Ocean warming-acidification synergism undermines dissolved organic matter assembly.

Understanding the influence of synergisms on natural processes is a critical step toward determining the full-extent of anthropogenic stressors. As carbon emissions continue unabated, two major stressors--warming and acidification--threaten marine systems on several scales. Here, we report that a moderate temperature increase (from 30°C to 32°C) is sufficient to slow--even hinder--the ability of dissolved organic matter, a major carbon pool, to self-assemble to form marine microgels, which contribute to the particulate organic matter pool. Moreover, acidification lowers the temperature threshold at which we observe our results. These findings carry implications for the marine carbon cycle, as self-assembled marine microgels generate an estimated global seawater budget of ~1016 g C. We used laser scattering spectroscopy to test the influence of temperature and pH on spontaneous marine gel assembly. The results of independent experiments revealed that at a particular point, both pH and temperature block microgel formation (32°C, pH 8.2), and disperse existing gels (35°C). We then tested the hypothesis that temperature and pH have a synergistic influence on marine gel dispersion. We found that the dispersion temperature decreases concurrently with pH: from 32°C at pH 8.2, to 28°C at pH 7.5. If our laboratory observations can be extrapolated to complex marine environments, our results suggest that a warming-acidification synergism can decrease carbon and nutrient fluxes, disturbing marine trophic and trace element cycles, at rates faster than projected

Models of Social Groups in Blogosphere Based on Information about Comment Addressees and Sentiments

This work concerns the analysis of number, sizes and other characteristics of groups identified in the blogosphere using a set of models identifying social relations. These models differ regarding identification of social relations, influenced by methods of classifying the addressee of the comments (they are either the post author or the author of a comment on which this comment is directly addressing) and by a sentiment calculated for comments considering the statistics of words present and connotation. The state of a selected blog portal was analyzed in sequential, partly overlapping time intervals. Groups in each interval were identified using a version of the CPM algorithm, on the basis of them, stable groups, existing for at least a minimal assumed duration of time, were identified.Comment: Gliwa B., Ko\'zlak J., Zygmunt A., Models of Social Groups in Blogosphere Based on Information about Comment Addressees and Sentiments, in the K. Aberer et al. (Eds.): SocInfo 2012, LNCS 7710, pp. 475-488, Best Paper Awar

A Decision Support System for Market Segmentation - A Neural Networks Approach

Market segmentation refers to the subdividing of a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix [Kotler 1980]. The reason for segmenting a market is that consumers are often numerous, geographically dispersed, and heterogeneous, and therefore seek varying benefits from the products they buy. Consumers within a segment are expected to have homogeneous buying preferences whereas those in different segments tend to behave differently. By properly identifying the benefit segment of a firm\u27s product, the marketing manager can target the sales effort at specific groups of consumers rather than at the total population. The identification of consumer segments is of critical importance for key strategic issues in marketing involving the assessment of a firm\u27s opportunities and threats. The marketing manager must evaluate the potential of the firm\u27s products in the target segment and ultimately select the most promising strategy for the segment. In thisresearch, we introduce a new approach, a neural networks based method, to discover market segments and configure them into meaningful structures. The particular type of neural networks, the Self-Organizing Map networks, can be used as a decision support tool for supporting strategic decisions involving identifying and targeting market segments. The Self-Organizing Map (SOM) network, a variation of neural computing networks, is a categorization network developed by Kohonen. The theory of the SOM network is motivated by the observation of the operation of the brain. This paper presents the technique of SOM and shows how it may be applied as a clustering tool to market segmentation. A computer program for implementing the SOM neural networks is developed and the results will be compared with other clustering approaches. The study demonstrates the potential of using the Self-Organizing Map as the clustering tool for market segmentation

