Antibodies against the extracellular domain of human Notch1 receptor reveal the critical role of epidermal-growth-factor-like repeats 25-26 in ligand binding and receptor activation

The Notch signalling pathway is implicated in a wide variety of cellular processes throughout metazoan development. Although the downstream mechanism of Notch signalling has been extensively studied, the details of its ligand-mediated receptor activation are not clearly understood. Although the role of Notch ELRs EGF (epidermal growth factor)-like-repeats] 11-12 in ligand binding is known, recent studies have suggested interactions within different ELRs of the Notch receptor whose significance remains to be understood. Here, we report critical inter-domain interactions between human Notch1 ELRs 21-30 and the ELRs 11-15 that are modulated by calcium. Surface plasmon resonance analysis revealed that the interaction between ELRs 21-30 and ELRs 11-15 is similar to 10-fold stronger than that between ELRs 11-15 and the ligands. Although there was no interaction between Notch 1 ELRs 21-30 and the ligands in vitro, addition of pre-clustered Jagged1Fc resulted in the dissociation of the preformed complex between ELRs 21-30 and 11-15, suggesting that inter-domain interactions compete for ligand binding. Furthermore, the antibodies against ELRs 21-30 inhibited ligand binding to the full-length Notch1 and subsequent receptor activation, with the antibodies against ELRs 25-26 being the most effective. These results suggest that the ELRs 25-26 represent a cryptic ligand-binding site which becomes exposed only upon the presence of the ligand. Thus, using specific antibodies against various domains of the Notch1 receptor, we demonstrate that, although ELRs 11-12 are the principal ligand-binding site, the ELRs 25-26 serve as a secondary binding site and play an important role in receptor activation.

Toll-Like Receptors 7, 8, and 9 Expression and Function in Primary Human Cervical Cancer Langerhans Cells Evidence of Anergy

Objective: Human papillomavirus oncoproteins E6 and E7 down modulate Toll-like receptor (TLR) 9 expression in infected keratinocytes. We explored the status of expression and function of TLR7, TLR8, and TLR9 in primary human Langerhans cells (LCs) isolated from cervical tumors. Methodology: Single-cell suspensions were made from fresh tissues of squamous cell carcinoma (International Federation of Gynecology and Obstetrics stage IB2); myeloid dendritic cells were purified using CD1c magnetic activated cell separation kits. Langerhans cells were further flow sorted into CD1a(+)CD207(+) cells. Acute monocytic leukemia cell line THP-1-derived LCs (moLCs) formed the controls. mRNA from flow-sorted LCs was reverse transcribed to cDNA and TLR7, TLR8, and TLR9 amplified. Monocyte-derived Langerhans cells and cervical tumor LCs were stimulated with TLR7, TLR8, and TLR9 ligands. Culture supernatants were assayed for interleukin (IL) 1 beta, IL-6, IL-10, IL-12p70, interferon (IFN) alpha, interferon gamma, and tumor necrosis factor (TNF) alpha by Luminex multiplex bead array. Human papillomavirus was genotyped. Results: We have for the first time demonstrated that the acute monocytic leukemia cell line THP-1 can be differentiated into LCs in vitro. Although these moLCs. expressed all the 3 TLRs, tumor LCs expressed TLR7 and TLR8, but uniformly lacked TLR9. Also, moLCs secreted IL-6, IL-1 beta, and tumor necrosis factor alpha to TLR8 ligand and interferon alpha in response to TLR9 ligand; in contrast, tumor LCs did not express any cytokine to any of the 3 TLR ligands. Human papillomavirus type 16 was one of the common human papillomavirus types in all cases. Conclusions: Cervical tumor LCs lacked TLR9 expression and were functionally anergic to all the 3: TLR7, TLR8, and TLR9 ligands, which may play a crucial role in immune tolerance. The exact location of block(s) in TLR7 and TLR8 signaling needs to be investigated, which would have important immunotherapeutic implications.

Multilevel Dodecagonal Voltage Space Vector Generation using Flying Capacitor topology for Induction motor drives

In this paper, a multilevel flying capacitor inverter topology suitable for generating multilevel dodecagonal space vectors for an induction motor drive, is proposed. Because of the dodecagonal space vectors, it has increased modulation range with the absence of all 6n +/- 1, (n=odd) harmonics in the phase voltage and currents. The topology, realized by flying capacitor three level inverters feeding an open-end winding induction motor, does not suffer the neutral point voltage imbalance issues seen in NPC inverters and the capacitors have inherent charge-balancing capability with PWM control using switching state redundancies. Furthermore, the proposed technique uses lesser number of power supplies compared to cascaded H-bridge or NPC based dodecagonal schemes and has better ride-through capability. Finally, the voltage control is obtained through a simple carrier-based space vector PWM scheme implemented on a DSP.

