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In this article, the evolution of BFT (bluefin tuna) sizes in fattening cages is studied, for which it was necessary to perform exhaustive monitoring with stereoscopic cameras and an exhaustive analysis of the data using automatic procedures. Exploring the size evolution of BFT over a long period is an important step in inferring their growth patterns, which are essential for designing smart aquaculture and sustainable fishing, and even assessing their health status. An important objective of this work was to verify whether tuna in captivity, in addition to fattening, grow in length. To this end, our autonomous monitoring system, equipped with stereoscopic cameras, was installed from 28 July 2020 to 23 May 2021 in a fattening cage in the Mediterranean containing 724 free-swimming tuna. This system provides thousands of images that, grouped by time intervals, allow us to conduct our studies. An automatic procedure, already introduced in a previous work and capable of processing large volumes of data, is used to estimate the length and width of individuals in ventral stereoscopic images of fish, and the evolution over time is analysed for each biometric characteristic. However, verifying the evolution of length and width based only on means or medians of these measurements may be inconsistent and insufficiently accurate to support our study objectives, as individuals of different sizes and ages may grow at different rates. Therefore, a modal analysis (Bhattacharya's method) was undertaken to identify the cohorts within the population. The results showed that each modal length surpassed the length of the next cohort and that there was accelerated growth in cages compared to the wild. In addition, we proved that using a length–width–weight relationship to estimate fish weight gives more accurate results than traditional length–weight relationships for fish fattened in cages. [ABSTRACT FROM AUTHOR]

Due to global warming and the effects associated with it, the wine industry is facing important challenges during the winemaking process and the production of high-quality wines. In this study, mistelas and naturally sweet wines were produced with the 'Pedro Ximénez' grapevine cultivar, overripened by sun drying and fermented with and without the presence of grape skins. Some oenological parameters related to alcoholic fermentation and low-molecular-weight polyphenols and furans were considered. Naturally sweet wines with skins presence showed a higher value of viable biomass than those with grape skins absence. However, in terms of density and ethanol production, sweet wines with grape skins absence presented lower and higher values, respectively, than the other elaborations. No significant differences in the organic acids and low-molecular-weight polyphenols and furans contents, with respect to the presence or absence of grape skins, were observed. In this sense, this research proves that the production of sweet wines from sun-dried grapes with the presence/absence of grape skins during alcoholic fermentation could be a possible choice in areas where agro-climatic conditions make it possible. [ABSTRACT FROM AUTHOR]

This paper presents a non-invasive fully automatic procedure to obtain highly accurate fish length estimation in adult Bluefin Tuna, based on a stereoscopic vision system and a deformable model of the fish ventral silhouette. The present work takes a geometric tuna model, which was previously developed by the same authors to discriminate fish in 2D images, and proposes new models to enhance the capabilities of the automatic procedure, from fish discrimination to accurate 3D length estimation. Fish length information is an important indicator of the health of wild fish stocks and for predicting biomass using length-weight relations. The proposal pays special attention to parts of the fish silhouette that have special relevance for accurate length estimation. The models have been designed to best fit the rear part of the fish, in particular the caudal peduncle, and a width parameter has been added to better fit the silhouette. Moreover, algorithms have been developed to extract snout tip and caudal peduncle features, allowing better initialization of model parameters. Snout Fork Length (SFL) measurements using the different models are extracted from images recorded with a stereoscopic vision system in a sea cage containing 312 adult Atlantic Bluefin Tuna. The automatic measurements are compared with two ground truths: one configured with semiautomatic measurements of favourable selected samples and one with real SFL measurements of the tuna stock collected at harvesting. Comparison with the semiautomatic measurements demonstrates that the combination of improved geometric models and feature extraction algorithms delivers good results in terms of fish length estimation error (up to 90% of the samples bounded in a 3% error margin) and number of automatic measurements (up to 950 samples out of 1000). When compared with real SFL measurements of the tuna stock, the system provides a high number of automatic detections (up to 6706 in a video of 135 min duration, i.e., 50 automatic measurements per minute of recording) and highly accurate length measurements, obtaining no statistically significant difference between automatic and real SFL frequency distributions. This procedure could be extended to other species to assess the size distribution of stocks, as discussed in the paper. [ABSTRACT FROM AUTHOR]

