LakeMIP Kivu: evaluating the representation of a large, deep tropical lake by a set of one-dimensional lake models

The African great lakes are of utmost importance for the local economy (fishing), as well as being essential to the survival of the local people. During the past decades, these lakes experienced fast changes in ecosystem structure and functioning, and their future evolution is a major concern. In this study, for the first time a set of one-dimensional lake models are evaluated for Lake Kivu (2.28°S; 28.98°E), East Africa. The unique limnology of this meromictic lake, with the importance of salinity and subsurface springs in a tropical high-altitude climate, presents a worthy challenge to the seven models involved in the Lake Model Intercomparison Project (LakeMIP). Meteorological observations from two automatic weather stations are used to drive the models, whereas a unique dataset, containing over 150 temperature profiles recorded since 2002, is used to assess the model’s performance. Simulations are performed over the freshwater layer only (60 m) and over the average lake depth (240 m), since salinity increases with depth below 60 m in Lake Kivu and some lake models do not account for the influence of salinity upon lake stratification. All models are able to reproduce the mixing seasonality in Lake Kivu, as well as the magnitude and seasonal cycle of the lake enthalpy change. Differences between the models can be ascribed to variations in the treatment of the radiative forcing and the computation of the turbulent heat fluxes. Fluctuations in wind velocity and solar radiation explain inter-annual variability of observed water column temperatures. The good agreement between the deep simulations and the observed meromictic stratification also shows that a subset of models is able to account for the salinity- and geothermal-induced effects upon deep-water stratification. Finally, based on the strengths and weaknesses discerned in this study, an informed choice of a one-dimensional lake model for a given research purpose becomes possible.

Personal neglect-a disorder of body representation?

The cognitive mechanisms underlying personal neglect are not well known. One theory postulates that personal neglect is due to a disorder of contralesional body representation. In the present study, we have investigated whether personal neglect is best explained by impairments in the representation of the contralesional side of the body, in particular, or a dysfunction of the mental representation of the contralesional space in general. For this, 22 patients with right hemisphere cerebral lesions (7 with personal neglect, 15 without personal neglect) and 13 healthy controls have been studied using two experimental tasks measuring representation of the body and extrapersonal space. In the tasks, photographs of left and right hands as well as left and right rear-view mirrors presented from the front and the back had to be judged as left or right. Our results show that patients with personal neglect made more errors when asked to judge stimuli of left hands and left rear-view mirrors than either patients without personal neglect or healthy controls. Furthermore, regression analyses indicated that errors in interpreting left hands were the best predictor of personal neglect, while other variables such as extrapersonal neglect, somatosensory or motor impairments, or deficits in left extrapersonal space representation had no predictive value of personal neglect. These findings suggest that deficient body representation is the major mechanism underlying personal neglect.

Interference with gesture production by theta burst stimulation over left inferior frontal cortex

The traditional view of a predominant inferior parietal representation of gestures has been recently challenged by neuroimaging studies demonstrating that gesture production and discrimination may critically depend on inferior frontal lobe function. The aim of the present work was therefore to investigate the effect of transient disruption of these brain sites by continuous theta burst stimulation (cTBS) on gesture production and recognition.

Proteome analysis of human plasma and amniotic fluid by Off-Gel isoelectric focusing followed by nano-LC-MS/MS

This paper presents a comparative proteomic analysis of human maternal plasma and amniotic fluid (AF) samples from the same patient at term of pregnancy in order to find specific AF proteins as markers of premature rupture of membranes, a complication frequently observed during pregnancy. Maternal plasma and the corresponding AF were immunodepleted in order to remove the six most abundant proteins before the systematic analysis of their protein composition. The protein samples were then fractionated by IEF Off-Gel electrophoresis (OGE), digested and analyzed with nano-LC-MS/MS separation, revealing a total of 73 and 69 proteins identified in maternal plasma and AF samples, respectively. The proteins identified in AF have been compared to those identified in the mother plasma as well as to the reference human plasma protein list reported by Anderson et al. (Mol. Cell. Proteomics 2004, 3, 311-326). This comparison showed that 26 proteins were exclusively present in AF and not in plasma among which 10 have already been described to be placenta or pregnancy specific. As a further validation of the method, plasma proteins fractionated by OGE and analysed by nano-LC-MS/MS have been compared to the Swiss 2-D PAGE reference map by reconstructing a map that matches 2-D gel and OGE experimental data. This representation shows that 36 of 49 reference proteins could be identified in both data sets, and that isoform shifts in pI are well conserved in the OGE data sets.

