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Herein, we report a Pd(0)-catalyzed cascade reaction of intramolecular indolyl isocyanide-insertion, isocyanide-initiated scaffold-rearrangement with indolyl migration and redox-neutral process, which affords an efficient access to indolylisoindolinones. Isocyanide as a C(sp3)H–N synthon and the alkyl motif of isocyanide as a hydride source have been explored for the first time. [ABSTRACT FROM AUTHOR]

We investigate how to make a simpler version of an existing algorithm, named C³E, from Consensus between Classification and Clustering Ensembles, more user-friendly by automatically tuning its main parameters with the use of metaheuristics. In particular, C³E based on a Squared Loss function, C³E-SL, assumes an optimization procedure that takes as input class membership estimates from existing classifiers, as well as a similarity matrix from a cluster ensemble operating solely on the new target data, to provide a consolidated classification of the target data. To do so, two parameters have to be defined a priori, namely: the relative importance of classifier and cluster ensembles and the number of iterations of the algorithm. In some practical applications, these parameters can be optimized via time consuming grid search approaches based on cross-validation procedures. This paper shows that seven metaheuristics for parameter optimization yield classifiers as accurate as those obtained from grid search, but taking half the running time. More precisely, and by assuming a trade-off between user-friendliness and accuracy, experiments performed on twenty real-world datasets suggest that CMA-ES, DE, and SaDE are the best alternatives to optimize the C³E-SL parameters. [ABSTRACT FROM AUTHOR]

Unsupervised models can provide supplementary soft constraints to help classify new "target" data because similar instances in the target set are more likely to share the same class label. Such models can also help detect possible differences between training and target distributions, which is useful in applications where concept drift may take place, as in transfer learning settings. This article describes a general optimization framework that takes as input class membership estimates from existing classifiers learned on previously encountered "source" (or training) data, as well as a similarity matrix from a cluster ensemble operating solely on the target (or test) data to be classified, and yields a consensus labeling of the target data. More precisely, the application settings considered are nontransductive semisupervised and transfer learning scenarios where the training data are used only to build an ensemble of classifiers and are subsequently discarded before classifying the target data. The framework admits a wide range of loss functions and classification/ clustering methods. It exploits properties of Bregman divergences in conjunction with Legendre duality to yield a principled and scalable approach. A variety of experiments show that the proposed framework can yield results substantially superior to those provided by naïvely applying classifiers learned on the original task to the target data. In addition, we show that the proposed approach, even not being conceptually transductive, can provide better results compared to some popular transductive learning techniques. [ABSTRACT FROM AUTHOR]

Ubiquitous applications of Deep neural networks (DNNs) in different artificial intelligence systems have led to their adoption in solving challenging visualization problems in recent years. While sophisticated DNNs offer an impressive generalization, it is imperative to comprehend the quality, confidence, robustness, and uncertainty associated with their prediction. A thorough understanding of these quantities produces actionable insights that help application scientists make informed decisions. Unfortunately, the intrinsic design principles of the DNNs cannot beget prediction uncertainty, necessitating separate formulations for robust uncertainty-aware models for diverse visualization applications. To that end, this contribution demonstrates how the prediction uncertainty and sensitivity of DNNs can be estimated efficiently using various methods and then interactively compared and contrasted for deep image synthesis tasks. Our inspection suggests that uncertainty-aware deep visualization models generate illustrations of informative and superior quality and diversity. Furthermore, prediction uncertainty improves the robustness and interpretability of deep visualization models, making them practical and convenient for various scientific domains that thrive on visual analyses.

The advent and spread of novel coronavirus (nCoV) has posed a new public health crisis since December 2019. Several cases of unexplained pneumonia occurred in Wuhan, Hubei Province, China, only a month before the Chinese Spring festival. After the diagnosis of bronchoalveolar fluid samples of people infected, the new coronavirus was identified using nextgeneration sequence technology. This work aims to provide information regarding COVID-19 that will help the researchers to identify the vital therapeutic targets for SARS-CoV-2 and also will provide insights into some significant findings of recent times highlighted by scientific communities around the globe. In this review, we have tried to explore multiple aspects related to COVID-19, including epidemiology, etiology, COVID-19 variants, vaccine candidates, potential therapeutic targets, the role of natural products, and computational studies in drug design and development, repurposing, and analysis of crystal structures available for COVID-19 related protein structures. Druggable targets include all viral enzymes and proteins involved in viral replication and regulation of host cellular machines. The medical community tracks several therapies to combat the infection by investigating various antiviral and immunomodulatory mechanisms. While some vaccines are approved in this worldwide health crisis, a more precise therapy or drug is formally recommended to be used against SARS-CoV-2 infection. Natural products other than synthetic drugs have been tested by in silico analysis against COVID-19. However, important issues still need to be addressed regarding in vivo bioavailability and better efficacy., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Herein, we report a Pd(0)-catalyzed cascade reaction of intramolecular indolyl isocyanide-insertion, isocyanide-initiated scaffold-rearrangement with indolyl migration and redox-neutral process, which affords an efficient access to indolylisoindolinones. Isocyanide as a C (sp 3 ) H-N synthon and the alkyl motif of isocyanide as a hydride source have been explored for the first time.

2-Arylquinazolines with a range of alkyl polyamines as side chain/ring functional motifs at the 4th-position were considered for antileishmanial study with the rationale that related heterocyclic scaffolds and polyamine functionalities are present in drugs, clinical trial agents, natural products and anti-parasitic/leishmanial agents. Synthesis involves construction of the 2-arylquinazolin-4-one ring and deoxyamination via deoxychlorination followed by S N Ar-based amination or a methodology of S N Ar-deoxyamination driven by BOP-mediated hydroxyl-activation. Various alkyl-polyamines important for activities were incorporated. A total of 26 compounds were prepared and screened against Leishmania donovani ( Ld ) promastigote cells using the MTT assay. Most of the investigated series of compounds showed characteristic leishmanicidal properties. Several compounds showed pronounced leishmanicidal activities (IC 50 : 5-6.5 μM) with higher efficiency than the antileishmanial drug miltefosine (IC 50 : 10.5 μM), and relatively less cytotoxicity to macrophage host cells (SI: 9.27-13.5) compared to miltefosine (SI: 3.42). Important pharmacophoric skeletons were identified., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)