A Study of Photosynthesis in Clear Lake, lowa

The oxygen and carbon-14 methods were used to measure photosynthesis in Clear Lake, Iowa during 1958 and 1959. Differences in the rates of photosynthesis at widely separated stations were generally small. Daily variations in the rate of photosynthesis were not greater than two-fold. The correlation between the rate of photosynthesis and the incident illumination was 0.81, and the efficiency of utilization of incident light energy was 0.72 per cent. The net gain of organic matter at the phytoplankton level during the period May 1 to November 1 was equivalent to 3480 pounds of glucose per acre

Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization

Artificial autonomous agents and robots interacting in complex environments are required to continually acquire and fine-tune knowledge over sustained periods of time. The ability to learn from continuous streams of information is referred to as lifelong learning and represents a long-standing challenge for neural network models due to catastrophic forgetting. Computational models of lifelong learning typically alleviate catastrophic forgetting in experimental scenarios with given datasets of static images and limited complexity, thereby differing significantly from the conditions artificial agents are exposed to. In more natural settings, sequential information may become progressively available over time and access to previous experience may be restricted. In this paper, we propose a dual-memory self-organizing architecture for lifelong learning scenarios. The architecture comprises two growing recurrent networks with the complementary tasks of learning object instances (episodic memory) and categories (semantic memory). Both growing networks can expand in response to novel sensory experience: the episodic memory learns fine-grained spatiotemporal representations of object instances in an unsupervised fashion while the semantic memory uses task-relevant signals to regulate structural plasticity levels and develop more compact representations from episodic experience. For the consolidation of knowledge in the absence of external sensory input, the episodic memory periodically replays trajectories of neural reactivations. We evaluate the proposed model on the CORe50 benchmark dataset for continuous object recognition, showing that we significantly outperform current methods of lifelong learning in three different incremental learning scenario

Some Measurements of Primary Production in East and West Okoboji Lakes, Dickinson County, Iowa

Many methods have been employed to measure the organic production of phytoplankton communities in natural waters. These methods include measurements of, (a) the ash-free weight of seston, (b) the chlorophyll content of the water, (c) phytoplankton abundance, (d) plant volume, (e) oxygen evolution, and (f) the assimilation of carbon-14. The introduction (3) of a simple technique for the application of carbon-14 to production studies has opened the way for a more direct approach to the problem. Because the significance of data obtained by the use of indirect methods is necessarily obscure, production studies frequently employ two or more methods. Although the carbon-14 method is considered to give a direct measurement of production, it was decided that a more comprehensive picture of production factors would be obtained by measuring both oxygen evolution and carbon-14 assimilation. The data reported were collected in June, July, and August of 1957, during the initial phase of a project organized in June 1957 by Dr. K. D. Carlander, Department of Zoology and Entomology, and Dr. J. D. Dodd, Department of Botany and Plant Pathology, for the purpose of measuring primary production in some Iowa lakes

Lifelong learning of human actions with deep neural network self-organization

Lifelong learning is fundamental in autonomous robotics for the acquisition and fine-tuning of knowledge through experience. However, conventional deep neural models for action recognition from videos do not account for lifelong learning but rather learn a batch of training data with a predefined number of action classes and samples. Thus, there is the need to develop learning systems with the ability to incrementally process available perceptual cues and to adapt their responses over time. We propose a self-organizing neural architecture for incrementally learning to classify human actions from video sequences. The architecture comprises growing self-organizing networks equipped with recurrent neurons for processing time-varying patterns. We use a set of hierarchically arranged recurrent networks for the unsupervised learning of action representations with increasingly large spatiotemporal receptive fields. Lifelong learning is achieved in terms of prediction-driven neural dynamics in which the growth and the adaptation of the recurrent networks are driven by their capability to reconstruct temporally ordered input sequences. Experimental results on a classification task using two action benchmark datasets show that our model is competitive with state-of-the-art methods for batch learning also when a significant number of sample labels are missing or corrupted during training sessions. Additional experiments show the ability of our model to adapt to non-stationary input avoiding catastrophic interference

The pd <--> pi+ t reaction around the Delta resonance

The pd pi+ t process has been calculated in the energy region around the Delta-resonance with elementary production/absorption mechanisms involving one and two nucleons. The isobar degrees of freedom have been explicitly included in the two-nucleon mechanism via pi-- and rho-exchange diagrams. No free parameters have been employed in the analysis since all the parameters have been fixed in previous studies on the simpler pp pi+ d process. The treatment of the few-nucleon dynamics entailed a Faddeev-based calculation of the reaction, with continuum calculations for the initial p-d state and accurate solutions of the three-nucleon bound-state equation. The integral cross-section was found to be quite sensitive to the NN interaction employed while the angular dependence showed less sensitivity. Approximately a 4% effect was found for the one-body mechanism, for the three-nucleon dynamics in the p-d channel, and for the inclusion of a large, possibly converged, number of three-body partial states, indicating that these different aspects are of comparable importance in the calculation of the spin-averaged observables.Comment: 40 Pages, RevTex, plus 5 PostScript figure

Estimating time-to-onset of adverse drug reactions from spontaneous reporting databases.

International audienceBACKGROUND: Analyzing time-to-onset of adverse drug reactions from treatment exposure contributes to meeting pharmacovigilance objectives, i.e. identification and prevention. Post-marketing data are available from reporting systems. Times-to-onset from such databases are right-truncated because some patients who were exposed to the drug and who will eventually develop the adverse drug reaction may do it after the time of analysis and thus are not included in the data. Acknowledgment of the developments adapted to right-truncated data is not widespread and these methods have never been used in pharmacovigilance. We assess the use of appropriate methods as well as the consequences of not taking right truncation into account (naïve approach) on parametric maximum likelihood estimation of time-to-onset distribution. METHODS: Both approaches, naïve or taking right truncation into account, were compared with a simulation study. We used twelve scenarios for the exponential distribution and twenty-four for the Weibull and log-logistic distributions. These scenarios are defined by a set of parameters: the parameters of the time-to-onset distribution, the probability of this distribution falling within an observable values interval and the sample size. An application to reported lymphoma after anti TNF-¿ treatment from the French pharmacovigilance is presented. RESULTS: The simulation study shows that the bias and the mean squared error might in some instances be unacceptably large when right truncation is not considered while the truncation-based estimator shows always better and often satisfactory performances and the gap may be large. For the real dataset, the estimated expected time-to-onset leads to a minimum difference of 58 weeks between both approaches, which is not negligible. This difference is obtained for the Weibull model, under which the estimated probability of this distribution falling within an observable values interval is not far from 1. CONCLUSIONS: It is necessary to take right truncation into account for estimating time-to-onset of adverse drug reactions from spontaneous reporting databases

Populist Mobilization: A New Theoretical Approach to Populism*

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112280/1/j.1467-9558.2011.01388.x.pd

International Consensus Statement on Rhinology and Allergy: Rhinosinusitis

Background: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICAR‐RS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICAR‐RS‐2021 as well as updates to the original 140 topics. This executive summary consolidates the evidence‐based findings of the document. Methods: ICAR‐RS presents over 180 topics in the forms of evidence‐based reviews with recommendations (EBRRs), evidence‐based reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results: ICAR‐RS‐2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidence‐based management algorithm is provided. Conclusion: This ICAR‐RS‐2021 executive summary provides a compilation of the evidence‐based recommendations for medical and surgical treatment of the most common forms of RS