The Late Miocene Gomphothere Amahuacatherium peruvium (Proboscidea: Gomphotheriidae) from Amazonian Peru: Implications for the great american faunal interchange - [Boletín D 23]

Se presentan en detalle los caracteres osteológicos del proboscideo Amahuacatherium peruvium (Proboscidea: Gomphotheriidae). Este proboscideo fue recuperado de los depósitos del Mioceno (Chasicoan) expuestos a lo largo del río Madre de Dios en las tierras bajas al sudeste del Perú. Este proboscideo fue un gomphothere tetrabelodonte y brevirostrino con mandíbulas inferiores que conservan los incisivos y molares con un patrón de esmalte ligeramente complicado. El Amahuacatherium peruvium proviene de la parte baja de la discordancia que se formó durante un período de gran erosión en toda la cuenca del Amazonas al comienzo del Mioceno tardío, cuando el nivel del mar comenzó a descender globalmente hace doce millones de años. Este taxón representa la ocurrencia más temprana de proboscideos, o de cualquier mamífero norteamericano en América del Sur, también representa la ocurrencia más temprana en América del Norte y en América del Sur de cualquier participante en el gran intercambio faunal americano. Algunos proboscideos norteamericanos pueden derivarse de linajes que se originaron en América del Sur durante el Mioceno tardío o Plioceno. Se propone que los proboscideos de América del Norte dispersados en América del Sur siguieron una ruta a través de Panamá vía las serranías de San Blas que conectó al arco de Baudo y el terreno alóctono de Chocó y finalizó en las colinas Istmina de Colombia. El informe presenta bibliografía

Liver Transplantation to Provide Low-Density-Lipoprotein Receptors and Lower Plasma Cholesterol in a Child with Homozygous Familial Hypercholesterolemia

A six-year-old girl with severe hypercholesterolemia and atherosclerosis had two defective genes at the low-density-lipoprotein (LDL) receptor locus, as determined by biochemical studies of cultured fibroblasts. One gene, inherited from the mother, produced no LDL receptors; the other gene, inherited from the father, produced a receptor precursor that was not transported to the cell surface and was unable to bind LDL. The patient degraded intravenously administered 125I-LDL at an extremely low rate, indicating that her high plasma LDL-cholesterol level was caused by defective receptor-mediated removal of LDL from plasma. After transplantation of a liver and a heart from a normal donor, the patient's plasma LDL-cholesterol level declined by 81 per cent, from 988 to 184 mg per deciliter. The fractional catabolic rate for intravenously administered 125I-LDL, a measure of functional LDL receptors in vivo, increased by 2.5-fold. Thus, the transplanted liver, with its normal complement of LDL receptors, was able to remove LDL cholesterol from plasma at a nearly normal rate. We conclude that a genetically determined deficiency of LDL receptors can be largely reversed by liver transplantation. These data underscore the importance of hepatic LDL receptors in controlling the plasma level of LDL cholesterol in human beings. (N Engl J Med 1984; 311: 1658–64.). © 1984, Massachusetts Medical Society. All rights reserved

Upper cenozoic chronostratigraphy of the southwestern Amazon Basin

The lack of numerical age dates for upper Cenozoic strata of the Amazon Basin has prevented resolution of its geologic history and accurate dating of important paleofaunas. Here we present results of magnetostratigraphy and 40Ar/39Ar dating of two volcanic ash deposits from the Madre de Dios Formation of eastern Peru. The two ash ages, 9.01 ± 0.28 Ma and 3.12 ± 0.02 Ma, provide the first numerical age data necessary for accurate interpretation of late Tertiary sedimentation in Amazonia and establish approximate time constraints for the last major cycle of Cenozoic deposition within the southwestern Amazon Basin. The older ash age also provides a minimum age for numerous Amazonian paleofaunas, which allows a more definitive correlation of these paleofaunas with those in other regions of South America

Islet Autoantibody Standardization Program 2018 Workshop:Interlaboratory Comparison of Glutamic Acid Decarboxylase Autoantibody Assay Performance

