A preliminary evaluation of a low-cost multispectral sensor for non-destructive evaluation of olive fruits’ fat content

[Abstract] This study presents a preliminary evaluation of a low-cost multispectral device for the non-destructive assessment of olive fruits’ fat content. The developed device integrates a multispectral sensor, with a spectral response of 18 channels falling in a range from 410 to 940 nm, a calibrated light source, and a programmable board, in a ‘gun’-shaped device whose trigger activates sample reading. The device was used to measure 50 intact olive samples, which were subsequently chemically analysed to determine their actual fat content. Then, the multispectral readings from the 18 channels were used as input variables to train a neural network, using the actual fat content registers as reference data. The measured results, in terms of root-mean-square-error and coefficient of determination, shows promising capabilities of the developed low-cost device in the prediction of fat content of intact olives, what stands up for further development and experimentation.This work was supported by grant PID2020- 119217RA-I00 funded by MCIN/AEI/ 10.13039/501100011033, and grant IJC2019-040114-I funded by MCIN/AEI/ 10.13039/501100011033, and also by project TIColiVA with grant P18-RTJ-4539 funded by the Regional Government of Andalusia through the “PAIDI, Plan Andaluz de Investigación, Desarrollo e Innovación”. The authors would also like to thank “Cooperativa Nuestra Señora de la Oliva”, for generously providing the olive samples with which this research was conducted.Ministerio de Ciencia e Innovación; 10.13039/501100011033Junta de Andalucía; P18-RTJ-453

Vineyard pruning weight assessment by machine vision: towards an on-the-go measurement system

Aim: Pruning weight is an indicator of vegetative growth and vigour in grapevine. Traditionally, it is manually determined, which is time-consuming and labour-demanding. This study aims at providing a new, non-invasive and low-cost method for pruning weight estimation in commercial vineyards based on computer vision. Methods and results: The methodology relies on computer-based analysis of RGB images captured manually and on-the-go in a VSP Tempranillo vineyard. Firstly, the pruning weight estimation was evaluated using manually taken photographs using a controlled background. These images were analysed to generate a model of wood pruning weight estimation, resulting in a coefficient of determination (R2) of 0.91 (p<0.001) and a root-mean-square error (RMSE) of 87.7 g. After this, a mobile sensor platform (modified ATV) was used to take vine images automatically and on-the-go without background. These RGB images were analysed using a fully automated computer vision algorithm, resulting in R2 = 0.75 (p<0.001) and RMSE = 147.9 g. Finally, the mobile sensor platform was also used to sample a commercial VSP vineyard to map the spatial variability of wood pruning weight, and hereafter vine vigour. Conclusions: The results showed that the developed computer vision methodology was able to estimate the vine pruning weight in commercial vineyards and to map the spatial variation of the pruning weight across a vineyard. Significance and impact of the study: The presented methodology may become a valuable tool for the wine industry for rapid assessment and mapping of vine vigour. This information can be used to support decision making on pruning, fertilization and canopy management

Estimación de los componentes del racimo mediante análisis de imagen

El peso de baya, así como el número de bayas y peso del racimo son parámetros fundamentales en la estimación del rendimiento en la industria vitivinícola y de uva de mesa. En la actualidad, los métodos utilizados para estimar y predecir el rendimiento productivo del viñedo son destructivos, lentos, y requieren elevada cantidad de mano de obra. En este trabajo se presenta una nueva metodología, basada en el análisis de imagen, para determinar los componentes del racimo de forma rápida y económica. Se fotografiaron racimos de siete variedades de uva (Vitis vinifera L.) distintas en condiciones de laboratorio y se determinaron los componentes del racimo de forma manual después de la adquisición de imágenes. El tratamiento de las imágenes incluyó el desarrollo de dos algoritmos basados en las estrategias de Canny y LIP (Logarithmic Image Processing) para encontrar los contornos de las bayas, como paso previo a la detección de las mismas mediante la Transformada de Hough. Asimismo, se comparó la capacidad de los algoritmos desarrollados utilizando una única imagen por racimo o cuatro imágenes por racimo, obtenidas de diferentes orientaciones. Los mejores resultados (R2 entre 69%-95% en detección del número de bayas por racimo, y R2 entre 65%-97% en la estimación del peso de racimo) se obtuvieron utilizando cuatro imágenes por racimo y aplicando el algoritmo de Canny. Asimismo, la capacidad del modelo basado en análisis de imagen para predecir el peso de baya fue 84%. La novedosa metodología desarrollada y presentada en este trabajo ha permitido la estimación de los componentes del racimo de forma rápida y económica, en comparación con los métodos manuales actuales