Modulating the granularity of category formation by global cortical states

The unsupervised categorization of sensory stimuli is typically attributed to feedforward processing in a hierarchy of cortical areas. This purely sensory-driven view of cortical processing, however, ignores any internal modulation, e.g., by top-down attentional signals or neuromodulator release. To isolate the role of internal signaling on category formation, we consider an unbroken continuum of stimuli without intrinsic category boundaries. We show that a competitive network, shaped by recurrent inhibition and endowed with Hebbian and homeostatic synaptic plasticity, can enforce stimulus categorization. The degree of competition is internally controlled by the neuronal gain and the strength of inhibition. Strong competition leads to the formation of many attracting network states, each being evoked by a distinct subset of stimuli and representing a category. Weak competition allows more neurons to be co-active, resulting in fewer but larger categories. We conclude that the granularity of cortical category formation, i.e., the number and size of emerging categories, is not simply determined by the richness of the stimulus environment, but rather by some global internal signal modulating the network dynamics. The model also explains the salient non-additivity of visual object representation observed in the monkey inferotemporal (IT) cortex. Furthermore, it offers an explanation of a previously observed, demand-dependent modulation of IT activity on a stimulus categorization task and of categorization-related cognitive deficits in schizophrenic patients.

Preservation and representation of human bones and it's importance for anthropological Analysis - an introduction

This paper presents problems arising from the lack of standardized methods for recording skeletal remains. Using practical examples it is shown how preservation and representation of bones can distort observations and how this can be reduced by systematic data acquisition.

A Concept of Semantics Extraction from Web Data by Induction of Fuzzy Ontologies

Traditionally, ontologies describe knowledge representation in a denotational, formalized, and deductive way. In addition, in this paper, we propose a semiotic, inductive, and approximate approach to ontology creation. We define a conceptual framework, a semantics extraction algorithm, and a first proof of concept applying the algorithm to a small set of Wikipedia documents. Intended as an extension to the prevailing top-down ontologies, we introduce an inductive fuzzy grassroots ontology, which organizes itself organically from existing natural language Web content. Using inductive and approximate reasoning to reflect the natural way in which knowledge is processed, the ontology’s bottom-up build process creates emergent semantics learned from the Web. By this means, the ontology acts as a hub for computing with words described in natural language. For Web users, the structural semantics are visualized as inductive fuzzy cognitive maps, allowing an initial form of intelligence amplification. Eventually, we present an implementation of our inductive fuzzy grassroots ontology Thus,this paper contributes an algorithm for the extraction of fuzzy grassroots ontologies from Web data by inductive fuzzy classification.

Sparse representation based action and gesture recognition

In this paper we present a solution to the problem of action and gesture recognition using sparse representations. The dictionary is modelled as a simple concatenation of features computed for each action or gesture class from the training data, and test data is classified by finding sparse representation of the test video features over this dictionary. Our method does not impose any explicit training procedure on the dictionary. We experiment our model with two kinds of features, by projecting (i) Gait Energy Images (GEIs) and (ii) Motion-descriptors, to a lower dimension using Random projection. Experiments have shown 100% recognition rate on standard datasets and are compared to the results obtained with widely used SVM classifier.

Improved detection of amnestic MCI by means of discriminative vector quantization of single-trial cognitive ERP responses

Cognitive event-related potentials (ERPs) are widely employed in the study of dementive disorders. The morphology of averaged response is known to be under the influence of neurodegenerative processes and exploited for diagnostic purposes. This work is built over the idea that there is additional information in the dynamics of single-trial responses. We introduce a novel way to detect mild cognitive impairment (MCI) from the recordings of auditory ERP responses. Using single trial responses from a cohort of 25 amnestic MCI patients and a group of age-matched controls, we suggest a descriptor capable of encapsulating single-trial (ST) response dynamics for the benefit of early diagnosis. A customized vector quantization (VQ) scheme is first employed to summarize the overall set of ST-responses by means of a small-sized codebook of brain waves that is semantically organized. Each ST-response is then treated as a trajectory that can be encoded as a sequence of code vectors. A subject's set of responses is consequently represented as a histogram of activated code vectors. Discriminating MCI patients from healthy controls is based on the deduced response profiles and carried out by means of a standard machine learning procedure. The novel response representation was found to improve significantly MCI detection with respect to the standard alternative representation obtained via ensemble averaging (13% in terms of sensitivity and 6% in terms of specificity). Hence, the role of cognitive ERPs as biomarker for MCI can be enhanced by adopting the delicate description of our VQ scheme.