Nearly constant switching frequency hysteresis current controller for general n-level inverter fed induction motor drive

A current-error space phasor based hysteresis controller with nearly constant switching frequency is proposed for a general n-level voltage source inverter fed three-phase induction motor drive. Like voltage-controlled space vector PWM (SVPWM), the proposed controller can precisely detect sub-sector changes and for switching it selects only the nearest switching voltage vectors using the information of the estimated fundamental stator voltages along α and β axes. It provides smooth transition between voltage levels, including operation in over modulation region. Due to adjacent switching amongst the nearest switching vectors forming a triangular sub-sector, in which tip of the fundamental stator voltage vector of the machine lies, switching loss is reduced while keeping the current-error space phasor within the varying parabolic boundary. Appropriate dimension and orientation of this parabolic boundary ensures similar switching frequency spectrum like constant switching frequency SVPWM-based induction motor (IM) drive. Inherent advantages of multi-level inverter and space phasor based current hysteresis controller are retained. The proposed controller is simulated as well as implemented on a 5-level inverter fed 7.5 kW open-end winding IM drive.

Sensory-organ-like response determines the magnetism of zigzag-edged honeycomb nanoribbons

We present an analytical effective theory for the magnetic phase diagram for zigzag-edge terminated honeycomb nanoribbons described by a Hubbard model with an interaction parameter U. We show that the edge magnetic moment varies as ln U and uncover its dependence on the width W of the ribbon. The physics of this owes its origin to the sensory-organ-like response of the nanoribbons, demonstrating that considerations beyond the usual Stoner-Landau theory are necessary to understand the magnetism of these systems. A first-order magnetic transition from an antiparallel orientation of the moments on opposite edges to a parallel orientation occurs upon doping with holes or electrons. The critical doping for this transition is shown to depend inversely on the width of the ribbon. Using variational Monte Carlo calculations, we show that magnetism is robust to fluctuations. Additionally, we show that the magnetic phase diagram is generic to zigzag-edge terminated nanostructures such as nanodots. Furthermore, we perform first-principles modeling to show how such magnetic transitions can be realized in substituted graphene nanoribbons. DOI: 10.1103/PhysRevB.87.085412

A sequential dual method for structural SVMs

In many real world prediction problems the output is a structured object like a sequence or a tree or a graph. Such problems range from natural language processing to compu- tational biology or computer vision and have been tackled using algorithms, referred to as structured output learning algorithms. We consider the problem of structured classifi- cation. In the last few years, large margin classifiers like sup-port vector machines (SVMs) have shown much promise for structured output learning. The related optimization prob -lem is a convex quadratic program (QP) with a large num-ber of constraints, which makes the problem intractable for large data sets. This paper proposes a fast sequential dual method (SDM) for structural SVMs. The method makes re-peated passes over the training set and optimizes the dual variables associated with one example at a time. The use of additional heuristics makes the proposed method more efficient. We present an extensive empirical evaluation of the proposed method on several sequence learning problems.Our experiments on large data sets demonstrate that the proposed method is an order of magnitude faster than state of the art methods like cutting-plane method and stochastic gradient descent method (SGD). Further, SDM reaches steady state generalization performance faster than the SGD method. The proposed SDM is thus a useful alternative for large scale structured output learning.

Infinite push: a new support vector ranking algorithm that directly optimizes accuracy at the absolute top of the list

Ranking problems have become increasingly important in machine learning and data mining in recent years, with applications ranging from information retrieval and recommender systems to computational biology and drug discovery. In this paper, we describe a new ranking algorithm that directly maximizes the number of relevant objects retrieved at the absolute top of the list. The algorithm is a support vector style algorithm, but due to the different objective, it no longer leads to a quadratic programming problem. Instead, the dual optimization problem involves l1, ∞ constraints; we solve this dual problem using the recent l1, ∞ projection method of Quattoni et al (2009). Our algorithm can be viewed as an l∞-norm extreme of the lp-norm based algorithm of Rudin (2009) (albeit in a support vector setting rather than a boosting setting); thus we refer to the algorithm as the ‘Infinite Push’. Experiments on real-world data sets confirm the algorithm’s focus on accuracy at the absolute top of the list.

