Capability of the Invasive Tree Prosopis glandulosa Torr. to Remediate Soil Treated with Sewage Sludge

Sewage sludge improves agricultural soil and plant growth, but there are hazards associated with its use, including high metal(loid) contents. An experimental study was conducted under greenhouse conditions to examine the effects of sewage sludge on growth of the invasive tree Prosopis glandulosa, as well as to determine its phytoremediation capacity. Plants were established and grown for seven months along a gradient of sewage sludge content. Plant traits, soil properties, and plant and soil concentrations of N, P, K, Cd, Pb, Cu, Ni, Zn, Cr, Co, As, and Fe were recorded. The addition of sewage sludge led to a significant decrease in soil pH, and Ni, Co, and As concentrations, as well as an increase in soil organic matter and the concentrations of N, P, Cu, Zn, and Cr. Increasing sewage sludge content in the growth medium raised the total uptake of most metals by P. glandulosa plants due to higher biomass accumulation (taller plants with more leaves) and higher metal concentrations in the plant tissues. P. glandulosa concentrated more Cd, Pb, Cu, Zn, and Fe in its below-ground biomass (BGB) than in its above-ground biomass (AGB). P. glandulosa concentrated Ni, Co, and As in both BGB and AGB. P. glandulosa has potential as a biotool for the phytoremediation of sewage sludges and sewage-amended soils in arid and semi-arid environments, with a potential accumulation capability for As in plant leaves

The Role of Sustainability in Brand Equity Value in the Financial Sector

The behavior of firms is changing as new kinds of businesses evolve. In particular, companies are now seeking to optimize their value, especially their intangible value¿referred to as brand equity value¿which has many behavioral drivers. The analysis of brand equity determinants in the financial sector (e.g., ethical investments, sustainability and firm behavior) has received little attention. The methodology used in this study included the collection of information from publicly listed companies, followed by the execution of a statistical analysis to study the correlations between brand equity values and their determinants. We aimed to close this gap by raising the awareness of the positive impacts of sustainable investments in the financial sector and the need for a managerial implementation model to build a sustainability-oriented brand value. The objective of this research was to examine the relationships between elements such as sustainability scores or diversity measures and firms' brand value. Considering sectoral and regional effects, we observed a positive relationship between environmental and social governance scores and brand equity value

TimeSpec4LULC: a global multispectral time series database for training LULC mapping models with machine learning

Land use and land cover (LULC) mapping are of paramount importance to monitor and understand the structure and dynamics of the Earth system. One of the most promising ways to create accurate global LULC maps is by building good quality state-of-the-art machine learning models. Building such models requires large and global datasets of annotated time series of satellite images, which are not available yet. This paper presents TimeSpec4LULC (https://doi.org/10.5281/zenodo.5913554; Khaldi et al., 2022), a smart open-source global dataset of multispectral time series for 29 LULC classes ready to train machine learning models. TimeSpec4LULC was built based on the seven spectral bands of the MODIS sensors at 500 m resolution, from 2000 to 2021, and was annotated using spatial–temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE). The 22-year monthly time series of the seven bands were created globally by (1) applying different spatial–temporal quality assessment filters on MODIS Terra and Aqua satellites; (2) aggregating their original 8 d temporal granularity into monthly composites; (3) merging Terra + Aqua data into a combined time series; and (4) extracting, at the pixel level, 6 076 531 time series of size 262 for the seven bands along with a set of metadata: geographic coordinates, country and departmental divisions, spatial–temporal consistency across LULC products, temporal data availability, and the global human modification index. A balanced subset of the original dataset was also provided by selecting 1000 evenly distributed samples from each class such that they are representative of the entire globe. To assess the annotation quality of the dataset, a sample of pixels, evenly distributed around the world from each LULC class, was selected and validated by experts using very high resolution images from both Google Earth and Bing Maps imagery. This smartly, pre-processed, and annotated dataset is targeted towards scientific users interested in developing various machine learning models, including deep learning networks, to perform global LULC mapping.This work was partially supported by DETECTOR (grant no. A-RNM-256-UGR18, Universidad de Granada/FEDER), LifeWatch SmartEcoMountains (grant no. LifeWatch-2019-10-UGR-01, Ministerio de Ciencia e Innovación/Universidad de Granada/FEDER), BBVA DeepSCOP (Ayudas Fundación BBVA a Equipos de Investigación Científica 2018), DeepL-ISCO (grant no. A-TIC-458-UGR18, Ministerio de Ciencia e Innovación/FEDER), SMART-DASCI (grant no. TIN2017-89517-P, Ministerio de Ciencia e Innovación/Universidad de Granada/FEDER), BigDDL-CET (grant no. P18-FR-4961, Ministerio de Ciencia e Innovación/Universidad de Granada/FEDER), RESISTE (grant no. P18-RT-1927, Consejería de Economía, Conocimiento y Universidad from the Junta de Andalucía/FEDER), Ecopotential (grant no. 641762, European Commission), PID2020-119478GB-I00, the Consellería de Educación, Cultura y Deporte de la Generalitat Valenciana, the European Social Fund (grant no. APOSTD/2021/188), the European Research Council (ERC grant no. 647038/BIODESERT), and the Group on Earth Observations and Google Earth Engine (Essential Biodiversity Variables – ScaleUp project)

