Miracle-dz/UniFAR-FTUs huggingface dataset:
A study of interaction between FITC- labeled antibody and optical fiber surface

The immobilization of antibodies on solid surface has been widely studied in elucidating immunoassay and in developing immunosensor. In this paper, FITC-labeled goat anti-human IgG were covalently immobilized on 3-aminopropyltriethoxysilane (APTES) and glutaraldehyde (Glu) to modify the distal end of a fiber-optic probe. The higher density and stability of FITC-labeled goat anti-human IgG could be obtained on the fiber surface modified with APTES-Glu than that on the non-modified fiber surface by evanescent excited fluorescence spectroscopy. Furthermore, red shift of the FITC fluorescence dye emission peak was observed on different fiber surface, exhibiting different interaction between FITC- labeled antibody and surface. Finally, under these conditions antigen-antibody reaction was investigated as an immunosensor. Detection limit of the evanescent wave optic-fiber biosensor is 10ng/ml.
Apo- and holo-structures of 3alpha-hydroxysteroid dehydrogenase from Pseudomonas sp. B-0831. Loop-helix transition induced by coenzyme binding.

Bacterial 3alpha-hydroxysteroid dehydrogenase, which belongs to a short-chain dehydrogenase/reductase family and forms a dimer composed of two 26-kDa subunits, catalyzes the oxidoreduction of hydroxysteroids in a coenzyme-dependent manner. This enzyme also catalyzes the oxidoreduction of nonsteroid compounds that play an important role in xenobiotic metabolism of bacteria. We performed an x-ray analysis on the crystal of Ps3alphaHSD, the enzyme from Pseudomonas sp. B-0831 complexed with NADH. The resulting crystal structure at 1.8A resolution showed that Ps3alphaHSD exists as a structural heterodimer composed of apo- and holo-subunits. A distinct structural difference between them was found in the 185-207-amino acid region, where the structure in the apo-subunit is disordered whereas that in the holo-subunit consists of two alpha-helices. This fact proved that the NADH binding allows the helical structures to form the substrate binding pocket even in the absence of the substrate, although the region corresponds to the so-called "substrate-binding loop. " The induction of alpha-helices in solution by the coenzyme binding was also confirmed by the CD experiment. In addition, the CD experiment revealed that the helix-inducing ability of NADH is stronger than that of NAD. We discuss the negative cooperativity for the coenzyme binding, which is caused by the effect of the structural change transferred between the subunits of the heterodimer.
Fetal functional imaging portrays heterogeneous development of emerging human brain networks

The functional connectivity architecture of the adult human brain enables complex cognitive processes, and exhibits a remarkably complex structure shared across individuals. We are only beginning to understand its heterogeneous structure, ranging from a strongly hierarchical organization in sensorimotor areas to widely distributed networks in areas such as the parieto-frontal cortex. Our study relied on the functional magnetic resonance imaging (fMRI) data of 32 fetuses with no detectable morphological abnormalities. After adapting functional magnetic resonance acquisition, motion correction, and nuisance signal reduction procedures of resting-state functional data analysis to fetuses, we extracted neural activity information for major cortical and subcortical structures. Resting fMRI networks were observed for increasing regional functional connectivity from 21st to 38th gestational weeks (GWs) with a network-based statistical inference approach. The overall connectivity network, short range, and interhemispheric connections showed sigmoid expansion curve peaking at the 26–29 GW. In contrast, long-range connections exhibited linear increase with no periods of peaking development. Region-specific increase of functional signal synchrony followed a sequence of occipital (peak: 24.8 GW), temporal (peak: 26 GW), frontal (peak: 26.4 GW), and parietal expansion (peak: 27.5 GW). We successfully adapted functional neuroimaging and image post-processing approaches to correlate macroscopical scale activations in the fetal brain with gestational age. This in vivo study reflects the fact that the mid-fetal period hosts events that cause the architecture of the brain circuitry to mature, which presumably manifests in increasing strength of intra- and interhemispheric functional macro connectivity.
A Bayesian approach for stochastic white matter tractography

White matter fiber bundles in the human brain can be located by tracing the local water diffusion in diffusion weighted magnetic resonance imaging (MRI) images. In this paper, a novel Bayesian modeling approach for white matter tractography is presented. The uncertainty associated with estimated white matter fiber paths is investigated, and a method for calculating the probability of a connection between two areas in the brain is introduced. The main merits of the presented methodology are its simple implementation and its ability to handle noise in a theoretically justified way. Theory for estimating global connectivity is also presented, as well as a theorem that facilitates the estimation of the parameters in a constrained tensor model of the local water diffusion profile
Geometrically projected discrete dislocation dynamics

