SL(2,R)-geometric phase space and (2+2)-dimensions

We propose an alternative geometric mathematical structure for arbitrary phase space. The main guide in our approach is the hidden SL(2,R)-symmetry which acts on the phase space changing coordinates by momenta and vice versa. We show that the SL(2,R)-symmetry is implicit in any symplectic structure. We also prove that in any sensible physical theory based on the SL(2,R)-symmetry the signature of the flat target "spacetime" must be associated with either one-time and one-space or at least two-time and two-space coordinates. We discuss the consequences as well as possible applications of our approach on different physical scenarios.Comment: 17 pages, no figure

Macroeconomic co-benefits of DRR investment: Assessment using the Dynamic Model of Multi-hazard Mitigation CoBenefits (DYNAMMICs) model

This contributing paper aims to bridge the important knowledge gap between the conceptualization of disaster risk reduction (DRR) benefits and its formal theoretical underpinnings and methods for quantifying benefits. This study also introduces a new macroeconomic framework for quantifying the multiple benefits of DRR investment. One of the most widely adopted framings, ‘Triple Dividends’ (Tanner et al. 2015), presents a series of narratives in which DRR investment not only protects but also fosters growth and other societal welfare but it falls short in how to quantify such multiple benefits. Given that the interaction of disaster risk with DRR investment and the macroeconomy is complex, the lack of detailed understanding regarding such dynamics limits the ability to effectively design a set of DRR investment options that yield synergies between DRR and other development aspirations. The Dynamic Model of Multi-hazard Mitigation CoBenefits (DYNAMMICs) framework serves to model the macro-economic benefits of DRR investments in this study. This study finds that, unlike the narrative-based approach used in the previous studies, the DYNAMMICs framework has demonstrated the complexity associated with the triple dividends concept. Different DRR investment options yielded a wide ranging trajectories of the 1st, 2nd and 3rd dividends, suggesting that a more careful analysis is needed to capture the full potential of DRR investment to foster the triple dividends. However, the DYNAMMICs framework proposed in this study is capable of evaluating multiple hazards and investment options at the same time and offers a way to evaluate the potential of DRR investment to create synergies with other development goals

How are network centrality metrics related to interest rates in the Mexican secured and unsecured interbank markets?

In financial stability, it is essential to know the determinants of interest rates in interbank markets because they are important vehicles for liquidity allocation among banks and are relevant for monetary policy transmission. Recent research indicates that banks with excess liquidity exercise their market power by rationing liquidity during periods of financial stress. This confirms the value of knowing the banks connections and identifying liquidity spreaders in such markets to manage contagion risk, liquidity hoarding and to preserve financial stability. In addition to well studied bank features such as size, liquidity and credit risk, we study which network metrics relate to interest rates during different periods. Using transaction level data on unsecured and secured lending, we apply an approach that employs network theory, econometric models and machine learning to analyze the structural properties of the secured and unsecured interbank markets in Mexico. Our findings support the “too-interconnected-to-fail” hypothesis. In the secured interbank market, PageRank shows a relationship with interest rates, while metrics associated with the notion of influence and systemic risk (Katz and DebtRank) are relevant in the unsecured interbank market. In general, a bank with high centrality lends at higher rates and gets funding at lower rates

Dry Needling for Spine Related Disorders: a Scoping Review

Introduction/Background: The depth and breadth of research on dry needling (DN) has not been evaluated specifically for symptomatic spine related disorders (SRD) from myofascial trigger points (TrP), disc, nerve and articular structures not due to serious pathologies. Current literature appears to support DN for treatment of TrP. Goals of this review include identifying research published on DN treatment for SRD, sites of treatment and outcomes studied. Methods: A scoping review was conducted following Levac et al.’s five part methodological framework to determine the current state of the literature regarding DN for patients with SRD. Results: Initial and secondary search strategies yielded 55 studies in the cervical (C) region (71.43%) and 22 in the thoracolumbar-pelvic (TLP) region (28.57%). Most were randomized controlled trials (60% in C, 45.45% in TLP) and clinical trials (18.18% in C, 22.78% in TLP). The most commonly treated condition was TrP for both the C and TLP regions. In the C region, DN was provided to 23 different muscles, with the trapezius as treatment site in 41.88% of studies. DN was applied to 31 different structures in the TLP region. In the C region, there was one treatment session in 23 studies (41.82%) and 2–6 treatments in 25 (45.45%%). For the TLP region, one DN treatment was provided in 8 of the 22 total studies (36.36%) and 2–6 in 9 (40.9%). The majority of experimental designs had DN as the sole intervention. For both C and TLP regions, visual analogue scale, pressure pain threshold and range of motion were the most common outcomes. Conclusion: For SRD, DN was primarily applied to myofascial structures for pain or TrP diagnoses. Many outcomes were improved regardless of diagnosis or treatment parameters. Most studies applied just one treatment which may not reflect common clinical practice. Further research is warranted to determine optimal treatment duration and frequency. Most studies looked at DN as the sole intervention. It is unclear whether DN alone or in addition to other treatment procedures would provide superior outcomes. Functional outcome tools best suited to tracking the outcomes of DN for SRD should be explored.https://doi.org/10.1186/s12998-020-00310-

