Effects of high dietary inclusion of Arthrospira platensis, either extruded or supplemented with a super-dosing multi-enzyme mixture, on broiler growth performance and major meat quality parameters.

BACKGROUND: This investigation assessed the effects of high dietary inclusion of Spirulina (Arthrospira platensis) on broiler chicken growth performance, meat quality and nutritional attributes. For this, 120 male broiler chicks were housed in 40 battery brooders (three birds per brooder). Initially, for 14 days, a standard corn and soybean meal diet was administered. Subsequently, from days 14 to 35, chicks were assigned to one of the four dietary treatments (n = 10 per treatment): (1) control diet (CTR); (2) diet with 15% Spirulina (SP); (3) diet with 15% extruded Spirulina (SPE); and (4) diet with 15% Spirulina plus a super-dosing enzymes supplement (0.20% pancreatin extract and 0.01% lysozyme) (SPM). RESULTS: Throughout the experimental period, both SP and SPM diets resulted in decreased final body weight and body weight gain compared to control (p < 0.001), with the SPE diet showing comparable results to CTR. The SPE diet prompted an increase in average daily feed intake (p = 0.026). However, all microalga treatments increased the feed conversion ratio compared to CTR. Dietary inclusion of Spirulina notably increased intestinal content viscosity (p < 0.010), which was mitigated by the SPM diet. Spirulina supplementation led to lower pH levels in breast meat 24 h post-mortem and heightened the b* colour value in both breast and thigh meats (p < 0.010). Furthermore, Spirulina contributed to an increased accumulation of total carotenoids, n-3 polyunsaturated fatty acids (PUFA), and saturated fatty acids (SFA), while diminishing n-6 PUFA, thus altering the n-6/n-3 and PUFA/SFA ratios favourably (p < 0.001). However, it also reduced zinc concentration in breast meat (p < 0.001). CONCLUSIONS: The findings indicate that high Spirulina levels in broiler diets impair growth due to increased intestinal viscosity, and that extrusion pre-treatment mitigates this effect. Despite reducing digesta viscosity, a super-dosing enzyme mix did not improve growth. Data also indicates that Spirulina enriches meat with antioxidants and n-3 PUFA but reduces α-tocopherol and increases saturated fats. Reduced zinc content in meat suggests the need for Spirulina biofortification to maintain its nutritional value.

The Portuguese mertolenga cattle breed: a review.

This review summarizes the state of the art regarding the phylogenetic origins, recent history and present-day main traits and uses of the Mertolenga cattle breed from Southern Portugal, particularly those related to production performances and product quality. Named after the historical city of Mértola, in southern Portugal, the Mertolenga is one of the fifteen autochthonous bovine breeds of Portugal. It is a cattle breed thoroughly adapted to the poor Mediterranean pastures of the southern regions of the Iberian Peninsula. It is used predominantly in an extensive to semi-intensive sylvopastoral production system called montado, where pastures are combined with helm and cork trees. Its productive traits allow for a good adaptation to the intense dry heat and pasture shortage during the summer, and compensatory growth in autumn and spring, when pastures regenerate. They are small to medium sized animals, with well-balanced bodies, roan, red, or, less often, red-spotted coats, and known for their nervous temperament. Although this breed experienced a severe decline in numbers in the 1970s and classified as endangered in the 1990s, the work of a few breeders led to the establishment of larger Mertolenga breed inventories, starting from a limited base. For this reason, the entire breed has today a strong influence from a very few herds and sires. Reproduction is still mostly achieved using natural mating, and the males are often kept with the breeding females all year long. It is a heterogeneous breed both phenotypically and genetically. Recent phylogenetic studies have revealed the Mertolenga as a one of the most genetically diverse breeds in the country and in the Iberian Peninsula and helped classify this breed, once believed to be a variety of the Alentejana breed. These studies also showed genetic relations with other breeds in the Iberian Peninsula. Mertolenga beef currently benefits from several certifications, the most important one being the PDO - Protected Denomination of Origin.

Ketamine-dexmedetomidine combination for sedation in pediatric major surgery in a low-income country.

