Intrinsic activity in the fly brain gates visual information during behavioral choices

The small insect brain is often described as an input/output system that executes reflex-like behaviors. It can also initiate neural activity and behaviors intrinsically, seen as spontaneous behaviors, different arousal states and sleep. However, less is known about how intrinsic activity in neural circuits affects sensory information processing in the insect brain and variability in behavior. Here, by simultaneously monitoring Drosophila's behavioral choices and brain activity in a flight simulator system, we identify intrinsic activity that is associated with the act of selecting between visual stimuli. We recorded neural output (multiunit action potentials and local field potentials) in the left and right optic lobes of a tethered flying Drosophila, while its attempts to follow visual motion (yaw torque) were measured by a torque meter. We show that when facing competing motion stimuli on its left and right, Drosophila typically generate large torque responses that flip from side to side. The delayed onset (0.1-1 s) and spontaneous switch-like dynamics of these responses, and the fact that the flies sometimes oppose the stimuli by flying straight, make this behavior different from the classic steering reflexes. Drosophila, thus, seem to choose one stimulus at a time and attempt to rotate toward its direction. With this behavior, the neural output of the optic lobes alternates; being augmented on the side chosen for body rotation and suppressed on the opposite side, even though the visual input to the fly eyes stays the same. Thus, the flow of information from the fly eyes is gated intrinsically. Such modulation can be noise-induced or intentional; with one possibility being that the fly brain highlights chosen information while ignoring the irrelevant, similar to what we know to occur in higher animals

Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities

Global change drivers are known to interact in their effects on biodiversity, but much research to date ignores this complexity. As a consequence, there are problems in the attribution of biodiversity change to different drivers and, therefore, our ability to manage habitats and landscapes appropriately. Few studies explicitly acknowledge and account for interactive (i.e., nonadditive) effects of land use and climate change on biodiversity. One reason is that the mechanisms by which drivers interact are poorly understood. We evaluate such mechanisms, including interactions between demographic parameters, evolutionary trade-offs and synergies and threshold effects of population size and patch occupancy on population persistence. Other reasons for the lack of appropriate research are limited data availability and analytical issues in addressing interaction effects. We highlight the influence that attribution errors can have on biodiversity projections and discuss experimental designs and analytical tools suited to this challenge. Finally, we summarize the risks and opportunities provided by the existence of interaction effects. Risks include ineffective conservation management; but opportunities also arise, whereby the negative impacts of climate change on biodiversity can be reduced through appropriate land management as an adaptation measure. We hope that increasing the understanding of key mechanisms underlying interaction effects and discussing appropriate experimental and analytical designs for attribution will help researchers, policy makers, and conservation practitioners to better minimize risks and exploit opportunities provided by land use-climate change interactions

New live screening of plant-nematode interactions in the rhizosphere

Abstract Free living nematodes (FLN) are microscopic worms found in all soils. While many FLN species are beneficial to crops, some species cause significant damage by feeding on roots and vectoring viruses. With the planned legislative removal of traditionally used chemical treatments, identification of new ways to manage FLN populations has become a high priority. For this, more powerful screening systems are required to rapidly assess threats to crops and identify treatments efficiently. Here, we have developed new live assays for testing nematode responses to treatment by combining transparent soil microcosms, a new light sheet imaging technique termed Biospeckle Selective Plane Illumination Microscopy (BSPIM) for fast nematode detection, and Confocal Laser Scanning Microscopy for high resolution imaging. We show that BSPIM increased signal to noise ratios by up to 60 fold and allowed the automatic detection of FLN in transparent soil samples of 1.5 mL. Growing plant root systems were rapidly scanned for nematode abundance and activity, and FLN feeding behaviour and responses to chemical compounds observed in soil-like conditions. This approach could be used for direct monitoring of FLN activity either to develop new compounds that target economically damaging herbivorous nematodes or ensuring that beneficial species are not negatively impacted

Multidimentional proteomics for cell biology

The proteome is a dynamic system in which each protein has interconnected properties — dimensions — that together contribute to the phenotype of a cell. Measuring these properties has proved challenging owing to their diversity and dynamic nature. Advances in mass spectrometry-based proteomics now enable the measurement of multiple properties for thousands of proteins, including their abundance, isoform expression, turnover rate, subcellular localization, post-translational modifications and interactions. Complementing these experimental developments are new data analysis, integration and visualization tools as well as data-sharing resources. Together, these advances in the multidimensional analysis of the proteome are transforming our understanding of various cellular and physiological processes

