor state, which in its ionized state forms a positive space charge that causes the positron to experience large electric fields. This observation suggests a new positron annihilation-based deep-level transient technique applicable to semi-insulating materials. © 1994 The American Physical Society.published_or_final_versio

Sequence Effect of Self-Assembling Peptides on the Complexation and In Vitro Delivery of the Hydrophobic Anticancer Drug Ellipticine

A special class of self-assembling peptides has been found to be capable of stabilizing the hydrophobic anticancer agent ellipticine in aqueous solution. Here we study the effect of peptide sequence on the complex formation and its anticancer activity in vitro. Three peptides, EAK16-II, EAK16-IV and EFK16-II, were selected to have either a different charge distribution (EAK16-II vs. EAK16-IV) or a varying hydrophobicity (EAK16-II vs. EFK16-II). Results on their complexation with ellipticine revealed that EAK16-II and EAK16-IV were able to stabilize protonated ellipticine or ellipticine microcrystals depending on the peptide concentration; EFK16-II could stabilize neutral ellipticine molecules and ellipticine microcrystals. These different molecular states of ellipticine were expected to affect ellipticine delivery. The anticancer activity of these complexes was tested against two cancer cell lines: A549 and MCF-7, and related to the cell viability. The viability results showed that the complexes with protonated ellipticine were effective in eradicating both cancer cells (viability <0.05), but their dilutions in water were not stable, leading to a fast decrease in their toxicity. In contrast, the complexes formulated with EFK16-II were relatively stable upon dilution, but their original toxicity was relatively low compared to that with protonated ellipticine. Overall, the charge distribution of the peptides seemed not to affect the complex formation and its therapeutic efficacy in vitro; however, the increase in hydrophobicity of the peptides significantly altered the state of stabilized ellipticine and increased the stability of the complexes. This work provides essential information for peptide sequence design in the development of self-assembling peptide-based delivery of hydrophobic anticancer drugs

An improved parameter estimation and comparison for soft tissue constitutive models containing an exponential function

Motivated by the well-known result that stiffness of soft tissue is proportional to the stress, many of the constitutive laws for soft tissues contain an exponential function. In this work, we analyze properties of the exponential function and how it affects the estimation and comparison of elastic parameters for soft tissues. In particular, we find that as a consequence of the exponential function there are lines of high covariance in the elastic parameter space. As a result, one can have widely varying mechanical parameters defining the tissue stiffness but similar effective stress–strain responses. Drawing from elementary algebra, we propose simple changes in the norm and the parameter space, which significantly improve the convergence of parameter estimation and robustness in the presence of noise. More importantly, we demonstrate that these changes improve the conditioning of the problem and provide a more robust solution in the case of heterogeneous material by reducing the chances of getting trapped in a local minima. Based upon the new insight, we also propose a transformed parameter space which will allow for rational parameter comparison and avoid misleading conclusions regarding soft tissue mechanics

Modification of Hydrophilic and Hydrophobic Surfaces Using an Ionic-Complementary Peptide

Ionic-complementary peptides are novel nano-biomaterials with a variety of biomedical applications including potential biosurface engineering. This study presents evidence that a model ionic-complementary peptide EAK16-II is capable of assembling/coating on hydrophilic mica as well as hydrophobic highly ordered pyrolytic graphite (HOPG) surfaces with different nano-patterns. EAK16-II forms randomly oriented nanofibers or nanofiber networks on mica, while ordered nanofibers parallel or oriented 60° or 120° to each other on HOPG, reflecting the crystallographic symmetry of graphite (0001). The density of coated nanofibers on both surfaces can be controlled by adjusting the peptide concentration and the contact time of the peptide solution with the surface. The coated EAK16-II nanofibers alter the wettability of the two surfaces differently: the water contact angle of bare mica surface is measured to be <10°, while it increases to 20.3±2.9° upon 2 h modification of the surface using a 29 µM EAK16-II solution. In contrast, the water contact angle decreases significantly from 71.2±11.1° to 39.4±4.3° after the HOPG surface is coated with a 29 µM peptide solution for 2 h. The stability of the EAK16-II nanofibers on both surfaces is further evaluated by immersing the surface into acidic and basic solutions and analyzing the changes in the nanofiber surface coverage. The EAK16-II nanofibers on mica remain stable in acidic solution but not in alkaline solution, while they are stable on the HOPG surface regardless of the solution pH. This work demonstrates the possibility of using self-assembling peptides for surface modification applications