Anorectal malformations are one of the most frequent congenital malformations treated by pediatric surgeons. In low-income countries, the surgical and anesthetic management of children in need of these procedures can be challenging. Limited oxygen supply, lack of equipment, especially pediatric, and intensive care units make the use of regional anesthesia appealing. We present a series of four cases of anorectal malformations corrections in Guinea Bissau, in children up to 13 months of age, under regional anesthesia and sedation with ketodex, a mixture of ketamine and dexmedetomidine (in a proportion of 1 mg to 1 µg). No child developed respiratory depression requiring airway intervention or supplemental oxygen, or had hemodynamic instability.

Development of a bench-scale photobioreactor with a novel recirculation system for continuous cultivation of microalgae.

Microalgae cultivation can be used to increase the sustainability of carbon emitting processes, converting the CO2 from exhaust gases into fuels, food and chemicals. Many of the carbon emitting industries operate in a continuous manner, for periods that can span days or months, resulting in a continuous stream of gas emissions. Biogenic CO2 from industrial microbiological processes is one example, since in many cases it becomes unsustainable to stop these processes on a daily or weekly basis. To correctly sequester these emissions, microalgae systems must be operated under continuous constant conditions, requiring photobioreactors (PBRs) that can act as chemostats for long periods of time. However, in order to optimize culture parameters or study metabolic responses, bench-scale setups are necessary. Currently there is a lack of studies and design alternatives using chemostat, since most works focus on batch assays or semi-continuous cultures. Therefore, this work focused on the development of a continuous bench-scale PBR, which combines a retention vessel, a photocollector and a degasser, with an innovative recirculation system, that allows it to operate as an autotrophic chemostat, to study carbon sequestration from a biogenic CO2-rich constant air stream. To assess its applicability, the PBR was used to cultivate the green microalga Haematococcus pluvialis using as sole carbon source the CO2 produced by a coupled heterotrophic bacterial chemostat. An air stream containing ≈0.35 vol% of CO2, was fed to the system, and it was evaluated in terms of stability, carbon fixation and biomass productivity, for dilution rates ranging from 0.1 to 0.5 d-1. The PBR was able to operate under chemostat conditions for more than 100 days, producing a stable culture that generated proportional responses to the stimuli it was subjected to, attaining a maximum biomass productivity of 183 mg/L/d with a carbon fixation efficiency of ≈39% at 0.3 d-1. These results reinforce the effectiveness of the developed PBR system, making it suitable for laboratory-scale studies of continuous photoautotrophic microalgae cultivation.

Uptake of lipid core nanoparticles by fragments of tissues collected during cerebral tumor excision surgeries: hypotheses for use in drug targeting therapy.

PURPOSE: Malignant cerebral tumors have poor prognosis and the blood-brain barrier is a major hindrance for most drugs to reach those tumors. Lipid nanoparticles (LDE) that bind to lipoprotein receptors may carry anticancer drugs and penetrate the cells through those receptors that are overexpressed in gliomas. The aim was to investigate the in vivo uptake of LDE by human cerebral tumors. METHODS: Twelve consecutive patients (4 with glioblastomas, 1 meduloblastoma, 1 primary lymphoma, 2 with non-cerebral metastases and 4 with benign tumors) scheduled for tumor excision surgery were injected intravenously, 12 h before surgery, with LDE labeled 14C-cholesterol oleate. Fragments of tumors and of normal head tissues (muscle, periosteum, dura mater) discarded by the surgeon were submitted to lipid extraction and radioactive counting. RESULTS: Tumor LDE uptake (range: 10-283 d.p.m./g of tissue) was not lower than that of normal tissues (range: 20-263 d.p.m./g). Malignant tumor uptake was threefold greater than benign tumor uptake (140 ± 93 vs 46 ± 18 d.p.m./g, p < 0.05). Results show that LDE can concentrate in brain malignant tumors and may be used to carry drugs directed against those tumors. CONCLUSION: As LDE was previously shown to markedly decrease drug toxicity this new therapeutic strategy should be tested in future trials.

Alzheimer's diagnosis using deep learning in segmenting and classifying 3D brain MR images.