BACKGROUND: The Islet Autoantibody Standardization Program (IASP) aims to improve the performance of immunoassays measuring type 1 diabetes (T1D)-associated autoantibodies and the concordance of results among laboratories. IASP organizes international interlaboratory assay comparison studies in which blinded serum samples are distributed to participating laboratories, followed by centralized collection and analysis of results, providing participants with an unbiased comparative assessment. In this report, we describe the results of glutamic acid decarboxylase autoantibody (GADA) assays presented in the IASP 2018 workshop. METHODS: In May 2018, IASP distributed to participants uniquely coded sera from 43 new-onset T1D patients, 7 multiple autoantibody-positive nondiabetic individuals, and 90 blood donors. Results were analyzed for the following metrics: sensitivity, specificity, accuracy, area under the ROC curve (ROC-AUC), partial ROC-AUC at 95% specificity (pAUC95), and concordance of qualitative and quantitative results. RESULTS: Thirty-seven laboratories submitted results from a total of 48 different GADA assays adopting 9 different formats. The median ROC-AUC and pAUC95 of all assays were 0.87 [interquartile range (IQR), 0.83-0.89] and 0.036 (IQR, 0.032-0.039), respectively. Large differences in pAUC95 (range, 0.001-0.0411) were observed across assays. Of formats widely adopted, bridge ELISAs showed the best median pAUC95 (0.039; range, 0.036-0.041). CONCLUSIONS: Several novel assay formats submitted to this study showed heterogeneous performance. In 2018, the majority of the best performing GADA immunoassays consisted of novel or established nonradioactive tests that proved on a par or superior to the radiobinding assay, the previous gold standard assay format for GADA measurement

