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Efficient conversion of methane to value-added products such as olefins and aromatics has been in pursuit for the past few decades. The demand has increased further due to the recent discoveries of shale gas reserves. Oxidative and non-oxidative coupling of methane (OCM and NOCM) have been actively researched, although catalysts with commercially viable conversion rates are not yet available. Recently, Sr 2 F e 1.5 + 0.075 M o 0.5 O 6 − δ (SFMO-075Fe) has been reported to activate methane in an electrochemical OCM (EC-OCM) set up with a C2 selectivity of 82.2%1. However, alkaline earth metal-based materials are known to suffer chemical instability in carbon-rich environments. Hence, here we evaluated the chemical stability of SFMO in carbon-rich conditions with varying oxygen concentrations at temperatures relevant for EC-OCM. SFMO-075Fe showed good methane activation properties especially at low overpotentials but suffered poor chemical stability as observed via thermogravimetric, powder XRD, and XPS measurements where SrCO3 was observed to be a major decomposition product along with SrMoO3 and MoC. Nevertheless, our study demonstrates that electrochemical methods could be used to selectively activate methane towards partial oxidation products such as ethylene at low overpotentials while higher applied biases result in the complete oxidation of methane to carbon dioxide and water. Catalytic conversion of methane to ethene is limited by poor conversion, selectivity, and catalyst stability. Here a solid oxide electrolysis process is reported as a promising method for selective, partial oxidation of methane with low overpotentials. [ABSTRACT FROM AUTHOR]

Language models are at the heart of numerous works, notably in the text mining and information retrieval communities. These statistical models aim at extracting word distributions, from simple unigram models to recurrent approaches with latent variables that capture subtle dependencies in texts. However, those models are learned from word sequences only, and authors' identities, as well as publication dates, are seldom considered. We propose a neural model, based on recurrent language modeling (e.g., LSTM), which aims at capturing language diffusion tendencies in author communities through time. By conditioning language models with author and dynamic temporal vector states, we are able to leverage the latent dependencies between the text contexts. The model captures language evolution of authors via a shared temporal prediction function in a latent space, which allows to handle a variety of modeling tasks, including completion and prediction of language models through time. Experiments show the performances of the approach, compared to several temporal and non-temporal language baselines on two real-world corpora. [ABSTRACT FROM AUTHOR]

Transcranial Direct brain stimulation (tDCS) is commonly used in order to modulate cortical networks activity during physiological processes through the application of weak electrical fields with scalp electrodes. Cathodal stimulation has been shown to decrease brain excitability in the context of epilepsy, with variable success. However, the cellular mechanisms responsible for the acute and the long-lasting effect of tDCS remain elusive. Using a novel approach of computational modeling that combines detailed but functionally integrated neurons we built a physiologically-based thalamocortical column. This model comprises 10,000 individual neurons made of pyramidal cells, and 3 types of gamma-aminobutyric acid (GABA) -ergic cells (VIP, PV, and SST) respecting the anatomy, layers, projection, connectivity and neurites orientation. Simulating realistic electric fields in term of intensity, main results showed that 1) tDCS effects are best explained by modulation of the presynaptic probability of release 2) tDCS affects the dynamic of cortical network only if a sufficient number of neurons are modulated 3)VIP GABAergic interneurons of the superficial layer of the cortex are especially affected by tDCS 4) Long lasting effect depends on glutamatergic synaptic plasticity. [ABSTRACT FROM AUTHOR]

We investigate how adversarial learning may be used for various animation tasks related to human motion synthesis. We propose a learning framework that we decline for building various models corresponding to various needs: a random synthesis generator that randomly produces realistic motion capture trajectories; conditional variants that allow controlling the synthesis by providing high-level features that the animation should match; a style transfer model that allows transforming an existing animation in the style of another one. Our work is built on the adversarial learning strategy that has been proposed in the machine learning field very recently (2014) for learning accurate generative models on complex data, and that has been shown to provide impressive results, mainly on image data. We report both objective and subjective evaluation results on motion capture data performed under emotion, the Emilya Dataset. Our results show the potential of our proposals for building models for a variety of motion synthesis tasks. [ABSTRACT FROM AUTHOR]

We introduce a dynamical spatio-temporal model formalized as a recurrent neural network for modeling time series of spatial processes, i.e., series of observations sharing temporal and spatial dependencies. The model learns these dependencies through a structured latent dynamical component, while a decoder predicts the observations from the latent representations. We consider several variants of this model, corresponding to different prior hypothesis about the spatial relations between the series. The model is used for the tasks of forecasting and data imputation. It is evaluated and compared to state-of-the-art baselines, on a variety of forecasting and imputation problems representative of different application areas: epidemiology, geo-spatial statistics, and car traffic prediction. The experiments also show that this approach is able to learn relevant spatial relations without prior information. [ABSTRACT FROM AUTHOR]

