Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques

Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network

This study reports a statistical analysis of monthly sunspot number time series and observes non homogeneity and asymmetry within it. Using Mann-Kendall test a linear trend is revealed. After identifying stationarity within the time series we generate autoregressive AR(p) and autoregressive moving average (ARMA(p,q)). Based on minimization of AIC we find 3 and 1 as the best values of p and q respectively. In the next phase, autoregressive neural network (AR-NN(3)) is generated by training a generalized feedforward neural network (GFNN). Assessing the model performances by means of Willmott's index of second order and coefficient of determination, the performance of AR-NN(3) is identified to be better than AR(3) and ARMA(3,1).Comment: 17 pages, 4 figure

Measurement of the νe−\nu_e-Nucleus Charged-Current Double-Differential Cross Section at <Eν>=\left< E_{\nu} \right> = 2.4 GeV using NOvA

The inclusive electron neutrino charged-current cross section is measured in the NOvA near detector using $8.02\times10^{20}$ protons-on-target (POT) in the NuMI beam. The sample of GeV electron neutrino interactions is the largest analyzed to date and is limited by $\simeq$ 17\% systematic rather than the $\simeq$ 7.4\% statistical uncertainties. The double-differential cross section in final-state electron energy and angle is presented for the first time, together with the single-differential dependence on $Q^{2}$ (squared four-momentum transfer) and energy, in the range 1 GeV $\leq E_{\nu} <$6 GeV. Detailed comparisons are made to the predictions of the GENIE, GiBUU, NEUT, and NuWro neutrino event generators. The data do not strongly favor a model over the others consistently across all three cross sections measured, though some models have especially good or poor agreement in the single differential cross section vs. $Q^{2}$

The usefulness of leptin measurements and ultrasound fat thickness for assessment of body fat reserves of Awassi lambs

The objective of this study was to investigate the usefulness of leptin measurements for predicting back fat thickness and the amount of carcase fat in lambs with varying body weights (BWs). Blood samples were taken from 20 male Awassi lambs at 20, 25, 30, 35, and 40 kg BW. Tail, omental, dissected, and total fat were measured at slaughter (40 kg BW). Ultrasound fat thickness (UFT) increased with BW (p .05 for 40 kg BW). The introduction of UFT and BW as independent variables in addition to leptin in the multiple regression equations improved the predictions for carcase fat only (57.6%, p  .05 for leptin, leptin + UFT and leptin + BW, respectively). Leptin was a single predictor for omentum and dissected fat (41.4%, p  .05). The other variables (UFT and BW) were not a predictor (p > .05) except for total fat (p > .05). These results indicate that serum leptin concentration in association with BW and UFT could be used to estimate total fat (tail, omental and dissectible carcase fat) in male Awassi lambs

The effect of cold atmospheric plasma (NO) alone and in combination with NPH insulin on the full-thickness excisional wound healing in a diabetic rat model

This study was planned to investigate an alternative treatment modality in diabetic wound healing. In this experimental study, the efficacy of both cold atmospheric plasma/nitric oxide (NO) and NPH insulin ointment, recently known to have beneficial effects on wound healing, was investigated in diabetic wound healing. Twenty-four (24) diabetic rats were divided into four groups DC, DI, DNO and DINO (diabetic control, diabetic insulin, diabetic nitric oxide, diabetic insulin + nitric oxide groups). No treatment was applied to the DC group, NPH insulin was applied to the DI group, CAP/NO was applied to the DNO group, and CAP/NO + NPH insulin was applied to the DINO group once daily for 14 days. The wound area reduction and the wound contraction rate were calculated on the basis of the tissue sections taken, and histopathological and genetic analyses were carried out. Compared to the control group, exogenous NO gas was found to be a potent antibacterial agent in the diabetic wound healing, causing a reduction in the wound area (P = 0.034), an increased contraction rate (P = 0.021), epithelialisation (P = 0.02), collagen organisation (P = 0.006) and a reduction in the number of inflammatory cells (P = 0.002). A significant increase in the expression of IL-8 mRNA was observed (P = 0.026). It was concluded that NPH insulin alone contributes to wound healing, but it is not necessary to use it together with exogenous NO gas