Cesarean section in the People's Republic of China: current perspectives.

OBJECTIVE: To review the current knowledge on the prevalence, reasons, and consequences of cesarean sections in the People's Republic of China. METHODS: Peer-reviewed articles were systematically searched on PubMed. The following Chinese databases were comprehensively searched: the China National Knowledge Infrastructure, Wanfang, and the VIP information. The databases were searched from inception to September 1, 2013. Two reviewers independently screened the titles and abstracts for eligibility. Full texts of eligible papers were reviewed, where relevant references were hand-searched and reviewed. FINDINGS: Sixty articles were included from PubMed, 17 articles were intentionally picked out from Chinese journals, and five additional articles were added, for a total of 82 articles for the analysis. With a current national rate near 40%, the literature consistently reported a rapid rise of cesarean sections in the People's Republic of China in the past decades, irrespective of where people lived or their socioeconomic standing. Nonclinical factors were considered as the main drivers fueling the rise of cesareans in the People's Republic of China. There was a lively debate on whether women's preferences or providers' distorted financial incentives affected the rise in cesarean sections. However, recent evidence suggests that it might be the People's Republic of China's health development approach - focusing on specialized care and marginalizing primary care - that is playing a role. Although 30 articles were identified studying the consequences of cesareans, the methodologies are in general weak and the themes are out of focus. CONCLUSION: The overuse of cesareans is rising alarmingly in the People's Republic of China and has become a real public health problem. No consensus has been made on the leverage factors that drive the cesarean epidemic, particularly for those nonclinical factors. The more macro level structural factors may have played a part, though further research is warranted to understand the mechanisms. Knowledge of the consequences of cesareans, particularly for women, is limited in the People's Republic of China, leaving a substantial literature gap

A Bayesian Quasi-Likelihood Design for Identifying the Minimum Effective Dose and Maximum Utility Dose in Dose-Ranging Studies

Most existing dose-ranging study designs focus on assessing the dose-efficacy relationship and identifying the minimum effective dose. There is an increasing interest in optimizing the dose based on the benefit-risk tradeoff. We propose a Bayesian quasi-likelihood dose-ranging design that jointly considers safety and efficacy to simultaneously identify the minimum effective dose and the maximum utility dose to optimize the benefit-risk tradeoff. The binary toxicity endpoint is modeled using a beta-binomial model. The efficacy endpoint is modeled using the quasi-likelihood approach to accommodate various types of data (e.g. binary, ordinal or continuous) without imposing any parametric assumptions on the dose-response curve. Our design utilizes a utility function as a measure of benefit-risk tradeoff and adaptively assign patients to doses based on the doses\u27 likelihood of being the minimum effective dose and maximum utility dose. The design takes a group-sequential approach. At each interim, the doses that are deemed overly toxic or futile are dropped. At the end of the trial, we use posterior probability criteria to assess the strength of the dose-response relationship for establishing the proof-of-concept. If the proof-of-concept is established, we identify the minimum effective dose and maximum utility dose. Our simulation study shows that compared with some existing designs, the Bayesian quasi-likelihood dose-ranging design is robust and yields competitive performance in establishing proof-of-concept and selecting the minimum effective dose. Moreover, it includes an additional feature for further maximum utility dose selection

Does Repetition Affect Acceptance? A Social Robot Adoption Model for Technologically-Savvy Users in the Caribbean

There is little research on use and adoption factors for social robots in the Caribbean. In one pilot study, the Zenbo companion robot was used to evaluate potential social robot use in a Caribbean setting. An informal observation from that study was the existence of communication failure–participants frequently repeated commands to the robot. Based on this observation, we have undertaken this study to identify the factors that affect robot adoption among technologically-savvy Caribbean users (undergraduate Computer Science and Information Technology (IT) students) and create a technology adoption model for this type of user. Our model shows that communication failure, manifested as repetition, has no effect on technology acceptance. Additionally, social attitudes towards robots, like the perception of competence and warmth, also have no effect on adoption. This social robot adoption model is the first of its kind for the Caribbean and helps contextualize factors that can affect social robots’ adoption in the region

