FSPE: Visualization of Hyperspectral Imagery Using Faithful Stochastic Proximity Embedding

Hyperspectral image visualization reduces color bands to three, but prevailing linear methods fail to address data characteristics, and nonlinear embeddings are computationally demanding. Qualitative evaluation of embedding is also lacking. We propose faithful stochastic proximity embedding (FSPE), which is a scalable and nonlinear dimensionality reduction method. FSPE considers the nonlinear characteristics of spectral signatures, yet it avoids the costly computation of geodesic distances that are often required by other nonlinear methods. Furthermore, we employ a pixelwise metric that measures the quality of hyperspectral image visualization at each pixel. FSPE outperforms the state-of-art methods by at least 12% on average and up to 25% in the qualitative measure. An implementation on graphics processing units is two orders of magnitude faster than the baseline. Our method opens the path to high-fidelity and real-time analysis of hyperspectral images

Structures from Distances in Two and Three Dimensions using Stochastic Proximity Embedding

The point placement problem is to determine the locations of a set of distinct points uniquely (up to translation and reflection) by making the fewest possible pairwise distance queries of an adversary. Deterministic and randomized algorithms are available if distances are known exactly. In this thesis, we discuss a 1-round algorithm for approximate point placement in the plane in an adversarial model. The distance query graph presented to the adversary is chordal. The remaining distances are uniquely determined using the Stochastic Proximity Embedding (SPE) method due to Agrafiotis, and the layout of the points is also generated from SPE. We have also computed the distances uniquely using a distance matrix completion algorithm for chordal graphs, based on a result by Bakonyi and Johnson. The layout of the points is determined using the traditional Young- Householder approach. We compared the layout of both the method and discussed briefly inside. The modified version of SPE is proposed to overcome the highest translation embedding that the method faces when dealing with higher learning rates. We also discuss the computation of molecular structures in three-dimensional space, with only a subset of the pairwise atomic distances available. The subset of distances is obtained using the Philips model for creating artificial backbone chain of molecular structures. We have proposed the Degree of Freedom Approach to solve this problem and carried out our implementation using SPE and the Distance matrix completion Approac

Modeling Stroke Diagnosis with the Use of Intelligent Techniques

The purpose of this work is to test the efficiency of specific intelligent classification algorithms when dealing with the domain of stroke medical diagnosis. The dataset consists of patient records of the ”Acute Stroke Unit”, Alexandra Hospital, Athens, Greece, describing patients suffering one of 5 different stroke types diagnosed by 127 diagnostic attributes / symptoms collected during the first hours of the emergency stroke situation as well as during the hospitalization and recovery phase of the patients. Prior to the application of the intelligent classifier the dimensionality of the dataset is further reduced using a variety of classic and state of the art dimensionality reductions techniques so as to capture the intrinsic dimensionality of the data. The results obtained indicate that the proposed methodology achieves prediction accuracy levels that are comparable to those obtained by intelligent classifiers trained on the original feature space

Študija strukturne dinamike jedrnega receptorja PPARγ s simulacijami molekulske dinamike