Cost-Effectiveness of Chagas Disease Vector Control Strategies in Northwestern Argentina

Despite decreasing rates of prevalence and incidence, Chagas disease remains a serious problem in Latin America, especially for the rural poor. Without vaccines, control and prevention rely mostly on residual spraying of insecticides. Under the aegis of the Southern Cone Initiative, and in agreement with global trends in decentralization of the health systems, in 1992 the Argentinean vector control launched a new vector control program based on community participation. The present study represents the first thorough evaluation of the overall performance of such vector control program and the first comparative assessment of the cost-effectiveness of different vector control strategies in a highly endemic rural area of northwestern Argentina. Supported by results of independent studies, the present work shows that in rural, poor and dispersed areas of the Gran Chaco region, the implementation of a mixed (i.e., vertical attack phase followed by horizontal surveillance) strategy constantly supervised and supported by national or local vector control programs would be the most cost-effective option to interrupt vector-borne transmission of Chagas disease

Analytical, experimental, and Monte Carlo system response matrix for pinhole SPECT reconstruction

PURPOSE: To assess the performance of two approaches to the system response matrix (SRM) calculation in pinhole single photon emission computed tomography (SPECT) reconstruction. METHODS: Evaluation was performed using experimental data from a low magnification pinhole SPECT system that consisted of a rotating flat detector with a monolithic scintillator crystal. The SRM was computed following two approaches, which were based on Monte Carlo simulations (MC-SRM) and analytical techniques in combination with an experimental characterization (AE-SRM). The spatial response of the system, obtained by using the two approaches, was compared with experimental data. The effect of the MC-SRM and AE-SRM approaches on the reconstructed image was assessed in terms of image contrast, signal-to-noise ratio, image quality, and spatial resolution. To this end, acquisitions were carried out using a hot cylinder phantom (consisting of five fillable rods with diameters of 5, 4, 3, 2, and 1 mm and a uniform cylindrical chamber) and a custom-made Derenzo phantom, with center-to-center distances between adjacent rods of 1.5, 2.0, and 3.0 mm. RESULTS: Good agreement was found for the spatial response of the system between measured data and results derived from MC-SRM and AE-SRM. Only minor differences for point sources at distances smaller than the radius of rotation and large incidence angles were found. Assessment of the effect on the reconstructed image showed a similar contrast for both approaches, with values higher than 0.9 for rod diameters greater than 1 mm and higher than 0.8 for rod diameter of 1 mm. The comparison in terms of image quality showed that all rods in the different sections of a custom-made Derenzo phantom could be distinguished. The spatial resolution (FWHM) was 0.7 mm at iteration 100 using both approaches. The SNR was lower for reconstructed images using MC-SRM than for those reconstructed using AE-SRM, indicating that AE-SRM deals better with the projection noise than MC-SRM. CONCLUSIONS: The authors' findings show that both approaches provide good solutions to the problem of calculating the SRM in pinhole SPECT reconstruction. The AE-SRM was faster to create and handle the projection noise better than MC-SRM. Nevertheless, the AE-SRM required a tedious experimental characterization of the intrinsic detector response. Creation of the MC-SRM required longer computation time and handled the projection noise worse than the AE-SRM.Nevertheless, the MC-SRM inherently incorporates extensive modeling of the system and therefore experimental characterization was not required