Safety and immediate humoral response of COVID-19 vaccines in chronic kidney disease patients:the SENCOVAC study

BACKGROUND: Chronic kidney disease (CKD) patients are at high-risk for severe Covid-19. The multicentric, observational and prospective SENCOVAC study aims to describe the humoral response and safety of SARS-CoV-2 vaccines in CKD patients. Safety and immediate humoral response results are reported here. METHODS: Four cohorts of patients were included: kidney transplant (KT) recipients, haemodialysis (HD), peritoneal dialysis (PD) and non-dialysis CKD patients from 50 Spanish centres. Adverse events after vaccine doses were recorded. At baseline and on day 28 after the last vaccine dose, anti-Spike antibodies were measured and compared between cohorts. Factors associated with development of anti-Spike antibodies were analyzed. RESULTS: 1746 participants were recruited: 1116 HD, 171 PD, 176 non-dialysis CKD patients and 283 KT recipients. Most patients (98%) received mRNA vaccines. At least one vaccine reaction developed after the first dose in 763 (53.5%) and after the second dose in 741 (54.5%) of patients. Anti-Spike antibodies were measured in the first 301 patients. At 28 days, 95% of patients had developed antibodies: 79% of KT, 98% of HD, 99% of PD and 100% of non-dialysis CKD patients (p<0.001). In a multivariate adjusted analysis, absence of an antibody response was independently associated to KT (OR 20.56, p = 0.001) and to BNT162b2 vaccine (OR 6.03, p = 0.023). CONCLUSION: The rate of anti-Spike antibody development after vaccination in KT patients was low but in other CKD patients it approached 100%; suggesting that KT patients require persistent isolation measures and booster doses of a Covid-19 vaccine. Potential differences between Covid-19 vaccines should be explored in prospective controlled studies

Presbyopia:Effectiveness of correction strategies

Presbyopia is a global problem affecting over a billion people worldwide. The prevalence of unmanaged presbyopia is as high as 50% of those over 50 years of age in developing world populations due to a lack of awareness and accessibility to affordable treatment, and is even as high as 34% in developed countries. Definitions of presbyopia are inconsistent and varied, so we propose a redefinition that states “presbyopia occurs when the physiologically normal age-related reduction in the eye's focusing range reaches a point, when optimally corrected for distance vision, that the clarity of vision at near is insufficient to satisfy an individual's requirements”. Presbyopia is inevitable if one lives long enough, but intrinsic and extrinsic risk factors including cigarette smoking, pregnancy history, hyperopic or astigmatic refractive error, ultraviolet radiation, female sex (although accommodation is similar to males), hotter climates and some medical conditions such as diabetes can accelerate the onset of presbyopic symptoms. Whilst clinicians can ameliorate the symptoms of presbyopia with near vision spectacle correction, bifocal and progressive spectacle lenses, monovision, translating or multifocal contact lenses, monovision, extended depth of focus, multifocal (refractive, diffractive and asymmetric designs) or ‘accommodating’ intraocular lenses, corneal inlays, scleral expansion, laser refractive surgery (corneal monovision, corneal shrinkage, corneal multifocal profiles and lenticular softening), pharmacologic agents, and electro-stimulation of the ciliary muscle, none fully overcome presbyopia in all patients. While the restoration of natural accommodation or an equivalent remains elusive, guidance is gives on presbyopic correction evaluation techniques

The Athena X-ray Integral Field Unit: a consolidated design for the system requirement review of the preliminary definition phase