PPARγ is a nuclear receptor protein that has a central role in promoting adipocyte growth and differentiation, as well as regulating serum glucose and triglyceride levels. Under normal physiological conditions, PPARγ is a target of several post-translational modifications (PTMs) that modulate its transcriptional activity. One particular PTM, phosphorylation of Ser245 in the ligand-binding domain (LBD) by cyclin-dependent kinase 5 (CDK5) leads to down-regulation of specific genes that promote insulin sensitivity, increasing the risk of type 2 diabetes and cardiovascular diseases. Earlier studies have shown that some PPARγ ligands can lead to the inhibition of Ser245 phosphorylation by CDK5 and restore the basal gene expression of PPARγ and increase insulin sensitivity. The mechanism of this inhibition from the molecular point of view is not completely understood. While it has been previously suggested that ligand binding leads to the establishment of a series of interactions that effectively prevent the formation of PPARγ-CDK5 interaction surface, to date, there are no experimental structures to support this hypothesis. To that end, we used molecular dynamics simulations to investigate the effects of different ligands on the structure and dynamics of this region of the protein. Using both classical and accelerated dynamics approaches, we studied PPARγ-ligand complexes containing ligands with three different activation profiles, the full agonist rosiglitazone, partial agonist MRL24 and a non-agonist decanoic acid, that resembles an endogenous PPARγ ligand. Our observations show that the binding of a ligand to the active site of PPARγ correlates well with the global stability of PPARγ LBD, especially in the case of rosiglitazone, effectively restricting the dynamics and conformational space of residues, encompassing the PPARγ-CDK5 interaction surface. Further research is required to discern the difference between ligands of distinct inhibitory and agonistic profiles on the dynamical and conformational properties of PPARγ.PPARγ je jedrni receptor, ki ima osrednjo vlogo pri promociji rasti in diferenciacije adipocitov ter regulaciji nivoja glukoze in trigliceridov v krvi. Pri normalnih fizioloških pogojih je PPARγ tarča številnih post-translacijskih modifikacij, ki uravnavajo transkripcijsko aktivnost tega receptorja. Postranslacijska modifikacija PPARγ s strani od ciklina odvisne kinaze 5 (CDK5) vodi v supresijo specifičnih genov za povečevanje inzulinske občutljivosti, kar vodi k povečanemu tveganju za pojav sladkorne bolezni tipa 2 in srčno-žilnih obolenj. Predhodne študije so pokazale, da inhibicija fosforilacije PPARγ s strani CDK5, z vezavo liganda v aktivno mesto PPARγ povrne bazalni nivo transkripcije in ponovno poveča inzulinsko občutljivost posameznika. Mehanizem inhibicije na molekulski ravni ni popolnima razjasnjen. Čeprav se domneva, da se z vezavo liganda v aktivno mesto PPARγ vzpostavijo kritične interakcije med ligandom in proteinom, ki preprečijo molekulsko prepoznavanje CDK5, do sedaj še ni bilo nobenih eksperimentalnih izsledkov, ki bi potrjevale to hipotezo. V ta namen smo s simulacijami molekulske dinamike preučili vpliv različnih ligandov na strukturo in dinamiko interakcijske površine PPARγ za interakcijo s CDK5. S pomočjo metod klasične in pospešene molekulske dinamike smo podrobneje preučili komplekse PPARγ z ligandi treh aktivacijskih profilov, polnim agonistom rosiglitazone, delnim agonistom MRL24 in neagonistom dekanojsko kislino iz skupine srednjeverižnih maščobnih kislin. Naša opažanja so, da vezava liganda v aktivno mesto PPARγ dobro korelira z globalno stabilnostjo ligand-vezavne domene PPARγ. Nadaljnje raziskave bi bile potrebne za razločevanje razlik med ligandi z različnimi inhibitornimi in agonističnimi lastnostmi pri njihoven vplivu na dinamiko in konformacijske lastnosti PPARγ

Iterative focused screening with biological fingerprints identifies selective Asc-1 inhibitors distinct from traditional high throughput screening

N-methyl-d-aspartate receptors (NMDARs) mediate glutamatergic signaling that is critical to cognitive processes in the central nervous system, and NMDAR hypofunction is thought to contribute to cognitive impairment observed in both schizophrenia and Alzheimer’s disease. One approach to enhance the function of NMDAR is to increase the concentration of an NMDAR coagonist, such as glycine or d-serine, in the synaptic cleft. Inhibition of alanine–serine–cysteine transporter-1 (Asc-1), the primary transporter of d-serine, is attractive because the transporter is localized to neurons in brain regions critical to cognitive function, including the hippocampus and cortical layers III and IV, and is colocalized with d-serine and NMDARs. To identify novel Asc-1 inhibitors, two different screening approaches were performed with whole-cell amino acid uptake in heterologous cells stably expressing human Asc-1: (1) a high-throughput screen (HTS) of 3 M compounds measuring 35S l-cysteine uptake into cells attached to scintillation proximity assay beads in a 1536 well format and (2) an iterative focused screen (IFS) of a 45 000 compound diversity set using a 3H d-serine uptake assay with a liquid scintillation plate reader in a 384 well format. Critically important for both screening approaches was the implementation of counter screens to remove nonspecific inhibitors of radioactive amino acid uptake. Furthermore, a 15 000 compound expansion step incorporating both on- and off-target data into chemical and biological fingerprint-based models for selection of additional hits enabled the identification of novel Asc-1-selective chemical matter from the IFS that was not identified in the full-collection HTS

