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This paper aims to evaluate the statistical association between exposure to air pollution and forced expiratory volume in the first second (FEV1) in both asthmatic and non-asthmatic children and teenagers, in which the response variable FEV1 was repeatedly measured on a monthly basis, characterizing a longitudinal experiment. Due to the nature of the data, an robust linear mixed model (RLMM), combined with a robust principal component analysis (RPCA), is proposed to handle the multicollinearity among the covariates and the impact of extreme observations (high levels of air contaminants) on the estimates. The Huber and Tukey loss functions are considered to obtain robust estimators of the parameters in the linear mixed model (LMM). A finite sample size investigation is conducted under the scenario where the covariates follow linear time series models with and without additive outliers (AO). The impact of the time-correlation and the outliers on the estimates of the fixed effect parameters in the LMM is investigated. In the real data analysis, the robust model strategy evidenced that RPCA exhibits three principal component (PC), mainly related to relative humidity (Hmd), particulate matter with a diameter smaller than 10 µm (PM10) and particulate matter with a diameter smaller than 2.5 µm (PM2.5).

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The aim of this study was to validate an HPLC-UV method to assess vitamin D status by determining the linearity and precision of the 25-hydroxyvitamin D3 (25(OH)D3) calibration curve, the limits of detection, quantitation and robustness of the method, and its accuracy. A second stock solution of 25(OH)D3 was prepared (500 ng/mL), and working dilutions (5, 10, 20, 30, 40, and 50 ng/mL) were prepared for a calibration curve. The HPLC equipment had a UV-Vis diode-array detector and utilized an AcclaimTM 120 C18 column (5 µm, 4.6 × 250 mm) with a flow rate of 1.2 mL/min, a column temperature of 30 °C, and the standards and samples were maintained at 4 °C, with an injection volume of 100 µL. Detection of 25(OH)D3 was determined at 265 nm, with a retention time of 4.0 min. The validation was conducted according to the FDA Validation of Analytical Procedures: Guidance for Industry. Vitamin D was extracted from plasma samples using acetonitrile (ACN)-0.1% formic acid (2:1 v/v), and the percentage of recovery was calculated. The proposed method conditions gave excellent linearity (R2 = 0.9989) and the linearity coefficient was R2 > 0.99 for 25(OH)D3. The detection and quantification limits were 1.1703 ng/mL and 3.5462 ng/mL, respectively. Decreasing or increasing the reading temperature by 1 °C decreased the response units (AU) of vitamin D, 25(OH)D3. When the current flow rate decreased by 0.2 mL/min (1.0 mL/min), the retention time increased to 4.913 min, whereas an increase of 0.2 mL/min of the proposed flow rate (1.4 mL/min) decreased the retention time to 3.500 min. The percentage of recovery varied from 92.2% to 97.1%. The proposed method to quantify a vitamin D metabolite (25(OH)D3) in human plasma samples was reliable and validated.

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Small ruminant farming is of socio-economic and environmental importance to many rural communities around the world. The SMARTER H2020 project aims to redefine genetic selection criteria to increase the sustainability of the sector. The objective of this study was to analyse the selection and breeding management practices of small ruminant producers and breeders, linked with socio-technical elements that shape them. The study is based on farm surveys using semi-structured interviews conducted in five countries (France, Spain, Italy, Greece, and Uruguay) across 272 producers and breeders of 13 sheep and goat breeds, and 15 breed × system combinations. The information was collected in four sections. The first and second sections dealt with general elements of structure and management of the system and the flock/herd. The third section focused on selection and breeding management practices: criteria for culling and replacement of females, selection criteria for males, use of estimated breeding values and global indexes, and preferences for indexing new traits to increase the sustainability of their system. The fourth section aimed to collect socio-technical information. We used a data abstraction method to standardise the representation of these data. A mixed data factor analysis followed by a hierarchical ascending classification allowed the characterisation of three profiles of selection and breeding management: (1) a profile of producers (n = 93) of small flocks/herds, with little knowledge or use of genetic selection and improvement tools (selection index, artificial insemination, performance recording); these farmers do not feel that new traits are needed to improve the sustainability of their system. (2) a profile of producers (n = 34) of multibreed flocks/herds that rely significantly on grazing; they are familiar with genetic tools, they currently use AI; they would like the indexes to include more health and robustness characteristics, to make their animals more resistant and to increase the sustainability of their system. And (3) a profile of producers or breeders (n = 145) of large flocks/herds, with specific culling criteria; these farmers are satisfied with the current indexes to maintain the sustainability of their system. These results are elements that can be used by private breeding companies and associations to support the evolution of selection objectives to increase the resilience of animals and to improve the sustainability of the small ruminant breeding systems.

