EVALUACIÓN DE CINCO MÉTODOS PARA EL PRONÓSTICO Y EL ANÁLISIS DE TENDENCIA DE LA PRODUCCIÓN AGRÍCOLA DE PANAMÁ: : UNA HERRAMIENTA PARA LAS INSTITUCIONES Y EMPRESAS DEL SECTOR

As an orientation for agricultural science managers and practitioners to improve budget planning and projected financial statements, five forecasting methods and the trend of linear regression, over a time series, for 30 agricultural crops in Panama were analyzed. The five forecasting methods applied were Simple Moving Average (SMA), Weighted Moving Average (WMA), Exponential Smoothing (SE), Simple Linear Regression (SLR) and Polynomial Regression (PR) and the only trend method used was SLR. The data was run in Excel software. The results of the basic statistics (see table 1) showed that, of 30 items analyzed in 2019, in 16 cases production was below the average of the historical series. Table 2 shows that, of the 30 items predicted, 6 cases corresponded to the SMA, 14 cases to the WMA, 9 cases to the SE and in one case to the PR method.  In the trend analysis, for after 2019, of the 30 cases analyzed, 19 items showed a tendency to decrease, 9 showed an increase and two to were predicted to maintain constant production.  It is concluded that the forecasts to produce agricultural crops may vary from one method to another, depending also on addition to the data and time, a high coefficient of determination of the regression model, a result in positive real numbers and the lowest percentage error of absolute deviation.Como una orientación para que los administradores y profesionales de las ciencias agrícolas mejoren la planificación de presupuestos y estados financieros proyectados, se analizaron cinco métodos de pronósticos y la tendencia de la regresión lineal, en una serie de tiempo, para 30 cultivos agrícolas en Panamá. Los cinco métodos de pronósticos aplicados fueron el Promedio Móvil Simple (PMS), el Promedio Móvil Ponderado (PMP), la Suavización Exponencial (SE), la Regresión lineal simple (RLS) y la Regresión Polinómica (RP) y el único método de tendencia utilizado fue el RLS. Los datos se corrieron en el software Excel. Los resultados de las estadísticas básicas (ver tabla1) mostraron que, de 30 rubros analizados en el 2019, en 16 casos la producción estuvo por debajo del promedio de la serie histórica. En la tabla 2 se observa que, de los 30 rubros pronosticados, 6 casos correspondieron al PMS, 14 casos al PMP, 9 casos a la SE y en un caso al método de la RP.  En el análisis de tendencia, para después del año 2019, de los 30 casos analizados, 19 rubros mostraron tendencia al decrecimiento, 9 mostraron al incremento y dos a mantener la producción constante.  Se concluye que los pronósticos para la producción de cultivos agrícolas pueden variar de un método a otro, dependiendo además de la data y del tiempo, de un alto coeficiente de determinación del modelo de regresión, de un resultado en números reales positivos y del menor error porcentual de desviación absoluta

Evidence for Reductive Genome Evolution and Lateral Acquisition of Virulence Functions in Two Corynebacterium pseudotuberculosis Strains

Ruiz JC, D'Afonseca V, Silva A, et al. Evidence for Reductive Genome Evolution and Lateral Acquisition of Virulence Functions in Two Corynebacterium pseudotuberculosis Strains. PLoS ONE. 2011;6(4): e18551.Background: Corynebacterium pseudotuberculosis, a Gram-positive, facultative intracellular pathogen, is the etiologic agent of the disease known as caseous lymphadenitis (CL). CL mainly affects small ruminants, such as goats and sheep; it also causes infections in humans, though rarely. This species is distributed worldwide, but it has the most serious economic impact in Oceania, Africa and South America. Although C. pseudotuberculosis causes major health and productivity problems for livestock, little is known about the molecular basis of its pathogenicity. Methodology and Findings: We characterized two C. pseudotuberculosis genomes (Cp1002, isolated from goats; and CpC231, isolated from sheep). Analysis of the predicted genomes showed high similarity in genomic architecture, gene content and genetic order. When C. pseudotuberculosis was compared with other Corynebacterium species, it became evident that this pathogenic species has lost numerous genes, resulting in one of the smallest genomes in the genus. Other differences that could be part of the adaptation to pathogenicity include a lower GC content, of about 52%, and a reduced gene repertoire. The C. pseudotuberculosis genome also includes seven putative pathogenicity islands, which contain several classical virulence factors, including genes for fimbrial subunits, adhesion factors, iron uptake and secreted toxins. Additionally, all of the virulence factors in the islands have characteristics that indicate horizontal transfer. Conclusions: These particular genome characteristics of C. pseudotuberculosis, as well as its acquired virulence factors in pathogenicity islands, provide evidence of its lifestyle and of the pathogenicity pathways used by this pathogen in the infection process. All genomes cited in this study are available in the NCBI Genbank database (http://www.ncbi.nlm.nih.gov/genbank/) under accession numbers CP001809 and CP001829

