Temperature-dependent infrared reflectivity studies of multiferroic TbMnO3: Evidence for spin-phonon coupling

We have measured near normal incidence far-infrared (FIR) reflectivity spectra of a single crystal of TbMnO3 from 10 K to 300 K in the spectral range of 50 cm(-1)-700 cm(-1). Fifteen transverse optic (TO) and longitudinal optic (LO) modes are identified in the imaginary part of the dielectric function epsilon(2)(omega) and energy loss function Im(-1/epsilon(omega)), respectively. Some of the observed phonon modes show anomalous softening below the magnetic transition temperature T-N (similar to 46 K). We attribute this anomalous softening to the spin-phonon coupling caused by phonon modulation of the superexchange integral between the Mn3+ spins. The effective charge of oxygen (Z(O)) calculated using the measured LO-TO splitting increases below TN.

Distributed nonlinear optimization algorithms for general network problems

There are a number of large networks which occur in many problems dealing with the flow of power, communication signals, water, gas, transportable goods, etc. Both design and planning of these networks involve optimization problems. The first part of this paper introduces the common characteristics of a nonlinear network (the network may be linear, the objective function may be non linear, or both may be nonlinear). The second part develops a mathematical model trying to put together some important constraints based on the abstraction for a general network. The third part deals with solution procedures; it converts the network to a matrix based system of equations, gives the characteristics of the matrix and suggests two solution procedures, one of them being a new one. The fourth part handles spatially distributed networks and evolves a number of decomposition techniques so that we can solve the problem with the help of a distributed computer system. Algorithms for parallel processors and spatially distributed systems have been described.There are a number of common features that pertain to networks. A network consists of a set of nodes and arcs. In addition at every node, there is a possibility of an input (like power, water, message, goods etc) or an output or none. Normally, the network equations describe the flows amoungst nodes through the arcs. These network equations couple variables associated with nodes. Invariably, variables pertaining to arcs are constants; the result required will be flows through the arcs. To solve the normal base problem, we are given input flows at nodes, output flows at nodes and certain physical constraints on other variables at nodes and we should find out the flows through the network (variables at nodes will be referred to as across variables).The optimization problem involves in selecting inputs at nodes so as to optimise an objective function; the objective may be a cost function based on the inputs to be minimised or a loss function or an efficiency function. The above mathematical model can be solved using Lagrange Multiplier technique since the equalities are strong compared to inequalities. The Lagrange multiplier technique divides the solution procedure into two stages per iteration. Stage one calculates the problem variables % and stage two the multipliers lambda. It is shown that the Jacobian matrix used in stage one (for solving a nonlinear system of necessary conditions) occurs in the stage two also.A second solution procedure has also been imbedded into the first one. This is called total residue approach. It changes the equality constraints so that we can get faster convergence of the iterations.Both solution procedures are found to coverge in 3 to 7 iterations for a sample network.The availability of distributed computer systems — both LAN and WAN — suggest the need for algorithms to solve the optimization problems. Two types of algorithms have been proposed — one based on the physics of the network and the other on the property of the Jacobian matrix. Three algorithms have been deviced, one of them for the local area case. These algorithms are called as regional distributed algorithm, hierarchical regional distributed algorithm (both using the physics properties of the network), and locally distributed algorithm (a multiprocessor based approach with a local area network configuration). The approach used was to define an algorithm that is faster and uses minimum communications. These algorithms are found to converge at the same rate as the non distributed (unitary) case.