DynaSim: a MATLAB toolbox for neural modeling and simulation

[EN] DynaSim is an open-source MATLAB/GNU Octave toolbox for rapid prototyping of neural models and batch simulation management. It is designed to speed up and simplify the process of generating, sharing, and exploring network models of neurons with one or more compartments. Models can be specified by equations directly (similar to XPP or the Brian simulator) or by lists of predefined or custom model components. The higher-level specification supports arbitrarily complex population models and networks of interconnected populations. DynaSim also includes a large set of features that simplify exploring model dynamics over parameter spaces, running simulations in parallel using both multicore processors and high-performance computer clusters, and analyzing and plotting large numbers of simulated data sets in parallel. It also includes a graphical user interface (DynaSim GUI) that supports full functionality without requiring user programming. The software has been implemented in MATLAB to enable advanced neural modeling using MATLAB, given its popularity and a growing interest in modeling neural systems. The design of DynaSim incorporates a novel schema for model specification to facilitate future interoperability with other specifications (e.g., NeuroML, SBML), simulators (e.g., NEURON, Brian, NEST), and web-based applications (e.g., Geppetto) outside MATLAB. DynaSim is freely available at http://dynasimtoolbox.org. This tool promises to reduce barriers for investigating dynamics in large neural models, facilitate collaborative modeling, and complement other tools being developed in the neuroinformatics community.This material is based upon research supported by the U.S. Army Research Office under award number ARO W911NF-12-R-0012-02, the U.S. Office of Naval Research under award number ONR MURI N00014-16-1-2832, and the National Science Foundation under award number NSF DMS-1042134 (Cognitive Rhythms Collaborative: A Discovery Network)Sherfey, JS.; Soplata, AE.; Ardid-Ramírez, JS.; Roberts, EA.; Stanley, DA.; Pittman-Polletta, BR.; Kopell, NJ. (2018). DynaSim: a MATLAB toolbox for neural modeling and simulation. Frontiers in Neuroinformatics. 12:1-15. https://doi.org/10.3389/fninf.2018.00010S11512Bokil, H., Andrews, P., Kulkarni, J. E., Mehta, S., & Mitra, P. P. (2010). Chronux: A platform for analyzing neural signals. Journal of Neuroscience Methods, 192(1), 146-151. doi:10.1016/j.jneumeth.2010.06.020Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J. M., … Destexhe, A. (2007). Simulation of networks of spiking neurons: A review of tools and strategies. Journal of Computational Neuroscience, 23(3), 349-398. doi:10.1007/s10827-007-0038-6Börgers, C., & Kopell, N. (2005). Effects of Noisy Drive on Rhythms in Networks of Excitatory and Inhibitory Neurons. Neural Computation, 17(3), 557-608. doi:10.1162/0899766053019908Ching, S., Cimenser, A., Purdon, P. L., Brown, E. N., & Kopell, N. J. (2010). Thalamocortical model for a propofol-induced  -rhythm associated with loss of consciousness. Proceedings of the National Academy of Sciences, 107(52), 22665-22670. doi:10.1073/pnas.1017069108Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9-21. doi:10.1016/j.jneumeth.2003.10.009Durstewitz, D., Seamans, J. K., & Sejnowski, T. J. (2000). Neurocomputational models of working memory. Nature Neuroscience, 3(S11), 1184-1191. doi:10.1038/81460EatonJ. W. BatemanD. HaubergS. WehbringR. GNU Octave Version 4.2.0 Manual: A High-Level Interactive Language for Numerical Computations2016Ermentrout, B. (2002). Simulating, Analyzing, and Animating Dynamical Systems. doi:10.1137/1.9780898718195FitzHugh, R. (1955). Mathematical models of threshold phenomena in the nerve membrane. The Bulletin of Mathematical Biophysics, 17(4), 257-278. doi:10.1007/bf02477753Gewaltig, M.-O., & Diesmann, M. (2007). NEST (NEural Simulation Tool). Scholarpedia, 2(4), 1430. doi:10.4249/scholarpedia.1430Gleeson, P., Crook, S., Cannon, R. C., Hines, M. L., Billings, G. O., Farinella, M., … Silver, R. A. (2010). NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail. PLoS Computational Biology, 6(6), e1000815. doi:10.1371/journal.pcbi.1000815Goodman, D. (2008). Brian: a simulator for spiking neural networks in Python. Frontiers in Neuroinformatics, 2. doi:10.3389/neuro.11.005.2008Goodman, D. F. M. (2009). The Brian simulator. Frontiers in Neuroscience, 3(2), 192-197. doi:10.3389/neuro.01.026.2009Hines, M. L., & Carnevale, N. T. (1997). The NEURON Simulation Environment. Neural Computation, 9(6), 1179-1209. doi:10.1162/neco.1997.9.6.1179Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4), 500-544. doi:10.1113/jphysiol.1952.sp004764Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., … Wang. (2003). The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics, 19(4), 524-531. doi:10.1093/bioinformatics/btg015Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14(6), 1569-1572. doi:10.1109/tnn.2003.820440Kopell, N., Ermentrout, G. B., Whittington, M. A., & Traub, R. D. (2000). Gamma rhythms and beta rhythms have different synchronization properties. Proceedings of the National Academy of Sciences, 97(4), 1867-1872. doi:10.1073/pnas.97.4.1867Kramer, M. A., Roopun, A. K., Carracedo, L. M., Traub, R. D., Whittington, M. A., & Kopell, N. J. (2008). Rhythm Generation through Period Concatenation in Rat Somatosensory Cortex. PLoS Computational Biology, 4(9), e1000169. doi:10.1371/journal.pcbi.1000169Lorenz, E. N. (1963). Deterministic Nonperiodic Flow. 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Detection of antibodies directed to the N-terminal region of GAD is dependent on assay format and contributes to differences in the specificity of GAD autoantibody assays for type 1 diabetes

Autoantibodies to glutamate decarboxylase (GADA) are sensitive markers of islet autoimmunity and type 1 diabetes. They form the basis of robust prediction models and are widely used for recruitment of subjects at high risk of type 1 diabetes to prevention trials. However GADA are also found in many individuals at low risk of diabetes progression. To identify the sources of diabetes irrelevant GADA reactivity therefore, we analyzed data from the 2009 and 2010 Diabetes Autoantibody Standardization Program GADA workshop and found that binding of healthy control sera varied according to assay type. Characterization of control sera found positive by radiobinding assay, but negative by ELISA showed that many of these sera reacted to epitopes in the N-terminal region of the molecule. This finding prompted development of an N-terminally truncated GAD65 radiolabel, (35)S-GAD65(96-585), which improved the performance of most GADA radiobinding assays (RBAs) participating in an Islet Autoantibody Standardization Program GADA substudy. These detailed workshop comparisons have identified a source of disease-irrelevant signals in GADA RBAs and suggest that N-terminally truncated GAD labels will enable more specific measurement of GADA in type 1 diabetes

Differential contributions of synaptic and intrinsic inhibitory currents to speech segmentation via flexible phase-locking in neural oscillators

