A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry

The process of unfolding the neutron energy spectrum has been subject of research for many years. Monte Carlo, iterative methods, the bayesian theory, the principle of maximum entropy are some of the methods used. The drawbacks associated with traditional unfolding procedures have motivated the research of complementary approaches. Back Propagation Neural Networks (BPNN), have been applied with success in neutron spectrometry and dosimetry domains, however, the structure and learning parameters are factors that highly impact in the networks performance. In ANN domain, Generalized Regression Neural Network (GRNN) is one of the simplest neural networks in term of network architecture and learning algorithm. The learning is instantaneous, requiring no time for training. Opposite to BPNN, a GRNN would be formed instantly with just a 1-pass training on the development data. In the network development phase, the only hurdle is to optimize the hyper-parameter, which is known as sigma, governing the smoothness of the network. The aim of this work was to compare the performance of BPNN and GRNN in the solution of the neutron spectrometry problem. From results obtained it can be observed that despite the very similar results, GRNN performs better than BPNN

A neutron spectrum unfolding code based on generalized regression artificial neural networks

The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the network topology and the long training time. Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN). That's mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum, provided that the optimal values of spread has been determined and that the dataset adequately represents the problem space. In addition, GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation

Animal Models of Rheumatoid Arthritis

Autoimmunity is a condition in which the host organizes an immune response against its own antigens. Rheumatoid arthritis (RA) is an autoimmune disease of unknown etiology, characterized by the presence of chronic inflammatory infiltrates, the development of destructive arthropathy, bone erosion, and degradation of the articular cartilage and subchondral bone. There is currently no treatment that resolves the disease, only the use of palliatives, and not all patients respond to pharmacologic therapy. According to RA multifactorial origin, several in vivo models have been used to evaluate its pathophysiology as well as to identify the usefulness of biomarkers to predict, to diagnose, or to evaluate the prognosis of the disease. This chapter focuses on the most common in vivo models used for the study of RA, including those related with genetic, immunological, hormonal, and environmental interactions. Similarly, the potential of these models to understand RA pathogenesis and to test preventive and therapeutic strategies of autoimmune disorder is also highlighted. In conclusion, of all the animal models discussed, the CIA model could be considered the most successful by generating arthritis using type II collagen and adjuvants and evaluating therapeutic compounds both intra-articularly and systemically

Generalized Regression Neural Networks with Application in Neutron Spectrometry

The aim of this research was to apply a generalized regression neural network (GRNN) to predict neutron spectrum using the rates count coming from a Bonner spheres system as the only piece of information. In the training and testing stages, a data set of 251 different types of neutron spectra, taken from the International Atomic Energy Agency compilation, were used. Fifty-one predicted spectra were analyzed at testing stage. Training and testing of GRNN were carried out in the MATLAB environment by means of a scientific and technological tool designed based on GRNN technology, which is capable of solving the neutron spectrometry problem with high performance and generalization capability. This computational tool automates the pre-processing of information, the training and testing stages, the statistical analysis, and the post-processing of the information. In this work, the performance of feed-forward backpropagation neural networks (FFBPNN) and GRNN was compared in the solution of the neutron spectrometry problem. From the results obtained, it can be observed that despite very similar results, GRNN performs better than FFBPNN because the former could be used as an alternative procedure in neutron spectrum unfolding methodologies with high performance and accuracy

Associated factors for mortality in a COVID-19 colombian cohort : is the third wave relevant when Mu variant was predominant epidemiologically?

Q1Q1Pacientes con COVID-19Objectives: To evaluate the association between Colombia's third wave when the Mu variant was predominant epidemiologically (until 75%) in Colombia and COVID-19 all-cause in-hospital mortality. Methods: In this retrospective cohort, we included hospitalized patients ≥18 years with SARS-CoV-2 infection between March 2020 to September 2021 in ten hospitals from three cities in Colombia. Description analysis, survival, and multivariate Cox regression analyses were performed to evaluate the association between the third epidemic wave and in-hospital mortality. Results: A total of 25,371 patients were included. The age-stratified time-to-mortality curves showed differences according to epidemic waves in patients ≥75 years (log-rank test p = 0.012). In the multivariate Cox analysis, the third wave was not associated with increased mortality relative to the first wave (aHR 0.95; 95%CI 0.84–1.08), but there was an interaction between age ≥75 years and the third wave finding a lower HR for mortality (aHR 0.56, 95%CI 0.36–0.86). Conclusions: We did not find an increase in in-hospital mortality during the third epidemic wave in which the Mu variant was predominant in Colombia. The reduced hazard in mortality in patients ≥75 years hospitalized in the third wave could be explained by the high coverage of SARS-CoV-2 vaccination in this population and patients with underlying conditions.https://orcid.org/0000-0003-1833-1599https://orcid.org/0000-0001-5363-5729https://orcid.org/0000-0001-6964-2229https://orcid.org/0000-0003-3975-2835https://orcid.org/0000-0001-9441-4375Revista Internacional - IndexadaA1N

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Role of glucose-1-phosphate and glucose-6-phosphate in glycogen synthesis by pigeon liver homogenate

