Mathematical models for vulnerable plaques

A plaque is an accumulation and swelling in the artery walls and typically consists of cells, cell debris, lipids, calcium deposits and fibrous connective tissue. A person is likely to have many plaques inside his/her body even if they are healthy. However plaques may become "vulnerable", "high-risk" or "thrombosis-prone" if the person engages in a high-fat diet and does not exercise regularly. In this study group, we proposed two mathematical models to describe plaque growth and rupture. The first model is a mechanical one that approximately treats the plaque as an inflating elastic balloon. In this model, the pressure inside the core increases and then decreases suggesting that plaque stabilization and prevention of rupture is possible. The second model is a biochemical one that focuses on the role of MMPs in degrading the fibrous plaque cap. The cap stress, MMP concentration, plaque volume and cap thickness are coupled together in a system of phenomenological equations. The equations always predict an eventual rupture since the volume, stresses and MMP concentrations generally grow without bound. The main weakness of the model is that many of the important parameters that control the behavior of the plaque are unknown. The two simple models suggested by this group could serve as a springboard for more realistic theoretical studies. But most importantly, we hope they will motivate more experimental work to quantify some of the important mechanical and biochemical properties of vulnerable plaques

Multiple magnetic transitions in multiferroic BiMnO3

The magnetic phase variations under hydrostatic pressure on multiferroic BiMnO3 have been examined by the dc magnetization [Mg(T)], magnetic hysteresis [Ueff(H)], and ac susceptibility [X'g(T)]. Three magnetic transitions, manifested as kinks I, II, and III on the Mg(T)], curves, were identified at 8.7 and 9.4 kbar. With increasing pressure, transition temperatures of kink I and kink II TkI and TkII tend to decrease, but the temperature of kink III TkIII showed more complex variation. Under increasing magnetic field, TkI and TkII increase; however, TkIII decreases. Combining [Mg(T)] curves with Ueff(H) and X'g(T), more detailed properties of these three kinks would be shown as follows. Kink I is a long-range soft ferromagnetic transition which occurs at TkI 100 K under ambient pressure but is suppressed completely at 11.9 kbar. Kink II emerges at 8.7 kbar along with TkII 93 K which is also long-range soft ferromagnetic but canted in nature. Kink III, a canted antiferromagnetic transition, appears at TkIII 72.5 K along with kink II also at 8.7 kbar. The proposed phase diagrams at ambient pressure, 9.4 and 11.9 kbar show the different magnetic features of BiMnO3. These findings are believed to result from the variations in crystal structure influenced by the external pressure. These results also indicate the common complicatedComment: 6 paages, 5 gifures, published in Physical Review B 80, 184426/200

Validation of satellite retrievals of cloud microsphysics and liquid water path using observations from FIRE

Cloud effective radii (r(sub e)) and cloud liquid water path (LWP) are derived from ISCCP spatially sampled satellite data and validated with ground-based pyranometer and microwave radiometer measurements taken on San Nicolas Island during the 1987 FIRE IFO. Values of r(sub e) derived from the ISCCP data are also compared to values retrieved by a hybrid method that uses the combination of LWP derived from microwave measurement and optical thickness derived from GOES data. The results show that there is significant variability in cloud properties over a 100 km x 80 km area and that the values at San Nicolas Island are not necessarily representative of the surrounding cloud field. On the other hand, even though there were large spatial variations in optical depth, the r(sub e) values remained relatively constant (with sigma less than or equal to 2-3 microns in most cases) in the marine stratocumulus. Furthermore, values of r(sub e) derived from the upper portion of the cloud generally are representative of the entire stratiform cloud. When LWP values are less than 100 g m(exp -2), then LWP values derived from ISCCP data agree well with those values estimated from ground-based microwave measurements. In most cases LWP differences were less than 20 g m(exp -2). However, when LWP values become large (e.g., greater than or equal to 200 g m(exp -2)), then relative differences may be as large as 50%- 100%. There are two reasons for this discrepancy in the large LWP clouds: (1) larger vertical inhomogeneities in precipitating clouds and (2) sampling errors on days of high spatial variability of cloud optical thicknesses. Variations of r(sub e) in stratiform clouds may indicate drizzle: clouds with droplet sizes larger than 15 microns appear to be associated with drizzling, while those less than 10 microns are indicative of nonprecipitating clouds. Differences in r(sub e) values between the GOES and ISCCP datasets are found to be 0.16 +/- 0.98 micron

