Abstract
The recent increasing interest in the application of radiology contrasting agents to create transparency in biological tissues implies that the diffusion properties of those agents need evaluation. The comparison of those properties with the ones obtained for other optical clearing agents allows to perform an optimized agent selection to create optimized transparency in clinical applications. In this study, the evaluation and comparison of the diffusion properties of gadobutrol and glycerol in skeletal muscle was made, showing that although gadobutrol has a higher molar mass than glycerol, its low viscosity allows for a faster diffusion in the muscle. The characterization of the tissue dehydration and refractive index matching mechanisms of optical clearing was made in skeletal muscle, namely by the estimation of the diffusion coefficients for water, glycerol and gadobutrol. The estimated tortuosity values of glycerol (2.2) and of gadobutrol (1.7) showed a longer path-length for glycerol in the muscle.

Abstract
The pancreas is a highly important organ, since it produces insulin and prevents the occurrence of diabetes. Although rare, pancreatic cancer is highly lethal, with a small life expectancy after being diagnosed. The pancreas is one of the organs less studied in the field of biophotonics. With the objective of acquiring information that can be used in the development of future applications to diagnose and treat pancreas diseases, the spectral optical properties of the rabbit pancreas were evaluated in a broad-spectral range, between 200 and 1000 nm. The method used to obtain such optical properties is simple, based almost on direct calculations from spectral measurements. The optical properties obtained show similar wavelength dependencies to the ones obtained for other tissues, but a further analysis on the spectral absorption coefficient showed that the pancreas tissues contain pigments, namely melanin, and lipofuscin. Using a simple calculation, it was possible to retrieve similar contents of these pigments from the absorption spectrum of the pancreas, which indicates that they accumulate in the same proportion as a result of the aging process. Such pigment accumulation was camouflaging the real contents of DNA, hemoglobin, and water, which were precisely evaluated after subtracting the pigment absorption.

Abstract
Many relevant sound events occur in urban scenarios, and robust classification models are required to identify abnormal and relevant events correctly. These models need to identify such events within valuable time, being effective and prompt. It is also essential to determine for how much time these events prevail. This article presents an extensive analysis developed to identify the best-performing model to successfully classify a broad set of sound events occurring in urban scenarios. Analysis and modelling of Transformer models were performed using available public datasets with different sets of sound classes. The Transformer models' performance was compared to the one achieved by the baseline model and end-to-end convolutional models. Furthermore, the benefits of using pre-training from image and sound domains and data augmentation techniques were identified. Additionally, complementary methods that have been used to improve the models' performance and good practices to obtain robust sound classification models were investigated. After an extensive evaluation, it was found that the most promising results were obtained by employing a Transformer model using a novel Adam optimizer with weight decay and transfer learning from the audio domain by reusing the weights from AudioSet, which led to an accuracy score of 89.8% for the UrbanSound8K dataset, 95.8% for the ESC-50 dataset, and 99% for the ESC-10 dataset, respectively.

Abstract
Audio recognition can be used in smart cities for security, surveillance, manufacturing, autonomous vehicles, and noise mitigation, just to name a few. However, urban sounds are everyday audio events that occur daily, presenting unstructured characteristics containing different genres of noise and sounds unrelated to the sound event under study, making it a challenging problem. Therefore, the main objective of this literature review is to summarize the most recent works on this subject to understand the current approaches and identify their limitations. Based on the reviewed articles, it can be realized that Deep Learning (DL) architectures, attention mechanisms, data augmentation techniques, and pretraining are the most crucial factors to consider while creating an efficient sound classification model. The best-found results were obtained by Mushtaq and Su, in 2020, using a DenseNet-161 with pretrained weights from ImageNet, and NA-1 and NA-2 as augmentation techniques, which were of 97.98%, 98.52%, and 99.22% for UrbanSound8K, ESC-50, and ESC-10 datasets, respectively. Nonetheless, the use of these models in real-world scenarios has not been properly addressed, so their effectiveness is still questionable in such situations.

Abstract
This article focuses on a sensorial glove prototype capable of acquiring hand motion and estimating its pose. The presented solution features twelve inertial measurement units (IMUs) to track hand orientation. The sensors are attached to a glove to decrease the project cost. The system also focuses on sensor fusion algorithms for the IMUs and further implementations, presenting the algebraic quaternion algorithm (AQUA), used because of its modularity and intuitive implementation. An adaptation of a human hand model is proposed, explaining its advantages and its limitations. Considering that the calibration is a very important process in gyroscope performance, the online and offline calibration data was analyzed, pointing out its challenges and improvements. To better visualize the model and sensors a simulation was conducted in Unity.

Abstract
Background: American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard.Objective: We aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA. Methods: We searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study. Results: Our search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression. Conclusions: Although high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition. Trial Registration: PROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339(J Med Internet Res 2022;24(9):e39452) doi: 10.2196/39452