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Currently, industrial bioproducts are less competitive than chemically produced goods due to the shortcomings of conventional microbial hosts. Thus, is essential developing robust bacteria for improved cell tolerance to process-specific parameters. In this context, metagenomic approaches from extreme environments can provide useful biological parts to improve bacterial robustness. Here, in order to build genetic constructs that increase bacterial resistance to diverse stress conditions, we recovered novel protein-encoding sequences related to stress-resistance from metagenomic databases using an in silico approach based on Hidden-Markov-Model profiles. For this purpose, we used metagenomic shotgun sequencing data from microbial communities of extreme environments to identify genes encoding chaperones and other proteins that confer resistance to stress conditions. We identified and characterized 10 novel protein-encoding sequences related to the DNA-binding protein HU, the ATP-dependent protease ClpP, and the chaperone protein DnaJ. By expressing these genes in Escherichia coli under several stress conditions (including high temperature, acidity, oxidative and osmotic stress, and UV radiation), we identified five genes conferring resistance to at least two stress conditions when expressed in E. coli. Moreover, one of the identified HU coding-genes which was retrieved from an acidic soil metagenome increased E. coli tolerance to four different stress conditions, implying its suitability for the construction of a synthetic circuit directed to expand broad bacterial resistance.

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Research Highlight: Leimberger, K.G., Hadley, A.S., & Betts, M.G. (2023). Plant-hummingbird pollination networks exhibit minimal rewiring after experimental removal of a locally abundant plant species. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.13935. In this paper, Leimberger, Hadley and Betts (2023) explore the effects of removing a locally abundant plant species on plant-hummingbird pollination networks. They experimentally prevented access of hummingbirds to flowers of Heliconia tortuosa and assessed subsequent changes in the interactions between plants and hummingbirds. Their main hypothesis postulated that the loss of a highly connected species would lead to interaction rewiring and niche expansions by hummingbirds, decreasing individual, species and network specialization. However, they found that the overall structure of the plant-hummingbird networks remains mostly unaltered, with limited rewiring and minimal changes in specialization. The main contributions of this study can be summarized as (i) it adds to a limited number of manipulative studies on the capacity of species to rewire their interactions following the loss of partners, and importantly, it is the first study from the tropics and with vertebrate pollinators, for which experimental studies at appropriate scales is intrinsically more challenging; and (ii) innovates by evaluating change in specialization for the individual level, carried out through pollen sampling on the body of hummingbirds. The limited change in species interactions highlights that network stability through interaction rewiring may have been overestimated in previous studies, calling for further manipulative studies in the field. At the same time, it also indicated that even the loss of a highly abundant plant species has an overall small effect on network structure. Thus, this study contributes timely findings regarding the capacity of ecological communities to respond to species extinctions.

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This paper presents a robust decoupling Model Predictive Controller (MPC) for constrained linear systems with bounded disturbances. The usual explicit decouplers are combined with a suitable augmented state-space representation in order to ensure recursive feasibility, robust constraint satisfaction, and cross-coupling reduction during set-point changes. The proposed approach is flexible with respect to the decoupler choice and the robust MPC algorithm. A robust MPC for tracking piecewise constant references with an artificial target based on nominal prediction is proposed in order to achieve offset-free piecewise constant reference tracking. Two case studies are presented to illustrate the effectiveness of the proposed decoupling strategy.

