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Debido a la búsqueda de alternativas económicas para la disminución de contaminantes en aguas residuales, se emplea como opción el uso de las cáscaras de naranja y banano como adsorbente para remover metales pesados. Los experimentos se efectuaron utilizando biomasas previamente tratadas para obtener los adsorbentes. Durante los ensayos, se variaron las condiciones de operación: pH, tiempo, cantidad de adsorbente; y como constantes: velocidad de agitación, tamaño de partícula y temperatura. Se determinó la disminución de Cr(VI) mediante colorimetría y Cu(II) por Espectrofotometría UV Visible y la caracterización de los adsorbentes por Espectroscopía Infrarroja por Transformada de Fourier (FTIR). Se aplicaron modelos cinéticos de pseudo primer orden, pseudo segundo orden y Elovich. Los resultados mostraron porcentajes de remoción favorables para el Cr(VI) usando la CN (91,11 %) a pH 4, 3 g/L, 2 h y la CB (92,96 %) a pH 3, 4 g/L, 4 h. Mientras que para el Cu(II) empleando la CN (97,10 %) a pH 5,5, 5 g/L, 30 min y la CB (69,57 %) a pH 5,8, 3 g/L, 30 min, asimismo las concentraciones finales de ambos metales cumplen con la normativa TULSMAN. Se concluye que el uso de ambos bioadsorbentes disminuye metales pesados como el Cr(VI) y Cu(II) en aguas residuales domésticas.

Additional file 3 Fig. S3. Methodology for manual phenotyping of maize ears. (A) Sample ear from the biological diversity panel. (B) Sample ear from the environmental diversity panel grown under water deficit conditions showing partial pollination and/or ovary and grain abortion. Red areas: example of grains used for manual phenotyping of basal (BGC), median (MGC) and apical (AGC) number of grains per cohort, particularly difficult to characterize on "scattered" ears. To standardize the measurement, the method consisted in counting the number of rows (= lines of grains along the ear perpendicular to the cohorts) with at least one grain per row for each third of the ear. Green areas: example of grains used for manual phenotyping of the average number of cohorts per ear (CN). The measurement was repeated 4 times around the ear. White zone: fertile zone of the ear (FZ), where the surface occupied by grains visually represents more than 50% of the visible surface of the ear. Grey zones: basal (BA) and apical (AA) abortion zones, where the surface occupied by grains visually represents less than 50% of the visible surface of the ear. The lengths of the FZ, BA, and AA zones were measured manually along the main axis of the ear.

Additional file 4 Fig. S4. (A) Illustration for 3 ears of the processing used to extract ear length from ear masks. The black line represents the centerline of pixels along the main axis of the ear (vertical axis, starting from the bottom to the top of the ear), the measured ear length is the number of pixels in this line. (B) Illustration of the processing procedure to calculate the number of grains per cohort. Grain objects were reduced to a one-pixel wide vertical line along the principal axis. (C) Illustration of the processing procedure to calculate the number of grain cohorts. Grain objects were reduced to a one-pixel wide horizontal line along the ear axis perpendicular to the principal axis.

Additional file 8 Fig. S8. Comparison of Earbox (y) and reference (x) data. (A) Number of grains per cohort in the basal third. (B) Number of grains per cohort in the apical third. (C) Length of the basal aborted zone in centimeters. (D) Length of the apical aborted zone in centimeters. Green dots: ears from biological diversity panel; red dots: ears from environmental diversity panel. Empty red dots: scattered ears of the environmental diversity panel (Fig.3B). Grey line: bisector line. Black line: linear regression of data. R²: correlation coefficient between x and y values, RMSE: root mean square error, n: number of observations in each graph.

Additional file 1 Fig. S1. Choice of ear rotation angle based on Earbox specifications. (A) Heatmap of the theoretical cumulative ear percentage seen in all 6 images for roller rotation angles between 0° and 360° and diameters between 2cm and 6cm. Values are calculated assuming 120° of the ear circumferences is captured in each image. The dotted line represents the selected roller rotation angle, for which no critical diameter is encountered, capturing 100% of the information from each ear. (B) Match between theoritical and measured rotation angle for a set of ears chosen to represent diameter diversity in maize. Dots: measured and theoritical rotation angles. Colors: ear diameters. Dotted line: y = x. Solid line: linear regression. R²: correlation coefficient between x and y values, RMSE: root mean square error.