This article presents a non-invasive fully automatic procedure for Bluefin Tuna sizing, based on a stereoscopic vision system and a deformable model of the fish ventral silhouette. An image processing procedure is performed on each video frame to extract individual fish, followed by a fitting procedure to adjust the fish model to the extracted targets, adapting it to the bending movements of the fish. The proposed system is able to give accurate measurements of tuna snout fork length (SFL) and widths at five predefined silhouette points without manual intervention. In this work, the system is used to study size evolution in adult Atlantic Bluefin Tuna (Thunnus Thynnus) over time in a growing farm. The dataset is composed of 12 pairs of videos, which were acquired once a month in 2015, between July and October, in three grow-out cages of tuna aquaculture facilities on the west Mediterranean coast. Each grow out cage contains between 300 and 650 fish on an approximate volume of 20 000 m³. Measurements were automatically obtained for the 4 consecutive months after caging and suggest a fattening process: SFL shows an increase of just a few centimetres (2%) while the maximum width (A1) shows a relative increase of more than 20%, mostly in the first 2months in farm. Moreover, a linear relation (with coefficient of determination R² > 0.98) between SFL and widths for each month is deduced, and a fattening factor (F) is introduced. The validity of the measurements is proved by comparing 15 780 SFL measurements, obtained with our automatic system in the last month, versus ground truth data of a high percentage of the stock under study (1143 out of 1579), obtaining no statistically significant difference. This procedure could be extended to other species to assess the size distribution of stocks, as discussed in the article. [ABSTRACT FROM AUTHOR]

This paper proposes a robust deformable adaptive 2D model, based on computer vision methods, that automatically fits the body (ventral silhouette) of Bluefin tuna while swimming. Our model (without human intervention) adjusts to fish shape and size, obtaining fish orientation, bending to fit their flexion motion and has proved robust enough to overcome possible segmentation inaccuracies. Once the model has been successfully fitted to the fish it can ensure that the detected object is a tuna and not parts of fish or other objects. Automatic requirements of the fishing industry like biometric measurement, specimen counting or catch biomass estimation could then be addressed using a stereoscopic system and meaningful information extracted from our model. We also introduce a fitting procedure based on a fitting parameter – Fitting Error Index (FEI) – which permits us to know the quality of the results. In the experiments our model has achieved very high success rates (up to 90%) discriminating individuals in highly complex images acquired for us in real conditions in the Mediterranean Sea. Conclusions and future improvements to the proposed model are also discussed. [ABSTRACT FROM AUTHOR]

Corn (Zea mays) kernel processing companies evaluate the quality of kernels to determine the price of a batch. Human inspectors in labs inspect a reduced set of kernels to estimate the proportion of damaged kernels in any given lot. The visual differences between good and damaged kernels may be minor and, therefore, difficult to discern. Our goal is to design a computer vision system that enables the automatic evaluation of the quality of corn lots. To decide if an individual kernel can be accepted or rejected, it is necessary to design a method to detect defects, as well as quantify the defective proportions. A setup to work in-line and an approach to identify damaged kernels that combines algorithm-based computer vision techniques of novelty detection and principal component analysis (PCA) is presented. Experiments were carried out in three colour spaces using 450 dent corn kernels previously classified by experts. Results show that the method is promising (92% success) but extensions are recommended to further improve results. [ABSTRACT FROM AUTHOR]

Abstract: One of the main problems in the post-harvest processing of citrus is the detection of visual defects in order to classify the fruit depending on their appearance. Species and cultivars of citrus present a high rate of unpredictability in texture and colour that makes it difficult to develop a general, unsupervised method able of perform this task. In this paper we study the use of a general approach that was originally developed for the detection of defects in random colour textures. It is based on a Multivariate Image Analysis strategy and uses Principal Component Analysis to extract a reference eigenspace from a matrix built by unfolding colour and spatial data from samples of defect-free peel. Test images are also unfolded and projected onto the reference eigenspace and the result is a score matrix which is used to compute defective maps based on the statistic. In addition, a multiresolution scheme is introduced in the original method to speed up the process. Unlike the techniques commonly used for the detection of defects in fruits, this is an unsupervised method that only needs a few samples to be trained. It is also a simple approach that is suitable for real-time compliance. Experimental work was performed on 120 samples of oranges and mandarins from four different cultivars: Clemenules, Marisol, Fortune, and Valencia. The success ratio for the detection of individual defects was 91.5%, while the classification ratio of damaged/sound samples was 94.2%. These results show that the studied method can be suitable for the task of citrus inspection. [Copyright &y& Elsevier]