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Cell recruitment is a process by which a differentiated cell induces neighboring cells to adopt its same cell fate. In Drosophila, cells expressing the protein encoded by the wing selector gene, vestigial (vg), drive a feed-forward recruitment signal that expands the Vg pattern as a wave front. However, previous studies on Vg pattern formation do not reveal these dynamics. Here, we use live imaging to show that multiple cells at the periphery of the wing disc simultaneously activate a fluorescent reporter of the recruitment signal, suggesting that cells may be recruited without the need for their contact neighbors be recruited in advance. In support of this observation, when Vg expression is inhibited either at the dorsal-ventral boundary or away from it, the activation of the recruitment signal still occurs at a distance, suggesting that Vg expression is not absolutely required to send or propagate the recruitment signal. However, the strength and extent of the recruitment signal is clearly compromised. We conclude that a feed-forward, contact-dependent cell recruitment process is not essential for Vg patterning, but it is necessary for robustness. Overall, our findings reveal a previously unidentified role of cell recruitment as a robustness-conferring cell differentiation mechanism.

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The functions of living organisms are affected by different kinds of perturbation, both internal and external, which in many cases have functional effects and phenotypic impact. The effects of these perturbations become particularly relevant for multicellular organisms with complex body patterns and cell type heterogeneity, where transcriptional programs controlled by gene regulatory networks determine, for example, the cell fate during embryonic development. Therefore, an essential aspect of development in these organisms is the ability to maintain the functionality of their genetic developmental programs even in the presence of genetic variation, changing environmental conditions and biochemical noise, a property commonly termed robustness. We discuss the implication of different molecular mechanisms of robustness involved in neurodevelopment, which is characterized by the interplay of many developmental programs at a molecular, cellular and systemic level. We specifically focus on processes affecting the function of gene regulatory networks, encompassing transcriptional regulatory elements and post-transcriptional processes such as miRNA-based regulation, but also higher order regulatory organization, such as gene network topology. We also present cases where impairment of robustness mechanisms can be associated with neurodevelopmental disorders, as well as reasons why understanding these mechanisms should represent an important part of the study of gene regulatory networks driving neural development.

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Organ growth driven by cell proliferation is an exponential process. As a result, even small variations in proliferation rates, when integrated over a relatively long developmental time, will lead to large differences in size. How organs robustly control their final size despite perturbations in cell proliferation rates throughout development is a long-standing question in biology. Using a mathematical model, we show that in the developing wing of the fruit fly, Drosophila melanogaster, variations in proliferation rates of wing-committed cells are inversely proportional to the duration of cell recruitment, a differentiation process in which a population of undifferentiated cells adopt the wing fate by expressing the selector gene, vestigial. A time-course experiment shows that vestigial-expressing cells increase exponentially while recruitment takes place, but slows down when recruitable cells start to vanish, suggesting that undifferentiated cells may be driving proliferation of wing-committed cells. When this observation is incorporated in our model, we show that the duration of cell recruitment robustly determines a final wing size even when cell proliferation rates of wing-committed cells are perturbed. Finally, we show that this control mechanism fails when perturbations in proliferation rates affect both wing-committed and recruitable cells, providing an experimentally testable hypothesis of our model.

