From Collapse to Freezing in Random Heteropolymers

We consider a two-letter self-avoiding (square) lattice heteropolymer model of N_H (out ofN) attracting sites. At zero temperature, permanent links are formed leading to collapse structures for any fraction rho_H=N_H/N. The average chain size scales as R = N^{1/d}F(rho_H) (d is space dimension). As rho_H --> 0, F(rho_H) ~ rho_H^z with z={1/d-nu}=-1/4 for d=2. Moreover, for 0 < rho_H < 1, entropy approaches zero as N --> infty (being finite for a homopolymer). An abrupt decrease in entropy occurs at the phase boundary between the swollen (R ~ N^nu) and collapsed region. Scaling arguments predict different regimes depending on the ensemble of crosslinks. Some implications to the protein folding problem are discussed.Comment: 4 pages, Revtex, figs upon request. New interpretation and emphasis. Submitted to Europhys.Let

Vascular liver anatomy and main variants: what the radiologist must know

Advances in surgical techniques are extremely demanding regarding the accuracy and level of detail expected for display of the vascular anatomy of the liver. Precise knowledge of the arterial, portal and hepatic vein territories are mandatory whenever a liver intervention is planned. Sectional anatomy can now be routinely performed on multidetector computed tomography (MDCT) with volumetric data and isotropic voxel display, by means of sub-millimetric slice thickness acquisition. The relevant vascular information can thus be gathered, reviewed and post-processed with unprecedented clarity, obviating the need for digital subtraction angiography. The scope of the present paper is to review the normal vascular liver anatomy, its most relevant variants including additional sources of vascular inflow. Apart from providing the surgeon with a detailed vascular and parenchymal roadmap knowledge of imaging findings may avoid potential confusion with pathologic processes

Overview of ImageCLEFcoral 2019 task

Understanding the composition of species in ecosystems on a large scale is key to developing effective solutions for marine conservation, hence there is a need to classify imagery automatically and rapidly. In 2019, ImageCLEF proposed for the first time the ImageCLEFcoral task. The task requires participants to automatically annotate and localize benthic substrate (such as hard coral, soft coral, algae and sponge) in a collection of images originating from a growing, large-scale dataset from coral reefs around the world as part of monitoring programmes. In its first edition, five groups participated submitting 20 runs using a variety of machine learning and deep learning approaches. Best runs achieved 0.24 in the annotation and localisation subtask and 0.04 on the pixel-wise parsing subtask in terms of MAP 0.5 IoU scores which measures the Mean Average Precision (MAP) when using the performance measure of Intersection over Union (IoU) bigger to 0.5 of the ground truth

Predicting the evolution of neck pain episodes in routine clinical practice.

BACKGROUND: The objective of this study was to develop models for predicting the evolution of a neck pain (NP) episode. METHODS: Three thousand two hundred twenty-five acute and chronic patients seeking care for NP, were recruited consecutively in 47 health care centers. Data on 37 variables were gathered, including gender, age, employment status, duration of pain, intensity of NP and pain referred down to the arm (AP), disability, history of neck surgery, diagnostic procedures undertaken, imaging findings, clinical diagnosis, and treatments used. Three separate multivariable logistic regression models were developed for predicting a clinically relevant improvement in NP, AP and disability at 3 months. RESULTS: Three thousand one (93.5%%) patients attended follow-up. For all the models calibration was good. The area under the ROC curve was ≥0.717 for pain and 0.664 for disability. Factors associated with a better prognosis were: a) For all the outcomes: pain being acute (vs. chronic) and having received neuro-reflexotherapy. b) For NP: nonspecific pain (vs. pain caused by disc herniation or spinal stenosis), no signs of disc degeneration on imaging, staying at work, and being female. c) For AP: nonspecific NP and no signs of disc degeneration on imaging. d) For disability: staying at work and no signs of facet joint degeneration on imaging. CONCLUSIONS: A prospective registry can be used for developing valid predictive models to quantify the odds that a given patient with NP will experience a clinically relevant improvement

Differential response to dexamethasone on the TXB2 release in guinea-pig alveolar macrophages induced by zymosan and cytokines

Glucocorticosteroids reduce the production of inflammatory mediators but this effect may depend on the stimulus. We have compared the time course of the effect of dexamethasone on the thromboxane B2 (TXB2) release induced by cytokine stimulation and zymosan in guinea-pig alveolar macrophages. Interleukin-1β (IL-1β), tumour necrosis factor-α (TNF-α) and opsonized zymosan (OZ), all stimulate TXB2 release. High concentrations of dexamethasone (1–10 μM) inhibit the TXB2 production induced by both cytokines and OZ, but the time course of this response is different. Four hours of incubation with dexamethasone reduce the basal TXB2 release and that induced by IL-1β and TNF-α, but do not modify the TXB2 release induced by OZ. However, this stimulus was reduced after 24 h incubation. Our results suggest that the antiinflammatory activity of glucocorticosteroids shows some dependence on stimulus and, therefore, may have more than one mechanism involved