Towards an integrated crowdsourcing definition

Crowdsourcing is a relatively recent concept that encompasses many practices. This diversity leads to the blurring of the limits of crowdsourcing that may be identified virtually with any type of internet-based collaborative activity, such as co-creation or user innovation. Varying definitions of crowdsourcing exist, and therefore some authors present certain specific examples of crowdsourcing as paradigmatic, while others present the same examples as the opposite. In this article, existing definitions of crowdsourcing are analysed to extract common elements and to establish the basic characteristics of any crowdsourcing initiative. Based on these existing definitions, an exhaustive and consistent definition for crowdsourcing is presented and contrasted in 11 cases.Estelles Arolas, E.; González-Ladrón-De-Guevara, F. (2012). Towards an integrated crowdsourcing definition. Journal of Information Science. 32(2):189-200. doi:10.1177/0165551512437638S189200322Vukovic, M., & Bartolini, C. (2010). Towards a Research Agenda for Enterprise Crowdsourcing. Leveraging Applications of Formal Methods, Verification, and Validation, 425-434. doi:10.1007/978-3-642-16558-0_36Brabham, D. C. (2008). Crowdsourcing as a Model for Problem Solving. Convergence: The International Journal of Research into New Media Technologies, 14(1), 75-90. doi:10.1177/1354856507084420Vukovic, M. (2009). Crowdsourcing for Enterprises. 2009 Congress on Services - I. doi:10.1109/services-i.2009.56Doan, A., Ramakrishnan, R., & Halevy, A. Y. (2011). Crowdsourcing systems on the World-Wide Web. Communications of the ACM, 54(4), 86. doi:10.1145/1924421.1924442Brabham, D. C. (2008). Moving the crowd at iStockphoto: The composition of the crowd and motivations for participation in a crowdsourcing application. First Monday, 13(6). doi:10.5210/fm.v13i6.2159Huberman, B. A., Romero, D. M., & Wu, F. (2009). Crowdsourcing, attention and productivity. Journal of Information Science, 35(6), 758-765. doi:10.1177/0165551509346786Andriole, S. J. (2010). Business impact of Web 2.0 technologies. Communications of the ACM, 53(12), 67. doi:10.1145/1859204.1859225Denyer, D., Tranfield, D., & van Aken, J. E. (2008). Developing Design Propositions through Research Synthesis. Organization Studies, 29(3), 393-413. doi:10.1177/0170840607088020Egger, M., Smith, G. D., & Altman, D. G. (Eds.). (2001). Systematic Reviews in Health Care. doi:10.1002/9780470693926Tatarkiewicz, W. (1980). A History of Six Ideas. doi:10.1007/978-94-009-8805-7Cosma, G., & Joy, M. (2008). Towards a Definition of Source-Code Plagiarism. IEEE Transactions on Education, 51(2), 195-200. doi:10.1109/te.2007.906776Brabham, D. C. (2009). Crowdsourcing the Public Participation Process for Planning Projects. Planning Theory, 8(3), 242-262. doi:10.1177/1473095209104824Alonso, O., & Lease, M. (2011). Crowdsourcing 101. Proceedings of the fourth ACM international conference on Web search and data mining - WSDM ’11. doi:10.1145/1935826.1935831Bederson, B. B., & Quinn, A. J. (2011). Web workers unite! addressing challenges of online laborers. Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems - CHI EA ’11. doi:10.1145/1979742.1979606Grier, D. A. (2011). Not for All Markets. Computer, 44(5), 6-8. doi:10.1109/mc.2011.155Heer, J., & Bostock, M. (2010). Crowdsourcing graphical perception. Proceedings of the 28th international conference on Human factors in computing systems - CHI ’10. doi:10.1145/1753326.1753357Heymann, P., & Garcia-Molina, H. (2011). Turkalytics. Proceedings of the 20th international conference on World wide web - WWW ’11. doi:10.1145/1963405.1963473Kazai, G. (2011). In Search of Quality in Crowdsourcing for Search Engine Evaluation. Advances in Information Retrieval, 165-176. doi:10.1007/978-3-642-20161-5_17La Vecchia, G., & Cisternino, A. (2010). Collaborative Workforce, Business Process Crowdsourcing as an Alternative of BPO. Lecture Notes in Computer Science, 425-430. doi:10.1007/978-3-642-16985-4_40Liu, E., & Porter, T. (2010). Culture and KM in China. VINE, 40(3/4), 326-333. doi:10.1108/03055721011071449Oliveira, F., Ramos, I., & Santos, L. (2010). Definition of a Crowdsourcing Innovation Service for the European SMEs. Lecture Notes in Computer Science, 412-416. doi:10.1007/978-3-642-16985-4_37Porta, M., House, B., Buckley, L., & Blitz, A. (2008). Value 2.0: eight new rules for creating and capturing value from innovative technologies. Strategy & Leadership, 36(4), 10-18. doi:10.1108/10878570810888713Ribiere, V. M., & Tuggle, F. D. (Doug). (2010). Fostering innovation with KM 2.0. VINE, 40(1), 90-101. doi:10.1108/03055721011024955Sloane, P. (2011). The brave new world of open innovation. Strategic Direction, 27(5), 3-4. doi:10.1108/02580541111125725Wexler, M. N. (2011). Reconfiguring the sociology of the crowd: exploring crowdsourcing. International Journal of Sociology and Social Policy, 31(1/2), 6-20. doi:10.1108/01443331111104779Whitla, P. (2009). Crowdsourcing and Its Application in Marketing Activities. Contemporary Management Research, 5(1). doi:10.7903/cmr.1145Yang, J., Adamic, L. A., & Ackerman, M. S. (2008). Crowdsourcing and knowledge sharing. Proceedings of the 9th ACM conference on Electronic commerce - EC ’08. doi:10.1145/1386790.1386829Brabham, D. C. (2010). MOVING THE CROWD AT THREADLESS. Information, Communication & Society, 13(8), 1122-1145. doi:10.1080/13691181003624090Giudice, K. D. (2010). Crowdsourcing credibility: The impact of audience feedback on Web page credibility. Proceedings of the American Society for Information Science and Technology, 47(1), 1-9. doi:10.1002/meet.14504701099Stewart, O., Huerta, J. M., & Sader, M. (2009). Designing crowdsourcing community for the enterprise. Proceedings of the ACM SIGKDD Workshop on Human Computation - HCOMP ’09. doi:10.1145/1600150.1600168Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50(4), 370-396. doi:10.1037/h0054346Veal, A. J. (Ed.). (2002). Leisure and tourism policy and planning. doi:10.1079/9780851995465.0000Dahlander, L., & Gann, D. M. (2010). How open is innovation? Research Policy, 39(6), 699-709. doi:10.1016/j.respol.2010.01.01

