Identification of Nonlinear State-Space Systems from Heterogeneous Datasets

This paper proposes a new method to identify nonlinear state-space systems from heterogeneous datasets. The method is described in the context of identifying biochemical/gene networks (i.e., identifying both reaction dynamics and kinetic parameters) from experimental data. Simultaneous integration of various datasets has the potential to yield better performance for system identification. Data collected experimentally typically vary depending on the specific experimental setup and conditions. Typically, heterogeneous data are obtained experimentally through (a) replicate measurements from the same biological system or (b) application of different experimental conditions such as changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. We formulate here the identification problem using a Bayesian learning framework that makes use of “sparse group” priors to allow inference of the sparsest model that can explain the whole set of observed, heterogeneous data. To enable scale up to large number of features, the resulting non-convex optimisation problem is relaxed to a re-weighted Group Lasso problem using a convex-concave procedure. As an illustrative example of the effectiveness of our method, we use it to identify a genetic oscillator (generalised eight species repressilator). Through this example we show that our algorithm outperforms Group Lasso when the number of experiments is increased, even when each single time-series dataset is short. We additionally assess the robustness of our algorithm against noise by varying the intensity of process noise and measurement noise

Trapping of the transport-segment DNA by the ATPase domains of a type II topoisomerase

Type II topoisomerases alter DNA topology to control DNA supercoiling and chromosome segregation and are targets of clinically important anti-infective and anticancer therapeutics. They act as ATP-operated clamps to trap a DNA helix and transport it through a transient break in a second DNA. Here, we present the first X-ray crystal structure solved at 2.83 Å of a closed clamp complete with trapped T-segment DNA obtained by co-crystallizing the ATPase domain of S. pneumoniae topoisomerase IV with a nonhydrolyzable ATP analogue and 14-mer duplex DNA. The ATPase dimer forms a 22 Å protein hole occupied by the kinked DNA bound asymmetrically through positively charged residues lining the hole, and whose mutagenesis impacts the DNA decatenation, DNA relaxation and DNA-dependent ATPase activities of topo IV. These results and a side-bound DNA-ParE structure help explain how the T-segment DNA is captured and transported by a type II topoisomerase, and reveal a new enzyme–DNA interface for drug discovery

Weather in stellar atmosphere: the dynamics of mercury clouds in alpha Andromedae

The formation of long-lasting structures at the surfaces of stars is commonly ascribed to the action of strong magnetic fields. This paradigm is supported by observations of evolving cool spots in the Sun and active late-type stars, and stationary chemical spots in the early-type magnetic stars. However, results of our seven-year monitoring of mercury spots in non-magnetic early-type star alpha Andromedae show that the picture of magnetically-driven structure formation is fundamentally incomplete. Using an indirect stellar surface mapping technique, we construct a series of 2-D images of starspots and discover a secular evolution of the mercury cloud cover in this star. This remarkable structure formation process, observed for the first time in any star, is plausibly attributed to a non-equilibrium, dynamical evolution of the heavy-element clouds created by atomic diffusion and may have the same underlying physics as the weather patterns on terrestrial and giant planets.Comment: 10 pages, 2 figures; to be published in Nature Physic

Elevated CO₂ does not increase eucalypt forest productivity on a low-phosphorus soil

Rising atmospheric CO2 stimulates photosynthesis and productivity of forests, offsetting CO2 emissions. Elevated CO2 experiments in temperate planted forests yielded ~23% increases in productivity over the initial years. Whether similar CO2 stimulation occurs in mature evergreen broadleaved forests on low-phosphorus (P) soils is unknown, largely due to lack of experimental evidence. This knowledge gap creates major uncertainties in future climate projections as a large part of the tropics is P-limited. Here,we increased atmospheric CO2 concentration in a mature broadleaved evergreen eucalypt forest for three years, in the first large-scale experiment on a P-limited site. We show that tree growth and other aboveground productivity components did not significantly increase in response to elevated CO2 in three years, despite a sustained 19% increase in leaf photosynthesis. Moreover, tree growth in ambient CO2 was strongly P-limited and increased by ~35% with added phosphorus. The findings suggest that P availability may potentially constrain CO2-enhanced productivity in P-limited forests; hence, future atmospheric CO2 trajectories may be higher than predicted by some models. As a result, coupled climate-carbon models should incorporate both nitrogen and phosphorus limitations to vegetation productivity in estimating future carbon sinks

Network inference analysis identifies an APRR2-like gene linked to pigment accumulation in tomato and pepper fruits

Carotenoids represent some of the most important secondary metabolites in the human diet, and tomato (Solanum lycopersicum) is a rich source of these health-promoting compounds. In this work, a novel and fruit-related regulator of pigment accumulation in tomato has been identified by artificial neural network inference analysis and its function validated in transgenic plants. A tomato fruit gene regulatory network was generated using artificial neural network inference analysis and transcription factor gene expression profiles derived from fruits sampled at various points during development and ripening. One of the transcription factor gene expression profiles with a sequence related to an Arabidopsis (Arabidopsis thaliana) ARABIDOPSIS PSEUDO RESPONSE REGULATOR2-LIKE gene (APRR2-Like) was up-regulated at the breaker stage in wild-type tomato fruits and, when overexpressed in transgenic lines, increased plastid number, area, and pigment content, enhancing the levels of chlorophyll in immature unripe fruits and carotenoids in red ripe fruits. Analysis of the transcriptome of transgenic lines overexpressing the tomato APPR2-Like gene revealed up-regulation of several ripening-related genes in the overexpression lines, providing a link between the expression of this tomato gene and the ripening process. A putative ortholog of the tomato APPR2-Like gene in sweet pepper (Capsicum annuum) was associated with pigment accumulation in fruit tissues. We conclude that the function of this gene is conserved across taxa and that it encodes a protein that has an important role in ripening