SCANN: Synthesis of Compact and Accurate Neural Networks

Deep neural networks (DNNs) have become the driving force behind recent artificial intelligence (AI) research. An important problem with implementing a neural network is the design of its architecture. Typically, such an architecture is obtained manually by exploring its hyperparameter space and kept fixed during training. This approach is time-consuming and inefficient. Another issue is that modern neural networks often contain millions of parameters, whereas many applications and devices require small inference models. However, efforts to migrate DNNs to such devices typically entail a significant loss of classification accuracy. To address these challenges, we propose a two-step neural network synthesis methodology, called DR+SCANN, that combines two complementary approaches to design compact and accurate DNNs. At the core of our framework is the SCANN methodology that uses three basic architecture-changing operations, namely connection growth, neuron growth, and connection pruning, to synthesize feed-forward architectures with arbitrary structure. SCANN encapsulates three synthesis methodologies that apply a repeated grow-and-prune paradigm to three architectural starting points. DR+SCANN combines the SCANN methodology with dataset dimensionality reduction to alleviate the curse of dimensionality. We demonstrate the efficacy of SCANN and DR+SCANN on various image and non-image datasets. We evaluate SCANN on MNIST and ImageNet benchmarks. In addition, we also evaluate the efficacy of using dimensionality reduction alongside SCANN (DR+SCANN) on nine small to medium-size datasets. We also show that our synthesis methodology yields neural networks that are much better at navigating the accuracy vs. energy efficiency space. This would enable neural network-based inference even on Internet-of-Things sensors.Comment: 13 pages, 8 figure

EEG-Based Multi-Class Workload Identification Using Feature Fusion and Selection

The effectiveness of workload identification is one of the critical aspects in a monitoring instrument of mental state. In this field, the workload is usually recognised as binary classes. There are scarce studies towards multi-class workload identification because the challenge of the success of workload identification is much tough, even though one more workload class is added. Besides, most of the existing studies only utilized spectral power features from individual channels but ignoring abundant inter-channel features that represent the interactions between brain regions. In this study, we utilized features representing intra-channel information and inter-channel information to classify multiple classes of workload based on EEG. We comprehensively compared each category of features contributing to workload identification and elucidated the roles of feature fusion and feature selection for the workload identification. The results demonstrated that feature combination (83.12% in terms of accuracy) enhanced the classification performance compared to individual feature categories (i.e., band power features, 75.90%; connection features, 81.72%, in terms of accuracy). With the F-score feature selection, the classification accuracy was further increased to 83.47%. When the features of graph metric were fused, the accuracy was reached to 84.34%. Our study provided comprehensive performance comparisons between methods and feature categories for the multi-class workload identification and demonstrated that feature selection and fusion played an important role in the enhancement of workload identification. These results could facilitate further studies of multi-class workload identification and practical application of workload identification

A Self-Organizing Algorithm for Modeling Protein Loops

Protein loops, the flexible short segments connecting two stable secondary structural units in proteins, play a critical role in protein structure and function. Constructing chemically sensible conformations of protein loops that seamlessly bridge the gap between the anchor points without introducing any steric collisions remains an open challenge. A variety of algorithms have been developed to tackle the loop closure problem, ranging from inverse kinematics to knowledge-based approaches that utilize pre-existing fragments extracted from known protein structures. However, many of these approaches focus on the generation of conformations that mainly satisfy the fixed end point condition, leaving the steric constraints to be resolved in subsequent post-processing steps. In the present work, we describe a simple solution that simultaneously satisfies not only the end point and steric conditions, but also chirality and planarity constraints. Starting from random initial atomic coordinates, each individual conformation is generated independently by using a simple alternating scheme of pairwise distance adjustments of randomly chosen atoms, followed by fast geometric matching of the conformationally rigid components of the constituent amino acids. The method is conceptually simple, numerically stable and computationally efficient. Very importantly, additional constraints, such as those derived from NMR experiments, hydrogen bonds or salt bridges, can be incorporated into the algorithm in a straightforward and inexpensive way, making the method ideal for solving more complex multi-loop problems. The remarkable performance and robustness of the algorithm are demonstrated on a set of protein loops of length 4, 8, and 12 that have been used in previous studies

Spike pattern recognition by supervised classification in low dimensional embedding space

© The Author(s) 2016. This article is published with open access at Springerlink.com under the terms of the Creative Commons Attribution License 4.0, (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts’ manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min−1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.Peer reviewedFinal Published versio