Trends in consanguineous marriage in Karnataka, South India, 1980–89

Analysis of data on 106,848 marriages in the cities of Bangalore and Mysore, South India, between 1980 and 1989 showed that levels of consanguineous marriage varied between cities through time and by religion. The average coefficient of inbreeding was higher in Bangalore (F = 0·0339) than in Mysore (F = 0·0203), principally reflecting large-scale, post-Independence rural migration into Bangalore. Although there was some evidence of a decline in consanguineous marriages in Mysore, there was no convincing support in either city for earlier projections of a rapid reduction in the popularity of unions between close biological relatives.

Molecular Marriage through Partner Preferences in Covalent Cage Formation and Cage-to-Cage Transformation

Unprecedented self-sorting of three-dimensional purely organic cages driven by dynamic covalent bonds is described. Four different cages were first synthesized by condensation of two triamines and two dialdehydes separately. When a mixture of all the components was allowed to react, only two cages were formed, which suggests a high-fidelity self-recognition. The issue of the preference of one triamine for a particular dialdehyde was further probed by transforming a non-preferred combination to either of the two preferred combinations by reacting it with the appropriate triamine or dialdehyde.

Hydrogen-Bond-Driven Controlled Molecular Marriage in Covalent Cages

A supramolecular approach that uses hydrogen-bonding interaction as a driving force to accomplish exceptional self-sorting in the formation of imine-based covalent organic cages is discussed. Utilizing the dynamic covalent chemistry approach from three geometrically similar dialdehydes (A, B, and D) and the flexible triamine tris(2-aminoethyl)amine (X), three new 3+2] self-assembled nanoscopic organic cages have been synthesized and fully characterized by various techniques. When a complex mixture of the dialdehydes and triamine X was subjected to reaction, it was found that only dialdehyde B (which has OH groups for H-bonding) reacted to form the corresponding cage B3X2 selectively. Surprisingly, the same reaction in the absence of aldehyde B yielded a mixture of products. Theoretical and experimental investigations are in complete agreement that the presence of the hydroxyl moiety adjacent to the aldehyde functionality in B is responsible for the selective formation of cage B3X2 from a complex reaction mixture. This spectacular selection was further analyzed by transforming a nonpreferred (non-hydroxy) cage into a preferred (hydroxy) cage B3X2 by treating the former with aldehyde B. The role of the H-bond in partner selection in a mixture of two dialdehydes and two amines has also been established. Moreover, an example of unconventional imine bond metathesis in organic cage-to-cage transformation is reported.

Sunlight-Induced Covalent Marriage of Two Triply Interlocked Pd-6 Cages and Their Facile Thermal Separation

A template-free triply interlocked Pd-6 cage (2) was synthesized by two-component self-assembly of cis-blocked 90 degrees acceptor cis-(tmen)Pd(NO3)(2) (M) and 1,3,5-tris((E)-2-(pyridin-3-yl)vinyl)benzene (L). Assembly 2 was characterized by H-1 NMR and ESI-MS, and the structure was confirmed by X-ray crystallography, which revealed a parallel conformation of the olefin double bonds belonging to the adjacent cages in the solid state at a distance of 3.656 angstrom, thereby indicating the feasibility of 2+2] photochemical reaction. Two adjacent interlocked cages were covalently married together by intermolecular 2+2] cycloaddition in a single crystal-to-single crystal fashion upon exposure to sunlight/UV irradiation. Most surprisingly, the covalently married pair was easily separated thermally in aqueous medium under mild reaction conditions.

Constraining uncertainty in regional hydrologic impacts of climate change: Nonstationarity in downscaling

Regional impacts of climate change remain subject to large uncertainties accumulating from various sources, including those due to choice of general circulation models (GCMs), scenarios, and downscaling methods. Objective constraints to reduce the uncertainty in regional predictions have proven elusive. In most studies to date the nature of the downscaling relationship (DSR) used for such regional predictions has been assumed to remain unchanged in a future climate. However,studies have shown that climate change may manifest in terms of changes in frequencies of occurrence of the leading modes of variability, and hence, stationarity of DSRs is not really a valid assumption in regional climate impact assessment. This work presents an uncertainty modeling framework where, in addition to GCM and scenario uncertainty, uncertainty in the nature of the DSR is explored by linking downscaling with changes in frequencies of such modes of natural variability. Future projections of the regional hydrologic variable obtained by training a conditional random field (CRF) model on each natural cluster are combined using the weighted Dempster-Shafer (D-S) theory of evidence combination. Each projection is weighted with the future projected frequency of occurrence of that cluster (''cluster linking'') and scaled by the GCM performance with respect to the associated cluster for the present period (''frequency scaling''). The D-S theory was chosen for its ability to express beliefs in some hypotheses, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The methodology is tested for predicting monsoon streamflow of the Mahanadi River at Hirakud Reservoir in Orissa, India. The results show an increasing probability of extreme, severe, and moderate droughts due to limate change. Significantly improved agreement between GCM predictions owing to cluster linking and frequency scaling is seen, suggesting that by linking regional impacts to natural regime frequencies, uncertainty in regional predictions can be realistically quantified. Additionally, by using a measure of GCM performance in simulating natural regimes, this uncertainty can be effectively constrained.

