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This retrospective study evaluates the influence of an invasive strategy of urgent coronary revascularization on the in-hospital mortality of patients with acute myocardial infarction (AMI) complicated early by cardiogenic shock. Among 1,981 patients with AMI admitted to our institution from 1994 to 1997, 162 patients (8.2%) developed cardiogenic shock unrelated to mechanical complications. The strategy of management was considered invasive if an urgent coronary angiography was indicated within 24 hours of symptom onset. Every other strategy was considered conservative. Fifty-seven patients who developed the shock late or after a revascularization procedure, or who died on admission, were excluded. The strategy was invasive in 73 patients (70%). Five of them died before angiography could be performed and 65 underwent angioplasty (success rate 72%). By univariate analysis the invasive strategy was associated with a lower mortality than conservative strategy (71% vs 91%, p = 0.03), but this association disappeared after adjustment for baseline characteristics. Older age, nonsmoking, and previous ischemic heart disease were independent predictors of mortality. In conclusion, we have failed to demonstrate that a strategy of urgent coronary revascularization within 24 hours of symptom onset for patients with AMI complicated by cardiogenic shock is independently associated with a lower in-hospital mortality. This strategy was limited by the high mortality within 1 hour of admission in patients with cardiogenic shock, the modest success rate of angioplasty in this setting, and the powerful influence of some adverse baseline characteristics on prognosis., Depto. de Medicina, Fac. de Medicina, TRUE, pub

Producción Científica, In this paper a family of non-autonomous scalar parabolic PDEs over a general compact and connected flow is considered. The existence or not of a neighbourhood of zero where the problems are linear has an influence on the methods used and on the dynamics of the induced skew-product semiflow. That is why two cases are distinguished: linear-dissipative and purely dissipative problems. In both cases, the structure of the global and pullback attractors is studied using principal spectral theory. Besides, in the purely dissipative setting, a simple condition is given, involving both the underlying linear dynamics and some properties of the nonlinear term, to determine the nontrivial sections of the attractor, FEDER Ministerio de Economía y Competitividad MTM2015-66330-P y RTI2018-096523-B-I00, Universidad de Valladolid PIP-TCESC-2020

Though the lateral frontal cortex is broadly implicated in cognitive control, functional MRI (fMRI) studies suggest fine-grained distinctions within this region. To examine this question electrophysiologically, we placed electrodes on the lateral frontal cortex in patients undergoing awake craniotomy for tumor resection. Patients performed verbal tasks with a manipulation of attentional switching, a canonical control demand. Power in the high gamma range (70e250 Hz) distinguished electrodes based on their location within a highresolution fMRI network parcellation of the frontal lobe. Electrodes within the canonical fronto-parietal control network showed increased power in the switching condition, a result absent in electrodes within default mode, language and somato-motor networks. High gamma results contrasted with spatially distributed power decreases in the beta range (12e30 Hz). These results confirm the importance of fine-scale functional distinctions within the human frontal lobe, and pave the way for increased precision of functional mapping in tumor surgeries.

Though the lateral frontal cortex is broadly implicated in cognitive control, functional MRI (fMRI) studies suggest fine-grained distinctions within this region. To examine this question electrophysiologically, we placed electrodes on the lateral frontal cortex in patients undergoing awake craniotomy for tumor resection. Patients performed verbal tasks with a manipulation of attentional switching, a canonical control demand. Power in the high gamma range (70–250 Hz) distinguished electrodes based on their location within a high-resolution fMRI network parcellation of the frontal lobe. Electrodes within the canonical fronto-parietal control network showed increased power in the switching condition, a result absent in electrodes within default mode, language and somato-motor networks. High gamma results contrasted with spatially distributed power decreases in the beta range (12–30 Hz). These results confirm the importance of fine-scale functional distinctions within the human frontal lobe, and pave the way for increased precision of functional mapping in tumor surgeries.

[EN] In this work, we study a lower bound of the condition number of a matrix by its combined matrix. In particular, we construct a special combined matrix in such a way that the sums of its columns are lower bounds of the condition number of the matrix. Cases for special matrices as unitary matrices are considered.