In supervised classification, data representation is usually considered at the dataset level: one looks for the 'best' representation of data assuming it to be the same for all the data in the data space. We propose a different approach where the representations used for classification are tailored to each datum in the data space. One immediate goal is to obtain sparse datum-wise representations: our approach learns to build a representation specific to each datum that contains only a small subset of the features, thus allowing classification to be fast and efficient. This representation is obtained by way of a sequential decision process that sequentially chooses which features to acquire before classifying a particular point; this process is learned through algorithms based on Reinforcement Learning. The proposed method performs well on an ensemble of medium-sized sparse classification problems. It offers an alternative to global sparsity approaches, and is a natural framework for sequential classification problems. The method extends easily to a whole family of sparsity-related problem which would otherwise require developing specific solutions. This is the case in particular for cost-sensitive and limited-budget classification, where feature acquisition is costly and is often performed sequentially. Finally, our approach can handle non-differentiable loss functions or combinatorial optimization encountered in more complex feature selection problems. [ABSTRACT FROM AUTHOR]

Purpose: The imaging potential of a new F-labelled methionine derivative, S-(3-[F]fluoropropyl)- d-homocysteine (F-D-FPHCys), and its selectivity for amino acid transporter subtypes were investigated in vitro and by imaging of human tumour xenografts. Methods: Expression of members of the system L (LAT isoforms 1-4 and 4F2hc) and ASCT (ASCT isoforms 1 and 2) amino acid transporter subclasses were assessed by quantitative real-time PCR in four human tumour models, including A431 squamous cell carcinoma, PC3 prostate cancer, and Colo 205 and HT-29 colorectal cancer lines. The first investigations for the characterization of F-D-FPHCys were in vitro uptake studies by comparing it with [1-C]- l-methionine (C-MET) and in vivo by PET imaging. In addition, the specific involvement of LAT1 transporters in F-D-FPHCys accumulation was tested by silencing LAT1 mRNA transcription with siRNAs. To determine the proliferative activity in tumour xenografts ex vivo, Ki-67 staining was used as a biomarker. Results: A431 cells showed the highest F-D-FPHCys uptake in vitro and in vivo followed by Colo 205, PC3 and HT-29. A similar pattern of retention was observed with C-MET. F-D-FPHCys retention was strongly correlated with LAT1 expression both in vitro ( R = 0.85) and in vivo ( R = 0.99). Downregulation of LAT1 by siRNA inhibited F-D-FPHCys uptake, demonstrating a clear dependence on this transporter for tumour uptake. Furthermore, F-D-FPHCys accumulation mirrored cellular proliferation. Conclusion: The favourable properties of F-D-FPHCys make this tracer a promising imaging probe for detection of tumours as well as for the noninvasive evaluation and monitoring of tumour growth. [ABSTRACT FROM AUTHOR]

We consider here the task of multi-label classification for data organized in a multi-relational graph. We propose the IMMCA model-Iterative Multi-label Multi-relational Classification Algorithm-a general algorithm for solving the inference and learning problems for this task. Inference is performed iteratively by propagating scores according to the multi-relational structure of the data. We detail two instances of this general model, implementing two different label propagation schemes on the multigraph. To the best of our knowledge, this is the first collective classification method able to handle multiple relations and to perform multi-label classification in multigraphs. Tests are performed for two generic applications, image annotation and document classification, on different social datasets. For image annotation, we have been using Flickr datasets of different sizes and with different configurations, with multiple relations such as authorship, friendship, or textual similarities. For document classification, we used the Cora classical benchmark plus an Email corpus. Additional experiments on artificial data allow us to analyze further the behavior of the model. [ABSTRACT FROM AUTHOR]

We formalize the problem of Structured Prediction as a Reinforcement Learning task. We first define a Structured Prediction Markov Decision Process (SP-MDP), an instantiation of Markov Decision Processes for Structured Prediction and show that learning an optimal policy for this SP-MDP is equivalent to minimizing the empirical loss. This link between the supervised learning formulation of structured prediction and reinforcement learning (RL) allows us to use approximate RL methods for learning the policy. The proposed model makes weak assumptions both on the nature of the Structured Prediction problem and on the supervision process. It does not make any assumption on the decomposition of loss functions, on data encoding, or on the availability of optimal policies for training. It then allows us to cope with a large range of structured prediction problems. Besides, it scales well and can be used for solving both complex and large-scale real-world problems. We describe two series of experiments. The first one provides an analysis of RL on classical sequence prediction benchmarks and compares our approach with state-of-the-art SP algorithms. The second one introduces a tree transformation problem where most previous models fail. This is a complex instance of the general labeled tree mapping problem. We show that RL exploration is effective and leads to successful results on this challenging task. This is a clear confirmation that RL could be used for large size and complex structured prediction problems. [ABSTRACT FROM AUTHOR]

