1,254 research outputs found
What Is the Storage Effect, Why Should It Occur in Cancers, and How Can It Inform Cancer Therapy?
Intratumor heterogeneity is a feature of cancer that is associated with progression, treatment resistance, and recurrence. However, the mechanisms that allow diverse cancer cell lineages to coexist remain poorly understood. The storage effect is a coexistence mechanism that has been proposed to explain the diversity of a variety of ecological communities, including coral reef fish, plankton, and desert annual plants. Three ingredients are required for there to be a storage effect: (1) temporal variability in the environment, (2) buffered population growth, and (3) species-specific environmental responses. In this article, we argue that these conditions are observed in cancers and that it is likely that the storage effect contributes to intratumor diversity. Data that show the temporal variation within the tumor microenvironment are needed to quantify how cancer cells respond to fluctuations in the tumor microenvironment and what impact this has on interactions among cancer cell types. The presence of a storage effect within a patient’s tumors could have a substantial impact on how we understand and treat cancer
NMFS / Interagency Working Group Evaluation of CITES Criteria and Guidelines.
EXECUTIVE SUMMARY: At present, the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) criteria used to assess whether a population qualifies for inclusion in the CITES Appendices relate to (A) size of the population, (B) area of distribution of the population, and (C) declines in the size of the population. Numeric guidelines are provided as indicators of a small population (less than 5,000 individuals), a small subpopulation (less than 500 individuals), a restricted area of distribution for a population (less than 10,000 km2), a restricted area of distribution for a subpopula-tion (less than 500 km2), a high rate of decline (a decrease of 50% or more in total within 5 years or two generations whichever is longer or, for a small wild population, a decline of 20% or more in total within ten years or three generations whichever is longer), large fluctuations (population size or area of distribution varies widely, rapidly and frequently, with a variation greater than one order of magnitude), and a short-term fluctuation (one of two years or less).
The Working Group discussed several broad issues of relevance to the CITES criteria and guidelines. These included the importance of the historical extent of decline versus the recent rate of decline; the utility and validity of incorporating relative population productivity into decline criteria; the utility of absolute numbers for defining small populations or small areas; the appropriateness of generation times as time frames for examining declines; the importance of the magnitude and frequency of fluctuations as factors affecting risk of extinction; and the overall utility of numeric thresh-olds or guidelines
The Development of Techie Times
Summer 2020 provided the motivation and opportunity to move summer outreach programs into the virtual world. Faculty and students in the Purdue University School of Engineering Technology moved face-to-face programs into a middle school program called Techie Times. This program was designed to provide students with an organized platform occurring just before the school year started, allowing them to learn at home, working with family, or independently. The program was designed to take place nonconsecutively over eight days, covering five various STEM topics. Some of these activities were already a part of the middle school curriculum; others were not. That provided an opportunity to engage students and teach them principles that support various engineering technology curricula. Students were recruited from across the country. Students were placed into three cohorts sorted by biological age and then into