Multi-Label Multi-Kernel Transfer Learning for Human Protein Subcellular Localization

Recent years have witnessed much progress in computational modelling for protein subcellular localization. However, the existing sequence-based predictive models demonstrate moderate or unsatisfactory performance, and the gene ontology (GO) based models may take the risk of performance overestimation for novel proteins. Furthermore, many human proteins have multiple subcellular locations, which renders the computational modelling more complicated. Up to the present, there are far few researches specialized for predicting the subcellular localization of human proteins that may reside in multiple cellular compartments. In this paper, we propose a multi-label multi-kernel transfer learning model for human protein subcellular localization (MLMK-TLM). MLMK-TLM proposes a multi-label confusion matrix, formally formulates three multi-labelling performance measures and adapts one-against-all multi-class probabilistic outputs to multi-label learning scenario, based on which to further extends our published work GO-TLM (gene ontology based transfer learning model for protein subcellular localization) and MK-TLM (multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization) for multiplex human protein subcellular localization. With the advantages of proper homolog knowledge transfer, comprehensive survey of model performance for novel protein and multi-labelling capability, MLMK-TLM will gain more practical applicability. The experiments on human protein benchmark dataset show that MLMK-TLM significantly outperforms the baseline model and demonstrates good multi-labelling ability for novel human proteins. Some findings (predictions) are validated by the latest Swiss-Prot database. The software can be freely downloaded at http://soft.synu.edu.cn/upload/msy.rar

Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence

BACKGROUND: Knowing the submitochondria localization of a mitochondria protein is an important step to understand its function. We develop a method which is based on an extended version of pseudo-amino acid composition to predict the protein localization within mitochondria. This work goes one step further than predicting protein subcellular location. We also try to predict the membrane protein type for mitochondrial inner membrane proteins. RESULTS: By using leave-one-out cross validation, the prediction accuracy is 85.5% for inner membrane, 94.5% for matrix and 51.2% for outer membrane. The overall prediction accuracy for submitochondria location prediction is 85.2%. For proteins predicted to localize at inner membrane, the accuracy is 94.6% for membrane protein type prediction. CONCLUSION: Our method is an effective method for predicting protein submitochondria location. But even with our method or the methods at subcellular level, the prediction of protein submitochondria location is still a challenging problem. The online service SubMito is now available at

Gene ontology based transfer learning for protein subcellular localization

<p>Abstract</p> <p>Background</p> <p>Prediction of protein subcellular localization generally involves many complex factors, and using only one or two aspects of data information may not tell the true story. For this reason, some recent predictive models are deliberately designed to integrate multiple heterogeneous data sources for exploiting multi-aspect protein feature information. Gene ontology, hereinafter referred to as <it>GO</it>, uses a controlled vocabulary to depict biological molecules or gene products in terms of biological process, molecular function and cellular component. With the rapid expansion of annotated protein sequences, gene ontology has become a general protein feature that can be used to construct predictive models in computational biology. Existing models generally either concatenated the <it>GO </it>terms into a flat binary vector or applied majority-vote based ensemble learning for protein subcellular localization, both of which can not estimate the individual discriminative abilities of the three aspects of gene ontology.</p> <p>Results</p> <p>In this paper, we propose a Gene Ontology Based Transfer Learning Model (<it>GO-TLM</it>) for large-scale protein subcellular localization. The model transfers the signature-based homologous <it>GO </it>terms to the target proteins, and further constructs a reliable learning system to reduce the adverse affect of the potential false <it>GO </it>terms that are resulted from evolutionary divergence. We derive three <it>GO </it>kernels from the three aspects of gene ontology to measure the <it>GO </it>similarity of two proteins, and derive two other spectrum kernels to measure the similarity of two protein sequences. We use simple non-parametric cross validation to explicitly weigh the discriminative abilities of the five kernels, such that the time & space computational complexities are greatly reduced when compared to the complicated semi-definite programming and semi-indefinite linear programming. The five kernels are then linearly merged into one single kernel for protein subcellular localization. We evaluate <it>GO-TLM </it>performance against three baseline models: <it>MultiLoc, MultiLoc-GO </it>and <it>Euk-mPLoc </it>on the benchmark datasets the baseline models adopted. 5-fold cross validation experiments show that <it>GO-TLM </it>achieves substantial accuracy improvement against the baseline models: 80.38% against model <it>Euk-mPLoc </it>67.40% with <it>12.98% </it>substantial increase; 96.65% and 96.27% against model <it>MultiLoc-GO </it>89.60% and 89.60%, with <it>7.05% </it>and <it>6.67% </it>accuracy increase on dataset <it>MultiLoc plant </it>and dataset <it>MultiLoc animal</it>, respectively; 97.14%, 95.90% and 96.85% against model <it>MultiLoc-GO </it>83.70%, 90.10% and 85.70%, with accuracy increase <it>13.44%</it>, <it>5.8% </it>and <it>11.15% </it>on dataset <it>BaCelLoc plant</it>, dataset <it>BaCelLoc fungi </it>and dataset <it>BaCelLoc animal </it>respectively. For <it>BaCelLoc </it>independent sets, <it>GO-TLM </it>achieves 81.25%, 80.45% and 79.46% on dataset <it>BaCelLoc plant holdout</it>, dataset <it>BaCelLoc plant holdout </it>and dataset <it>BaCelLoc animal holdout</it>, respectively, as compared against baseline model <it>MultiLoc-GO </it>76%, 60.00% and 73.00%, with accuracy increase <it>5.25%</it>, <it>20.45% </it>and <it>6.46%</it>, respectively.</p> <p>Conclusions</p> <p>Since direct homology-based <it>GO </it>term transfer may be prone to introducing noise and outliers to the target protein, we design an explicitly weighted kernel learning system (called Gene Ontology Based Transfer Learning Model, <it>GO-TLM</it>) to transfer to the target protein the known knowledge about related homologous proteins, which can reduce the risk of outliers and share knowledge between homologous proteins, and thus achieve better predictive performance for protein subcellular localization. Cross validation and independent test experimental results show that the homology-based <it>GO </it>term transfer and explicitly weighing the <it>GO </it>kernels substantially improve the prediction performance.</p