Three-dimensional discrete dislocation dynamics methods (3D-DDD) have been developed to explicitly track the motion of individual dislocations under applied stress. At present, these methods are limited to plastic strains of about one percent or less due to high computational cost associated with the interactions between large numbers of dislocations. This limitation motivates the construction of minimalistic approaches to efficiently simulate the motion of dislocations for higher strains and longer time scales. In the present study, we propose geometrically projected discrete dislocation dynamics (GP-DDD), a method in which dislocation loops are modeled as geometrical objects that maintain their shape with a constant number of degrees of freedom as they expand. We present an example where rectangles composed of two screw and two edge dislocation segments are used for modeling gliding dislocation loops. We use this model to simulate single slip loading of copper and compare the results with detailed 3D-DDD simulations. We discuss the regimes in which GP-DDD is able to adequately capture the variation of the flow stress with strain rate in the single slip loading condition. A simulation using GP-DDD requires ∼40 times fewer degrees of freedom for a copper single slip loading case, thus reducing computational time and complexity.
Sum of Non-Concave Utilities Maximization for MIMO Interference Systems

To evaluate system capacity, past works on Multiple-Input-Multiple-Output (MIMO) systems with mutually interfering links have focused on maximizing the sum of mutual information as the objective criterion. Since the ultimate goal of a MIMO system is to support network applications used by consumers, we consider the non-concave sigmoid utility function, which is the recommended choice according to Shenker for modeling consumer satisfaction in applications with inelastic network traffic. We formulate the sum of utilities maximization as a global optimization problem with polynomial constraints and a rational objective function. Using a technique known as moment relaxation, we derive a sequence of Semidefinite Programming (SDP) problems whose optimal objective values converge to the global maximum sum of utilities. In our simulation examples, we employ our optimization model to determine the average global maxima sum of utilities by optimizing the covariance matrices of the transmitters. We then compare the results with those attainable by the alternative non-uniform optimal power control model that optimizes only the eigenvalues of the covariance matrices. By examining performance differences between the two models, we obtain insights about how interference and excessive data-rate requirements imposed by the application can impede link-consumers' ability to maximize their sum of utilities.
Stabilization of Cu-Ni based persistent current switch

Stabilization of a persistent current switch with a parallel connection is described. The current transfer behavior for a seven-strand conductor, wound as a persistent current switch, was studied. Each strand current was measured by using Rogowski coils. In the current ramp, negative current flowed into the center strand, which was inverted to a positive direction in about a 2-min time constant. Strand quench was induced by a heater wound in each strand. When one of the outer strands was quenched by its heater, the current was mainly transferred to the nearest three strands including the center strand in about 2 ms. These experimental results agreed well with the calculated results considering the self/mutual inductance for a parallel conductor and the joint resistance. The transferred current of several tens of amperes, which flowed with a few orders of 10000 A/s ramp rate, resulted in the degradation of the whole switch quench current from the conductor critical value. <<ETX>>
Detecting Concept Drift with Support Vector Machines

For many learning tasks where data is collected over an extended period of time, its underlying distribution is likely to change. A typical example is information (cid:12)ltering, i.e. the adaptive classi(cid:12)cation of documents with respect to a particular user interest. Both the interest of the user and the document content change over time. A (cid:12)ltering sys-tem should be able to adapt to such concept changes. This paper proposes a new method to recognize and handle concept changes with support vector machines. The method maintains a window on the training data. The key idea is to automatically adjust the window size so that the estimated generalization error is minimized. The new approach is both theoretically well-founded as well as e(cid:11)ective and e(cid:14)cient in practice. Since it does not require complicated parameterization, it is simpler to use and more robust than comparable heuristics. Experiments with simulated concept drift scenarios based on real-world text data compare the new method with other window management approaches. We show that it can e(cid:11)ectively select an appropriate window size in a robust way.
A Multi-pass Algorithm for Accurate Audio-to-Score Alignment

Most current audio-to-score alignment algorithms work on the level of score time frames; i.e., they cannot differentiate between several notes occurring at the same discrete time within the score. This level of accuracy is sufficient for a variety of applications. However, for those that deal with, for example, musical expression analysis such microtimings might also be of interest. Therefore, we propose a method that estimates the onset times of individual notes in a post-processing step. Based on the initial alignment and a feature obtained by matrix factorization, those notes for which the confidence in the alignment is high are chosen as anchor notes. The remaining notes in between are revised, taking into account the additional information about these anchors and the temporal relations given by the score. We show that this method clearly outperforms a reference method that uses the same features but does not differentiate between anchor and non-anchor notes.
Lmx1b-targeted cis-regulatory modules involved in limb dorsalization