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BACKGROUND: Research on gene duplication is abundant and comes from a wide range of approaches, from high-throughput analyses and experimental evolution to bioinformatics and theoretical models. Notwithstanding, a consensus is still lacking regarding evolutionary mechanisms involved in evolution through gene duplication as well as the conditions that affect them. We argue that a better understanding of evolution through gene duplication requires considering explicitly that genes do not act in isolation. It demands studying how the perturbation that gene duplication implies percolates through the web of gene interactions. Due to evolution's contingent nature, the paths that lead to the final fate of duplicates must depend strongly on the early stages of gene duplication, before gene copies have accumulated distinctive changes. METHODS: Here we use a widely-known model of gene regulatory networks to study how gene duplication affects network behavior in early stages. Such networks comprise sets of genes that cross-regulate. They organize gene activity creating the gene expression patterns that give cells their phenotypic properties. We focus on how duplication affects two evolutionarily relevant properties of gene regulatory networks: mitigation of the effect of new mutations and access to new phenotypic variants through mutation. RESULTS: Among other observations, we find that those networks that are better at maintaining the original phenotype after duplication are usually also better at buffering the effect of single interaction mutations and that duplication tends to enhance further this ability. Moreover, the effect of mutations after duplication depends on both the kind of mutation and genes involved in it. We also found that those phenotypes that had easier access through mutation before duplication had higher chances of remaining accessible through new mutations after duplication. CONCLUSION: Our results support that gene duplication often mitigates the impact of new mutations and that this effect is not merely due to changes in the number of genes. The work that we put forward helps to identify conditions under which gene duplication may enhance evolvability and robustness to mutations.

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The assessment of the presence of genotype by environment interaction (GxE) in beef cattle is very important in tropical countries with diverse climatic conditions and production systems. The present study aimed to assess the presence of GxE by using different reaction norm models for eleven traits related to growth, reproduction, and visual score in Nellore cattle. We studied five reaction norm models (RNM), fitting a linear model considering homoscedastic residual variance (RNM_homo), and four models considering heteroskedasticity, being linear (RNM_hete), quadratic (RNM_quad), linear spline (RNM_l-l), and quadratic spline (RNM_q-q). There was the presence of GxE for age at first calving (AFC), scrotal circumference (SC), weaning to yearling weight gain (WYG), and yearling weight (YW). The best models were RNM_l-l for YW and RNM_q-q for AFC, SC, and WYG. The heritability estimates for RNM_l-l ranged from 0.07 to 0.20, 0.42 to 0.61, 0.24 to 0.42, and 0.47 to 0.63 for AFC, SC, WYG, and YW, respectively. The heteroskedasticity in reaction norm models improves the assessment of the presence of GxE for YW, WYG, AFC, and SC. Additionally, the trajectories of reaction norms for these traits seem to be affected by a non-linear component, and selecting robust animals for these traits is an alternative to increase production and reduce environmental sensitivity.

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Feed restriction (FR) occurs commonly in sheep production systems in the Brazilian semi-arid region and can cause physiological changes in the progeny. We assessed the effects of the FR pre and postnatal on the performance and carcass traits of Morada Nova lambs. Twenty-four lambs born from a group of 68 ewes were distributed in three treatments of 8 replicates as follows: ewes fed ad libitum pre and postnatal (AL-AL); postnatal restriction (POSTN-R): included lambs born from ewes fed ad libitum in the last third of pregnancy with FR postnatal; and prenatal feeding restriction (PREN-AL): comprised lambs born from ewes subjected to FR in the last third of pregnancy but ad libitum postnatal. Slaughter body weight (20.96 kg); total weight gain (11.34 kg); average daily weight (0.096 kg); fasting carcass weight (19.45); hot carcass weight (9.33 kg); and cold carcass weight (9.11 kg) were smaller (P < 0.05) in POSTN-R but similar between AL-AL and PREN-AL. Rib (0.47 kg), shoulder (0.85 kg), loin (0.50 kg), and chest-flank (0.97 kg) had lower weight in lambs under POSTN-R (P < 0.05). The treatments did not affect the tissue composition of the 12th rib. The non-carcass components and carcass traits are the variables that best discriminate animals under FR plans. The postpartum FR in native lambs has an impact on the performance and carcass weight of the progeny, unlike lambs submitted to prepartum FR, which demonstrates the adaptation to intrauterine nutritional deficiency in Morada Nova ewes to produce lambs with heavy carcasses in regions semi-arid.