Hydrologic drought prediction under climate change: Uncertainty modeling with Dempster-Shafer and Bayesian approaches

Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in regional climate change impacts. In this paper, an uncertainty modeling framework is evaluated, which uses a generalized uncertainty measure to combine GCM, scenario and downscaling uncertainties. The Dempster-Shafer (D-S) evidence theory is used for representing and combining uncertainty from various sources. A significant advantage of the D-S framework over the traditional probabilistic approach is that it allows for the allocation of a probability mass to sets or intervals, and can hence handle both aleatory or stochastic uncertainty, and epistemic or subjective uncertainty. This paper shows how the D-S theory can be used to represent beliefs in some hypotheses such as hydrologic drought or wet conditions, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The D-S approach has been used in this work for information synthesis using various evidence combination rules having different conflict modeling approaches. A case study is presented for hydrologic drought prediction using downscaled streamflow in the Mahanadi River at Hirakud in Orissa, India. Projections of n most likely monsoon streamflow sequences are obtained from a conditional random field (CRF) downscaling model, using an ensemble of three GCMs for three scenarios, which are converted to monsoon standardized streamflow index (SSFI-4) series. This range is used to specify the basic probability assignment (bpa) for a Dempster-Shafer structure, which represents uncertainty associated with each of the SSFI-4 classifications. These uncertainties are then combined across GCMs and scenarios using various evidence combination rules given by the D-S theory. A Bayesian approach is also presented for this case study, which models the uncertainty in projected frequencies of SSFI-4 classifications by deriving a posterior distribution for the frequency of each classification, using an ensemble of GCMs and scenarios. Results from the D-S and Bayesian approaches are compared, and relative merits of each approach are discussed. Both approaches show an increasing probability of extreme, severe and moderate droughts and decreasing probability of normal and wet conditions in Orissa as a result of climate change. (C) 2010 Elsevier Ltd. All rights reserved.

Probabilistic extension to realistic abductive reasoning model

In this paper, we give a method for probabilistic assignment to the Realistic Abductive Reasoning Model, The knowledge is assumed to be represented in the form of causal chaining, namely, hyper-bipartite network. Hyper-bipartite network is the most generalized form of knowledge representation for which, so far, there has been no way of assigning probability to the explanations, First, the inference mechanism using realistic abductive reasoning model is briefly described and then probability is assigned to each of the explanations so as to pick up the explanations in the decreasing order of plausibility.

Role of feedback in mammalian vision: a new hypothesis and a computational model

This paper presents a novel hypothesis on the function of massive feedback pathways in mammalian visual systems. We propose that the cortical feature detectors compete not for the right to represent the output at a point, but for exclusive rights to abstract and represent part of the underlying input. Feedback can do this very naturally. A computational model that implements the above idea for the problem of line detection is presented and based on that we suggest a functional role for the thalamo-cortical loop during perception of lines. We show that the model successfully tackles the so called Cross problem. Based on some recent experimental results, we discuss the biological plausibility of our model. We also comment on the relevance of our hypothesis (on the role of feedback) to general sensory information processing and recognition. (C) 1998 Published by Elsevier Science Ltd. All rights reserved.

Expansions of tau hadronic spectral function moments in a nonpower QCD perturbation theory with tamed large order behavior

The moments of the hadronic spectral functions are of interest for the extraction of the strong coupling alpha(s) and other QCD parameters from the hadronic decays of the tau lepton. Motivated by the recent analyses of a large class of moments in the standard fixed-order and contour-improved perturbation theories, we consider the perturbative behavior of these moments in the framework of a QCD nonpower perturbation theory, defined by the technique of series acceleration by conformal mappings, which simultaneously implements renormalization-group summation and has a tame large-order behavior. Two recently proposed models of the Adler function are employed to generate the higher-order coefficients of the perturbation series and to predict the exact values of the moments, required for testing the properties of the perturbative expansions. We show that the contour-improved nonpower perturbation theories and the renormalization-group-summed nonpower perturbation theories have very good convergence properties for a large class of moments of the so-called ``reference model,'' including moments that are poorly described by the standard expansions. The results provide additional support for the plausibility of the description of the Adler function in terms of a small number of dominant renormalons.