The immobilization of proteins on gold-coated magnetic nanoparticles and the subsequent recognition of the targeted proteins provide an effective means for the separation of proteins via application of a magnetic filed. A key challenge is the ability to fabricate such nanoparticles with the desired core-shell nanostructure. In this article, we report findings of the fabrication and characterization of gold-coated iron oxide (Fe2O3 and Fe3O4) core@shell nanoparticles (Fe oxide@Au) toward novel functional biomaterials. A hetero-interparticle coalescence strategy has been demonstrated for fabricating Fe oxide@Au nanoparticles that exhibit controllable sizes ranging from 5 to 100 nm and high monodispersity. Composition and surface analyses have proven that the resulting nanoparticles consist of the Fe2O3 core and the Au shell. The magnetically active Fe oxide core and thiolate-active Au shell were shown to be viable for exploiting the Au surface protein-binding reactivity for bioassay and the Fe oxide core magnetism for magnetic bioseparation. These findings are entirely new and could form the basis for fabricating magnetic nanoparticles as biomaterials with tunable size, magnetism, and surface binding properties.

Controlled release of membrane-tethered, dormant precursors is an intriguing activation mechanism that regulates diverse cellular functions in eukaryotes. An exquisite example is the proteolytic activation of membrane-bound transcription factors. The proteolytic cleavage liberates active transcription factors from the membranes that can enter the nucleus and evokes rapid transcriptional responses to incoming stimuli. Here, we show that a membrane-bound NAC (for NAM, ATAF1/2, CUC2) transcription factor, designated NTM1 (for NAC with transmembrane motif1), is activated by proteolytic cleavage through regulated intramembrane proteolysis and mediates cytokinin signaling during cell division in Arabidopsis thaliana. Cell proliferation was greatly reduced in an Arabidopsis mutant with retarded growth and serrated leaves in which a transcriptionally active NTM1 form was constitutively expressed. Accordingly, a subset of cyclin-dependent kinase (CDK) inhibitor genes (the KIP-related proteins) was induced in this mutant with a significant reduction in histone H4 gene expression and in CDK activity. Consistent with a role for NTM1 in cell cycling, a Ds element insertional mutant was morphologically normal but displayed enhanced hypocotyl growth with accelerated cell division. Interestingly, cytokinins were found to regulate NTM1 activity by controlling its stability. These results indicate that the membrane-mediated activation of NTM1 defines a molecular mechanism by which cytokinin signaling is tightly regulated during cell cycling.

Shoot apical meristem (SAM) development is coordinately regulated by two interdependent signaling events: one maintaining stem cell identity and the other governing the initiation of lateral organs from the flanks of the SAM. The signaling networks involved in this process are interconnected and are regulated by multiple molecular mechanisms. Class III homeodomainleucine zipper (HD-ZIP III) proteins are the most extensively studied transcription factors involved in this regulation. However, how different signals are integrated to maintain stem cell identity and to pattern lateral organ polarity remains unclear. Here, we demonstrated that a small ZIP protein, ZPR3, and its functionally redundant homolog, ZPR4, negatively regulate the HD-ZIP III activity in SAM development. ZPR3 directly interacts with PHABULOSA (PHB) and other HD-ZIP III proteins via the ZIP motifs and forms nonfunctional heterodimers. Accordingly, a double mutant, zpr3-2 zpr4-2, exhibits an altered SAM activity with abnormal stem cell maintenance. However, the mutant displays normal patterning of leaf polarity. In addition, we show that PHB positively regulates ZPR3 expression. We therefore propose that HD-ZIP III activity in regulating SAM development is modulated by, among other things, a feedback loop involving the competitive inhibitors ZPR3 and ZPR4.

Synthesis of nanomaterials with multi-imaging modality is of great importance in clinical molecular imaging and diagnostics. This work reports novel synthetic strategy to create ultrasmall and hexagonal upconversion nanoparticles (UCNPs), -NaGdF 4 : Yb 3+ , Er 3+ , and -NaGdF 4 : Yb 3+ , Tm 3+ , with inherent magnetic and efficient upconversion properties. The use of new combination of lanthanide chloride and sodium TFA as the precursors for UCNPs gave the best results in terms of size (10-40 nm), crystallinity and morphology, and proved to be cost-and time-saving. Water solubilization of both NaGdF 4 : Yb 3+ , Er 3+ , and -NaGdF 4 : Yb 3+ , Tm 3+ UCNPs was achieved by homogeneous polymer coating using amphiphilic poly(acrylic acid) derivatives. The strong upconversion and magnetic properties were maintained after extensive polymer coating process. To see the potential of the UCNPs for biological applications, the surface of NaGdF 4 : Yb 3+ , Er 3+ UCNPs were functionalized with Ni-nitrilotriacetate (NiNTA) moiety. The remarkable specificity of these NiNTAUCNPs for the oligohistidine peptide was clearly shown by both magnetic resonance and optical imaging. Finally, the cellular uptake of these UCNPs was investigated by fluorescence microscope using spectral imaging technique.