This paper evaluates the impact of using deep learning techniques in an automatic fish sizing process. Automatic fish sizing with a non-invasive approach involves working with different views of the fish's body and changing environments, being the stage of extraction of individuals in the image and the quality of the segmentation essential to obtain good sizing measurements. The goal of this work is to improve the results and functionality achieved in our previous studies with conventional segmentation methods based on local thresholding, where different limitations were observed, mainly the necessity of parameters tuning and a high computational cost. The number of detections must also increase significantly to increase the reliability of the statistical results. An approach using convolutional neural networks is proposed for fish detection and segmentation in videos acquired under real conditions, which eliminates the engineering procedure of parameter adjustment and generalises the solution for fish segmentation to deal with different environmental conditions (illumination and water turbidity) and background variability. The results show that the fish sizing procedure is enhanced thanks to the improvement in fish image instance segmentation. In particular, the number of fish measurements increases by up to 2.45 times when using Mask R-CNN and the PointRend module, thus increasing the accuracy of the fish length estimation, and the number of measurements per minute of computing time increases by up to 3.5 times. Our proposal obtains highly accurate fish length estimations in juvenile bluefin tuna based on a stereoscopic vision system and a deformable model of the fish's silhouette, both from the ventral and dorsal perspectives. An important improvement is achieved by applying CNN, as demonstrated by the number of segmented instances, the time required to segment an instance, and the accuracy of the fish sizing achieved. [ABSTRACT FROM AUTHOR]

A number of methods for temporal alignment, feature extraction and clustering of electrocardiographic signals are proposed. The ultimate aim of the paper is to find a method to automatically reduce the quantity of beats to examine in a long-term electrocardiographic signal, known as Holter signal, without loss of valuable information for the diagnosis. These signals include thousands of beats and therefore visual inspection is difficult and cumbersome. All the elements involved in each stage will be described and a thorough experimental study will be presented. The electrocardiograph signals used in the experiments belong to the well-known MIT database, where many different waveforms can be found. Based on the results of the experiments, a complete process is proposed to obtain the significant beats present within a signal, with a reasonable computational cost. Hence, cardiologists will only have to examine a small but fully representative subset of beats, making this method a very useful tool for medical decision support systems. [Copyright &y& Elsevier]

Objective: Describe the medium-term safety of the tension free vaginal tape obturator (TVT-O) procedure in terms of complications, cure and changes in quality of life (QoL) after the surgery. Materials and methods: Descriptive historical cohort that included women over 18 years of age who underwent TVT-O due to objectively proven stress urinary incontinence, urethral hypermobility or mixed urinary incontinence in which the stress component predominated, confirmed on urodynamic testing between July 2013-April 2017, in a reference hospital located in the city of Murcia Spain. Women with previous anti-incontinence surgery, concomitant vaginal surgery and planning pregnancy were excluded. Follow-up was determined for each patient based on the time elapsed between surgery and the time when the research protocol was applied. Complications were stratified according to the modified Clavien-Dindo classification; also we evaluated subjective cure rate, quality of life using the ICIQ-SF score, before and after surgery. Results: The mean age was 52.6 (SD± 10.5) years and 80.1% of patients were at least overweight. The incidence of complications at 12 months was: 8.3% (12/144). We did not detect complications after this period in the followed patients at 24, 36 and 48 months. The subjective cure determined at 12, 24, 36 and 48 months was 62.5% (90/144), 59.09% (55/88), 50.81% (31/61) and 50% (7/14), respectively. There was a significant improvement in quality of life, as determined by the ICQ-SF mean score before and after surgery (13.76[6,34] vs 3.84[5.76]; p<0.05). Conclusions: The TVT-O surgery is a safe therapy associated with a low complication incidence at 12 months, an acceptable subjective cure rate in stress urinary incontinence, and quality-of-life improvement. Classifications of complications related to the insertion of the prosthesis and of those inherent to surgery, such as urinary tract infection, are required. [ABSTRACT FROM AUTHOR]

