山东省平阴县农村地区出生缺陷聚集性分析

目的 分析山东省平阴县农村地区出生缺陷聚集性分布特征,为进一步病因学研究和防治提供线索和依据。方法 收集平阴县2004-2016年出生缺陷登记资料,描述出生缺陷地理分布,计算每个村庄的出生缺陷发病例数,对出生缺陷数据资料进行Poisson分布和负二项分布拟合优度检验。结果 出生缺陷发病数据不符合Poisson分布(χ²=30.33,P<0.001),但符合负二项分布(χ²=4.56,P=0.336)。结论 平阴县农村地区出生缺陷在村级层面上具有空间聚集性。     

Male Inmate Profiles and Their Biological Correlates

Objective:

Borderline and antisocial personality disorders (PDs) share common clinical features (impulsivity, aggressiveness, substance use disorders [SUDs], and suicidal behaviours) that are greatly overrepresented in prison populations. These disorders have been associated biologically with testosterone and cortisol levels. However, the associations are ambiguous and the subject of controversy, perhaps because these heterogeneous disorders have been addressed as unitary constructs. A consideration of profiles of people, rather than of exclusive diagnoses, might yield clearer relationships.

Methods:

In our study, multiple correspondence analysis and cluster analysis were employed to identify subgroups among 545 newly convicted inmates. The groups were then compared in terms of clinical features and biological markers, including levels of cortisol, testosterone, estradiol, progesterone, and sulfoconjugated dehydroepiandrosterone (DHEA-S).

Results:

Four clusters with differing psychiatric, criminal, and biological profiles emerged. Clinically, one group had intermediate scores for each of the tested clinical features. Another group comprised people with little comorbidity. Two others displayed severe impulsivity, PD, and SUD. Biologically, cortisol levels were lowest in the last 2 groups and highest in the group with less comorbidity. In keeping with previous findings reported in the literature, testosterone was higher in a younger population with severe psychiatric symptoms. However, some apparently comparable behavioural outcomes were found to be related to distinct biological profiles. No differences were observed for estradiol, progesterone, or DHEA-S levels.

Conclusions:

The results not only confirm the importance of biological markers in the study of personality features but also demonstrate the need to consider the role of comorbidities and steroid coregulation.     

Correlation between Defects and Electrical Performances of Ion-Irradiated 4H-SiC p–n Junctions

In this study, 4H-SiC p–n junctions were irradiated with 700 keV He⁺ ions in the fluence range 1.0 × 10¹² to 1.0 × 10¹⁵ ions/cm². The effects of irradiation were investigated by current–voltage (I–V) and capacitance–voltage (C–V) measurements, while deep-level transient spectroscopy (DLTS) was used to study the traps introduced by irradiation defects. Modifications of the device’s electrical performances were observed after irradiation, and two fluence regimes were identified. In the low fluence range (≤10¹³ ions/cm²), I–V characteristics evidenced an increase in series resistance, which can be associated with the decrease in the dopant concentration, as also denoted by C–V measurements. In addition, the pre-exponential parameter of junction generation current increased with fluence due to the increase in point defect concentration. The main produced defect states were the Z_1/2, RD_1/2, and EH_6/7 centers, whose concentrations increased with fluence. At high fluence (>10¹³ ions/cm²), I–V curves showed a strong decrease in the generation current, while DLTS evidenced a rearrangement of defects. The detailed electrical characterization of the p–n junction performed at different temperatures highlights the existence of conduction paths with peculiar electrical properties introduced by high fluence irradiation. The results suggest the formation of localized highly resistive regions (realized by agglomeration of point defects) in parallel with the main junction.     

Spatiotemporal Analysis of COVID-19 Incidence Data

(1) Background: A better understanding of COVID-19 dynamics in terms of interactions among individuals would be of paramount importance to increase the effectiveness of containment measures. Despite this, the research lacks spatiotemporal statistical and mathematical analysis based on large datasets. We describe a novel methodology to extract useful spatiotemporal information from COVID-19 pandemic data. (2) Methods: We perform specific analyses based on mathematical and statistical tools, like mathematical morphology, hierarchical clustering, parametric data modeling and non-parametric statistics. These analyses are here applied to the large dataset consisting of about 19,000 COVID-19 patients in the Veneto region (Italy) during the entire Italian national lockdown. (3) Results: We estimate the COVID-19 cumulative incidence spatial distribution, significantly reducing image noise. We identify four clusters of connected provinces based on the temporal evolution of the incidence. Surprisingly, while one cluster consists of three neighboring provinces, another one contains two provinces more than 210 km apart by highway. The survival function of the local spatial incidence values is modeled here by a tapered Pareto model, also used in other applied fields like seismology and economy in connection to networks. Model’s parameters could be relevant to describe quantitatively the epidemic. (4) Conclusion: The proposed methodology can be applied to a general situation, potentially helping to adopt strategic decisions such as the restriction of mobility and gatherings.     

