Two-Dimensional Time-Domain Antenna Arrays for Optimum Steerable Energy Pattern with Low Side Lobes

This document presents the synthesis of different two-dimensional time-domain antenna arrays for steerable energy patterns with side lobe levels. The research is focused on the uniform and nonuniform distributions of true-time exciting delays and positions of antenna elements. The uniform square array, random array, uniform concentric ring array, and rotated nonuniform concentric ring array geometries are particularly studied. These geometries are synthesized by using the well-known sequential quadratic programming. The synthesis regards the optimal true-time exciting delays and optimal positions of pulsed antenna elements. The results show the capabilities of the different antenna arrays to steer the beam in their energy pattern in time domain and how their performance is in frequency domain after the synthesis in time domain

Physiological Profile of Male Competitive and Recreational Surfers

Surfing consists of both high- and low-intensity paddling of varying durations, using both the aerobic and anaerobic systems. Surf-specific physiological studies lack adequate group sample sizes, and V[Combining Dot Above]O2peak values are yet to determine the differences between competitive and recreational surfers. The purpose of this study was therefore to provide a comprehensive physiological profile of both recreational and competitive surfers. This multisite study involved 62 male surfers, recreational (n = 47) and competitive (n = 15). Anthropometric measurements were conducted followed by dual-energy x-ray absorptiometry, anaerobic testing and finally aerobic testing. V[Combining Dot Above]O2peak was significantly greater in competitive surfers than in recreational surfers (M = 40.71 ± 3.28 vs. 31.25 ± 6.31 ml·kg·min, p \u3c 0.001). This was also paralleled for anaerobic power (M = 303.93 vs. 264.58 W) for competitive surfers. Arm span and lean total muscle mass was significantly (p ≤ 0.01) correlated with key performance variables (V[Combining Dot Above]O2peak and anaerobic power). No significant (p ≥ 0.05) correlations were revealed between season rank and each of the variables of interest (V[Combining Dot Above]O2peak and anaerobic power). Key performance variables (V[Combining Dot Above]O2peak and anaerobic power) are significantly higher in competitive surfers, indicating that this is both an adaptation and requirement in this cohort. This battery of physiological tests could be used as a screening tool to identify an athlete\u27s weaknesses or strengths. Coaches and clinicians could then select appropriate training regimes to address weaknesses

Impact of outdoor air pollution on severity and mortality in COVID-19 pneumonia

The relationship between exposure to air pollution and the severity of coronavirus disease 2019 (COVID-19) pneumonia and other outcomes is poorly understood. Beyond age and comorbidity, risk factors for adverse outcomes including death have been poorly studied. The main objective of our study was to examine the relationship between exposure to outdoor air pollution and the risk of death in patients with COVID-19 pneumonia using individual-level data. The secondary objective was to investigate the impact of air pollutants on gas exchange and systemic inflammation in this disease. This cohort study included 1548 patients hospitalised for COVID-19 pneumonia between February and May 2020 in one of four hospitals. Local agencies supplied daily data on environmental air pollutants ($PM_{10}$, $PM_{2.5}$, $O_3$, $NO_2$, $NO$ and $NO_X$) and meteorological conditions (temperature and humidity) in the year before hospital admission (from January 2019 to December 2019). Daily exposure to pollution and meteorological conditions by individual postcode of residence was estimated using geospatial Bayesian generalised additive models. The influence of air pollution on pneumonia severity was studied using generalised additive models which included: age, sex, Charlson comorbidity index, hospital, average income, air temperature and humidity, and exposure to each pollutant. Additionally, generalised additive models were generated for exploring the effect of air pollution on C-reactive protein (CRP) level and Sp$O_2$/Fi$O_2$ at admission. According to our results, both risk of COVID-19 death and CRP level increased significantly with median exposure to $PM_{10}$, $NO_2$, $NO$ and $NO_X$, while higher exposure to $NO_2$, $NO$ and $NO_X$ was associated with lower Sp$O_2$/Fi$O_2$ ratios. In conclusion, after controlling for socioeconomic, demographic and health-related variables, we found evidence of a significant positive relationship between air pollution and mortality in patients hospitalised for COVID-19 pneumonia. Additionally, inflammation (CRP) and gas exchange (Sp$O_2$/Fi$O_2$) in these patients were significantly related to exposure to air pollution