Mortality following development of breast cancer while using oestrogen or oestrogen plus progestin: a computer record-linkage study

The literature on the relationship between breast cancer mortality and postmenopausal oestrogen and combined oestrogen/progestin therapy is seemingly contradictory. This study explored survival after exposure to oestrogen or oestrogen plus progestin at or in the year prior to breast cancer diagnosis. Information on patients first diagnosed with invasive breast cancer between 1993 and 1998 was linked with outpatient pharmacy data from 1992 to 2000. Patients were classified according to use of oestrogen alone or oestrogen plus progestin at or in the year prior to diagnosis. Compared to nonusers, and adjusting for age at diagnosis, race/ethnicity, tumour size and grade, oestrogen receptor status, surgery status, and chemotherapy and hormone therapy for breast cancer treatment, oestrogen plus progestin users had lower all-cause mortality (stage I hazard ratio (HR)=0.69, 95% confidence interval (CI)=0.48–0.99; stage II HR=0.53, 95% CI=0.39–0.72) and breast cancer mortality (stage I HR=0.52, 95% CI=0.26–1.04; stage II HR=0.69, 95% CI=0.48–0.98). Oestrogen users experienced little or no survival benefit for all-cause mortality (stage I HR=1.04, 95% CI=0.77–1.42; stage II HR=0.86, 95% CI=0.65–1.14) or breast cancer mortality (stage I HR=1.23, 95% CI 0.72–2.10; stage II HR=1.01, 95% CI 0.72–1.41). Our findings suggest, relative to nonusers, a lower risk of death from all causes and from breast cancer in patients who were diagnosed with breast cancer while exposed to oestrogen plus progestin, but not in patients exposed to oestrogen only

Genome-Wide Modeling of Transcription Preinitiation Complex Disassembly Mechanisms using ChIP-chip Data

Apparent occupancy levels of proteins bound to DNA in vivo can now be routinely measured on a genomic scale. A challenge in relating these occupancy levels to assembly mechanisms that are defined with biochemically isolated components lies in the veracity of assumptions made regarding the in vivo system. Assumptions regarding behavior of molecules in vivo can neither be proven true nor false, and thus is necessarily subjective. Nevertheless, within those confines, connecting in vivo protein-DNA interaction observations with defined biochemical mechanisms is an important step towards fully defining and understanding assembly/disassembly mechanisms in vivo. To this end, we have developed a computational program PathCom that models in vivo protein-DNA occupancy data as biochemical mechanisms under the assumption that occupancy levels can be related to binding duration and explicitly defined assembly/disassembly reactions. We exemplify the process with the assembly of the general transcription factors (TBP, TFIIB, TFIIE, TFIIF, TFIIH, and RNA polymerase II) at the genes of the budding yeast Saccharomyces. Within the assumption inherent in the system our modeling suggests that TBP occupancy at promoters is rather transient compared to other general factors, despite the importance of TBP in nucleating assembly of the preinitiation complex. PathCom is suitable for modeling any assembly/disassembly pathway, given that all the proteins (or species) come together to form a complex