BACKGROUND AND OBJECTIVES: Dementia is one of the brain diseases with serious symptoms such as memory loss, and thinking problems. According to the World Alzheimer Report 2016, in the world, there are 47 million people having dementia and it can be 131 million by 2050. There is no standard method to diagnose dementia, and consequently unable to access the treatment effectively. Hence, the computational diagnosis of the disease from brain Magnetic Resonance Image (MRI) scans plays an important role in supporting the early diagnosis. Alzheimer's Disease (AD), a common type of Dementia, includes problems related to disorientation, mood swings, not managing self-care, and behavioral issues. In this article, we present a new computational method to diagnosis Alzheimer's disease from 3D brain MR images. METHODS: An efficient approach to diagnosis Alzheimer's disease from brain MRI scans is proposed comprising two phases: I) segmentation and II) classification, both based on deep learning. After the brain tissues are segmented by a model that combines Gaussian Mixture Model (GMM) and Convolutional Neural Network (CNN), a new model combining Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM) is used to classify Alzheimer's disease based on the segmented tissues. RESULTS: We present two evaluations for segmentation and classification. For comparison, the new method was evaluated using the AD-86 and AD-126 datasets leading to Dice 0.96 for segmentation in both datasets and accuracies 0.88, and 0.80 for classification, respectively. CONCLUSION: Deep learning gives prominent results for segmentation and feature extraction in medical image processing. The combination of XGboost and SVM improves the results obtained.

Convolutional Neural Networks and Heuristic Methods for Crowd Counting: A Systematic Review.

The crowd counting task has become a pillar for crowd control as it provides information concerning the number of people in a scene. It is helpful in many scenarios such as video surveillance, public safety, and future event planning. To solve such tasks, researchers have proposed different solutions. In the beginning, researchers went with more traditional solutions, while recently the focus is on deep learning methods and, more specifically, on Convolutional Neural Networks (CNNs), because of their efficiency. This review explores these methods by focusing on their key differences, advantages, and disadvantages. We have systematically analyzed algorithms and works based on the different models suggested and the problems they are trying to solve. The main focus is on the shift made in the history of crowd counting methods, moving from the heuristic models to CNN models by identifying each category and discussing its different methods and architectures. After a deep study of the literature on crowd counting, the survey partitions current datasets into sparse and crowded ones. It discusses the reviewed methods by comparing their results on the different datasets. The findings suggest that the heuristic models could be even more effective than the CNN models in sparse scenarios.

Using Deep Neural Networks for Human Fall Detection Based on Pose Estimation.

Requests for caring for and monitoring the health and safety of older adults are increasing nowadays and form a topic of great social interest. One of the issues that lead to serious concerns is human falls, especially among aged people. Computer vision techniques can be used to identify fall events, and Deep Learning methods can detect them with optimum accuracy. Such imaging-based solutions are a good alternative to body-worn solutions. This article proposes a novel human fall detection solution based on the Fast Pose Estimation method. The solution uses Time-Distributed Convolutional Long Short-Term Memory (TD-CNN-LSTM) and 1Dimentional Convolutional Neural Network (1D-CNN) models, to classify the data extracted from image frames, and achieved high accuracies: 98 and 97% for the 1D-CNN and TD-CNN-LSTM models, respectively. Therefore, by applying the Fast Pose Estimation method, which has not been used before for this purpose, the proposed solution is an effective contribution to accurate human fall detection, which can be deployed in edge devices due to its low computational and memory demands.

9-DOF IMU-Based Attitude and Heading Estimation Using an Extended Kalman Filter with Bias Consideration.

The attitude and heading reference system (AHRS) is an important concept in the area of navigation, image stabilization, and object detection and tracking. Many studies and works have been conducted in this regard to estimate the accurate orientation of rigid bodies. In most research in this area, low-cost MEMS sensors are employed, but since the system's response will diverge over time due to integration drift, it is necessary to apply proper estimation algorithms. A two-step extended Kalman Filter (EKF) algorithm is used in this study to estimate the orientation of an IMU. A 9-DOF device is used for this purpose, including a 6-DOF IMU with a three-axis gyroscope and a three-axis accelerometer, and a three-axis magnetometer. In addition, to have an accurate algorithm, both IMU and magnetometer biases and disturbances are modeled and considered in the real-time filter. After applying the algorithm to the sensor's output, an accurate orientation as well as unbiased angular velocity, linear acceleration, and magnetic field were achieved. In order to demonstrate the reduction of noise power, fast Fourier transform (FFT) diagrams are used. The effect of the initial condition on the response of the system is also investigated.