smaller groups to enhance interactions. Volunteers moderated the smaller groups representing corporate engineering retirees, university professors, and others interested in helping. The volunteers were provided with information to support the principles being learned in the activity of the day. They asked the students to demonstrate what they did at home and then asked them questions about what they learned from the activity. In the older age groups, volunteers generated hypotheses and tested them to see if they worked, thus providing a challenge for the older and more experienced students. This camp proved to be well-timed on the summer calendar. Parents expressed their pleasure in their students becoming a bit more disciplined as they transitioned from their summer activities to the upcoming school year. This paper will review the program’s curriculum, observations by the parents/guardians, and feedback from the students. The program is an example of a well-transformed outreach program that engaged and enlightened students
Crafting Stories in the Domestic Archive
The Knitting and Crochet Guild archive, Holmfirth, West Yorkshire hosts a vast array of hand-made items, including clothing, artefacts, yarns and samples, as well as tools, pattern leaflets, booklets and magazines. This article explores how the collection was used as a starting point for engaging students in new experiential encounters with the archive, as both a concept and as a container for material histories of the past. Two theoretical frameworks of investigation provide an intertwining methodology for reading the project: the first operates as a feminist narrative of intervention in the history of textile craft making, and the second considers how the ‘thought-images’ of Walter Benjamin provide a tool for thinking through student responses. It is argued that as a repository of the home-crafts, Lee Mills provides historical materialism with the experiential investigation it needs for a critical pedagogy of the present
Prefrontal oscillations modulate the propagation of neuronal activity required for working memory
[EN] Cognition involves using attended information, maintained in working memory (WM), to guide action. During a cognitive task, a correct response requires flexible, selective gating so that only the appropriate information flows from WM to downstream effectors that carry out the response. In this work, we used biophysically-detailed modeling to explore the hypothesis that network oscillations in prefrontal cortex (PFC), leveraging local inhibition, can independently gate responses to items in WM. The key role of local inhibition was to control the period between spike bursts in the outputs, and to produce an oscillatory response no matter whether the WM item was maintained in an asynchronous or oscillatory state. We found that the WM item that induced an oscillatory population response in the PFC output layer with the shortest period between spike bursts was most reliably propagated. The network resonant frequency (i.e., the input frequency that produces the largest response) of the output layer can be flexibly tuned by varying the excitability of deep layer principal cells. Our model suggests that experimentally-observed modulation of PFC beta-frequency (15-30 Hz) and gamma -frequency (30-80 Hz) oscillations could leverage network resonance and local inhibition to govern the flexible routing of signals in service to cognitive processes like gating outputs from working memory and the selection of rule-based actions. Importantly, we show for the first time that nonspecific changes in deep layer excitability can tune the output gate's resonant frequency, enabling the specific selection of signals encoded by populations in asynchronous or fast oscillatory states. More generally, this represents a dynamic mechanism by which adjusting network excitability can govern the propagation of asynchronous and oscillatory signals