Recent Advances Concerning Certain Class of Geophysical Flows

This paper is devoted to reviewing several recent developments concerning certain class of geophysical models, including the primitive equations (PEs) of atmospheric and oceanic dynamics and a tropical atmosphere model. The PEs for large-scale oceanic and atmospheric dynamics are derived from the Navier-Stokes equations coupled to the heat convection by adopting the Boussinesq and hydrostatic approximations, while the tropical atmosphere model considered here is a nonlinear interaction system between the barotropic mode and the first baroclinic mode of the tropical atmosphere with moisture. We are mainly concerned with the global well-posedness of strong solutions to these systems, with full or partial viscosity, as well as certain singular perturbation small parameter limits related to these systems, including the small aspect ratio limit from the Navier-Stokes equations to the PEs, and a small relaxation-parameter in the tropical atmosphere model. These limits provide a rigorous justification to the hydrostatic balance in the PEs, and to the relaxation limit of the tropical atmosphere model, respectively. Some conditional uniqueness of weak solutions, and the global well-posedness of weak solutions with certain class of discontinuous initial data, to the PEs are also presented.Comment: arXiv admin note: text overlap with arXiv:1507.0523

Deep Learning for Forecasting Stock Returns in the Cross-Section

Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achieves high performance mainly in image recognition and speech recognition, has attracted attention in the machine learning field. This paper implements deep learning to predict one-month-ahead stock returns in the cross-section in the Japanese stock market and investigates the performance of the method. Our results show that deep neural networks generally outperform shallow neural networks, and the best networks also outperform representative machine learning models. These results indicate that deep learning shows promise as a skillful machine learning method to predict stock returns in the cross-section.Comment: 12 pages, 2 figures, 8 tables, accepted at PAKDD 201

Remodelling of human atrial K+ currents but not ion channel expression by chronic β-blockade

Chronic β-adrenoceptor antagonist (β-blocker) treatment in patients is associated with a potentially anti-arrhythmic prolongation of the atrial action potential duration (APD), which may involve remodelling of repolarising K+ currents. The aim of this study was to investigate the effects of chronic β-blockade on transient outward, sustained and inward rectifier K+ currents (ITO, IKSUS and IK1) in human atrial myocytes and on the expression of underlying ion channel subunits. Ion currents were recorded from human right atrial isolated myocytes using the whole-cell-patch clamp technique. Tissue mRNA and protein levels were measured using real time RT-PCR and Western blotting. Chronic β-blockade was associated with a 41% reduction in ITO density: 9.3 ± 0.8 (30 myocytes, 15 patients) vs 15.7 ± 1.1 pA/pF (32, 14), p < 0.05; without affecting its voltage-, time- or rate dependence. IK1 was reduced by 34% at −120 mV (p < 0.05). Neither IKSUS, nor its increase by acute β-stimulation with isoprenaline, was affected by chronic β-blockade. Mathematical modelling suggested that the combination of ITO- and IK1-decrease could result in a 28% increase in APD90. Chronic β-blockade did not alter mRNA or protein expression of the ITO pore-forming subunit, Kv4.3, or mRNA expression of the accessory subunits KChIP2, KChAP, Kvβ1, Kvβ2 or frequenin. There was no reduction in mRNA expression of Kir2.1 or TWIK to account for the reduction in IK1. A reduction in atrial ITO and IK1 associated with chronic β-blocker treatment in patients may contribute to the associated action potential prolongation, and this cannot be explained by a reduction in expression of associated ion channel subunits

Regulation of mitochondrial biogenesis in erythropoiesis by mTORC1-mediated protein translation.

Advances in genomic profiling present new challenges of explaining how changes in DNA and RNA are translated into proteins linking genotype to phenotype. Here we compare the genome-scale proteomic and transcriptomic changes in human primary haematopoietic stem/progenitor cells and erythroid progenitors, and uncover pathways related to mitochondrial biogenesis enhanced through post-transcriptional regulation. Mitochondrial factors including TFAM and PHB2 are selectively regulated through protein translation during erythroid specification. Depletion of TFAM in erythroid cells alters intracellular metabolism, leading to elevated histone acetylation, deregulated gene expression, and defective mitochondria and erythropoiesis. Mechanistically, mTORC1 signalling is enhanced to promote translation of mitochondria-associated transcripts through TOP-like motifs. Genetic and pharmacological perturbation of mitochondria or mTORC1 specifically impairs erythropoiesis in vitro and in vivo. Our studies support a mechanism for post-transcriptional control of erythroid mitochondria and may have direct relevance to haematologic defects associated with mitochondrial diseases and ageing

3D Face Reconstruction from Light Field Images: A Model-free Approach

Reconstructing 3D facial geometry from a single RGB image has recently instigated wide research interest. However, it is still an ill-posed problem and most methods rely on prior models hence undermining the accuracy of the recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI) obtained from light field cameras and learn CNN models that recover horizontal and vertical 3D facial curves from the respective horizontal and vertical EPIs. Our 3D face reconstruction network (FaceLFnet) comprises a densely connected architecture to learn accurate 3D facial curves from low resolution EPIs. To train the proposed FaceLFnets from scratch, we synthesize photo-realistic light field images from 3D facial scans. The curve by curve 3D face estimation approach allows the networks to learn from only 14K images of 80 identities, which still comprises over 11 Million EPIs/curves. The estimated facial curves are merged into a single pointcloud to which a surface is fitted to get the final 3D face. Our method is model-free, requires only a few training samples to learn FaceLFnet and can reconstruct 3D faces with high accuracy from single light field images under varying poses, expressions and lighting conditions. Comparison on the BU-3DFE and BU-4DFE datasets show that our method reduces reconstruction errors by over 20% compared to recent state of the art

Explanation of the Colour Change in Alexandrites.