Purpose: Although a number of prognostic indicators have been developed, it is still difficult to predict the biological behaviour of all cancer types. 99mTc-(V)-DMSA(V DMSA) uptake and focal adhesion kinase (FAK) expression and activation level could be potential agents for this purpose. We hypothesised the existence of a correlation between V DMSA, whose uptake is linked to phosphate ions, essential compounds for tumour growth and cell proliferation, and the adhesion protein FAK, whose elevated expression and level of constitutive activation are implicated in cancer progression. The aim of this study was to assess the relationship between VDMSA incorporation rate and FAK expression and activation by phosphorylation on tyrosine 397 residue. Methods: We determined V DMSA uptake in six different cancer cell lines and we measured FAK expression and activation by using Western Blotting analysis. Correlations with factors known to be associated with poor prognosis, such as invasive potential, resistance to chemotherapy and proliferation rate, were also investigated. Results: The cell lines exhibited different V DMSA incorporation rates. In addition, these cells showed the same FAK expression, but various degrees of activation. A correlation was observed between V DMSA uptake and level of FAK phosphorylation and between V DMSA or constitutive FAK activation and proliferation rate. However, no correlation was shown between these parameters and the other factors tested, i.e. invasive potential and anticancer drug resistance. Conclusion: The results of this in vitro study clearly demonstrate that phosphorylation of FAK, proliferation rate and V DMSA uptake are closely related. Because proliferation and a high level of constitutive FAK activation are linked to cancer progression, it can be assumed that in vivo V DMSA uptake reflects tumour aggressiveness. [ABSTRACT FROM AUTHOR]

Purpose: Pentavalent 99mTc-dimercaptosuccinic acid [99mTc-(V)DMSA or (V)1)MSA] is a marker of phosphate transport, entering cells specifically through type III NaPi co-transporters. Phosphate ion is known to he involved in cell metabolism, including the apoptotic cell death process As phosphate accumulation decreases during apoptosis, we investigated the influence of type III NaPi co-transporter activity on (V)DMSA uptake during this type of cell death Methods: Uptake of (V)DMSA and phosphate was compared in a leukemic cell line (U937) in vitro model after induction of apoptosis by a chemotherapeutic agent, etoposide (VP16). (V)DMSA biodistribution in nude mice during apoptosis was also investigated in a U937 xenograft in vivo model The percentage of apoptosis in vitro and in vivo was determined with annexin V fluorescein by flow cytometry. Results: The in vitro results showed that, in parallel with the decrease in phosphate uptake during apoptosis, (V)DMSA accumulation is negatively correlated with the percentage of apoptosis. Biodistribution studies showed decreased accumulation of (V)DMSA in tumours after treatment with VP16. Animal studies also confirmed an inverse correlation between percentage of apoptosis in tumours and (V)DMSA uptake. Conclusion: The activity of type III NaPi co-transporter is inhibited during the early stages of apoptosis, leading to differential incorporation of (V)DMSA in viable cells and apoptotic cells both in vitro and in vivo. [ABSTRACT FROM AUTHOR]

In vivo studies have demonstrated that pentavalent technetium-99m dimercaptosuccinic acid [[sup 99m]Tc-(V)-DMSA] may be a useful tumour imaging agent. Several studies have suggested that [sup 99m]Tc-(V)-DMSA uptake may be related to the structural similarity between the [sup 99m]Tc-(V)-DMSA core and the PO[sub 4][sup 3–] anion. As phosphate ions enter cells via NaPi cotransporters, we investigated whether [sup 99m]Tc-(V)-DMSA uptake is mediated by NaPi cotransporters. [sup 99m]Tc-(V)-DMSA and phosphate uptake kinetics were compared in three cancer cell lines (MCF-7, G152 and MG-63) under several conditions (with and without sodium and NaPi cotransporter inhibitor and at different pH). Determination of molecular NaPi cotransporter mRNA expression was performed by reverse-transcriptase polymerase chain reaction (Rt-PCR) assay. Results obtained in the presence of NaPi inhibitor, in sodium-free medium and at alkaline pH showed that [sup 99m]Tc-(V)-DMSA accumulation is linked to NaPi cotransporter functionality. MCF-7 and G152 exhibited the same tracer uptake, whereas MG-63 showed the highest phosphate accumulation and the lowest [sup 99m]Tc-(V)-DMSA uptake. These results were in accordance with mRNA NaPi expression, i.e. all cell lines expressed NaPi type III but MG-63 also co-expressed NaPi type I. The total level of NaPi cotransporter was highly correlated with phosphate accumulation, while the level of type III was related to [sup 99m]Tc-(V)-DMSA uptake. We have demonstrated that [sup 99m]Tc-(V)-DMSA uptake is specifically mediated by NaPi type III in cancer cells. [ABSTRACT FROM AUTHOR]