The parameter spaces of hierarchical systems such as multilayer perceptrons include singularities due to the symmetry and degeneration of hidden units. A parameter space forms a geometrical manifold, called the neuromanifold in the case of neural networks. Such a model is identified with a statistical model, and a Riemannian metric is given by the Fisher information matrix. However, the matrix degenerates at singularities. Such a singular structure is ubiquitous not only in multilayer perceptrons but also in the gaussian mixture probability densities, ARMA time-series model, and many other cases. The standard statistical paradigm of the Cramér-Rao theorem does not hold, and the singularity gives rise to strange behaviors in parameter estimation, hypothesis testing, Bayesian inference, model selection, and in particular, the dynamics of learning from examples. Prevailing theories so far have not paid much attention to the problem caused by singularity, relying only on ordinary statistical theories developed for regular (nonsingular) models. Only recently have researchers remarked on the effects of singularity, and theories are now being developed. This article gives an overview of the phenomena caused by the singularities of statistical manifolds related to multilayer perceptrons and gaussian mixtures. We demonstrate our recent results on these problems. Simple toy models are also used to show explicit solutions. We explain that the maximum likelihood estimator is no longer subject to the gaussian distribution even asymptotically, because the Fisher information matrix degenerates, that the model selection criteria such as AIC, BIC, and MDL fail to hold in these models, that a smooth Bayesian prior becomes singular in such models, and that the trajectories of dynamics of learning are strongly affected by the singularity, causing plateaus or slow manifolds in the parameter space. The natural gradient method is shown to perform well because it takes the singular geometrical structure into account. The generalization error and the training error are studied in some examples.

The natural gradient learning method is known to have ideal performances for on-line training of multilayer perceptrons. It avoids plateaus, which give rise to slow convergence of the backpropagation method. It is Fisher efficient, whereas the conventional method is not. However, for implementing the method, it is necessary to calculate the Fisher information matrix and its inverse, which is practically very difficult. This article proposes an adaptive method of directly obtaining the inverse of the Fisher information matrix. It generalizes the adaptive Gauss-Newton algorithms and provides a solid theoretical justification of them. Simulations show that the proposed adaptive method works very well for realizing natural gradient learning.

Although there is clinical demand for new technology that can accurately measure Parkinsonian tremors, automatic scoring of Parkinsonian tremors using machine-learning approaches has not yet been employed. This study aims to fill this gap by proposing machine-learning algorithms as a way to predict the Unified Parkinson’s Disease Rating Scale (UPDRS), which are similar to how neurologists rate scores in actual clinical practice. In this study, the tremor signals of 85 patients with Parkinson’s disease (PD) were measured using a wrist-watch-type wearable device consisting of an accelerometer and a gyroscope. The displacement and angle signals were calculated from the measured acceleration and angular velocity, and the acceleration, angular velocity, displacement, and angle signals were used for analysis. Nineteen features were extracted from each signal, and the pairwise correlation strategy was used to reduce the number of feature dimensions. With the selected features, a decision tree (DT), support vector machine (SVM), discriminant analysis (DA), random forest (RF), and k-nearest-neighbor (kNN) algorithm were explored for automatic scoring of the Parkinsonian tremor severity. The performance of the employed classifiers was analyzed using accuracy, recall, and precision, and compared to other findings in similar studies. Finally, the limitations and plans for further study are discussed.

Spinocerebellar ataxia (SCA) presents heterogeneous clinical phenotypes, and parkinsonism is reported in diverse SCA subtypes. Both levodopa responsive Parkinson disease (PD) like phenotype and atypical parkinsonism have been described especially in SCA2, SCA3, and SCA17 with geographic differences in prevalence. SCA2 is the most frequently reported subtype of SCA related to parkinsonism worldwide. Parkinsonism in SCA2 has unique genetic characteristics, such as low number of expansions and interrupted structures, which may explain the sporadic cases with low penetrance. Parkinsonism in SCA17 is more remarkable in Asian populations especially in Korea. In addition, an unclear cutoff of the pathologic range is the key issue in SCA17 related parkinsonism. SCA3 is more common in western cohorts. SCA6 and SCA8 have also been reported with a PD-like phenotype. Herein, we reviewed the epidemiologic, clinical, genetic, and pathologic features of parkinsonism in SCAs.