2016 United Kingdom national guideline on the sexual health care of men who have sex with men.

This guideline is intended for use in UK Genitourinary medicine clinics and sexual health services but is likely to be of relevance in all sexual health settings, including general practice and Contraception and Sexual Health (CASH) services, where men who have sex with men (MSM) seek sexual health care or where addressing the sexual health needs of MSM may have public health benefits. For the purposes of this document, MSM includes all gay, bisexual and all other males who have sex with other males and both cis and trans men. This document does not provide guidance on the treatment of particular conditions where this is covered in other British Association for Sexual Health and HIV (BASHH) Guidelines but outlines best practice in multiple aspects of the sexual health care of MSM. Where prevention of sexually transmitted infections including HIV can be addressed as an integral part of clinical care, this is consistent with the concept of combination prevention and is included. The document is designed primarily to provide guidance on the direct clinical care of MSM but also makes reference to the design and delivery of services with the aim of supporting clinicians and commissioners in providing effective services. Methodology This document was produced in accordance with the guidance set out in the BASHH CEG's document 'Framework for guideline development and assessment' published in 2010 at http://www.bashh.org/guidelines and with reference to the Agree II instrument. Following the production of the updated framework in April 2015, the GRADE system for assessing evidence was adopted and the draft recommendations were regraded. Search strategy (see also Appendix 1) Ovid Medline 1946 to December 2014, Medline daily update, Embase 1974 to December 2014, Pubmed NeLH Guidelines Database, Cochrane library from 2000 to December 2014. Search language English only. The search for Section 3 was conducted on PubMed to December 2014. Priority was given to peer-reviewed papers published in scientific journals, although for many issues evidence includes conference abstracts listed on the Embase database. In addition, for 'Identification of problematic recreational drug and alcohol use' section and 'Sexual problems and dysfunctions in MSM' section, searches included PsycINFO. Methods Article titles and abstracts were reviewed and if relevant the full text article was obtained. Priority was given to randomised controlled trial and systematic review evidence, and recommendations made and graded on the basis of best available evidence. Piloting and feedback The first draft of the guideline was circulated to the writing group and to a small group of relevant experts, third sector partners and patient representatives who were invited to comment on the whole document and specifically on particular sections. The revised draft was reviewed by the CEG and then reviewed by the BASHH patient/public panel and posted on the BASHH website for public consultation. The final draft was piloted before publication. Guideline update The guidelines will be reviewed and revised in five years' time, 2022

MIDGROUND OBJECT DETECTION IN REAL WORLD VIDEO SCENES

Traditional video scene analysis depends on accurate background modeling to identify salient foreground objects. However, in many important surveillance applications, saliency is defined by the appearance of a new non-ephemeral object that is between the foreground and background. This midground realm is defined by a temporal window following the object’s appearance; but it also depends on adaptive background modeling to allow detection with scene variations (e.g., occlusion, small illumination changes). The human visual system is illsuited for midground detection. For example, when surveying a busy airline terminal, it is difficult (but important) to detect an unattended bag which appears in the scene. This paper introduces a midground detection technique which emphasizes computational and storage efficiency. The approach uses a new adaptive, pixel-level modeling technique derived from existing backgrounding methods. Experimental results demonstrate that this technique can accurately and efficiently identify midground objects in real-world scenes, including PETS2006 and AVSS2007 challenge datasets. 1

Multimodal Mean Adaptive Backgrounding for Embedded Real-Time Video Surveillance,” Embedded Computer Vision Workshop (ECVW07

Automated video surveillance applications require accurate separation of foreground and background image content. Cost sensitive embedded platforms place realtime performance and efficiency demands on techniques to accomplish this task. In this paper we evaluate pixel-level foreground extraction techniques for a low cost integrated surveillance system. We introduce a new adaptive technique, multimodal mean (MM), which balances accuracy, performance, and efficiency to meet embedded system requirements. Our evaluation compares several pixel-level foreground extraction techniques in terms of their computation and storage requirements, and functional accuracy for three representative video sequences. The proposed MM algorithm delivers comparable accuracy of the best alternative (Mixture o

Midground Object Detection in Real World Video Scenes,

Traditional video scene analysis depends on accurate background modeling to identify salient foreground objects. However, in many important surveillance applications, saliency is defined by the appearance of a new non-ephemeral object that is between the foreground and background. This midground realm is defined by a temporal window following the object's appearance; but it also depends on adaptive background modeling to allow detection with scene variations (e.g., occlusion, small illumination changes). The human visual system is ill-suited for midground detection. For example, when surveying a busy airline terminal, it is difficult (but important) to detect an unattended bag which appears in the scene. This paper introduces a midground detection technique which emphasizes computational and storage efficiency. The approach uses a new adaptive, pixel-level modeling technique derived from existing backgrounding methods. Experimental results demonstrate that this technique can accurately and efficiently identify midground objects in real-world scenes, including PETS2006 and A VSS2007 challenge datasets

Embedded Real-Time Surveillance Using Multimodal Mean Background Modeling

Automated video surveillance applications require accurate separation of foreground and background image content. Cost-sensitive embedded platforms place real-time performance and efficiency demands on techniques to accomplish this task. In this chapter, we evaluate pixel-level foreground extraction techniques for a low-cost integrated surveillance system. We introduce a new adaptive background modeling technique, multimodal mean (MM), which balances accuracy, performance, and efficiency to meet embedded system requirements. Our evaluation compares several pixel-level foreground extraction techniques in terms of their computation and storage requirements, and functional accuracy for three representative video sequences. The proposed MM algorithm delivers comparable accuracy of the best alternative (mixture of Gaussians) with a 6× improvement in execution time and an 18% reduction in required storage on an eBox-2300 embedded platform