Quantifying and filtering knowledge generated by literature based discovery

Background Literature based discovery (LBD) automatically infers missed connections between concepts in literature. It is often assumed that LBD generates more information than can be reasonably examined. Methods We present a detailed analysis of the quantity of hidden knowledge produced by an LBD system and the effect of various filtering approaches upon this. The investigation of filtering combined with single or multi-step linking term chains is carried out on all articles in PubMed. Results The evaluation is carried out using both replication of existing discoveries, which provides justification for multi-step linking chain knowledge in specific cases, and using timeslicing, which gives a large scale measure of performance. Conclusions While the quantity of hidden knowledge generated by LBD can be vast, we demonstrate that (a) intelligent filtering can greatly reduce the number of hidden knowledge pairs generated, (b) for a specific term, the number of single step connections can be manageable, and (c) in the absence of single step hidden links, considering multiple steps can provide valid links

Reversibility of apoptosis in cancer cells

Apoptosis is a cell suicide programme characterised by unique cellular events such as mitochondrial fragmentation and dysfunction, nuclear condensation, cytoplasmic shrinkage and activation of apoptotic protease caspases, and these serve as the noticeable apoptotic markers for the commitment of cell demise. Here, we show that, however, the characterised apoptotic dying cancer cells can regain their normal morphology and proliferate after removal of apoptotic inducers. In addition, we demonstrate that reversibility of apoptosis occurs in various cancer cell lines, and in different apoptotic stimuli. Our findings show that cancer cells can survive after initiation of apoptosis, thereby revealing an unexpected potential escape mechanism of cancer cells from chemotherapy

Agreement among Health Care Professionals in Diagnosing Case Vignette-Based Surgical Site Infections

OBJECTIVE: To assess agreement in diagnosing surgical site infection (SSI) among healthcare professionals involved in SSI surveillance. METHODS: Case-vignette study done in 2009 in 140 healthcare professionals from seven specialties (20 in each specialty, Anesthesiologists, Surgeons, Public health specialists, Infection control physicians, Infection control nurses, Infectious diseases specialists, Microbiologists) in 29 University and 36 non-University hospitals in France. We developed 40 case-vignettes based on cardiac and gastrointestinal surgery patients with suspected SSI. Each participant scored six randomly assigned case-vignettes before and after reading the SSI definition on an online secure relational database. The intraclass correlation coefficient (ICC) was used to assess agreement regarding SSI diagnosis on a seven-point Likert scale and the kappa coefficient to assess agreement for superficial or deep SSI on a three-point scale. RESULTS: Based on a consensus, SSI was present in 21 of 40 vignettes (52.5%). Intraspecialty agreement for SSI diagnosis ranged across specialties from 0.15 (95% confidence interval, 0.00-0.59) (anesthesiologists and infection control nurses) to 0.73 (0.32-0.90) (infectious diseases specialists). Reading the SSI definition improved agreement in the specialties with poor initial agreement. Intraspecialty agreement for superficial or deep SSI ranged from 0.10 (-0.19-0.38) to 0.54 (0.25-0.83) (surgeons) and increased after reading the SSI definition only among the infection control nurses from 0.10 (-0.19-0.38) to 0.41 (-0.09-0.72). Interspecialty agreement for SSI diagnosis was 0.36 (0.22-0.54) and increased to 0.47 (0.31-0.64) after reading the SSI definition. CONCLUSION: Among healthcare professionals evaluating case-vignettes for possible surgical site infection, there was large disagreement in diagnosis that varied both between and within specialties

The Viscoelastic Properties of Passive Eye Muscle in Primates. II: Testing the Quasi-Linear Theory

We have extensively investigated the mechanical properties of passive eye muscles, in vivo, in anesthetized and paralyzed monkeys. The complexity inherent in rheological measurements makes it desirable to present the results in terms of a mathematical model. Because Fung's quasi-linear viscoelastic (QLV) model has been particularly successful in capturing the viscoelastic properties of passive biological tissues, here we analyze this dataset within the framework of Fung's theory