Monoclonal Antibodies against Hepatitis C Genotype 3a Virus Like Particle Inhibit Virus Entry in Cell Culture System

The envelope protein (E1-E2) of Hepatitis C virus (HCV) is a major component of the viral structure. The glycosylated envelope protein is considered to be important for initiation of infection by binding to cellular receptor(s) and also known as one of the major antigenic targets to host immune response. The present study was aimed at identifying mouse monoclonal antibodies which inhibit binding of virus like particles of HCV to target cells. The first step in this direction was to generate recombinant HCV-like particles (HCV-LPs) specific for genotypes 3a of HCV (prevalent in India) using the genes encoding core, E1 and E2 envelop proteins in a baculovirus expression system. The purified HCV-LPs were characterized by ELISA and electron microscopy and were used to generate monoclonal antibodies (mAbs) in mice. Two monoclonal antibodies (E8G9 and H1H10) specific for the E2 region of envelope protein of HCV genotype 3a, were found to reduce the virus binding to Huh7 cells. However, the mAbs generated against HCV genotype 1b (D2H3, G2C7, E1B11) were not so effective. More importantly, mAb E8G9 showed significant inhibition of the virus entry in HCV JFH1 cell culture system. Finally, the epitopic regions on E2 protein which bind to the mAbs have also been identified. Results suggest a new therapeutic strategy and provide the proof of concept that mAb against HCV-LP could be effective in preventing virus entry into liver cells to block HCV replication.

Identification of coherent synchronous generators in a multi- machine power system using support vector clustering

This paper illustrates the application of a new technique, based on Support Vector Clustering (SVC) for the direct identification of coherent synchronous generators in a large interconnected Multi-Machine Power Systems. The clustering is based on coherency measures, obtained from the time domain responses of the generators following system disturbances. The proposed clustering algorithm could be integrated into a wide-area measurement system that enables fast identification of coherent clusters of generators for the construction of dynamic equivalent models. An application of the proposed method is demonstrated on a practical 15 generators 72-bus system, an equivalent of Indian Southern grid in an attempt to show the effectiveness of this clustering approach. The effects of short circuit fault locations on coherency are also investigated.

Damage identification of beam-like structures with contiguous and distributed damage

Structural health monitoring of existing infrastructure is currently an active field of research, where elaborate experimental programs and advanced analytical methods are used in identifying the current state of health of critical structures. Change of static deflection as the indicator of damage is the simplest tool in a structural health monitoring scenario of bridges that is least exploited in damage identification strategies. In this paper, some simple and elegant equations based on loss of symmetry due to damage are derived and presented for identification of damage in a bridge girder modeled as a simply supported beam using changes in static deflections and dynamic parameters. A single contiguous and distributed damage, typical of reinforced or prestressed concrete structures, is assumed for the structure. The methodology is extended for a base-line-free as well as base-line-inclusive measurement. Measurement strategy involves application of loads only at two symmetric points one at a time and deflection measurements at those symmetric points as well as at the midspan of the beam. A laboratory-based experiment is used to validate the approach. Copyright (c) 2012 John Wiley & Sons, Ltd.

Simplified implementation of space vector PWM strategies for a three level inverter

Space vector based PWM strategies for three-level inverters have a broader choice of switching sequences to generate the required reference vector than triangle comparison based PWM techniques. However, space vector based PWM involves numerous steps which are computationally intensive. A simplified algorithm is proposed here, which is shown to reduce the computation time significantly. The developed algorithm is used to implement synchronous and asynchronous conventional space vector PWM, synchronized modified space vector PWM and an asynchronous advanced bus-clamping PWM technique on a low-cost dsPIC digital controller. Experimental results are presented for a comparative evaluation of the performance of different PWM methods.

Linear Coding Schemes for the Distributed Computation of Subspaces

Let X-1,..., X-m be a set of m statistically dependent sources over the common alphabet F-q, that are linearly independent when considered as functions over the sample space. We consider a distributed function computation setting in which the receiver is interested in the lossless computation of the elements of an s-dimensional subspace W spanned by the elements of the row vector X-1,..., X-m]Gamma in which the (m x s) matrix Gamma has rank s. A sequence of three increasingly refined approaches is presented, all based on linear encoders. The first approach uses a common matrix to encode all the sources and a Korner-Marton like receiver to directly compute W. The second improves upon the first by showing that it is often more efficient to compute a carefully chosen superspace U of W. The superspace is identified by showing that the joint distribution of the {X-i} induces a unique decomposition of the set of all linear combinations of the {X-i}, into a chain of subspaces identified by a normalized measure of entropy. This subspace chain also suggests a third approach, one that employs nested codes. For any joint distribution of the {X-i} and any W, the sum-rate of the nested code approach is no larger than that under the Slepian-Wolf (SW) approach. Under the SW approach, W is computed by first recovering each of the {X-i}. For a large class of joint distributions and subspaces W, the nested code approach is shown to improve upon SW. Additionally, a class of source distributions and subspaces are identified, for which the nested-code approach is sum-rate optimal.

Sonic hedgehog-Dependent Induction of MicroRNA 31 and MicroRNA 150 Regulates Mycobacterium bovis BCG-Driven Toll-Like Receptor 2 Signaling

Hedgehog (HH) signaling is a significant regulator of cell fate decisions during embryogenesis, development, and perpetuation of various disease conditions. Testing whether pathogen-specific HH signaling promotes unique innate recognition of intracellular bacteria, we demonstrate that among diverse Gram-positive or Gram-negative microbes, Mycobacterium bovis BCG, a vaccine strain, elicits a robust activation of Sonic HH (SHH) signaling in macrophages. Interestingly, sustained tumor necrosis factor alpha (TNF-alpha) secretion by macrophages was essential for robust SHH activation, as TNF-alpha(-/-) macrophages exhibited compromised ability to activate SHH signaling. Neutralization of TNF-alpha or blockade of TNF-alpha receptor signaling significantly reduced the infection-induced SHH signaling activation both in vitro and in vivo. Intriguingly, activated SHH signaling downregulated M. bovis BCG-mediated Toll-like receptor 2 (TLR2) signaling events to regulate a battery of genes associated with divergent functions of M1/M2 macrophages. Genome-wide expression profiling as well as conventional gain-of-function or loss-of-function analysis showed that SHH signaling-responsive microRNA 31 (miR-31) and miR-150 target MyD88, an adaptor protein of TLR2 signaling, thus leading to suppression of TLR2 responses. SHH signaling signatures could be detected in vivo in tuberculosis patients and M. bovis BCG-challenged mice. Collectively, these investigations identify SHH signaling to be what we believe is one of the significant regulators of host-pathogen interactions.

Sequential alternating proximal method for scalable sparse structural SVMs

Structural Support Vector Machines (SSVMs) have recently gained wide prominence in classifying structured and complex objects like parse-trees, image segments and Part-of-Speech (POS) tags. Typical learning algorithms used in training SSVMs result in model parameters which are vectors residing in a large-dimensional feature space. Such a high-dimensional model parameter vector contains many non-zero components which often lead to slow prediction and storage issues. Hence there is a need for sparse parameter vectors which contain a very small number of non-zero components. L1-regularizer and elastic net regularizer have been traditionally used to get sparse model parameters. Though L1-regularized structural SVMs have been studied in the past, the use of elastic net regularizer for structural SVMs has not been explored yet. In this work, we formulate the elastic net SSVM and propose a sequential alternating proximal algorithm to solve the dual formulation. We compare the proposed method with existing methods for L1-regularized Structural SVMs. Experiments on large-scale benchmark datasets show that the proposed dual elastic net SSVM trained using the sequential alternating proximal algorithm scales well and results in highly sparse model parameters while achieving a comparable generalization performance. Hence the proposed sequential alternating proximal algorithm is a competitive method to achieve sparse model parameters and a comparable generalization performance when elastic net regularized Structural SVMs are used on very large datasets.

Support vector machine based high speed protection relay for EHV/UHV transmission line

This paper presents a fast and accurate relaying technique for a long 765kv UHV transmission line based on support vector machine. For a long EHV/UHV transmission line with large distributed capacitance, a traditional distance relay which uses a lumped parameter model of the transmission line can cause malfunction of the relay. With a frequency of 1kHz, 1/4th cycle of instantaneous values of currents and voltages of all phases at the relying end are fed to Support Vector Machine(SVM). The SVM detects fault type accurately using 3 milliseconds of post-fault data and reduces the fault clearing time which improves the system stability and power transfer capability. The performance of relaying scheme has been checked with a typical 765kV Indian transmission System which is simulated using the Electromagnetic Transients Program(EMTP) developed by authors in which the distributed parameter line model is used. More than 15,000 different short circuit fault cases are simulated by varying fault location, fault impedance, fault incidence angle and fault type to train the SVM for high speed accurate relaying. Simulation studies have shown that the proposed relay provides fast and accurate protection irrespective of fault location, fault impedance, incidence time of fault and fault type. And also the proposed scheme can be used as augmentation for the existing relaying, particularly for Zone-2, Zone-3 protection.