More Money, Fewer Problems? Cross-Level Effects of Economic Deprivation on Political Representation

While equal political representation of all citizens is a fundamental democratic goal, it is hampered empirically in a multitude of ways. This study examines how the societal level of economic inequality affects the representation of relatively poor citizens by parties and governments. Using CSES survey data for citizens’ policy preferences and expert placements of political parties, empirical evidence is found that in economically more unequal societies, the party system represents the preferences of relatively poor citizens worse than in more equal societies. This moderating effect of economic equality is also found for policy congruence between citizens and governments, albeit slightly less clear-cut.

Keeping citizens, companies, and organizations informed by Web KnowARR

For the main part, electronic government (or e-government for short) aims to put digital public services at disposal for citizens, companies, and organizations. To that end, in particular, e-government comprises the application of Information and Communications Technology (ICT) to support government operations and provide better governmental services (Fraga, 2002) as possible with traditional means. Accordingly, e-government services go further as traditional governmental services and aim to fundamentally alter the processes in which public services are generated and delivered, after this manner transforming the entire spectrum of relationships of public bodies with its citizens, businesses and other government agencies (Leitner, 2003). To implement this transformation, one of the most important points is to inform the citizen, business, and/or other government agencies faithfully and in an accessible way. This allows all the partaking participants of governmental affairs for a transition from passive information access to active participation (Palvia and Sharma, 2007). In addition, by a corresponding handling of the participants' data, a personalization towards these participants may even be accomplished. For instance, by creating significant user profiles as a kind of participants' tailored knowledge structures, a better-quality governmental service may be provided (i.e., expressed by individualized governmental services). To create such knowledge structures, thus known information (e.g., a social security number) can be enriched by vague information that may be accurate to a certain degree only. Hence, fuzzy knowledge structures can be generated, which help improve governmental-participants relationship. The Web KnowARR framework (Portmann and Thiessen, 2013; Portmann and Pedrycz, 2014; Portmann and Kaltenrieder, 2014), which I introduce in my presentation, allows just all these participants to be automatically informed about changes of Web content regarding a- respective governmental action. The name Web KnowARR thereby stands for a self-acting entity (i.e. instantiated form the conceptual framework) that knows or apprehends the Web. In this talk, the frameworks respective three main components from artificial intelligence research (i.e. knowledge aggregation, representation, and reasoning), as well as its specific use in electronic government will be briefly introduced and discussed.

Reassembling a system from the sensor to cerebral representation: the olfactory system in vitro.

An odorant's code is represented by activity in a dispersed ensemble of olfactory sensory neurons in the nose, activation of a specific combination of groups of mitral cells in the olfactory bulb and is considered to be mapped at divergent locations in the olfactory cortex. We present here an in vitro model of the mammalian olfactory system developed to gain easy access to all stations of the olfactory pathway. Mouse olfactory epithelial explants are cocultured with a brain slice that includes the olfactory bulb and olfactory cortex areas and maintains the central olfactory pathway intact and functional. Organotypicity of bulb and cortex is preserved and mitral cell axons can be traced to their target areas. Calcium imaging shows propagation of mitral cell activity to the piriform cortex. Long term coculturing with postnatal olfactory epithelial explants restores the peripheral olfactory pathway. Olfactory receptor neurons renew and progressively acquire a mature phenotype. Axons of olfactory receptor neurons grow out of the explant and rewire into the olfactory bulb. The extent of reinnervation exhibits features of a postlesion recovery. Functional imaging confirms the recovery of part of the peripheral olfactory pathway and shows that activity elicited in olfactory receptor neurons or the olfactory nerves is synaptically propagated into olfactory cortex areas. This model is the first attempt to reassemble a sensory system in culture, from the peripheral sensor to the site of cortical representation. It will increase our knowledge on how neuronal circuits in the central olfactory areas integrate sensory input and counterbalance damage.