National policy-makers speak out: are researchers giving them what they need?

The objective of this empirical study was to understand the perspectives and attitudes of policy-makers towards the use and impact of research in the health sector in low- and middle-income countries. The study used data from 83 semi-structured, in-depth interviews conducted with purposively selected policy-makers at the national level in Argentina, Egypt, Iran, Malawi, Oman and Singapore. The interviews were structured around an interview guide developed based on existing literature and in consultation with all six country investigators. Transcripts were processed using a thematic-analysis approach. Policy-makers interviewed for this study were unequivocal in their support for health research and the high value they attribute to it. However, they stated that there were structural and informal barriers to research contributing to policy processes, to the contribution research makes to knowledge generally, and to the use of research in health decision-making specifically. Major findings regarding barriers to evidence-based policy-making included poor communication and dissemination, lack of technical capacity in policy processes, as well as the influence of the political context. Policy-makers had a variable understanding of economic analysis, equity and burden of disease measures, and were vague in terms of their use in national decisions. Policy-maker recommendations regarding strategies for facilitating the uptake of research into policy included improving the technical capacity of policy-makers, better packaging of research results, use of social networks, and establishment of fora and clearinghouse functions to help assist in evidence-based policy-makin

Electronically Controllable Phase Shifter with Progressive Impedance Transformation at K Band

This communication presents the design of a two-port electronically tunable phase shifter at K band. The phase shifter consists of a 3 dB hybrid coupler loaded with reflective phase-controllable circuits. The reflective circuits are formed by varactors and non-sequential impedance transformers which increase the operational bandwidth and the provided phase shift. The final phase shifter design is formed by two loaded-coupler stages of phase shifting to guarantee a complete phase turn. An 18 GHz phase shifter design with dynamic range of 600 degrees of phase shift is depicted in this document. The prototype is manufactured and validated through measurements showing good agreement with the simulation results.This work has been partially supported by the TIN2016-75097-P, RTI2018-102002-A-I00, and EQC2018- 004988-P projects of the Spanish National Program of Research, Development, and Innovation and project B-TIC-402-UGR18 of Junta de Andalucí

Supernova progenitor stars in the initial range of 23 to 33 solar masses and their relation with the SNR Cas A

