Nervous glucose sensing regulates postnatal β cell proliferation and glucose homeostasis.

How glucose sensing by the nervous system impacts the regulation of β cell mass and function during postnatal development and throughout adulthood is incompletely understood. Here, we studied mice with inactivation of glucose transporter 2 (Glut2) in the nervous system (NG2KO mice). These mice displayed normal energy homeostasis but developed late-onset glucose intolerance due to reduced insulin secretion, which was precipitated by high-fat diet feeding. The β cell mass of adult NG2KO mice was reduced compared with that of WT mice due to lower β cell proliferation rates in NG2KO mice during the early postnatal period. The difference in proliferation between NG2KO and control islets was abolished by ganglionic blockade or by weaning the mice on a carbohydrate-free diet. In adult NG2KO mice, first-phase insulin secretion was lost, and these glucose-intolerant mice developed impaired glucagon secretion when fed a high-fat diet. Electrophysiological recordings showed reduced parasympathetic nerve activity in the basal state and no stimulation by glucose. Furthermore, sympathetic activity was also insensitive to glucose. Collectively, our data show that GLUT2-dependent control of parasympathetic activity defines a nervous system/endocrine pancreas axis that is critical for β cell mass establishment in the postnatal period and for long-term maintenance of β cell function.

A new aggregation method for strategic decision making and its application in assignment theory

A new aggregation method for decision making is presented by using induced aggregation operators and the index of maximum and minimum level. Its main advantage is that it can assess complex reordering processes in the aggregation that represent complex attitudinal characters of the decision maker such as psychological or personal factors. A wide range of properties and particular cases of this new approach are studied. A further generalization by using hybrid averages and immediate weights is also presented. The key issue in this approach against the previous model is that we can use the weighted average and the ordered weighted average in the same formulation. Thus, we are able to consider the subjective attitude and the degree of optimism of the decision maker in the decision process. The paper ends with an application in a decision making problem based on the use of the assignment theory.

The Uncertain generalized probabilistic weighted average and its application in the theory of expertons

A new model for dealing with decision making under risk by considering subjective and objective information in the same formulation is here presented. The uncertain probabilistic weighted average (UPWA) is also presented. Its main advantage is that it unifies the probability and the weighted average in the same formulation and considering the degree of importance that each case has in the analysis. Moreover, it is able to deal with uncertain environments represented in the form of interval numbers. We study some of its main properties and particular cases. The applicability of the UPWA is also studied and it is seen that it is very broad because all the previous studies that use the probability or the weighted average can be revised with this new approach. Focus is placed on a multi-person decision making problem regarding the selection of strategies by using the theory of expertons.

International union of basic and clinical pharmacology. XCI. structure, function, and pharmacology of acid-sensing ion channels and the epithelial Na+ channel.

The epithelial Na(+) channel (ENaC) and the acid-sensing ion channels (ASICs) form subfamilies within the ENaC/degenerin family of Na(+) channels. ENaC mediates transepithelial Na(+) transport, thereby contributing to Na(+) homeostasis and the maintenance of blood pressure and the airway surface liquid level. ASICs are H(+)-activated channels found in central and peripheral neurons, where their activation induces neuronal depolarization. ASICs are involved in pain sensation, the expression of fear, and neurodegeneration after ischemia, making them potentially interesting drug targets. This review summarizes the biophysical properties, cellular functions, and physiologic and pathologic roles of the ASIC and ENaC subfamilies. The analysis of the homologies between ENaC and ASICs and the relation between functional and structural information shows many parallels between these channels, suggesting that some mechanisms that control channel activity are shared between ASICs and ENaC. The available crystal structures and the discovery of animal toxins acting on ASICs provide a unique opportunity to address the molecular mechanisms of ENaC and ASIC function to identify novel strategies for the modulation of these channels by pharmacologic ligands.