The Athena X-ray Integral Unit (X-IFU) is the high resolution X-ray spectrometer studied since 2015 for flying in the mid-30s on the Athena space X-ray Observatory. Athena is a versatile observatory designed to address the Hot and Energetic Universe science theme, as selected in November 2013 by the Survey Science Committee. Based on a large format array of Transition Edge Sensors (TES), X-IFU aims to provide spatially resolved X-ray spectroscopy, with a spectral resolution of 2.5 eV (up to 7 keV) over a hexagonal field of view of 5 arc minutes (equivalent diameter). The X-IFU entered its System Requirement Review (SRR) in June 2022, at about the same time when ESA called for an overall X-IFU redesign (including the X-IFU cryostat and the cooling chain), due to an unanticipated cost overrun of Athena. In this paper, after illustrating the breakthrough capabilities of the X-IFU, we describe the instrument as presented at its SRR (i.e. in the course of its preliminary definition phase, so-called B1), browsing through all the subsystems and associated requirements. We then show the instrument budgets, with a particular emphasis on the anticipated budgets of some of its key performance parameters, such as the instrument efficiency, spectral resolution, energy scale knowledge, count rate capability, non X-ray background and target of opportunity efficiency. Finally, we briefly discuss the ongoing key technology demonstration activities, the calibration and the activities foreseen in the X-IFU Instrument Science Center, touch on communication and outreach activities, the consortium organisation and the life cycle assessment of X-IFU aiming at minimising the environmental footprint, associated with the development of the instrument. Thanks to the studies conducted so far on X-IFU, it is expected that along the design-to-cost exercise requested by ESA, the X-IFU will maintain flagship capabilities in spatially resolved high resolution X-ray spectroscopy, enabling most of the original X-IFU related scientific objectives of the Athena mission to be retained. The X-IFU will be provided by an international consortium led by France, The Netherlands and Italy, with ESA member state contributions from Belgium, Czech Republic, Finland, Germany, Poland, Spain, Switzerland, with additional contributions from the United States and Japan.The French contribution to X-IFU is funded by CNES, CNRS and CEA. This work has been also supported by ASI (Italian Space Agency) through the Contract 2019-27-HH.0, and by the ESA (European Space Agency) Core Technology Program (CTP) Contract No. 4000114932/15/NL/BW and the AREMBES - ESA CTP No.4000116655/16/NL/BW. This publication is part of grant RTI2018-096686-B-C21 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. This publication is part of grant RTI2018-096686-B-C21 and PID2020-115325GB-C31 funded by MCIN/AEI/10.13039/501100011033

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

New, Low-Cost, Hand-Held Multispectral Device for In-Field Fruit-Ripening Assessment

The state of ripeness at harvest is a key piece of information for growers as it determines the market price of the yield. This has been traditionally assessed by destructive chemical methods, which lead to low-spatiotemporal resolution in the monitorization of crop development and poor responsiveness for growers. These limitations have shifted the focus to remote-sensing, spectroscopy-based approaches. However, most of the research focusing on these approaches has been accomplished with expensive equipment, which is exorbitant for most users. To combat this issue, this work presents a low-cost, hand-held, multispectral device with original hardware specially designed to face the complexity related to in-field use. The proposed device is based on a development board (AS7265x, AMS AG) that has three sensor chips with a spectral response of eighteen channels in a range from 410 to 940 nm. The proposed device was evaluated in a red-grape field experiment. Briefly, it was used to acquire the spectral signature of eighty red-grape samples in the vineyard. Subsequently, the grape samples were analysed using standard chemical methods to generate ground-truth values of ripening status indicators (soluble solid content (SSC) and titratable acidity (TA)). The eighteen pre-process reflectance measurements were used as input for training artificial neural network models to estimate the two target parameters (SSC and TA). The developed estimation models were evaluated through a leave-one-out cross-validation approach obtaining promising results (R2 = 0.70, RMSE = 1.21 for SSC; and R2 = 0.67, RMSE = 0.91 for TA)