English obesity policies: To govern and not to govern

Problem definitions constitute a crucial part of the policy process. In 2008 the Labour Government presented a plan to reduce the obesity prevalence in England. Only three years later the Conservative–Liberal Government introduced a plan on the same topic, which it presented as new and innovative. The aim of this study is to analyse the respective governments’ problematisations of obesity and to identify similarities and differences. Despite the different hues of the two governments, the programmes are surprisingly similar. They seek to simultaneously govern and not to govern. They adhere to liberal ideals of individual choice and they also suggest initiatives that will lead people to choose certain behaviours. Both governments encourage the food and drink industry to support their policies voluntarily, rather than obliging them to do so, although Labour is somewhat more inclined to use statutory measures. The Conservative–Liberal plan does not represent many new ideas. The plans are characterised by the paradox that they convey both ideas and ideals about freedom of choice as well as about state interventions to influence people's choices, which could be seen as incompatible, but as the study shows in practice they are not.     

以临床胜任力为导向的CBL在急危重症护理本科教学中的实践

目的　探讨临床胜任力为导向的病例教学（case-based learning，CBL）在急危重症护理学本科教学中的可行性和有效性。方法　将急危重症护理本科生120人随机分为CBL教学组和传统教学组。根据每次实习学生人数，CBL教学组分为数个小组，每小组固定一个带教教师，并以小组为单位进行临床教学实践；传统教学组采用既往的临床实习教学方式，即一个学生固定跟随一个带教教师。采用理论考试、技能考试以及问卷调查等多种考核方式评价两组的教学效果，采用SPSS 21.0软件行t检验和卡方检验。结果　与传统教学组相比，CBL教学组在理论考试[（92.5±3.0） vs. （85.3±3.3）]和技能考试[（93.1±4.5） vs. （88.1±3.4）]方面均更好，且差异有统计学意义（P<0.05）；在对教学满意度调查方面，CBL教学组优于传统教学组。结论　以临床胜任力为导向的CBL教学应用于急危重症护理本科教学是可行的，能有效培养学生的临床思维和提高实际的临床胜任力。     

新型管理模式在护理实习教学管理中的应用探讨

目的探讨在进行护理实习教学管理期间新型管理模式应用可行性。方法选择我院2018年1月-2019年2月102例实习护生作为试验对象;数字奇偶法分组后探究每组教学管理模式;对照组(51名):选择传统护理教学管理模式展开;试验组(51名):选择新型护理教学管理模式展开;比较两组护实习护生行为规范合格率、平均业务学习出勤率以及教学满意度评分结果。结果试验组实习护生规范合格率(98.04%)高于对照组(64.71%)(P<0.05);试验组实习护生平均业务学习出勤率(98.04%)高于对照组(62.75%)(P<0.05);试验组实习护生各项教学满意度评分均高于对照组(P<0.05)。结论医院实习护生在接受新型护理实习教学管理后,对于规范合格率的提升,平均业务学习出勤率的提升以及教学满意度评分的提升,均获得显著效果,最终为医院实习护生的学习效率以及护理安全提升奠定了基础。     

PBL与思维导图相结合教学模式在临床教学中的应用

目的探讨PBL与思维导图相结合教学模式在临床教学中的应用。方法本文研究对象为在我院见习的临床本科生,为大理大学2015级一个班级,共55名学生,数据收集时间为2019年1月;按照授课方式的差异将学生分为实验班与对照班,实验班27名学生,对照班28名学生;对照班实施常规教学法,实验班实施PBL与思维导图相结合教学法。结果实验班教学结束测试得分为(85.60±3.46)分,对照班教学结束测试得分为(77.48±3.12)分,差异具有统计学意义(P<0.05);实验班教学方式满意度评分为(90.35±4.12)分,对照班教学方式满意度评分为(80.25±4.05)分,差异具有统计学意义(P<0.05)。结论在临床教学中采用PBL与思维导图相结合教学模式,能够提高学生的专业知识掌握水平与对教学模式的认可度。     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.     

Switch From Twice-daily Tacrolimus to Once-daily,Prolonged-release Tacrolimus in Kidney Transplantation: Long-term Outcome

Background

One daily dose of tacrolimus (QDT) improves adherence in kidney transplant (KT) recipients. A switch from twice-daily tacrolimus (BDT) to QDT showed similar efficacy and safety.

Prospective Analysis Using a Novel CNN Algorithm to Distinguish Atypical Ductal Hyperplasia From Ductal Carcinoma in Situ in Breast

IntroductionWe previously developed a convolutional neural networks (CNN)-based algorithm to distinguish atypical ductal hyperplasia (ADH) from ductal carcinoma in situ (DCIS) using a mammographic dataset. The purpose of this study is to further validate our CNN algorithm by prospectively analyzing an unseen new dataset to evaluate the diagnostic performance of our algorithm.Materials and MethodsIn this institutional review board-approved study, a new dataset composed of 280 unique mammographic images from 140 patients was used to test our CNN algorithm. All patients underwent stereotactic-guided biopsy of calcifications and underwent surgical excision with available final pathology. The ADH group consisted of 122 images from 61 patients with the highest pathology diagnosis of ADH. The DCIS group consisted of 158 images from 79 patients with the highest pathology diagnosis of DCIS. Two standard mammographic magnification views (craniocaudal and mediolateral/lateromedial) of the calcifications were used for analysis. Calcifications were segmented using an open source software platform 3D slicer and resized to fit a 128 × 128 pixel bounding box. Our previously developed CNN algorithm was used. Briefly, a 15 hidden layer topology was used. The network architecture contained 5 residual layers and dropout of 0.25 after each convolution. Diagnostic performance metrics were analyzed including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. The “positive class” was defined as the pure ADH group in this study and thus specificity represents minimizing the amount of falsely labeled pure ADH cases.ResultsArea under the receiver operating characteristic curve was 0.90 (95% confidence interval, ± 0.04). Diagnostic accuracy, sensitivity, and specificity was 80.7%, 63.9%, and 93.7%, respectively.ConclusionProspectively tested on new unseen data, our CNN algorithm distinguished pure ADH from DCIS using mammographic images with high specificity.     

Easy Words: Reference Resolution in a Malevolent Referent World

This article describes early stages in the acquisition of a first vocabulary by infants and young children. It distinguishes two major stages, the first of which operates by a stand‐alone word‐to‐world pairing procedure and the second of which, using the evidence so acquired, builds a domain‐specific syntax‐sensitive structure‐to‐world pairing procedure. As we show, the first stage of learning is slow, restricted in character, and to some extent errorful, whereas the second procedure is determinative, rapid, and essentially errorless. Our central claim here is that the early, referentially based learning procedure succeeds at all because it is reined in by attention‐focusing properties of word‐to‐world timing and related indicants of referential intent.