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Biological adaptations depend on natural selection sorting out those individuals that exhibit characters fit to their environment. Selection, in turn, depends on the phenotypic variation present in a population. Thus, evolutionary outcomes depend, to a certain extent, on the kind of variation that organisms can produce through random genetic perturbation, that is, their phenotypic variability. Moreover, the properties of developmental mechanisms that produce the organisms affect their phenotypic variability. Two of these properties are modularity and robustness. Modularity is the degree to which interactions occur mostly within groups of the system's elements and scarcely between elements in different groups. Robustness is the propensity of a system to endure perturbations while preserving its phenotype. In this paper, we used a model of gene regulatory networks (GRNs) to study the relationship between modularity and robustness in developmental processes and how modularity affects the variation that random genetic mutations produce in the expression patterns of GRNs. Our results show that modularity and robustness are correlated in multifunctional GRNs and that selection for one of these properties affects the other as well. We contend that these observations may help to understand why modularity and robustness are widespread in biological systems. Additionally, we found that modular networks tend to produce new expression patterns with subtle changes localized in the expression of a few groups of genes. This effect in the phenotypic variability of modular GRNs may bear important consequences for adaptive evolution: it may help to adjust the expression of one group of genes at a time, with few alterations on other previously evolved expression patterns.

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Carbendazim (CBZ) is a fungicide employed in grape crop disease controls, and its maximum residue limit in food is regulated by specialized agencies. This study aimed to determine the CBZ content in the grape juices in a semi-quantitative classification model based on portable Ultraviolet-Visible spectroscopy and partial least squares with discriminant analysis. The sensitivity and specificity of the obtained model ranged from 83 to 100%, with the external validation set. These results are therefore promising for industrial application, and the model presents robustness for the evaluation of grape juices produced from a different grape variety. The VIP scores allowed identifying important variables involved in class modeling. This study suggests a methodology that is fast and demands minimal sample preparation (only dilution), besides being less expensive compared to the traditional methods, free of reagent/solvent, contributing to quality control in the juice industry.

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Live-attenuated vaccines have been historically used to successfully prevent numerous diseases caused by a broad variety of RNA viruses due to their ability to elicit strong and perdurable immune-protective responses. In recent years, various strategies have been explored to achieve viral attenuation by rational genetic design rather than using classic and empirical approaches, based on successive passages in cell culture. A deeper understanding of evolutionary implications of distinct viral genomic compositional aspects, as well as substantial advances in synthetic biology technologies, have provided a framework to achieve new viral attenuation strategies. Herein, we will discuss different approaches that are currently applied to modify compositional features of viruses in order to develop novel live-attenuated vaccines.

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We use large-scale mutagenesis data and computer simulations to quantify the mutational robustness of protein-coding genes by taking into account constraints arising from protein function and the genetic code. Analyses of the distribution of amino acid substitutions from 18 mutagenesis studies revealed an average of 45% of neutral variants; while mutagenesis data of 12 proteins artificially designed under no other constraints but stability, reach an average of 60%. Simulations using a lattice protein model allow us to contrast these estimates to the expected mutational robustness of protein families by generating unbiased samples of foldable sequences, which we find to have 30% of neutral variants. In agreement with mutagenesis data of designed proteins, the model shows that maximally robust protein families might access up to twice the amount of neutral variants observed in the unbiased samples (i.e. 60%). A biophysical model of protein-ligand binding suggests that constraints associated to molecular function have only a moderate impact on robustness of approximately 5 to 10% of neutral variants; and that the direction of this effect depends on the relation between functional performance and thermodynamic stability. Although the genetic code constraints the access of a gene's nucleotide sequence to only 30% of the full distribution of amino acid mutations, it provides an extra 15 to 20% of neutral variants to the estimations above, such that the expected, observed, and maximal robustness of protein-coding genes are approximately 50, 65, and 75%, respectively. We discuss our results in the light of three main hypothesis put forward to explain the existence of mutationally robust genes.