SlideImages: A Dataset for Educational Image Classification

In the past few years, convolutional neural networks (CNNs) have achieved impressive results in computer vision tasks, which however mainly focus on photos with natural scene content. Besides, non-sensor derived images such as illustrations, data visualizations, figures, etc. are typically used to convey complex information or to explore large datasets. However, this kind of images has received little attention in computer vision. CNNs and similar techniques use large volumes of training data. Currently, many document analysis systems are trained in part on scene images due to the lack of large datasets of educational image data. In this paper, we address this issue and present SlideImages, a dataset for the task of classifying educational illustrations. SlideImages contains training data collected from various sources, e.g., Wikimedia Commons and the AI2D dataset, and test data collected from educational slides. We have reserved all the actual educational images as a test dataset in order to ensure that the approaches using this dataset generalize well to new educational images, and potentially other domains. Furthermore, we present a baseline system using a standard deep neural architecture and discuss dealing with the challenge of limited training data.Comment: 8 pages, 2 figures, to be presented at ECIR 202

PLS-based soft-sensor to predict ammonium concentration evolution in hollow fibre membrane contactors for nitrogen recovery

[EN] Hollow fibre membrane contactors (HFMC) have emerged as a promising technology for nitrogen-recovery that can be implemented in wastewater treatment plants (WWTPs) to promote circular economy. In this process, a hydrophobic membrane allows the transference of free-ammonia across the hollow fibres. During its operation, the ammonium concentration decreases, and real-time measurements would be of great value for process monitoring, optimization and control. Ammonium probes exist, but they are expensive and present noticeably maintenance costs. In this work, results from eight N-recovery experiments performed at different pH values using real supernatant of a full-scale anaerobic digester were analysed in terms of the time-evolution profiles of pH and total ammonium nitrogen (TAN). The pH revealed to carry relevant information related to the TAN concentration, as it decreased in the feed solution due to free ammonia stripping. The pH is an inexpensive-to measure process variable that can be routinely acquired in any WWTP. Therefore, a data-driven soft-sensor has been developed. It uses the pH, its derivative, and the pH increments after each reagent dosing as input signals, to estimate the TAN concentration via PLS. An extended PLS-model incorporating interaction terms, quadratic and cubic forms of the three input variables improved the TAN concentration estimation. The developed soft-sensor was able to accurately reproduce the evolution of TAN concentration (in the range 0-1000 mgNH(4)(+)-N/L with R-2 > 0.97 and RMSE < 40 mg/L) during the HFMC process operation, thus making it possible to monitor the process as well as enabling future development of different control and optimization strategies.This research was financially supported by the Spanish Ministry of Economy and Competitiveness (MINECO projects CTM2014-54980-C2-1/2-R and CTM2017-86751-C2-1/2-R) with the European Regional Development Fund (ERDF) as well as the Universitat Polite`cnica de Vale`ncia via a pre-doctoral FPI fellowship to Guillermo Noriega.Aguado García, D.; Noriega-Hevia, G.; Ferrer, J.; Seco, A.; Serralta Sevilla, J. (2022). PLS-based soft-sensor to predict ammonium concentration evolution in hollow fibre membrane contactors for nitrogen recovery. Journal of Water Process Engineering. 47:1-7. https://doi.org/10.1016/j.jwpe.2022.102735174

Improved heuristic drift elimination with magnetically-aided dominant directions (MiHDE) for pedestrian navigation in complex buildings

The main problem of pedestrian dead-reckoning (PDR) using only a body-attached inertial measurement unit is the accumulation of heading errors. The heading provided by magnetometers in indoor buildings is in general not reliable and therefore it is commonly not used. Recently, a new method was proposed called heuristic drift elimination (HDE) that minimises the heading error when navigating in buildings. It assumes that the majority of buildings have their corridors parallel to each other, or they intersect at right angles, and consequently most of the time the person walks along a straight path with a heading constrained to one of the four possible directions. In this article we study the performance of HDE-based methods in complex buildings, i.e. with pathways also oriented at 45°, long curved corridors, and wide areas where non-oriented motion is possible. We explain how the performance of the original HDE method can be deteriorated in complex buildings, and also, how severe errors can appear in the case of false matches with the building's dominant directions. Although magnetic compassing indoors has a chaotic behaviour, in this article we analyse large data-sets in order to study the potential use that magnetic compassing has to estimate the absolute yaw angle of a walking person. Apart from these analysis, this article also proposes an improved HDE method called Magnetically-aided Improved Heuristic Drift Elimination (MiHDE), that is implemented over a PDR framework that uses foot-mounted inertial navigation with an extended Kalman filter (EKF). The EKF is fed with the MiHDE-estimated orientation error, gyro bias corrections, as well as the confidence over that corrections. We experimentally evaluated the performance of the proposed MiHDE-based PDR method, comparing it with the original HDE implementation. Results show that both methods perform very well in ideal orthogonal narrow-corridor buildings, and MiHDE outperforms HDE for non-ideal trajectories (e.g. curved paths) and also makes it robust against potential false dominant direction matchings