SeqNLS: Nuclear Localization Signal Prediction Based on Frequent Pattern Mining and Linear Motif Scoring

Nuclear localization signals (NLSs) are stretches of residues in proteins mediating their importing into the nucleus. NLSs are known to have diverse patterns, of which only a limited number are covered by currently known NLS motifs. Here we propose a sequential pattern mining algorithm SeqNLS to effectively identify potential NLS patterns without being constrained by the limitation of current knowledge of NLSs. The extracted frequent sequential patterns are used to predict NLS candidates which are then filtered by a linear motif-scoring scheme based on predicted sequence disorder and by the relatively local conservation (IRLC) based masking. The experiment results on the newly curated Yeast and Hybrid datasets show that SeqNLS is effective in detecting potential NLSs. The performance comparison between SeqNLS with and without the linear motif scoring shows that linear motif features are highly complementary to sequence features in discerning NLSs. For the two independent datasets, our SeqNLS not only can consistently find over 50% of NLSs with prediction precision of at least 0.7, but also outperforms other state-of-the-art NLS prediction methods in terms of F1 score or prediction precision with similar or higher recall rates. The web server of the SeqNLS algorithm is available at http://mleg.cse.sc.edu/seqNLS

Critical current density and vortex pinning in tetragonal FeS1−x_{1-x}Sex_{x} (x=0,0.06x=0,0.06)

We report critical current density ($J_c$) in tetragonal FeS single crystals, similar to iron based superconductors with much higher superconducting critical temperatures ($T_{c}$'s). The $J_c$ is enhanced 3 times by 6\% Se doping. We observe scaling of the normalized vortex pinning force as a function of reduced field at all temperatures. Vortex pinning in FeS and FeS$_{0.94}$Se$_{0.06}$ shows contribution of core-normal surface-like pinning. Reduced temperature dependence of $J_c$ indicates that dominant interaction of vortex cores and pinning centers is via scattering of charge carriers with reduced mean free path ($\delta$$l$), in contrast to K$_x$Fe$_{2-y}$Se$_2$ where spatial variations in $T_{c}$ ($\delta$$T_{c}$) prevails.Comment: 5 pages, 4 figure

STING-mediated disruption of calcium homeostasis chronically activates ER stress and primes T cell death

STING gain-of-function mutations cause lung disease and T cell cytopenia through unknown mechanisms. Here, we found that these mutants induce chronic activation of ER stress and unfolded protein response (UPR), leading to T cell death by apoptosis in th

Strain induced half-metal to semiconductor transition in GdN

We have investigated the electronic structure and magnetic properties of GdN as a function of unit cell volume. Based on the first-principles calculations of GdN, we observe that there is a transformation in conduction properties associated with the volume increase: first from halfmetallic to semi-metallic, then ultimately to semiconducting. We show that applying stress can alter the carrier concentration as well as mobility of the holes and electrons in the majority spin channel. In addition, we found that the exchange parameters depend strongly on lattice constant, thus the Curie temperature of this system can be enhanced by applying stress or doping impurities.Comment: 9 pages, 3 figure

Translocator protein in late stage Alzheimer\u27s disease and Dementia with Lewy bodies brains

OBJECTIVE: Increased translocator protein (TSPO), previously known as the peripheral benzodiazepine receptor (PBR), in glial cells of the brain has been used as a neuroinflammation marker in the early and middle stages of neurodegenerative diseases, such as Alzheimer\u27s disease (AD) and Dementia with Lewy Bodies (DLB). In this study, we investigated the changes in TSPO density with respect to late stage AD and DLB. METHODS: TSPO density was measured in multiple regions of postmortem human brains in 20 different cases: seven late stage AD cases (Braak amyloid average: C; Braak tangle average: VI; Aged 74-88, mean: 83 ± 5 years), five DLB cases (Braak amyloid average: C; Braak tangle average: V; Aged 79-91, mean: 84 ± 4 years), and eight age-matched normal control cases (3 males, 5 females: aged 77-92 years; mean: 87 ± 6 years). Measurements were taken by quantitative autoradiography using [ RESULTS: No significant changes were found in TSPO density of the frontal cortex, striatum, thalamus, or red nucleus of the AD and DLB brains. A significant reduction in TSPO density was found in the substantia nigra (SN) of the AD and DLB brains compared to that of age-matched healthy controls. INTERPRETATION: This distinct pattern of TSPO density change in late stage AD and DLB cases may imply the occurrence of microglia dystrophy in late stage neurodegeneration. Furthermore, TSPO may not only be a microglia activation marker in early stage AD and DLB, but TSPO may also be used to monitor microglia dysfunction in the late stage of these diseases

Minimalist Ensemble Algorithms for Genome-Wide Protein Localization Prediction

Background Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms. Results This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors. Conclusions We proposed a method for rational design of minimalist ensemble algorithms using feature selection and classifiers. The proposed minimalist ensemble algorithm based on logistic regression can achieve equal or better prediction performance while using only half or one-third of individual predictors compared to other ensemble algorithms. The results also suggested that meta-predictors that take advantage of a variety of features by combining individual predictors tend to achieve the best performance. The LR ensemble server and related benchmark datasets are available at http://mleg.cse.sc.edu/LRensemble/cgi-bin/predict.cgi

Large deformation of spherical vesicle studied by perturbation theory and Surface evolver

With tangent angle perturbation approach the axial symmetry deformation of a spherical vesicle in large under the pressure changes is studied by the elasticity theory of Helfrich spontaneous curvature model.Three main results in axial symmetry shape: biconcave shape, peanut shape, and one type of myelin are obtained. These axial symmetry morphology deformations are in agreement with those observed in lipsome experiments by dark-field light microscopy [Hotani, J. Mol. Biol. 178, (1984) 113] and in the red blood cell with two thin filaments (myelin) observed in living state (see, Bessis, Living Blood Cells and Their Ultrastructure, Springer-Verlag, 1973). Furthermore, the biconcave shape and peanut shape can be simulated with the help of a powerful software, Surface Evolver [Brakke, Exp. Math. 1, 141 (1992) 141], in which the spontaneous curvature can be easy taken into account.Comment: 16 pages, 6 EPS figures and 2 PS figure

Facial expression cloning optimization method based Laplace operator.

In view of the reality of facial expression cloning and efficiency of expression reconstruction, a novel method based on motion capture data is proposed. After capturing the data of six fundamental expressions, it normalizes these data to make them in the same range. Then 41 points are chosen in critical areas of facial expression and it gets cloning expression using Laplace deformation algorithm with convex weight which can preserve the details of facial expression to avoid the low fidelity of uniform weights and unstable calculation of cotangent weights. Experimental results show that this method can generate realistic and natural expression animations and the efficiency of facial expression cloning is improved significantly