throughout neocortex.This work was supported by the U.S. Army Research Office under award number ARO W911NF-12-R-0012-02 to N. K., the U.S. Office of Naval Research under award number ONR MURI N00014-16-1-2832 to M. H. and E. M., the National Institute of Mental Health under award number NIMH R37MH087027 to E. M., and The MIT Picower Institute Faculty Innovation Fund to E. M. We would like to acknowledge Joachim Hass and Michelle McCarthy for early discussions of our modeling results, as well as Andre Bastos and Mikael Lundqvist for discussions relating our modeling work to their experiments.Sherfey, J.; Ardid-RamÃrez, JS.; Miller, EK.; Hasselmo, ME.; Kopell, NJ. (2020). Prefrontal oscillations modulate the propagation of neuronal activity required for working memory. Neurobiology of Learning and Memory. 173:1-13. https://doi.org/10.1016/j.nlm.2020.107228113173Adams, N. E., Sherfey, J. S., Kopell, N. J., Whittington, M. A., & LeBeau, F. E. N. (2017). Hetereogeneity in Neuronal Intrinsic Properties: A Possible Mechanism for Hub-Like Properties of the Rat Anterior Cingulate Cortex during Network Activity. eneuro, 4(1), ENEURO.0313-16.2017. doi:10.1523/eneuro.0313-16.2017Akam, T., & Kullmann, D. M. (2010). Oscillations and Filtering Networks Support Flexible Routing of Information. Neuron, 67(2), 308-320. doi:10.1016/j.neuron.2010.06.019Amiez, C., Joseph, J.-P., & Procyk, E. (2005). Anterior cingulate error-related activity is modulated by predicted reward. European Journal of Neuroscience, 21(12), 3447-3452. doi:10.1111/j.1460-9568.2005.04170.xArdid, S., Sherfey, J. S., McCarthy, M. M., Hass, J., Pittman-Polletta, B. R., & Kopell, N. (2019). Biased competition in the absence of input bias revealed through corticostriatal computation. Proceedings of the National Academy of Sciences, 116(17), 8564-8569. doi:10.1073/pnas.1812535116Ardid, S., & Wang, X.-J. (2013). A Tweaking Principle for Executive Control: Neuronal Circuit Mechanism for Rule-Based Task Switching and Conflict Resolution. Journal of Neuroscience, 33(50), 19504-19517. doi:10.1523/jneurosci.1356-13.2013Ardid, S., Wang, X.-J., & Compte, A. (2007). An Integrated Microcircuit Model of Attentional Processing in the Neocortex. Journal of Neuroscience, 27(32), 8486-8495. doi:10.1523/jneurosci.1145-07.2007Ardid, S., Wang, X.-J., Gomez-Cabrero, D., & Compte, A. (2010). Reconciling Coherent Oscillation with Modulationof Irregular Spiking Activity in Selective Attention:Gamma-Range Synchronization between Sensoryand Executive Cortical Areas. Journal of Neuroscience, 30(8), 2856-2870. doi:10.1523/jneurosci.4222-09.2010Baddeley, A. D. and Hitch, G. (1974). Working Memory. In Bower, G.H., editor, Psychology of Learning and Motivation, volume 8, pages 47–89. Academic Press.Badre, D., & Frank, M. J. (2011). Mechanisms of Hierarchical Reinforcement Learning in Cortico-Striatal Circuits 2: Evidence from fMRI. Cerebral Cortex, 22(3), 527-536. doi:10.1093/cercor/bhr117Barbas, H. (2015). General Cortical and Special Prefrontal Connections: Principles from Structure to Function. Annual Review of Neuroscience, 38(1), 269-289. doi:10.1146/annurev-neuro-071714-033936Bhandari, A., & Badre, D. (2018). Learning and transfer of working memory gating policies. Cognition, 172, 89-100. doi:10.1016/j.cognition.2017.12.001Brette, R., & Guigon, E. (2003). Reliability of Spike Timing Is a General Property of Spiking Model Neurons. Neural Computation, 15(2), 279-308. doi:10.1162/089976603762552924Börgers, C., & Kopell, N. (2005). Effects of Noisy Drive on Rhythms in Networks of Excitatory and Inhibitory Neurons. Neural Computation, 17(3), 557-608. doi:10.1162/0899766053019908Brincat, S. L., & Miller, E. K. (2016). Prefrontal Cortex Networks Shift from External to Internal Modes during Learning. Journal of Neuroscience, 36(37), 9739-9754. doi:10.1523/jneurosci.0274-16.2016Buschman, T. J., Denovellis, E. L., Diogo, C., Bullock, D., & Miller, E. K. (2012). Synchronous