Multi wavelength observations of Cassiopeia A (Cas A) have provided us with a strong evidence for the presence of circumstellar material surrounding the progenitor star. It has been suggested that its progenitor was a massive star with a strong mass loss. But, despite the large amount of observational data from optical, IR, radio and x-ray observations, the identity of Cas A progenitor is still elusive. In this work, we compute stellar and circumstellar numerical models to look for the progenitor of Cas A. The models will be compared with the observational constraints. We have computed stellar evolution models to get time-dependent wind parameters and surface abundances. We have chosen a set of probable progenitor stars, with initial masses of 23, 28, 29, 30 and 33 \Mo, with mass loss. The derived mass loss rates and wind terminal velocities are used to simulate the evolution of the circumstellar medium. Our stellar set gives distinct SN progenitors: RSG, luminous blue super giants (LBSGs) and Wolf-Rayet (WR) stars. The 23 \Mo star explodes as a RSG in a $\rm \rho \sim r^{-2}$ dense, free streaming wind surrounded by a thin, compressed, RSG shell. The 28 \Mo star explodes as a LBSG, and the SN blast wave interacts with a low density, free streaming wind surrounded by an unstable and massive ''RSG+LBSG'' shell. Finally, the 30 and 33 \Mo stars explode as WR stars surrounded by fast, WR winds that terminate in highly fragmented ''WR+RSG shell''. We have compared the surface chemical abundances of our stellar models with the observational abundances in Cas A. The abundance analysis shows that the progenitor was a star with an initial mass of the order of 30 \Mo, while the hydrodynamical analysis favors progenitors of initial masses around 23.Comment: 12 pages, 11 figures, accepted for publication in Astronomy & Astrophysic

Does inter-vertebral range of motion increase after spinal manipulation? A prospective cohort study.

Background: Spinal manipulation for nonspecific neck pain is thought to work in part by improving inter-vertebral range of motion (IV-RoM), but it is difficult to measure this or determine whether it is related to clinical outcomes. Objectives: This study undertook to determine whether cervical spine flexion and extension IV-RoM increases after a course of spinal manipulation, to explore relationships between any IV-RoM increases and clinical outcomes and to compare palpation with objective measurement in the detection of hypo-mobile segments. Method: Thirty patients with nonspecific neck pain and 30 healthy controls matched for age and gender received quantitative fluoroscopy (QF) screenings to measure flexion and extension IV-RoM (C1-C6) at baseline and 4-week follow-up between September 2012-13. Patients received up to 12 neck manipulations and completed NRS, NDI and Euroqol 5D-5L at baseline, plus PGIC and satisfaction questionnaires at follow-up. IV-RoM accuracy, repeatability and hypo-mobility cut-offs were determined. Minimal detectable changes (MDC) over 4 weeks were calculated from controls. Patients and control IV-RoMs were compared at baseline as well as changes in patients over 4 weeks. Correlations between outcomes and the number of manipulations received and the agreement (Kappa) between palpated and QF-detected of hypo-mobile segments were calculated. Results: QF had high accuracy (worst RMS error 0.5o) and repeatability (highest SEM 1.1o, lowest ICC 0.90) for IV-RoM measurement. Hypo-mobility cut offs ranged from 0.8o to 3.5o. No outcome was significantly correlated with increased IV-RoM above MDC and there was no significant difference between the number of hypo-mobile segments in patients and controls at baseline or significant increases in IV-RoMs in patients. However, there was a modest and significant correlation between the number of manipulations received and the number of levels and directions whose IV-RoM increased beyond MDC (Rho=0.39, p=0.043). There was also no agreement between palpation and QF in identifying hypo-mobile segments (Kappa 0.04-0.06). Conclusions: This study found no differences in cervical sagittal IV-RoM between patients with non-specific neck pain and matched controls. There was a modest dose-response relationship between the number of manipulations given and number of levels increasing IV-RoM - providing evidence that neck manipulation has a mechanical effect at segmental levels. However, patient-reported outcomes were not related to this

Extraction of pharmacokinetic evidence of drug-drug interactions from the literature

Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F10.93, MCC0.74, iAUC0.99) and sentences (F10.76, MCC0.65, iAUC0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.National Institutes of Health, National Library of Medicine Program, grant 01LM011945-01 "BLR: Evidence-based Drug-Interaction Discovery: In-Vivo, In-Vitro and Clinical," a grant from the Indiana University Collaborative Research Program 2013, "Drug-Drug Interaction Prediction from Large-scale Mining of Literature and Patient Records," as well as a grant from the joint program between the Fundação Luso-Americana para o Desenvolvimento (Portugal) and National Science Foundation (USA), 2012-2014, "Network Mining For Gene Regulation And Biochemical Signaling.