Sound Classification and Processing of Urban Environments: A Systematic Literature Review.

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.

Transformers for Urban Sound Classification-A Comprehensive Performance Evaluation.

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.

Binaural Acoustic Scene Classification Using Wavelet Scattering, Parallel Ensemble Classifiers and Nonlinear Fusion.

The analysis of ambient sounds can be very useful when developing sound base intelligent systems. Acoustic scene classification (ASC) is defined as identifying the area of a recorded sound or clip among some predefined scenes. ASC has huge potential to be used in urban sound event classification systems. This research presents a hybrid method that includes a novel mathematical fusion step which aims to tackle the challenges of ASC accuracy and adaptability of current state-of-the-art models. The proposed method uses a stereo signal, two ensemble classifiers (random subspace), and a novel mathematical fusion step. In the proposed method, a stable, invariant signal representation of the stereo signal is built using Wavelet Scattering Transform (WST). For each mono, i.e., left and right, channel, a different random subspace classifier is trained using WST. A novel mathematical formula for fusion step was developed, its parameters being found using a Genetic algorithm. The results on the DCASE 2017 dataset showed that the proposed method has higher classification accuracy (about 95%), pushing the boundaries of existing methods.

Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning.

Heart sounds convey important information regarding potential heart diseases. Currently, heart sound classification attracts many researchers from the fields of telemedicine, digital signal processing, and machine learning-among others-mainly to identify cardiac pathology as quickly as possible. This article proposes chaogram as a new transform to convert heart sound signals to colour images. In the proposed approach, the output image is, therefore, the projection of the reconstructed phase space representation of the phonocardiogram (PCG) signal on three coordinate planes. This has two major benefits: (1) it makes possible to apply deep convolutional neural networks to heart sounds and (2) it is also possible to employ a transfer learning scheme by converting a heart sound signal to an image. The performance of the proposed approach was verified on the PhysioNet dataset. Due to the imbalanced data on this dataset, it is common to assess the results quality using the average of sensitivity and specificity, which is known as score, instead of accuracy. In this study, the best results were achieved using the InceptionV3 model, which achieved a score of 88.06%.

Weakly Supervised Video Anomaly Detection Based on 3D Convolution and LSTM.

Weakly supervised video anomaly detection is a recent focus of computer vision research thanks to the availability of large-scale weakly supervised video datasets. However, most existing research works are limited to the frame-level classification with emphasis on finding the presence of specific objects or activities. In this article, a new neural network architecture is proposed to efficiently extract the prominent features for detecting whether a video contains anomalies. A video is treated as an integral input and the detection follows the procedure of video-label assignment. The extraction of spatial and temporal features is carried out by three-dimensional convolutions, and then their relationship is further modeled using an LSTM network. The concise structure of the proposed method enables high computational efficiency, and extensive experiments demonstrate its effectiveness.

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System.

With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios.

Diagnosis of Leukaemia in Blood Slides Based on a Fine-Tuned and Highly Generalisable Deep Learning Model.

Leukaemia is a dysfunction that affects the production of white blood cells in the bone marrow. Young cells are abnormally produced, replacing normal blood cells. Consequently, the person suffers problems in transporting oxygen and in fighting infections. This article proposes a convolutional neural network (CNN) named LeukNet that was inspired on convolutional blocks of VGG-16, but with smaller dense layers. To define the LeukNet parameters, we evaluated different CNNs models and fine-tuning methods using 18 image datasets, with different resolution, contrast, colour and texture characteristics. We applied data augmentation operations to expand the training dataset, and the 5-fold cross-validation led to an accuracy of 98.61%. To evaluate the CNNs generalisation ability, we applied a cross-dataset validation technique. The obtained accuracies using cross-dataset experiments on three datasets were 97.04, 82.46 and 70.24%, which overcome the accuracies obtained by current state-of-the-art methods. We conclude that using the most common and deepest CNNs may not be the best choice for applications where the images to be classified differ from those used in pre-training. Additionally, the adopted cross-dataset validation approach proved to be an excellent choice to evaluate the generalisation capability of a model, as it considers the model performance on unseen data, which is paramount for CAD systems.