Oscillatory Neural Ensembles for Rules in the Prefrontal Cortex. Neuron, 76(4), 838-846. doi:10.1016/j.neuron.2012.09.029Cannon, J., McCarthy, M. M., Lee, S., Lee, J., Börgers, C., Whittington, M. A., & Kopell, N. (2013). Neurosystems: brain rhythms and cognitive processing. European Journal of Neuroscience, 39(5), 705-719. doi:10.1111/ejn.12453Cho, R. Y., Konecky, R. O., & Carter, C. S. (2006). Impairments in frontal cortical  synchrony and cognitive control in schizophrenia. Proceedings of the National Academy of Sciences, 103(52), 19878-19883. doi:10.1073/pnas.0609440103Compte, A. (2000). Synaptic Mechanisms and Network Dynamics Underlying Spatial Working Memory in a Cortical Network Model. Cerebral Cortex, 10(9), 910-923. doi:10.1093/cercor/10.9.910DeFelipe, J. (1997). Types of neurons, synaptic connections and chemical characteristics of cells immunoreactive for calbindin-D28K, parvalbumin and calretinin in the neocortex. Journal of Chemical Neuroanatomy, 14(1), 1-19. doi:10.1016/s0891-0618(97)10013-8Douglas, R. J., & Martin, K. A. C. (2004). NEURONAL CIRCUITS OF THE NEOCORTEX. Annual Review of Neuroscience, 27(1), 419-451. doi:10.1146/annurev.neuro.27.070203.144152Durstewitz, D., & Seamans, J. K. (2002). The computational role of dopamine D1 receptors in working memory. Neural Networks, 15(4-6), 561-572. doi:10.1016/s0893-6080(02)00049-7Durstewitz, D., Seamans, J. K., & Sejnowski, T. J. (2000). Dopamine-Mediated Stabilization of Delay-Period Activity in a Network Model of Prefrontal Cortex. Journal of Neurophysiology, 83(3), 1733-1750. doi:10.1152/jn.2000.83.3.1733Frank, M. J., & Badre, D. (2011). Mechanisms of Hierarchical Reinforcement Learning in Corticostriatal Circuits 1: Computational Analysis. Cerebral Cortex, 22(3), 509-526. doi:10.1093/cercor/bhr114FRANK, M. J., LOUGHRY, B., & O’REILLY, R. C. (2001). Interactions between frontal cortex and basal ganglia in working memory: A computational model. Cognitive, Affective, & Behavioral Neuroscience, 1(2), 137-160. doi:10.3758/cabn.1.2.137Hasselmo, M. E., & Stern, C. E. (2018). A network model of behavioural performance in a rule learning task. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1744), 20170275. doi:10.1098/rstb.2017.0275Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735Kaski, S., & Kohonen, T. (1994). Winner-take-all networks for physiological models of competitive learning. Neural Networks, 7(6-7), 973-984. doi:10.1016/s0893-6080(05)80154-6Kerns, J. G., Cohen, J. D., MacDonald, A.W., Cho, R.Y., Stenger, V.A., and Carter, C.S. (2004). Anterior cingulate conflict monitoring and adjustments in control. Science (New York, N.Y.), 303(5660):1023–1026.Komorowski, R. W., Garcia, C. G., Wilson, A., Hattori, S., Howard, M. W., & Eichenbaum, H. (2013). Ventral Hippocampal Neurons Are Shaped by Experience to Represent Behaviorally Relevant Contexts. Journal of Neuroscience, 33(18), 8079-8087. doi:10.1523/jneurosci.5458-12.2013Kriete, T., & Noelle, D. C. (2011). Generalisation benefits of output gating in a model of prefrontal cortex. Connection Science, 23(2), 119-129. doi:10.1080/09540091.2011.569881Kritzer, M. F., & Goldman-Rakic, P. S. (1995). Intrinsic circuit organization of the major layers and sublayers of the dorsolateral prefrontal cortex in the rhesus monkey. The Journal of Comparative Neurology, 359(1), 131-143. doi:10.1002/cne.903590109Levitt, J. B., Lewis, D. A., Yoshioka, T., & Lund, J. S. (1993). Topography of pyramidal neuron intrinsic connections in macaque monkey prefrontal cortex (areas 9 and 46). The Journal of Comparative Neurology, 338(3), 360-376. doi:10.1002/cne.903380304Lundqvist, M., Compte, A., & Lansner, A. (2010). Bistable, Irregular Firing and Population Oscillations in a Modular Attractor Memory Network. PLoS Computational Biology, 6(6), e1000803. doi:10.1371/journal.pcbi.1000803Lundqvist, M., Herman, P., Warden, M. R., Brincat, S. L., & Miller, E. K. (2018). Gamma and beta bursts during working memory readout suggest roles in its volitional control. Nature Communications, 9(1). doi:10.1038/s41467-017-02791-8Lundqvist, M., Rose, J., Herman, P., Brincat, S. L., Buschman, T. J., & Miller, E. K. (2016). Gamma and Beta Bursts Underlie Working Memory. Neuron, 90(1), 152-164. doi:10.1016/j.neuron.2016.02.028Mante, V., Sussillo, D., Shenoy, K. V., & Newsome, W. T. (2013). Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature, 503(7474), 78-84. doi:10.1038/nature12742Melrose, R. J., Poulin, R. M., & Stern, C. E. (2007). An fMRI investigation of the role of the basal ganglia in reasoning. Brain Research, 1142, 146-158. doi:10.1016/j.brainres.2007.01.060Miller, E. K. (2000). The prefontral cortex and cognitive control. Nature Reviews Neuroscience, 1(1), 59-65. doi:10.1038/35036228O’Reilly, R. C., & Frank, M. J. (2006). Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia. Neural Computation, 18(2), 283-328. doi:10.1162/089976606775093909Parnaudeau, S., O’Neill, P.-K., Bolkan, S. S., Ward, R. D., Abbas, A. I., Roth, B. L., … Kellendonk, C. (2013). Inhibition of Mediodorsal Thalamus Disrupts Thalamofrontal Connectivity and Cognition. Neuron, 77(6), 1151-1162. doi:10.1016/j.neuron.2013.01.038Nunez, P. L., & Srinivasan, R. (2006). Electric fields of the Brain: The Neurophysics of EEG. Oxford University Press. Google-Books-ID: fUv54as56_8C.Renart, A., Rocha, J. d. l., Bartho, P., Hollender, L., Parga, N., Reyes, A., Harris, K. D. (2010). The Asynchronous State in Cortical Circuits. Science, 327(5965):587–590.Richardson, M. J. E., Brunel, N., & Hakim, V. (2003). From Subthreshold to Firing-Rate Resonance. Journal of Neurophysiology, 89(5), 2538-2554. doi:10.1152/jn.00955.2002Rotstein, H. G. (2017). Spiking Resonances In Models With The Same Slow Resonant And Fast Amplifying Currents But Different Subthreshold Dynamic Properties. bioRxiv, page 128611.Seamans, J. K., Lapish, C. C., & Durstewitz, D. (2008). Comparing the prefrontal cortex of rats and primates: Insights from electrophysiology. Neurotoxicity Research, 14(2-3), 249-262. doi:10.1007/bf03033814Shen, Z., Popov, V., Delahay, A. B., & Reder, L. M. (2017). Item strength affects working memory capacity. Memory & Cognition, 46(2), 204-215. doi:10.3758/s13421-017-0758-4Sherfey, J. S., Ardid, S., Hass, J., Hasselmo, M. E., & Kopell, N. J. (2018). Flexible resonance in prefrontal networks with strong feedback inhibition. PLOS Computational Biology, 14(8), e1006357. doi:10.1371/journal.pcbi.1006357Sherfey, J. S., Soplata, A. E., Ardid, S., Roberts, E. A., Stanley, D. A., Pittman-Polletta, B.R., and Kopell, N.J. (2018b). DynaSim: A MATLAB Toolbox for Neural Modeling and Simulation. Frontiers in Neuroinformatics, 12.Siegel, M., Warden, M. R., & Miller, E. K. (2009). Phase-dependent neuronal coding of objects in short-term memory. Proceedings of the National Academy of Sciences, 106(50), 21341-21346. doi:10.1073/pnas.0908193106Tegnér, J., Compte, A., & Wang, X.-J. (2002). The dynamical stability of reverberatory neural circuits. Biological Cybernetics, 87(5-6), 471-481. doi:10.1007/s00422-002-0363-9Tzur, G., & Berger, A. (2009). Fast and slow brain rhythms in rule/expectation violation tasks: Focusing on evaluation processes by excluding motor action. Behavioural Brain Research, 198(2), 420-428. doi:10.1016/j.bbr.2008.11.041Zhu, H., Paschalidis, I. C., Chang, A., Stern, C. E., & Hasselmo, M. E. (2020). A neural circuit model for a contextual association task inspired by recommender systems. Hippocampus, 30(4), 384-395. doi:10.1002/hipo.23194Zhu, H., Paschalidis, I. C., & Hasselmo, M. E. (2018). Neural circuits for learning context-dependent associations of stimuli. Neural Networks, 107, 48-60. doi:10.1016/j.neunet.2018.07.01
Systems of Support: The Educators with Disabilities Caucus and Its Mentoring Program
In the field of education, critics have described the experiences of beginning teaching as “sink or swim, trial by fire, or boot camp experiences.
