How should the sustainability of the location of dry ports be measured? A proposed methodology using Bayesian networks and multi-criteria decision analysis

The global economic structure, with its decentralized production and the consequent increase in freight traffic all over the world, creates considerable problems and challenges for the freight transport sector. This situation has led shipping to become the most suitable and cheapest way to transport goods. Thus, ports are configured as nodes with critical importance in the logistics supply chain as a link between two transport systems, sea and land. Increase in activity at seaports is producing three undesirable effects: increasing road congestion, lack of open space in port installations and a significant environmental impact on seaports. These adverse effects can be mitigated by moving part of the activity inland. Implementation of dry ports is a possible solution and would also provide an opportunity to strengthen intermodal solutions as part of an integrated and more sustainable transport chain, acting as a link between road and railway networks. In this sense, implementation of dry ports allows the separation of the links of the transport chain, thus facilitating the shortest possible routes for the lowest capacity and most polluting means of transport. Thus, the decision of where to locate a dry port demands a thorough analysis of the whole logistics supply chain, with the objective of transferring the largest volume of goods possible from road to more energy efficient means of transport, like rail or short-sea shipping, that are less harmful to the environment. However, the decision of where to locate a dry port must also ensure the sustainability of the site. Thus, the main goal of this article is to research the variables influencing the sustainability of dry port location and how this sustainability can be evaluated. With this objective, in this paper we present a methodology for assessing the sustainability of locations by the use of Multi-Criteria Decision Analysis (MCDA) and Bayesian Networks (BNs). MCDA is used as a way to establish a scoring, whilst BNs were chosen to eliminate arbitrariness in setting the weightings using a technique that allows us to prioritize each variable according to the relationships established in the set of variables. In order to determine the relationships between all the variables involved in the decision, giving us the importance of each factor and variable, we built a K2 BN algorithm. To obtain the scores of each variable, we used a complete cartography analysed by ArcGIS. Recognising that setting the most appropriate location to place a dry port is a geographical multidisciplinary problem, with significant economic, social and environmental implications, we consider 41 variables (grouped into 17 factors) which respond to this need. As a case of study, the sustainability of all of the 10 existing dry ports in Spain has been evaluated. In this set of logistics platforms, we found that the most important variables for achieving sustainability are those related to environmental protection, so the sustainability of the locations requires a great respect for the natural environment and the urban environment in which they are framed.

Transforming neural computations and representing time

Motifs of neural circuitry seem surprisingly conserved over different areas of neocortex or of paleocortex, while performing quite different sensory processing tasks. This apparent paradox may be resolved by the fact that seemingly different problems in sensory information processing are related by transformations (changes of variables) that convert one problem into another. The same basic algorithm that is appropriate to the recognition of a known odor quality, independent of the strength of the odor, can be used to recognize a vocalization (e.g., a spoken syllable), independent of whether it is spoken quickly or slowly. To convert one problem into the other, a new representation of time sequences is needed. The time that has elapsed since a recent event must be represented in neural activity. The electrophysiological hallmarks of cells that are involved in generating such a representation of time are discussed. The anatomical relationships between olfactory and auditory pathways suggest relevant experiments. The neurophysiological mechanism for the psychophysical logarithmic encoding of time duration would be of direct use for interconverting olfactory and auditory processing problems. Such reuse of old algorithms in new settings and representations is related to the way that evolution develops new biochemistry.

Expression of the mouse cerberus-related gene, Cerr1, suggests a role in anterior neural induction and somitogenesis

The Xenopus cerberus gene encodes a secreted factor that is expressed in the anterior endomesoderm of gastrula stage embryos and can induce the formation of ectopic heads when its mRNA is injected into Xenopus embryos [Bouwmeester, T., Kim, S., Lu, B. & De Robertis, E. M. (1996) Nature (London) 382, 595–601]. Here we describe the existence of a cerberus-related gene, Cerr1, in the mouse. Cerr1 encodes a putative secreted protein that is 48% identical to cerberus over a 110-amino acid region. Analysis of a mouse interspecific backcross panel demonstrated that Cerr1 mapped to the central portion of mouse chromosome 4. In early gastrula stage mouse embryos, Cerr1 is expressed in the anterior visceral endoderm and in the anterior definitive endoderm. In somite stage embryos, Cerr1 expression is restricted to the most recently formed somites and in the anterior presomitic mesoderm. Germ layer explant recombination assays demonstrated that Cerr1-expressing somitic-presomitic mesoderm, but not older Cerr1-nonexpressing somitic mesoderm, was able to mimic the anterior neuralizing ability of anterior mesendoderm and maintain Otx2 expression in competent ectoderm. In most Lim1−/− headless embryos, Cerr1 expression in the anterior endoderm was weak or absent. These results suggest that Cerr1 may play a role in anterior neural induction and somite formation during mouse development.

