Superfluid Bosons and Flux Liquids: Disorder, Thermal Fluctuations, and Finite-Size Effects

The influence of different types of disorder (both uncorrelated and correlated) on the superfluid properties of a weakly interacting or dilute Bose gas, as well as on the corresponding quantities for flux line liquids in high-temperature superconductors at low magnetic fields are reviewed, investigated and compared. We exploit the formal analogy between superfluid bosons and the statistical mechanics of directed lines, and explore the influence of the different "imaginary time" boundary conditions appropriate for a flux line liquid. For superfluids, we discuss the density and momentum correlations, the condensate fraction, and the normal-fluid density as function of temperature for two- and three-dimensional systems subject to a space- and time-dependent random potential as well as conventional point-, line-, and plane-like defects. In the case of vortex liquids subject to point disorder, twin boundaries, screw dislocations, and various configurations of columnar damage tracks, we calculate the corresponding quantities, namely density and tilt correlations, the ``boson'' order parameter, and the tilt modulus. The finite-size corrections due to periodic vs. open "imaginary time" boundary conditions differ in interesting and important ways. Experimental implications for vortex lines are described briefly.Comment: 78 pages, RevTex, 4 figures included (sorry, there are no ps-files for the remaining 2 figures; if needed, please send mail to [email protected]); brief erratum appended (2 pages

Mode-coupling theory of the stress-tensor autocorrelation function of a dense binary fluid mixture

We present a generalized mode-coupling theory for a dense binary fluid mixture. The theory is used to calculate molecular-scale renormalizations to the stress-tensor autocorrelation function (STAF) and to the long-wavelength zero-frequency shear viscosity. As in the case of a dense simple fluid, we find that the STAF appears to decay as $t^{-3/2}$ over an intermediate range of time. The coefficient of this long-time tail is more than two orders of magnitude larger than that obtained from conventional mode-coupling theory. Our study focuses on the effect of compositional disorder on the decay of the STAF in a dense mixture.Comment: Published; withdrawn since ordering in the archive gives misleading impression of new publicatio

Vortex Line Fluctuations in Model High Temperature Superconductors

We carry out Monte Carlo simulations of the uniformly frustrated 3d XY model as a model for vortex line fluctuations in a high Tc superconductor. A density of vortex lines of f=1/25 is considered. We find two sharp phase transitions. The low T phase is an ordered vortex line lattice. The high T normal phase is a vortex line liquid with much entangling, cutting, and loop excitations. An intermediate phase is found which is characterized as a vortex line liquid of disentangled lines. In this phase, the system displays superconducting properties in the direction parallel to the magnetic field, but normal behavior in planes perpendicular to the magnetic field.Comment: 38 pages, LaTeX 15 figures (upon request to [email protected]

Burkholderia pseudomallei Capsular Polysaccharide Recognition by a Monoclonal Antibody Reveals Key Details toward a Biodefense Vaccine and Diagnostics against Melioidosis.

Burkholderia pseudomallei is the bacterium responsible for melioidosis, an infectious disease with high mortality rates. Since melioidosis is a significant public health concern in endemic regions and the organism is currently classified as a potential biothreat agent, the development of effective vaccines and rapid diagnostics is a priority. The capsular polysaccharide (CPS) expressed by B. pseudomallei is a highly conserved virulence factor and a protective antigen. Because of this, CPS is considered an attractive antigen for use in the development of both vaccines and diagnostics. In the present study, we describe the interactions of CPS with the murine monoclonal antibody (mAb) 4C4 using a multidisciplinary approach including organic synthesis, molecular biology techniques, surface plasmon resonance, and nuclear magnetic spectroscopy. Using these methods, we determined the mode of binding between mAb 4C4 and native CPS or ad hoc synthesized capsular polysaccharide fragments. Interestingly, we demonstrated that the O-acetyl moiety of CPS is essential for the interaction of the CPS epitope with mAb 4C4. Collectively, our results provide important insights into the structural features of B. pseudomallei CPS that enable antibody recognition that may help the rational design of CPS-based vaccine candidates. In addition, our findings confirm that the mAb 4C4 is suitable for use in an antibody-based detection assay for diagnosis of B. pseudomallei infections