Modeling regional-scale wildland fire emissions with the wildland fire emissions information system
As carbon modeling tools become more comprehensive, spatial data are needed to improve quantitative maps of carbon emissions from fire. The Wildland Fire Emissions Information System (WFEIS) provides mapped estimates of carbon emissions from historical forest fires in the United States through a web browser. WFEIS improves access to data and provides a consistent approach to estimating emissions at landscape, regional, and continental scales. The system taps into data and tools developed by the U.S. Forest Service to describe fuels, fuel loadings, and fuel consumption and merges information from the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration on fire location and timing. Currently, WFEIS provides web access to Moderate Resolution Imaging Spectroradiometer (MODIS) burned area for North America and U.S. fire-perimeter maps from the Monitoring Trends in Burn Severity products from the USGS, overlays them on 1-km fuel maps for the United States, and calculates fuel consumption and emissions with an open-source version of the Consume model. Mapped fuel moisture is derived from daily meteorological data from remote automated weather stations. In addition to tabular output results, WFEIS produces multiple vector and raster formats. This paper provides an overview of the WFEIS system, including the web-based system functionality and datasets used for emissions estimates. WFEIS operates on the web and is built using open-source software components that work with open international standards such as keyhole markup language (KML). Examples of emissions outputs from WFEIS are presented showing that the system provides results that vary widely across the many ecosystems of North America and are consistent with previous emissions modeling estimates and products
The Massive Progenitor of the Type II-Linear Supernova 2009kr
We present early-time photometric and spectroscopic observations of supernova (SN) 2009kr in NGC 1832. We find that its properties to date support its classification as Type II-linear (SN II-L), a relatively rare subclass of core-collapse supernovae (SNe). We have also identified a candidate for the SN progenitor star through comparison of pre-explosion, archival images taken with WFPC2 on board the Hubble Space Telescope with SN images obtained using adaptive optics plus NIRC2 on the 10 m Keck-II telescope. Although the host galaxy's substantial distance (similar to 26 Mpc) results in large uncertainties in the relative astrometry, we find that if this candidate is indeed the progenitor, it is a highly luminous (M(V)(0) = -7.8 mag) yellow supergiant with initial mass similar to 18-24 M(circle dot). This would be the first time that an SN II-L progenitor has been directly identified. Its mass may be a bridge between the upper initial mass limit for the more common Type II-plateau SNe and the inferred initial mass estimate for one Type II-narrow SN.Hungarian OTKA K76816NSF AST-0707769, AST-0908886Sylvia & Jim Katzman FoundationTABASGO FoundationNASA through STScI AR-11248, GO-10877Harvard UniversityUC BerkeleyUniversity of VirginiaNASA/Swift NNX09AQ66GDOEAstronom
Exchangeable zinc pool size at birth in Pakistani small for gestational age and appropriate for gestational age infants do not differ but are lower than in US infants
Objectives: Small for gestational age (SGA) infants are more susceptible to infectious morbidity and growth faltering compared to their appropriate for gestational age (AGA) counterparts. Zinc supplementation of SGA infants may be beneficial but the underlying susceptibility to zinc deficiency of SGA infants has not been examined.Methods: In a community-based, observational, longitudinal study in a peri-urban settlement of Karachi, Pakistan, we compared the size of the exchangeable zinc pools (EZPs) in term SGA and AGA infants at birth and at 6 months of age, hypothesizing that the EZP would be lower in the SGA group. To measure EZP size, a zinc stable isotope was intravenously administered within 48 hours of birth (n = 17 and 22) at 6 months (n = 11 and 14) in SGA and AGA infants, respectively. Isotopic enrichment in urine was used to determine EZP.Results: No significant difference was detected in the mean (±standard deviation) EZP between SGA and AGA infants at birth, with values of 9.8 ± 3.5 and 10.1 ± 4.1 mg/kg, respectively (P = 0.86), or at 6 months. Longitudinal EZP measurements demonstrated a significant decline in EZP relative to body weight in both groups at 6 months (P \u3c 0.001). Mean EZP (adjusted for body weight) size at birth for the combined Pakistani groups was significantly lower than AGA infants at birth in the United States (P = 0.017).Conclusions: These results did not support a difference in zinc endowment between SGA and AGA Pakistani infants. They, however, do suggest lower in utero zinc transfer to the fetus in a setting where poor maternal nutritional status may confer a high susceptibility to postnatal zinc deficiency
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