Chronic suppression of testicular function by constant infusion of gonadotropin-releasing hormone agonist and testosterone supplementation in the bonnet monkey (Macaca radiata)

Objective: To study the efficacy of long-term buserelin acetate infusion to desensitize pituitary and block testicular function in adult male monkeys (Macaca radiata). Animals: Proven fertile male monkeys exhibiting normal testicular function. Protocol: Each of the control (n = 5) and experimental monkeys (n = 10) received a fresh miniosmotic pump every 21 days, whereas pumps in controls delivered vehicle of experimentals released 50-mu-g buserelin acetate every 24 hours. On day 170 (renewed every 60 days) a silastic capsule containing crystalline testosterone (T) was implanted in the experimental monkeys. At the end of 3 years, treatment was stopped, and recovery of testicular function and fertility monitored. Results: (1) Treatment resulted in marked reduction of nocturnal but not basal serum T; (2) the pituitary remained desensitized to buserelin acetate throughout the 3-year period; (3) animals were largely azoospermic with occasional oligospermia exhibited by two monkeys; and (4) withdrawal of treatment restored testicular function, with 70% of animals regaining fertility. Conclusion: Long-term infertility (but restorable) can be induced in male monkeys by constant infusion of buserelin acetate and T.

A Huber-loss-driven clustering technique and its application to robust cell detection in confocal microscopy images

We address the problem of detecting cells in biological images. The problem is important in many automated image analysis applications. We identify the problem as one of clustering and formulate it within the framework of robust estimation using loss functions. We show how suitable loss functions may be chosen based on a priori knowledge of the noise distribution. Specifically, in the context of biological images, since the measurement noise is not Gaussian, quadratic loss functions yield suboptimal results. We show that by incorporating the Huber loss function, cells can be detected robustly and accurately. To initialize the algorithm, we also propose a seed selection approach. Simulation results show that Huber loss exhibits better performance compared with some standard loss functions. We also provide experimental results on confocal images of yeast cells. The proposed technique exhibits good detection performance even when the signal-to-noise ratio is low.

Skeletal Muscle Degeneration and Regeneration in Mice and Flies

Many aspects of skeletal muscle biology are remarkably similar between mammals and tiny insects, and experimental models of mice and flies (Drosophila) provide powerful tools to understand factors controlling the growth, maintenance, degeneration (atrophy and necrosis), and regeneration of normal and diseased muscles, with potential applications to the human condition. This review compares the limb muscles of mice and the indirect flight muscles of flies, with respect to the mechanisms of adult myofiber formation, homeostasis, atrophy, hypertrophy, and the response to muscle degeneration, with some comment on myogenic precursor cells and common gene regulatory pathways. There is a striking similarity between the species for events related to muscle atrophy and hypertrophy, without contribution of any myoblast fusion. Since the flight muscles of adult flies lack a population of reserve myogenic cells (equivalent to satellite cells), this indicates that such cells are not required for maintenance of normal muscle function. However, since satellite cells are essential in postnatal mammals for myogenesis and regeneration in response to myofiber necrosis, the extent to which such regeneration might be possible in flight muscles of adult flies remains unclear. Common cellular and molecular pathways for both species are outlined related to neuromuscular disorders and to age-related loss of skeletal muscle mass and function (sarcopenia). The commonality of events related to skeletal muscles in these disparate species (with vast differences in size, growth duration, longevity, and muscle activities) emphasizes the combined value and power of these experimental animal models.

Unraveling the Intricate Organization of Mammalian Mitochondrial Presequence Translocases: Existence of Multiple Translocases for Maintenance of Mitochondrial Function

Mitochondria are indispensable organelles implicated in multiple aspects of cellular processes, including tumorigenesis. Heat shock proteins play a critical regulatory role in accurately delivering the nucleus-encoded proteins through membrane-bound presequence translocase (Tim23 complex) machinery. Although altered expression of mammalian presequence translocase components had been previously associated with malignant phenotypes, the overall organization of Tim23 complexes is still unsolved. In this report, we show the existence of three distinct Tim23 complexes, namely, B1, B2, and A, involved in the maintenance of normal mitochondrial function. Our data highlight the importance of Magmas as a regulator of translocase function and in dynamically recruiting the J-proteins DnaJC19 and DnaJC15 to individual translocases. The basic housekeeping function involves translocases B1 and B2 composed of Tim17b isoforms along with DnaJC19, whereas translocase A is nonessential and has a central role in oncogenesis. Translocase B, having a normal import rate, is essential for constitutive mitochondrial functions such as maintenance of electron transport chain complex activity, organellar morphology, iron-sulfur cluster protein biogenesis, and mitochondrial DNA. In contrast, translocase A, though dispensable for housekeeping functions with a comparatively lower import rate, plays a specific role in translocating oncoproteins lacking presequence, leading to reprogrammed mitochondrial functions and hence establishing a possible link between the TIM23 complex and tumorigenicity.

