Centro Internacional Franco-Argentino de Ciencias de la Información y de Sistemas

Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.

With sustained growth of software complexity, finding security vulnerabilities in operating systems has become an important necessity. Nowadays, OS are shipped with thousands of binary executables. Unfortunately, methodologies and tools for an OS scale program testing within a limited time budget are still missing.

In this paper we introduce a general-purpose software tool for discrete event system specification (DEVS) modeling and simulation oriented to the simulation of hybrid systems. The environment, called PowerDEVS, allows atomic DEVS models to be defined in C++ language that can then be coupled graphically in hierarchical block diagrams to create more complex systems. The environment automatically translates the graphically coupled models into a C++ code which executes the simulation. A remarkable feature of PowerDEVS is the possibility to perform simulations under a real-time operating system (RTAI) synchronizing with a real-time clock, which permits the design and automatic implementation of synchronous and asynchronous digital controllers. Combined with its continuous system simulation library, PowerDEVS is also an efficient tool for real-time simulation of physical systems. Another feature is the interconnection between PowerDEVS and the numerical package Scilab. PowerDEVS simulations can make use of Scilab workspace variables and functions, and the results can be sent back to Scilab for further processing and data analysis. In addition to describing the main features of the software tool, the article also illustrates its use with some examples which show its simplicity and efficiency.

In this article, we study the impact of stealthy attacks on the cyber-physical system (CPS) modeled as a stochastic linear system. An attack is characterised by a malicious injection into the system through input, output or both, and it is called stealthy (resp. strictly stealthy) if it produces bounded changes (resp. no changes) in the detection residue. Correspondingly, a CPS is called vulnerable (resp. strictly vulnerable) if it can be destabilized by a stealthy attack (resp. strictly stealthy attack). We provide necessary and sufficient conditions for the vulnerability and strictly vulnerability. For the invulnerable case, we also provide a performance bound for the difference between healthy and attacked system. Numerical examples are provided to illustrate the theoretical results.

This paper presents an overview of the recent developments of modifier-adaptation schemes for real-time optimization of uncertain processes. These schemes have the ability to reach plant optimality upon convergence despite the presence of structural plant-model mismatch. Modifier Adaptation has its origins in the technique of Integrated System Optimization and Parameter Estimation, but differs in the definition of the modifiers and in the fact that no parameter estimation is required. This paper reviews the fundamentals of Modifier Adaptation and provides an overview of several variants and extensions. Furthermore, the paper discusses different methods for estimating the required gradients (or modifiers) from noisy measurements. We also give an overview of the application studies available in the literature. Finally, the paper briefly discusses open issues so as to promote future research in this area.

Durable resistance to the rust diseases of wheat ( Triticum aestivum L.) can be achieved by developing lines that have race‐nonspecific adult plant resistance conferred by multiple minor slow‐rusting genes. Genomic selection (GS) is a promising tool for accumulating favorable alleles of slow‐rusting genes. In this study, five CIMMYT wheat populations evaluated for resistance were used to predict resistance to stem rust ( Puccinia graminis ) and yellow rust ( Puccinia striiformis ) using Bayesian least absolute shrinkage and selection operator (LASSO) (BL), ridge regression (RR), and support vector regression with linear or radial basis function kernel models. All parents and populations were genotyped using 1400 Diversity Arrays Technology markers and different prediction problems were assessed. Results show that prediction ability for yellow rust was lower than for stem rust, probably due to differences in the conditions of infection of both diseases. For within population and environment, the correlation between predicted and observed values (Pearson's correlation [ρ]) was greater than 0.50 in 90% of the evaluations whereas for yellow rust, ρ ranged from 0.0637 to 0.6253. The BL and RR models have similar prediction ability, with a slight superiority of the BL confirming reports about the additive nature of rust resistance. When making predictions between environments and/or between populations, including information from another environment or environments or another population or populations improved prediction.