Effective Rheology of Bubbles Moving in a Capillary Tube

We calculate the average volumetric flux versus pressure drop of bubbles moving in a single capillary tube with varying diameter, finding a square-root relation from mapping the flow equations onto that of a driven overdamped pendulum. The calculation is based on a derivation of the equation of motion of a bubble train from considering the capillary forces and the entropy production associated with the viscous flow. We also calculate the configurational probability of the positions of the bubbles.Comment: 4 pages, 1 figur

International study to evaluate PCR methods for detection of Trypanosoma cruzi DNA in blood samples from Chagas disease patients

A century after its discovery, Chagas disease, caused by the parasite Trypanosoma cruzi, still represents a major neglected tropical threat. Accurate diagnostics tools as well as surrogate markers of parasitological response to treatment are research priorities in the field. The polymerase chain reaction (PCR) has been proposed as a sensitive laboratory tool for detection of T. cruzi infection and monitoring of parasitological treatment outcome. However, high variation in accuracy and lack of international quality controls has precluded reliable applications in the clinical practice and comparisons of data among cohorts and geographical regions. In an effort towards harmonization of PCR strategies, 26 expert laboratories from 16 countries evaluated their current PCR procedures against sets of control samples, composed by serial dilutions of T.cruzi DNA from culture stocks belonging to different lineages, human blood spiked with parasite cells and blood samples from Chagas disease patients. A high variability in sensitivities and specificities was found among the 48 reported PCR tests. Out of them, four tests with best performance were selected and further evaluated. This study represents a crucial first step towards device of a standardized operative procedure for T. cruzi PCR.Fil: Schijman, Alejandro G. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET). Laboratorio de Biología Molecular de la Enfermedad de Chagas (LabMECh); Argentina.Fil: Bisio, Margarita. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET). Laboratorio de Biología Molecular de la Enfermedad de Chagas (LabMECh); Argentina.Fil: Orellana, Liliana. Universidad de Buenos Aires. Instituto de Cálculo; Argentina.Fil: Sued, Mariela. Universidad de Buenos Aires. Instituto de Cálculo; Argentina.Fil: Duffy, Tomás. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET). Laboratorio de Biología Molecular de la Enfermedad de Chagas (LabMECh); Argentina.Fil: Mejia Jaramillo, Ana M. Universidad de Antioquia. Grupo Chagas; Colombia.Fil: Cura, Carolina. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET). Laboratorio de Biología Molecular de la Enfermedad de Chagas (LabMECh); Argentina.Fil: Auter, Frederic. French Blood Services; Francia.Fil: Veron, Vincent. Universidad de Parasitología. Laboratorio Hospitalario; Guayana Francesa.Fil: Qvarnstrom, Yvonne. Centers for Disease Control. Department of Parasitic Diseases; Estados Unidos.Fil: Deborggraeve, Stijn. Institute of Tropical Medicine; Bélgica.Fil: Hijar, Gisely. Instituto Nacional de Salud; Perú.Fil: Zulantay, Inés. Facultad de Medicina; Chile.Fil: Lucero, Raúl Horacio. Universidad Nacional del Nordeste; Argentina.Fil: Velázquez, Elsa. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Parasitología Dr. Mario Fatala Chaben; Argentina.Fil: Tellez, Tatiana. Universidad Mayor de San Simon. Centro Universitario de Medicina Tropical; Bolivia.Fil: Sanchez Leon, Zunilda. Universidad Nacional de Asunción. Instituto de Investigaciones en Ciencias de la Salud; Paraguay.Fil: Galvão, Lucia. Faculdade de Farmácia; Brasil.Fil: Nolder, Debbie. Hospital for Tropical Diseases. London School of Tropical Medicine and Hygiene Department of Clinical Parasitology; Reino Unido.Fil: Monje Rumi, María. Universidad Nacional de Salta. Laboratorio de Patología Experimental; Argentina.Fil: Levi, José E. Hospital Sirio Libanês. Blood Bank; Brasil.Fil: Ramirez, Juan D. Universidad de los Andes. Centro de Investigaciones en Microbiología y Parasitología Tropical; Colombia.Fil: Zorrilla, Pilar. Instituto Pasteur; Uruguay.Fil: Flores, María. Instituto de Salud Carlos III. Centro de Mahahonda; España.Fil: Jercic, Maria I. Instituto Nacional De Salud. Sección Parasitología; Chile.Fil: Crisante, Gladys. Universidad de los Andes. Centro de Investigaciones Parasitológicas J.F. Torrealba; Venezuela.Fil: Añez, Néstor. Universidad de los Andes. Centro de Investigaciones Parasitológicas J.F. Torrealba; Venezuela.Fil: De Castro, Ana M. Universidade Federal de Goiás. Instituto de Patologia Tropical e Saúde Pública (IPTSP); Brasil.Fil: Gonzalez, Clara I. Universidad Industrial de Santander. Grupo de Inmunología y Epidemiología Molecular (GIEM); Colombia.Fil: Acosta Viana, Karla. Universidad Autónoma de Yucatán. Departamento de Biomedicina de Enfermedades Infecciosas y Parasitarias Laboratorio de Biología Celular; México.Fil: Yachelini, Pedro. Universidad Católica de Santiago del Estero. Instituto de Biomedicina; Argentina.Fil: Torrico, Faustino. Universidad Mayor de San Simon. Centro Universitario de Medicina Tropical; Bolivia.Fil: Robello, Carlos. Instituto Pasteur; Uruguay.Fil: Diosque, Patricio. Universidad Nacional de Salta. Laboratorio de Patología Experimental; Argentina.Fil: Triana Chavez, Omar. Universidad de Antioquia. Grupo Chagas; Colombia.Fil: Aznar, Christine. Universidad de Parasitología. Laboratorio Hospitalario; Guayana Francesa.Fil: Russomando, Graciela. Universidad Nacional de Asunción. Instituto de Investigaciones en Ciencias de la Salud; Paraguay.Fil: Büscher, Philippe. Institute of Tropical Medicine; Bélgica.Fil: Assal, Azzedine. French Blood Services; Francia.Fil: Guhl, Felipe. Universidad de los Andes. Centro de Investigaciones en Microbiología y Parasitología Tropical; Colombia.Fil: Sosa Estani, Sergio. ANLIS Dr.C.G.Malbrán. Centro Nacional de Diagnóstico e Investigación en Endemo-Epidemias; Argentina.Fil: DaSilva, Alexandre. Centers for Disease Control. Department of Parasitic Diseases; Estados Unidos.Fil: Britto, Constança. Instituto Oswaldo Cruz/FIOCRUZ. Laboratório de Biologia Molecular e Doenças Endêmicas; Brasil.Fil: Luquetti, Alejandro. Laboratório de Pesquisa de Doença de Chagas; Brasil.Fil: Ladzins, Janis. World Health Organization (WHO). Special Programme for Research and Training in Tropical Diseases (TDR); Suiza