Evaluación de cinco métodos para el pronóstico y el análisis de tendencia de la producción agrícola de Panamá: una herramienta para las instituciones y empresas del sector

Como una orientación para que los administradores y profesionales de las ciencias agrícolas mejoren la planificación de presupuestos y estados financieros proyectados, se analizaron cinco métodos de pronósticos y la tendencia de la regresión lineal, en una serie de tiempo, para 30 cultivos agrícolas en Panamá. Los cinco métodos de pronósticos aplicados fueron el Promedio Móvil Simple(PMS), el Promedio Móvil Ponderado(PMP), la Suavización Exponencial (SE), la Regresión lineal simple (RLS) y la Regresión Polinómica(RP) y el único método de tendencia utilizado fue el RLS. Los datos se corrieron en el software Excel. Los resultados de las estadísticas básicas (ver tabla1) mostraron que, de 30 rubros analizados en el 2019, en 16 casos la producción estuvo por debajo del promedio de la serie histórica. En la tabla 2 se observa que, de los 30 rubros pronosticados, 6 casos correspondieron al PMS, 14 casos al PMP, 9 casos a la SE y en un caso al método de la RP. En el análisis de tendencia, para después del año 2019, de los 30 casos analizados, 19 rubros mostraron tendencia al decrecimiento, 9 mostraron al incremento y dos a mantener la producción constante. Se concluye que los pronósticos para la producción de cultivos agrícolas pueden variar de un método a otro, dependiendo además de la data y del tiempo, de un alto coeficiente de determinación del modelo de regresión, de un resultado en números reales positivos y del menor error porcentual de desviación absoluta

Challenges, facilitators and barriers to screening study participants in early disease stages-experience from the MACUSTAR study

Background!#!Recruiting asymptomatic participants with early disease stages into studies is challenging and only little is known about facilitators and barriers to screening and recruitment of study participants. Thus we assessed factors associated with screening rates in the MACUSTAR study, a multi-centre, low-interventional cohort study of early stages of age-related macular degeneration (AMD).!##!Methods!#!Screening rates per clinical site and per week were compiled and applicable recruitment factors were assigned to respective time periods. A generalized linear mixed-effects model including the most relevant recruitment factors identified via in-depth interviews with study personnel was fitted to the screening data. Only participants with intermediate AMD were considered.!##!Results!#!A total of 766 individual screenings within 87 weeks were available for analysis. The mean screening rate was 0.6 ± 0.9 screenings per week among all sites. The participation at investigator teleconferences (relative risk increase 1.466, 95% CI [1.018-2.112]), public holidays (relative risk decrease 0.466, 95% CI [0.367-0.591]) and reaching 80% of the site's recruitment target (relative risk decrease 0.699, 95% CI [0.367-0.591]) were associated with the number of screenings at an individual site level.!##!Conclusions!#!Careful planning of screening activities is necessary when recruiting early disease stages in multi-centre observational or low-interventional studies. Conducting teleconferences with local investigators can increase screening rates. When planning recruitment, seasonal and saturation effects at clinical site level need to be taken into account.!##!Trial registration!#!ClinicalTrials.gov NCT03349801 . Registered on 22 November 2017