Biocontrol and plant growth promoting traits of two avocado rhizobacteria are orchestrated by the emission of diffusible and volatile compounds

Avocado (Persea americana Mill.) is a tree crop of great social and economic importance. However, the crop productivity is hindered by fast-spreading diseases, which calls for the search of new biocontrol alternatives to mitigate the impact of avocado phytopathogens. Our objectives were to evaluate the antimicrobial activity of diffusible and volatile organic compounds (VOCs) produced by two avocado rhizobacteria (Bacillus A8a and HA) against phytopathogens Fusarium solani, Fusarium kuroshium, and Phytophthora cinnamomi, and assess their plant growth promoting effect in Arabidopsis thaliana. We found that, in vitro, VOCs emitted by both bacterial strains inhibited mycelial growth of the tested pathogens by at least 20%. Identification of bacterial VOCs by gas chromatography coupled to mass spectrometry (GC–MS) showed a predominance of ketones, alcohols and nitrogenous compounds, previously reported for their antimicrobial activity. Bacterial organic extracts obtained with ethyl acetate significantly reduced mycelial growth of F. solani, F. kuroshium, and P. cinnamomi, the highest inhibition being displayed by those from strain A8a (32, 77, and 100% inhibition, respectively). Tentative identifications carried out by liquid chromatography coupled to accurate mass spectrometry of diffusible metabolites in the bacterial extracts, evidenced the presence of some polyketides such as macrolactins and difficidin, hybrid peptides including bacillaene, and non-ribosomal peptides such as bacilysin, which have also been described in Bacillus spp. for antimicrobial activities. The plant growth regulator indole-3-acetic acid was also identified in the bacterial extracts. In vitro assays showed that VOCs from strain HA and diffusible compounds from strain A8a modified root development and increased fresh weight of A. thaliana. These compounds differentially activated several hormonal signaling pathways involved in development and defense responses in A. thaliana, such as auxin, jasmonic acid (JA) and salicylic acid (SA); genetic analyses suggested that developmental stimulation of the root system architecture by strain A8a was mediated by the auxin signaling pathway. Furthermore, both strains were able to enhance plant growth and decreased the symptoms of Fusarium wilt in A. thaliana when soil-inoculated. Collectively, our results evidence the potential of these two rhizobacterial strains and their metabolites as biocontrol agents of avocado pathogens and as biofertilizers

Analyzing multitarget activity landscapes using protein-ligand interaction fingerprints: interaction cliffs.

This is the original submitted version, before peer review. The final peer-reviewed version is available from ACS at http://pubs.acs.org/doi/abs/10.1021/ci500721x.Activity landscape modeling is mostly a descriptive technique that allows rationalizing continuous and discontinuous SARs. Nevertheless, the interpretation of some landscape features, especially of activity cliffs, is not straightforward. As the nature of activity cliffs depends on the ligand and the target, information regarding both should be included in the analysis. A specific way to include this information is using protein-ligand interaction fingerprints (IFPs). In this paper we report the activity landscape modeling of 507 ligand-kinase complexes (from the KLIFS database) including IFP, which facilitates the analysis and interpretation of activity cliffs. Here we introduce the structure-activity-interaction similarity (SAIS) maps that incorporate information on ligand-target contact similarity. We also introduce the concept of interaction cliffs defined as ligand-target complexes with high structural and interaction similarity but have a large potency difference of the ligands. Moreover, the information retrieved regarding the specific interaction allowed the identification of activity cliff hot spots, which help to rationalize activity cliffs from the target point of view. In general, the information provided by IFPs provides a structure-based understanding of some activity landscape features. This paper shows examples of analyses that can be carried out when IFPs are added to the activity landscape model.M-L is very grateful to CONACyT (No. 217442/312933) and the Cambridge Overseas Trust for funding. AB thanks Unilever for funding and the European Research Council for a Starting Grant (ERC-2013- StG-336159 MIXTURE). J.L.M-F. is grateful to the School of Chemistry, Department of Pharmacy of the National Autonomous University of Mexico (UNAM) for support. This work was supported by a scholarship from the Secretariat of Public Education and the Mexican government

Impacto cuantitativo de la contaminación en la probabilidad de muerte por neumonía por SARS-CoV-2

