Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models

The health and function of tissue rely on its vasculature network to provide reliable blood perfusion. Volumetric imaging approaches, such as multiphoton microscopy, are able to generate detailed 3D images of blood vessels that could contribute to our understanding of the role of vascular structure in normal physiology and in disease mechanisms. The segmentation of vessels, a core image analysis problem, is a bottleneck that has prevented the systematic comparison of 3D vascular architecture across experimental populations. We explored the use of convolutional neural networks to segment 3D vessels within volumetric in vivo images acquired by multiphoton microscopy. We evaluated different network architectures and machine learning techniques in the context of this segmentation problem. We show that our optimized convolutional neural network architecture, which we call DeepVess, yielded a segmentation accuracy that was better than both the current state-of-the-art and a trained human annotator, while also being orders of magnitude faster. To explore the effects of aging and Alzheimer's disease on capillaries, we applied DeepVess to 3D images of cortical blood vessels in young and old mouse models of Alzheimer's disease and wild type littermates. We found little difference in the distribution of capillary diameter or tortuosity between these groups, but did note a decrease in the number of longer capillary segments ($>75\mu m$) in aged animals as compared to young, in both wild type and Alzheimer's disease mouse models.Comment: 34 pages, 9 figure

The Improvement of Automatic Skin Cancer Detection Algorithm Based on CVQ technique

Nowadays, by increasing the number of deaths related to skin cancer, this kind of cancer has been converted as one of the important issues in humans' life. However, the main key is early detection of skin cancer in order to save the life of people. By considering this fact that there is a near similarity between cancer moles and normal ones, attention to artificial systems with the ability of distinguishing between these kinds of moles can be very important, undoubtedly. The accuracy of this kind of system must be considered in order to find better results, especially in the cases which are related to human‘s life. In this paper, with regard to the fact that the raising of a kind of skin cancer, Melanoma, has increasing, we have employed neural networks in the aim of function improvement of an approach based on compressed image technique, namely, Classified Vector Quantization (CVQ) technique. This suggested method has been examined on some images and the results show that this method is a proper way in order to automatic skin cancer detection

The Improvement of Automatic Skin Cancer Detection Algorithm Based on CVQ technique

Nowadays, by increasing the number of deaths related to skin cancer, this kind of cancer has been converted as one of the important issues in humans' life. However, the main key is early detection of skin cancer in order to save the life of people. By considering this fact that there is a near similarity between cancer moles and normal ones, attention to artificial systems with the ability of distinguishing between these kinds of moles can be very important, undoubtedly. The accuracy of this kind of system must be considered in order to find better results, especially in the cases which are related to human‘s life. In this paper, with regard to the fact that the raising of a kind of skin cancer, Melanoma, has increasing, we have employed neural networks in the aim of function improvement of an approach based on compressed image technique, namely, Classified Vector Quantization (CVQ) technique. This suggested method has been examined on some images and the results show that this method is a proper way in order to automatic skin cancer detection

Techno-environmental assessment and machine learning-based optimization of a novel dual-source multi-generation energy system

The utilization of high-temperature hybrid energy systems has a vital and promising role in reducing environmental pollutants and coping with climate change. So, in the present research, a dual-source multigeneration energy system composed of a gas turbine, a supercritical carbon dioxide recompression Brayton cycle, an organic Rankine cycle, an absorption refrigeration system, and a reverse osmosis desalination unit is designed and analyzed from thermodynamic, environmental and economic perspectives. The system supplies power with a stable load to follow the changes in the demand side which is important for off-grid distributed energy systems. The dual-source operation of the system makes it possible to generate sustainable electricity leading to less utilization of fossil fuels in the gas turbine subsystem and reduction in environmental pollution, and furthermore, malfunctioning of a subsystem will not lead to the failure of the entire plant. Three multi-objective optimizations with different objective functions are accomplished using artificial neural network from data learning and genetic and Greywolf algorithms to obtain the best-operating conditions. Under the base conditions, for the total input energy of 699 MW to the entire system, the energy and exergy efficiencies, the unit exergy cost of products, the carbon dioxide emission index, and the payback period, respectively, were found to be 45%, 54%, 15.3 $/GJ, 112.2 kg/MWh, and 7.2 years. The net output power of the proposed system was calculated as 288.2 MW. A sensitivity analysis revealed that with a change in the pressure ratio of the supercritical carbon dioxide cycle, the net generated power and overall efficiency take maximum values of 293.9 MW while the unit exergy cost of products and carbon dioxide emission index take minimum values of 15.3$/GJ and 110.1 kg/MWh, respectively. Furthermore, increasing the pressure ratio of the gas turbine leads to maximum values of 45 and 54% in overall energy and exergy efficiencies, respectively

