On multi-view learning with additive models

In many scientific settings data can be naturally partitioned into variable groupings called views. Common examples include environmental (1st view) and genetic information (2nd view) in ecological applications, chemical (1st view) and biological (2nd view) data in drug discovery. Multi-view data also occur in text analysis and proteomics applications where one view consists of a graph with observations as the vertices and a weighted measure of pairwise similarity between observations as the edges. Further, in several of these applications the observations can be partitioned into two sets, one where the response is observed (labeled) and the other where the response is not (unlabeled). The problem for simultaneously addressing viewed data and incorporating unlabeled observations in training is referred to as multi-view transductive learning. In this work we introduce and study a comprehensive generalized fixed point additive modeling framework for multi-view transductive learning, where any view is represented by a linear smoother. The problem of view selection is discussed using a generalized Akaike Information Criterion, which provides an approach for testing the contribution of each view. An efficient implementation is provided for fitting these models with both backfitting and local-scoring type algorithms adjusted to semi-supervised graph-based learning. The proposed technique is assessed on both synthetic and real data sets and is shown to be competitive to state-of-the-art co-training and graph-based techniques.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS202 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

ada: An R Package for Stochastic Boosting

Boosting is an iterative algorithm that combines simple classification rules with "mediocre" performance in terms of misclassification error rate to produce a highly accurate classification rule. Stochastic gradient boosting provides an enhancement which incorporates a random mechanism at each boosting step showing an improvement in performance and speed in generating the ensemble. ada is an R package that implements three popular variants of boosting, together with a version of stochastic gradient boosting. In addition, useful plots for data analytic purposes are provided along with an extension to the multi-class case. The algorithms are illustrated with synthetic and real data sets.

Software for Implementing the Sequential Elimination of Level Combinations Algorithm

Genetic algorithms (GAs) are a popular technology to search for an optimum in a large search space. Using new concepts of forbidden array and weighted mutation, Mandal, Wu, and Johnson (2006) used elements of GAs to introduce a new global optimization technique called sequential elimination of level combinations (SELC), that efficiently finds optimums. A SAS macro, and MATLAB and R functions are developed to implement the SELC algorithm.

Software for Implementing the Sequential Elimination of Level Combinations Algorithm

Genetic algorithms (GAs) are a popular technology to search for an optimum in a large search space. Using new concepts of forbidden array and weighted mutation, Mandal, Wu, and Johnson (2006) used elements of GAs to introduce a new global optimization technique called sequential elimination of level combinations (SELC), that efficiently finds optimums. A SAS macro, and MATLAB and R functions are developed to implement the SELC algorithm

Group II metabotropic glutamate receptor type 2 allosteric potentiators prevent sodium lactate-induced panic-like response in panic-vulnerable rats

Rats with chronic inhibition of GABA synthesis by infusion of l-allyglycine, a glutamic acid decarboxylase inhibitor, into their dorsomedial/perifornical hypothalamus are anxious and exhibit panic-like cardio-respiratory responses to treatment with intravenous (i.v.) sodium lactate (NaLac) infusions, in a manner similar to what occurs in patients with panic disorder. We previously showed that either NMDA receptor antagonists or metabotropic glutamate receptor type 2/3 receptor agonists can block such a NaLac response, suggesting that a glutamate mechanism is contributing to this panic-like state. Using this animal model of panic, we tested the efficacy of CBiPES and THIIC, which are selective group II metabotropic glutamate type 2 receptor allosteric potentiators (at 10-30 mg/kg i.p.), in preventing NaLac-induced panic-like behavioral and cardiovascular responses. The positive control was alprazolam (3mg/kg i.p.), a clinically effective anti-panic benzodiazepine. As predicted, panic-prone rats given a NaLac challenge displayed NaLac-induced panic-like cardiovascular (i.e. tachycardia and hypertensive) responses and "anxiety" (i.e. decreased social interaction time) and "flight" (i.e. increased locomotion) -associated behaviors; however, systemic injection of the panic-prone rats with CBiPES, THIIC or alprazolam prior to the NaLac dose blocked all NaLac-induced panic-like behaviors and cardiovascular responses. These data suggested that in a rat animal model, selective group II metabotropic glutamate type 2 receptor allosteric potentiators show an anti-panic efficacy similar to alprazolam

The History of Flow Chemistry at Eli Lilly and Company

Flow chemistry was initially used for speed to early phase material delivery in the development laboratories, scaling up chemical transformations that we would not or could not scale up batch for safety reasons. Some early examples included a Newman Kwart Rearrangement, Claisen rearrangement, hydroformylation, and thermal imidazole cyclization. Next, flow chemistry was used to enable safe scale up of hazardous chemistries to manufacturing plants. Examples included high pressure hydrogenation, aerobic oxidation, and Grignard formation reactions. More recently, flow chemistry was used in Small Volume Continuous (SVC) processes, where highly potent oncolytic molecules were produced by fully continuous processes at about 10 kg/day including reaction, extraction, distillation, and crystallization, using disposable equipment contained in fume hoods

Blood donor behaviour, motivations and the need for a systematic cross-cultural perspective: the example of moral outrage and health and non-health based philanthropy across seven countries

Background: Blood donation is a prosocial altruistic act that is motived by the mechanisms that underlie altruism (e.g., warm-glow, reciprocity, fairness/trust). Because there is consistent evidence that altruism and its mechanisms show cross-cultural variability, in the present paper we make the case for a cross-cultural perspective in blood donor research. Methods: We analyse a subset of variables from a larger study, with samples drawn from seven countries (England, Malta, the Netherlands, Australia, USA, Hungary, Italy: average N per country = 282). This subset of variables focuses on health (organ donor registration) and non-health (volunteering, donating money) philanthropy, family traditions of helping, and moral outrage as predictors of blood donor status. Results: We show two cross-cultural universals: 1) organ donor registration in opt-in countries is positively associated with blood donor status and 2) non-health philanthropy is generally unrelated to blood donor status. We also show two country specific effects: 1) a family tradition for helping is associated with blood donor status in Italy only and 2) moral outrage is a predictor only in the USA. Conclusions: We contend that these findings provide proof of principle why a cross-cultural perspective on blood donor behaviour is needed