Hepatotoxicity is associated with major changes in liver gene expression induced by xenobiotic exposure. Understanding the underlying mechanisms is critical for its clinical diagnosis and treatment. MicroRNAs are key regulators of gene expression that control mRNA stability and translation, during normal development and pathology. The canonical technique to measure gene transcript levels is Real-Time qPCR, which has been successfully modified to determine the levels of microRNAs as well. However, in order to obtain accurate data in a multi-step method like RT-qPCR, the normalization with endogenous, stably expressed reference genes is mandatory. Since the expression stability of candidate reference genes varies greatly depending on experimental factors, the aim of our study was to identify a combination of genes for optimal normalization of microRNA and mRNA qPCR expression data in experimental models of acute hepatotoxicity. Rats were treated with four traditional hepatotoxins: acetaminophen, carbon tetrachloride, D-galactosamine and thioacetamide, and the liver expression levels of two groups of candidate reference genes, one for microRNA and the other for mRNA normalization, were determined by RT-qPCR in compliance with the MIQE guidelines. In the present study, we report that traditional reference genes such as U6 spliceosomal RNA, Beta Actin and Glyceraldehyde-3P-dehydrogenase altered their expression in response to classic hepatotoxins and therefore cannot be used as reference genes in hepatotoxicity studies. Stability rankings of candidate reference genes, considering only those that did not alter their expression, were determined using geNorm, NormFinder and BestKeeper software packages. The potential candidates whose measurements were stable were further tested in different combinations to find the optimal set of reference genes that accurately determine mRNA and miRNA levels. Finally, the combination of MicroRNA-16/5S Ribosomal RNA and Beta 2 Microglobulin/18S Ribosomal RNA were validated as optimal reference genes for microRNA and mRNA quantification, respectively, in rat models of acute hepatotoxicity.

Dementia is becoming increasingly prevalent in Latin America, contrasting with stable or declining rates in North America and Europe. This scenario places unprecedented clinical, social, and economic burden upon patients, families, and health systems. The challenges prove particularly pressing for conditions with highly specific diagnostic and management demands, such as frontotemporal dementia. Here we introduce a research and networking initiative designed to tackle these ensuing hurdles, the Multi-partner consortium to expand dementia research in Latin America (ReDLat). First, we present ReDLat's regional research framework, aimed at identifying the unique genetic, social, and economic factors driving the presentation of frontotemporal dementia and Alzheimer's disease in Latin America relative to the US. We describe ongoing ReDLat studies in various fields and ongoing research extensions. Then, we introduce actions coordinated by ReDLat and the Latin America and Caribbean Consortium on Dementia (LAC-CD) to develop culturally appropriate diagnostic tools, regional visibility and capacity building, diplomatic coordination in local priority areas, and a knowledge-to-action framework toward a regional action plan. Together, these research and networking initiatives will help to establish strong cross-national bonds, support the implementation of regional dementia plans, enhance health systems' infrastructure, and increase translational research collaborations across the continent.

Interoception (the sensing of inner-body signals) is a multi-faceted construct with major relevance for basic and clinical neuroscience research. However, the neurocognitive signatures of this domain (cutting across behavioral, electrophysiological, and fMRI connectivity levels) are rarely reported in convergent or systematic fashion. Additionally, various controversies in the field might reflect the caveats of standard interoceptive accuracy (IA) indexes, mainly based on heartbeat detection (HBD) tasks. Here we profit from a novel IA index (md) to provide a convergent multidimensional and multi-feature approach to cardiac interoception. We found that outcomes from our IA-md index are associated with -and predicted by- canonical markers of interoception, including the hd-EEG-derived heart-evoked potential (HEP), fMRI functional connectivity within interoceptive hubs (insular, somatosensory, and frontal networks), and socio-emotional skills. Importantly, these associations proved more robust than those involving current IA indexes. Furthermore, this pattern of results persisted when taking into consideration confounding variables (gender, age, years of education, and executive functioning). This work has relevant theoretical and clinical implications concerning the characterization of cardiac interoception and its assessment in heterogeneous samples, such as those composed of neuropsychiatric patients.

In this paper we present the Rosario dataset, a collection of sensor data for autonomous mobile robotics in agricultural scenes. The dataset is motivated by the lack of realistic sensor readings gathered by a mobile robot in such environments. It consists of six sequences recorded in soybean fields showing real and challenging cases: highly repetitive scenes, reflection, and burned images caused by direct sunlight and rough terrain among others. The dataset was conceived in order to provide a benchmark and contribute to the agricultural simultaneous localization and mapping (SLAM)/odometry and sensor fusion research. It contains synchronized readings of several sensors: wheel odometry, inertial measurement unit (IMU), stereo camera, and a Global Positioning System real-time kinematics (GPS-RTK) system. The dataset is publicly available from http://www.cifasis-conicet.gov.ar/robot/ .