Thermodynamics modelling and optimisation of a biogas fueled decentralised poly-generation system using machine learning techniques

In the forthcoming era of smart energy systems, decentralised solutions are gaining increasing prominence due to their superior adaptability for interconnecting sectors, reduced inefficiencies, and environmentally friendly operation. This study introduces a new medium-scale biogas-based power plant that utilises a gas turbine to meet the energy needs of a specific locality, encompassing electricity, heating, cooling, and water supply, all whilst considering the system's environmental impact. To optimise the plant's performance, three different multi-objective optimisation scenarios employing machine learning methodologies and Greywolf algorithms with distinct objective functions are analysed. Under the base conditions, the proposed plant showcases impressive capabilities, delivering 1372 kW of electricity, 246.2 kW of heating, 293.3 kW of cooling, and 4.1 kg/s of distilled water. It operates with first and second law thermodynamics efficiencies of 72.3% and 41.4%, respectively, while maintaining a CO2 emission index of 0.778 kgCO2/kWh. Furthermore, the net present value and investment return period for the investment are estimated to be approximately 4.4 million USD and 4 years, respectively. Through optimisation (scenario 1) that prioritises maximising efficiency while minimising product costs and environmental impact, the following parameters are achieved: an exergy efficiency of 42.7%, a cost of products at 28.8 $/GJ, and a reduced CO2 emission index of 0.762 kgCO2/kWh. The results reveal that the proposed system not only excels in efficiency but also proves to be economically viable and environmentally beneficial

Segmentation of anatomical layers and imaging artifacts in intravascular polarization sensitive optical coherence tomography using attending physician and boundary cardinality losses

Intravascular ultrasound and optical coherence tomography are widely available for assessing coronary stenoses and provide critical information to optimize percutaneous coronary intervention. Intravascular polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization state of the light scattered by the vessel wall in addition to conventional cross-sectional images of subsurface microstructure. This affords reconstruction of tissue polarization properties and reveals improved contrast between the layers of the vessel wall along with insight into collagen and smooth muscle content. Here, we propose a convolutional neural network model, optimized using two new loss terms (Boundary Cardinality and Attending Physician), that takes advantage of the additional polarization contrast and classifies the lumen, intima, and media layers in addition to guidewire and plaque shadows. Our model segments the media boundaries through fibrotic plaques and continues to estimate the outer media boundary behind shadows of lipid-rich plaques. We demonstrate that our multi-class classification model outperforms existing methods that exclusively use conventional OCT data, predominantly segment the lumen, and consider subsurface layers at most in regions of minimal disease. Segmentation of all anatomical layers throughout diseased vessels may facilitate stent sizing and will enable automated characterization of plaque polarization properties for investigation of the natural history and significance of coronary atheromas.</p

Segmentation of anatomical layers and imaging artifacts in intravascular polarization sensitive optical coherence tomography using attending physician and boundary cardinality losses