Now published in PLOS Computational Biology doi: 10.1371/journal.pcbi.1008783.Current hypotheses suggest that speech segmentation – the initial division and grouping of the speech stream into candidate phrases, syllables, and phonemes for further linguistic processing – is executed by a hierarchy of oscillators in auditory cortex. Theta (~3-12 Hz) rhythms play a key role by phase-locking to recurring acoustic features marking syllable boundaries. Reliable synchronization to quasi-rhythmic inputs, whose variable frequency can dip below cortical theta frequencies (down to ~1 Hz), requires “flexible” theta oscillators whose underlying neuronal mechanisms remain unknown. Using biophysical computational models, we found that the flexibility of phase-locking in neural oscillators depended on the types of hyperpolarizing currents that paced them. Simulated cortical theta oscillators flexibly phase-locked to slow inputs when these inputs caused both (i) spiking and (ii) the subsequent buildup of outward current sufficient to delay further spiking until the next input. The greatest flexibility in phase-locking arose from a synergistic interaction between intrinsic currents that was not replicated by synaptic currents at similar timescales. Flexibility in phase-locking enabled improved entrainment to speech input, optimal at mid-vocalic channels, which in turn supported syllabic-timescale segmentation through identification of vocalic nuclei. Our results suggest that synaptic and intrinsic inhibition contribute to frequency-restricted and -flexible phase-locking in neural oscillators, respectively. Their differential deployment may enable neural oscillators to play diverse roles, from reliable internal clocking to adaptive segmentation of quasi-regular sensory inputs like speech. Author summary: Oscillatory activity in auditory cortex is believed to play an important role in auditory and speech processing. One suggested function of these rhythms is to divide the speech stream into candidate phonemes, syllables, words, and phrases, to be matched with learned linguistic templates. This requires brain rhythms to flexibly synchronize with regular acoustic features of the speech stream. How neuronal circuits implement this task remains unknown. In this study, we explored the contribution of inhibitory currents to flexible phase-locking in neuronal theta oscillators, believed to perform initial syllabic segmentation. We found that a combination of specific intrinsic inhibitory currents at multiple timescales, present in a large class of cortical neurons, enabled exceptionally flexible phase-locking, which could be used to precisely segment speech by identifying vowels at mid-syllable. This suggests that the cells exhibiting these currents are a key component in the brain’s auditory and speech processing architecture.https://journals.plos.org/ploscompbiol/article/peerReview?id=10.1371/journal.pcbi.100878

Differential contributions of synaptic and intrinsic inhibitory currents to speech segmentation via flexible phase-locking in neural oscillators

Current hypotheses suggest that speech segmentation-the initial division and grouping of the speech stream into candidate phrases, syllables, and phonemes for further linguistic processing-is executed by a hierarchy of oscillators in auditory cortex. Theta (∼3-12 Hz) rhythms play a key role by phase-locking to recurring acoustic features marking syllable boundaries. Reliable synchronization to quasi-rhythmic inputs, whose variable frequency can dip below cortical theta frequencies (down to ∼1 Hz), requires "flexible" theta oscillators whose underlying neuronal mechanisms remain unknown. Using biophysical computational models, we found that the flexibility of phase-locking in neural oscillators depended on the types of hyperpolarizing currents that paced them. Simulated cortical theta oscillators flexibly phase-locked to slow inputs when these inputs caused both (i) spiking and (ii) the subsequent buildup of outward current sufficient to delay further spiking until the next input. The greatest flexibility in phase-locking arose from a synergistic interaction between intrinsic currents that was not replicated by synaptic currents at similar timescales. Flexibility in phase-locking enabled improved entrainment to speech input, optimal at mid-vocalic channels, which in turn supported syllabic-timescale segmentation through identification of vocalic nuclei. Our results suggest that synaptic and intrinsic inhibition contribute to frequency-restricted and -flexible phase-locking in neural oscillators, respectively. Their differential deployment may enable neural oscillators to play diverse roles, from reliable internal clocking to adaptive segmentation of quasi-regular sensory inputs like speech.Wellcome Trust; P50 MH109429 - NIMH NIH HHS; R01 MH111439 - NIMH NIH HHS; 098353 - Wellcome TrustPublished versio

Policy perils of ignoring uncertainty in oil palm research

Peer reviewe