Skeletal Muscle Degeneration and Regeneration in Mice and Flies

Many aspects of skeletal muscle biology are remarkably similar between mammals and tiny insects, and experimental models of mice and flies (Drosophila) provide powerful tools to understand factors controlling the growth, maintenance, degeneration (atrophy and necrosis), and regeneration of normal and diseased muscles, with potential applications to the human condition. This review compares the limb muscles of mice and the indirect flight muscles of flies, with respect to the mechanisms of adult myofiber formation, homeostasis, atrophy, hypertrophy, and the response to muscle degeneration, with some comment on myogenic precursor cells and common gene regulatory pathways. There is a striking similarity between the species for events related to muscle atrophy and hypertrophy, without contribution of any myoblast fusion. Since the flight muscles of adult flies lack a population of reserve myogenic cells (equivalent to satellite cells), this indicates that such cells are not required for maintenance of normal muscle function. However, since satellite cells are essential in postnatal mammals for myogenesis and regeneration in response to myofiber necrosis, the extent to which such regeneration might be possible in flight muscles of adult flies remains unclear. Common cellular and molecular pathways for both species are outlined related to neuromuscular disorders and to age-related loss of skeletal muscle mass and function (sarcopenia). The commonality of events related to skeletal muscles in these disparate species (with vast differences in size, growth duration, longevity, and muscle activities) emphasizes the combined value and power of these experimental animal models.

A Fuzzy Approximation Scheme for Sequential Learning in Pattern Recognition

An adaptive learning scheme, based on a fuzzy approximation to the gradient descent method for training a pattern classifier using unlabeled samples, is described. The objective function defined for the fuzzy ISODATA clustering procedure is used as the loss function for computing the gradient. Learning is based on simultaneous fuzzy decisionmaking and estimation. It uses conditional fuzzy measures on unlabeled samples. An exponential membership function is assumed for each class, and the parameters constituting these membership functions are estimated, using the gradient, in a recursive fashion. The induced possibility of occurrence of each class is useful for estimation and is computed using 1) the membership of the new sample in that class and 2) the previously computed average possibility of occurrence of the same class. An inductive entropy measure is defined in terms of induced possibility distribution to measure the extent of learning. The method is illustrated with relevant examples.

Support vector machine applied to settlement of shallow foundations on cohesionless soils

The determination of settlement of shallow foundations on cohesionless soil is an important task in geotechnical engineering. Available methods for the determination of settlement are not reliable. In this study, the support vector machine (SVM), a novel type of learning algorithm based on statistical theory, has been used to predict the settlement of shallow foundations on cohesionless soil. SVM uses a regression technique by introducing an ε – insensitive loss function. A thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement. The study shows that SVM has the potential to be a useful and practical tool for prediction of settlement of shallow foundation on cohesionless soil.

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sqa <.km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing epsilon-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability ofRVM over the SVM model.

Analysis of EXAFS data of complex systems

Analysis of EXAFS data of complex systems containing more than one phase and one type of coordination, has been discussed. It is shown that a modified treatment of EXAFS function as well as the amplitude ratio plots provide useful means of obtaining valuable structural information. The systems investigated are: biphasic Ni+NiO mixture, NiAl2O4 with two coordinations for Ni, NiO+NiAl2O4 mixture, CoS+CoO system and Ni dispersed on Al2O3. The results obtained with these systems have been most satisfactory and serve to illustrate the utility and the applicability of the innovations described in this paper.

Nonlinear analysis of adhesively bonded lap joints considering viscoplasticity in adhesives

This paper presents nonlinear finite element analysis of adhesively bonded joints considering the elastoviscoplastic constitutive model of the adhesive material and the finite rotation of the joint. Though the adherends have been assumed to be linearly elastic, the yielding of the adhesive is represented by a pressure sensitive modified von Mises yield function. The stress-strain relation of the adhesive is represented by the Ramberg-Osgood relation. Geometric nonlinearity due to finite rotation in the joint is accounted for using the Green-Lagrange strain tensor and the second Piola-Kirchhoff stress tensor in a total Lagrangian formulation. Critical time steps have been calculated based on the eigenvalues of the transition matrices of the viscoplastic model of the adhesive. Stability of the viscoplastic solution and time dependent behaviour of the joints are examined. A parametric study has been carried out with particular reference to peel and shear stress along the interface. Critical zones for failure of joints have been identified. The study is of significance in the design of lap joints as well as on the characterization of adhesive strength. (C) 1999 Elsevier Science Ltd. All rights reserved.

A comparative study of ordinary kriging and support vector machine models for the spatial variability of rock depth in Bangalore

In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 sq.km. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, ordinary kriging and Support Vector Machine (SVM) models have been developed. In ordinary kriging, the knowledge of the semivariogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of Bangalore, where field measurements are not available. A cross validation (Q1 and Q2) analysis is also done for the developed ordinary kriging model. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing e-insensitive loss function has been used to predict the reduced level of rock from a large set of data. A comparison between ordinary kriging and SVM model demonstrates that the SVM is superior to ordinary kriging in predicting rock depth.

Familial Diarrhea Syndrome Caused by an Activating GUCY2C Mutation

BACKGROUND Familial diarrhea disorders are, in most cases, severe and caused by recessive mutations. We describe the cause of a novel dominant disease in 32 members of a Norwegian family. The affected members have chronic diarrhea that is of early onset, is relatively mild, and is associated with increased susceptibility to inflammatory bowel disease, small-bowel obstruction, and esophagitis. METHODS We used linkage analysis, based on arrays with single-nucleotide polymorphisms, to identify a candidate region on chromosome 12 and then sequenced GUCY2C, encoding guanylate cyclase C (GC-C), an intestinal receptor for bacterial heat-stable enterotoxins. We performed exome sequencing of the entire candidate region from three affected family members, to exclude the possibility that mutations in genes other than GUCY2C could cause or contribute to susceptibility to the disease. We carried out functional studies of mutant GC-C using HEK293T cells. RESULTS We identified a heterozygous missense mutation (c.2519G -> T) in GUCY2C in all affected family members and observed no other rare variants in the exons of genes in the candidate region. Exposure of the mutant receptor to its ligands resulted in markedly increased production of cyclic guanosine monophosphate (cGMP). This may cause hyperactivation of the cystic fibrosis transmembrane regulator (CFTR), leading to increased chloride and water secretion from the enterocytes, and may thus explain the chronic diarrhea in the affected family members. CONCLUSIONS Increased GC-C signaling disturbs normal bowel function and appears to have a proinflammatory effect, either through increased chloride secretion or additional effects of elevated cellular cGMP. Further investigation of the relevance of genetic variants affecting the GC-C-CFTR pathway to conditions such as Crohn's disease is warranted. (Funded by Helse Vest Western Norway Regional Health Authority] and the Department of Science and Technology, Government of India.)

Noise tolerance under risk minimization

In this paper, we explore noise-tolerant learning of classifiers. We formulate the problem as follows. We assume that there is an unobservable training set that is noise free. The actual training set given to the learning algorithm is obtained from this ideal data set by corrupting the class label of each example. The probability that the class label of an example is corrupted is a function of the feature vector of the example. This would account for most kinds of noisy data one encounters in practice. We say that a learning method is noise tolerant if the classifiers learnt with noise-free data and with noisy data, both have the same classification accuracy on the noise-free data. In this paper, we analyze the noise-tolerance properties of risk minimization (under different loss functions). We show that risk minimization under 0-1 loss function has impressive noise-tolerance properties and that under squared error loss is tolerant only to uniform noise; risk minimization under other loss functions is not noise tolerant. We conclude this paper with some discussion on the implications of these theoretical results.