Graphical Markov models, unifying results and their interpretation

Graphical Markov models combine conditional independence constraints with graphical representations of stepwise data generating processes.The models started to be formulated about 40 years ago and vigorous development is ongoing. Longitudinal observational studies as well as intervention studies are best modeled via a subclass called regression graph models and, especially traceable regressions. Regression graphs include two types of undirected graph and directed acyclic graphs in ordered sequences of joint responses. Response components may correspond to discrete or continuous random variables and may depend exclusively on variables which have been generated earlier. These aspects are essential when causal hypothesis are the motivation for the planning of empirical studies. To turn the graphs into useful tools for tracing developmental pathways and for predicting structure in alternative models, the generated distributions have to mimic some properties of joint Gaussian distributions. Here, relevant results concerning these aspects are spelled out and illustrated by examples. With regression graph models, it becomes feasible, for the first time, to derive structural effects of (1) ignoring some of the variables, of (2) selecting subpopulations via fixed levels of some other variables or of (3) changing the order in which the variables might get generated. Thus, the most important future applications of these models will aim at the best possible integration of knowledge from related studies.Comment: 34 Pages, 11 figures, 1 tabl

Biodiesel Purification Using Polymeric Nanofiltration Composite Membranes Highly Resistant to Harsh Conditions

Biodiesel as alternative for conventional diesel fuel is mainly produced by the catalytic reaction of triglycerides with an alcohol. In this work, the purification of biodiesel was carried out with two lab-made solvent-resistant composite nanofiltration membranes of poly(vinylidene difluoride) (PVDF) as support and poly(dimethylsiloxane) as coating layer. Biodiesel was obtained from the esterification of partially refined soy oil with bioethanol (EtOH) and NaOH as catalyst. The best biodiesel purification performance was achieved with the PVDF-12SI membrane reaching high retention of glycerol, total glycerides, and soap. PVDF-SI membranes were found to have an excellent stability for biodiesel permeation, achieving a flux recovery ratio of EtOH as high as 0.94 after twenty cycles of use.Fil: Torres, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Toledo Arana, Javier Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Ochoa, Nelio Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Física Aplicada "Dr. Jorge Andrés Zgrablich". Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Instituto de Física Aplicada "Dr. Jorge Andrés Zgrablich"; ArgentinaFil: Marchese, Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Física Aplicada "Dr. Jorge Andrés Zgrablich". Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Instituto de Física Aplicada "Dr. Jorge Andrés Zgrablich"; ArgentinaFil: Pagliero, Cecilia Liliana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentin

Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge

Previous studies of artificial intelligence (AI) applied to dermatology have shown AI to have higher diagnostic classification accuracy than expert dermatologists; however, these studies did not adequately assess clinically realistic scenarios, such as how AI systems behave when presented with images of disease categories that are not included in the training dataset or images drawn from statistical distributions with significant shifts from training distributions. We aimed to simulate these real-world scenarios and evaluate the effects of image source institution, diagnoses outside of the training set, and other image artifacts on classification accuracy, with the goal of informing clinicians and regulatory agencies about safety and real-world accuracy.We designed a large dermoscopic image classification challenge to quantify the performance of machine learning algorithms for the task of skin cancer classification from dermoscopic images, and how this performance is affected by shifts in statistical distributions of data, disease categories not represented in training datasets, and imaging or lesion artifacts. Factors that might be beneficial to performance, such as clinical metadata and external training data collected by challenge participants, were also evaluated. 25?331 training images collected from two datasets (in Vienna [HAM10000] and Barcelona [BCN20000]) between Jan 1, 2000, and Dec 31, 2018, across eight skin diseases, were provided to challenge participants to design appropriate algorithms. The trained algorithms were then tested for balanced accuracy against the HAM10000 and BCN20000 test datasets and data from countries not included in the training dataset (Turkey, New Zealand, Sweden, and Argentina). Test datasets contained images of all diagnostic categories available in training plus other diagnoses not included in training data (not trained category). We compared the performance of the algorithms against that of 18 dermatologists in a simulated setting that reflected intended clinical use.64 teams submitted 129 state-of-the-art algorithm predictions on a test set of 8238 images. The best performing algorithm achieved 58·8% balanced accuracy on the BCN20000 data, which was designed to better reflect realistic clinical scenarios, compared with 82·0% balanced accuracy on HAM10000, which was used in a previously published benchmark. Shifted statistical distributions and disease categories not included in training data contributed to decreases in accuracy. Image artifacts, including hair, pen markings, ulceration, and imaging source institution, decreased accuracy in a complex manner that varied based on the underlying diagnosis. When comparing algorithms to expert dermatologists (2460 ratings on 1269 images), algorithms performed better than experts in most categories, except for actinic keratoses (similar accuracy on average) and images from categories not included in training data (26% correct for experts vs 6% correct for algorithms, p<0·0001). For the top 25 submitted algorithms, 47·1% of the images from categories not included in training data were misclassified as malignant diagnoses, which would lead to a substantial number of unnecessary biopsies if current state-of-the-art AI technologies were clinically deployed.We have identified specific deficiencies and safety issues in AI diagnostic systems for skin cancer that should be addressed in future diagnostic evaluation protocols to improve safety and reliability in clinical practice.Melanoma Research Alliance and La Marató de TV3.Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved

