Findings from the University of East Anglia's evaluation of the Ipswich/Suffolk multi-agency strategy on prostitution following the five murders in 2006

This paper provides a summary of the main findings of an evaluation of a new multi-agency Strategy set up to tackle on-street sex-working, after five prostitutes were murdered in the English county town of Ipswich. It focuses on the outcomes of the Strategy’s four objectives, including their cost-effectiveness. It also offers an insight into the lives of the women who were previously involved in street sex-working, the means by which the Strategy helped them to move towards exiting this work, and the ways in which younger people identified as being at risk of entering it might be prevented from doing so

Getting Involved on a College Campus

According to Astin’s Theory of Involvement, what a student gains from being involved is directly proportional to both the qualitative and quantitative amount of involvement (Astin, 1999). In addition, research has proven that there is a positive correlation in student involvement with retention and academics (Kuh and Pike, 2005). Getting involved on campus is an ongoing process, and it is important for students to utilize a university’s recourses to help guide them in the possibilities. A university’s Student Activities office is just one of many areas on campus that students can find a myriad of ways to get involved. Students can seek guidance in finding the perfect club or organizations, run for student government, find a passion within a volunteer project, and much more. This video highlights the benefits of getting involved, such as gaining essential leadership skills and forming a core social group, in addition to ways the Student Activities office can be helpful in achieving these goals. Additionally, this video explains the reverse; the negative consequences that come from a lack of involvement

Comparing Sex Buyers With Men Who Do Not Buy Sex: New Data on Prostitution and Trafficking.

We investigated attitudes and behaviors associated with prostitution and sexual aggression among 101 men who buy sex and 101 age-, education-, and ethnicity-matched men who did not buy sex. Both groups tended to accept rape myths, be aware of harms of prostitution and trafficking, express ambivalence about the nature of prostitution, and believe that jail time and public exposure are the most effective deterrents to buying sex. Sex buyers were more likely than men who did not buy sex to report sexual aggression and likelihood to rape. Men who bought sex scored higher on measures of impersonal sex and hostile masculinity and had less empathy for prostituted women, viewing them as intrinsically different from other women. When compared with non-sex-buyers, these findings indicate that men who buy sex share certain key characteristics with men at risk of committing sexual aggression as documented by research based on the leading scientific model of the characteristics of non-criminal sexually aggressive men, the Confluence Model of sexual aggression

Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas)

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Houskeeper, H. F., Rosenthal, I. S., Cavanaugh, K. C., Pawlak, C., Trouille, L., Byrnes, J. E. K., Bell, T. W., & Cavanaugh, K. C. Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas). Plos One, 17(1), (2022): e0257933, https://doi.org/10.1371/journal.pone.0257933.Giant kelp populations that support productive and diverse coastal ecosystems at temperate and subpolar latitudes of both hemispheres are vulnerable to changing climate conditions as well as direct human impacts. Observations of giant kelp forests are spatially and temporally uneven, with disproportionate coverage in the northern hemisphere, despite the size and comparable density of southern hemisphere kelp forests. Satellite imagery enables the mapping of existing and historical giant kelp populations in understudied regions, but automating the detection of giant kelp using satellite imagery requires approaches that are robust to the optical complexity of the shallow, nearshore environment. We present and compare two approaches for automating the detection of giant kelp in satellite datasets: one based on crowd sourcing of satellite imagery classifications and another based on a decision tree paired with a spectral unmixing algorithm (automated using Google Earth Engine). Both approaches are applied to satellite imagery (Landsat) of the Falkland Islands or Islas Malvinas (FLK), an archipelago in the southern Atlantic Ocean that supports expansive giant kelp ecosystems. The performance of each method is evaluated by comparing the automated classifications with a subset of expert-annotated imagery (8 images spanning the majority of our continuous timeseries, cumulatively covering over 2,700 km of coastline, and including all relevant sensors). Using the remote sensing approaches evaluated herein, we present the first continuous timeseries of giant kelp observations in the FLK region using Landsat imagery spanning over three decades. We do not detect evidence of long-term change in the FLK region, although we observe a recent decline in total canopy area from 2017–2021. Using a nitrate model based on nearby ocean state measurements obtained from ships and incorporating satellite sea surface temperature products, we find that the area of giant kelp forests in the FLK region is positively correlated with the nitrate content observed during the prior year. Our results indicate that giant kelp classifications using citizen science are approximately consistent with classifications based on a state-of-the-art automated spectral approach. Despite differences in accuracy and sensitivity, both approaches find high interannual variability that impedes the detection of potential long-term changes in giant kelp canopy area, although recent canopy area declines are notable and should continue to be monitored carefully.This work was funded by the National Aeronautics and Space Administration as part of the Citizen Science for Earth Systems Program (https://earthdata.nasa.gov/esds/competitive-programs/csesp) with grant #80NSSC18M0103 (awarded to JEKB), which also provided salary to HFH, and by the National Science Foundation through the Santa Barbara Coastal Long-Term Environmental Research (https://sbclter.msi.ucsb.edu) program with grants #OCE 0620276 and 1232779. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