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Wireless sensor networks are used in many location-dependent applications. The location of sensor nodes is commonly carried out in a distributed way for energy saving and network robustness, where the handling of these characteristics is still a great challenge. It is very desirable that distributed algorithms invest as few iterations as possible with the highest accuracy on position estimates. This research proposes a range-based and robust localization method, derived from the Newton scheme, that can be applied over isotropic and anisotropic networks in presence of outliers in the pair-wise distance measurements. The algorithm minimizes the error of position estimates using a hop-weighted function and a scaling factor that allows a significant improvement on position estimates in only few iterations. Simulations demonstrate that our proposed algorithm outperforms other similar algorithms under anisotropic networks.

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We present a robustness analysis of an inter-cities mobility complex network, motivated by the challenge of the COVID-19 pandemic and the seek for proper containment strategies. Brazilian data from 2016 are used to build a network with more than five thousand cities (nodes) and twenty-seven states with the edges representing the weekly flow of people between cities via terrestrial transports. Nodes are systematically isolated (removed from the network) either at random (failures) or guided by specific strategies (targeted attacks), and the impacts are assessed with three metrics: the number of components, the size of the giant component, and the total remaining flow of people. We propose strategies to identify which regions should be isolated first and their impact on people mobility. The results are compared with the so-called reactive strategy, which consists of isolating regions ordered by the date the first case of COVID-19 appeared. We assume that the nodes' failures abstract individual municipal and state initiatives that are independent and possess a certain level of unpredictability. Differently, the targeted attacks are related to centralized strategies led by the federal government in agreement with municipalities and states. Removing a node means completely restricting the mobility of people between the referred city/state and the rest of the network. Results reveal that random failures do not cause a high impact on mobility restraint, but the coordinated isolation of specific cities with targeted attacks is crucial to detach entire network areas and thus prevent spreading. Moreover, the targeted attacks perform better than the reactive strategy for the three analyzed robustness metrics.

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In recent years, advanced threats against Cyber-Physical Systems (CPSs), such as Distributed Denial of Service (DDoS) attacks, are increasing. Furthermore, traditional machine learning-based intrusion detection systems (IDSs) often fail to efficiently detect such attacks when corrupted datasets are used for IDS training. To face these challenges, this paper proposes a novel error-robust multidimensional technique for DDoS attack detection. By applying the well-known Higher Order Singular Value Decomposition (HOSVD), initially, the average value of the common features among instances is filtered out from the dataset. Next, the filtered data are forwarded to machine learning classification algorithms in which traffic information is classified as a legitimate or a DDoS attack. In terms of results, the proposed scheme outperforms traditional low-rank approximation techniques, presenting an accuracy of 98.94%, detection rate of 97.70% and false alarm rate of 4.35% for a dataset corruption level of 30% with a random forest algorithm applied for classification. In addition, for error-free conditions, it is found that the proposed approach outperforms other related works, showing accuracy, detection rate and false alarm rate of 99.87%, 99.86% and 0.16%, respectively, for the gradient boosting classifier.

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Gonzalo Moratorio works in the field of experimental evolution of viruses. In this mSphere of Influence article, he reflects on how the papers "Virus attenuation by genome-scale changes in codon pair bias" by Coleman et al. (Science 320:1784-1787, 2008, https://doi.org/10.1126/science.1155761) and "Codon usage determines the mutational robustness, evolutionary capacity, and virulence of an RNA virus" by Lauring et al. (Cell Host Microbe 12:623-632, 2012, https://doi.org/10.1016/j.chom.2012.10.008) made an impact on his thinking about how to employ synthetic biology to study experimental evolution of viruses.

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