Mercadoroberson4908

While c-MYC is well established as a proto-oncogene, its structure and function as a transcription factor have made c-MYC a difficult therapeutic target. To identify small-molecule inhibitors targeting c-MYC for anticancer therapy, we designed a high-throughput screening (HTS) strategy utilizing cellular assays. The novel approach for the HTS was based on the detection of cellular c-MYC protein, with active molecules defined as those that specifically decreased c-MYC protein levels in cancer cells. The assay was based on a dual antibody detection system using Förster/fluorescence resonance energy transfer (FRET) and was utilized to detect endogenous c-MYC protein in the MYC amplified cancer cell lines DMS273 and Colo320 HSR. The assays were miniaturized to 1536-well plate format and utilized to screen the GlaxoSmithKline small-molecule collection of approximately 2 million compounds. In addition to the HTS assay, follow-up assays were developed and used to triage and qualify compounds. Two cellular assays used to eliminate false-positive compounds from the initially selected HTS hits were (1) a cellular toxicity assay and (2) an unstable protein reporter assay. Three positive selection assays were subsequently used to qualify compounds (1) 384-well cell cycle flow cytometry, (2) 384-well cell growth, and (3) c-MYC gene signature reverse transcription quantitative PCR (RT-qPCR). The HTS and follow-up assays successfully identified three compounds that specifically decreased c-MYC protein levels in cancer cells and phenocopied c-MYC siRNA in terms of cell growth inhibition and gene signatures. The HTS, triage, and three compounds identified are described.One particular weakness of psychology that was left implicit by Meehl is the fact that psychological theories tend to be verbal theories, permitting at best ordinal predictions. Such predictions do not enable the high-risk tests that would strengthen our belief in the verisimilitude of theories but instead lead to the practice of null-hypothesis significance testing, a practice Meehl believed to be a major reason for the slow theoretical progress of soft psychology. The rising popularity of meta-analysis has led some to argue that we should move away from significance testing and focus on the size and stability of effects instead. Proponents of this reform assume that a greater emphasis on quantity can help psychology to develop a cumulative body of knowledge. The crucial question in this endeavor is whether the resulting numbers really have theoretical meaning. Psychological science lacks an undisputed, preexisting domain of observations analogous to the observations in the space-time continuum in physics. It is argued that, for this reason, effect sizes do not really exist independently of the adopted research design that led to their manifestation. Consequently, they can have no bearing on the verisimilitude of a theory.Psychology endeavors to develop theories of human capacities and behaviors on the basis of a variety of methodologies and dependent measures. We argue that one of the most divisive factors in psychological science is whether researchers choose to use computational modeling of theories (over and above data) during the scientific-inference process. Modeling is undervalued yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us toward better science by forcing us to conceptually analyze, specify, and formalize intuitions that otherwise remain unexamined-what we dub open theory. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Here, we present scientific inference in psychology as a path function in which each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above the stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability crises and persistent failure at coherent theory building. This is because without formal modeling we lack open and transparent theorizing. We also explain how to formalize, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all.Candida albicans is a common fungus of the human microbiota. While generally a harmless commensal in healthy individuals, several factors can lead to its overgrowth and cause a range of complications within the host, from localized superficial infections to systemic life-threatening disseminated candidiasis. A major virulence factor of C. albicans is its ability to form biofilms, a closely packed community of cells that can grow on both abiotic and biotic substrates, including implanted medical devices and mucosal surfaces. These biofilms are extremely hard to eradicate, are resistant to conventional antifungal treatment and are associated with high morbidity and mortality rates, making biofilm-associated infections a major clinical challenge. Here, we review the current knowledge of the processes involved in C. albicans biofilm formation and development, including the central processes of adhesion, extracellular matrix production and the transcriptional network that regulates biofilm development. We also consider the advantages of the biofilm lifestyle and explore polymicrobial interactions within multispecies biofilms that are formed by C. albicans and selected microbial species.The extensive application of long-range corrected hybrid functionals highlights the importance of further improving their accuracy. Unlike common long-range corrected hybrid functionals mainly focusing on the exchange part, range-separated correlation and its role in long-range corrected hybrid functionals are the main concerns of this work. To this end, we present theory on the derivation of the range-separated correlation, whose reliability and validity are proved by the agreement with the full CI on the test of the short-range correlation energy. The tests on various properties indicate that the long-range part of the LYP functional cannot effectively capture the long-range correlation effect required in LC-BLYP, whose absence instead results in a better XC functional. MRTX-1257 This new functional significantly improves LC-BLYP on all the tests in this work, with an accuracy on par with or even greater than the widely recognized CAM-B3LYP method for some applications, while maintaining the important -1/r asymptotic behavior of the XC potential.