Casimir Forces at Tricritical Points: Theory and Possible Experiments

Using field-theoretical methods and exploiting conformal invariance, we study Casimir forces at tricritical points exerted by long-range fluctuations of the order-parameter field. Special attention is paid to the situation where the symmetry is broken by the boundary conditions (extraordinary transition). Besides the parallel-plate configuration, we also discuss the geometries of two separate spheres and a single sphere near a planar wall, which may serve as a model for colloidal particles immersed in a fluid. In the concrete case of ternary mixtures a quantitative comparison with critical Casimir and van der Waals forces shows that, especially with symmetry-breaking boundaries, the tricritical Casimir force is considerably stronger than the critical one and dominates also the competing van der Waals force.Comment: 18 pages, Latex, 3 postscript figures, uses Elsevier style file

Vortex structure and resistive transitions in high-Tc superconductors

The nature of the resistive transition for a current applied parallel to the magnetic field in high-Tc materials is investigated by numerical simulation on the three dimensional Josephson junction array model. It is shown by using finite size scaling that for samples with disorder the critical temperature Tp for the c axis resistivity corresponds to a percolation phase transition of vortex lines perpendicularly to the applied field. The value of Tp is higher than the critical temperature for j perpendicular to H, but decreases with the thickness of the sample and with anisotropy. We predict that critical behavior around Tp should reflect in experimentally accessible quantities, as the I-V curves.Comment: 8 pages + 6 figure

Women with psychotic episodes during pregnancy show increased markers of placental damage with Tenney-Parker changes

y. Psychosis is a hazardous and functionally disruptive psychiatric condition which may affect women in pregnancy, entailing negative consequences for maternofetal well-being. The precise pathophysiological basis and consequences of a psychotic episode in pregnancy remain to be further elucidated. The placenta is a pivotal tissue with many functions in the gestational period, critically influencing the fate and development of pregnancy. Although detrimental alterations have been observed in women undergoing severe psychiatric disorders in pregnancy, there are little studies evaluating the consequences of suffering from a psychotic episode in the placental tissue In this work, we have evaluated the histopathological consequences of a first episode of psychosis in pregnancy (FE-PW; N=22) and compare them with healthy pregnant women (HC-PW; N=20) by using histological, immunohistochemical and gene expression techniques. Our results define that the placental tissue of FE-PW display an increase in the number of placental villi, bridges, syncytial knots and syncytial knots/villi. Besides, we have also observed an enhanced gene and protein expression in FE-PW of the hypoxic marker HIF-1α, together with the apoptotic markers BAX and Bcl-2. To our knowledge, this is the first study demonstrating significant histopathological changes in the placenta of women suffering a new-onset psychotic episode in pregnancy. Further studies should be aimed at deepening the knowledge about the pernicious effects of psychosis in the maternofetal tissues, as well as the potential implications of these alterations

Infraestructura tecnológica de servicios semánticos para la Web Semántica

This project aims at creating a network of distributed interoperable semantic services for building more complex ones. These services will be available in semantic Web service libraries, so that they can be invoked by other systems (e.g., semantic portals, software agents, etc.). Thus, to accomplish this objective, the project proposes: a) To create specific technology for developing and composing Semantic Web Services. b) To migrate the WebODE ontology development workbench to this new distributed interoperable semantic service architecture. c) To develop new semantic services (ontology learning, ontology mappings, incremental ontology evaluation, and ontology evolution). d) To develop technological support that eases semantic portal interoperability, using Web services and Semantic Web Services. The project results will be open source, so as to improve their technological transfer. The quality of these results is ensured by a benchmarking process. Keywords: Ontologies and Semantic We

ℓ-space spectroscopy of the Cosmic Microwave Background with the BOOMERanG experiment

The BOOMERanG experiment has recently produced detailed maps of the Cosmic Microwave Background, where sub-horizon structures are resolved with good signal to noise ratio. A power spectrum (spherical harmonics) analysis of the maps detects three peaks, at multipoles ℓ = (213_(-13)^(+10)),(541_(-32)^(+20))(845_(-25)^(+12)). In this paper we discuss the data analysis and the implications of these results for cosmology

Artificial Intelligence-assisted automated heart failure detection and classification from electronic health records

AimsElectronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients.Methods and resultsWe used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies.ConclusionsOur study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes

Artificial Intelligence-assisted automated heart failure detection and classification from electronic health records

AimsElectronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients.Methods and resultsWe used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies.ConclusionsOur study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes

Distinct Neural Signatures of Outcome Monitoring After Selection and Execution Errors

Losing a point in tennis could result from poor shot selection or faulty stroke execution. To explore how the brain responds to these different types of errors, we examined feedback-locked EEG activity while participants completed a modified version of a standard three-armed bandit probabilistic reward task. Our task framed unrewarded outcomes as the result of either errors of selection or errors of execution. We examined whether amplitude of a medial frontal negativity (the feedback-related negativity [FRN]) was sensitive to the different forms of error attribution. Consistent with previous reports, selection errors elicited a large FRN relative to rewards, and amplitude of this signal correlated with behavioral adjustment after these errors. A different pattern was observed in response to execution errors. These outcomes produced a larger FRN, a frontocentral attenuation in activity preceding this component, and a subsequent enhanced error positivity in parietal sites. Notably, the only correlations with behavioral adjustment were with the early frontocentral attenuation and amplitude of the parietal signal; FRN differences between execution errors and rewarded trials did not correlate with subsequent changes in behavior. Our findings highlight distinct neural correlates of selection and execution error processing, providing insight into how the brain responds to the different classes of error that determine future action

The `Friction' of Vacuum, and other Fluctuation-Induced Forces

The static Casimir effect describes an attractive force between two conducting plates, due to quantum fluctuations of the electromagnetic (EM) field in the intervening space. {\it Thermal fluctuations} of correlated fluids (such as critical mixtures, super-fluids, liquid crystals, or electrolytes) are also modified by the boundaries, resulting in finite-size corrections at criticality, and additional forces that effect wetting and layering phenomena. Modified fluctuations of the EM field can also account for the `van der Waals' interaction between conducting spheres, and have analogs in the fluctuation--induced interactions between inclusions on a membrane. We employ a path integral formalism to study these phenomena for boundaries of arbitrary shape. This allows us to examine the many unexpected phenomena of the dynamic Casimir effect due to moving boundaries. With the inclusion of quantum fluctuations, the EM vacuum behaves essentially as a complex fluid, and modifies the motion of objects through it. In particular, from the mechanical response function of the EM vacuum, we extract a plethora of interesting results, the most notable being: (i) The effective mass of a plate depends on its shape, and becomes anisotropic. (ii) There is dissipation and damping of the motion, again dependent upon shape and direction of motion, due to emission of photons. (iii) There is a continuous spectrum of resonant cavity modes that can be excited by the motion of the (neutral) boundaries.Comment: RevTex, 2 ps figures included. The presentation is completely revised, and new sections are adde