《昆山市卫生监督体系现代化建设研究-建设篇》课题概述

《昆山市卫生监督体系现代化建设研究-建设篇》课题概述包括研究背景和意义、研究目标和内容、研究方法和资料来源、资料质量控制和技术路线图。本研究自2012年5月启动,调研历时7个月,对昆山市卫生监督的执法依据和职能任务进行了梳理,对昆山市卫生监督机构(市所和11家分所)的人、财、物、职能开展情况和管理相对人情况等进行普查,旨在了解昆山市卫生监督体系建设的成效,存在的问题,并提出完善昆山市卫生监督体系建设的发展思路,为全国县级卫生监督机构的发展提供参考。     

Different Pearl Indices in studies of hormonal contraceptives in the United States: impact of study population

Objective

To examine the impact of subject characteristics on efficacy as measured by the Pearl Index (PI) in clinical trials and to make study populations similar by matching.

Methods

Our analysis used US data from four large Phase III studies. We compared results from one fertility control patch study with pooled data from three studies with virtually identical design on oral hormonal contraceptives. First, we identified three characteristics that had the most impact on the PI. Second, we used these three variables and matched subjects from the patch study with those from the oral contraceptive (OC) studies. Finally, we calculated the PIs for matched and unmatched subjects from both the patch study and the OC studies.

Results

A total of 3706 subjects were included in our analysis. The variables ‘Hispanic ethnicity’, ‘previous pregnancy’ and ‘previous use of hormonal contraceptives’ had the most impact on the PI. The PIs for the matched patch cohort and the matched OC cohort were 2.97 and 2.48, respectively. Those for the unmatched patch cohort and the unmatched OC cohort were 10.17 and 0.90, respectively.

Conclusion

Subject characteristics strongly influence the PI in clinical studies of hormonal contraceptives. In particular, Hispanic ethnicity, previous pregnancies and no previous use of hormonal contraceptives result in a higher PI.

Implications

PIs from different clinical trials cannot be meaningfully compared unless subject characteristics that have most impact on the PI are similar or are made to be similar statistically as we did here by matching.     

Learning Markov random walks for robust subspace clustering and estimation

Markov Random Walks (MRW) has proven to be an effective way to understand spectral clustering and embedding. However, due to less global structural measure, conventional MRW (e.g., the Gaussian kernel MRW) cannot be applied to handle data points drawn from a mixture of subspaces. In this paper, we introduce a regularized MRW learning model, using a low-rank penalty to constrain the global subspace structure, for subspace clustering and estimation. In our framework, both the local pairwise similarity and the global subspace structure can be learnt from the transition probabilities of MRW. We prove that under some suitable conditions, our proposed local/global criteria can exactly capture the multiple subspace structure and learn a low-dimensional embedding for the data, in which giving the true segmentation of subspaces. To improve robustness in real situations, we also propose an extension of the MRW learning model based on integrating transition matrix learning and error correction in a unified framework. Experimental results on both synthetic data and real applications demonstrate that our proposed MRW learning model and its robust extension outperform the state-of-the-art subspace clustering methods.     

Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs)

In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature.     

Unsupervised classification of major depression using functional connectivity MRI

The current diagnosis of psychiatric disorders including major depressive disorder based largely on self‐reported symptoms and clinical signs may be prone to patients' behaviors and psychiatrists' bias. This study aims at developing an unsupervised machine learning approach for the accurate identification of major depression based on single resting‐state functional magnetic resonance imaging scans in the absence of clinical information. Twenty‐four medication‐naive patients with major depression and 29 demographically similar healthy individuals underwent resting‐state functional magnetic resonance imaging. We first clustered the voxels within the perigenual cingulate cortex into two subregions, a subgenual region and a pregenual region, according to their distinct resting‐state functional connectivity patterns and showed that a maximum margin clustering‐based unsupervised machine learning approach extracted sufficient information from the subgenual cingulate functional connectivity map to differentiate depressed patients from healthy controls with a group‐level clustering consistency of 92.5% and an individual‐level classification consistency of 92.5%. It was also revealed that the subgenual cingulate functional connectivity network with the highest discriminative power primarily included the ventrolateral and ventromedial prefrontal cortex, superior temporal gyri and limbic areas, indicating that these connections may play critical roles in the pathophysiology of major depression. The current study suggests that subgenual cingulate functional connectivity network signatures may provide promising objective biomarkers for the diagnosis of major depression and that maximum margin clustering‐based unsupervised machine learning approaches may have the potential to inform clinical practice and aid in research on psychiatric disorders. Hum Brain Mapp 35:1630–1641, 2014. © 2013 Wiley Periodicals, Inc.