Osteoarthritis (OA) affects nearly 240 million people worldwide. Knee OA is the most common type of arthritis, especially in older adults. Physicians measure the severity of knee OA according to the Kellgren and Lawrence (KL) scale through visual inspection of X-ray or MR images. We propose a semi-automatic CADx model based on Deep Siamese convolutional neural networks and a fine-tuned ResNet-34 to simultaneously detect OA lesions in the two knees according to the KL scale. The training was done using a public dataset, whereas the validations were performed with a private dataset. Some problems of the imbalanced dataset were solved using transfer learning. The model results average of the multi-class accuracy is 61%, presenting better performance results for classifying classes KL-0, KL-3, and KL-4 than KL-1 and KL-2. The classification results were compared and validated using the classification of experienced radiologists.

The management and operation of wastewater treatment plants (WWTP) usually involve the release into the atmosphere of malodorous substances with the potential to reduce the quality of life of people living nearby. In this type of facility, anaerobic degradation processes contribute to the generation of hydrogen sulfide (H₂S), often at quite high concentrations; thus, the presence of this chemical compound in the atmosphere can be a good indicator of the occurrence and intensity of the olfactory impact in a specific area. The present paper describes the experimental and modelling work being carried out by CEAM-UMH in the surroundings of several wastewater treatment plants located in the Valencia Autonomous Community (Spain). This work has permitted the estimation of H₂S emission rates at different WWTPs under different environmental and operating conditions. Our methodological approach for analyzing and describing the most relevant aspects of the olfactory impact consisted of several experimental campaigns involving intensive field measurements using passive samplers in the vicinity of several WWTPs, in combination with numerical simulation results from a diagnostic dispersion model. A meteorological tower at each WWTP provided the input values for the dispersion code, ensuring a good fit of the advective component and therefore more confidence in the modelled concentration field in response to environmental conditions. Then, comparisons between simulated and experimental H₂S concentrations yielded estimates of the global emission rate for this substance at several WWTPs at different time periods. The results obtained show a certain degree of temporal and spatial (between-plant) variability (possibly due to both operational and environmental conditions). Nevertheless, and more importantly, the results show a high degree of uniformity in the estimates, which consistently stay within the same order of magnitude.

Positron emission tomography (PET) is a functional non-invasive imaging modality that uses radioactive substances (radiotracers) to measure changes in metabolic processes. Advances in scanner technology and data acquisition in the last decade have led to the development of more sophisticated PET devices with good spatial resolution (1–3 mm of full width at half maximum (FWHM)). However, there are involuntary motions produced by the patient inside the scanner that lead to image degradation and potentially to a misdiagnosis. The adverse effect of the motion in the reconstructed image increases as the spatial resolution of the current scanners continues improving. In order to correct this effect, motion correction techniques are becoming increasingly popular and further studied. This work presents a simulation study of an image motion correction using a frame-based algorithm. The method is able to cut the acquired data from the scanner in frames, taking into account the size of the object of study. This approach allows working with low statistical information without losing image quality. The frames are later registered using spatio-temporal registration developed in a multi-level way. To validate these results, several performance tests are applied to a set of simulated moving phantoms. The results obtained show that the method minimizes the intra-frame motion, improves the signal intensity over the background in comparison with other literature methods, produces excellent values of similarity with the ground-truth (static) image and is able to find a limit in the patient-injected dose when some prior knowledge of the lesion is present.

A monitoring program based on an indirect method was conducted to assess the approximation of the olfactory impact in several wastewater treatment plants (in the present work, only one is shown). The method uses H₂S passive sampling using Palmes-type diffusion tubes impregnated with silver nitrate and fluorometric analysis employing fluorescein mercuric acetate. The analytical procedure was validated in the exposure chamber. Exposure periods of at least 4 days are recommended. The quantification limit of the procedure is 0.61 ppb for a 5-day sampling, which allows the H₂S immission (ground concentration) level to be measured within its low odor threshold, from 0.5 to 300 ppb. Experimental results suggest an exposure time greater than 4 days, while recovery efficiency of the procedure, 93.0 ± 1.8%, seems not to depend on the amount of H₂S collected by the samplers within their application range. The repeatability, expressed as relative standard deviation, is lower than 7%, which is within the limits normally accepted for this type of sampler. Statistical comparison showed that this procedure and the reference method provide analogous accuracy. The proposed procedure was applied in two experimental campaigns, one intensive and the other extensive, and concentrations within the H₂S low odor threshold were quantified at each sampling point. From these results, it can be concluded that the procedure shows good potential for monitoring the olfactory impact around facilities where H₂S emissions are dominant.