Commentary: Coordinated infraslow neural and cardiac oscillations mark fragility and offline periods in mammalian sleep.

We read with interest the paper by Lecci et al. (2017), who showed oscillations of the electroencephalographic (EEG) spectral power in the sigma band (10\u201315 Hz) during non-rapid-eye-movement (NREM) sleep at frequencies in the infra-slow range (ISO = 0.001\u20130.1 Hz). The occurrence of this rhythm (sigma-ISO) in human subjects and mice, and its correlation with autonomic and behavioral components suggest that it reflects a fundamental physiological mechanism

Detecting coagulation time in cheese making by means of computer vision and machine learning techniques

Cheese production, a globally cherished culinary tradition, faces challenges in ensuring consistent product quality and production efficiency. The critical phase of determining cutting time during curd formation significantly influences cheese quality and yield. Traditional methods often struggle to address variability in coagulation conditions, particularly in small-scale factories. In this paper, we present several key practical contributions to the field, including the introduction of CM-IDB, the first publicly available image dataset related to the cheese-making process. Also, we propose an innovative artificial intelligence-based approach to automate the detection of curd-firming time during cheese production using a combination of computer vision and machine learning techniques. The proposed method offers real-time insights into curd firmness, aiding in predicting optimal cutting times. Experimental results show the effectiveness of integrating sequence information with single image features, leading to improved classification performance. In particular, deep learning-based features demonstrate excellent classification capability when integrated with sequence information. The study suggests the suitability of the proposed approach for integration into real-time systems, especially within dairy production, to enhance product quality and production efficiency

Automatic Monitoring Cheese Ripeness Using Computer Vision and Artificial Intelligence

Ripening is a very important process that contributes to cheese quality, as its characteristics are determined by the biochemical changes that occur during this period. Therefore, monitoring ripening time is a fundamental task to market a quality product in a timely manner. However, it is difficult to accurately determine the degree of cheese ripeness. Although some scientific methods have also been proposed in the literature, the conventional methods adopted in dairy industries are typically based on visual and weight control. This study proposes a novel approach aimed at automatically monitoring the cheese ripening based on the analysis of cheese images acquired by a photo camera. Both computer vision and machine learning techniques have been used to deal with this task. The study is based on a dataset of 195 images (specifically collected from an Italian dairy industry), which represent Pecorino cheese forms at four degrees of ripeness. All stages but the one labeled as 'day 18', which has 45 images, consist of 50 images. These images have been handled with image processing techniques and then classified according to the degree of ripening, i.e., 18, 22, 24, and 30 days. A 5-fold cross-validation strategy was used to empirically evaluate the performance of the models. During this phase, each training fold was augmented online. This strategy allowed to use 624 images for training, leaving 39 original images per fold for testing. Experimental results have demonstrated the validity of the approach, showing good performance for most of the trained models

A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19

COVID-19 is an ongoing global pandemic of coronavirus disease 2019, which may cause severe acute respiratory syndrome. This disease highlighted the limitations of health systems worldwide regarding managing the pandemic. In particular, the lack of diagnostic tests that can quickly and reliably detect infected patients has contributed to the spread of the virus. Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) and antigen tests, which are the main diagnostic tests for COVID-19, showed their limitations during the pandemic. In fact, RT-PCR requires several hours to provide a diagnosis and is not properly accurate, thus generating a high number of false negatives. Unlike RT-PCR, antigen tests provide rapid diagnosis but are less accurate in detecting COVID-19 positive patients. Medical imaging is an alternative diagnostic test for COVID-19. In particular, chest computed tomography allows detecting lung infections related to the disease with high accuracy. However, visual analysis of a chest scan generated by computed tomography is a demanding activity for radiologists, making widespread use of this test unfeasible. Therefore, it is essential to lighten their work with automated tools able to provide accurate diagnosis in a short time. To deal with this challenge, in this work, an approach based on 3D Inception CNNs is proposed. Specifically, 3D Inception-V1 and Inception-V3 models have been built and compared. Then, soft-voting ensemble classifier models have been separately built on these models to boost the performance. As for the individual models, results showed that Inception-V1 outperformed Inception-V3 according to different measures. As for the ensemble classifier models, the outcome of experiments pointed out that the adopted voting strategy boosted the performance of individual models. The best results have been achieved enforcing soft voting on Inception-V1 models

Commercial sponge fishing in Libya: Historical records, present status and perspectives

