Pd(II)-catalyzed hydroarylations/hydroalkenylations of terminal alkynes: regioselective synthesis of allylic, homoallylic, and 1,3-diene systems.

A Pd-catalyzed regioselective hydroarylation of terminal alkynes containing a heteroatom has been developed via carbopalladation for the synthesis of allylic ethers, amines, and homoallylic alcohols. Moreover, hydroalkenylation of alkynes produces a variety of stereodefined 1,4-dienes with high regioselectivity. The important features of the present protocol are that it is highly regioselective, operationally rapid, and scalable with a huge substrate scope using only 3 mol% of PdCl2(PPh3)2 catalyst in the presence of a mild base KOAc.

Iodine-Catalyzed Regioselective Synthesis of Diphenyl-Substituted Carbazoles via [4 + 2] Annulation of ß-Formyl Ketones with Indoles.

The [4 + 2] annulation of ß-formyl ketones with an indole has been developed for the regioselective synthesis of diphenyl-substituted carbazoles in the presence of a catalytic amount of iodine. The 1,4-dicarbonyl compound containing a phenyl group at the α-position of an aldehyde group reacts more readily with indoles to form carbazole derivatives. Using this method, a variety of carbazole derivatives can be readily accessed under mild reaction conditions.

One-Pot Knoevenagel/Imination/6π-Azaelectrocyclization Sequence for the Synthesis of Disubstituted Nicotinonitriles.

We report on a copper-catalyzed three-component reaction for the synthesis of disubstituted nicotinonitriles using 3-bromopropenals, benzoylacetonitriles, and ammonium acetate (NH4OAc). The Knoevenagel-type condensation of 3-bromopropenals with benzoylacetonitriles gives δ-bromo-2,4-dienones that contain strategically placed functional groups that react with the ammonia generated in situ to give the corresponding azatrienes. These azatrienes can then be transformed into trisubstituted pyridines under the reaction conditions via a reaction sequence involving 6π-azaelectrocyclization and aromatization.

Iodine-Catalyzed Annulation Reaction of Ortho-Formylarylketones with Indoles: A General Strategy for the Synthesis of Indolylbenzo[b]carbazoles.

The iodine-catalyzed cascade reaction of ortho-formylarylketones with indoles for the synthesis of indolylbenzo[b]carbazoles is reported. The reaction is initiated in the presence of iodine by two successive nucleophilic additions of indoles with an aldehyde group of ortho-formylarylketones, and the ketone does not undergo a nucleophilic addition and only involves in the Friedel-Crafts-type cyclization. A variety of substrates are tested, and the efficiency of this reaction is demonstrated with gram-scale reactions.

Development of a Novel Transformation of Spiking Neural Classifier to an Interpretable Classifier.

This article presents a new approach for providing an interpretation for a spiking neural network classifier by transforming it to a multiclass additive model. The spiking classifier is a multiclass synaptic efficacy function-based leaky-integrate-fire neuron (Mc-SEFRON) classifier. As a first step, the SEFRON classifier for binary classification is extended to handle multiclass classification problems. Next, a new method is presented to transform the temporally distributed weights in a fully trained Mc-SEFRON classifier to shape functions in the feature space. A composite of these shape functions results in an interpretable classifier, namely, a directly interpretable multiclass additive model (DIMA). The interpretations of DIMA are also demonstrated using the multiclass Iris dataset. Further, the performances of both the Mc-SEFRON and DIMA classifiers are evaluated on ten benchmark datasets from the UCI machine learning repository and compared with the other state-of-the-art spiking neural classifiers. The performance study results show that Mc-SEFRON produces similar or better performances than other spiking neural classifiers with an added benefit of interpretability through DIMA. Furthermore, the minor differences in accuracies between Mc-SEFRON and DIMA indicate the reliability of the DIMA classifier. Finally, the Mc-SEFRON and DIMA are tested on three real-world credit scoring problems, and their performances are compared with state-of-the-art results using machine learning methods. The results clearly indicate that DIMA improves the classification accuracy by up to 12% over other interpretable classifiers indicating a better quality of interpretations on the highly imbalanced credit scoring datasets.

Barriers to Cancer Screening Uptake in Women: A Qualitative Study from Tamil Nadu, India.

