Miracle-dz/UniFAR-FTUs huggingface dataset:
Language Mapping with Dense Array EEG Source Localization: Implications for Neurosurgical Planning

Original approval signatures are on file with the University of Oregon Graduate School. Current language mapping protocols for neurosurgical planning are invasive, expensive, and not suitable for all surgical candidates. We investigated the potential of dense array EEG to determine hemispheric dominance for language and localize current sources of semantic and lower level language functions in the brain using a semantic decision task, a phonological decision task, and an acoustic decision task. Source estimates of N400-window ERPs (N365, N480) and the Late Positive Complex (LPC) localized strongly to medial temporal regions. Overall source estimates revealed a slight left lateralized network, with more posterior engagement for the semantic condition and more anterior engagement for the phonological condition. Source localization of the resulting t-test wave from the semantic – phonological highlighted a stronger left lateralized pattern of activation encompassing more of the semantic network. As a first pass these results are promising, but need to be investigated on individual subject ERPs. (2011). From sound to meaning: changes in EEG source-localized brain activity with foreign-language training. In D. vii ACKNOWLEDGMENTS I wish to express sincere appreciation to my committee member Catherine Poulsen for her enthusiasm, critical guidance, and availability in the preparation of this manuscript. Also, special thanks to the science team at Electrical Geodesics Inc., my advisor Don Tucker and Phan Luu, for their guidance in this research. I also want to thank my committee member Dare Baldwin for helping to keep me on track and always having her door open. Of course thanks to thank my lab mates Alison Waters and Ida Moab for their support and wisdom, and the research assistant team in the brain electrophysiology lab, namely Jenn Lewis, assistance in participant scheduling and stimulus preparation. In addition, special thanks are due to my family Jim Nelson, Tracie Nelson, and Jimmy Nelson for all their support and encouragement.
miR-7b, a microRNA up-regulated in the hypothalamus after chronic hyperosmolar stimulation, inhibits Fos translation

The transcription factor activator protein 1 (AP-1) is formed through the dimerization of immediate–early genes Fos and Jun family members. Activator protein 1 is known as a pivotal regulator of major biological events such as cell proliferation, differentiation, organogenesis, memory formation, and apoptosis. During a search for microRNAs (miRNAs; small, endogenous, noncoding RNAs that repress gene expression of target mRNAs in animals posttranscriptionally) that are differentially expressed in the mouse paraventricular and supraoptic nuclei after 10 days of drinking 2% saline, one candidate microRNA that is relatively highly expressed, mmu-miR-7b (miR-7b), was studied further because sequence analysis suggested a likely interaction with the 3′ untranslated region of Fos mRNA. We show that miR-7b expression inhibits Fos translation in vitro and that it and its host gene are prominently expressed in the PVN and other brain areas, including the suprachiasmatic nucleus. No effect on Fos mRNA levels was observed. Normally, Fos is expressed at low to undetectable levels in cells, but it shows rapid induction and decay after acute stimuli. Various pathways have been identified through which Fos family proteins are degraded; our results indicate a significant additional mechanism by which Fos protein and activity may be regulated.
Choice, Commitment and Time Horizon

Timberlake, Gawley, and Lucas (1986) found that rats were unable to anticipate future resources (food) that were delayed by 16 minutes or more. The 15 minute period during which the rats were able to anticipate food which would be available in the future IS called the time horizon. The present experiment sought an explanation of the fact that the animals in the Timberlake et al (1986) study could not anticipate free food beyond 15 minutes and to also examine whether the time horizon of rats can be lengthened. In most seSSIOns, a single response bar (left or right) was presented at the start of the session. One bar was associated with 30 minutes of a progressive ratio schedule. The other bar was associated with the same progressive ratio schedule, followed by 5 minutes of free food. The bar presented alternated randomly from day to day. Once every 5 sessions, both bars were presented at the start of the session, and the animals chose between them. None of the animals consistently chose the PR and FF schedule suggesting that in this contingency, rats cannot "anticipate" over a 30 minute gap.
Mutations in fibrillar collagens (types I, II, III, and XI), fibril‐associated collagen (type IX), and network‐forming collagen (type X) cause a spectrum of diseases of bone, cartilage, and blood vessels