Natural bath sponges (genera Spongia and Hippospongia, Porifera, Demospongiae) have been harvested for millennia to be used as aids to beauty and body tools, in traditional and modem medicine as well as in painting. Recently, a series of severe epidemics have affected Mediterranean commercial sponges fostering the overexploitation of remaining fishing grounds. Furthermore, Mediterranean bath sponges attain the highest prices compared to Caribbean or Indo-Pacific ones but little or no correct information on origin is transferred to the final buyer. A complex network of re-selling activities and the lack of labelling make it almost impossible to track the pathway of sponge trade. Some of the finest Mediterranean natural bath sponges come from Libya. Nevertheless, little information on Libyan sponge banks and trade have been available mostly given the former international ban. Under an Italian-Libyan joint-project it was possible to assess the past and present situation of sponge fishing in Libya, roughly covering a period of 150 years. After rather low production in years 1860-1879, average crop exceeded 40 t/year between 1880 and 1929. The peak was recorded in years 1920-1929 (almost 70 t/year on average). Today Libyan sponge fishery and trade are mostly confined to the eastern area of the country. Less than 10 t/year are currently harvested. According to a preliminary SCUBA diving survey along the Libyan coasts, sponges belonging to the order Dictyoceratida appear to be the most conspicuous sessile invertebrates in the investigated areas. Here, sponges belonging to the genera Ircinia and Sarcotragus (commonly defined "wild sponges" with no commercial value) appear to be more abundant than those belonging to the genera Spongia and Hippospongia. Sustainable approaches to the exploitation of this valuable natural resource such as sponge farming are proposed and discussed. (c) 2007 Elsevier B.V. All rights reserved

Cognitive Impairment and Age-Related Vision Disorders: Their Possible Relationship and the Evaluation of the Use of Aspirin and Statins in a 65 Years-and-Over Sardinian Population

Neurological disorders (Alzheimer’s disease, vascular and mixed dementia) and visual loss (cataract, age-related macular degeneration, glaucoma, and diabetic retinopathy) are among the most common conditions that afflict people of at least 65 years of age. An increasing body of evidence is emerging, which demonstrates that memory and vision impairment are closely, significantly, and positively linked and that statins and aspirin may lessen the risk of developing age-related visual and neurological problems. However, clinical studies have produced contradictory results. Thus, the intent of the present study was to reliably establish whether a relationship exist between various types of dementia and age-related vision disorders, and to establish whether statins and aspirin may or may not have beneficial effects on these two types of disorders. We found that participants with dementia and/or vision problems were more likely to be depressed and displayed worse functional ability in basic and instrumental activities of daily living than controls. Mini mental state examination scores were significantly lower in patients with vision disorders compared to subjects without vision disorders. A closer association with macular degeneration was found in subjects with Alzheimer’s disease than in subjects without dementia or with vascular dementia, mixed dementia, or other types of age-related vision disorders. When we considered the associations between different types of dementia and vision disorders and the use of statins and aspirin, we found a significant positive association between Alzheimer’s disease and statins on their own or in combination with aspirin, indicating that these two drugs do not appear to reduce the risk of Alzheimer’s disease or improve its clinical evolution and may, on the contrary, favor its development. No significant association in statin use alone, aspirin use alone, or the combination of these was found in subjects without vision disorders but with dementia, and, similarly, none in subjects with vision disorders but without dementia. Overall, these results confirm the general impression so far; namely, that macular degeneration may contribute to cognitive disorders (Alzheimer’s disease in particular). In addition, they also suggest that, while statin and aspirin use may undoubtedly have some protective effects, they do not appear to be magic pills against the development of cognitive impairment or vision disorders in the elderly

Sponges architecture by colour: new insights into the fibres morphogenesis, skeletal spatial layout and morpho-anatomical traits of a marine horny sponge species (Porifera)

This paper focuses on the skeletal architecture and morphotraits of the Mediterranean horny sponge Sarcotragus spinosulus (Demospongiae, Keratosa, Dictyoceratida, Irciniidae). This special endoskeletal system consists of a dense, variably complex connective architecture, which extends throughout the entire sponge body and is embedded in an abundant jelly-like extracellular matrix (ECM). To investigate the topographic arrangement and micro-morphotraits of these connective structures in detail and by colour, also during morphogenetic processes, histology techniques using light microscopy are essential. New information is provided on the coordinated morphogenetic processes that characterize the growth and assembly of collagenic prototype structures in the matrix of fibrous skeletal elements and drive skeleton remodelling. Our results also highlight some novelties and some remarkable peculiarities of fibrous, filamentous and fibrillar components at the levels of both composition and structure. The morphofunctional significance of skeletal architecture is suggested in the background of the anatomical complexity of S. spinosulus