Detecting spatial patterns with the cumulant function. Part II: An application to El Nino

The spatial coherence of a measured variable (e.g. temperature or pressure) is often studied to determine the regions where this variable varies the most or to find teleconnections, i.e. correlations between specific regions. While usual methods to find spatial patterns, such as Principal Components Analysis (PCA), are constrained by linear symmetries, the dependence of variables such as temperature or pressure at different locations is generally nonlinear. In particular, large deviations from the sample mean are expected to be strongly affected by such nonlinearities. Here we apply a newly developed nonlinear technique (Maxima of Cumulant Function, MCF) for the detection of typical spatial patterns that largely deviate from the mean. In order to test the technique and to introduce the methodology, we focus on the El Nino/Southern Oscillation and its spatial patterns. We find nonsymmetric temperature patterns corresponding to El Nino and La Nina, and we compare the results of MCF with other techniques, such as the symmetric solutions of PCA, and the nonsymmetric solutions of Nonlinear PCA (NLPCA). We found that MCF solutions are more reliable than the NLPCA fits, and can capture mixtures of principal components. Finally, we apply Extreme Value Theory on the temporal variations extracted from our methodology. We find that the tails of the distribution of extreme temperatures during La Nina episodes is bounded, while the tail during El Ninos is less likely to be bounded. This implies that the mean spatial patterns of the two phases are asymmetric, as well as the behaviour of their extremes.Comment: 15 pages, 7 figure

The investigation into highly efficient heptazine-based polymeric photocatalysts for visible light-driven solar fuel synthesis

Artificial photosynthesis has been regarded as a promising method to generate fuels in a much greener way by utilising inexhaustible solar energy via water splitting and CO2 conversion. Polymeric semiconductors have been recently identified as promising photocatalysts due to their comparatively low cost and ease modification of the electronic structure. However, the majority only respond to a limited wavelength region ( 420 nm), leading to a 10.3% apparent quantum yield (λ = 420 nm). Both theoretical calculations and spectroscopies have attributed such superior performance to enhanced charge separation and narrow bandgap. Such new polymer was then coupled with an inorganic photocatalyst to construct a Z-scheme system, which successfully splits water into both H2 and O2 in a stoichiometry ratio. Further, an efficient strategy was demonstrated to stepwise tailor the bandgap of polymeric photocatalysts from 2.7 to 1.9 eV by carefully manipulating the O/N linker/terminal atoms in the heptazine chains. These polymers work stably and efficiently for both H2 and O2 evolution (420 nm < λ < 710 nm), exhibiting nearly 20 times higher activity compared to g-C3N4 with high AQYs under visible light irradiation. Experimental and theoretical results have attributed the narrowed band gap and enhanced charge separation to the oxygen incorporation into the linker/terminal position. Based on this success, a more challenging multi-electron photochemical process of visible light-driven CO2 reduction in water was investigated using junctions consisting of the novel polymers and two kinds of carbon quantum dots (CQD) cocatalysts. The novel CQD was synthesised via a microwave-assisted method while the other CQD fabricated via sonication of glucose was reported as reduction cocatalysts (redCQD). In CO2 reduction reactions, the novel CQD/polymer junctions selectively produce methanol and O2 while the redCQD/polymer junction generates CO only. Ultrafast spectroscopies revealed that novel CQD works as a hole acceptor in the junctions, different from the redCQD as an electron acceptor. Electrons reach the surface of polymers to reduce CO2 to produce methanol while holes accumulate on CQD to oxidise water. Microwave-assisted CQD shows more favourable water adsorption instead of methanol adsorption compared with polymers, thus facilitating methanol production instead of CO. Therefore, the function of CQD is a key reason for such high selectivity

Associative Pattern Recognition for Biological Regulation Data

In the last decade, bioinformatics data has been accumulated at an unprecedented rate, thanks to the advancement in sequencing technologies. Such rapid development poses both challenges and promising research topics. In this dissertation, we propose a series of associative pattern recognition algorithms in biological regulation studies. In particular, we emphasize efficiently recognizing associative patterns between genes, transcription factors, histone modifications and functional labels using heterogeneous data sources (numeric, sequences, time series data and textual labels). In protein-DNA associative pattern recognition, we introduce an efficient algorithm for affinity test by searching for over-represented DNA sequences using a hash function and modulo addition calculation. This substantially improves the efficiency of \textit{next generation sequencing} data analysis. In gene regulatory network inference, we propose a framework for refining weak networks based on transcription factor binding sites, thus improved the precision of predicted edges by up to 52%. In histone modification code analysis, we propose an approach to genome-wide combinatorial pattern recognition for histone code to function associative pattern recognition, and achieved improvement by up to $38.1\%$. We also propose a novel shape based modification pattern analysis approach, using this to successfully predict sub-classes of genes in flowering-time category. We also propose a combination to combination associative pattern recognition, and achieved better performance compared against multi-label classification and bidirectional associative memory methods. Our proposed approaches recognize associative patterns from different types of data efficiently, and provides a useful toolbox for biological regulation analysis. This dissertation presents a road-map to associative patterns recognition at genome wide level

Meteoritic ablation and fusion spherules in Antarctic ice

In the course of two Antarctic expeditions in 1980/1981 and 1982/1983 approximately 4 metric tons of documented ice samples were collected from the Atka Bay Ice Shelf, Antarctica, and subsequently shipped for cosmic dust studies. After filtration of the melt water, approximately 700 Antarctic spherules (AAS) in the size range of 5 to 500 microns were handpicked from the filter residue under optical microscopes. For the chemical investigation of single dust grains the following techniques were applied: scanning electron microscopy (SEM), X-ray analysis (EDAX), instrumental neutron activation analysis (INAA), laser microprobe mass analysis (LAMMA), and accelerator mass spectroscopy (AMS). For more than 95% of the total mass the bulk and trace elements were determined in single grain analyses using EDAX, INAA, and LAMMA. The element pattern of the dust particles was compared with that of typical terrestrial material and meteoritic matter. The majority of the spherules exhibited elemental compositions compatible with meteoritic element patterns