Introducción La evidencia científica disponible señala que la contaminación del aire exterior podría agravar la severidad de la COVID-19 y por ende, incrementar las probabilidades de fallecimiento. Material y métodos Estudio observacional longitudinal retrospectivo de cohortes, multicéntrico en 4 hospitales: 2 en Bizkaia (1 urbano, 1 urbano-rural), Valencia y Barcelona (urbanos). Se incluyeron ingresos por neumonía SARS-CoV-2 en el primer pico epidémico de COVID-19 (febrero-mayo 2020). Para determinar la exposición a contaminación por PM$_{10}$ y NO$_{2}$, se obtuvieron los datos publicados por los organismos autonómicos de calidad del aire, para 2019 y 1er semestre 2020. Se utilizó un Modelo Aditivo Generalizado (GAM) para estimar el nivel diario de contaminante en cada código postal, en función de las coordenadas geográficas y la altitud de las estaciones de medición [Figura 1]. Para determinar la exposición crónica, se calcularon media y máximo en 2019; la aguda se caracterizó por media y máximo en los 7 días anteriores al ingreso. Se estudió la razón de probabilidades (‘odds ratio’, OR) de muerte frente a supervivencia entre nuestra cohorte. Se modeló mediante un GAM con regresión logística, incorporando como efectos fijos sexo, edad y contaminante; hospital como efecto aleatorio e índice de comorbilidad de Charlson como función suave mediantes splines penalizados. Resultados De los 1548 pacientes reclutados, 243 (15.7%) fallecieron durante su hospitalización y/o 30 días postingreso. Según los modelos [Tabla 1], existe evidencia estadística significativa de que la exposición crónica a PM$_{10}$ y NO$_{2}$ incrementan la probabilidad de muerte por neumonía SARS-CoV-2. Compensando por sexo, edad y Charlson -todos factores relacionados positivamente con el OR de muerte- así como por hospital; por cada incremento de 10 μg/m$^{3}$ en el nivel de PM$_{10}$ (máximo anual) el OR aumenta en 10.5%, linealmente proporcional al incremento en la contaminación. Mientras, cada 10 μg/m$^{3}$ más de NO2 (media anual) aumentan OR en 35.7%; cada 10 μg/m$^{3}$ más en exposición aguda a NO2 (media semana pre-ingreso): 62.9%; y NO$_{2}$ (máximo semana): 34.4%. Conclusiones Se cuantificaron y compensaron los efectos de los factores sexo, edad, Charlson y hospital. A igualdad de estos, incrementos en la exposición crónica y aguda a PM$_{10}$ y NO$_{2}$ aumentan de manera lineal y estadísticamente significativa la probabilidad de muerte por neumonía SARS-CoV-2

Predicción de la gravedad de neumonías por SARS-CoV-2 a partir de información clínica y contaminación, mediante inteligencia artificial

Introducción La contaminación del aire exterior se ha relacionado con mayor gravedad de las infecciones respiratorias. Por tanto, su inclusión en algoritmos predictivos podrían añadir información para pronosticar la gravedad de neumonías SARS-CoV-2. Material y métodos Estudio observacional longitudinal retrospectivo de cohortes, multicéntrico en 4 hospitales. Se incluyeron ingresos por neumonía SARS-CoV-2 en el primer pico epidémico de COVID-19 (febrero-mayo 2020). Se recogieron hasta 93 variables clínicas, analíticas y radiológicas por cada paciente (sexo, edad, peso, comorbilidades, síntomas, variables fisiológicas en urgencias, sangre, gasometría, etc.). Además, se calcularon los niveles exposición a contaminación por PM$_{10}$, PM$_{2.5}$, O$_{3}$, NO$_{2}$, NO, NO$_{X}$, SO$_{2}$ y CO en su código postal. En función de la evolución clínica de la neumonía, se definieron 3 niveles de gravedad [Tabla 1]. Para predecir dicha gravedad, se desarrolló un algoritmo de inteligencia artificial (IA), tipo ‘Random Forest’ con balanceo y ajuste automático de sus parámetros internos. El algoritmo se entrenó y evaluó mediante 20 repeticiones de validación cruzada 10-fold (90% entrenamiento, 10% validación), estratificando aleatoriamente por hospital y gravedad. Resultados En los conjuntos de validación, el algoritmo alcanzó una capacidad predictiva (área bajo la curva ROC) promedio AUC=0.834 para gravedad nivel 0, AUC=0.724 para 1 y AUC=0.850 para 2 [Figura 1]. Sin la información de contaminantes, su capacidad predictiva se degradó ligeramente (AUCs = 0.829, 0.722, 0.844; respectivamente). Conclusiones Nuestro algoritmo IA es capaz de predecir de manera satisfactoria la evolución de la gravedad en la neumonía; en particular para los casos más leves y más severos. El algoritmo IA extrae las reglas más relevantes a partir principalmente de la información clínica, analítica y radiológica de cada individuo; no obstante, la incorporación de la exposición a contaminantes mejora ligeramente la capacidad predictiva. El impacto de la contaminación podría estar ya reflejado en las analíticas de sangre, a través de su efecto en los niveles de inflamación del paciente (PCT, PCR, LDH, etc.)