End-to-end Interpretable Learning of Non-blind Image Deblurring

Non-blind image deblurring is typically formulated as a linear least-squares problem regularized by natural priors on the corresponding sharp picture's gradients, which can be solved, for example, using a half-quadratic splitting method with Richardson fixed-point iterations for its least-squares updates and a proximal operator for the auxiliary variable updates. We propose to precondition the Richardson solver using approximate inverse filters of the (known) blur and natural image prior kernels. Using convolutions instead of a generic linear preconditioner allows extremely efficient parameter sharing across the image, and leads to significant gains in accuracy and/or speed compared to classical FFT and conjugate-gradient methods. More importantly, the proposed architecture is easily adapted to learning both the preconditioner and the proximal operator using CNN embeddings. This yields a simple and efficient algorithm for non-blind image deblurring which is fully interpretable, can be learned end to end, and whose accuracy matches or exceeds the state of the art, quite significantly, in the non-uniform case.Comment: Accepted at ECCV2020 (poster

Release of PLGA–encapsulated dexamethasone from microsphere loaded porous surfaces

The aim of the present study was to investigate the morphology and function of a drug eluting metallic porous surface produced by the immobilization of poly lactide-co-glycolide microspheres bearing dexamethasone onto plasma electrolytically oxidized Ti–6Al–7Nb medical alloy. Spheres of 20 μm diameter were produced by an oil-in-water emulsion/solvent evaporation method and thermally immobilized onto titanium discs. The scanning electron microscopy investigations revealed that the size distribution and morphology of the attached spheres had not changed significantly. The drug release profiles following degradation in phosphate buffered saline for 1000 h showed that, upon immobilisation, the spheres maintained a sustained release, with a triphasic profile similar to the non-attached system. The only significant change was an increased release rate during the first 100 h. This difference was attributed to the effect of thermal attachment of the spheres to the surface

Anti-HIV-1 activity of cellulose acetate phthalate: Synergy with soluble CD4 and induction of "dead-end" gp41 six-helix bundles

BACKGROUND: Cellulose acetate phthalate (CAP), a promising candidate microbicide for prevention of sexual transmission of the human immunodeficiency virus type 1 (HIV-1) and other sexually transmitted disease (STD) pathogens, was shown to inactivate HIV-1 and to block the coreceptor binding site on the virus envelope glycoprotein gp120. It did not interfere with virus binding to CD4. Since CD4 is the primary cellular receptor for HIV-1, it was of interest to study CAP binding to HIV-1 complexes with soluble CD4 (sCD4) and its consequences, including changes in the conformation of the envelope glycoprotein gp41 within virus particles. METHODS: Enzyme-linked immunosorbent assays (ELISA) were used to study CAP binding to HIV-1-sCD4 complexes and to detect gp41 six-helix bundles accessible on virus particles using antibodies specific for the α-helical core domain of gp41. RESULTS: 1) Pretreatment of HIV-1 with sCD4 augments subsequent binding of CAP; 2) there is synergism between CAP and sCD4 for inhibition of HIV-1 infection; 3) treatment of HIV-1 with CAP induced the formation of gp41 six-helix bundles. CONCLUSIONS: CAP and sCD4 bind to distinct sites on HIV-1 IIIB and BaL virions and their simultaneous binding has profound effects on virus structure and infectivity. The formation of gp41 six-helical bundles, induced by CAP, is known to render the virus incompetent for fusion with target cells thus preventing infection

Modelling personal thermal sensations using C-Support Vector Classification (C-SVC) algorithm

The personalised conditioning system (PCS) is widely studied. Potentially, it is able to reduce energy consumption while securing occupants’ thermal comfort requirements. It has been suggested that automatic optimised operation schemes for PCS should be introduced to avoid energy wastage and discomfort caused by inappropriate operation. In certain automatic operation schemes, personalised thermal sensation models are applied as key components to help in setting targets for PCS operation. In this research, a novel personal thermal sensation modelling method based on the C-Support Vector Classification (C-SVC) algorithm has been developed for PCS control. The personal thermal sensation modelling has been regarded as a classification problem. During the modelling process, the method ‘learns’ an occupant’s thermal preferences from his/her feedback, environmental parameters and personal physiological and behavioural factors. The modelling method has been verified by comparing the actual thermal sensation vote (TSV) with the modelled one based on 20 individual cases. Furthermore, the accuracy of each individual thermal sensation model has been compared with the outcomes of the PMV model. The results indicate that the modelling method presented in this paper is an effective tool to model personal thermal sensations and could be integrated within the PCS for optimised system operation and control