Intravascular ultrasound and optical coherence tomography are widely available for assessing coronary stenoses and provide critical information to optimize percutaneous coronary intervention. Intravascular polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization state of the light scattered by the vessel wall in addition to conventional cross-sectional images of subsurface microstructure. This affords reconstruction of tissue polarization properties and reveals improved contrast between the layers of the vessel wall along with insight into collagen and smooth muscle content. Here, we propose a convolutional neural network model, optimized using two new loss terms (Boundary Cardinality and Attending Physician), that takes advantage of the additional polarization contrast and classifies the lumen, intima, and media layers in addition to guidewire and plaque shadows. Our model segments the media boundaries through fibrotic plaques and continues to estimate the outer media boundary behind shadows of lipid-rich plaques. We demonstrate that our multi-class classification model outperforms existing methods that exclusively use conventional OCT data, predominantly segment the lumen, and consider subsurface layers at most in regions of minimal disease. Segmentation of all anatomical layers throughout diseased vessels may facilitate stent sizing and will enable automated characterization of plaque polarization properties for investigation of the natural history and significance of coronary atheromas.</p

Safety and Efficacy of Incentive Spirometer in Covid-19 Pneumonia; a Randomized Clinical Trial

Introduction: Various treatment protocols have been recommended since the beginning of the COVID-19 pandemic and have gradually evolved. This study aimed to assess the effectiveness and safety of incentive spirometer exercise (ISE) in outcomes of hospitalized patients with moderate-to-severe COVID-19 pneumonia. Methods: A 3-month single-blind, two parallel-armed randomized controlled trial was conducted at Imam Hossein Hospital, Tehran, Iran. Participants aged >18 years with documented COVID-19 pneumonia were randomly allocated to 2 groups of IS (ISE in addition to the usual treatment) and control (usual care alone). The IS group was also asked to perform ISE after discharge for three months. The primary outcomes were peripheral O2 saturation (SpO2), VBG parameters (pCO2, PH, HCO3), dyspnea level measured by Modified Borg Scale (MBS), length of hospital stay (LOS), and respiratory rate (RR). Secondary outcomes included mortality rate, intubation rate (IR), and ICU admission rate. Results: A total of 160 eligible patients were randomly assigned to either the IS (n = 80) or control (n=80) groups. Although there were no significant differences in primary and secondary outcomes between the groups post-intervention, adjusted analysis showed that participants allocated to the IS group had significantly higher SpO2 levels and lower RR, MBS levels, and LOS. Also, the adjusted model analysis showed a marginal statistically significant difference between groups in secondary outcomes, such as IR, the 1-month mortality rate, and the 3-month mortality rate. Conclusion: It seems that adding the ISE to usual care in the early treatment setting of COVID-19 patients resulted in a relatively significant increase in SpO2 levels, improved respiratory status, and marginally decreased LOS. Additionally, ISE minimally reduced ICU admissions and intubation rates, with no significant impact on in-hospital or long-term mortality in patients with COVID-19 pneumonia

Brain capillary networks across species : a few simple organizational requirements are sufficient to reproduce both structure and function

Despite the key role of the capillaries in neurovascular function, a thorough characterization of cerebral capillary network properties is currently lacking. Here, we define a range of metrics (geometrical, topological, flow, mass transfer, and robustness) for quantification of structural differences between brain areas, organs, species, or patient populations and, in parallel, digitally generate synthetic networks that replicate the key organizational features of anatomical networks (isotropy, connectedness, space-filling nature, convexity of tissue domains, characteristic size). To reach these objectives, we first construct a database of the defined metrics for healthy capillary networks obtained from imaging of mouse and human brains. Results show that anatomical networks are topologically equivalent between the two species and that geometrical metrics only differ in scaling. Based on these results, we then devise a method which employs constrained Voronoi diagrams to generate 3D model synthetic cerebral capillary networks that are locally randomized but homogeneous at the network-scale. With appropriate choice of scaling, these networks have equivalent properties to the anatomical data, demonstrated by comparison of the defined metrics. The ability to synthetically replicate cerebral capillary networks opens a broad range of applications, ranging from systematic computational studies of structure-function relationships in healthy capillary networks to detailed analysis of pathological structural degeneration, or even to the development of templates for fabrication of 3D biomimetic vascular networks embedded in tissue-engineered constructs