Versatile Convolutional Networks Applied to Computed Tomography and Magnetic Resonance Image Segmentation.

Medical image segmentation has seen positive developments in recent years but remains challenging with many practical obstacles to overcome. The applications of this task are wide-ranging in many fields of medicine, and used in several imaging modalities which usually require tailored solutions. Deep learning models have gained much attention and have been lately recognized as the most successful for automated segmentation. In this work we show the versatility of this technique by means of a single deep learning architecture capable of successfully performing segmentation on two very different types of imaging: computed tomography and magnetic resonance. The developed model is fully convolutional with an encoder-decoder structure and high-resolution pathways which can process whole three-dimensional volumes at once, and learn directly from the data to find which voxels belong to the regions of interest and localize those against the background. The model was applied to two publicly available datasets achieving equivalent results for both imaging modalities, as well as performing segmentation of different organs in different anatomic regions with comparable success.

Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review.

Radiation oncology for prostate cancer is important as it can decrease the morbidity and mortality associated with this disease. Planning for this modality of treatment is both fundamental, time-consuming and prone to human-errors, leading to potentially avoidable delays in start of treatment. A fundamental step in radiotherapy planning is contouring of radiation targets, where medical specialists contouring, i.e., segment, the boundaries of the structures to be irradiated. Automating this step can potentially lead to faster treatment planning without a decrease in quality, while increasing time available to physicians and also more consistent treatment results. This can be framed as an image segmentation task, which has been studied for many decades in the fields of Computer Vision and Machine Learning. With the advent of Deep Learning, there have been many proposals for different network architectures achieving high performance levels. In this review, we searched the literature for those methods and describe them briefly, grouping those based on Computed Tomography (CT) or Magnetic Resonance Imaging (MRI). This is a booming field, evidenced by the date of the publications found. However, most publications use data from a very limited number of patients, which presents an obstacle to deep learning models training. Although the performance of the models has achieved very satisfactory results, there is still room for improvement, and there is arguably a long way before these models can be used safely and effectively in clinical practice.

Sex estimation using the mandibular canine index components.

The mandibular canine index (MCI) has been described as a suitable methodology for sex estimation in forensic scenarios but there are contradictory reports about its accuracy. Moreover, the two mandibular canine teeth must be available, which is not always a viable option. The aim of this study was to strip the MCI by analyzing the MCI itself and its components, in order to optimize its use for sex estimation. The mesiodistal dimensions of the mandibular canine crown and the mandibular canine arch width were measured in a sample of 120 cast models. Five predictor variables were considered in this study: the standard MCI, a variation of the MCI using the left canine, and MCI components (MD43, MD33 and D33-43). Multivariate binary logistic regression was performed using stepwise forward approach to select the most statistical relevant variables on the probability of a cast being from a female. The estimated probability was then analyzed with respect to performance in sex classification (ROC analysis and optimal cut-offs accuracy) and compared with the performance of the univariate variables. MCI43 and MCI33 presented the lowest performance (64.2% and 63.3% respectively), and the highest overall accuracy was attained using the MD43 and MD33 (85.8% in both cases). The multivariate logistic model obtained (using MD43 and MD33) exhibited the same accuracy as the logistic model based solely on MD43 (85.8%). Our results suggest that MD43 should be used instead of MCI for sex estimation.

Resident-to-resident elder mistreatment (R-REM): a study in residential structures for elderly people (ERI) in Portugal.

This study explores patterns of resident-to-resident elder mistreatment (R-REM) in Portuguese Residential Structures for Elderly People (ERI: Estruturas Residenciais para Idosos). Results display a serious situation of R-REM, which occurs in different patterns.