Lmx1b is a homeodomain transcription factor responsible for limb dorsalization. Despite striking double-ventral (loss-of-function) and double-dorsal (gain-of-function) limb phenotypes, no direct gene targets in the limb have been confirmed. To determine direct targets, we performed a chromatin immunoprecipitation against Lmx1b in mouse limbs at embryonic day 12.5 followed by next-generation sequencing (ChIP-seq). Nearly 84% (n=617) of the Lmx1b-bound genomic intervals (LBIs) identified overlap with chromatin regulatory marks indicative of potential cis-regulatory modules (PCRMs). In addition, 73 LBIs mapped to CRMs that are known to be active during limb development. We compared Lmx1b-bound PCRMs with genes regulated by Lmx1b and found 292 PCRMs within 1 Mb of 254 Lmx1b-regulated genes. Gene ontological analysis suggests that Lmx1b targets extracellular matrix production, bone/joint formation, axonal guidance, vascular development, cell proliferation and cell movement. We validated the functional activity of a PCRM associated with joint-related Gdf5 that provides a mechanism for Lmx1b-mediated joint modification and a PCRM associated with Lmx1b that suggests a role in autoregulation. This is the first report to describe genome-wide Lmx1b binding during limb development, directly linking Lmx1b to targets that accomplish limb dorsalization. Summary: Correlating Lmx1b-binding sites with Lmx1b-regulated genes during mouse limb development uncovers cis-regulatory modules and their gene targets involved in limb dorsal-ventral identity.
Overlay Networks with Linear Capacity Constraints

Overlay networks are virtual networks residing over the IP network; consequently, overlay links may share hidden lower level bottlenecks. Previous work has assumed an independent overlay model: a graph with independent link capacities. We introduce a model of overlays that incorporates correlated link capacities and linear capacity constraints (LCC) to formulate hidden shared bottlenecks; we refer to these as LCC-overlays. We define metrics to qualitatively measure overlay quality in terms of its accuracy (in representing the true network topology) and efficiency (that is, performance). Through analysis and simulations, we show that LCC-overlay is perfectly accurate and, hence, enjoys much higher efficiency than the inaccurate independent overlay. We discover that even a highly restricted LCC class-node-based LCC-yields near-optimal accuracy and significantly higher efficiency. We study two network flow problems in the context of LCC-graphs: widest path and maximum flow. We prove that Widest Path with LCC is NP-complete. We formulate Maximum Flow with LCC as a linear program and propose an efficient distributed algorithm to solve it. Based on the LCC model, we further study the problem of optimizing delay while still maintaining optimal or near-optimal bandwidth. We also outline a distributed algorithm to efficiently construct an overlay with node-based LCC.
Comparison of Deep-Water Viromes from the Atlantic Ocean and the Mediterranean Sea

The aim of this study was to compare the composition of two deep-sea viral communities obtained from the Romanche Fracture Zone in the Atlantic Ocean (collected at 5200 m depth) and the southwest Mediterranean Sea (from 2400 m depth) using a pyro-sequencing approach. The results are based on 18.7% and 6.9% of the sequences obtained from the Atlantic Ocean and the Mediterranean Sea, respectively, with hits to genomes in the non-redundant viral RefSeq database. The identifiable richness and relative abundance in both viromes were dominated by archaeal and bacterial viruses accounting for 92.3% of the relative abundance in the Atlantic Ocean and for 83.6% in the Mediterranean Sea. Despite characteristic differences in hydrographic features between the sampling sites in the Atlantic Ocean and the Mediterranean Sea, 440 virus genomes were found in both viromes. An additional 431 virus genomes were identified in the Atlantic Ocean and 75 virus genomes were only found in the Mediterranean Sea. The results indicate that the rather contrasting deep-sea environments of the Atlantic Ocean and the Mediterranean Sea share a common core set of virus types constituting the majority of both virus communities in terms of relative abundance (Atlantic Ocean: 81.4%; Mediterranean Sea: 88.7%).
Design space exploration in multi-objective hierarchical SOC design

Nowadays, multi-objective hierarchical design methodology has received more attention for its applicability to deal with complex SOC design. Reducing decision data across hierarchy levels is crucial to the hierarchical designs. However, previous works overlooked the importance of this feature and just nested the optimizing procedures of multiple levels. This paper discussed the way to compress decision data across hierarchical levels. Pareto-optimal theory was employed and developed to explore the design space of multi-objective hierarchical system. Furthermore, this paper proved that, under the independence condition, optimization in each hierarchical level could be performed independently. This is the very first time to explore the design space of multi-objective hierarchical system formally, which contributes to the promotion of novel hierarchical partition and synthesis methodology.
Staging telephony service creation: a language approach