The problem of exactly differentiating a signal with bounded second derivative is considered. A class of differentiators is proposed, which converge to the derivative of such a signal within a fixed, i.e., a finite and uniformly bounded convergence time. A tuning procedure is derived that allows to assign an arbitrary, predefined upper bound for this convergence time. It is furthermore shown that this bound can be made arbitrarily tight by appropriate tuning. The usefulness of the procedure is demonstrated by applying it to the well-known uniform robust exact differentiator, which is included in the considered class of differentiators as a special case.

Pearson's correlation coefficient (ρ) is the most commonly reported metric of the success of prediction in genomic selection (GS). However, in real breeding ρ may not be very useful for assessing the quality of the regression in the tails of the distribution, where individuals are chosen for selection. This research used 14 maize and 16 wheat data sets with different trait-environment combinations. Six different models were evaluated by means of a cross-validation scheme (50 random partitions each, with 90% of the individuals in the training set and 10% in the testing set). The predictive accuracy of these algorithms for selecting individuals belonging to the best α=10, 15, 20, 25, 30, 35, 40% of the distribution was estimated using Cohen's kappa coefficient (κ) and an ad hoc measure, which we call relative efficiency (RE), which indicates the expected genetic gain due to selection when individuals are selected based on GS exclusively. We put special emphasis on the analysis for α=15%, because it is a percentile commonly used in plant breeding programmes (for example, at CIMMYT). We also used ρ as a criterion for overall success. The algorithms used were: Bayesian LASSO (BL), Ridge Regression (RR), Reproducing Kernel Hilbert Spaces (RHKS), Random Forest Regression (RFR), and Support Vector Regression (SVR) with linear (lin) and Gaussian kernels (rbf). The performance of regression methods for selecting the best individuals was compared with that of three supervised classification algorithms: Random Forest Classification (RFC) and Support Vector Classification (SVC) with linear (lin) and Gaussian (rbf) kernels. Classification methods were evaluated using the same cross-validation scheme but with the response vector of the original training sets dichotomised using a given threshold. For α=15%, SVC-lin presented the highest κ coefficients in 13 of the 14 maize data sets, with best values ranging from 0.131 to 0.722 (statistically significant in 9 data sets) and the best RE in the same 13 data sets, with values ranging from 0.393 to 0.948 (statistically significant in 12 data sets). RR produced the best mean for both κ and RE in one data set (0.148 and 0.381, respectively). Regarding the wheat data sets, SVC-lin presented the best κ in 12 of the 16 data sets, with outcomes ranging from 0.280 to 0.580 (statistically significant in 4 data sets) and the best RE in 9 data sets ranging from 0.484 to 0.821 (statistically significant in 5 data sets). SVC-rbf (0.235), RR (0.265) and RHKS (0.422) gave the best κ in one data set each, while RHKS and BL tied for the last one (0.234). Finally, BL presented the best RE in two data sets (0.738 and 0.750), RFR (0.636) and SVC-rbf (0.617) in one and RHKS in the remaining three (0.502, 0.458 and 0.586). The difference between the performance of SVC-lin and that of the rest of the models was not so pronounced at higher percentiles of the distribution. The behaviour of regression and classification algorithms varied markedly when selection was done at different thresholds, that is, κ and RE for each algorithm depended strongly on the selection percentile. Based on the results, we propose classification method as a promising alternative for GS in plant breeding.

Global brain states are frequently placed within a unidimensional continuum by correlational studies, ranging from states of deep unconsciousness to ordinary wakefulness. An alternative is their multidimensional and mechanistic characterization in terms of different cognitive capacities, using computational models to reproduce the underlying neural dynamics. We explore this alternative by introducing a semi-empirical model linking regional activation and long-range functional connectivity in the different brain states visited during the natural wake-sleep cycle. Our model combines functional magnetic resonance imaging (fMRI) data, in vivo estimates of structural connectivity, and anatomically-informed priors to constrain the independent variation of regional activation. The best fit to empirical data was achieved using priors based on functionally coherent networks, with the resulting model parameters dividing the cortex into regions presenting opposite dynamical behavior. Frontoparietal regions approached a bifurcation from dynamics at a fixed point governed by noise, while sensorimotor regions approached a bifurcation from oscillatory dynamics. In agreement with human electrophysiological experiments, sleep onset induced subcortical deactivation with low correlation, which was subsequently reversed for deeper stages. Finally, we introduced periodic forcing of variable intensity to simulate external perturbations, and identified the key regions relevant for the recovery of wakefulness from deep sleep. Our model represents sleep as a state with diminished perceptual gating and the latent capacity for global accessibility that is required for rapid arousals. To the extent that the qualitative characterization of local dynamics is exhausted by the dichotomy between unstable and stable behavior, our work highlights how expanding the model parameter space can describe states of consciousness in terms of multiple dimensions with interpretations given by the choice of anatomically-informed priors.