This review summarizes the data on 278 different mutations found to date in the genes for types I, II, III, IX, X, and XI collagens from 317 apparently unrelated patients. A majority (217 mutations; 78% of the total) of the mutations are single‐base and either change the codon of a critical amino acid (63%), or lead to abnormal RNA splicing (13%). Most of the amino acid substitutions are those of a bulkier amino acid for the obligatory glycine of the repeating‐Gly‐X‐Y‐sequence of the collagen triple helix (155; 56%). Altogether, 26 different mutations (9.4% of the mutations) occur in more than one unrelated individual. The 65 patients in whom the 26 mutations were characterized constitute almost one‐fifth (20.5%) of the 317 patients analyzed. The mutations in types I, II, III, IX, X, and XI collagens cause a wide spectrum of diseases of bone, cartilage, and blood vessels, including osteogenesis imperfecta, a variety of chondrodysplasias, types IV and VII of the Ehlers‐Danlos syndrome, and, rarely, some forms of osteoporosis, osteoarthritis, and familial aneurysms. Hum Mutat 9:300–315, 1997. © 1997 Wiley‐Liss, Inc.
Locality-Constrained Concept Factorization

Matrix factorization based techniques, such as non-negative matrix factorization (NMF) and concept factorization (CF), have attracted great attention in dimension reduction and data clustering. Both of them are linear learning problems and lead to a sparse representation of the data. However, the sparsity obtained by these methods does not always satisfy locality conditions, thus the obtained data representation is not the best. This paper introduces a locality-constrained concept factorization method which imposes a locality constraint onto the traditional concept factorization. By requiring the concepts (basis vectors) to be as close to the original data points as possible, each data can be represented by a linear combination of only a few basis concepts. Thus our method is able to achieve sparsity and locality at the same time. We demonstrate the effectiveness of this novel algorithm through a set of evaluations on real world applications.
Manner of Interaction of Heterogeneous Claudin Species within and between Tight Junction Strands

In tight junctions (TJs), TJ strands are associated laterally with those of adjacent cells to form paired strands to eliminate the extracellular space. Claudin-1 and -2, integral membrane proteins of TJs, reconstitute paired TJ strands when transfected into L fibroblasts. Claudins comprise a multigene family and more than two distinct claudins are coexpressed in single cells, raising the questions of whether heterogeneous claudins form heteromeric TJ strands and whether claudins interact between each of the paired strands in a heterophilic manner. To answer these questions, we cotransfected two of claudin-1, -2, and -3 into L cells, and detected their coconcentration at cell–cell borders as elaborate networks. Immunoreplica EM confirmed that distinct claudins were coincorporated into individual TJ strands. Next, two L transfectants singly expressing claudin-1, -2, or -3 were cocultured and we found that claudin-3 strands laterally associated with claudin-1 and -2 strands to form paired strands, whereas claudin-1 strands did not interact with claudin-2 strands. We concluded that distinct species of claudins can interact within and between TJ strands, except in some combinations. This mode of assembly of claudins could increase the diversity of the structure and functions of TJ strands.
Incremental cryptography and application to virus protection

The goal ofincremental cryptography is to design cryptographic algorithms with the property that having applied the algorithm to a document, it is possible to quickly update the result of the algorithm for a modifled document, rather than having to re-compute it from scratch. In settings where cryptographic algorithms such as encryption or signatures are frequently applied to changing documents, dramatic e‐ciency improvements can be achieved. One such setting is the use of authentication tags for virus protection. We consider documents that can be modifled by powerful (and realistic) document modiflcation operations such as insertion and deletion of character-strings (or equivalently cut and paste of text). We provide e‐cient incremental signature and message authentication schemes supporting the above document modiflcation operations. They meet a strong notion of tamper-proof security which is appropriate for the virus protection setting. We initiate a study of incremental encryption, providing deflnitions as well as solutions. Finally, we raise the novel issue of \privacy" of incremental authentication schemes.
Elsevier Editorial System(tm) for Journal of Multivariate Analysis Manuscript Draft Best Regards, Bayesian Nonlinear Regression for Large P Small N Problems *manuscript Click Here to View Linked References

Statistical modelling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large $p$ small $n$ problem. We develop nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik's $\epsilon$-insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS). This provides a full probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also considered the relevance vector machine (RVM) introduced by Bishop, Tipping and others. Instead of the original treatment of the RVM relying on the use of type II maximum likelihood estimates of the hyper-parameters, we put a prior on the hyper-parameters and use Markov chain Monte Carlo technique for computation. We apply our model for prediction of blood glucose concentration in diabetics using florescence based optics. We have extended the full Bayesian support vector regression (SVR) and relevance vector regression (RVR) models when the response is multivariate. We have also proposed an empirical Bayes RVM and SVM. The multivariate version of the SVM and RVM is illustrated with a prediction problem in the near-infrared (NIR) spectroscopy. A simulation study is also undertaken to check the prediction accuracy of our models To: The Editor Journal of Multivariate Analysis Dear Editor, We have attached our manuscript``Bayesian Nonlinear regression for large p small n problems'' and would like to be considered for publication in the Journal of Multivariate Analysis. If you have any question, please feel free to contact me at the number 979-845-3141 or using the email bmallick@stat.tamu. edu. Abstract Statistical modelling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. We develop nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik's-insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS). This provides a full probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also considered the relevance vector machine (RVM) introduced by Bishop, Tipping and others. Instead of the original treatment of the RVM relying on the use of type II maximum likelihood estimates of the hyper-parameters, we put a prior on the hyper-parameters and use Markov chain Monte Carlo technique for computation. …
Parenting and the psychological development of a representative sample of triplets conceived by assisted reproduction.