Harvesting strategy and N fertilization influence ¹³⁴Cs uptake by forage plants

Selostus: Korjuuaika ja typpilannoitus vaikuttavat rehukasvien radiocesiumpitoisuuteen

The social disembedding of business theory and practice. A (neo-)institutional analysis of the homo economicus and corporate social responsibility, and the inherent responsibility of business scholars

Aim Structure of the Thesis In the first article, I focus on the context in which the Homo Economicus was constructed - i.e., the conception of economic actors as fully rational, informed, egocentric, and profit-maximizing. I argue that the Homo Economicus theory was developed in a specific societal context with specific (partly tacit) values and norms. These norms have implicitly influenced the behavior of economic actors and have framed the interpretation of the Homo Economicus. Different factors however have weakened this implicit influence of the broader societal values and norms on economic actors. The result is an unbridled interpretation and application of the values and norms of the Homo Economicus in the business environment, and perhaps also in the broader society. In the second article, I show that the morality of many economic actors relies on isomorphism, i.e., the attempt to fit into the group by adopting the moral norms surrounding them. In consequence, if the norms prevailing in a specific group or context (such as a specific region or a specific industry) change, it can be expected that actors with an 'isomorphism morality' will also adapt their ethical thinking and their behavior -for the 'better' or for the 'worse'. The article further describes the process through which corporations could emancipate from the ethical norms prevailing in the broader society, and therefore develop an institution with specific norms and values. These norms mainly rely on mainstream business theories praising the economic actor's self-interest and neglecting moral reasoning. Moreover, because of isomorphism morality, many economic actors have changed their perception of ethics, and have abandoned the values prevailing in the broader society in order to adopt those of the economic theory. Finally, isomorphism morality also implies that these economic actors will change their morality again if the institutional context changes. The third article highlights the role and responsibility of business scholars in promoting a systematic reflection and self-critique of the business system and develops alternative models to fill the moral void of the business institution and its inherent legitimacy crisis. Indeed, the current business institution relies on assumptions such as scientific neutrality and specialization, which seem at least partly challenged by two factors. First, self-fulfilling prophecy provides scholars with an important (even if sometimes undesired) normative influence over practical life. Second, the increasing complexity of today's (socio-political) world and interactions between the different elements constituting our society question the strong specialization of science. For instance, economic theories are not unrelated to psychology or sociology, and economic actors influence socio-political structures and processes, e.g., through lobbying (Dobbs, 2006; Rondinelli, 2002), or through marketing which changes not only the way we consume, but more generally tries to instill a specific lifestyle (Cova, 2004; M. K. Hogg & Michell, 1996; McCracken, 1988; Muniz & O'Guinn, 2001). In consequence, business scholars are key actors in shaping both tomorrow's economic world and its broader context. A greater awareness of this influence might be a first step toward an increased feeling of civic responsibility and accountability for the models and theories developed or taught in business schools.

Cytosolic sensing of extracellular self-DNA transported into monocytes by the antimicrobial peptide LL37.

The intracellular location of nucleic acid sensors prevents recognition of extracellular self-DNA released by dying cells. However, on forming a complex with the endogenous antimicrobial peptide LL37, extracellular DNA is transported into endosomal compartments of plasmacytoid dendritic cells, leading to activation of Toll-like receptor-9 and induction of type I IFNs. Whether LL37 also transports self-DNA into nonplasmacytoid dendritic cells, leading to type I IFN production via other intracellular DNA receptors is unknown. Here we found that LL37 very efficiently transports self-DNA into monocytes, leading the production of type I IFNs in a Toll-like receptor-independent manner. This type I IFN induction was mediated by double-stranded B form DNA, regardless of its sequence, CpG content, or methylation status, and required signaling through the adaptor protein STING and TBK1 kinase, indicating the involvement of cytosolic DNA sensors. Thus, our study identifies a novel link between the antimicrobial peptides and type I IFN responses involving DNA-dependent activation of cytosolic sensors in monocytes.

The activity of the anti-apoptotic fragment generated by the caspase-3/p120 RasGAP stress-sensing module displays strict Akt isoform specificity.