Mixing by polymers: experimental test of decay regime of mixing

By using high molecular weight fluorescent passive tracers with different diffusion coefficients and by changing the fluid velocity we study dependence of a characteristic mixing length on the Peclet number, $Pe$, which controls the mixing efficiency. The mixing length is found to be related to $Pe$ by a power law, $L_{mix}\propto Pe^{0.26\pm 0.01}$, and increases faster than expected for an unbounded chaotic flow. Role of the boundaries in the mixing length abnormal growth is clarified. The experimental findings are in a good quantitative agreement with the recent theoretical predictions.Comment: 4 pages,5 figures. accepted for publication in PR

A targeted gene panel that covers coding, non-coding and short tandem repeat regions improves the diagnosis of patients with neurodegenerative diseases

Genetic testing for neurodegenerative diseases (NDs) is highly challenging because of genetic heterogeneity and overlapping manifestations. Targeted-gene panels (TGPs), coupled with next-generation sequencing (NGS), can facilitate the profiling of a large repertoire of ND-related genes. Due to the technical limitations inherent in NGS and TGPs, short tandem repeat (STR) variations are often ignored. However, STR expansions are known to cause such NDs as Huntington\u27s disease and spinocerebellar ataxias type 3 (SCA3). Here, we studied the clinical utility of a custom-made TGP that targets 199 NDs and 311 ND-associated genes on 118 undiagnosed patients. At least one known or likely pathogenic variation was found in 54 patients; 27 patients demonstrated clinical profiles that matched the variants; and 16 patients whose original diagnosis were refined. A high concordance of variant calling were observed when comparing the results from TGP and whole-exome sequencing of four patients. Our in-house STR detection algorithm has reached a specificity of 0.88 and a sensitivity of 0.82 in our SCA3 cohort. This study also uncovered a trove of novel and recurrent variants that may enrich the repertoire of ND-related genetic markers. We propose that a combined comprehensive TGPs-bioinformatics pipeline can improve the clinical diagnosis of NDs

Broadband energy-efficient optical modulation by hybrid integration of silicon nanophotonics and organic electro-optic polymer

Silicon-organic hybrid integrated devices have emerging applications ranging from high-speed optical interconnects to photonic electromagnetic-field sensors. Silicon slot photonic crystal waveguides (PCWs) filled with electro-optic (EO) polymers combine the slow-light effect in PCWs with the high polarizability of EO polymers, which promises the realization of high-performance optical modulators. In this paper, a broadband, power-efficient, low-dispersion, and compact optical modulator based on an EO polymer filled silicon slot PCW is presented. A small voltage-length product of V{\pi}*L=0.282Vmm is achieved, corresponding to an unprecedented record-high effective in-device EO coefficient (r33) of 1230pm/V. Assisted by a backside gate voltage, the modulation response up to 50GHz is observed, with a 3-dB bandwidth of 15GHz, and the estimated energy consumption is 94.4fJ/bit at 10Gbit/s. Furthermore, lattice-shifted PCWs are utilized to enhance the optical bandwidth by a factor of ~10X over other modulators based on non-band-engineered PCWs and ring-resonators.Comment: 12 pages, 4 figures, SPIE Photonics West Conference 201

Microwaves reduce water refractive index

Microwaves, long used as a convenient household appliance, have been increasingly used in industrial processes such as organic synthesis and oil processing. It has been proposed that microwaves can enhance these chemical processes via a non-thermal effect. Here we report the instantaneous effect of microwaves on the permittivity and phase velocity of light in water through the in-situ measurement of changes in refractive index. Microwave irradiation was found to reduce the water refractive index (RI) sharply. The reduction increased as a function of microwave power to a far greater extent than expected from the change in temperature. The phase velocity of light in water increases up to ~ 5% (RI of 1.27) during microwave irradiation. Upon stopping irradiation, the return to the equilibrium RI was delayed by up to 30 min. Our measurement shows that microwaves have a profound non-thermal and long-lasting effect on the properties of water. Further investigation is planned to verify if the observed RI reduction is restricted to the region near the surface or deep inside water bulk. The observation suggests a relationship between microwave-induced and the enhanced aqueous reactions