Revealing hidden species distribution with pheromones: the case of Synanthedon vespiformis (Lepidoptera: Sesiidae) in Sweden

Synanthedon vespiformis L. (Lepidoptera: Sesiidae) is considered a rare insect in Sweden, discovered in 1860, with only a few observations recorded until a sex pheromone attractant became available recently. This study details a national survey conducted using pheromones as a sampling method for this species. Through pheromone trapping we captured 439 specimens in Southern Sweden at 77 sites, almost tripling the number of previously reported records for this species. The results suggest that S. vespiformis is truly a rare species with a genuinely scattered distribution, but can be locally abundant. Habitat analyses were conducted in order to test the relationship between habitat quality and the number of individuals caught. In Sweden, S. vespiformis is thought to be associated with oak hosts, but our attempts to predict its occurrence by the abundance of oaks yielded no significant relationships. We therefore suggest that sampling bias and limited knowledge on distribution may have led to the assumption that this species is primarily reliant on oaks in the northern part of its range, whereas it may in fact be polyphagous, similar to S. vespiformis found as an agricultural pest in Central and Southern Europe. We conclude that pheromones can massively enhance sampling potential for this and other rare lepidopteran species. Large-scale pheromone-based surveys provide a snapshot of true presences and absences across a considerable part of a species national distribution range, and thus for the first time provide a viable means of systematically assessing changes in distribution over time with high spatiotemporal resolution

The practical politics of sharing personal data

The focus of this paper is upon how people handle the sharing of personal data as an interactional concern. A number of ethnographic studies of domestic environments are drawn upon in order to articulate a range of circumstances under which data may be shared. In particular a distinction is made between the in situ sharing of data with others around you and the sharing of data with remote parties online. A distinction is also drawn between circumstances of purposefully sharing data in some way and circumstances where the sharing of data is incidental or even unwitting. On the basis of these studies a number of the organisational features of how people seek to manage the ways in which their data is shared are teased out. The paper then reflects upon how data sharing practices have evolved to handle the increasing presence of digital systems in people’s environments and how these relate to the ways in which people traditionally orient to the sharing of information. In conclusion a number of ways are pointed out in which the sharing of data remains problematic and there is a discussion of how systems may need to adapt to better support people’s data sharing practices in the future

Prejudice and misconceptions about tuberculosis and HIV in rural and urban communities in Ethiopia: a challenge for the TB/HIV control program

<p>Abstract</p> <p>Background</p> <p>In Ethiopia, where HIV and tuberculosis (TB) are very common, little is known about the prejudice and misconceptions of rural communities towards People living with HIV/AIDS (PLHA) and TB.</p> <p>Methods</p> <p>We conducted a cross sectional study in Gilgel Gibe Field Research area (GGFRA) in southwest Ethiopia to assess the prejudice and misconceptions of rural and urban communities towards PLHA and TB. The study population consisted of 862 randomly selected adults in GGFRA. Data were collected by trained personnel using a pretested structured questionnaire. To triangulate the findings, 8 focus group discussions among women and men were done.</p> <p>Results</p> <p>Of the 862 selected study participants, 750(87%) accepted to be interviewed. The mean age of the respondents was 31.2 (SD ± 11.0). Of the total interviewed individuals, 58% of them were females. More than half of the respondents did not know the possibility of transmission of HIV from a mother to a child or by breast feeding. For fear of contagion of HIV, most people do not want to eat, drink, and share utensils or clothes with a person living with HIV/AIDS. A higher proportion of females [OR = 1.5, (95% CI: 1.0, 2.2)], non-literate individuals [OR = 2.3, (95%CI: 1.4, 3.6)], rural residents [OR = 3.8, (95%CI: 2.2, 6.6)], and individuals who had poor knowledge of HIV/AIDS [OR = 2.8, (95%CI: 1.8, 2.2)] were more likely to have high prejudice towards PLHA than respectively males, literates, urban residents and individuals with good knowledge. Exposure to cold air was implicated as a major cause of TB. Literates had a much better knowledge about the cause and methods of transmission and prevention of TB than non-literates. More than half of the individuals (56%) had high prejudice towards a patient with TB. A larger proportion of females [OR = 1.3, (95% CI: 1.0, 1.9)] and non-literate individuals [OR = 1.4, (95% CI: 1.1, 2.0)] had high prejudice towards patients with TB than males and literate individuals.</p> <p>Conclusion</p> <p>TB/HIV control programs in collaboration with other partners should invest more in social mobilization and education of the communities to rectify the widespread prejudice and misconceptions.</p

Privacy Disclosures Detection in Natural-Language Text Through Linguistically-Motivated Artificial Neural Networks

An increasing number of people are sharing information through text messages, emails, and social media without proper privacy checks. In many situations, this could lead to serious privacy threats. This paper presents a methodology for providing extra safety precautions without being intrusive to users. We have developed and evaluated a model to help users take control of their shared information by automatically identifying text (i.e., a sentence or a transcribed utterance) that might contain personal or private disclosures. We apply off-the-shelf natural language processing tools to derive linguistic features such as part-of-speech, syntactic dependencies, and entity relations. From these features, we model and train a multichannel convolutional neural network as a classifier to identify short texts that have personal, private disclosures. We show how our model can notify users if a piece of text discloses personal or private information, and evaluate our approach in a binary classification task with 93% accuracy on our own labeled dataset, and 86% on a dataset of ground truth. Unlike document classification tasks in the area of natural language processing, our framework is developed keeping the sentence level context into consideration