Partial coherence and frustration in self‐organizing spherical grids

Nearby grid cells have been observed to express a remarkable degree of long‐range order, which is often idealized as extending potentially to infinity. Yet their strict periodic firing and ensemble coherence are theoretically possible only in flat environments, much unlike the burrows which rodents usually live in. Are the symmetrical, coherent grid maps inferred in the lab relevant to chart their way in their natural habitat? We consider spheres as simple models of curved environments and waiting for the appropriate experiments to be performed, we use our adaptation model to predict what grid maps would emerge in a network with the same type of recurrent connections, which on the plane produce coherence among the units. We find that on the sphere such connections distort the maps that single grid units would express on their own, and aggregate them into clusters. When remapping to a different spherical environment, units in each cluster maintain only partial coherence, similar to what is observed in disordered materials, such as spin glasses.     

基于多层次集成学习的骨质疏松辅助诊断研究

目的 原发性骨质疏松是一种起病隐匿、病程较长,在中老年人中高发的疾病,其可引起包括骨折在内的一系列严重症状,是我国中老年人致残致死的主要原因之一。与骨质疏松相关的生理检验指标有很多,如何筛选利用这些指标为诊断服务、建立诊断模型,尚未有成熟、统一的方法。方法 利用人工智能相关技术,对临床骨质疏松患者指标使用多种特征相关性算法进行特征选择,并在此基础上提出了一种多层次的集成学习框架：SAB-SVMKNN算法,其通过将内部同质学习器集成和外部异质学习器集成结合,将集成学习中的Boosting算法和Bagging算法使用Stacking进行集成,构建性能更强,适应性更好地诊断预测模型。结果 使用特征选择从原始数据中的31项临床指标中筛选了对于骨质疏松最重要的8种相关特征,通过这种方式使各模型准确率平均提高了9.2%,且该研究对应的模型准确率提升18.6%,最终达到了94.8%的准确率。结论 特征选择对于临床诊断和骨质疏松疾病的研究具有重要意义,该研究构建的预测模型可以有助于提高医生的诊断准确率。     

基于FastICA与ICEEMDAN的人脸视频心率检测

现有的非接触式心率检测方法存在噪声干扰、准确率低等问题。针对这些问题,提出一种基于FastICA与改进的自适应噪声完全集合经验模态分解(ICEEMDAN)相结合的算法,采用人脸视频进行心率检测。用摄像头采集人脸视频,并从视频中提取R、G、B通道源信号,即皮肤颜色变化信号,分别求出RGB这3个颜色通道的像素平均值;然后利用 FastICA对RGB这3组像素平均值进行解混,得到3组独立源信号,再用ICEEMDAN将其中一组独立源信号进行模态分解,并选取合适频段内的固有模式函数(IMF)估计心率的信号,最后用频谱分析计算得到心率。设计实验对8名人员进行了人脸视频检测,将检测结果与多参数监护仪进行对比分析。实验结果表明,该方法与多参数监护仪测量结果的平均误差与均方根误差均小于1 beat/min,因此基于FastICA与ICEEMDAN的人脸视频心率检测对人体心率检测具有良好的稳定性和准确性。     

甘肃省卫生人力资源公平性及预测研究

目的对甘肃省卫生人力资源配置现状及公平性进行分析,对2019—2023年需求数量进行预测,为甘肃省卫生人力资源发展规划提供参考建议。方法运用洛伦兹曲线和基尼系数对甘肃省卫生人力资源配置的公平性进行研究;运用时间序列模型对甘肃省2019—2023年卫生人力资源需求量进行预测。结果 2018年甘肃省所属每千人口卫生技术人员、执业(助理)医师和注册护士分别为5.2名、2.0名和1.9名。按人口配置的基尼系数均小于0.4的临界值;按地理配置的基尼系数均超过0.6,处于高度不公平状态。预测到2023年甘肃省卫生技术人员、执业(助理)医师和注册护士的人数分别为167 471人、40 534人和53 657人。结论甘肃省卫生人力资源配置的人口公平性优于地理公平性;卫生人力资源发展速度缓慢,医护比不合理,全省医疗人力资源不足;甘肃省在未来五年对卫生人力资源的需求量很大,需要进一步重视卫生人力资源的开发,扩大人才队伍建设规模。     

