Dibenzofuran Derivatives Inspired from Cercosporamide as Dual Inhibitors of Pim and CLK1 Kinases.

Pim kinases (proviral integration site for Moloney murine leukemia virus kinases) are overexpressed in various types of hematological malignancies and solid carcinomas, and promote cell proliferation and survival. Thus, Pim kinases are validated as targets for antitumor therapy. In this context, our combined efforts in natural product-inspired library generation and screening furnished very promising dibenzo[b,d]furan derivatives derived from cercosporamide. Among them, lead compound 44 was highlighted as a potent Pim-1/2 kinases inhibitor with an additional nanomolar IC50 value against CLK1 (cdc2-like kinases 1) and displayed a low micromolar anticancer potency towards the MV4-11 (AML) cell line, expressing high endogenous levels of Pim-1/2 kinases. The design, synthesis, structure-activity relationship, and docking studies are reported herein and supported by enzyme, cellular assays, and Galleria mellonella larvae testing for acute toxicity.

Benzofuro[3,2-d]pyrimidines inspired from cercosporamide CaPkc1 inhibitor: Synthesis and evaluation of fluconazole susceptibility restoration.

In a context of growing resistance to classical antifungal therapy, the design of new drugs targeting alternative pathways is highly expected. Benzofuro[3,2-d]pyrimidine derivatives, derived from (-)-cercosporamide, were synthesized and evaluated as potential Candida albicans PKC inhibitors in the aim of restoring susceptibility to azole treatment. Co-administration assay of benzofuropyrimidinedione 23 and fluconazole highlighted a synergistic effect on inhibition of cell growth of a Candida albicans resistant strain.

Efficient selection between hierarchical cognitive models: Cross-validation with variational Bayes.

Model comparison is the cornerstone of theoretical progress in psychological research. Common practice overwhelmingly relies on tools that evaluate competing models by balancing in-sample descriptive adequacy against model flexibility, with modern approaches advocating the use of marginal likelihood for hierarchical cognitive models. Cross-validation is another popular approach but its implementation remains out of reach for cognitive models evaluated in a Bayesian hierarchical framework, with the major hurdle being its prohibitive computational cost. To address this issue, we develop novel algorithms that make variational Bayes (VB) inference for hierarchical models feasible and computationally efficient for complex cognitive models of substantive theoretical interest. It is well known that VB produces good estimates of the first moments of the parameters, which gives good predictive densities estimates. We thus develop a novel VB algorithm with Bayesian prediction as a tool to perform model comparison by cross-validation, which we refer to as CVVB. In particular, CVVB can be used as a model screening device that quickly identifies bad models. We demonstrate the utility of CVVB by revisiting a classic question in decision making research: what latent components of processing drive the ubiquitous speed-accuracy tradeoff? We demonstrate that CVVB strongly agrees with model comparison via marginal likelihood, yet achieves the outcome in much less time. Our approach brings cross-validation within reach of theoretically important psychological models, making it feasible to compare much larger families of hierarchically specified cognitive models than has previously been possible. To enhance the applicability of the algorithm, we provide Matlab code together with a user manual so users can easily implement VB and/or CVVB for the models considered in this article and their variants. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

An internet of radiation sensor system (IoRSS) to detect radioactive sources out of regulatory control.

A radioactive source that is not under regulatory control, either because it has never been under regulatory control or because it has been abandoned, lost, misplaced, stolen, or otherwise transferred without proper authorization, is considered an orphan source. Orphan sources are usually gathered as scrap metal because of their heavy metallic containers. Melting an orphan source with scrap metal produces contaminated recycled metal and waste; the consequences will be extremely serious for humans and the environment, affecting the economy and social stability. In this paper, we propose and develop an Internet of Radiation Sensor System (IoRSS) to detect radioactive sources out of regulatory control in scrap metal recycling and production facilities. It is a complete IoT system consisting of a network of wirelessly connected radiometric devices that optimizes the detection, localization, and identification of radioactive sources by integrating data from multiple portable radiation detectors. The proposed IoRSS system creates a robust and flexible network architecture along with advanced data fusion algorithms that combine information from many detectors. The IoRSS system provides advanced search and monitoring capabilities in a large coverage area and in difficult operational environments.