How we continue bonds with deceased persons:The proximity-seeking behavior scale

Continuing bonds is a multifaceted process, encompassing perceptions, beliefs, illusions and hallucinations, and overt behaviors. We developed the Proximity-Seeking Behavior Scale (PSBS) to assess overt behavior to continue bonds with the deceased person. We had 694 bereaved adults complete an online survey. Exploratory and confirmatory factor analyses yielded a one-factor model for PSBS items. PSBS reliability was good. PSBS scores correlated positively with rumination and yearning, feeling connected to the deceased person, and prolonged grief and depression symptoms. The PSBS appears a reliable and valid instrument to assess proximity-seeking behaviors

Epidemiology of facial fractures: Incidence, prevalence and years lived with disability estimates from the Global Burden of Disease 2017 study

Background: The Global Burden of Disease Study (GBD) has historically produced estimates of causes of injury such as falls but not the resulting types of injuries that occur. The objective of this study was to estimate the global incidence, prevalence and years lived with disability (YLDs) due to facial fractures and to estimate the leading injurious causes of facial fracture. Methods: We obtained results from GBD 2017. First, the study estimated the incidence from each injury cause (eg, falls), and then the proportion of each cause that would result in facial fracture being the most disabling injury. Incidence, prevalence and YLDs of facial fractures are then calculated across causes. Results: Globally, in 2017, there were 7 538 663 (95% uncertainty interval 6 116 489 to 9 4

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

A Novel Method for Multispectral Image Classification by Using Social Spider Optimization Algorithm Integrated to Fuzzy C-Mean Clustering

In remote sensing, Fuzzy C-Means clustering (FCM) is a robust method in determining membership grades of a pixel belonging to 1 or more classes. This paper proposes a novel approach by using the social spider optimization (SSO) algorithm in solving the search for optimal cluster centers in FCM. Hanoi, the capital of Vietnam, was chosen as a case study because of its spatial complexity. Multispectral satellite datasets of Landsat 8, Sentinel 2A and SPOT 7 were used. The experiment started with the segmentation process, followed by an examination of the model, then the results were compared with several conventional clustering methods. For accuracy assessment, the FCM minimizing objective functions, user and producer accuracies and overall accuracy were used. The results showed that SSO significantly improved the performance of FCM and outperformed the benchmarked classifiers or other common optimization algorithms. It could be concluded that the model was successfully deployed in the study area and could be suggested as an alternative solution for urban pattern detection. In a broader sense, classification methods will be enriched with the active and fast-growing contribution of nature-inspired algorithms