The open-endedness of telephony platforms is creating expectations among users, ranging from end-users to administrators, to create services dedicated to their activities. Not only is the population of developers heterogeneous, but the technologies underlying modern telephony range over a variety of areas such as multimedia, databases, web services, and distributed systems. This situation drastically widens the expertise required for service creation. This paper proposes an approach to coping with the heterogeneity of both the service developers and the technologies underlying modern telephony. Our approach is based on programming languages. It consists of providing a language that is specific to each developer community with respect to its expertise (e.g., programming skills) and the target application area (e.g., administration). Such languages, called Domain-Specific Languages (DSLs), are organized in layers, accounting for abstraction levels. Our layered approach to telephony service creation is illustrated by two high-level DSLs for end-user service creation, requiring no programming skills, and an expressive DSL enabling the development of expert-level telephony services. We show that layering DSLs greatly facilitates their implementation and verification of telephony-specific properties by leveraging on high-level tools.
MRA Versus DSA for Follow-Up of Coiled Intracranial Aneurysms: A Meta-Analysis

SUMMARY: MR angiography is proposed as a safer and less expensive alternative to the reference standard, DSA, in the follow-up of intracranial aneurysms treated with endovascular coil occlusion. We performed a systematic review and meta-analysis to evaluate the accuracy of TOF-MRA and contrast-enhanced MRA in detecting residual flow in the follow-up of coiled intracranial aneurysms. Literature was reviewed through the PubMed, Cochrane, and EMBASE data bases. In comparison with DSA, the sensitivity of TOF-MRA was 86% (95% CI: 82–89%), with a specificity of 84% (95% CI: 81–88%), for the detection of any recurrent flow. For contrast-enhanced MRA, the sensitivity and specificity were 86% (95% CI: 82–89%) and 89% (95% CI: 85–92%), respectively. Both TOF-MRA and contrast-enhanced MRA are shown to be highly accurate for detection of any recanalization in intracranial aneurysms treated with endovascular coil occlusion.
ERK signaling pathway regulates sleep duration through activity-induced gene expression during wakefulness

Activity-induced activation of the kinase ERK in cortical neurons increases the duration of sleep. ERK preps the brain for sleep Why do we feel more tired and tend to sleep longer and more deeply after a period of sleep deprivation or after a day that was full of physical or mental activity? Findings from Mikhail et al. using mice and cultured cortical neurons suggest that sleep deprivation or activity during the waking hours stimulates gene expression dependent in part on the kinase ERK. Signaling by the ERK pathway increased the duration of sleep and affected sleep patterns. Genetic deletion of ERK in cortical neurons or intracranial injection of an ERK inhibitor reduced the amount of sleep and extended the duration of wakefulness in mice. The findings support a previously reported link between sleep and ERK in flies to provide insight into the mechanisms regulating sleep in mammals. Wakefulness is accompanied by experience-dependent synaptic plasticity and an increase in activity-regulated gene transcription. Wake-induced genes are certainly markers of neuronal activity and may also directly regulate the duration of and need for sleep. We stimulated murine cortical cultures with the neuromodulatory signals that are known to control wakefulness in the brain and found that norepinephrine alone or a mixture of these neuromodulators induced activity-regulated gene transcription. Pharmacological inhibition of the various signaling pathways involved in the regulation of gene expression indicated that the extracellular signal–regulated kinase (ERK) pathway is the principal one mediating the effects of waking neuromodulators on gene expression. In mice, ERK phosphorylation in the cortex increased and decreased with wakefulness and sleep. Whole-body or cortical neuron–specific deletion of Erk1 or Erk2 significantly increased the duration of wakefulness in mice, and pharmacological inhibition of ERK phosphorylation decreased sleep duration and increased the duration of wakefulness bouts. Thus, this signaling pathway, which is highly conserved from Drosophila to mammals, is a key pathway that links waking experience–induced neuronal gene expression to sleep duration and quality.
FWDA: a Fast Wishart Discriminant Analysis with its Application to Electronic Health Records Data Classification

Linear Discriminant Analysis (LDA) on Electronic Health Records (EHR) data is widely-used for early detection of diseases. Classical LDA for EHR data classification, however, suffers from two handicaps: the ill-posed estimation of LDA parameters (e.g., covariance matrix), and the "linear inseparability" of EHR data. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fast Wishart Discriminant Analysis, that makes predictions in an ensemble way. Specifically, FWDA first surrogates the distribution of inverse covariance matrices using a Wishart distribution estimated from the training data, then "weighted-averages" the classification results of multiple LDA classifiers parameterized by the sampled inverse covariance matrices via a Bayesian Voting scheme. The weights for voting are optimally updated to adapt each new input data, so as to enable the nonlinear classification. Theoretical analysis indicates that FWDA possesses a fast convergence rate and a robust performance on high dimensional data. Extensive experiments on large-scale EHR dataset show that our approach outperforms state-of-the-art algorithms by a large margin.