Alexandrites are remarkable and rare gemstones. They display an extraordinary colour change according to the ambient lighting, from emerald green in daylight to ruby red in incandescent light from tungsten lamps or candles. While this colour change has been correctly attributed to chromium impurities and their absorption band in the yellow region of the visible light spectrum, no adequate explanation of the mechanism has been given. Here, the alexandrite effect is fully explained by considering the von Kries model of the human colour constancy mechanism. This implies that our colour constancy mechanism is real (objective) and primarily attuned to correct for the colour temperature of black-body illuminants

Social density processes regulate the functioning and performance of foraging human teams

Social density processes impact the activity and order of collective behaviours in a variety of biological systems. Much effort has been devoted to understanding how density of people affects collective human motion in the context of pedestrian flows. However, there is a distinct lack of empirical data investigating the effects of social density on human behaviour in cooperative contexts. Here, we examine the functioning and performance of human teams in a central-place foraging arena using high-resolution GPS data. We show that team functioning (level of coordination) is greatest at intermediate social densities, but contrary to our expectations, increased coordination at intermediate densities did not translate into improved collective foraging performance, and foraging accuracy was equivalent across our density treatments. We suggest that this is likely a consequence of foragers relying upon visual channels (local information) to achieve coordination but relying upon auditory channels (global information) to maximise foraging returns. These findings provide new insights for the development of more sophisticated models of human collective behaviour that consider different networks for communication (e.g. visual and vocal) that have the potential to operate simultaneously in cooperative contexts

Temporal Model Adaptation for Person Re-Identification

Person re-identification is an open and challenging problem in computer vision. Majority of the efforts have been spent either to design the best feature representation or to learn the optimal matching metric. Most approaches have neglected the problem of adapting the selected features or the learned model over time. To address such a problem, we propose a temporal model adaptation scheme with human in the loop. We first introduce a similarity-dissimilarity learning method which can be trained in an incremental fashion by means of a stochastic alternating directions methods of multipliers optimization procedure. Then, to achieve temporal adaptation with limited human effort, we exploit a graph-based approach to present the user only the most informative probe-gallery matches that should be used to update the model. Results on three datasets have shown that our approach performs on par or even better than state-of-the-art approaches while reducing the manual pairwise labeling effort by about 80%

Definition of the σW regulon of Bacillus subtilis in the absence of stress

Bacteria employ extracytoplasmic function (ECF) sigma factors for their responses to environmental stresses. Despite intensive research, the molecular dissection of ECF sigma factor regulons has remained a major challenge due to overlaps in the ECF sigma factor-regulated genes and the stimuli that activate the different ECF sigma factors. Here we have employed tiling arrays to single out the ECF σW regulon of the Gram-positive bacterium Bacillus subtilis from the overlapping ECF σX, σY, and σM regulons. For this purpose, we profiled the transcriptome of a B. subtilis sigW mutant under non-stress conditions to select candidate genes that are strictly σW-regulated. Under these conditions, σW exhibits a basal level of activity. Subsequently, we verified the σW-dependency of candidate genes by comparing their transcript profiles to transcriptome data obtained with the parental B. subtilis strain 168 grown under 104 different conditions, including relevant stress conditions, such as salt shock. In addition, we investigated the transcriptomes of rasP or prsW mutant strains that lack the proteases involved in the degradation of the σW anti-sigma factor RsiW and subsequent activation of the σW-regulon. Taken together, our studies identify 89 genes as being strictly σW-regulated, including several genes for non-coding RNAs. The effects of rasP or prsW mutations on the expression of σW-dependent genes were relatively mild, which implies that σW-dependent transcription under non-stress conditions is not strictly related to RasP and PrsW. Lastly, we show that the pleiotropic phenotype of rasP mutant cells, which have defects in competence development, protein secretion and membrane protein production, is not mirrored in the transcript profile of these cells. This implies that RasP is not only important for transcriptional regulation via σW, but that this membrane protease also exerts other important post-transcriptional regulatory functions

Evidence of the Generation of Isosaccharinic Acids and Their Subsequent Degradation by Local Microbial Consortia within Hyper-Alkaline Contaminated Soils, with Relevance to Intermediate Level Radioactive Waste Disposal

The contamination of surface environments with hydroxide rich wastes leads to the formation of high pH (>11.0) soil profiles. One such site is a legacy lime works at Harpur Hill, Derbyshire where soil profile indicated in-situ pH values up to pH 12. Soil and porewater profiles around the site indicated clear evidence of the presence of the α and β stereoisomers of isosaccharinic acid (ISA) resulting from the anoxic, alkaline degradation of cellulosic material. ISAs are of particular interest with regards to the disposal of cellulosic materials contained within the intermediate level waste (ILW) inventory of the United Kingdom, where they may influence radionuclide mobility via complexation events occurring within a geological disposal facility (GDF) concept. The mixing of uncontaminated soils with the alkaline leachate of the site resulted in ISA generation, where the rate of generation in-situ is likely to be dependent upon the prevailing temperature of the soil. Microbial consortia present in the uncontaminated soil were capable of surviving conditions imposed by the alkaline leachate and demonstrated the ability to utilise ISAs as a carbon source. Leachate-contaminated soil was sub-cultured in a cellulose degradation product driven microcosm operating at pH 11, the consortia present were capable of the degradation of ISAs and the generation of methane from the resultant H2/CO2 produced from fermentation processes. Following microbial community analysis, fermentation processes appear to be predominated by Clostridia from the genus Alkaliphilus sp, with methanogenesis being attributed to Methanobacterium and Methanomassiliicoccus sp. The study is the first to identify the generation of ISA within an anthropogenic environment and advocates the notion that microbial activity within an ILW-GDF is likely to influence the impact of ISAs upon radionuclide migration