Environmental impacts of capital inequality evidence via G7 economies

RESULTS The proposed deep learning architectures have been successfully tested and evaluated on-line based on MRI datasets of brain tumor segmentation (BraTS 2019) challenge, including s336 cases as training data and 125 cases for validation data. The dice and Hausdorff distance scores of obtained segmentation results are about 0.81 to 0.84 and 9.8 to 19.7 correspondingly. CONCLUSION This study showed successful feasibility and comparative performance of applying different deep learning models in a new DeepSeg framework for automated brain tumor segmentation in FLAIR MR images. The proposed DeepSeg is open source and freely available at https//github.com/razeineldin/DeepSeg/.PURPOSE The registration of a preoperative 3D model, reconstructed, for example, from MRI, to intraoperative laparoscopy 2D images, is the main challenge to achieve augmented reality in laparoscopy. The current systems have a major limitation they require that the surgeon manually marks the occluding contours during surgery. This requires the surgeon to fully comprehend the non-trivial concept of occluding contours and surgeon time, directly impacting acceptance and usability. To overcome this limitation, we propose a complete framework for object-class occluding contour detection (OC2D), with application to uterus surgery. METHODS Our first contribution is a new distance-based evaluation score complying with all the relevant performance criteria. Our second contribution is a loss function combining cross-entropy and two new penalties designed to boost 1-pixel thickness responses. This allows us to train a U-Net end to end, outperforming all competing methods, which tends to produce thick responses. Our third contribution is a dataset of 3818 carefully labelled laparoscopy images of the uterus, which was used to train and evaluate our detector. RESULTS Evaluation shows that the proposed detector has a similar false false-negative rate to existing methods but substantially reduces both false-positive rate and response thickness. Finally, we ran a user study to evaluate the impact of OC2D against manually marked occluding contours in augmented laparoscopy. We used 10 recorded gynecologic laparoscopies and involved 5 surgeons. Using OC2D led to a reduction of 3 min and 53 s in surgeon time without sacrificing registration accuracy. CONCLUSIONS We provide a new set of criteria and a distance-based measure to evaluate an OC2D method. We propose an OC2D method which outperforms the state-of-the-art methods. The results obtained from the user study indicate that fully automatic augmented laparoscopy is feasible.PURPOSE The detection of clinically significant prostate cancer (PCa) is shown to greatly benefit from MRI-ultrasound fusion biopsy, which involves overlaying pre-biopsy MRI volumes (or targets) with real-time ultrasound images. In previous literature, machine learning models trained on either MRI or ultrasound data have been proposed to improve biopsy guidance and PCa detection. However, quantitative fusion of information from MRI and ultrasound has not been explored in depth in a large study. This paper investigates information fusion approaches between MRI and ultrasound to improve targeting of PCa foci in biopsies. METHODS We build models of fully convolutional networks (FCN) using data from a newly proposed ultrasound modality, temporal enhanced ultrasound (TeUS), and apparent diffusion coefficient (ADC) from 107 patients with 145 biopsy cores. The architecture of our models is based on U-Net and U-Net with attention gates. Models are built using joint training through intermediate and late fusion of thed enables future intra-operative deployment of this technology.INTRODUCTION The sodium-glucose cotransporter 2 (SGLT2) inhibitor ertugliflozin is approved for the treatment of adults with type 2 diabetes mellitus (T2DM). This analysis was conducted on safety data pooled from phase 3 studies using ertugliflozin 5 mg or 15 mg versus placebo or an active comparator. METHODS The placebo pool (n = 1544) comprised data from three similarly designed 26-week placebo-controlled studies. The broad pool (n = 4849) comprised these three placebo-controlled studies plus four placebo- or active-controlled studies with treatment durations of up to 104 weeks. RESULTS In the placebo pool, there were no notable differences across groups in the incidence of adverse events (AEs), serious AEs, or AEs resulting in discontinuation from study medication, while associations were observed with genital mycotic infection in both females (3.0%, 9.1%, and 12.2% in the placebo, ertugliflozin 5 mg, and ertugliflozin 15 mg groups, respectively) and males (0.4%, 3.7%, 4.2%), thirst (0.2%, 1.3%, 1.0%), andor other SGLT2 inhibitors. TRIAL REGISTRATION Clinicaltrials.gov indentifier, NCT02033889, NCT01958671, NCT02036515, NCT01986855, NCT02099110, NCT02226003, NCT01999218.The biorefinery concept makes use of renewable lignocellulosic biomass to produce commodities sustainably. A synthetic microbial consortium can enable the simultaneous utilization of sugars such as glucose and xylose to produce biochemicals, where each consortium member converts one sugar into the target product. check details In this study, woody biomass was used to generate glucose and xylose after pretreatment with 20% (w/v) sulfuric acid and 60-min reaction time. We compared several strategies for detoxification with charcoal and sodium borohydride treatments to improve the fermentability of this hydrolysate in a defined medium for the production of the growth-associated product pyruvate. In shake flask culture, the highest pyruvate yield on xylose of 0.8 g/g was found using pH 6 charcoal-treated hydrolysate. In bioreactor studies, a consortium of two engineered E. coli strains converted the mixture of glucose and xylose in batch studies to 12.8 ± 2.7 g/L pyruvate in 13 h. These results demonstrate that lignocellulosic biomass as the sole carbon source can be used to produce growth-related products after employing suitable detoxification strategies.This study examined whether cognitive processes in preschool, conceptualized as a unitary construct of executive control (EC) as well as foundational cognitive abilities (FCA), predict both maladaptive and adaptive functioning in middle childhood and mediate associations between early childhood socio-familial stress and those functional outcomes. Performance-based, multidimensional, and age-appropriate measures of EC and FCA were collected in a laboratory setting from 313 preschool-age children at age 5, along with questionnaire data from children and their parents on three dimensions of early socio-familial stress and parent smoking. Parent, teacher, and child self-report data on 285 of these children were obtained when they were in grade 3 or 4. Middle childhood data were used to create indices of maladaptive and adaptive functioning. A bi-factor structural equation modeling analysis captured distinct dimensions of preschool EC and FCA and was used to test the hypothesized pathways. EC had a statistically significant negative association with later maladaptive functioning.