Current-density functional theory of time-dependent linear response in quantal fluids: recent progress

Vignale and Kohn have recently formulated a local density approximation to the time-dependent linear response of an inhomogeneous electron system in terms of a vector potential for exchange and correlation. The vector potential depends on the induced current density through spectral kernels to be evaluated on the homogeneous electron-gas. After a brief review of their theory, the case of inhomogeneous Bose superfluids is considered, with main focus on dynamic Kohn-Sham equations for the condensate in the linear response regime and on quantal generalized hydrodynamic equations in the weak inhomogeneity limit. We also present the results of calculations of the exchange-correlation spectra in both electron and superfluid boson systems.Comment: 12 pages, 2 figures, Postscript fil

Swordtail Fry Attend to Chemical and Visual Cues in Detecting Predators and Conspecifics

Predation pressure and energy requirements present particularly salient opposing selective pressures on young fish. Thus, fry are expected to possess sophisticated means of detecting predators and resources. Here we tested the hypotheses that fry of the swordtail fish Xiphophorus birchmanni use chemical and visual cues in detection of predators and conspecifics. To test these hypotheses we presented young (<7 day-old) fry with combinations of visual and chemical stimuli from adult conspecifics and predators. We found that exposure to predator odors resulted in shoal tightening similar to that observed when fry were presented with visual cues alone. In trials with conspecific stimuli, fry were particularly attracted to adult conspecifics when presented simultaneous visual and chemical stimuli compared to the visual stimulus alone. These results show that fry attend to the odors of adult conspecifics, whose presence in a particular area may signal the location of resources as well as an absence of predators. This is one of the first studies to show that such young fish use chemical and visual cues in predator detection and in interactions with conspecifics. Previous research in X. birchmanni has shown that anthropogenic alteration of the chemical environment disrupts intraspecific chemical communication among adults; we suggest that because fry use the same chemosensory pathways to detect predators and conspecifics, alteration of the chemical environment may critically disrupt predator and resource detection

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

BACKGROUND: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. RESULTS: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. CONCLUSION: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features

Long-term prognosis of symptomatic isolated middle cerebral artery disease in Korean stroke patients

<p>Abstract</p> <p>Background</p> <p>This study aimed to investigate the long-term mortality and recurrence rate of stroke in first-time stroke patients with symptomatic isolated middle cerebral artery disease (MCAD) under medical management.</p> <p>Methods</p> <p>We identified 141 first ever stroke patients (mean age, 64.4 ± 12.5 years; 53% male) with symptomatic isolated MCAD. MCAD was defined as significant stenosis of more than 50% or occlusion of the MCA as revealed by MR angiography. The median follow-up was 27.7 months. We determined a cumulative rate of stroke recurrence and mortality by Kaplan-Meier survival analyses and sought predictors using the Cox proportional hazard model.</p> <p>Results</p> <p>The cumulative composite outcome rate (stroke recurrence or any-cause death) was 14%, 19%, 22%, and 28% at years 1, 2, 3, and 5, respectively. The annual recurrence rate of stroke was 4.1%. The presence of diabetes mellitus was the only significant independent predictor of stroke recurrence or any cause of death in multivariate analyses of Cox proportional hazard model adjusted for any plausible potential confounding factors.</p> <p>Conclusions</p> <p>We estimated the long-term prognosis of stroke patients with isolated symptomatic MCAD under current medical management in Korea. Diabetes mellitus was found to be a significant predictor for stroke recurrence and mortality.</p

Inferring stabilizing mutations from protein phylogenies : application to influenza hemagglutinin

One selection pressure shaping sequence evolution is the requirement that a protein fold with sufficient stability to perform its biological functions. We present a conceptual framework that explains how this requirement causes the probability that a particular amino acid mutation is fixed during evolution to depend on its effect on protein stability. We mathematically formalize this framework to develop a Bayesian approach for inferring the stability effects of individual mutations from homologous protein sequences of known phylogeny. This approach is able to predict published experimentally measured mutational stability effects (ΔΔG values) with an accuracy that exceeds both a state-of-the-art physicochemical modeling program and the sequence-based consensus approach. As a further test, we use our phylogenetic inference approach to predict stabilizing mutations to influenza hemagglutinin. We introduce these mutations into a temperature-sensitive influenza virus with a defect in its hemagglutinin gene and experimentally demonstrate that some of the mutations allow the virus to grow at higher temperatures. Our work therefore describes a powerful new approach for predicting stabilizing mutations that can be successfully applied even to large, complex proteins such as hemagglutinin. This approach also makes a mathematical link between phylogenetics and experimentally measurable protein properties, potentially paving the way for more accurate analyses of molecular evolution

Bioactive compounds of plum mango (Bouea macrophylla Griffith)

The fruit of Bouea macrophylla referred as Plum mango or Gandaria is a popular seasonal fruit, which is widely consumed in the Malay subcontinent. There is ample of traditional knowledge available among the locals on the use of leaves, bark, fruits and seeds of this plant. However, very limited research information and scientific report is available on their composition, phytochemicals or on the bioactive compounds. In the present chapter, we have aimed towards comprehensively providing information on nutritional value, functional qualities, health promoting bioactive compounds and volatile constituents of this underutilized fruit

Mechanistic Insight into the Reactivation of BCAII Enzyme from Denatured and Molten Globule States by Eukaryotic Ribosomes and Domain V rRNAs

In all life forms, decoding of messenger-RNA into polypeptide chain is accomplished by the ribosome. Several protein chaperones are known to bind at the exit of ribosomal tunnel to ensure proper folding of the nascent chain by inhibiting their premature folding in the densely crowded environment of the cell. However, accumulating evidence suggests that ribosome may play a chaperone role in protein folding events in vitro. Ribosome-mediated folding of denatured proteins by prokaryotic ribosomes has been studied extensively. The RNA-assisted chaperone activity of the prokaryotic ribosome has been attributed to the domain V, a span of 23S rRNA at the intersubunit side of the large subunit encompassing the Peptidyl Transferase Centre. Evidently, this functional property of ribosome is unrelated to the nascent chain protein folding at the exit of the ribosomal tunnel. Here, we seek to scrutinize whether this unique function is conserved in a primitive kinetoplastid group of eukaryotic species Leishmania donovani where the ribosome structure possesses distinct additional features and appears markedly different compared to other higher eukaryotic ribosomes. Bovine Carbonic Anhydrase II (BCAII) enzyme was considered as the model protein. Our results manifest that domain V of the large subunit rRNA of Leishmania ribosomes preserves chaperone activity suggesting that ribosome-mediated protein folding is, indeed, a conserved phenomenon. Further, we aimed to investigate the mechanism underpinning the ribosome-assisted protein reactivation process. Interestingly, the surface plasmon resonance binding analyses exhibit that rRNA guides productive folding by directly interacting with molten globule-like states of the protein. In contrast, native protein shows no notable affinity to the rRNA. Thus, our study not only confirms conserved, RNA-mediated chaperoning role of ribosome but also provides crucial insight into the mechanism of the process

Improving the in silico assessment of pathogenicity for compensated variants

Understanding the functional sequelae of amino-acid replacements is of fundamental importance in medical genetics. Perhaps, the most intuitive way to assess the potential pathogenicity of a given human missense variant is by measuring the degree of evolutionary conservation of the substituted amino-acid residue, a feature that generally serves as a good proxy metric for the functional/structural importance of that residue. However, the presence of putatively compensated variants as the wild-type alleles in orthologous proteins of other mammalian species not only challenges this classical view of amino-acid essentiality but also precludes the accurate evaluation of the functional impact of this type of missense variant using currently available bioinformatic prediction tools. Compensated variants constitute at least 4% of all known missense variants causing human-inherited disease and hence represent an important potential source of error in that they are likely to be disproportionately misclassified as benign variants. The consequent under-reporting of compensated variants is exacerbated in the context of next-generation sequencing where their inappropriate exclusion constitutes an unfortunate natural consequence of the filtering and prioritization of the very large number of variants generated. Here we demonstrate the reduced performance of currently available pathogenicity prediction tools when applied to compensated variants and propose an alternative machine-learning approach to assess likely pathogenicity for this particular type of variant