Classification using ensemble learning under weighted misclassification loss

Binary classification rules based on covariates typically depend on simple loss functions such as zero-one misclassification. Some cases may require more complex loss functions. For example, individual-level monitoring of HIV-infected individuals on antiretroviral therapy requires periodic assessment of treatment failure, defined as having a viral load (VL) value above a certain threshold. In some resource limited settings, VL tests may be limited by cost or technology, and diagnoses are based on other clinical markers. Depending on scenario, higher premium may be placed on avoiding false-positives, which brings greater cost and reduced treatment options. Here, the optimal rule is determined by minimizing a weighted misclassification loss/risk. We propose a method for finding and cross-validating optimal binary classification rules under weighted misclassification loss. We focus on rules comprising a prediction score and an associated threshold, where the score is derived using an ensemble learner. Simulations and examples show that our method, which derives the score and threshold jointly, more accurately estimates overall risk and has better operating characteristics compared with methods that derive the score first and the cutoff conditionally on the score especially for finite samples.     

Lennard-Jones流体相平衡性质的Monte Carlo模拟

采用Monte Carlo方法,在巨正则系综下对Lennard Jones（L J）流体临界点和气液相平衡性质进行模拟。在完整标度理论的基础上,利用Q 参数法并结合直方图再加权技术进行有限尺寸标度外推得到L J流体临界温度Tc*=1.316 4±0.001 7,临界密度ρc*=0.316 7±0.000 7,这与已有报道结果相吻合。同时计算了L J流体近临界相平衡性质,并分析了L J流体的临界不对称性,研究发现,影响共存曲线直径的因素中,|t|1-α项占主导地位,|t|2β项的贡献较弱。     

寰枕交界区硬脊膜动静脉瘘的诊断与治疗(2例报告并文献复习)

目的探讨寰枕交界区硬脊膜动静脉瘘诊断与治疗。方法采用多模式影像学方法诊断,枕下后正中入路手术治疗寰枕交界区硬脊膜动静脉瘘患者2例,并结合文献进行分析。结果本组2例寰枕交界区硬脊膜动静脉瘘患者均表现为自发性蛛网膜下腔出血,均经DSA确诊,并经枕下后正中入路成功阻断瘘口后引流静脉。结论寰枕交界区硬脊膜动静脉瘘发病率低,缺乏特征性临床表现,诊断困难;多模态影像、尤其是DSA是确诊的主要方法;开颅手术切断瘘口后引流静脉是相对安全、有较高病灶闭塞率的治疗方法。     

Do regions of interest location and type influence liver stiffness measurement using magnetic resonance elastography?

PurposeTo assess the variability of liver stiffness measurements using magnetic resonance elastography (MRE) at 1.5 T, depending on different approaches of regions of interest (ROIs) drawing.Material and methodsFifty consecutive patients with successful liver MRE were included. There were 32 men and 18 women with a mean age of 52 ± 14 (SD) years (range: 20–85 years). MRE was acquired using a gradient recalled-echo MRE sequence. At the level of the portal bifurcation, one observer drawn in the right liver first 3 elliptical ROI and then one free-hand ROI, as large as possible based on the confidence map and the anatomy. Three additional elliptical ROIs were further drawn on the slice above and 3 other on the slice below, for a total of 9 elliptical ROIs. The average value of liver stiffness in the 3 elliptical ROIs of the central slice and the one from the 9 elliptical ROIs were computed. Three liver stiffness values were obtained for each patient from the 3 measurement methods (one free-hand ROI, 3 elliptical ROIs and 9 elliptical ROIs). Inter-method variability was assessed using the intra-class correlation coefficient (ICC) and Bland-Altman analysis.ResultsThe variability between the 3 methods was excellent with ICC > 0.978 (P < 0.0001). The Bland-Altman analysis revealed high agreement between the 3 methods with bias < 0.45 kPa and limits of agreement < ±1.13 kPa. The variability was lower when comparing a large free-hand ROI and the 3-elliptical ROIs, than when comparing the 9-elliptical ROIs to one of the other methods.ConclusionOur results show that the variability between the 3 methods of ROI drawing and placement is very low.