Heterogeneity of subcellular localization and electrophoretic mobility of survival motor neuron (SMN) protein in mammalian neural cells and tissues

Spinal muscular atrophy is caused by defects in the survival motor neuron (SMN) gene. To better understand the patterns of expression of SMN in neuronal cells and tissues, we raised a polyclonal antibody (abSMN) against a synthetic oligopeptide from SMN exon 2. AbSMN immunostaining in neuroblastoma cells and mouse and human central nervous system (CNS) showed intense labeling of nuclear “gems,” along with prominent nucleolar immunoreactivity in mouse and human CNS tissues. Strong cytoplasmic labeling was observed in the perikarya and proximal dendrites of human spinal motor neurons but not in their axons. Immunoblot analysis revealed a 34-kDa species in the insoluble protein fractions from human SY5Y neuroblastoma cells, embryonic mouse spinal cord cultures, and human CNS tissue. By contrast, a 38-kDa species was detected in the cytosolic fraction of SY5Y cells. We conclude that SMN protein is expressed prominently in both the cytoplasm and nucleus in multiple types of neurons in brain and spinal cord, a finding consistent with a role for SMN as a determinant of neuronal viability.

Caspase-1 is activated in neural cells and tissue with amyotrophic lateral sclerosis-associated mutations in copper-zinc superoxide dismutase

The mechanism by which mutations in the superoxide dismutase (SOD1) gene cause motor neuron degeneration in familial amyotrophic lateral sclerosis (ALS) is unknown. Recent reports that neuronal death in SOD1-familial ALS is apoptotic have not documented activation of cell death genes. We present evidence that the enzyme caspase-1 is activated in neurons expressing mutant SOD1 protein. Proteolytic processing characteristic of caspase-1 activation is seen both in spinal cords of transgenic ALS mice and neurally differentiated neuroblastoma (line N2a) cells with SOD1 mutations. This activation of caspase-1 is enhanced by oxidative challenge (xanthine/xanthine oxidase), which triggers cleavage and secretion of the interleukin 1β converting enzyme substrate, pro-interleukin 1β, and induces apoptosis. This N2a culture system should be an instructive in vitro model for further investigation of the proapoptotic properties of mutant SOD1.

bcl-x is expressed in embryonic and postnatal neural tissues and functions to prevent neuronal cell death.

Previous studies have implicated the bcl-2 protooncogene as a potential regulator of neuronal survival. However, mice lacking functional bcl-2 exhibited normal development and maintenance of the central nervous system (CNS). Since bcl-2 appears dispensable for neuronal survival, we have examined the expression and function of bcl-x, another member of the bcl-2 family of death regulatory genes. Bcl-2 is expressed in neuronal tissues during embryonic development but is down-regulated in the adult CNS. In contrast, Bcl-xL expression is retained in neurons of the adult CNS. Two different forms of bcl-x mRNA and their corresponding products, Bcl-xL and Bcl-x beta, were expressed in embryonic and adult neurons of the CNS. Microinjection of bcl-xL and bcl-x beta cDNAs into primary sympathetic neurons inhibited their death induced by nerve growth factor withdrawal. Thus, Bcl-x proteins appear to play an important role in the regulation of neuronal survival in the adult CNS.

Computational cost of GNG3D algorithm for mesh simplification

In this paper we present a study of the computational cost of the GNG3D algorithm for mesh optimization. This algorithm has been implemented taking as a basis a new method which is based on neural networks and consists on two differentiated phases: an optimization phase and a reconstruction phase. The optimization phase is developed applying an optimization algorithm based on the Growing Neural Gas model, which constitutes an unsupervised incremental clustering algorithm. The primary goal of this phase is to obtain a simplified set of vertices representing the best approximation of the original 3D object. In the reconstruction phase we use the information provided by the optimization algorithm to reconstruct the faces thus obtaining the optimized mesh. The computational cost of both phases is calculated, showing some examples.

Morphological classification of microtidal sand and gravel beaches

A new classification of microtidal sand and gravel beaches with very different morphologies is presented below. In 557 studied transects, 14 variables were used. Among the variables to be emphasized is the depth of the Posidonia oceanica. The classification was performed for 9 types of beaches: Type 1: Sand and gravel beaches, Type 2: Sand and gravel separated beaches, Type 3: Gravel and sand beaches, Type 4: Gravel and sand separated beaches, Type 5: Pure gravel beaches, Type 6: Open sand beaches, Type 7: Supported sand beaches, Type 8: Bisupported sand beaches and Type 9: Enclosed beaches. For the classification, several tools were used: discriminant analysis, neural networks and Support Vector Machines (SVM), the results were then compared. As there is no theory for deciding which is the most convenient neural network architecture to deal with a particular data set, an experimental study was performed with different numbers of neuron in the hidden layer. Finally, an architecture with 30 neurons was chosen. Different kernels were employed for SVM (Linear, Polynomial, Radial basis function and Sigmoid). The results obtained for the discriminant analysis were not as good as those obtained for the other two methods (ANN and SVM) which showed similar success.