Stantonbarr9372

Applications of neural networks (NNs) in medicine have increased dramatically in recent years. In order to train a NN that performs ECG segmentation, it can be very time consuming, or even completely prohibitive, to manually annotate fiducial points on enough QRST complexes to reach a high level of performance. Existing methods for time series data augmentation risk creating non-physiological ECG signals that may hamper NN training, and are unable to provide accurate fiducial point locations in the augmented data. We therefore developed ECGAug, a new method which generates an augmented training set of QRST signals (single beats or rhythm strips) with accurate fiducial point annotations. Our algorithm recombines a library of existing, annotated QRS complexes and T waves in physiologic ways, and then performs additional physiological transformations to generate a set of new annotated QRST complexes or rhythm strips to be used for NN training or validation of ECG annotation algorithms. In experiments where we trained NNs to annotate QRST complexes with a limited training dataset, QRST complexes added to the training dataset by ECGAug significantly improved NN performance. We present the ECGAug process, demonstrate its efficacy, and provide links for downloading the open source ECGAug software.The human respiratory network is a vital system that provides oxygen supply and nourishment to the whole body. Pulmonary diseases can cause severe respiratory problems, leading to sudden death if not treated timely. Many researchers have utilized deep learning systems (in both transfer learning and fine-tuning modes) to diagnose pulmonary disorders using chest X-rays (CXRs). read more However, such systems require exhaustive training efforts on large-scale (and well-annotated) data to effectively diagnose chest abnormalities (at the inference stage). Furthermore, procuring such large-scale data (in a clinical setting) is often infeasible and impractical, especially for rare diseases. With the recent advances in incremental learning, researchers have periodically tuned deep neural networks to learn different classification tasks with few training examples. Although, such systems can resist catastrophic forgetting, they treat the knowledge representations (which the network learns periodically) independently of each otheance compared to the conventional fine-tuning (transfer learning) approaches while significantly reducing the training and computational requirements.The purpose of this study is to develop a practical stripe artifacts correction framework on three-dimensional (3-D) time-of-flight magnetic resonance angiography (TOF-MRA) obtained by multiple overlapping thin slab acquisitions (MOTSA) technology. In this work, the stripe artifacts in TOF-MRA were considered as a part of image texture. To separate the image structure and the texture, the relative total variation (RTV) was firstly employed to smooth the TOF-MRA for generating the template image with fewer image textures. Then a residual image was generated, which was the difference between the template image and the raw TOF-MRA. The residual image was served as the image texture, which contained the image details and stripe artifacts. Then, we obtained the artifact image from the residual image via a filter in a specific direction since the image artifacts appeared as stripes. The image details were then produced from the difference between the artifact image and the image texture. To produce the corrected images, we finally compensated the image details to the RTV smoothing image. The proposed method was continued until the stripe artifacts during the iteration vary as little as possible. The digital phantom and the real patients' TOF-MRA were used to test the approach. The spatial uniformity was increased from 74% to 82% and the structural similarity was improved from 86% to 98% in the digital phantom test by using the proposed algorithm. Our approach proved to be highly successful in eliminating stripe artifacts in real patient data tests while retaining image details. The proposed iterative framework on TOF-MRA stripe artifact correction is effective and appealing for enhancing the imaging performance of multi-slab 3-D acquisitions.Globally, 10-20% of horticultural wastes are disposed in landfills leading to environmental pollution. Recycling these wastes as animal feedstuff will lessen food-feed competition and minimize environmental hazards. The present study was undertaken to determine the nutritional quality of fresh fruit and vegetable waste (F&VW) and their dietary inclusion on nutrient utilization, antioxidant status, greenhouse gases (GHG) emissions and potable water sparing efficacy in sheep. Three dietary combinations were formulated i.e. control (C)70% Cenchrus ciliaris hay +30% concentrate mixture (CM), diet with fruit waste (FWD)70% Cenchrus ciliaris hay +20% CM +10% FW and diet with vegetable waste (VWD)70% Cenchrus ciliaris hay +20% CM +10% VW for in vitro and in vivo evaluation of these wastes as potential livestock feed. Twenty-one adult ewes were allocated into 3 groups C, FWD and VWD and fed on the above three diets. Dry matter and crude protein digestibility were significantly enhanced by 5.5 and 7.2%; 7.3 and 7.6% in F&VW supplemented groups, respectively, without affecting feed intake. Plasma total antioxidant capacity (TAC) was improved by 32.2 and 26.3% in F&VW supplemented groups. Inclusion of F&VW biomass reduced annual methane (CH4) and nitrous oxide (N2O) emissions (kg CO2eq/sheep) by 3.12 and 4.55%; 15.18 and 14.92% and thus contributed to lowering of global warming potential by 4.00 and 5.27%, respectively. Furthermore, there was a net reduction of potable water consumption by 21.78 and 13.92% in F&VW supplemented groups, respectively. Therefore, it can be concluded that F&VW can be a potential feedstuff for ruminants and its efficient reuse would minimize environmental impacts associated with disposal of such waste in the landfills.

Adding ovarian function suppression (OFS) after chemotherapy improves survival in young women with moderate- and high-risk breast cancer. Assessment of ovarian function restoration after chemotherapy becomes critical for subsequent endocrine treatment and addressing fertility issues.

In the adding OFS after chemotherapy trial, patients who resumed ovarian function up to 2 years after chemotherapy were randomised to receive either 5 years of tamoxifen or adding 2 years of OFS with tamoxifen. Ovarian function was evaluated from enrolment to randomisation, and patients who did not randomise because of amenorrhoea for 2 years received tamoxifen and were followed up for 5 years. Prospectively collected consecutive hormone levels (proportion of patients with premenopausal follicle-stimulating hormone [FSH] levels<30 mIU/mLand oestradiol [E2] levels≥40pg/mL) and history of menstruation were available for 1067 patients with breast cancer.

Over 5 years of tamoxifen treatment, 69% of patients resumed menstruation and 98% and 74% of patients satisfied predefined ovarian function restoration as per serum FSH and E2 levels, respectively.