Wildfires are responsible for a substantial loss of forest ecosystem services globally and represent a major driving force of forest degradation across Latin American and the Caribbean (LAC). The detrimental effect of forest fires is particularly relevant in regions where fire has been historically absent or has rarely occurred. Nowadays, there is an increasing interest to promote and develop ecological restoration (ER) following fire. LAC constitutes a hotspot where work and interest in ER has been steadily increasing over the last decades, mainly due to the drive of a new generation of young and experienced ecologists and foresters. Despite the increasing attention in post-fire restoration in the region, there is a dearth of initiatives compiling and organizing all the available information on this topic. This work aims to address such constraint, providing current information on post-fire ER in LAC forests. After a brief contextualization of environmental and social consequences of wildfires, we collect and discuss recent advances on restoring degraded forests. From the conifer Mexican ecosystems to the Southern Patagonian evergreen forests, we look back over the last two decades (2000–2020) mainly discussing experiences of success and failure, as well as limitations of implementing approaches based on passive/natural restoration or active/assisted restoration. Furthermore, we also explore other aspects of the restoration process, including those related to social participation and community engagement (e.g. education in restored areas), the use of fire regulation and management to reduce fire risks and increase ecosystem resilience, educational aspects and intermediate approaches as agroforestry and silviculture practices. In the last sections, we identify three major categories of specific constraints that condition ER, including environmental limitations (biotic and abiotic factors), technical/management factors and the socio-economic challenge of restoration.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been successfully employed to understand the organisation of the human brain. Typically, the brain is parcellated into regions of interest (ROIs) and modelled as a graph where each ROI represents a node and association measures between ROI-specific blood-oxygen-level-dependent (BOLD) time series are edges. Recently, graph neural networks (GNNs) have seen a surge in popularity due to their success in modelling unstructured relational data. The latest developments with GNNs, however, have not yet been fully exploited for the analysis of rs-fMRI data, particularly with regards to its spatio-temporal dynamics. In this paper, we present a novel deep neural network architecture which combines both GNNs and temporal convolutional networks (TCNs) in order to learn from both the spatial and temporal components of rs-fMRI data in an end-to-end fashion. In particular, this corresponds to intra-feature learning (i.e., learning temporal dynamics with TCNs) as well as inter-feature learning (i.e., leveraging interactions between ROI-wise dynamics with GNNs). We evaluate our model with an ablation study using 35,159 samples from the UK Biobank rs-fMRI database, as well as in the smaller Human Connectome Project (HCP) dataset, both in a unimodal and in a multimodal fashion. We also demonstrate that out architecture contains explainability-related features which easily map to realistic neurobiological insights. We suggest that this model could lay the groundwork for future deep learning architectures focused on leveraging the inherently and inextricably spatio-temporal nature of rs-fMRI data.

Gambling in the modern era is pervasive owing to the variety of gambling opportunities available, including those that use technology (eg, online applications on smartphones). Although many people gamble recreationally without undue negative effects, a sizeable subset of individuals develop disordered gambling, which is associated with marked functional impairment including other mental health problems, relationship problems, bankruptcy, suicidality, and criminality. The National UK Research Network for Behavioural Addictions (NUK-BA) was established to promote understanding of, research into, and treatments for behavioural addictions including gambling disorder, which is the only formally recognised behavioural addiction. In this Health Policy paper, we outline the status of research and treatment for disordered gambling in the UK (including funding issues) and key research that should be conducted to establish the magnitude of the problem, vulnerability and resilience factors, the underlying neurobiology, long-term consequences, and treatment opportunities. In particular, we emphasise the need to: (1) conduct independent longitudinal research into the prevalence of disordered gambling (including gambling disorder and at-risk gambling), and gambling harms, including in vulnerable and minoritised groups; (2) select and refine the most suitable pragmatic measurement tools; (3) identify predictors (eg, vulnerability and resilience markers) of disordered gambling in people who gamble recreationally, including in vulnerable and minoritised groups; (4) conduct randomised controlled trials on psychological interventions and pharmacotherapy for gambling disorder; (5) improve understanding of the neurobiological basis of gambling disorder, including impulsivity and compulsivity, genetics, and biomarkers; and (6) develop clinical guidelines based on the best contemporary research evidence to guide effective clinical interventions. We also highlight the need to consider what