Classical limit for the scattering of Dirac particles in a magnetic field

We present a relativistic quantum calculation at first order in perturbation theory of the differential cross section for a Dirac particle scattered by a solenoidal magnetic field. The resulting cross section is symmetric in the scattering angle as those obtained by Aharonov and Bohm (AB) in the string limit and by Landau and Lifshitz (LL) for the non relativistic case. We show that taking pr_0\|sin(\theta/2)|/\hbar<<1 in our expression of the differential cross section it reduces to the one reported by AB, and if additionally we assume \theta << 1 our result becomes the one obtained by LL. However, these limits are explicitly singular in \hbar as opposed to our initial result. We analyze the singular behavior in \hbar and show that the perturbative Planck's limit (\hbar -> 0) is consistent, contrarily to those of the AB and LL expressions. We also discuss the scattering in a uniform and constant magnetic field, which resembles some features of QCD

On the Decoding Failure Rate of QC-MDPC Bit-Flipping Decoders

International audienceQuasi-cyclic moderate density parity check codes allow the design of McEliece-like public-key encryption schemes with compact keys and a security that provably reduces to hard decoding problems for quasi-cyclic codes.In particular, QC-MDPC are among the most promising code-based key encapsulation mechanisms (KEM) that are proposed to the NIST call for standardization of quantum safe cryptography (two proposals, BIKE and QC-MDPC KEM).The first generation of decoding algorithms suffers from a small, but not negligible, decoding failure rate (DFR in the order of 10⁻⁷ to 10⁻¹⁰). This allows a key recovery attack presented by Guo, Johansson, and Stankovski (GJS attack) at Asiacrypt 2016 which exploits a small correlation between the faulty message patterns and the secret key of the scheme, and limits the usage of the scheme to KEMs using ephemeral public keys. It does not impact the interactive establishment of secure communications (e.g. TLS), but the use of static public keys for asynchronous applications (e.g. email) is rendered dangerous.Understanding and improving the decoding of QC-MDPC is thus of interest for cryptographic applications. In particular, finding parameters for which the failure rate is provably negligible (typically as low as 2⁻⁶⁴ or 2⁻¹²⁸) would allow static keys and increase the applicability of the mentioned cryptosystems.We study here a simple variant of bit-flipping decoding, which we call step-by-step decoding. It has a higher DFR but its evolution can be modeled by a Markov chain, within the theoretical framework of Julia Chaulet's PhD thesis. We study two other, more efficient, decoders. One is the textbook algorithm. The other is (close to) the BIKE decoder. For all those algorithms we provide simulation results, and, assuming an evolution similar to the step-by-step decoder, we extrapolate the value of the DFR as a function of the block length. This will give an indication of how much the code parameters must be increased to ensure resistance to the GJS attack