A complete investigation of the Fourier space of complex intermetallic Mg[sub 39]A1[sub 61] is reported. X-ray patterns reveal the reciprocal space of the whole system consisting in selective reflections and coherent diffuse scattering. Their temperature dependence are studied. Selective reflections are interpreted as incommensurately modulated states and a structural analysis of the coherent diffuse scattering is presented. The same type of coherent diffuse scattering curve was also observed in several quasicrystalline alloys. In all the studied cases, the disorder is interpreted by the presence of icosahedral arrangements that are reminiscent of Frank units in supercooled alloys. [ABSTRACT FROM AUTHOR]

A method has been developed for the determination of 23 elements in marine plankton in which inductively coupled plasma (ICP) source mass spectrometry (MS) was used to quantify the elements in the solution after digestion in a mixture of hydrofluoric and nitric acids in sealed PTFE vessels in a microwave field. The procedure was validated by the analysis of a standard reference soil (SRM 2709 San Joaquin Soil) and a standard reference fresh water plankton (CRM 414). The method was applied to the analysis of several marine plankton samples grown under controlled conditions including several whose growth media had been enriched with selenium. Matrix induced signal suppressions and instrumental drift were corrected by internal standardization. The suitabilities of germanium, indium, rhodium, scandium and yttrium as internal standard elements were evaluated. Neither scandium nor yttrium could be used due to the presence of these elements in the samples, germanium was used for the determination of As, Co, Cu, Fe, Ni, Se, Si and Zn, indium was used for Al, Ba, Ca, Eu, Sr, and Tl, and rhodium was used for Cd, Cr, Hg, Mg, Pb, Sb, Sn, and V. For Al, Ca, Cr, Cu, Fe, Mg, Mn, Ni, Si, Sr, V, and Zn internal standardization did not completely compensate for the suppressive effect of the heavier elements and the solutions were diluted. However, for As, Ba, Cd, Co, Eu, Hg, Pb, Sb, Se, Sn and Tl, it was possible to obtain accurate results despite the 35–¶40% suppression in the signals. Isobaric overlap was only a problem in the cases of 42Ca and 78Se; 44Ca and 77Se, respectively, were used. Memory effects were only observed with Hg for which a nitric acid-sodium chloride solution was the most effective wash-out solution. The marine plankton samples were able to tolerate a higher concentration of Hg as the selenium concentration increased. [ABSTRACT FROM AUTHOR]

Consumption of selenium enriched plants or yeast-based nutritional supplements has been reported to provide anticarcinogenic benefits which are selenium compound dependent. Separation and identification of these selenium compounds is critical to understand the activity. Plants and yeast convert inorganic selenium in the soil or growth media into organoselenium compounds, probably following a route similar to the sulfur assimilatory pathway. Non-volatile selenium compounds produced include selenoamino acids, some of which have shown anticarcinogenic activity. Volatile compounds produced by chemical reaction of involatile precursors have also been found. An ion pair chromatographic method with ICP-MS detection for the separation of selenoamino acid standards potentially present in real samples is given. The method allows separation of selenoamino acids including such analytes as the cis-trans isomers of Se-1-propenyl- dl-selenocysteine. The method also provides the capability of determining the presence of selenoxides and possibly selenones, and tracking of other functionalities and reactions by selective derivatization. Alternatively, selenoamino acids are treated with ethylchloroformate to produce stable volatile derivatives which are amenable to GC separation with element specific atomic emission detection (GC-AED). Results of total selenium determination and speciation of selenium enriched yeast-based nutritional supplements, selenium enriched allium vegetables and bioremediation samples are presented. [ABSTRACT FROM AUTHOR]

Electroencephalogram, caudate nucleus temperature (Tc), ear skin temperature (Te) as well as cerebral blood flow (CBF) measured by a thermal clearance method, were recorded simultaneously and continuously in cats. After baseline recording in which we confirmed the increase of Tc during paradoxical sleep (PS), neuronal cell bodies of the mesencephalic reticular formation and/or the posterior hypothalamus (PH) were destroyed with ibotenic acid. Only PH lesions were followed by either a suppression of the increase or even a decrease of Tc during PS while Te variations were not modified. The decrease in CBF, which was always associated with Tc increase, was suppressed after the PH lesion. These results led us to the conclusion that the increase of Tc at the onset of PS is due to a decrease in CBF. Furthermore, it may be hypothetized that the decrease in CBF depend upon an active vasoconstriction process originating in the PH. [ABSTRACT FROM AUTHOR]