Towards automating underwater measurement of fish length: a comparison of semi-automatic and manual stereo–video measurements

Underwater stereo–video systems are widely used for counting and measuring fish in aquaculture, fisheries, and conservation management. Length measurements are generated from stereo–video recordings by a software operator using a mouse to locate the head and tail of a fish in synchronized pairs of images. This data can be used to compare spatial and temporal changes in the mean length and biomass or frequency distributions of populations of fishes. Since the early 1990s stereo–video has also been used for measuring the lengths of fish in aquaculture for quota and farm management. However, the costs of the equipment, software, the time, and salary costs involved in post processing imagery manually and the subsequent delays in the availability of length information inhibit the adoption of this technology. We present a semi-automatic method for capturing stereo–video measurements to estimate the lengths of fish. We compare the time taken to make measurements of the same fish measured manually from stereo–video imagery to that measured semi-automatically. Using imagery recorded during transfers of Southern Bluefin Tuna (SBT) from tow cages to grow out cages, we demonstrate that the semi-automatic algorithm developed can obtain fork length measurements with an error of less than 1% of the true length and with at least a sixfold reduction in operator time in comparison to manual measurements. Of the 22 138 SBT recorded we were able to measure 52.6% (11 647) manually and 11.8% (2614) semi-automatically. For seven of the eight cage transfers recorde,d there were no statistical differences in the mean length, weight, or length frequency between manual and semi-automatic measurements. When the data were pooled across the eight cage transfers, there was no statistical difference in mean length or weight between the stereo–video-based manual and semi-automated measurements. Hence, the presented semi-automatic system can be deployed to significantly reduce the cost involved in adoption of stereo–video technology

A randomized controlled multimodal behavioral intervention trial for improving antiepileptic drug adherence

Purpose: Medication nonadherence is one of the most important reasons for treatment failure in patients with epilepsy. The present study investigated the effectiveness of a multicomponent intervention to improve adherence to antiepileptic drug (AED) medication in patients with epilepsy. Methods: In a prospective, randomizedmulticenter trial, three sessions of face-to-facemotivational interviewing (MI) in combination with complementary behavior change techniques were compared with standard care.Motivational interviewing prompted change talk and self-motivated statements from the patients, planning their own medication intake regimen and also identifying and overcoming barriers thatmay prevent adherence. Participants were provided with calendars to self-monitor their medication taking behavior. A family member and the health-care teamwere invited to attend the last session ofMI in order to improve the collaboration and communication between patients, their caregiver or family member, and their health-care provider. At baseline and 6-month follow-up, psychosocial variables and medical adherence were assessed. Results: In total, 275 participantswere included in the study. Comparedwith the active control group, patients in the intervention group reported significantly highermedication adherence, aswell as stronger intention and perceptions of control for taking medication regularly. The intervention group also reported higher levels of action planning, coping planning, self-monitoring, and lower medication concerns. Conclusions: This study shows that MI can be effective in clinical practice to improvemedication adherence in patientswith epilepsy. It also provides evidence that combining volitional interventions, including action planning, coping planning, and self-monitoring withmotivational interviewing can promote the effectiveness of the medical treatments for epilepsy by improving adherenc

Comparing the excessive daytime sleepiness of obese and non-obese patients

Background: Obesity, particularly morbid obesity, has various physical and mental complications. Excessive daytime somnolence (EDS) is a sleep disorder that reduces individuals� performance capability and the accuracy of their short-term memory and causes learning problems. This retrospective study aimed to document the presence of EDS in a sample of obese patients in comparison to patients with a normal weight. Objectives: This article compares the excessive daytime sleepiness of obese and non-obese patients in the minimally invasive surgery research center in Tehran, Iran. Patients and Methods: In this case-control study, we compared excessive daytime sleepiness in 55 obese patients who were candidates for laparoscopic surgery, with a body mass index (BMI) of equal to or greater than 30 kg/m2, with 55 controls with a normal BMI (19.5-24.9 kg/m2). The process of selecting the control group in our case-control study is matching in group levels, so that the controls are similar to the case group with regard to certain key characteristics, such as age, sex, and race. The sleep assessment was based on the Epworth sleepiness scale (ESS) questionnaire. Analysis of variance (ANOVA) was used to compare the means of quantitative data, such as the ESS score of groups. Results: Sleepiness was not affected by gender in cases or controls. The sleepiness prevalence was 29 (52.7) in the cases group and 17 (30.9) in the control group (OR = 2.493 (95 CI 1.144-5.435)). The mean ESS scores in cases and controls were 7.82 ± 3.86 and 10.54±6.15, respectively (P = 0.007). Moreover, the prevalence of sleepiness and the mean ESS scores in class III of obesity differed significantly from the controls (16 (57.1) vs. 17 (30.9)) (OR = 2.980 (95 CI 1.162-7.645)) and (11.04±5.93 vs. 7.82±3.86) (P = 0.013), respectively. Conclusions: Our findings suggest a strong relationship between EDS and obesity, particularly morbid obesity. Therefore, physicians must be familiar with EDS as a mixed clinical entity indicating careful assessment and specific treatment planning. © 2016, Iranian Red Crescent Medical Journal

Reconstruction of three-dimensional porous media using generative adversarial neural networks

To evaluate the variability of multi-phase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a novel method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image datasets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that GANs can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.Comment: 21 pages, 20 figure