Soybean yield is limited primarily by abiotic constraints. No transgenic soybean with improved abiotic stress tolerance is commercially available. We transformed soybean plants with genetic constructs able to express the sunflower transcription factor HaHB4, which confers drought tolerance to Arabidopsis and wheat. One line (b10H) carrying the sunflower promoter was chosen among three independent lines because it exhibited the best performance in seed yield, and was evaluated in the greenhouse and in 27 field trials in different environments in Argentina. In greenhouse experiments, transgenic plants showed increased seed yield under stress conditions together with greater epicotyl diameter, larger xylem area, and increased water use efficiency compared with controls. They also exhibited enhanced seed yield in warm and dry field conditions. This response was accompanied by an increase in seed number that was not compensated by a decrease in individual seed weight. Transcriptome analysis of plants from a field trial with maximum difference in seed yield between genotypes indicated the induction of genes encoding redox and heat shock proteins in b10H. Collectively, our results indicate that soybeans transformed with HaHB4 are expected to have a reduced seed yield penalty when cultivated in warm and dry conditions, which constitute the best target environments for this technology.

Proton transfer reaction-mass spectrometry (PTR-MS), a direct injection mass spectrometric technique based on an efficient implementation of chemical ionisation, allows for fast and high-sensitivity monitoring of volatile organic compounds (VOCs). The first implementations of PTR-MS, based on quadrupole mass analyzers (PTR-Quad-MS), provided only the nominal mass of the ions measured and thus little chemical information. To partially overcome these limitations and improve the analytical capability of this technique, the coupling of proton transfer reaction ionisation with a time-of-flight mass analyser has been recently realised and commercialised (PTR-TOF-MS). Here we discuss the very first application of this new instrument to agro-industrial problems and dairy science in particular. As a case study, we show here that the rapid PTR-TOF-MS fingerprinting coupled with data-mining methods can quickly verify whether the storage condition of the milk affects the final quality of cheese and we provide relevant examples of better compound identification in comparison with the previous PTR-MS implementations. In particular, 'Trentingrana' cheese produced by four different procedures for milk storage are compared both in the case of winter and summer production. It is indeed possible to set classification models with low prediction errors and to identify the chemical formula of the ion peaks used for classification, providing evidence of the role that this novel spectrometric technique can play for fundamental and applied agro-industrial themes.

We have studied the magnetic properties at room temperature of Fe1−xGax (FeGa) epitaxial thin films grown on ZnSe/GaAs(100) for 0.14≤x≤0.29 range concentration, and film thicknesses, d = 36 and 72 nm. The study was performed by means of magnetometric measurements and magnetic force microscopy scans. Increasing x promotes the loss of the four-fold magnetic-crystalline anisotropy associated to an Fe-like behavior, which is lost completely above x = 0.20. Stripe domains with rotatable anisotropy are observed even in samples in which the theoretical conditions for stripe appearance are not completely fulfilled. An unexpected “saw-tooth” stripe structure has been found under certain conditions.

BACKGROUND: In living organisms, small heat shock proteins (sHSPs) are triggered in response to stress situations. This family of proteins is large in plants and, in the case of tomato (Solanum lycopersicum), 33 genes have been identified, most of them related to heat stress response and to the ripening process. Transcriptomic and proteomic studies have revealed complex patterns of expression for these genes. In this work, we investigate the coregulation of these genes by performing a computational analysis of their promoter architecture to find regulatory motifs known as heat shock elements (HSEs). We leverage the presence of sHSP members that originated from tandem duplication events and analyze the promoter architecture diversity of the whole sHSP family, focusing on the identification of HSEs. RESULTS: We performed a search for conserved genomic sequences in the promoter regions of the sHSPs of tomato, plus several other proteins (mainly HSPs) that are functionally related to heat stress situations or to ripening. Several computational analyses were performed to build multiple sequence motifs and identify transcription factor binding sites (TFBS) homologous to HSF1AE and HSF21 in Arabidopsis. We also investigated the expression and interaction of these proteins under two heat stress situations in whole tomato plants and in protoplast cells, both in the presence and in the absence of heat shock transcription factor A2 (HsfA2). The results of these analyses indicate that different sHSPs are up-regulated depending on the activation or repression of HsfA2, a key regulator of HSPs. Further, the analysis of protein-protein interaction between the sHSP protein family and other heat shock response proteins (Hsp70, Hsp90 and MBF1c) suggests that several sHSPs are mediating alternative stress response through a regulatory subnetwork that is not dependent on HsfA2. CONCLUSIONS: Overall, this study identifies two regulatory motifs (HSF1AE and HSF21) associated with the sHSP family in tomato which are considered genomic HSEs. The study also suggests that, despite the apparent redundancy of these proteins, which has been linked to gene duplication, tomato sHSPs showed different up-regulation and different interaction patterns when analyzed under different stress situations.

