Perceptual Consciousness, Short-Term Memory, and Overflow: Replies to Beck, Orlandi and Franklin, and Phillips

A reply to commentators -- Jake Beck, Nico Orlandi and Aaron Franklin, and Ian Phillips -- on our paper "Does perceptual consciousness overflow cognitive access?"

Prochlorococcus, Synechococcus and picoeukaryotic phytoplankton abundance climatology in the global ocean from quantitative niche models.

Dataset: phytoplankton climatologyProchlorococcus, Synechococcus and picoeukaryotic phytoplankton estimated mean cell abundance (cells/ml) in 1-degree grids for 25 layers from 0m to 200 m depth. Cell abundance was estimated with quantitative niche models for each lineage (Flombaum et al., 2013; Flombaum et al., 2020), inputs from the monthly mean of temperature and nitrate from the World Ocean Atlas, and PAR from MODIS-Aqua Level-3 Mapped Photosynthetically Available Radiation Data Version 2018. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/811147NSF Division of Ocean Sciences (NSF OCE) OCE-1848576, Agencia Nacional de Promoción Científica y Tecnológica () PICT-2017-3020, Universidad de Buenos Aires () UBACyT 20020170100620B

Rhesus Monkeys (Macaca mulatta) Spontaneously Compute Addition Operations Over Large Numbers

Mathematics is a uniquely human capacity. Studies of animals and human infants reveal, however, that this capacity builds on language-independent mechanisms for quantifying small numbers (< 4) precisely and large numbers approximately. It is unclear whether animals and human infants can spontaneously tap mechanisms for quantifying large numbers to compute mathematical operations. Moreover, all available work on addition operations in non-human animals has confounded number with continuous perceptual properties (e.g. volume, contour length) that correlate with number. This study shows that rhesus monkeys spontaneously compute addition operations over large numbers, as opposed to continuous extents, and that the limit on this ability is set by the ratio difference between two numbers as opposed to their absolute difference.Psycholog

Interactions among resource partitioning, sampling effect, and facilitation on the biodiversity effect: A modeling approach

Resource partitioning, facilitation, and sampling effect are the three mechanisms behind the biodiversity effect, which is depicted usually as the effect of plant-species richness on aboveground net primary production. These mechanisms operate simultaneously but their relative importance and interactions are difficult to unravel experimentally. Thus, niche differentiation and facilitation have been lumped together and separated from the sampling effect. Here, we propose three hypotheses about interactions among the three mechanisms and test them using a simulation model. The model simulated water movement through soil and vegetation, and net primary production mimicking the Patagonian steppe. Using the model, we created grass and shrub monocultures and mixtures, controlled root overlap and grass water-use efficiency (WUE) to simulate gradients of biodiversity, resource partitioning and facilitation. The presence of shrubs facilitated grass growth by increasing its WUE and in turn increased the sampling effect whereas root overlap (resource partitioning) had, on average, no effect on sampling effect. Interestingly, resource partitioning and facilitation interacted so the effect of facilitation on sampling effect decreased as resource partitioning increased. Sampling effect was enhanced by the difference between the two functional groups in their efficiency in using resources. Morphological and physiological differences make one group outperform the other, once those differences were established further differences did not enhance the sampling effect. In addition, grass WUE and root overlap positively influence the biodiversity effect but showed no interactions.Fil: Flombaum, Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinacion Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; ArgentinaFil: Sala, Osvaldo Esteban. Arizona State University. School of Life Sciences and School of Sustainability; Estados UnidosFil: Rastetter, Edward B.. Marine Biological Laboratory. The Ecosystem Center; Estados Unido

Prochlorococcus, Synechococcus, and picoeukaryotic phytoplankton abundances in the global ocean

Marine picophytoplankton is the most abundant photosynthetic group on Earth; however, it is still underrepresented in dynamic ecosystem models. Major constraints for understanding its role in the ecosystem at a global scale are sparse data and lack of a baseline description of its distribution. Here, we present three datasets to assess the global abundance of the principal groups of picophytoplankton, Prochlorococcus, Synechococcus, and picoeukaryotic phytoplankton: (1) a compilation of 109,045 field observations with ancillary environmental data, (2) a global monthly climatology of 1° grids from 0 to 200 m, and (3) four climate scenarios projections, from the Coupled Model Intercomparison Project 5, spanning years 1901 to 2100. Together this set of observational and modeled data can improve our understanding of the role of picophytoplankton in the global ecosystem.Fil: Visintini Adomaitis, Natalia Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Martiny, Adam Camilo. University of California at Irvine; Estados UnidosFil: Flombaum, Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentin

Ifosfamide May Be Safely Used in Patients with End Stage Renal Disease on Hemodialysis

Background. Pharmacokinetic data on clearance of ifosfamide in hemodialysis patients are limited. Consequently, these patients are excluded from therapy with this agent. We review the outcomes for patients at our institution with end stage renal disease on dialysis who received ifosfamide for metastatic sarcoma. Patients and Methods. We treated three patients with end stage renal disease on hemodialysis with escalating doses of ifosfamide. Data on radiographic response to therapy, WBC and platelet counts, signs or symptoms of infection, neuropathy and bladder toxicity are reported. Starting doses of ifosfamide were based on review of the literature available with subsequent modifications based on each patient's prior exposure to myelosuppressive agents and on symptoms of neurotoxicity and the degree of myelosuppression following each cycle of chemotherapy. Results. Myelosuppression was the most common side effect from therapy, but no patient developed a life threatening infection, neurotoxicity, or hematuria. One patient developed epistaxis in the setting of thrombocytopenia while on warfarin therapy. All patients had clinical evidence for therapeutic response and two had documented radiographic improvement following ifosfamide administration. Conclusion. Ifosfamide can be used safely in combination with hemodialysis in patients with end stage renal disease

Biomass rcp85 CMIP5 data - mean picophytoplankton surface biomass estimated for the climate models under the Representative Concentration Pathway 8.5

Dataset: Biomass rcp85 CMIP5 dataMean picophytoplankton surface biomass (mg/m3) estimated for the climate models (CanESM2, CESM1 BGC, GFDL ESM2G, HadGEM2 ES, IPSL CM5A MR, MIROC ESM, MPI, and NorESM1 ME) under the Representative Concentration Pathway 8.5 (RCP8.5) – equivalent to a radiative forcing of 8.5 W m-2 in 2100) scenario. Light fields were identical across simulations. Picophytoplankton biomass results from the sum of the biomass estimated for the Prochlorococcus, Synechococcus, and picoeukaryotic phytoplankton. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/783527NSF Division of Ocean Sciences (NSF OCE) OCE-1848576, Agencia Nacional de Promoción Científica y Tecnológica () PICT-2017-3020, Universidad de Buenos Aires () UBACyT 20020170100620B

Problems for the Purported Cognitive Penetration of Perceptual Color Experience and Macpherson’s Proposed Mechanism

Fiona Macpherson (2012) argues that various experimental results provide strong evidence in favor of the cognitive penetration of perceptual color experience. Moreover, she proposes a mechanism for how such cognitive penetration occurs. We argue, first, that the results on which Macpherson relies do not provide strong grounds for her claim of cognitive penetrability; and, second, that, if the results do reflect cognitive penetrability, then time-course considerations raise worries for her proposed mechanism. We base our arguments in part on several of our own experiments, reported herein

Global cell abundance of picoeukaryotic phytoplankton, predicted by neural network models using average temperatures and nitrate from the World Ocean Atlas 2005

Dataset: Global picoeukaryotic phytoplankton distributionGlobal cell abundance of picoeukaryotic phytoplankton, predicted by our neural network models using average temperatures and nitrate from the World Ocean Atlas 2005 (1°x1° resolution), and 8 d average PAR and K490 values derived from satellite data (SeaWiFS 0.083°x0.083°) and obtained as an output cells/ml for each set of conditions in a 1°x1° resolution. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/783537NSF Division of Ocean Sciences (NSF OCE) OCE-1848576, Agencia Nacional de Promoción Científica y Tecnológica () PICT-2017-3020, Universidad de Buenos Aires () UBACyT 20020170100620B

Biomass historic CMIP5 data - mean picophytoplankton surface biomass estimated for climate models under the Historical scenario

Dataset: Biomass historic CMIP5 dataMean picophytoplankton surface biomass (mg/m3) estimated for the climate models (CanESM2, CESM1 BGC, GFDL ESM2G, HadGEM2 ES, IPSL CM5A MR, MIROC ESM, MPI, and NorESM1 ME) under the Historical scenario. Light fields were identical across simulations. Picophytoplankton biomass results from the sum of the biomass estimated for the Prochlorococcus, Synechococcus, and picoeukaryotic phytoplankton. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/783516NSF Division of Ocean Sciences (NSF OCE) OCE-1848576, Agencia Nacional de Promoción Científica y Tecnológica () PICT-2017-3020, Universidad de Buenos Aires () UBACyT 20020170100620B