Physician perceptions of primary prevention: qualitative base for the conceptual shaping of a practice intervention tool

BACKGROUND: A practice intervention must have its basis in an understanding of the physician and practice to secure its benefit and relevancy. We used a formative process to characterize primary care physician attitudes, needs, and practice obstacles regarding primary prevention. The characterization will provide the conceptual framework for the development of a practice tool to facilitate routine delivery of primary preventive care. METHODS: A focus group of primary care physician Opinion Leaders was audio-taped, transcribed, and qualitatively analyzed to identify emergent themes that described physicians' perceptions of prevention in daily practice. RESULTS: The conceptual worth of primary prevention, including behavioral counseling, was high, but its practice was significantly countered by the predominant clinical emphasis on and rewards for secondary care. In addition, lack of health behavior training, perceived low self-efficacy, and patient resistance to change were key deterrents to primary prevention delivery. Also, the preventive focus in primary care is not on cancer, but on predominant chronic nonmalignant conditions. CONCLUSIONS: The success of the future practice tool will be largely dependent on its ability to "fit" primary prevention into the clinical culture of diagnoses and treatment sustained by physicians, patients, and payers. The tool's message output must be formatted to facilitate physician delivery of patient-tailored behavioral counseling in an accurate, confident, and efficacious manner. Also, the tool's health behavior messages should be behavior-specific, not disease-specific, to draw on shared risk behaviors of numerous diseases and increase the likelihood of perceived salience and utility of the tool in primary care

Beckman Access versus the Bayer ACS:180 and the Abbott AxSYM cardiac Troponin-I real-time immunoassays: an observational prospective study

BACKGROUND: Reliability of cardiac troponin-I assays under real-time conditions has not been previously well studied. Most large published cTnI trials have utilized protocols which required the freezing of serum (or plasma) for delayed batch cTnI analysis. We sought to correlate the presence of the acute ischemic coronary syndrome (AICS) to troponin-I values obtained in real-time by three random-mode analyzer immunoassay systems: the Beckman ACCESS (BA), the Bayer ACS:180 (CC) and the Abbott AxSYM (AX). METHODS: This was an observational prospective study at a university tertiary referral center. Serum from a convenience sampling of telemetry patients was analyzed in real-time for troponin-I by either the BA-CC (Arm-1) or BA-AX (Arm-2) assay pairs. Presence of the AICS was determined retrospectively and then correlated with troponin-I results. RESULTS: 100 patients were enrolled in Arm-1 (38 with AICS) and 94 in Arm-2 (48 with AICS). The BA system produced 51% false positives in Arm-1, 44% in Arm-2, with negative predictive values of 92% and 100% respectively. In Arm-1, the BA and the CC assays had sensitivities of 97% and 63% and specificities of 18% and 87%. In Arm-2, the BA and the AX assays had sensitivities of 100% and 83% and specificities of 11% and 78%. CONCLUSIONS: In real-time analysis, the performance of the AxSYM and ACS:180 assay systems produced more accurate troponin-I results than the ACCESS system

A Model for the Interplay of Receptor Recycling and Receptor-Mediated Contact in T Cells

Orientation of organelles inside T cells (TC) toward antigen-presenting cells (APC) ensures that the immune response is properly directed, but the orientation mechanisms remain largely unknown. Structural dynamics of TC are coupled to dynamics of T-cell receptor (TCR), which recognizes antigen on the APC surface. Engagement of the TCR triggers its internalization followed by delayed polarized recycling to the plasma membrane through the submembrane recycling compartment (RC), which organelle shares intracellular location with the TC effector apparatus. TCR engagement also triggers TC-APC interface expansion enabling further receptor engagement. To analyze the interplay of the cell-cell contact and receptor dynamics, we constructed a new numerical model. The new model displays the experimentally observed selective stabilization of the contact initiated next to the RC, and only transient formation of contact diametrically opposed to the RC. In the general case wherein the TC-APC contact is initiated in an arbitrary orientation to the RC, the modeling predicts that the contact dynamics and receptor recycling can interact, resulting effectively in migration of the contact to the TC surface domain adjacent to the submembrane RC. Using three-dimensional live-cell confocal microscopy, we obtain data consistent with this unexpected behavior. We conclude that a TC can stabilize its contact with an APC by aligning it with the polarized intracellular traffic of TCR. The results also suggest that the orientation of TC organelles, such as the RC and the effector apparatus, toward the APC can be achieved without any intracellular translocation of the organelles