1. 1. Glucose-1-C14, G-6-P-1-C14 and G-1-P-l-C14 have been incubated with a pigeon liver homogenate. Incorporation of C14 into glycogen and CO2 were measured. 2. 2. The relative incorporation of C14 from G-6-P-1-C14 and glucose-1-C14 into glycogen and CO2 together with the relative incorporation of C14 from G-1-P-l-C14 and glucose-1-C14 into glycogen and CO2 were calculated. From these results, it is postulated that G-6-P is not a necessary intermediate in glycogen biosynthesis from glucose; G-1-P would be the first intermediate and the metabolic cross that leads to glycogen and CO2. 3. 3. It is suggested that G-1-P is formed directly from glucose through the reactions catalyzed by phosphoglucokinase (E.C. 2.7.1.10) and phosphodismutase (E.C. 2.7.1.41). 4. 4. This last hypothesis is supported by the fact that G-1-P and G-1,6-diP stimulated C14 incorporation from glucose-1-C14 into both CO2 and glycogen. Other minor facts obtained from this work and others from the literature help to s

Differential steroidogenic response of human luteal cell subpopulations

The differentia] capacity for steroid synthesis of human luteal cell subpopulations was investigated in a well defined cell culture system. Corpora lutea were enzymatically dissociated, and the two cell types were obtained by a discontinous Percoll gradient Both cell types were cultured for 24 h with dibutyryl cAMP (1 mM), oestradiol (2.5 pM) and testosterone (1 MM). Steroid production was measured in the culture media and aromatase activity for both cell type subpopulations was also determined. Basal production of progesterone, oestradiol and testosterone was significantly greater in large cells than that in small cells (P < 0.05). Nevertheless, a greater response of small cells to several in-vitro treatments was observed. Thus, synthesis of progesterone, oestradiol and testosterone was significantly stimulated in these cells (P < 0.05) by dibutyryl cAMP. Interestingly, a 33-fold increase of progesterone production was also observed in the large luteal cell subpopulation. When oestradiol was added to the culture media, a 36% decrease of progesterone production (P < 0.05) by small cells was obtained, while progesterone synthesis by large cells was not significantly affected. Testosterone treatment of cells enhanced oestradiol production by both cell subtypes (P < 0.05), although the stimulatory action was greater in the small cell cultures (5.9-fold). These data indicate that the steroidogenic activity of the small cell subpopulation is highly dependent on endocrine and paracrine stimulatory mechanisms, while large cells possess a greater intrinsic steroidogenic capacity

Progesterone synthesis by human luteal cells: Modulation by estradiol

To assess the role of estradiol (E2) upon progesterone (P4) synthesis, a well defined human midluteal cell system was used. A dose-dependent inhibition of P4 synthesis with and without hCG was induced by E2. In addition, E2 had a dose related cumulative effect on pregnenolone as compared with control experiments (2-fold, P &lt; 0.05) as well as in hCG- stimulated conditions (3-fold, P &lt; 0.005). On the other hand, the concentrations of 20α-hydroxyprogesterone obtained in all experimental conditions were similar to control values, indicating that the catabolism of P4 was not modified. 3β-Hydroxysteroid dehydrogenase activity was significantly diminished (P &lt; 0.05) in the presence of E2. Finally, the kinetic studies on P4 synthesis from pregnenolone showed a competitive type of inhibition with a K1 of 2.22 x 10-6 mol/L. These data indicate an inhibition of 3β-hydroxysteroid dehydrogenase on human corpus luteum by E2

Endometrium and steroids, a pathologic overview

Normal endometrial function requires of cell proliferation and differentiation; therefore, disturbances in these processes could lead to pathological entities such as hyperplasia and endometrial adenocarcinoma, where cell proliferation is increased. The development of these pathologies is highly related to alterations in the levels and/or action of sexual steroids. In the present review, it has been analyzed how steroids, particularly estrogens, androgens and progestagens are involved in the etiopathogenesis of hyperplasia and endometrial endometrioid adenocarcinoma. The emphasis is given on pathological and pharmacological conditions that are presented as risk factors for endometrial pathologies, such as obesity, polycystic ovarian syndrome and hormone replacement postmenopausal women therapy, among others. Steroids alterations may promote changes at molecular level that enhance the development of hyperplasia and endometrioid cancer. In fact, there are solid data that indicate that estrogens stimulate cell -proliferation in this tissue; meanwhile, progestagens are able to stop cell proliferation and to increase differentiation. Nevertheless, the role of androgens is less clear, since there is contradictory information. It is most likely that the major contribution of steroids to the development of cell proliferation pathologies in endometria would be in early stages, where there is a high sensitivity to these molecules. This phenomenon is present even in stages previous to the occurrence of hyperplasia, like in the condition of polycystic ovarian syndrome, where the endometria have a greater sensitivity to steroids and high expression of cell cycle molecules. These abnormalities would contribute to the pathogenesis of hyperplasia and then in the progression to endometrioid adenocarcinoma.FONDECYT 1100299 1130053 CONICYT 24121153 CONICYT Doctoral National Fellowship 2110027