Experimental and numerical investigation of hydro power generator ventilation

Improvements in ventilation and cooling offer means to run hydro power generators at higher power output and at varying operating conditions. The electromagnetic, frictional and windage losses generate heat. The heat is removed by an air flow that is driven by fans and/or the rotor itself. The air flow goes through ventilation channels in the stator, to limit the electrical insulation temperatures. The temperature should be kept limited and uniform in both time and space, avoiding thermal stresses and hot-spots. For that purpose it is important that the flow of cooling air is distributed uniformly, and that flow separation and recirculation are minimized. Improvements of the air flow properties also lead to an improvement of the overall efficiency of the machine. A significant part of the windage losses occurs at the entrance of the stator ventilation channels, where the air flow turns abruptly from tangential to radial. The present work focuses exclusively on the air flow inside a generator model, and in particular on the flow inside the stator channels. The generator model design of the present work is based on a real generator that was previously studied. The model is manufactured taking into consideration the needs of both the experimental and numerical methodologies. Computational Fluid Dynamics (CFD) results have been used in the process of designing the experimental set-up. The rotor and stator are manufactured using rapid-prototyping and plexi-glass, yielding a high geometrical accuracy, and optical experimental access. A special inlet section is designed for accurate air flow rate and inlet velocity profile measurements. The experimental measurements include Particle Image Velocimetry (PIV) and total pressure measurements inside the generator. The CFD simulations are performed based on the OpenFOAM CFD toolbox, and the steady-state frozen rotor approach. Specific studies are performed, on the effect of adding “pick-up” to spacers, and the effects of the inlet fan blades on the flow rate through the model. The CFD results capture the experimental flow details to a reasonable level of accuracy

Independent Modular Networks

Monolithic neural networks that make use of a single set of weights to learn useful representations for downstream tasks explicitly dismiss the compositional nature of data generation processes. This characteristic exists in data where every instance can be regarded as the combination of an identity concept, such as the shape of an object, combined with modifying concepts, such as orientation, color, and size. The dismissal of compositionality is especially detrimental in robotics, where state estimation relies heavily on the compositional nature of physical mechanisms (e.g., rotations and transformations) to model interactions. To accommodate this data characteristic, modular networks have been proposed. However, a lack of structure in each module's role, and modular network-specific issues such as module collapse have restricted their usability. We propose a modular network architecture that accommodates the mentioned decompositional concept by proposing a unique structure that splits the modules into predetermined roles. Additionally, we provide regularizations that improve the resiliency of the modular network to the problem of module collapse while improving the decomposition accuracy of the model.Comment: ICRA23 RAP4Robots Worksho

Izražajnost kalretinina kao biomarkera rizika za metastatski karcinom mliječne žlijezde u pasa

Malignant breast tumors are the most common tumors in humans and are associated with a poor prognosis. An accurate animal model of human mammary gland tumorigenesis is needed to test novel diagnosis and treatment strategies. Dogs represent a promising model since they develop such tumors spontaneously. In the present study, three immunomarkers, including calretinin, c-Kit (CD117) and placental alkaline phosphatase (Plap), were used and compared with each other, in relation to estrogen and progesterone receptors and HER2 (triple markers), with the intention of malignancy grading. Enhanced expression of calretinin and placental alkaline phosphatase, without immunoreaction to c-Kit in neoplastic cells, is related to high-grade malignancy. Out of 50 tumors, 31 were metastasized, 29 of which (93.5%) were moderately to strongly calretinin positive (P<0.05). However, the results for c-Kit - and Plap+ in metastatic tumors were not reproducible. It may be concluded that calretinin could be introduced as a determinant biomarker in the diagnosis of breast cancer metastasis.Maligni tumori dojke najčešći su tumori u ljudi i povezani su s lošom prognozom. Da bi se testirali novi dijagnostički postupci i terapijske procedure u ljudi, potreban je prikladan životinjski model tumorogeneze mliječne žlijezde. Psi su potencijalno dobar model zbog spontanog razvoja ovakvih tumora. U ovom su istraživanju, s ciljem stupnjevanja malignosti, međusobno uspoređena tri imunomarkera, kalretinin, c-Kit (CD117) i placentalna alkalna fosfataza (Plap), a zatim su isti uspoređeni i s estrogenskim, progesteronskim te HER2 (trostrukim) markerima. Povećanje izražajnosti kalretinina i placentalne alkalne fosfataze, bez imunoreakcije na c-Kit u neoplastičnim stanicama povezano je s visokim stupnjem malignosti. Od 50 tumora, 31 je metastazirao, od kojih je 29 (93,5 %) bilo umjereno do izrazito pozitivno na kalretinin (P < 0,05). Doduše, rezultati za c-Ki ti Plap+ nisu bili ponovljivi. Zaključujemo da bi kalretinin mogao poslužiti kao biomarker u dijagnostici metatstatskog raka dojke

Molecular Detection and Genotyping of Intestinal Microspor-idia from Stray Dogs in Iran

Background: Microsporidia as one of the most important pathogens in veterinary and agricultural settings, have emerged in immunocompromised patients in Iran. To date, different Enterocytozoon bieneusi genotypes have been identified in humans and animals, supporting the possibility of zoonotic zoonosis transmission potential. The aim of this study was to evaluate the distribution of E. bieneusi genotypes among overpopulated stray dogs in vicinity of Tehran, the capital city of Iran. Methods: Totally, 75 stool and 75 urine samples were obtained from 75 stray dogs during the time period from Mar 2015 to Oct 2015. DNA extraction was performed on all the samples and specific fragment of small subunit ribosomal RNA gene of E. bieneusi and Encephalitozoon spp. was amplified. Furthermore, specific primers targeting the internal transcribed spacer region of E. bieneusi were applied to determine the genotype of the microorganism. Results: Microsporidia was detected in 5.3% of stool samples, while none of the urine samples was positive for microsporidia species. Overall, 440 bp fragment of E. bieneusi was amplified in all the samples and there was no amplification for Encephalitozoon spp. The results of sequencing of 410 bp fragment of internal transcribed spacer region showed that all the E. bieneusi were genotype D. Conclusion: E. bieneusi was the most prevalent microsporidian species in the stray dogs and all the positive isolates were characterized as genotype D

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19’s spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Early-infantile onset epilepsy and developmental delay caused by bi-allelic GAD1 variants.

Gamma-aminobutyric acid (GABA) and glutamate are the most abundant amino acid neurotransmitters in the brain. GABA, an inhibitory neurotransmitter, is synthesized by glutamic acid decarboxylase (GAD). Its predominant isoform GAD67, contributes up to ∼90% of base-level GABA in the CNS, and is encoded by the GAD1 gene. Disruption of GAD1 results in an imbalance of inhibitory and excitatory neurotransmitters, and as Gad1-/- mice die neonatally of severe cleft palate, it has not been possible to determine any potential neurological dysfunction. Furthermore, little is known about the consequence of GAD1 disruption in humans. Here we present six affected individuals from six unrelated families, carrying bi-allelic GAD1 variants, presenting with developmental and epileptic encephalopathy, characterized by early-infantile onset epilepsy and hypotonia with additional variable non-CNS manifestations such as skeletal abnormalities, dysmorphic features and cleft palate. Our findings highlight an important role for GAD1 in seizure induction, neuronal and extraneuronal development, and introduce GAD1 as a new gene associated with developmental and epileptic encephalopathy

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation