NETWORK TOPOLOGY IN HUMAN PROTEIN INTERACTION DATA PREDICTS FUNCTIONAL ASSOCIATION

High-throughput assays, such as yeast two-hybrid system, have generated a huge amount of protein-protein interaction (PPI) data in the past decade. This tremendously increases the need for developing reliable methods to systematically and automatically suggest protein functions and relationships between them. With the available PPI data, it is now possible to study the functions and relationships in the context of a large-scale network. To data, several network-based schemes have been provided to effectively annotate protein functions on a large scale. However, due to those inherent noises in high-throughput data generation, new methods and algorithms should be developed to increase the reliability of functional annotations. Previous work in a yeast PPI network (Samanta and Liang, 2003) has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional associations between proteins, and hence suggest their functions. One advantage of the work is that their algorithm is not sensitive to noises (false positives) in high-throughput PPI data. In this study, we improved their prediction scheme by developing a new algorithm and new methods which we applied on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting functionally associated proteins. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as independent and unbiased benchmarks to evaluate our algorithms and methods within the human PPI network. We showed that, compared with the previous work from Samanta and Liang, our algorithm and methods developed in this study improved the overall quality of functional inferences for human proteins. By applying the algorithms to the human PPI network, we obtained 4,233 significant functional associations among 1,754 proteins. Further comparisons of their KEGG and GO annotations allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made pathway analysis to identify several subclusters that are highly enriched in certain signaling pathways. Particularly, we performed a detailed analysis on a subcluster enriched in the transforming growth factor β signaling pathway (P<10-50) which is important in cell proliferation and tumorigenesis. Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotations in this post-genomic era.

Intravenous mesenchymal stem cell therapy for traumatic brain injury.

OBJECT: Cell therapy has shown preclinical promise in the treatment of many diseases, and its application is being translated to the clinical arena. Intravenous mesenchymal stem cell (MSC) therapy has been shown to improve functional recovery after traumatic brain injury (TBI). Herein, the authors report on their attempts to reproduce such observations, including detailed characterizations of the MSC population, non-bromodeoxyuridine-based cell labeling, macroscopic and microscopic cell tracking, quantification of cells traversing the pulmonary microvasculature, and well-validated measurement of motor and cognitive function recovery. METHODS: Rat MSCs were isolated, expanded in vitro, immunophenotyped, and labeled. Four million MSCs were intravenously infused into Sprague-Dawley rats 24 hours after receiving a moderate, unilateral controlled cortical impact TBI. Infrared macroscopic cell tracking was used to identify cell distribution. Immunohistochemical analysis of brain and lung tissues 48 hours and 2 weeks postinfusion revealed transplanted cells in these locations, and these cells were quantified. Intraarterial blood sampling and flow cytometry were used to quantify the number of transplanted cells reaching the arterial circulation. Motor and cognitive behavioral testing was performed to evaluate functional recovery. RESULTS: At 48 hours post-MSC infusion, the majority of cells were localized to the lungs. Between 1.5 and 3.7% of the infused cells were estimated to traverse the lungs and reach the arterial circulation, 0.295% reached the carotid artery, and a very small percentage reached the cerebral parenchyma (0.0005%) and remained there. Almost no cells were identified in the brain tissue at 2 weeks postinfusion. No motor or cognitive functional improvements in recovery were identified. CONCLUSIONS: The intravenous infusion of MSCs appeared neither to result in significant acute or prolonged cerebral engraftment of cells nor to modify the recovery of motor or cognitive function. Less than 4% of the infused cells were likely to traverse the pulmonary microvasculature and reach the arterial circulation, a phenomenon termed the "pulmonary first-pass effect," which may limit the efficacy of this therapeutic approach. The data in this study contradict the findings of previous reports and highlight the potential shortcomings of acute, single-dose, intravenous MSC therapy for TBI.

