A comparative study of efficacy of two different regimens of vaginal misoprostol in first trimester termination of pregnancy in a tertiary care hospital

Background: Misoprostol has been authorised as an effective medication for termination of pregnancy at different gestations, cervical ripening, labour inducing in term pregnancy, and possibly therapy of postpartum haemorrhage in the last two decades. Objectives were to compare the efficacy of two different regimens of vaginal misoprostol in first trimester termination of pregnancy. Methods: This was a randomized controlled trial conducted among 50 women in the reproductive age group with single live intrauterine gestation less than 12 weeks. All patients including both groups received mifepristone 200 mg oral administration as day 1 followed by group A received misoprostol 800 mg stat after 36 hours of mifepristone and group B received misoprostol 400 mg stat after 36 hours of mifepristone followed by 200 mg at 6 hourly 2 doses. Results: The mean age of the study participants was 26.48±3.77 and 24.72±3.33 in group A and B respectively. The 20% and 16% in group A had repeat dose and dilatation and curettage (D and C) done whereas only 8% had D and C done in group B. There was no significant difference between the prevalence side effects between the groups. Group B showed higher dissatisfaction than group A. The 36% and 8% of the study participants in group A and B respectively had incomplete abortion. Conclusions: Multidose regimen is found to be more effective in the first trimester termination of pregnancy than the single dose regimen

Synthesis and Characterisation of Nanomaterials

Development of synthesis protocols for realising nanomaterials over a range of sizes, shapes,and chemical compositions is an important aspect of nanotechnology. The remarkable size-dependent physico-chemical properties of nanoparticles have fascinated and inspired researchactivity in this direction. This paper describes some aspects on synthesis and characterisationof nanoparticles of metals, metal alloys, and oxides, either in the form of thin films or bulk shapes.A brief discussion on processing of two-phase nanocomposite magnets is also presented.Defence Science Journal, 2008, 58(4), pp.504-516, DOI:http://dx.doi.org/10.14429/dsj.58.167

Studies of vitamin A deficiency in children

The clinical features of 319 children with vitamin A deficiency observed in Coonoor and forty-nine children studied in Hyderabad have been discussed in detail. Estimations of the contents of vitamin A and carotene in the serum and the in vitro destruction of vitamin A by lysed red blood cells were carried out. There was a lack of association between the incidence of night blindness and other ocular signs of vitamin A deficiency. Although signs of vitamin A deficiency were always associated with a low dietary intake of carotene and vitamin A, the reverse did not always occur. The proportion of patients with vitamin A deficiency who had keratomalacia was much greater in Hyderabad than in Coonoor. Levels of vitamin A and carotene in the serum were low in all children with vitamin A deficiency. Levels of vitamin A in the serum of subjects with kwashiorkor but without clinical signs of vitamin A deficiency were also found to be significantly lower than the levels in apparently normal children. Treatment with a high protein diet without vitamin A supplementation brought about a significant increase in the levels of vitamin A in the serum in the latter. The in vitro destruction of vitamin A by lysed red blood cells of children suffering from vitamin A deficiency appeared to be higher than that of the normal controls Treatment with vitamin A lowered this abnormal in vitro destruction

Characterizing notions of omniprediction via multicalibration

A recent line of work shows that notions of multigroup fairness imply surprisingly strong notions of omniprediction: loss minimization guarantees that apply not just for a specific loss function, but for any loss belonging to a large family of losses. While prior work has derived various notions of omniprediction from multigroup fairness guarantees of varying strength, it was unknown whether the connection goes in both directions. In this work, we answer this question in the affirmative, establishing equivalences between notions of multicalibration and omniprediction. The new definitions that hold the key to this equivalence are new notions of swap omniprediction, which are inspired by swap regret in online learning. We show that these can be characterized exactly by a strengthening of multicalibration that we refer to as swap multicalibration. One can go from standard to swap multicalibration by a simple discretization; moreover all known algorithms for standard multicalibration in fact give swap multicalibration. In the context of omniprediction though, introducing the notion of swapping results in provably stronger notions, which require a predictor to minimize expected loss at least as well as an adaptive adversary who can choose both the loss function and hypothesis based on the value predicted by the predictor. Building on these characterizations, we paint a complete picture of the relationship between the various omniprediction notions in the literature by establishing implications and separations between them. Our work deepens our understanding of the connections between multigroup fairness, loss minimization and outcome indistinguishability and establishes new connections to classic notions in online learning

Scalable Text and Link Analysis with Mixed-Topic Link Models

Many data sets contain rich information about objects, as well as pairwise relations between them. For instance, in networks of websites, scientific papers, and other documents, each node has content consisting of a collection of words, as well as hyperlinks or citations to other nodes. In order to perform inference on such data sets, and make predictions and recommendations, it is useful to have models that are able to capture the processes which generate the text at each node and the links between them. In this paper, we combine classic ideas in topic modeling with a variant of the mixed-membership block model recently developed in the statistical physics community. The resulting model has the advantage that its parameters, including the mixture of topics of each document and the resulting overlapping communities, can be inferred with a simple and scalable expectation-maximization algorithm. We test our model on three data sets, performing unsupervised topic classification and link prediction. For both tasks, our model outperforms several existing state-of-the-art methods, achieving higher accuracy with significantly less computation, analyzing a data set with 1.3 million words and 44 thousand links in a few minutes.Comment: 11 pages, 4 figure