BACKGROUND The aim of the study was to examine the quality of parenting and the psychological development of three-year-old children in IVF/ICSI families with triplets. METHODS Comparisons were carried out between a representative sample of 10 families with triplets and matched groups of 15 families with twins and 30 families with singletons. The families were recruited from Follow-Up, a national organization in France that was established to study children conceived by assisted reproduction. Standardized measures of the mother's psychological well-being (parenting stress, depression and quality of marriage) and standardized measures of the child's psychological development (emotional/behavioural problems and general development) were completed by the mother. RESULTS Mothers with a multiple birth were found to experience greater difficulties in parenting than mothers of singletons, with no differences between mothers of triplets and mothers of twins. Regarding the children, there were no differences in emotional or behavioural problems between triplets, twins and singletons. However, there were indications of mild delay among triplets and twins in some aspects of language development in comparison with the singleton children. CONCLUSIONS The birth of triplets or twins does appear to cause difficulties for parents in the early years, however, the children themselves do not seem to experience markedly raised levels of psychological or developmental problems.
Lifelog-based estimation of activity diary for cognitive behavioral therapy

Cognitive Behavioral Therapy (CBT) shows promise for the effective treatment and prevention for depression. During the long-term treatment with CBT, users (e.g. patients with depression) are given some kind of homework in their daily lives to self-monitor their thoughts and behaviors by using some techniques taught by therapists (e.g. doctors) in the face-to-face session. In this paper, we present an application to support users to do their homework, especially self-monitoring of their behaviors. By collecting lifelogs of them via smart phones, the application estimates their behaviors (activities) based on their lifelogs and shows the results to support self-monitoring by themselves.
A general framework for experiment design in diffusion MRI and its application in measuring direct tissue‐microstructure features

This article introduces a new and general framework for optimizing the experiment design for diffusion MRI of samples with unknown orientation. An illustration then uses the framework to study the feasibility of measuring direct features of brain‐tissue microstructure in vivo. The study investigates the accuracy and precision with which we can estimate potentially important new biomarkers such as axon density and radius in white matter. Simulation experiments use a simple model of white matter based on CHARMED (composite hindered and restricted model of diffusion). The optimization finds acquisition protocols achievable on modern human and animal systems that consist of 120 measurements with fixed maximum gradient strengths. Axon radii in brain tissue are typically in the range 0.25–10 μm. Simulations suggest that estimates of radii in the range 5–10 μm have highest precision and that a maximum gradient strength of 0.07 T m−1 is sufficient to distinguish radii of 5, 10, and 20 μm. Smaller radii are more difficult to distinguish from one another but are identifiable as small. A maximum gradient strength of 0.2 T m−1 distinguishes radii of 1 and 2 μm. The simulations also suggest that axon densities and diffusivity parameters in the normal range for white matter are recoverable. The experiment‐design optimization has applications well beyond the current work to optimize the protocol for fitting any model of the diffusion process. Magn Reson Med 60:439–448, 2008. © 2008 Wiley‐Liss, Inc.
View planning for multi-stage object reconstruction

Previous work has presented a view planning concept for automated object reconstruction. The multi-stage concept comprises a rapid initial exploration stage to produce a low resolution approximate object model the rough model. With the rough model as the new knowledge base, a view plan is developed for a subsequent precision measurement stage capturing detailed object geometry in accordance with a pre-de ned model speci cation. This paper addresses the issue \How rough can the rough model be for e ective view planning? ". We present experimental results showing that good results are achievable for a reasonable level of rough model decimation, surface errors well within expected levels and sparse sampling of viewpoint space.
Miswiring the brain: Δ9‐tetrahydrocannabinol disrupts cortical development by inducing an SCG10/stathmin‐2 degradation pathway

