Natural History Of The Neotropical Arboreal Ant, Odontomachus Hastatus: Nest Sites, Foraging Schedule, And Diet.

The ecology of most arboreal ants remains poorly documented because of the difficulty in accessing ant nests and foragers in the forest canopy. This study documents the nesting and foraging ecology of a large (∼13 mm total length) arboreal trap-jaw ant, Odontomachus hastatus (Fabricius) (Hymenoptera: Formicidae) in a sandy plain forest on Cardoso Island, off the coast of Southeast Brazil. The results showed that O. hastatus nested in root clusters of epiphytic bromeliads, most commonly Vriesea procera (70% of nest plants). Mature O. hastatus colonies include one to several queens and about 500 workers. Foraging by O. hastatus is primarily nocturnal year-round, with increased foraging activity during the wet/warm season. The foragers hunt singly in the trees, preying on a variety of canopy-dwelling arthropods, with flies, moths, ants, and spiders accounting for > 60% of the prey captured. Although predators often have impacts on prey populations, the ecological importance of O. hastatus remains to be studied.124

Hybrid Deep Modeling of a GS115 (Mut+) Pichia pastoris Culture with State–Space Reduction

JP acknowledges the PhD grant SFRD/BD14610472019, Fundação para a Ciência e Tecnologia (FCT).Hybrid modeling workflows combining machine learning with mechanistic process descriptions are becoming essential tools for bioprocess digitalization. In this study, a hybrid deep modeling method with state–space reduction was developed and showcased with a P. pastoris GS115 Mut+ strain expressing a single-chain antibody fragment (scFv). Deep feedforward neural networks (FFNN) with varying depths were connected in series with bioreactor macroscopic material balance equations. The hybrid model structure was trained with a deep learning technique based on the adaptive moment estimation method (ADAM), semidirect sensitivity equations and stochastic regularization. A state–space reduction method was investigated based on a principal component analysis (PCA) of the cumulative reacted amount. Data of nine fed-batch P. pastoris 50 L cultivations served to validate the method. Hybrid deep models were developed describing process dynamics as a function of critical process parameters (CPPs). The state–space reduction method succeeded to decrease the hybrid model complexity by 60% and to improve the predictive power by 18.5% in relation to the nonreduced version. An exploratory design space analysis showed that the optimization of the feed of methanol and of inorganic elements has the potential to increase the scFv endpoint titer by 30% and 80%, respectively, in relation to the reference condition.publishersversionpublishe

a Python interface for SBML compatible hybrid modelling

This work was supported by the Associate Laboratory for Green Chemistry—LAQV which is financed by national funds from FCT/MCTES [UIDB/50006/2020 and UIDP/50006/2020]. This work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement number [101000733] (PROMICON project). The authors thank H. Mochao for useful implementation ideas. JP and LA acknowledge PhD grants [SFRD/BD14610472019 and 2021.07759.BD], Fundação para a Ciência e Tecnologia (FCT) and RSC the contract [CEECIND/01399/2017].Here we present sbml2hyb, an easy-to-use standalone Python tool that facilitates the conversion of existing mechanistic models of biological systems in Systems Biology Markup Language (SBML) into hybrid semiparametric models that combine mechanistic functions with machine learning (ML). The so-formed hybrid models can be trained and stored back in databases in SBML format. The tool supports a user-friendly export interface with an internal format validator. Two case studies illustrate the use of the sbml2hyb tool. Additionally, we describe HMOD, a new model format designed to support and facilitate hybrid models building. It aggregates the mechanistic model information with the ML information and follows as close as possible the SBML rules. We expect the sbml2hyb tool and HMOD to greatly facilitate the widespread usage of hybrid modeling techniques for biological systems analysis.publishersversionpublishe

combining First Principles with deep neural networks

JP acknowledges PhD grant SFRD/BD14610472019, This work has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no 101000733 (PROMICON).Numerous studies have reported the use of hybrid semiparametric systems that combine shallow neural networks with First Principles for bioprocess modeling. Here we revisit the general bioreactor hybrid model and introduce some deep learning techniques. Multi-layer networks with varying depths were combined with First Principles equations in the form of deep hybrid models. Deep learning techniques, namely the adaptive moment estimation method (ADAM), stochastic regularization and depth-dependent weights initialization were evaluated in a hybrid modeling context. Modified sensitivity equations are proposed for the computation of gradients in order to reduce CPU time for the training of deep hybrid models. The methods are illustrated with applications to a synthetic dataset and a pilot 50 L MUT+ Pichia pastoris process expressing a single chain antibody fragment. All in all, the results point to a systematic generalization improvement of deep hybrid models over its shallow counterpart. Moreover, the CPU cost to train the deep hybrid models is shown to be lower than for the shallow counterpart. In the pilot 50L MUT+ Pichia pastoris data set, the prediction accuracy was increased by 18.4% and the CPU decreased by 43.4%.publishersversionpublishe

Stand dynamics modulate water cycling and mortality risk in droughted tropical forest

Transpiration from the Amazon rainforest generates an essential water source at a global and local scale. However, changes in rainforest function with climate change can disrupt this process, causing significant reductions in precipitation across Amazonia, and potentially at a global scale. We report the only study of forest transpiration following a long-term (>10 year) experimental drought treatment in Amazonian forest. After 15 years of receiving half the normal rainfall, drought-related tree mortality caused total forest transpiration to decrease by 30%. However, the surviving droughted trees maintained or increased transpiration because of reduced competition for water and increased light availability, which is consistent with increased growth rates. Consequently, the amount of water supplied as rainfall reaching the soil and directly recycled as transpiration increased to 100%. This value was 25% greater than for adjacent nondroughted forest. If these drought conditions were accompanied by a modest increase in temperature (e.g., 1.5°C), water demand would exceed supply, making the forest more prone to increased tree mortality.Peer reviewe

lcc: an R package to estimate the concordance correlation, Pearson correlation and accuracy over time

Background and Objective: Observational studies and experiments in medicine, pharmacology and agronomy are often concerned with assessing whether different methods/raters produce similar values over the time when measuring a quantitative variable. This article aims to describe the statistical package lcc, for are, that can be used to estimate the extent of agreement between two (or more) methods over the time, and illustrate the developed methodology using three real examples. Methods: The longitudinal concordance correlation, longitudinal Pearson correlation, and longitudinal accuracy functions can be estimated based on fixed effects and variance components of the mixed-effects regression model. Inference is made through bootstrap confidence intervals and diagnostic can be done via plots, and statistical tests. Results: The main features of the package are estimation and inference about the extent of agreement using numerical and graphical summaries. Moreover, our approach accommodates both balanced and unbalanced experimental designs or observational studies, and allows for different within-group error structures, while allowing for the inclusion of covariates in the linear predictor to control systematic variations in the response. All examples show that our methodology is flexible and can be applied to many different data types. Conclusions: The lcc package, available on the CRAN repository, proved to be a useful tool to describe the agreement between two or more methods over time, allowing the detection of changes in the extent of agreement. The inclusion of different structures for the variance-covariance matrices of random effects and residuals makes the package flexible for working with different types of databases