The Need for Visits to Social and Vocational Programs for the Mentally Ill as Part of General Psychiatry Residency Training

Background. Comprehensive treatment planning for psychiatric illnesses should be based on a biopsychosocial model of treatment to address the acuity and chronicity of these disorders. To achieve this goal, knowledge about pharmacological, psychological, and social aspects of the treatment plan should be presented as an integral part of general psychiatry residency training. This survey study was conducted to examine how many programs provide training where residents have scheduled visits to social and vocational mental health service organizations in the community and to identify potential obstacles to including this rotation in general psychiatry residency training. Methods. A voluntary, anonymous survey was sent via SurveyMonkey® to the program directors of all general psychiatry residency programs in the United States. The survey consisted of five questions designed to assess if their programs had a rotation where residents visit social and vocational programs in the community designed for mentally ill patients to provide knowledge of the community mental health resources to their residents. Results. Of the 168 survey invitations issued, 73 (44%) responded. Fifty-six responders acknowledged that their residents were required to visit a community mental health organization, but their programs did not offer visits to community social and vocational programs. Seventeen program directors reported that their program did not provide this experience to their residents and indicated a desire to include such a rotation. Conclusions. Community mental health service organization visits should enhance knowledge of psychiatry residents about community mental health resources and indirectly promote better patient care. Information obtained from this survey should create discussion to work toward better psychiatric resident training

Vocal behaviour and feeding ecology of killer whales Orcinus orca around Shetland, UK

Killer whales Orcinus orca are sighted regularly off Shetland, UK, but little is known about their numbers, diet and population identity. We aimed to relate vocal behaviour to diet of killer whales around Shetland in order to investigate population structure and differences in feeding strategies. Fieldwork was conducted in the summers of 2008 and 2009. We located killer whales through a sightings network and shore-based scans and collected photo-ID data, behavioural information, feeding data and acoustic recordings from a small boat. The majority of encounters (n = 14) were of small groups (1 to 15 individuals) travelling close to shore and feeding on marine mammals. Two encounters were with large groups (20+ individuals) feeding on herring Clupea harengus farther offshore. Seal-hunting groups vocalised rarely, producing pulsed calls, echolocation clicks and whistles almost exclusively when surface-active or milling after a kill. Herring-eating groups were largely silent during one encounter, but very vocal during the other. Analysis of pulsed calls identified 6 stereotyped call types for seal-hunting groups and 7 for herring-eating groups. No call types were shared between both kinds of groups. The vocal behaviour of seal-hunting groups showed striking parallels to that of Pacific marine mammal specialists and presumably evolved to decrease detection by acoustically sensitive prey. One call type produced by Shetland herring-eating killer whales matched a vocalisation that a previous study had described from Iceland and identified as a possible herding call that may function to concentrate herring during feeding. These findings point to behavioural and dietary specialisation among Shetland killer whales, which should be taken into account when making management decisions affecting these animals

Fast and Accurate Retrieval of Methane Concentration From Imaging Spectrometer Data Using Sparsity Prior

The strong radiative forcing by atmospheric methane has stimulated interest in identifying natural and anthropogenic sources of this potent greenhouse gas. Point sources are important targets for quantification, and anthropogenic targets have the potential for emissions reduction. Methane point-source plume detection and concentration retrieval have been previously demonstrated using data from the Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG). Current quantitative methods have tradeoffs between computational requirements and retrieval accuracy, creating obstacles for processing real-time data or large data sets from flight campaigns. We present a new computationally efficient algorithm that applies sparsity and an albedo correction to matched the filter retrieval of trace gas concentration path length. The new algorithm was tested using the AVIRIS-NG data acquired over several point-source plumes in Ahmedabad, India. The algorithm was validated using the simulated AVIRIS-NG data, including synthetic plumes of known methane concentration. Sparsity and albedo correction together reduced the root-mean-squared error of retrieved methane concentration-path length enhancement by 60.7% compared with a previous robust matched filter method. Background noise was reduced by a factor of 2.64. The new algorithm was able to process the entire 300 flight line 2016 AVIRIS-NG India campaign in just over 8 h on a desktop computer with GPU acceleration

Fast and Accurate Retrieval of Methane Concentration from Imaging Spectrometer Data Using Sparsity Prior

The strong radiative forcing by atmospheric methane has stimulated interest in identifying natural and anthropogenic sources of this potent greenhouse gas. Point sources are important targets for quantification, and anthropogenic targets have potential for emissions reduction. Methane point source plume detection and concentration retrieval have been previously demonstrated using data from the Airborne Visible InfraRed Imaging Spectrometer Next Generation (AVIRIS-NG). Current quantitative methods have tradeoffs between computational requirements and retrieval accuracy, creating obstacles for processing real-time data or large datasets from flight campaigns. We present a new computationally efficient algorithm that applies sparsity and an albedo correction to matched filter retrieval of trace gas concentration-pathlength. The new algorithm was tested using AVIRIS-NG data acquired over several point source plumes in Ahmedabad, India. The algorithm was validated using simulated AVIRIS-NG data including synthetic plumes of known methane concentration. Sparsity and albedo correction together reduced the root mean squared error of retrieved methane concentration-pathlength enhancement by 60.7% compared with a previous robust matched filter method. Background noise was reduced by a factor of 2.64. The new algorithm was able to process the entire 300 flightline 2016 AVIRIS-NG India campaign in just over 8 hours on a desktop computer with GPU acceleration.Comment: 13 pages, 11 figure