Revision and Update of the Consensus Definitions of Invasive Fungal Disease From the European Organization for Research and Treatment of Cancer and the Mycoses Study Group Education and Research Consortium.

BACKGROUND: Invasive fungal diseases (IFDs) remain important causes of morbidity and mortality. The consensus definitions of the Infectious Diseases Group of the European Organization for Research and Treatment of Cancer and the Mycoses Study Group have been of immense value to researchers who conduct clinical trials of antifungals, assess diagnostic tests, and undertake epidemiologic studies. However, their utility has not extended beyond patients with cancer or recipients of stem cell or solid organ transplants. With newer diagnostic techniques available, it was clear that an update of these definitions was essential. METHODS: To achieve this, 10 working groups looked closely at imaging, laboratory diagnosis, and special populations at risk of IFD. A final version of the manuscript was agreed upon after the groups' findings were presented at a scientific symposium and after a 3-month period for public comment. There were several rounds of discussion before a final version of the manuscript was approved. RESULTS: There is no change in the classifications of "proven," "probable," and "possible" IFD, although the definition of "probable" has been expanded and the scope of the category "possible" has been diminished. The category of proven IFD can apply to any patient, regardless of whether the patient is immunocompromised. The probable and possible categories are proposed for immunocompromised patients only, except for endemic mycoses. CONCLUSIONS: These updated definitions of IFDs should prove applicable in clinical, diagnostic, and epidemiologic research of a broader range of patients at high-risk

Epidemiological trends in nosocomial candidemia in intensive care

BACKGROUND: Infection represents a frequent complication among patients in Intensive Care Units (ICUs) and mortality is high. In particular, the incidence of fungal infections, especially due to Candida spp., has been increasing during the last years. METHODS: In a retrospective study we studied the etiology of candidemia in critically ill patients over a five-year period (1999–2003) in the ICU of the San Martino University Hospital in Genoa, Italy. RESULTS: In total, 182 episodes of candidaemia were identified, with an average incidence of 2.22 episodes/10 000 patient-days/year (range 1.25–3.06 episodes). Incidence of candidemia increased during the study period from 1.25 in 1999 to 3.06/10 000 patient-days/year in 2003. Overall, 40% of the fungemia episodes (74/182) were due to C.albicans, followed by C. parapsilosis(23%), C.glabrata (15%), C.tropicalis (9%) and other species (13%). Candidemia due to non-albicans species increased and this was apparently correlated with an increasing use of azoles for prophylaxis or empirical treatment. CONCLUSION: The study demonstrates a shift in the species of Candida causing fungemia in a medical and surgical ICU population during a 5 year period. The knowledge of the local epidemiological trends in Candida species isolated in blood cultures is important to guide therapeutic choices