In the 1970s a novel branch of statistics emerged focusing its effort on the selection of a function for the pattern recognition problem that would fulfill a relationship between the quality of the approximation and its complexity. This theory is mainly devoted to problems of estimating dependencies in the case of limited sample sizes, and comprise all the empirical out-of sample generalization approaches; e.g. cross validation (CV). In this paper a data-driven approach based on concentration inequalities is designed for testing competing hypothesis or comparing different models. In this sense we derive a Statistical Agnostic (non-parametric) Mapping (SAM) for neuroimages at voxel or regional levels which is able to: relieve the problem of instability with limited sample sizes when estimating the actual risk via CV; and provide an alternative way of Family-wise error (FWE) corrected (p)-value maps in inferential statistics for hypothesis testing. Using several neuroimaging datasets (containing large and small effects) and random task group analyses to compute empirical familywise error rates, this novel framework resulted in a model validation method for small samples over dimension ratios, and a less-conservative procedure than FWE (p)-value correction to determine the significance maps from the inferences made using small upper bounds of the actual risk.

Producción Científica, In this paper a family of non-autonomous scalar parabolic PDEs over a general compact and connected flow is considered. The existence or not of a neighbourhood of zero where the problems are linear has an influence on the methods used and on the dynamics of the induced skew-product semiflow. That is why two cases are distinguished: linear-dissipative and purely dissipative problems. In both cases, the structure of the global and pullback attractors is studied using principal spectral theory. Besides, in the purely dissipative setting, a simple condition is given, involving both the underlying linear dynamics and some properties of the nonlinear term, to determine the nontrivial sections of the attractor, FEDER Ministerio de Economía y Competitividad MTM2015-66330-P y RTI2018-096523-B-I00, Universidad de Valladolid PIP-TCESC-2020

Background: Genetic risk is thought to drive clinical variation on a spectrum of schizophrenia-like traits, but the underlying changes in brain structure that mechanistically link genomic variation to schizotypal experience and behavior are unclear. Methods: We assessed schizotypy using a self-reported questionnaire and measured magnetization transfer as a putative microstructural magnetic resonance imaging marker of intracortical myelination in 68 brain regions in 248 healthy young people (14–25 years of age). We used normative adult brain gene expression data and partial least squares analysis to find the weighted gene expression pattern that was most colocated with the cortical map of schizotypy-related magnetization. Results: Magnetization was significantly correlated with schizotypy in the bilateral posterior cingulate cortex and precuneus (and for disorganized schizotypy, also in medial prefrontal cortex; all false discovery rate–corrected ps < .05), which are regions of the default mode network specialized for social and memory functions. The genes most positively weighted on the whole-genome expression map colocated with schizotypy-related magnetization were enriched for genes that were significantly downregulated in two prior case-control histological studies of brain gene expression in schizophrenia. Conversely, the most negatively weighted genes were enriched for genes that were transcriptionally upregulated in schizophrenia. Positively weighted (downregulated) genes were enriched for neuronal, specifically interneuronal, affiliations and coded a network of proteins comprising a few highly interactive “hubs” such as parvalbumin and calmodulin. Conclusions: Microstructural magnetic resonance imaging maps of intracortical magnetization can be linked to both the behavioral traits of schizotypy and prior histological data on dysregulated gene expression in schizophrenia.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) comorbid with sleep disturbances can produce profound disruption in daily life and negatively impact quality of life of both the child and the family. However, the temporal relationship between ADHD and sleep impairment is unclear, as are underlying common brain mechanisms. METHODS: This study used data from the Quebec Longitudinal Study of Child Development (n = 1601, 52% female) and the Adolescent Brain Cognitive Development Study (n = 3515, 48% female). Longitudinal relationships between symptoms were examined using cross-lagged panel models. Gray matter volume neural correlates were identified using linear regression. The transcriptomic signature of the identified brain-ADHD-sleep relationship was characterized by gene enrichment analysis. Confounding factors, such as stimulant drugs for ADHD and socioeconomic status, were controlled for. RESULTS: ADHD symptoms contributed to sleep disturbances at one or more subsequent time points in both cohorts. Lower gray matter volumes in the middle frontal gyrus and inferior frontal gyrus, amygdala, striatum, and insula were associated with both ADHD symptoms and sleep disturbances. ADHD symptoms significantly mediated the link between these structural brain abnormalities and sleep dysregulation, and genes were differentially expressed in the implicated brain regions, including those involved in neurotransmission and circadian entrainment. CONCLUSIONS: This study indicates that ADHD symptoms and sleep disturbances have common neural correlates, including structural changes of the ventral attention system and frontostriatal circuitry. Leveraging data from large datasets, these results offer new mechanistic insights into this clinically important relationship between ADHD and sleep impairment, with potential implications for neurobiological models and future therapeutic directions.

BACKGROUND: In 2009, the National Institute of Mental Health launched the Research Domain Criteria, an attempt to move beyond diagnostic categories and ground psychiatry within neurobiological constructs that combine different levels of measures (e.g., brain imaging and behavior). Statistical methods that can integrate such multimodal data, however, are often vulnerable to overfitting, poor generalization, and difficulties in interpreting the results. METHODS: We propose an innovative machine learning framework combining multiple holdouts and a stability criterion with regularized multivariate techniques, such as sparse partial least squares and kernel canonical correlation analysis, for identifying hidden dimensions of cross-modality relationships. To illustrate the approach, we investigated structural brain–behavior associations in an extensively phenotyped developmental sample of 345 participants (312 healthy and 33 with clinical depression). The brain data consisted of whole-brain voxel-based gray matter volumes, and the behavioral data included item-level self-report questionnaires and IQ and demographic measures. RESULTS: Both sparse partial least squares and kernel canonical correlation analysis captured two hidden dimensions of brain–behavior relationships: one related to age and drinking and the other one related to depression. The applied machine learning framework indicates that these results are stable and generalize well to new data. Indeed, the identified brain–behavior associations are in agreement with previous findings in the literature concerning age, alcohol use, and depression-related changes in brain volume. CONCLUSIONS: Multivariate techniques (such as sparse partial least squares and kernel canonical correlation analysis) embedded in our novel framework are promising tools to link behavior and/or symptoms to neurobiology and thus have great potential to contribute to a biologically grounded definition of psychiatric disorders.

The importance of branding in marketing strategies makes it essential to understand the elements that give value-added to the brands. The very nature of ecological brands adds value to them. Knowing the underlying emotions of the elements that add value to brands can justify the benefit of applying neu- romarketing to branding. The objective of this study is to justify the use of neuromarketing tools in the ecological branding strategy by analysing the existing literature on branding, ecological branding and neuromarketing. The existing relationship between the elements that give value-added to the brand and the emotional variables that neuromarketing measures, justifies the use of neuromarketing tools in the ecological branding strategy.

In this paper we obtain a detailed description of the global and cocycle attractors for the skew-product semiflows induced by the mild solutions of a family of scalar linear-dissipative parabolic problems over a minimal and uniquely ergodic flow. We consider the case of null upper Lyapunov exponent for the linear part of the problem. Then, basically two different types of attractors can appear, depending on whether the linear coefficient in the equations determines a bounded or an unbounded associated real cocycle. In the first case (the one for periodic equations), the structure of the attractor is simple, whereas in the second case (which happens in aperiodic equations), the attractor is a pinched set with a complicated structure. We describe situations in which the attractor is chaotic in measure in the sense of Li-Yorke. Besides, we obtain a non-autonomous discontinuous pitchfork bifurcation scenario for concave equations, applicable for instance to a linear-dissipative version of the Chafee-Infante equation.

Replication-defective (RD) recombinant simian virus 40 (SV40)-based gene delivery vectors hold a great potential for clinical applications because of their presumed non-immunogenicity and capacity to induce immune tolerance to the transgene products in humans. However, the clinical use of SV40 vectors has been hampered by the lack of a packaging cell line that produces replication-competent (RC) free SV40 particles in the vector production process. To solve this problem, we have adapted the current SV40 vector genome used for the production of vector particles and generated a novel Vero-based packaging cell line named SuperVero that exclusively expresses the SV40 large T antigen. SuperVero cells produce similar numbers of SV40 vector particles compared to the currently used packaging cell lines, albeit in the absence of contaminating RC SV40 particles. Our unique SV40 vector platform named SVac paves the way to clinically test a whole new generation of SV40-based therapeutics for a broad range of important diseases.

[EN] One of the problems for water supply systems with intermittent supply is the peak flow produced at some hours of the day, which is usually much larger than that in a system with continuous supply. The main consequence is the reduction of pressure and flow at the ends or highest points of the system network. This, in turn, generates inequity in water supply and complaints from users. To reduce the peak flow, some sectors of the system must be assigned a different supply schedule. As a result, the supply curve is modified, and the peak flow is reduced. This reorganization seeks some optimal allocation schedule and must be based on various quantitative and qualitative technical criteria. This paper hybridizes integer linear programming and multi-criteria analysis to contribute with a solution proposal to the technical management of intermittent water supply systems, which provides short-term results and requires little investment for implementation. This solution does not seek perpetuating intermittent water supply. On the contrary, this methodology can be a useful tool in gradual transition processes from intermittent to continuous supply. (C) 2016 Elsevier B.V. All rights reserved.

[EN] The most important factor in planning and operating water distribution systems is satisfying consumer demand. This means continuously providing users with quality water in adequate volumes at reasonable pressure, thus ensuring reliable water distribution. In recent years, the application of statistical, machine learning, and artificial intelligence methodologies has been fostered for water demand forecasting. However, there is still room for improvement; and new challenges regarding on-line predictive models for water demand have appeared. This work proposes applying support vector regression, as one of the currently better machine learning options for short-term water demand forecasting, to build a base prediction. On this model, a Fourier time series process is built to improve the base prediction. This addition produces a tool able to eliminate many of the errors and much of the bias inherent in a fixed regression structure when responding to new incoming time series data. The final hybrid process is validated using demand data from a water utility in Franca, Brazil. Our model, being a near real-time model for water demand, may be directly exploited in water management decision-making processes. (C) 2016 Elsevier B.V. All rights reserved.

[EN] Hybrid models have been used in many engineering applications in order to find better solutions and to reduce project costs. In this paper a methodology for the optimal design of collective working shift irrigation systems is proposed. The proposal is based on a hybrid model of optimization, which includes Linear Programming (LP) and Genetic Algorithms (GA). The method is applied to an irrigation network to check its effectiveness to minimize the total investment costs for pipelines. The results are compared to another hybrid model for optimization, which is based on Nonlinear Programming (NLP) and GA. The advantage of the developed method is a more cost effective design using discrete pipeline diameters. (C) 2016 Published by Elsevier Ltd.

[EN] Management of existing systems can be interpreted as sets of decisions to make regarding pumps and valves to create hydraulic conditions able to satisfy the demand without operational problems such as pressures lower or higher than the normative pressure values. However, among the large number of combinations, some of them manage to reduce energy consumption, by finding the best operating point for pumps, and also water losses, by finding the best operating point for pressure reducing valves (PRV). Several works may be found in the literature using recent and advanced optimization techniques to define pump and valve operation. However, the processing time to define operational rules is a limiting factor for real time decision-making. Taking into account the need to improve the models in terms of optimal rules to apply in near real-time operations, this work presents a hybrid model (simulator + optimizer) to find pump speeds and PRV set points, aiming at combining energy savings with pressure control while reducing water losses. PSO is applied as the main optimization algorithm, which can also work in cooperation with other bio-inspired concepts to deploy an effective and fast search algorithm. The results allow comparisons with other techniques and show the ability of PSO to find an optimal point of operation

Replication-defective (RD) recombinant simian virus 40 (SV40)-based gene delivery vectors hold a great potential for clinical applications because of their presumed non-immunogenicity and capacity to induce immune tolerance to the transgene products in humans. However, the clinical use of SV40 vectors has been hampered by the lack of a packaging cell line that produces replication-competent (RC) free SV40 particles in the vector production process. To solve this problem, we have adapted the current SV40 vector genome used for the production of vector particles and generated a novel Vero-based packaging cell line named SuperVero that exclusively expresses the SV40 large T antigen. SuperVero cells produce similar numbers of SV40 vector particles compared to the currently used packaging cell lines, albeit in the absence of contaminating RC SV40 particles. Our unique SV40 vector platform named SVac paves the way to clinically test a whole new generation of SV40-based therapeutics for a broad range of important diseases.

Fondos FEDER, Objectives: To analyze the impact of late presentation (LP) on overall mortality and causes of death and describe LP trends and risk factors (2004-2013). Methods: Cox models and logistic regression were used to analyze data from a nation-wide cohort in Spain. LP is defined as being diagnosed when CD4 < 350 cells/ml or AIDS. Results: Of 7165 new HIV diagnoses, 46.9% (CI95%:45.7-48.0) were LP, 240 patients died. First-year mortality was the highest (aHRLP.vs.nLP = 10.3[CI95%:5.5-19.3]); between 1 and 4 years post-diagnosis, aHRLP.vs.nLP = 1.9(1.2-3.0); and >4 years, aHRLP.vs.nLP = 1.5(0.7-3.1). First-year's main cause of death was HIV/AIDS (73%); and malignancies among those surviving >4 years (32%). HIV/AIDS-related deaths were more likely in LP (59.2% vs. 25.0%; p < 0.001). LP declined from 55.9% (2004-05) to 39.4% (2012-13), and reduced in 46.1% in men who have sex with men (MSM) and 37.6% in heterosexual men, but increased in 22.6% in heterosexual women. Factors associated with LP: sex (ORMEN.vs.WOMEN = 1.4[1.2-1.7]); age (OR31-40.vs.<30 = 1.6[1.4-1.8], OR41-50.vs.<30 = 2.2[1.8-2.6], OR>50.vs.<30 = 3.6[2.9-4.4]); behavior (ORInjectedDrugUse.vs.MSM = 2.8[2.0-3.8]; ORHeterosexual.vs.MSM = 2.2[1.7-3.0]); education (ORPrimaryEducation.vs.University = 1.5[1.1-2.0], ORLowerSecondary.vs.University = 1.3[1.1-1.5]); and geographical origin (ORSub-Saharan.vs.Spain = 1.6[1.3-2.0], ORLatin-American.vs.Spain = 1.4[1.2-1.8]). Conclusions: LP is associated with higher mortality, especially short-term- and HIV/AIDS-related mortality. Mid-term-, but not long-term mortality, remained also higher in LP than nLP. LP decreased in MSM and heterosexual men, not in heterosexual women. The groups most affected by LP are low educated, non-Spanish and heterosexual women., European Commission, Ministerio de Economía y Competitividad (España), Instituto de Salud Carlos III (España), Depto. de Medicina, Fac. de Medicina, TRUE, pub

The flexibility introduced by evolutionary algorithms (EAs) has allowed the use of virtually arbitrary objective functions and constraints even when evaluations require, as for real-world problems, running complex mathematical and/or procedural simulations of the systems under analysis. Even so, EAs are not a panacea. Traditionally, the solution search process has been totally oblivious of the specific problem being solved, and optimization processes have been applied regardless of the size, complexity, and domain of the problem. In this paper, we justify our claim that far-reaching benefits may be obtained from more directly influencing how searches are performed. We propose using data mining techniques as a step for dynamically generating knowledge that can be used to improve the efficiency of solution search processes. In this paper, we use Kohonen SOMs and show an application for a well-known benchmark problem in the water distribution system design literature. The result crystallizes the conceptual rules for the EA to apply at certain stages of the evolution, which reduces the search space and accelerates convergence. (C) 2015 Elsevier B.V. All rights reserved.

Producción Científica, We determine sufficient conditions for uniform and strict persistence in the case of skew-product semiflows generated by solutions of non-autonomous families of cooperative systems of ODEs or delay FDEs in terms of the principal spectrums of some associated linear skew-product semiflows which admit a continuous separation. Our conditions are also necessary in the linear case. We apply our results to a noncooperative almost periodic Nicholson system with a patch structure, whose persistence turns out to be equivalent to the persistence of the linearized system along the null solution., MINECO/FEDER MTM2015-66330

Ni nanoparticles have been selectively loaded inside or outside commercial carbon nanotubes by the wet impregnation method, and their catalytic performance on the conversion of guaiacol in batch reactor at 5 MPa and 573 K has been studied. Different characterization techniques were used to investigate the effect of confinement in nanotubes. The catalysts showed different selectivity to cyclohexene compared to cyclohexane was enhanced when the nanoparticles were loaded inside. This result was attributed to the different adsorption mode of the molecule inside the nanotubes.

In this work a technique to improve the condition number si of a simple eigenvalue lambda(i) of a matrix A is an element of C-nxn is given. This technique obtains a rank one updated matrix that is similar to A with the eigenvalue condition number of lambda(i) equal to one. More precisely, the similar updated matrix A + v(i)q*, where Av(i) = lambda(i)v(i) and q is a fixed vector, has s(i) = 1 and the remaining condition numbers are at most equal to the corresponding initial condition numbers. Moreover an expression to compute the vector q, using only the eigenvalue lambda(i) and its eigenvector v(i), is given. (C) 2016 Elsevier Ltd. All rights reserved.

Neuroligins (NLs) are cell-adhesion proteins that regulate synapse formation and function. Neuroligin 1 (NL1) promotes the formation of glutamatergic synapses and mediates long-term potentiation in mouse models. Thus, altered NL1 function could mediate the synaptic and memory deficits associated with Alzheimer's disease (AD). Here, we describe a frameshift mutation, c.875_876insTT, in the neuroligin 1 gene (NLGN1) in a patient with AD and familial history of AD. The insertion generates a premature stop codon in the extracellular domain of NL1 (p.Thr271fs). Expression of mutant NL1 shows accumulation of truncated NL1 proteins in the endoplasmic reticulum. In hippocampal neurons, the p.Thr271fs mutation abolishes the ability of NL1 to promote the formation of glutamatergic synapses. Our data support a role for inactivating mutations in NLGN1 in AD. Previous studies have reported rare mutations in X-linked NLGNL3 and NLGNL4 genes in patients with autism, which result in the inactivation of the mutant alleles. Therefore, together with a role in neurodevelopmental disorders, altered NL function could underlie the molecular mechanisms associated with brain diseases in the elderly.

When duplex stainless steels are heated, they can become sensitised and intermetallic phases can appear. In this work, samples of duplex stainless steel, UNS 1.4462, have been heated (850 degrees C for 1 and 2 h) in a heating unit that can be accommodated to a confocal microscope in order to study the morphological changes in-situ. The electrochemical behaviour of the samples has been analysed by means of conventional and localised electrochemical techniques. According to the results, there is a general decrease in the steel resistance to localised corrosion; this decrease can be related to defects in the formed passive film.

Decision making (DM) processes are becoming increasingly complex. The reasons are manifold. DM usually involves many aspects; some are purely technical, while others are subjective and derived from social, political, and environmental factors, among others. As a result, they involve items that are not easily comparable under the same units of measurement. Problems are made even more complex by the fact that current governance processes tend to involve all the stakeholders in the DM process. In this paper we consider the AHP methodology (analytic hierarchy process), which is used to build consistent aggregate results from data provided by decision makers. As some of the actors involved may not be completely familiar with all the criteria under consideration, it is common that the body of opinion, expressed in terms of pairwise comparison, is incomplete. To overcome this weakness, we propose a framework that enables users to provide data on their preferences in a partial and/or incomplete way and at different times. This article is an advance towards a dynamic model of AHP. The authors have addressed the problem of adding a new criterion or deleting obsolete criteria. Here, we address the consistent completion of a reciprocal matrix as a mechanism to obtain a consistent body of opinion issued in an incomplete manner by a specific actor. This feature is incorporated into a process of linearization previously introduced by the authors, which is concisely presented. Finally, we provide an application for leakage control in a water supply company. The adoption of suitable control leakage policies in water supply is a problem of enormous interest in the water industry, particularly in urban hydraulics.

The conversion of guaiacol, a lignin model compound, over SiO2-supported Re metal, oxide and sulfide catalysts in a batch reactor at 300 °C and 5 MPa of hydrogen pressure were studied. The oxide catalyst was prepared by wet impregnation with a loading of 2.5 atom of Re per nm2 of support. The sulfide and metal catalysts were prepared by sulfidation and reduction, respectively, of the Re oxide catalyst. The catalysts were characterized by N2 adsorption, X-ray photoelectron spectroscopy (XPS), temperature programmed reduction (TPR) and transmission electron microscopy (TEM). Catalytic activity was correlated with XPS analysis of the post-reaction catalysts. The ReOx/SiO2 catalyst was the most active and selective towards deoxygenated products, and this was attributed to increase in the concentration of metal oxide defect sites (due to in situ partial reduction of the oxide catalyst during the reaction). This result reveals the high potential of ReOx/SiO2 catalyst for bio-oil upgrading because it shows, among other things, that it is unnecessary to add undesirable sulfiding agent to maintain catalytic activity.

Achieving consistency in pair-wise comparisons between decision elements given by experts or stakeholders is of paramount importance in decision-making based on the AHP methodology. Several alternatives to improve consistency have been proposed in the literature. The linearization method (Benitez et al., 2011 [10]), derives a consistent matrix based on an original matrix of comparisons through a suitable orthogonal projection expressed in terms of a Fourier-like expansion. We propose a formula that provides in a very simple manner the consistent matrix closest to a reciprocal (inconsistent) matrix. In addition, this formula is computationally efficient since it only uses sums to perform the calculations. A corollary of the main result shows that the normalized vector of the vector, whose components are the geometric means of the rows of a comparison matrix, gives the priority vector only for consistent matrices. (C) 2014 Elsevier Inc. All rights reserved.

[EN] Data mining solutions can be applied in combination with evolutionary algorithms to extract relevant information from solution spaces analyzed during optimization processes regarding water distribution system (WDS) design. Firstly, results from data mining can be introduced into the evolutionary algorithms to guide the search of solutions. Secondly, data mining techniques can be used not only to help explore the population of potential solutions but also to exploit data regarding the behavior of the WDS under different work conditions. As a result, applications can be developed for supporting decision making regarding WDS models in both offline and online contexts.

The evolution of technologies of informatics and telecommunications has improved significantly the way water distribution systems can be managed. The use of quasi real-time information is becoming a desired standard in many water utilities. Nevertheless, tools for water distribution system analysis and for supporting decision making haveńt been integrated with the same speed. This research uses cloud computing capabilities for supporting analysis and decision making in water distribution systems. It includes the development of evolutionary algorithms ready to run in a cloud environment for searching the best possible decisions considering a set of objectives. The evaluation of solutions profits from the scalability of the cloud for making faster the solution search process.

[EN] This document shows some aspects regarding water leakage time propagation through the underground, in controlled laboratory conditions, using a drilled PVC pipe, through the interpretation of GPR images. The extraction of relevant patterns is possible since GPR interpretations allow to obtain surfaces and volumes that can be easily identified by non-highly specialized personnel in the analysis of raw data. The results of this study are promising since they help promote the realization of techniques able to validate models for generation, distribution and prediction of water leaks in water supply systems using GPR as a non-destructive method.

Sectorization is a process aimed at efficiently managing Water Supply Networks (WSNs). There are many large WSNs where the number of sectors into which they might be subdivided is initially unknown. Our method uses concepts derived from the Social Network Theory (SNT) such as centrality and community detection to obtain such subdivisions. The output is then evaluated through energy criteria. This methodology entails two very important benefits over other methods previously proposed. Firstly, it is applicable to WSNs depending on a network trunk and, secondly, it considers the technical know-how of the staff of the water utility.

Over-exploitation and pollution have been identified as the main problems facing the Silao-Romita aquifer in Guanajuato, Mexico. The objective of this paper is to analyze the current situation, characterized by a clear lack of legislative enforcement, dispersion of competences, and scarcity of economic resources, in order to establish a new prioritization of action plans, and choose from among three specific management options. One of the main challenges when addressing these problems in a holistic manner is the conflicting viewpoints of the sectors involved. As each stakeholder has a different perception, there is a clear need for appropriate mechanisms to reach a consensus in decision-making. To achieve the objective, we use the Analytic Hierarchy Process (AHP), because of its flexibility and the availability of mathematical axiomatic principles and techniques to obtain group preferences and priorities. In addition, we use several tools developed by the authors to obtain consistency, streamline the trade-off between stakeholder know-how and synthetic consistency, and consistently complete partial judgments given by some of the stakeholders. The problem of obtaining a consensus among the actors involved regarding criteria and alternatives is also considered. The obtained results are intended to serve as guidelines for conducting priority actions to help solve the general problem of the study area, and to identify the management model that best meets the needs of the aquifer, according to the actors involved. (C) 2013 Elsevier Ltd. All rights reserved.

In this paper a combination of the multi-agent paradigm and a very well known clustering technique is used for unsupervised classification of subsoil characteristics working on a collection of ground penetrating radar (GPR) survey files. The main objective is to assess the feasibility of extracting features and patterns from radargrams. By optimizing both the field work and the interpretation of the raw images our target is to obtain visualizations that are automatic, fast, and reliable so to suitably assess the characteristics of the prospected areas and extract relevant information. The architecture of the system may be split into three interrelated processes: (a) pre-processing, (b) hierarchical agglomerative clustering, and (c) retrieval and visualization. The proposed system shows the viability of arranging GPR data from survey files into clusters, thus reducing the amount of information to be dealt with, while preserving its reliability. The system also helps characterize subsoil properties in a very natural and fast way, favors GPR files interpretation by non-highly qualified personnel, and does not require any assumptions about subsoil parameters. A powerful tool to analyze underground components in water supply systems is thus generated that acts in a non-destructive way and supports decision-making in water supply management.