The caspase-3/p120 RasGAP module acts as a stress sensor that promotes pro-survival or pro-death signaling depending on the intensity and the duration of the stressful stimuli. Partial cleavage of p120 RasGAP generates a fragment, called fragment N, which protects stressed cells by activating Akt signaling. Akt family members regulate many cellular processes including proliferation, inhibition of apoptosis and metabolism. These cellular processes are regulated by three distinct Akt isoforms: Akt1, Akt2 and Akt3. However, which of these isoforms are required for fragment N mediated protection have not been defined. In this study, we investigated the individual contribution of each isoform in fragment N-mediated cell protection against Fas ligand induced cell death. To this end, DLD1 and HCT116 isogenic cell lines lacking specific Akt isoforms were used. It was found that fragment N could activate Akt1 and Akt2 but that only the former could mediate the protective activity of the RasGAP-derived fragment. Even overexpression of Akt2 or Akt3 could not rescue the inability of fragment N to protect cells lacking Akt1. These results demonstrate a strict Akt isoform requirement for the anti-apoptotic activity of fragment N.

Graph theory reveals dysconnected hubs in 22q11DS and altered nodal efficiency in patients with hallucinations.

Schizophrenia is postulated to be the prototypical dysconnection disorder, in which hallucinations are the core symptom. Due to high heterogeneity in methodology across studies and the clinical phenotype, it remains unclear whether the structural brain dysconnection is global or focal and if clinical symptoms result from this dysconnection. In the present work, we attempt to clarify this issue by studying a population considered as a homogeneous genetic sub-type of schizophrenia, namely the 22q11.2 deletion syndrome (22q11.2DS). Cerebral MRIs were acquired for 46 patients and 48 age and gender matched controls (aged 6-26, respectively mean age = 15.20 ± 4.53 and 15.28 ± 4.35 years old). Using the Connectome mapper pipeline (connectomics.org) that combines structural and diffusion MRI, we created a whole brain network for each individual. Graph theory was used to quantify the global and local properties of the brain network organization for each participant. A global degree loss of 6% was found in patients' networks along with an increased Characteristic Path Length. After identifying and comparing hubs, a significant loss of degree in patients' hubs was found in 58% of the hubs. Based on Allen's brain network model for hallucinations, we explored the association between local efficiency and symptom severity. Negative correlations were found in the Broca's area (p < 0.004), the Wernicke area (p < 0.023) and a positive correlation was found in the dorsolateral prefrontal cortex (DLPFC) (p < 0.014). In line with the dysconnection findings in schizophrenia, our results provide preliminary evidence for a targeted alteration in the brain network hubs' organization in individuals with a genetic risk for schizophrenia. The study of specific disorganization in language, speech and thought regulation networks sharing similar network properties may help to understand their role in the hallucination mechanism.

An Adaptive Field Detection Method For Bridge Scour Monitoring Using Motion-Sensing Radio Transponders (RFIDs), TR-617, 2014

A comprehensive field detection method is proposed that is aimed at developing advanced capability for reliable monitoring, inspection and life estimation of bridge infrastructure. The goal is to utilize Motion-Sensing Radio Transponders (RFIDS) on fully adaptive bridge monitoring to minimize the problems inherent in human inspections of bridges. We developed a novel integrated condition-based maintenance (CBM) framework integrating transformative research in RFID sensors and sensing architecture, for in-situ scour monitoring, state-of-the-art computationally efficient multiscale modeling for scour assessment.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Planning under partial observability by classical replanning: theory and experiments

Planning with partial observability can be formulated as a non-deterministic search problem in belief space. The problem is harder than classical planning as keeping track of beliefs is harder than keeping track of states, and searching for action policies is harder than searching for action sequences. In this work, we develop a framework for partial observability that avoids these limitations and leads to a planner that scales up to larger problems. For this, the class of problems is restricted to those in which 1) the non-unary clauses representing the uncertainty about the initial situation are nvariant, and 2) variables that are hidden in the initial situation do not appear in the body of conditional effects, which are all assumed to be deterministic. We show that such problems can be translated in linear time into equivalent fully observable non-deterministic planning problems, and that an slight extension of this translation renders the problem solvable by means of classical planners. The whole approach is sound and complete provided that in addition, the state-space is connected. Experiments are also reported.