Task-Oriented Conversation Generation Using Heterogeneous Memory Networks

How to incorporate external knowledge into a neural dialogue model is critically important for dialogue systems to behave like real humans. To handle this problem, memory networks are usually a great choice and a promising way. However, existing memory networks do not perform well when leveraging heterogeneous information from different sources. In this paper, we propose a novel and versatile external memory networks called Heterogeneous Memory Networks (HMNs), to simultaneously utilize user utterances, dialogue history and background knowledge tuples. In our method, historical sequential dialogues are encoded and stored into the context-aware memory enhanced by gating mechanism while grounding knowledge tuples are encoded and stored into the context-free memory. During decoding, the decoder augmented with HMNs recurrently selects each word in one response utterance from these two memories and a general vocabulary. Experimental results on multiple real-world datasets show that HMNs significantly outperform the state-of-the-art data-driven task-oriented dialogue models in most domains.Comment: Accepted as a long paper at EMNLP-IJCNLP 201

Better Guider Predicts Future Better: Difference Guided Generative Adversarial Networks

Predicting the future is a fantasy but practicality work. It is the key component to intelligent agents, such as self-driving vehicles, medical monitoring devices and robotics. In this work, we consider generating unseen future frames from previous obeservations, which is notoriously hard due to the uncertainty in frame dynamics. While recent works based on generative adversarial networks (GANs) made remarkable progress, there is still an obstacle for making accurate and realistic predictions. In this paper, we propose a novel GAN based on inter-frame difference to circumvent the difficulties. More specifically, our model is a multi-stage generative network, which is named the Difference Guided Generative Adversarial Netwok (DGGAN). The DGGAN learns to explicitly enforce future-frame predictions that is guided by synthetic inter-frame difference. Given a sequence of frames, DGGAN first uses dual paths to generate meta information. One path, called Coarse Frame Generator, predicts the coarse details about future frames, and the other path, called Difference Guide Generator, generates the difference image which include complementary fine details. Then our coarse details will then be refined via guidance of difference image under the support of GANs. With this model and novel architecture, we achieve state-of-the-art performance for future video prediction on UCF-101, KITTI.Comment: To appear in ACCV 201

Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models

The Gaussian process state-space model (GPSSM) has attracted extensive attention for modeling complex nonlinear dynamical systems. However, the existing GPSSM employs separate Gaussian processes (GPs) for each latent state dimension, leading to escalating computational complexity and parameter proliferation, thus posing challenges for modeling dynamical systems with high-dimensional latent states. To surmount this obstacle, we propose to integrate the efficient transformed Gaussian process (ETGP) into the GPSSM, which involves pushing a shared GP through multiple normalizing flows to efficiently model the transition function in high-dimensional latent state space. Additionally, we develop a corresponding variational inference algorithm that surpasses existing methods in terms of parameter count and computational complexity. Experimental results on diverse synthetic and real-world datasets corroborate the efficiency of the proposed method, while also demonstrating its ability to achieve similar inference performance compared to existing methods. Code is available at \url{https://github.com/zhidilin/gpssmProj}

基地医院首例特重型颅脑损伤行开颅手术患者的护理

To sum up the nursing experience of the first case of severe craniocerebral injury undergoing craniotomy operation in The 251st Hospital of People's Liberation Army, providing a reference base for future care and support, and to improve the base level of care treatment in patients with traumatic brain injury总结解放军第251医院医疗队在基地医院首例特重型颅脑损伤行开颅手术患者护理经验，为今后基地护理保障工作提供参考，提高基地颅脑损伤患者救治护理水平

Fusion of Diffusion Weighted MRI and Clinical Data for Predicting Functional Outcome after Acute Ischemic Stroke with Deep Contrastive Learning

Stroke is a common disabling neurological condition that affects about one-quarter of the adult population over age 25; more than half of patients still have poor outcomes, such as permanent functional dependence or even death, after the onset of acute stroke. The aim of this study is to investigate the efficacy of diffusion-weighted MRI modalities combining with structured health profile on predicting the functional outcome to facilitate early intervention. A deep fusion learning network is proposed with two-stage training: the first stage focuses on cross-modality representation learning and the second stage on classification. Supervised contrastive learning is exploited to learn discriminative features that separate the two classes of patients from embeddings of individual modalities and from the fused multimodal embedding. The network takes as the input DWI and ADC images, and structured health profile data. The outcome is the prediction of the patient needing long-term care at 3 months after the onset of stroke. Trained and evaluated with a dataset of 3297 patients, our proposed fusion model achieves 0.87, 0.80 and 80.45% for AUC, F1-score and accuracy, respectively, outperforming existing models that consolidate both imaging and structured data in the medical domain. If trained with comprehensive clinical variables, including NIHSS and comorbidities, the gain from images on making accurate prediction is not considered substantial, but significant. However, diffusion-weighted MRI can replace NIHSS to achieve comparable level of accuracy combining with other readily available clinical variables for better generalization.Comment: 12 pages, 5 figures, 5 table