International Study to Evaluate PCR Methods for Detection of Trypanosoma cruzi DNA in Blood Samples from Chagas Disease Patients

A century after its discovery, Chagas disease, caused by the parasite Trypanosoma cruzi, still represents a major neglected tropical threat. Accurate diagnostics tools as well as surrogate markers of parasitological response to treatment are research priorities in the field. The polymerase chain reaction (PCR) has been proposed as a sensitive laboratory tool for detection of T. cruzi infection and monitoring of parasitological treatment outcome. However, high variation in accuracy and lack of international quality controls has precluded reliable applications in the clinical practice and comparisons of data among cohorts and geographical regions. In an effort towards harmonization of PCR strategies, 26 expert laboratories from 16 countries evaluated their current PCR procedures against sets of control samples, composed by serial dilutions of T.cruzi DNA from culture stocks belonging to different lineages, human blood spiked with parasite cells and blood samples from Chagas disease patients. A high variability in sensitivities and specificities was found among the 48 reported PCR tests. Out of them, four tests with best performance were selected and further evaluated. This study represents a crucial first step towards device of a standardized operative procedure for T. cruzi PCR

Enhanced production of multi-strange hadrons in high-multiplicity proton-proton collisions

At sufficiently high temperature and energy density, nuclear matter undergoes a transition to a phase in which quarks and gluons are not confined: the quark-gluon plasma (QGP)(1). Such an exotic state of strongly interacting quantum chromodynamics matter is produced in the laboratory in heavy nuclei high-energy collisions, where an enhanced production of strange hadrons is observed(2-6). Strangeness enhancement, originally proposed as a signature of QGP formation in nuclear collisions(7), is more pronounced for multi-strange baryons. Several effects typical of heavy-ion phenomenology have been observed in high-multiplicity proton-proton (pp) collisions(8,9), but the enhanced production of multi-strange particles has not been reported so far. Here we present the first observation of strangeness enhancement in high-multiplicity proton-proton collisions. We find that the integrated yields of strange and multi-strange particles, relative to pions, increases significantly with the event charged-particle multiplicity. The measurements are in remarkable agreement with the p-Pb collision results(10,11), indicating that the phenomenon is related to the final system created in the collision. In high-multiplicity events strangeness production reaches values similar to those observed in Pb-Pb collisions, where a QGP is formed.Peer reviewe

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC