Dendrites and conformal symmetry

Progress toward characterization of structural and biophysical properties of neural dendrites together with recent findings emphasizing their role in neural computation, has propelled growing interest in refining existing theoretical models of electrical propagation in dendrites while advocating novel analytic tools. In this paper we focus on the cable equation describing electric propagation in dendrites with different geometry. When the geometry is cylindrical we show that the cable equation is invariant under the Schr\"odinger group and by using the dendrite parameters, a representation of the Schr\"odinger algebra is provided. Furthermore, when the geometry profile is parabolic we show that the cable equation is equivalent to the Schr\"odinger equation for the 1-dimensional free particle, which is invariant under the Schr\"odinger group. Moreover, we show that there is a family of dendrite geometries for which the cable equation is equivalent to the Schr\"odinger equation for the 1-dimensional conformal quantum mechanics.Comment: 19 page

Corporate social responsibility in portfolio selection: A "goal games" against nature approach

Nowadays, there is an uprising social pressure on big companies to incorporate into their decision-making process elements of the so-called social responsibility. Among the many implications of this fact, one relevant one is the need to include this new element in classic portfolio selection models. This paper meets this challenge by formulating a model that combines goal programming with "goal games" against nature in a scenario where the social responsibility is defined through the introduction of a battery of sustainability indicators amalgamated into a synthetic index. In this way, we have obtained an efficient model that only implies solving a small number of linear programming problems. The proposed approach has been tested and illustrated by using a case study related to the selection of securities in international markets

Multimodal data acquisition at SARS-CoV-2 drive through screening centers: Setup description and experiences in Saarland, Germany

SARS-CoV-2 drive through screening centers (DTSC) have been implemented worldwide as a fast and secure way of mass screening. We use DTSCs as a platform for the acquisition of multimodal datasets that are needed for the development of remote screening methods. Our acquisition setup consists of an array of thermal, infrared and RGB cameras as well as microphones and we apply methods from computer vision and computer audition for the contactless estimation of physiological parameters. We have recorded a multimodal dataset of DTSC participants in Germany for the development of remote screening methods and symptom identification. Acquisition in the early stages of a pandemic and in regions with high infection rates can facilitate and speed up the identification of infection specific symptoms and large-scale data acquisition at DTSC is possible without disturbing the flow of operation

Modelling and prediction of pain related neural firings using deep learning

We propose a deep learning approach to model and predict pain related neural firings from EEG data. In particular, we target for the first time differentiation between acute and chronic pain. Our modelling strategy followed three steps: 1) Feature extraction of EEG data using Petrosian Fractal Dimension (PFD) and Hjorth activity functions. 2) Source localization of neural firings to differentiate between acute and chronic pain. 3) Modelling and training of a deep learning model for the prediction of the related pain according to the feature extracted neural firings. Based on our results, an occipital brain activation for chronic pain and a temporal activation in the case of acute pain were recognized. Moreover, our long short-term memory (LSTM) based prediction model achieved an accuracy of 91.29% for identification of related pain. The performance of the model was evaluated using precision, recall and F1 scores. For acute pain it achieved scores of 0.90, 0.82, 0.86 and for chronic pain scores of 0.86, 0.93, 0.89 respectively. It is concluded that our approach not only shows better predictive accuracy than the results reported by previous studies, but also represents an important step towards identifying and evaluating pain when patients are incapable of self-reporting it or when the clinical observations are unobtainable or unreliable

Trends in mortality in septic patients according to the different organ failure during 15 years

Background The incidence of sepsis can be estimated between 250 and 500 cases/100.000 people per year and is responsible for up to 6% of total hospital admissions. Identified as one of the most relevant global health problems, sepsis is the condition that generates the highest costs in the healthcare system. Important changes in the management of septic patients have been included in recent years; however, there is no information about how changes in the management of sepsis-associated organ failure have contributed to reduce mortality. Methods A retrospective analysis was conducted from hospital discharge records from the Minimum Basic Data Set Acute-Care Hospitals (CMBD-HA in Catalan language) for the Catalan Health System (CatSalut). CMBD-HA is a mandatory population-based register of admissions to all public and private acute-care hospitals in Catalonia. Sepsis was defined by the presence of infection and at least one organ dysfunction. Patients hospitalized with sepsis were detected, according ICD-9-CM (since 2005 to 2017) and ICD-10-CM (2018 and 2019) codes used to identify acute organ dysfunction and infectious processes. Results Of 11.916.974 discharges from all acute-care hospitals during the study period (2005-2019), 296.554 had sepsis (2.49%). The mean annual sepsis incidence in the population was 264.1 per 100.000 inhabitants/year, and it increased every year, going from 144.5 in 2005 to 410.1 in 2019. Multiorgan failure was present in 21.9% and bacteremia in 26.3% of cases. Renal was the most frequent organ failure (56.8%), followed by cardiovascular (24.2%). Hospital mortality during the study period was 19.5%, but decreases continuously from 25.7% in 2005 to 17.9% in 2019 (p < 0.0001). The most important reduction in mortality was observed in cases with cardiovascular failure (from 47.3% in 2005 to 31.2% in 2019) (p < 0.0001). In the same way, mean mortality related to renal and respiratory failure in sepsis was decreased in last years (p < 0.0001). Conclusions The incidence of sepsis has been increasing in recent years in our country. However, hospital mortality has been significantly reduced. In septic patients, all organ failures except liver have shown a statistically significant reduction on associated mortality, with cardiovascular failure as the most relevant

Editorial: The new frontier in brain network physiology: from temporal dynamics to the principles of integration in physiological brain networks

Editorial on the Research Topic -The new frontier in brain network physiology: from temporal dynamics to the principles of integration in physiological brain network

Intermittent inotropic support with levosimendan in advanced heart failure as destination therapy: The LEVO-D registry

Advanced heart failure; Inotropes; Palliative careInsuficiencia cardiaca avanzada; Inotropos; Cuidados paliativosInsuficiència cardíaca avançada; Inòtrops; Cures pal·liativesAim Patients with advanced heart failure (AHF) who are not candidates to advanced therapies have poor prognosis. Some trials have shown that intermittent levosimendan can reduce HF hospitalizations in AHF in the short term. In this real-life registry, we describe the patterns of use, safety and factors related to the response to intermittent levosimendan infusions in AHF patients not candidates to advanced therapies. Methods and results Multicentre retrospective study of patients diagnosed with advanced heart failure, not HT or LVAD candidates. Patients needed to be on the optimal medical therapy according to their treating physician. Patients with de novo heart failure or who underwent any procedure that could improve prognosis were not included in the registry. Four hundred three patients were included; 77.9% needed at least one admission the year before levosimendan was first administered because of heart failure. Death rate at 1 year was 26.8% and median survival was 24.7 [95% CI: 20.4–26.9] months, and 43.7% of patients fulfilled the criteria for being considered a responder lo levosimendan (no death, heart failure admission or unplanned HF visit at 1 year after first levosimendan administration). Compared with the year before there was a significant reduction in HF admissions (38.7% vs. 77.9%; P 12 g/dL (+1.5), amiodarone use (−1.5) HF visit 1 year before levosimendan (−1.5) and heart rate >70 b.p.m. (−2). Patients with a score less than −1 had a very low probability of response (21.5% free of death or HF event at 1 year) meanwhile those with a score over 1.5 had the better chance of response (68.4% free of death or HF event at 1 year). LEVO-D score performed well in the ROC analysis. Conclusion In this large real-life series of AHF patients treated with levosimendan as destination therapy, we show a significant decrease of heart failure events during the year after the first administration. The simple LEVO-D Score could be of help when deciding about futile therapy in this population

Identification of previously unseen Asian elephants using visual data and semi-supervised learning

This paper presents a novel method to identify unseen Asian elephants that are not previously captured or identified in available data sets and re-identify previously seen Asian elephants using images of elephant ears, leveraging a semi-supervised learning approach. Ear patterns of unseen elephants are learnt for future re-identification. To aid our process, elephant ear patterns are used as a biomarker to uniquely identify individual Asian elephant, each of which is attached a descriptor. The main challenge is to learn and use a clustering technique to identify new classes (i.e., elephants) in unlabelled elephant ear image sets and leveraging this data in verifying the labelled images. This study proposes a systematic approach to address the problem to uniquely identify elephants, where we developed: (a) a self-supervised learning approach for training the representation of labelled and unlabelled image data to avoid unWanted, bias labelled data, (b) rank statistics for transferring the models’ knowledge of the labelled classes when clustering the unlabelled images, and, (c) improving the identification accuracy of both the classification and clustering algorithms by introducing a optimization problem when training with the data representation on the labelled and unlabelled image data sets. This approach was evaluated on seen (labelled) and unseen (unlabelled) elephants, where we achieved a significant accuracy of 86.89% with an NMI (Normalized Mutual Information) score of 0.9132 on identifying seen elephants. Similarly, an accuracy of 54.29% with an NMI score of 0.6250 was achieved on identifying unseen elephants from the unlabelled Asian elephant ear image data set. Findings of this research provides the ability to accurately identify elephants without having expert knowledge on the field. Our method can be used to uniquely identify elephants from their herds and then use it to track their travel patterns Which is greatly applicable in understanding the social organization of elephant herds, individual behavioural patterns, and estimating demographic parameters as a measure to reducing the human-elephant conflict in Sri Lanka