Non-motor symptom burden in patients with Parkinson's disease with impulse control disorders and compulsive behaviours : results from the COPPADIS cohort

The study was aimed at analysing the frequency of impulse control disorders (ICDs) and compulsive behaviours (CBs) in patients with Parkinson's disease (PD) and in control subjects (CS) as well as the relationship between ICDs/CBs and motor, nonmotor features and dopaminergic treatment in PD patients. Data came from COPPADIS-2015, an observational, descriptive, nationwide (Spain) study. We used the validated Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease-Rating Scale (QUIP-RS) for ICD/CB screening. The association between demographic data and ICDs/CBs was analyzed in both groups. In PD, this relationship was evaluated using clinical features and treatment-related data. As result, 613 PD patients (mean age 62.47 ± 9.09 years, 59.87% men) and 179 CS (mean age 60.84 ± 8.33 years, 47.48% men) were included. ICDs and CBs were more frequent in PD (ICDs 12.7% vs. 1.6%, p < 0.001; CBs 7.18% vs. 1.67%, p = 0.01). PD patients had more frequent previous ICDs history, premorbid impulsive personality and antidepressant treatment (p < 0.05) compared with CS. In PD, patients with ICDs/CBs presented younger age at disease onset, more frequent history of previous ICDs and premorbid personality (p < 0.05), as well as higher comorbidity with nonmotor symptoms, including depression and poor quality of life. Treatment with dopamine agonists increased the risk of ICDs/CBs, being dose dependent (p < 0.05). As conclusions, ICDs and CBs were more frequent in patients with PD than in CS. More nonmotor symptoms were present in patients with PD who had ICDs/CBs compared with those without. Dopamine agonists have a prominent effect on ICDs/CBs, which could be influenced by dose

Low exposure long-baseline neutrino oscillation sensitivity of the DUNE experiment

The Deep Underground Neutrino Experiment (DUNE) will produce world-leading neutrino oscillation measurements over the lifetime of the experiment. In this work, we explore DUNE's sensitivity to observe charge-parity violation (CPV) in the neutrino sector, and to resolve the mass ordering, for exposures of up to 100 kiloton-megawatt-years (kt-MW-yr). The analysis includes detailed uncertainties on the flux prediction, the neutrino interaction model, and detector effects. We demonstrate that DUNE will be able to unambiguously resolve the neutrino mass ordering at a 3$\sigma$ (5$\sigma$) level, with a 66 (100) kt-MW-yr far detector exposure, and has the ability to make strong statements at significantly shorter exposures depending on the true value of other oscillation parameters. We also show that DUNE has the potential to make a robust measurement of CPV at a 3$\sigma$ level with a 100 kt-MW-yr exposure for the maximally CP-violating values \delta_{\rm CP}} = \pm\pi/2. Additionally, the dependence of DUNE's sensitivity on the exposure taken in neutrino-enhanced and antineutrino-enhanced running is discussed. An equal fraction of exposure taken in each beam mode is found to be close to optimal when considered over the entire space of interest

A Gaseous Argon-Based Near Detector to Enhance the Physics Capabilities of DUNE

This document presents the concept and physics case for a magnetized gaseous argon-based detector system (ND-GAr) for the Deep Underground Neutrino Experiment (DUNE) Near Detector. This detector system is required in order for DUNE to reach its full physics potential in the measurement of CP violation and in delivering precision measurements of oscillation parameters. In addition to its critical role in the long-baseline oscillation program, ND-GAr will extend the overall physics program of DUNE. The LBNF high-intensity proton beam will provide a large flux of neutrinos that is sampled by ND-GAr, enabling DUNE to discover new particles and search for new interactions and symmetries beyond those predicted in the Standard Model