BACKGROUND: The uptake for cancer screening has been consistently poor in India despite the efforts of nation-wide screening programs. Understanding the barriers and enablers among community women would aid in increasing the proportion of cancer screening uptake. METHODS: Nineteen key informants including community women, service providers and a cancer survivor were interviewed using a semi-structured interview guide. Interviews were recorded and transcribed by the interviewers. Manual descriptive thematic analysis was conducted using deductive approach. Codes were given and extracted into categories which were later grouped to form themes. RESULTS: The mean age of participants was 38 years. Among the participants, 38.9% and 16.7% underwent breast and cervical cancer screening respectively. The psychosocial factors were the major barriers for screening uptake such as fear of screening procedure and fear of being diagnosed with cancer. The other factors include lack of awareness, cultural beliefs, in addition to financial difficulties and health care system-related factors. Change in government policies to conduct mandatory screening programs, incentivization and creating awareness were reported as enablers for increasing the screening uptake among women. CONCLUSION: Psychosocial factors, the major barriers for screening uptake in women have remained unchanged over the years. Increasing awareness campaigns, usage of decision-making aids and changes in government policies are crucial for improving the rate of uptake and successful implementation of national screening programs.

Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer.

BACKGROUND: Traditional cancer treatments have centered on cytotoxic drugs and general purpose chemotherapy that may not be tailored to treat specific cancers. Identification of molecular markers that are related to different types of cancers might lead to discovery of drugs that are patient and disease specific. This study aims to use microarray gene expression cancer data to identify biomarkers that are indicative of different types of cancers. Our aim is to provide a multi-class cancer classifier that can simultaneously differentiate between cancers and identify type-specific biomarkers, through the application of the Binary Coded Genetic Algorithm (BCGA) and a neural network based Extreme Learning Machine (ELM) algorithm. RESULTS: BCGA and ELM are combined and used to select a subset of genes that are present in the Global Cancer Mapping (GCM) data set. This set of candidate genes contains over 52 biomarkers that are related to multiple cancers, according to the literature. They include APOA1, VEGFC, YWHAZ, B2M, EIF2S1, CCR9 and many other genes that have been associated with the hallmarks of cancer. BCGA-ELM is tested on several cancer data sets and the results are compared to other classification methods. BCGA-ELM compares or exceeds other algorithms in terms of accuracy. We were also able to show that over 50% of genes selected by BCGA-ELM on GCM data are cancer related biomarkers. CONCLUSIONS: We were able to simultaneously differentiate between 14 different types of cancers, using only 92 genes, to achieve a multi-class classification accuracy of 95.4% which is between 21.6% and 38% higher than other results in the literature for multi-class cancer classification. Our findings suggest that computational algorithms such as BCGA-ELM can facilitate biomarker-driven integrated cancer research that can lead to a detailed understanding of the complexities of cancer.

Convenient methods for the synthesis of chiral amino alcohols and amines.

Simple, convenient methods have been developed using readily available, easy-to-handle reagents to access a variety of chiral amino alcohols and amines, which have considerable potential for applications in asymmetric organic transformations. Scholars from this laboratory in India have made significant contributions to this field, which is the subject of the current review.

Projection-based fast learning fully complex-valued relaxation neural network.

This paper presents a fully complex-valued relaxation network (FCRN) with its projection-based learning algorithm. The FCRN is a single hidden layer network with a Gaussian-like sech activation function in the hidden layer and an exponential activation function in the output layer. For a given number of hidden neurons, the input weights are assigned randomly and the output weights are estimated by minimizing a nonlinear logarithmic function (called as an energy function) which explicitly contains both the magnitude and phase errors. A projection-based learning algorithm determines the optimal output weights corresponding to the minima of the energy function by converting the nonlinear programming problem into that of solving a set of simultaneous linear algebraic equations. The resultant FCRN approximates the desired output more accurately with a lower computational effort. The classification ability of FCRN is evaluated using a set of real-valued benchmark classification problems from the University of California, Irvine machine learning repository. Here, a circular transformation is used to transform the real-valued input features to the complex domain. Next, the FCRN is used to solve three practical problems: a quadrature amplitude modulation channel equalization, an adaptive beamforming, and a mammogram classification. Performance results from this paper clearly indicate the superior classification/approximation performance of the FCRN.

A sequential learning algorithm for complex-valued self-regulating resource allocation network-CSRAN.

This paper presents a sequential learning algorithm for a complex-valued resource allocation network with a self-regulating scheme, referred to as complex-valued self-regulating resource allocation network (CSRAN). The self-regulating scheme in CSRAN decides what to learn, when to learn, and how to learn based on the information present in the training samples. CSRAN is a complex-valued radial basis function network with a sech activation function in the hidden layer. The network parameters are updated using a complex-valued extended Kalman filter algorithm. CSRAN starts with no hidden neuron and builds up an appropriate number of hidden neurons, resulting in a compact structure. Performance of the CSRAN is evaluated using a synthetic complex-valued function approximation problem, two real-world applications consisting of a complex quadrature amplitude modulation channel equalization, and an adaptive beam-forming problem. Since complex-valued neural networks are good decision makers, the decision-making ability of the CSRAN is compared with other complex-valued classifiers and the best performing real-valued classifier using two benchmark unbalanced classification problems from UCI machine learning repository. The approximation and classification results show that the CSRAN outperforms other existing complex-valued learning algorithms available in the literature.