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1. A New Proof of the Atomic Decomposition of Hardy Spaces

   Dekel, S; Kerkyacharian, G; Kyriazis, G; Petrushev, P (April 2018, Constructive Theory of Functions)

   A new proof is given of the atomic decomposition of Hardy spaces H^p, 0< p≤1, in the classical setting on R^n. The new method can be used to establish atomic decomposition of maximal Hardy spaces in general and nonclassical settings. 

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2. An update of the development of motor behavior

   https://doi.org/10.1002/wcs.1682  

   Franchak, John_M; Adolph, Karen_E (June 2024, WIREs Cognitive Science)

   Abstract This primer describes research on the development of motor behavior. We focus on infancy when basic action systems are acquired—posture, locomotion, manual actions, and facial actions—and we adopt a developmental systems perspective to understand the causes and consequences of developmental change. Experience facilitates improvements in motor behavior and infants accumulate immense amounts of varied everyday experience with all the basic action systems. At every point in development, perception guides behavior by providing feedback about the results of just prior movements and information about what to do next. Across development, new motor behaviors provide new inputs for perception. Thus, motor development opens up new opportunities for acquiring knowledge and acting on the world, instigating cascades of developmental changes in perceptual, cognitive, and social domains. This article is categorized under:Cognitive Biology > Cognitive DevelopmentPsychology > Motor Skill and PerformanceNeuroscience > Development 

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3. Calibration of 2‐D soil zymography for correct analysis of enzyme distribution

   https://doi.org/10.1111/ejss.12744  

   Guber, Andrey_K; Kravchenko, Alexandra_N; Razavi, Bahar_S; Blagodatskaya, Evgenia; Kuzyakov, Yakov (March 2019, European Journal of Soil Science)

   Soil zymography is a new technique developed to visualize two‐dimensional distributions of enzyme activities. The method consists of incubating a membrane saturated with an enzyme‐specific fluorogenic substrate on a surface of the soil sample, followed by recording the membrane image generated by a fluorescent product (e.g. MUF: methylumbelliferone) in ultraviolet light. Despite its relative ease of use, performing zymography involves multiple user‐made decisions that might affect the accuracy of enzyme activity estimates. Therefore, unification of the zymography methodology is required for correct estimations and comparisons of various studies. We evaluated the following methodological aspects of the implementation of zymography: (a) camera settings and image processing, (b) effects of evaporation and (c) calibration procedures. Camera settings (shutter speeds or exposure time) affected the intensity of background fluorescence and signal‐to‐noise ratios (SNR). However, because their combined effects varied depending on MUF concentrations, light and camera setting need to be optimized for the expected range of MUF concentrations prior to zymography. Evaporation of MUF solution from the membrane had no effect on fluorescence. Relations between MUF concentration and intensity of fluorescence during calibrations demonstrated a saturated pattern and were strongly affected by image noise outside the optimal range (e.g. 8–14 μmMUF pixel⁻¹). We developed a new calibration approach that is based on a piecewise linear regression. The new approach accounted for specific ranges of MUF concentration and uses nonuniformly saturated membranes, reflecting the real distribution of enzyme activities in soil. The new calibration algorithm eliminated biases of the standard calibration and resulted in greater accuracy in predicting MUF concentrations. HighlightsWe developed a new approach to calibration for 2‐D soil zymography.The approach accounted for spatial nonuniformity of soil zymograms.Standard calibration resulted in systematic underestimation of enzyme activity.Soil zymography requires pixel‐based calibration with nonuniformly saturated membranes. 

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4. A Transfer Learning Approach to Correct the Temporal Performance Drift of Clinical Prediction Models: Retrospective Cohort Study

   https://doi.org/10.2196/38053  

   Zhang, Xiangzhou; Xue, Yunfei; Su, Xinyu; Chen, Shaoyong; Liu, Kang; Chen, Weiqi; Liu, Mei; Hu, Yong (January 2022, JMIR Medical Informatics)

   BackgroundClinical prediction models suffer from performance drift as the patient population shifts over time. There is a great need for model updating approaches or modeling frameworks that can effectively use the old and new data. ObjectiveBased on the paradigm of transfer learning, we aimed to develop a novel modeling framework that transfers old knowledge to the new environment for prediction tasks, and contributes to performance drift correction. MethodsThe proposed predictive modeling framework maintains a logistic regression–based stacking ensemble of 2 gradient boosting machine (GBM) models representing old and new knowledge learned from old and new data, respectively (referred to as transfer learning gradient boosting machine [TransferGBM]). The ensemble learning procedure can dynamically balance the old and new knowledge. Using 2010-2017 electronic health record data on a retrospective cohort of 141,696 patients, we validated TransferGBM for hospital-acquired acute kidney injury prediction. ResultsThe baseline models (ie, transported models) that were trained on 2010 and 2011 data showed significant performance drift in the temporal validation with 2012-2017 data. Refitting these models using updated samples resulted in performance gains in nearly all cases. The proposed TransferGBM model succeeded in achieving uniformly better performance than the refitted models. ConclusionsUnder the scenario of population shift, incorporating new knowledge while preserving old knowledge is essential for maintaining stable performance. Transfer learning combined with stacking ensemble learning can help achieve a balance of old and new knowledge in a flexible and adaptive way, even in the case of insufficient new data. 

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5. Bacterial Viruses Subcommittee and Archaeal Viruses Subcommittee of the ICTV: update of taxonomy changes in 2021

   https://doi.org/10.1007/s00705-021-05205-9  

   Krupovic, Mart; Turner, Dann; Morozova, Vera; Dyall-Smith, Mike; Oksanen, Hanna M.; Edwards, Rob; Dutilh, Bas E.; Lehman, Susan M.; Reyes, Alejandro; Baquero, Diana P.; et al (August 2021, Archives of Virology)

   Abstract In this article, we – the Bacterial Viruses Subcommittee and the Archaeal Viruses Subcommittee of the International Committee on Taxonomy of Viruses (ICTV) – summarise the results of our activities for the period March 2020 – March 2021. We report the division of the former Bacterial and Archaeal Viruses Subcommittee in two separate Subcommittees, welcome new members, a new Subcommittee Chair and Vice Chair, and give an overview of the new taxa that were proposed in 2020, approved by the Executive Committee and ratified by vote in 2021. In particular, a new realm, three orders, 15 families, 31 subfamilies, 734 genera and 1845 species were newly created or redefined (moved/promoted). 

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