In this paper, a new monitoring system is proposed by connecting different research areas, such as statistical monitoring, as well as knowledge-based and history-based systems. Tools such as adaptive principal components analysis (APCA), fuzzy-logic (FL) methods, and artificial neural network (ANN) methods are integrated to develop an efficient fault detection, isolation, and estimation (FDIE) system, especially for large chemical plants. It is capable of detecting, classifying, and estimating several faulty process elements. The information given by this new monitoring system is able to support the proper decisions for connecting and transforming an existing decentralized control strategy to a fault-tolerant method, based on an on-line reconfiguration. Thus, the obtained FDIE system is a valuable tool that is able to improve the overall performance of large and complex nonlinear controlled plants. In this case, inherent faults in sensors and actuators are analyzed. The FDIE system is tested for single as well as sequential abnormal events on a pulp mill benchmark, which is one of the biggest processes in the fault-tolerant control (FTC) that is integrated into the FDIE areas analyzed in the literature. A complete set of simulation results, evaluated by different indexes, together with cost analysis about the process operational profits with and without an FDIE system, are used here, to demonstrate the effectiveness of the proposed methodology.

The autosomal gene Mst77F of Drosophila melanogaster is essential for male fertility. In 2010, Krsticevic et al. (Genetics 184: 295-307) found 18 Y-linked copies of Mst77F ("Mst77Y"), which collectively account for 20% of the functional Mst77F-like mRNA. The Mst77Y genes were severely misassembled in the then-available genome assembly and were identified by cloning and sequencing polymerase chain reaction products. The genomic structure of the Mst77Y region and the possible existence of additional copies remained unknown. The recent publication of two long-read assemblies of D. melanogaster prompted us to reinvestigate this challenging region of the Y chromosome. We found that the Illumina Synthetic Long Reads assembly failed in the Mst77Y region, most likely because of its tandem duplication structure. The PacBio MHAP assembly of the Mst77Y region seems to be very accurate, as revealed by comparisons with the previously found Mst77Y genes, a bacterial artificial chromosome sequence, and Illumina reads of the same strain. We found that the Mst77Y region spans 96 kb and originated from a 3.4-kb transposition from chromosome 3L to the Y chromosome, followed by tandem duplications inside the Y chromosome and invasion of transposable elements, which account for 48% of its length. Twelve of the 18 Mst77Y genes found in 2010 were confirmed in the PacBio assembly, the remaining six being polymerase chain reaction-induced artifacts. There are several identical copies of some Mst77Y genes, coincidentally bringing the total copy number to 18. Besides providing a detailed picture of the Mst77Y region, our results highlight the utility of PacBio technology in assembling difficult genomic regions such as tandemly repeated genes.

BACKGROUND: The search for cluster structure in microarray datasets is a base problem for the so-called "-omic sciences". A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that is not shaped in the form of compact clouds of points, forming arbitrary shapes or paths embedded in a high-dimensional space, as could be the case of some gene expression datasets. RESULTS: In this work we introduce the Penalized k-Nearest-Neighbor-Graph (PKNNG) based metric, a new tool for evaluating distances in such cases. The new metric can be used in combination with most clustering algorithms. The PKNNG metric is based on a two-step procedure: first it constructs the k-Nearest-Neighbor-Graph of the dataset of interest using a low k-value and then it adds edges with a highly penalized weight for connecting the subgraphs produced by the first step. We discuss several possible schemes for connecting the different sub-graphs as well as penalization functions. We show clustering results on several public gene expression datasets and simulated artificial problems to evaluate the behavior of the new metric. CONCLUSIONS: In all cases the PKNNG metric shows promising clustering results. The use of the PKNNG metric can improve the performance of commonly used pairwise-distance based clustering methods, to the level of more advanced algorithms. A great advantage of the new procedure is that researchers do not need to learn a new method, they can simply compute distances with the PKNNG metric and then, for example, use hierarchical clustering to produce an accurate and highly interpretable dendrogram of their high-dimensional data.