A proposal is described for an underwater sensor combining an acoustic device with an optical one to automatically size juvenile bluefin tuna from a ventral perspective. Acoustic and optical information is acquired when the tuna are swimming freely and the fish cross our combined sensor's field of view. Image processing techniques are used to identify and classify fish traces in acoustic data (echogram), while the video frames are processed by fitting a deformable model of the fishes' ventral silhouette. Finally, the fish are sized combining the processed acoustic and optical data, once the correspondence between the two kinds of data is verified. The proposed system is able to automatically give accurate measurements of the tuna's Snout-Fork Length (SFL) and width. In comparison with our previously validated automatic sizing procedure with stereoscopic vision, this proposal improves the samples per hour of computing time by 7.2 times in a tank with 77 juveniles of Atlantic bluefin tuna (Thunnus thynnus), without compromising the accuracy of the measurements. This work validates the procedure for combining acoustic and optical data for fish sizing and is the first step towards an embedded sensor, whose electronics and processing capabilities should be optimized to be autonomous in terms of the power supply and to enable real-time processing. [ABSTRACT FROM AUTHOR]

In this work, acoustic and computer vision techniques are combined to develop an automatic procedure for biomass estimation of tuna during transfers. A side scan sonar working at 200 kHz and a stereo camera, positioned facing towards the surface to record the ventral aspect of fish, are set as acquisition equipment. Moreover, a floating structure has been devised to place the sensors between cages in transfers, creating a transfer canal that allows data acquisition while fish swim from donor to receiving cage. Biomass assessment is computed by counting transferred tuna and sizing a representative sample of the stock. The number of transferred tuna is automatically deduced from acoustic echograms by means of image processing techniques, whereas tuna size is computed from the stereo videos using our automatic computer vision procedure based on a deformable model of the fish ventral silhouette. The results show that the system achieves automatic tuna counting with error below 10%, achieving around 1% error in the best configuration, and automatic tuna sizing of more than 20% of the stock, with highly accurate Snout Fork Length estimation when compared to true data from harvests. These results fulfil the requirements imposed by International Commission for the Conservation of Atlantic Tunas for compliant transfer operations. [ABSTRACT FROM AUTHOR]

• Estimation of Bluefin Tuna mean length in sea cages has been performed using underwater acoustic tools. • The combined use of optical and acoustical techniques has revealed a strong relationship between TS and ABFT length measured in fattening cages. • Good agreement was found between TS and the fish fork (without the need to know the tuna's tilt angle), even considering uncompensated TS variable. This paper proposes an indirect method to estimate Bluefin Tuna (Thunnus thynnus) biomass in cages using acoustic techniques. Two Simrad EK60 echosounders working at 120 and 200 kHz and a stereo camera were used to obtain target strength (TS) to fork length (FL) relationships for both operating frequencies. The equipment was placed at the bottom of a floating cage, facing towards the surface to record the ventral aspect of fish. The acoustic and optical recordings were automatically analysed and the combination of acoustic and optical results provided unequivocal TS-FL assignments. Good relationships between TS and FL were obtained for both frequencies even without discriminating data from different fish tilts and without using beam directivity compensation. Stronger correlations were obtained for compensated TS at 200 kHz when reduced tuna swimming tilt was considered. TS measurements were compared to MFS numerical predictions for a Bluefin swimbladder model, with the simulation results showing good agreement with experimental measurements. The results allow the mean tuna length to be predicted in growing or fattening cages from acoustic data raising the possibility of improving production management and of providing a useful tool for catch control estimations made by international organisations (like ICCAT) and government bodies. [ABSTRACT FROM AUTHOR]

Aim In this study, we investigated the adequacy of the healthcare response systems to detect and treat faecal incontinence in patients in the primary care areas (PCAs) in a core urban area of Barcelona. Method Patients with a diagnosis of faecal incontinence in the study area were identified from the electronic register of diagnostic codes, and from a manual review of electronic medical records of a random sample of the study population. The remaining variables were obtained through a structured telephone interview. Results In the study population ( n = 65 023) with a previously estimated prevalence of faecal incontinence of 13% [95% confidence interval (CI) 10-17%], 68 cases of faecal incontinence were detected by the health care system (prevalence: 0.10%; 95% CI, 0.08-0.13). Of these, 39 patients (68% women, 68 ± 18 years of age) were interviewed: 18 (46%) reported symptoms lasting for longer than 5-10 years and 20 (51%) had waited for more than 5 years before seeking medical advice. Only 8 (18%) had received any treatment for faecal incontinence, and 18 (46%) reported persistent faecal incontinence at the time of the interview (Vaizey severity score 13 ± 4/24). Conclusion The detection and treatment of faecal incontinence is insufficient in primary care services. Strategies to correct this are needed. [ABSTRACT FROM AUTHOR]