The complexity of dominating set reconfiguration

Suppose that we are given two dominating sets $D_s$ and $D_t$ of a graph $G$ whose cardinalities are at most a given threshold $k$. Then, we are asked whether there exists a sequence of dominating sets of $G$ between $D_s$ and $D_t$ such that each dominating set in the sequence is of cardinality at most $k$ and can be obtained from the previous one by either adding or deleting exactly one vertex. This problem is known to be PSPACE-complete in general. In this paper, we study the complexity of this decision problem from the viewpoint of graph classes. We first prove that the problem remains PSPACE-complete even for planar graphs, bounded bandwidth graphs, split graphs, and bipartite graphs. We then give a general scheme to construct linear-time algorithms and show that the problem can be solved in linear time for cographs, trees, and interval graphs. Furthermore, for these tractable cases, we can obtain a desired sequence such that the number of additions and deletions is bounded by $O(n)$, where $n$ is the number of vertices in the input graph

Optical characterisation of germanium optical fibres

Semiconductor core optical fibres are currently generating great interest as they promise to be a platform for the seamless incorporation of optoelectronic functionality into a new generation of all-fibre networks [1,2]. Although recent attentions have primarily focused on silicon as the material of choice for semiconductor photonics applications, germanium has some advantages over its counterpart. For example, it has higher nonlinearity, extended infrared transparency and has recently been demonstrated as a direct band gap laser medium [3]. Here we present the first optical characterisation of a germanium core optical fibre. The fibre was fabricated using a chemical micro fluidic deposition process [1] that uses GeH4 (germane) as a precursor to deposit amorphous germanium into the hole of a silica capillary. Figure 1 (a) shows an optical microscope image of the polished end face of a germanium fibre, with a 5.6 µm core diameter, which has been completely filled with the semiconductor material. Optical transmission measurements have been conducted over the wavelength range 2 µm to 11 µm, to confirm the broad mid-infrared operational window, and the guided output at 2.4 µm, imaged using a Spiricon Pyrocam III pyroelectric array camera, is shown in Figure 1 (b). At this wavelength the optical loss has been measured to be 20 dB/cm, which is comparable to losses measured for amorphous silicon fibres in the infrared. The potential for these germanium optical fibres to be used as optical modulators and infrared detectors will be discussed

Latent Space Model for Multi-Modal Social Data

With the emergence of social networking services, researchers enjoy the increasing availability of large-scale heterogenous datasets capturing online user interactions and behaviors. Traditional analysis of techno-social systems data has focused mainly on describing either the dynamics of social interactions, or the attributes and behaviors of the users. However, overwhelming empirical evidence suggests that the two dimensions affect one another, and therefore they should be jointly modeled and analyzed in a multi-modal framework. The benefits of such an approach include the ability to build better predictive models, leveraging social network information as well as user behavioral signals. To this purpose, here we propose the Constrained Latent Space Model (CLSM), a generalized framework that combines Mixed Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA) incorporating a constraint that forces the latent space to concurrently describe the multiple data modalities. We derive an efficient inference algorithm based on Variational Expectation Maximization that has a computational cost linear in the size of the network, thus making it feasible to analyze massive social datasets. We validate the proposed framework on two problems: prediction of social interactions from user attributes and behaviors, and behavior prediction exploiting network information. We perform experiments with a variety of multi-modal social systems, spanning location-based social networks (Gowalla), social media services (Instagram, Orkut), e-commerce and review sites (Amazon, Ciao), and finally citation networks (Cora). The results indicate significant improvement in prediction accuracy over state of the art methods, and demonstrate the flexibility of the proposed approach for addressing a variety of different learning problems commonly occurring with multi-modal social data.Comment: 12 pages, 7 figures, 2 table

Fixed-Parameter Tractability of Token Jumping on Planar Graphs

Suppose that we are given two independent sets $I_0$ and $I_r$ of a graph such that $|I_0| = |I_r|$, and imagine that a token is placed on each vertex in $I_0$. The token jumping problem is to determine whether there exists a sequence of independent sets which transforms $I_0$ into $I_r$ so that each independent set in the sequence results from the previous one by moving exactly one token to another vertex. This problem is known to be PSPACE-complete even for planar graphs of maximum degree three, and W[1]-hard for general graphs when parameterized by the number of tokens. In this paper, we present a fixed-parameter algorithm for the token jumping problem on planar graphs, where the parameter is only the number of tokens. Furthermore, the algorithm can be modified so that it finds a shortest sequence for a yes-instance. The same scheme of the algorithms can be applied to a wider class of graphs, $K_{3,t}$-free graphs for any fixed integer $t \ge 3$, and it yields fixed-parameter algorithms