Children exposed in utero to cannabis present permanent neurobehavioral and cognitive impairments. Psychoactive constituents from Cannabis spp. , particularly Δ9‐tetrahydrocannabinol (THC), bind to cannabinoid receptors in the fetal brain. However, it is unknown whether THC can trigger a cannabinoid receptor‐driven molecular cascade to disrupt neuronal specification. Here, we show that repeated THC exposure disrupts endocannabinoid signaling, particularly the temporal dynamics of CB1 cannabinoid receptor, to rewire the fetal cortical circuitry. By interrogating the THC‐sensitive neuronal proteome we identify Superior Cervical Ganglion 10 (SCG10)/stathmin‐2, a microtubule‐binding protein in axons, as a substrate of altered neuronal connectivity. We find SCG10 mRNA and protein reduced in the hippocampus of midgestational human cannabis‐exposed fetuses, defining SCG10 as the first cannabis‐driven molecular effector in the developing cerebrum. CB1 cannabinoid receptor activation recruits c‐Jun N‐terminal kinases to phosphorylate SCG10, promoting its rapid degradation in situ in motile axons and microtubule stabilization. Thus, THC enables ectopic formation of filopodia and alters axon morphology. These data highlight the maintenance of cytoskeletal dynamics as a molecular target for cannabis, whose imbalance can limit the computational power of neuronal circuitries in affected offspring.
Adaptive Handoff Algorithm for Multi-Beam GEO Mobile Satellite System

An adaptive handoff algorithm is proposed for the multi-beam GEO mobile satellite system. Compared with the traditional algorithms applied in the terrestrial cellular systems, the length of the averaging window and the hysteresis margin will be adjusted adaptively according to the velocity of the MS (Mobile Station) and the RSS (Received Signal Strength) measurement of the serving spot beam in the proposed algorithm. Simulation results show that the adaptive algorithm significantly decreases the link degradation rate, prevents the unnecessary handoff, reduces the handoff delay and has favorable adaptability to the high velocity MS. Moreover, the algorithm maintains a fairly low complexity and can flexibly achieve better tradeoff among various handoff performance criteria.
On flatness-based control for series-connected VSC for voltage dip mitigation

The grid connected series converter is a custom power device that is mainly used for mitigation of voltage sags. In this paper a novel direct flatness-based control (FBC) in cascade configuration, constituted by an outer voltage and an inner current controller, is presented. It can absorb and inject active and reactive power into the grid. Simulation model includes the practical limitation of voltage source converter output voltage and computational delay. Then, the simulations results for the FBC under balanced voltage dips are presented and compared with traditional proportional integral controller. Finally, the sensitivity analysis of the FBC for grid frequency, phase angle jump and inaccurate knowledge of system parameters are presented.
Augmented Lagrangian with Variable Splitting for Faster Non-Cartesian ${\rm L}_{1}$-SPIRiT MR Image Reconstruction

SPIRiT (iterative self-consistent parallel imaging reconstruction), and its sparsity-regularized variant L1-SPIRiT, are compatible with both Cartesian and non-Cartesian magnetic resonance imaging sampling trajectories. However, the non-Cartesian framework is more expensive computationally, involving a nonuniform Fourier transform with a nontrivial Gram matrix. We propose a novel implementation of the regularized reconstruction problem using variable splitting, alternating minimization of the augmented Lagrangian, and careful preconditioning. Our new method based on the alternating direction method of multipliers converges much faster than existing methods because of the preconditioners' heightened effectiveness. We demonstrate such rapid convergence substantially improves image quality for a fixed computation time. Our framework is a step forward towards rapid non-Cartesian L1-SPIRiT reconstructions.
Fast Krylov Methods for Clustering

At the heart of unsupervised clustering and semi-supervised clustering is the calculation of matrix eigenvalues(eigenvectors) or matrix inversion. In generally, its complexity is O(N). By using Krylov Subspace Methods and Fast Methods, we improve the performance to O(NlogN). We also make a thorough evaluation of errors introduced by the fast algorithm.
Topology-based cancer classification and related pathway mining using microarray data

Cancer classification is the critical basis for patient-tailored therapy, while pathway analysis is a promising method to discover the underlying molecular mechanisms related to cancer development by using microarray data. However, linking the molecular classification and pathway analysis with gene network approach has not been discussed yet. In this study, we developed a novel framework based on cancer class-specific gene networks for classification and pathway analysis. This framework involves a novel gene network construction, named ordering network, which exhibits the power-law node-degree distribution as seen in correlation networks. The results obtained from five public cancer datasets showed that the gene networks with ordering relationship are better than those with correlation relationship in terms of accuracy and stability of the classification performance. Furthermore, we integrated the ordering networks, classification information and pathway database to develop the topology-based pathway analysis for identifying cancer class-specific pathways, which might be essential in the biological significance of cancer. Our results suggest that the topology-based classification technology can precisely distinguish cancer subclasses and the topology-based pathway analysis can characterize the correspondent biochemical pathways even if there are subtle, but consistent, changes in gene expression, which may provide new insights into the underlying molecular mechanisms of tumorigenesis.