Additional file 1.

Additional file 6 Fig. S6. Output of the estimation of the number of cohorts along the perpendicular ear axis. For each position along the ear diameter (x), the number of cohorts (y) is calculated for each ear shown in Fig. 5 with the methodology presented in Supplementary Fig. S3. Black line, ear with white grains; red line, ear with vine grains; golden line, aborted ear with yellow grains. Dotted lines represent the maximum number of cohorts for each curve. The maximum number of cohorts is used as the output of the routine workflow and its average over the 6 ear sides is used for the correlation in Fig. 6. Each curve represents data from a single image/ear side.

Additional file 5 Fig. S5. Illustration of the image correction applied to project the distances and positions of the reference points onto a hypothetical circular section of the ear. Orange circle, boundaries of the ear. The reference image measurement (dref) is corrected using the horizontal distances measured between its extreme reference points (black dots) and the center of the ear (dmax and dmin). The final corrected measured is an estimate of the length of the arc resulting from the projection of dref onto a hypothetical perfect circle of radius (Rear).

Additional file 2 Fig. S2. Steps of the deep learning iteration workflow. (A) Steps of the deep learning (DL) workflow and (B,C,D) example images used for 3 typical ears representing the problems encountered and the manual correction performed. (B) Typical ear with good empirical grain masks used directly for training the first iteration of deep learning (B2) and requiring minimum manual corrections (B3 to B4) to produce the routine segmentation method (B5). (C, D) Typical ears with false detections and erroneous grain segmentations from the empirical segmentation (C2, D2) and requiring major manual corrections (step 3 to 4) used for training the second deep learning iteration to produce the routine segmentation method (C5, D5). Orange boxes, image acquisition. White boxes, image, or data processing. Green boxes processed images or data. Green areas, manual grain mask corrections.

Additional file 9 Fig. 9. Examples of grain dimensions as a function of cohort and position along the ear across 9 contrasting ears. Each point represents the average dimension of all grains classified in the same cohort (Fig. 5P, 5Q, 5R) across the 6 sides (images) of the ear. Red dots, average grain height in centimeters. Green dots, average grain width in centimeters. Error bars, standard deviation. Errors for position from ear base are shown but are smaller than the dots.

Additional file 7 Fig. S7. Illustration of the cohort classification algorithm. Grains are classified in cohorts (each identified by a color) by scanning the ear from bottom to top starting with the lowest grain (bold black dots on the barycenter). The classification is done sequentially, one cohort at a time. For each cohort, grains whose barycenter lie within a specific range of pixels along the main axis of the ear are classified into a common cohort, and removed for the rest of the classification process. Black dots: grain barycenter’s. Colors: grains classified in the same cohort.

Resumen En los últimos años, han aparecido evidencias que relacionan a un tipo de linfoma mamario con los implantes de silicona, lo que ha causado gran conmoción a nivel mundial. Este linfoma anaplástico de células grandes no Hodgkin (células T monoclonales), se ha visto asociado en mayoría de los casos, a las prótesis mamarias texturizadas. Es relativamente raro, ya que se puede presentar en 1 de cada 2.832 operados (as) y se puede manifestar como un seroma periprotésico o como una tumoración de la cápsula cicatrizal mamaria con o sin compromiso de la glándula y de los tejidos adyacentes.

A raíz de la amenaza para el medio ambiente que suponen otros menajes hechos a partir de materiales contaminantes, el presente ensayo propone la producción de un nuevo material cerámico sostenible hecho a partir de polenta de maíz y materiales orgánicos, de fácil descomposición, fabricación y de precio asequibleque pueda servir como alternativa. A través de diferentes ensayos hemos demostrado que sus propiedades son adecuadas para este fin.

Background: Intravenous ferric carboxymaltose has been shown to improve symptoms and quality of life in patients with chronic heart failure and iron deficiency. We aimed to evaluate the effect of ferric carboxymaltose, compared with placebo, on outcomes in patients who were stabilised after an episode of acute heart failure. Methods: AFFIRM-AHF was a multicentre, double-blind, randomised trial done at 121 sites in Europe, South America, and Singapore. Eligible patients were aged 18 years or older, were hospitalised for acute heart failure with concomitant iron deficiency (defined as ferritin