Unravelling animal exposure profiles of human Q fever cases in Queensland,Australia, using natural language processing

Q fever, caused by the zoonotic bacterium Coxiella burnetii, is a globally distributed emerging infectious disease. Livestock are the most important zoonotic transmission sources, yet infection in people without livestock exposure is common. Identifying potential exposure pathways is necessary to design effective interventions and aid outbreak prevention. We used natural language processing and graphical network methods to provide insights into how Q fever notifications are associated with variation in patient occupations or lifestyles. Using an 18‐year time‐series of Q fever notifications in Queensland, Australia, we used topic models to test whether compositions of patient answers to follow‐up exposure questionnaires varied between demographic groups or across geographical areas. To determine heterogeneity in possible zoonotic exposures, we explored patterns of livestock and game animal co‐exposures using Markov Random Fields models. Finally, to identify possible correlates of Q fever case severity, we modelled patient probabilities of being hospitalized as a function of particular exposures. Different demographic groups consistently reported distinct sets of exposure terms and were concentrated in different areas of the state, suggesting the presence of multiple transmission pathways. Macropod exposure was commonly reported among Q fever cases, even when exposure to cattle, sheep or goats was absent. Males, older patients and those that reported macropod exposure were more likely to be hospitalized due to Q fever infection. Our study indicates that follow‐up surveillance combined with text modelling is useful for unravelling exposure pathways in the battle to reduce Q fever incidence and associated morbidity.     

The contribution of local distribution substations and associated area distribution system to personal exposure to power frequency magnetic fields

A number of epidemiological studies has shown a significant correlation between wire coding, magnetic fields and childhood cancer, although a more recent study has not [McBride et al. (1999) Am. J. Epidemiol. 149 (9), 831-842]. In the UK there is currently no equivalent to wire-codes and there is some uncertainty about the extent to which the UK medium-voltage electricity distribution systems contribute to personal exposure and how this compares with US overhead supply systems. Studies on four different area types were carried out to measure magnetic field intensities from typical electricity supply utility substations and cabling in the vicinity of domestic housing. Typically at distances of two metres from the substations mean magnetic field intensities were 20 nano teslas (nT) or less, increasing to 0.98 microT or less at the closest public access point. The mean magnetic field exposure level sampled around the four main test areas varied between 0.012 and 0.27 microT increasing to 0.30-0.80 microT at road junctions.     

全中枢神经系统放射治疗最佳布野探讨

目的：探讨全中枢神经系统放射治疗中,脊髓布野及全脊髓野与全脑野间衔接的方法。方法：采用我院与浙江大学联合研制的平移扫描治疗装置,在^60Co治疗机上对全脊髓进行平移扫描照射以及用一个等中心、半束整体铅摸照射全脑。结果：根据测量全脊髓内及脊髓与全脑衔接处以及全脑内剂量分布满意,无冷点或热点,符合QA。讨论：全中枢神经系统放射治疗时,采用平移扫描治疗全脊髓及等中心半束整体铅模照射全脑．全脑、髓内以及衔接处剂量满意。     

Electric and magnetic fields of 50 Hz are not associated with sudden infant death syndrome

Sudden infant death syndrome (SIDS) is a multifactorial condition. Power frequency magnetic fields have been implicated in SIDS. Through the use of a case-control study measuring 50 Hz electric and magnetic fields at the SIDS baby's last head position, no association could be found between SIDS and either electric (p = 0.327) or magnetic (p = 0.827) 50 Hz fields.     

How language can help discrimination in the Neural Modelling Fields framework

The relationship between thought and language and, in particular, the issue of whether and how language influences thought is still a matter of fierce debate. Here we consider a discrimination task scenario to study language acquisition in which an agent receives linguistic input from an external teacher, in addition to sensory stimuli from the objects that exemplify the overlapping categories that make up the environment. Sensory and linguistic input signals are fused using the Neural Modelling Fields (NMF) categorization algorithm. We find that the agent with language is capable of differentiating object features that it could not distinguish without language. In this sense, the linguistic stimuli prompt the agent to redefine and refine the discrimination capacity of its sensory channels.     

Outcomes after vascular resection during curative-intent resection for hilar cholangiocarcinoma: a multi-institution study from the US extrahepatic biliary malignancy consortium

Background

Surgical resection is the cornerstone of curative-intent therapy for patients with hilar cholangiocarcinoma (HC). The role of vascular resection (VR) in the treatment of HC in western centres is not well defined.

Automated landmarking and labeling of fully and partially scanned spinal columns in CT images

The spinal column is one of the most distinguishable structures in CT scans of the superior part of the human body. It is not necessary to segment the spinal column in order to use it as a frame of reference. It is sufficient to place landmarks and the appropriate anatomical labels at intervertebral disks and vertebrae.In this paper, we present an automated system for landmarking and labeling spinal columns in 3D CT datasets. We designed this framework with two goals in mind. First, we relaxed input data requirements found in the literature, and we label both full and partial spine scans. Secondly, we intended to fulfill the performance requirement for daily clinical use and developed a high throughput system capable of processing thousands of slices in just a few minutes. To accomplish the aforementioned goals, we encoded structural knowledge from training data in probabilistic boosting trees and used it to detect efficiently the spinal canal, intervertebral disks, and three reference regions responsible for initializing the landmarking and labeling. Final landmarks and labels are selected by Markov Random Field-based matches of newly introduced 3-disk models.The framework has been tested on 36 CT images having at least one of the regions around the thoracic first ribs, the thoracic twelfth ribs, or the sacrum. In an average time of 2 min, we achieved a correct labeling in 35 cases with precision of 99.0% and recall of 97.2%. Additionally, we present results assuming none of the three reference regions could be detected.