The global retinoblastoma outcome study : a prospective, cluster-based analysis of 4064 patients from 149 countries

DATA SHARING : The study data will become available online once all analyses are complete.BACKGROUND : Retinoblastoma is the most common intraocular cancer worldwide. There is some evidence to suggest that major differences exist in treatment outcomes for children with retinoblastoma from different regions, but these differences have not been assessed on a global scale. We aimed to report 3-year outcomes for children with retinoblastoma globally and to investigate factors associated with survival. METHODS : We did a prospective cluster-based analysis of treatment-naive patients with retinoblastoma who were diagnosed between Jan 1, 2017, and Dec 31, 2017, then treated and followed up for 3 years. Patients were recruited from 260 specialised treatment centres worldwide. Data were obtained from participating centres on primary and additional treatments, duration of follow-up, metastasis, eye globe salvage, and survival outcome. We analysed time to death and time to enucleation with Cox regression models. FINDINGS : The cohort included 4064 children from 149 countries. The median age at diagnosis was 23·2 months (IQR 11·0–36·5). Extraocular tumour spread (cT4 of the cTNMH classification) at diagnosis was reported in five (0·8%) of 636 children from high-income countries, 55 (5·4%) of 1027 children from upper-middle-income countries, 342 (19·7%) of 1738 children from lower-middle-income countries, and 196 (42·9%) of 457 children from low-income countries. Enucleation surgery was available for all children and intravenous chemotherapy was available for 4014 (98·8%) of 4064 children. The 3-year survival rate was 99·5% (95% CI 98·8–100·0) for children from high-income countries, 91·2% (89·5–93·0) for children from upper-middle-income countries, 80·3% (78·3–82·3) for children from lower-middle-income countries, and 57·3% (52·1-63·0) for children from low-income countries. On analysis, independent factors for worse survival were residence in low-income countries compared to high-income countries (hazard ratio 16·67; 95% CI 4·76–50·00), cT4 advanced tumour compared to cT1 (8·98; 4·44–18·18), and older age at diagnosis in children up to 3 years (1·38 per year; 1·23–1·56). For children aged 3–7 years, the mortality risk decreased slightly (p=0·0104 for the change in slope). INTERPRETATION : This study, estimated to include approximately half of all new retinoblastoma cases worldwide in 2017, shows profound inequity in survival of children depending on the national income level of their country of residence. In high-income countries, death from retinoblastoma is rare, whereas in low-income countries estimated 3-year survival is just over 50%. Although essential treatments are available in nearly all countries, early diagnosis and treatment in low-income countries are key to improving survival outcomes.The Queen Elizabeth Diamond Jubilee Trust and the Wellcome Trust.https://www.thelancet.com/journals/langlo/homeam2023Paediatrics and Child Healt

A Review of Deep Learning Based Speech Synthesis

Speech synthesis, also known as text-to-speech (TTS), has attracted increasingly more attention. Recent advances on speech synthesis are overwhelmingly contributed by deep learning or even end-to-end techniques which have been utilized to enhance a wide range of application scenarios such as intelligent speech interaction, chatbot or conversational artificial intelligence (AI). For speech synthesis, deep learning based techniques can leverage a large scale of <text, speech> pairs to learn effective feature representations to bridge the gap between text and speech, thus better characterizing the properties of events. To better understand the research dynamics in the speech synthesis field, this paper firstly introduces the traditional speech synthesis methods and highlights the importance of the acoustic modeling from the composition of the statistical parametric speech synthesis (SPSS) system. It then gives an overview of the advances on deep learning based speech synthesis, including the end-to-end approaches which have achieved start-of-the-art performance in recent years. Finally, it discusses the problems of the deep learning methods for speech synthesis, and also points out some appealing research directions that can bring the speech synthesis research into a new frontier

The gut microbiome of the Sunda pangolin (Manis javanica) reveals its adaptation to specialized myrmecophagy

Background The gut microbiomes of mammals are closely related to the diets of their hosts. The Sunda pangolin (Manis javanica) is a specialized myrmecophage, but its gut microbiome has rarely been studied. Methods Using high-throughput Illumina barcoded 16S rRNA amplicons of nine fecal samples from nine captive Sunda pangolins, we investigated their gut microbiomes. Results The detected bacteria were classified into 14 phyla, 24 classes, 48 orders, 97 families, and 271 genera. The main bacterial phyla were Firmicutes (73.71%), Proteobacteria (18.42%), Actinobacteria (3.44%), and Bacteroidetes (0.51%). In the PCoA and neighbor-net network (PERMANOVA: pangolins vs. other diets, weighted UniFrac distance p < 0.01, unweighted UniFrac distance p < 0.001), the gut microbiomes of the Sunda pangolins were distinct from those of mammals with different diets, but were much closer to other myrmecophages, and to carnivores, while distant from herbivores. We identified some gut microbiomes related to the digestion of chitin, including Lactococcus, Bacteroides, Bacillus, and Staphylococcus species, which confirms that the gut microbiome of pangolins may help them to digest chitin. Significance The results will aid studies of extreme dietary adaption and the mechanisms of diet differentiation in mammals, as well as metagenomic studies, captive breeding, and ex situ conservation of pangolins

Multi-Task Deep Learning for User Intention Understanding in Speech Interaction Systems

Speech interaction systems have been gaining popularity in recent years. The main purpose of these systems is to generate more satisfactory responses according to users' speech utterances, in which the most critical problem is to analyze user intention. Researches show that user intention conveyed through speech is not only expressed by content, but also closely related with users' speaking manners (e.g. with or without acoustic emphasis). How to incorporate these heterogeneous attributes to infer user intention remains an open problem. In this paper, we define Intention Prominence (IP) as the semantic combination of focus by text and emphasis by speech, and propose a multi-task deep learning framework to predict IP. Specifically, we first use long short-term memory (LSTM) which is capable of modeling long short-term contextual dependencies to detect focus and emphasis, and incorporate the tasks for focus and emphasis detection with multi-task learning (MTL) to reinforce the performance of each other. We then employ Bayesian network (BN) to incorporate multimodal features (focus, emphasis, and location reflecting users' dialect conventions) to predict IP based on feature correlations. Experiments on a data set of 135,566 utterances collected from real-world Sogou Voice Assistant illustrate that our method can outperform the comparison methods over 6.9-24.5% in terms of F1-measure. Moreover, a real practice in the Sogou Voice Assistant indicates that our method can improve the performance on user intention understanding by 7%

Segmentation of Cerebral Vessels in Mouse TOF-MRA by an Attention Network

Research involving animals contributes to our comprehension of the pathology and progression of cerebrovascular diseases. Utilizing ultra-high field 3D time-of-flight magnetic resonance angiography (TOF-MRA) has facilitated noninvasive, high-resolution imaging of the vasculature in mice. Despite this advancement, there is currently a lack of tools for segmenting vasculature in 3D TOF-MRA images of mice.In this study, we introduce a novel approach employing an attention network for automatic segmentation of cerebral vessels in 11.7T TOF-MRA images of mice. The proposed method was trained and evaluated using 34 TOF-MRA volumes. In contrast to other state-of-the-art segmentation networks, our method demonstrated superior completeness in capturing cerebrovascular structures. Compared with manual labeling, the proposed method achieved a Dice similarity coefficient of 85.50%. This methodology can serve as an effective tool for angiography analysis in pre-clinical studies of cerebrovascular diseases.</p