Diffusion tensor imaging of rat spinal cord in vivo

Magnetic resonance imaging, with its exquisite soft tissue contrast, is an ideal modality for investigating spinal cord pathology. While conventional MRI techniques are very sensitive for spinal cord pathology, their specificity is somewhat limited. Diffusion MRI is an advanced technique which is a very sensitive and specific indicator of the integrity of white matter tracts. Diffusion imaging has been shown to detect early ischemic changes in white matter, while conventional imaging demonstrates no change. By acquiring the complete apparent diffusion tensor (ADT), tissue diffusion properties can be expressed in terms of quantitative and rotationally invariant parameters. ^ Systematic study of SCI in vivo requires controlled animal models such as the popular rat model. To date, studies of spinal cord using ADT imaging have been performed exclusively in fixed, excised spinal cords, introducing inevitable artifacts and losing the benefits of MRI's noninvasive nature. In vivo imaging reflects the actual in vivo tissue properties, and allows each animal to be imaged at multiple time points, greatly reducing the number of animals required to achieve statistical significance. Because the spinal cord is very small, the available signal-to-noise ratio (SNR) is very low. Prior spin-echo based ADT studies of rat spinal cord have relied on high magnetic field strengths and long imaging times—on the order of 10 hours—for adequate SNR. Such long imaging times are incompatible with in vivo imaging, and are not relevant for imaging the early phases following SCI. Echo planar imaging (EPI) is one of the fastest imaging methods, and is popular for diffusion imaging. However, EPI further lowers the image SNR, and is very sensitive to small imperfections in the magnetic field, such as those introduced by the bony spine. Additionally, The small field-of-view (FOV) needed for spinal cord imaging requires large imaging gradients which generate EPI artifacts. The addition of diffusion gradients introduces yet further artifacts. ^ This work develops a method for rapid EPI-based in vivo diffusion imaging of rat spinal cord. The method involves improving the SNR using an implantable coil; reducing magnetic field inhomogeneities by means of an autoshim, and correcting EPI artifacts by post-processing. New EPI artifacts due to diffusion gradients described, and post-processing correction techniques are developed. ^ These techniques were used to obtain rotationally invariant diffusion parameters from 9 animals in vivo, and were validated using the gold-standard, but slow, spinecho based diffusion sequence. These are the first reported measurements of the ADT in spinal cord in vivo . ^ Many of the techniques described are equally applicable toward imaging of human spinal cord. We anticipate that these techniques will aid in evaluating and optimizing potential therapies, and will lead to improved patient care. ^

THEORETICAL STUDIES ON THE METHODS OF RECONSTRUCTING PHYLOGENETIC TREES FROM DNA SEQUENCE DATA (MOLECULAR EVOLUTION, HOMINOID EVOLUTION, COMPUTER SIMULATION)

(1) A mathematical theory for computing the probabilities of various nucleotide configurations is developed, and the probability of obtaining the correct phylogenetic tree (model tree) from sequence data is evaluated for six phylogenetic tree-making methods (UPGMA, distance Wagner method, transformed distance method, Fitch-Margoliash's method, maximum parsimony method, and compatibility method). The number of nucleotides (m*) necessary to obtain the correct tree with a probability of 95% is estimated with special reference to the human, chimpanzee, and gorilla divergence. m* is at least 4,200, but the availability of outgroup species greatly reduces m* for all methods except UPGMA. m* increases if transitions occur more frequently than transversions as in the case of mitochondrial DNA. (2) A new tree-making method called the neighbor-joining method is proposed. This method is applicable either for distance data or character state data. Computer simulation has shown that the neighbor-joining method is generally better than UPGMA, Farris' method, Li's method, and modified Farris method on recovering the true topology when distance data are used. A related method, the simultaneous partitioning method, is also discussed. (3) The maximum likelihood (ML) method for phylogeny reconstruction under the assumption of both constant and varying evolutionary rates is studied, and a new algorithm for obtaining the ML tree is presented. This method gives a tree similar to that obtained by UPGMA when constant evolutionary rate is assumed, whereas it gives a tree similar to that obtained by the maximum parsimony tree and the neighbor-joining method when varying evolutionary rate is assumed. ^

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD.

Sex-Specific Differences in Pathogen Susceptibility in Honey Bees (Apis mellifera)

Sex-related differences in susceptibility to pathogens are a common phenomenon in animals. In the eusocial Hymenoptera the two female castes, workers and queens, are diploid and males are haploid. The haploid susceptibility hypothesis predicts that haploid males are more susceptible to pathogen infections compared to females. Here we test this hypothesis using adult male (drone) and female (worker) honey bees (Apis mellifera), inoculated with the gut endoparasite Nosema ceranae and/or black queen cell virus (BQCV). These pathogens were chosen due to previously reported synergistic interactions between Nosema apis and BQCV. Our data do not support synergistic interactions between N. ceranae and BQCV and also suggest that BQCV has limited effect on both drone and worker health, regardless of the infection level. However, the data clearly show that, despite lower levels of N. ceranae spores in drones than in workers, Nosema-infected drones had both a higher mortality and a lower body mass than non-infected drones, across all treatment groups, while the mortality and body mass of worker bees were largely unaffected by N. ceranae infection, suggesting that drones are more susceptible to this pathogen than workers. In conclusion, the data reveal considerable sex-specific differences in pathogen susceptibility in honey bees and highlight the importance of ultimate measures for determining susceptibility, such as mortality and body quality, rather than mere infection levels

Scientific rationale for Saturn׳s in situ exploration

Remote sensing observations meet some limitations when used to study the bulk atmospheric composition of the giant planets of our solar system. A remarkable example of the superiority of in situ probe measurements is illustrated by the exploration of Jupiter, where key measurements such as the determination of the noble gases׳ abundances and the precise measurement of the helium mixing ratio have only been made available through in situ measurements by the Galileo probe. This paper describes the main scientific goals to be addressed by the future in situ exploration of Saturn placing the Galileo probe exploration of Jupiter in a broader context and before the future probe exploration of the more remote ice giants. In situ exploration of Saturn׳s atmosphere addresses two broad themes that are discussed throughout this paper: first, the formation history of our solar system and second, the processes at play in planetary atmospheres. In this context, we detail the reasons why measurements of Saturn׳s bulk elemental and isotopic composition would place important constraints on the volatile reservoirs in the protosolar nebula. We also show that the in situ measurement of CO (or any other disequilibrium species that is depleted by reaction with water) in Saturn׳s upper troposphere may help constraining its bulk O/H ratio. We compare predictions of Jupiter and Saturn׳s bulk compositions from different formation scenarios, and highlight the key measurements required to distinguish competing theories to shed light on giant planet formation as a common process in planetary systems with potential applications to most extrasolar systems. In situ measurements of Saturn׳s stratospheric and tropospheric dynamics, chemistry and cloud-forming processes will provide access to phenomena unreachable to remote sensing studies. Different mission architectures are envisaged, which would benefit from strong international collaborations, all based on an entry probe that would descend through Saturn׳s stratosphere and troposphere under parachute down to a minimum of 10 bar of atmospheric pressure. We finally discuss the science payload required on a Saturn probe to match the measurement requirements.

Total Variation Blind Deconvolution: The Devil Is in the Details

In this paper we study the problem of blind deconvolution. Our analysis is based on the algorithm of Chan and Wong [2] which popularized the use of sparse gradient priors via total variation. We use this algorithm because many methods in the literature are essentially adaptations of this framework. Such algorithm is an iterative alternating energy minimization where at each step either the sharp image or the blur function are reconstructed. Recent work of Levin et al. [14] showed that any algorithm that tries to minimize that same energy would fail, as the desired solution has a higher energy than the no-blur solution, where the sharp image is the blurry input and the blur is a Dirac delta. However, experimentally one can observe that Chan and Wong's algorithm converges to the desired solution even when initialized with the no-blur one. We provide both analysis and experiments to resolve this paradoxical conundrum. We find that both claims are right. The key to understanding how this is possible lies in the details of Chan and Wong's implementation and in how seemingly harmless choices result in dramatic effects. Our analysis reveals that the delayed scaling (normalization) in the iterative step of the blur kernel is fundamental to the convergence of the algorithm. This then results in a procedure that eludes the no-blur solution, despite it being a global minimum of the original energy. We introduce an adaptation of this algorithm and show that, in spite of its extreme simplicity, it is very robust and achieves a performance comparable to the state of the art.