From fat droplets to floating forests: cross-domain transfer learning using a PatchGAN-based segmentation model

Many scientific domains gather sufficient labels to train machine algorithms through human-in-the-loop techniques provided by the Zooniverse.org citizen science platform. As the range of projects, task types and data rates increase, acceleration of model training is of paramount concern to focus volunteer effort where most needed. The application of Transfer Learning (TL) between Zooniverse projects holds promise as a solution. However, understanding the effectiveness of TL approaches that pretrain on large-scale generic image sets vs. images with similar characteristics possibly from similar tasks is an open challenge. We apply a generative segmentation model on two Zooniverse project-based data sets: (1) to identify fat droplets in liver cells (FatChecker; FC) and (2) the identification of kelp beds in satellite images (Floating Forests; FF) through transfer learning from the first project. We compare and contrast its performance with a TL model based on the COCO image set, and subsequently with baseline counterparts. We find that both the FC and COCO TL models perform better than the baseline cases when using >75% of the original training sample size. The COCO-based TL model generally performs better than the FC-based one, likely due to its generalized features. Our investigations provide important insights into usage of TL approaches on multi-domain data hosted across different Zooniverse projects, enabling future projects to accelerate task completion.Comment: 5 pages, 4 figures, accepted for publication at the Proceedings of the ACM/CIKM 2022 (Human-in-the-loop Data Curation Workshop

Impact of brain tissue filtering on neurostimulation fields: A modeling study

Electrical neurostimulation techniques, such as deep brain stimulation (DBS) and transcranial magnetic stimulation (TMS), are increasingly used in the neurosciences, e.g., for studying brain function, and for neurotherapeutics, e.g., for treating depression, epilepsy, and Parkinson's disease. The characterization of electrical properties of brain tissue has guided our fundamental understanding and application of these methods, from electrophysiologic theory to clinical dosing-metrics. Nonetheless, prior computational models have primarily relied on ex-vivo impedance measurements. We recorded the in-vivo impedances of brain tissues during neurosurgical procedures and used these results to construct MRI guided computational models of TMS and DBS neurostimulatory fields and conductance-based models of neurons exposed to stimulation. We demonstrated that tissues carry neurostimulation currents through frequency dependent resistive and capacitive properties not typically accounted for by past neurostimulation modeling work. We show that these fundamental brain tissue properties can have significant effects on the neurostimulatory-fields (capacitive and resistive current composition and spatial/temporal dynamics) and neural responses (stimulation threshold, ionic currents, and membrane dynamics). These findings highlight the importance of tissue impedance properties on neurostimulation and impact our understanding of the biological mechanisms and technological potential of neurostimulatory methods.United States. Defense Advanced Research Projects Agency (Contract W31P4Q-09-C-0117)National Institute of Neurological Disorders and Stroke (U.S.) (Award R43NS062530)National Institute of Neurological Disorders and Stroke (U.S.) (Award 1R44NS080632