A Passive WiFi Source Localization System based on Fine-grained Power-based Trilateration

Indoor localization systems become more interesting for researchers because of the attractiveness of business cases in various application fields. A WiFi-based passive localization system can provide user location information to third-party providers of positioning services. However, indoor localization techniques are prone to multipath and Non-Line Of Sight (NLOS) propagation, which lead to significant performance degradation. To overcome these problems, we provide a passive localization system for WiFi targets with several improved algorithms for localization. Through Software Defined Radio (SDR) techniques, we extract Channel Impulse Response (CIR) information at the physical layer. CIR is later adopted to mitigate the multipath fading problem. We propose to use a Nonlinear Regression (NLR) method to relate the filtered power information to propagation distances, which significantly improves the ranging accuracy compared to the commonly used log-distance path loss model. To mitigate the influence of ranging errors, a new trilateration algorithm is designed as well by combining Weighted Centroid and Constrained Weighted Least Square (WC-CWLS) algorithms. Experiment results show that our algorithm is robust against ranging errors and outperforms the linear least square algorithm and weighted centroid algorithm.

Range-based Weighted-likelihood Particle Filter for RSS-based Indoor Tracking

Attractive business cases in various application fields contribute to the sustained long-term interest in indoor localization and tracking by the research community. Location tracking is generally treated as a dynamic state estimation problem, consisting of two steps: (i) location estimation through measurement, and (ii) location prediction. For the estimation step, one of the most efficient and low-cost solutions is Received Signal Strength (RSS)-based ranging. However, various challenges - unrealistic propagation model, non-line of sight (NLOS), and multipath propagation - are yet to be addressed. Particle filters are a popular choice for dealing with the inherent non-linearities in both location measurements and motion dynamics. While such filters have been successfully applied to accurate, time-based ranging measurements, dealing with the more error-prone RSS based ranging is still challenging. In this work, we address the above issues with a novel, weighted likelihood, bootstrap particle filter for tracking via RSS-based ranging. Our filter weights the individual likelihoods from different anchor nodes exponentially, according to the ranging estimation. We also employ an improved propagation model for more accurate RSS-based ranging, which we suggested in recent work. We implemented and tested our algorithm in a passive localization system with IEEE 802.15.4 signals, showing that our proposed solution largely outperforms a traditional bootstrap particle filter.

Climate-mediated movement of an avian hybrid zone

The interaction between sibling species that share a zone of contact is a multifaceted relationship affected by climate change [ 1, 2 ]. Between sibling species, interactions may occur at whole-organism (direct or indirect competition) or genomic (hybridization and introgression) levels [ 3–5 ]. Tracking hybrid zone movements can provide insights about influences of environmental change on species interactions [ 1 ]. Here, we explore the extent and mechanism of movement of the contact zone between black-capped chickadees (Poecile atricapillus) and Carolina chickadees (Poecile carolinensis) at whole-organism and genomic levels. We find strong evidence that winter temperatures limit the northern extent of P. carolinensis by demonstrating a current-day association between the range limit of this species and minimum winter temperatures. We further show that this temperature limitation has been consistent over time because we are able to accurately hindcast the previous northern range limit under earlier climate conditions. Using genomic data, we confirm northward movement of this contact zone over the past decade and highlight temporally consistent differential—but limited—geographic introgression of alleles. Our results provide an informative example of the influence of climate change on a contact zone between sibling species.

Fate of accidental symmetries of the relativistic hydrogen atom in a spherical cavity

The non-relativistic hydrogen atom enjoys an accidental SO(4) symmetry, that enlarges the rotational SO(3) symmetry, by extending the angular momentum algebra with the Runge–Lenz vector. In the relativistic hydrogen atom the accidental symmetry is partially lifted. Due to the Johnson–Lippmann operator, which commutes with the Dirac Hamiltonian, some degeneracy remains. When the non-relativistic hydrogen atom is put in a spherical cavity of radius R with perfectly reflecting Robin boundary conditions, characterized by a self-adjoint extension parameter γ, in general the accidental SO(4) symmetry is lifted. However, for R=(l+1)(l+2)a (where a is the Bohr radius and l is the orbital angular momentum) some degeneracy remains when γ=∞ or γ = 2/R. In the relativistic case, we consider the most general spherically and parity invariant boundary condition, which is characterized by a self-adjoint extension parameter. In this case, the remnant accidental symmetry is always lifted in a finite volume. We also investigate the accidental symmetry in the context of the Pauli equation, which sheds light on the proper non-relativistic treatment including spin. In that case, again some degeneracy remains for specific values of R and γ.

The physiological consequences of crib-biting in horses in response to an ACTH challenge test

Stereotypies are repetitive and relatively invariant patterns of behavior, which are observed in a wide range of species in captivity. Stereotypic behavior occurs when environmental demands produce a physiological response that, if sustained for an extended period, exceeds the natural physiological regulatory capacity of the organism, particularly in situations that include unpredictability and uncontrollability. One hypothesis is that stereotypic behavior functions to cope with stressful environments, but the existing evidence is contradictory. To address the coping hypothesis of stereotypies, we triggered physiological reactions in 22 horses affected by stereotypic behavior (crib-biters) and 21 non-crib-biters (controls), using an ACTH challenge test. Following administration of an ACTH injection, we measured saliva cortisol every 30min and heart rate (HR) continuously for a period of 3h. We did not find any differences in HR or HR variability between the two groups, but crib-biters (Group CB) had significantly higher cortisol responses than controls (Group C; mean±SD: CB, 5.84±2.62ng/ml, C, 4.76±3.04ng/ml). Moreover, crib-biters that did not perform the stereotypic behavior during the 3-hour test period (Group B) had significantly higher cortisol levels than controls, which was not the case of crib-biters showing stereotypic behavior (Group A) (B, 6.44±2.38ng/ml A, 5.58±2.69ng/ml). Our results suggest that crib-biting is a coping strategy that helps stereotypic individuals to reduce cortisol levels caused by stressful situations. We conclude that preventing stereotypic horses from crib-biting could be an inappropriate strategy to control this abnormal behavior, as it prevents individuals from coping with situations that they perceive as stressful.

Identification of Novel Death-Associated Protein Kinase 2 Interaction Partners by Proteomic Screening Coupled with Bimolecular Fluorescence Complementation.

Death-associated protein kinase 2 (DAPK2) is a Ca(2+)/calmodulin-dependent Ser/Thr kinase that possesses tumor-suppressive functions and regulates programmed cell death, autophagy, oxidative stress, hematopoiesis, and motility. As only few binding partners of DAPK2 have been determined, the molecular mechanisms governing these biological functions are largely unknown. We report the identification of 180 potential DAPK2 interaction partners by affinity purification-coupled mass spectrometry, 12 of which are known DAPK binding proteins. A small subset of established and potential binding proteins detected in this screen was further investigated by bimolecular fluorescence complementation (BiFC) assays, a method to visualize protein interactions in living cells. These experiments revealed that α-actinin-1 and 14-3-3-β are novel DAPK2 binding partners. The interaction of DAPK2 with α-actinin-1 was localized at the plasma membrane, resulting in massive membrane blebbing and reduced cellular motility, whereas the interaction of DAPK2 with 14-3-3-β was localized to the cytoplasm, with no impact on blebbing, motility, or viability. Our results therefore suggest that DAPK2 effector functions are influenced by the protein's subcellular localization and highlight the utility of combining mass spectrometry screening with bimolecular fluorescence complementation to identify and characterize novel protein-protein interactions.