Latent class analysis variable selection

We propose a method for selecting variables in latent class analysis, which is the most common model-based clustering method for discrete data. The method assesses a variable's usefulness for clustering by comparing two models, given the clustering variables already selected. In one model the variable contributes information about cluster allocation beyond that contained in the already selected variables, and in the other model it does not. A headlong search algorithm is used to explore the model space and select clustering variables. In simulated datasets we found that the method selected the correct clustering variables, and also led to improvements in classification performance and in accuracy of the choice of the number of classes. In two real datasets, our method discovered the same group structure with fewer variables. In a dataset from the International HapMap Project consisting of 639 single nucleotide polymorphisms (SNPs) from 210 members of different groups, our method discovered the same group structure with a much smaller number of SNP

Probing vacuum birefringence by phase-contrast Fourier imaging under fields of high-intensity lasers

In vacuum high-intensity lasers can cause photon-photon interaction via the process of virtual vacuum polarization which may be measured by the phase velocity shift of photons across intense fields. In the optical frequency domain, the photon-photon interaction is polarization-mediated described by the Euler-Heisenberg effective action. This theory predicts the vacuum birefringence or polarization dependence of the phase velocity shift arising from nonlinear properties in quantum electrodynamics (QED). We suggest a method to measure the vacuum birefringence under intense optical laser fields based on the absolute phase velocity shift by phase-contrast Fourier imaging. The method may serve for observing effects even beyond the QED vacuum polarization.Comment: 14 pages, 9 figures. Accepted by Applied Physics

Retargeting azithromycin analogues to have dual-modality antimalarial activity

Background: Resistance to front-line antimalarials (artemisinin combination therapies) is spreading, and development of new drug treatment strategies to rapidly kill Plasmodium spp. malaria parasites is urgently needed. Azithromycin is a clinically used macrolide antibiotic proposed as a partner drug for combination therapy in malaria, which has also been tested as monotherapy. However, its slow-killing 'delayed-death' activity against the parasite's apicoplast organelle and suboptimal activity as monotherapy limit its application as a potential malaria treatment. Here, we explore a panel of azithromycin analogues and demonstrate that chemical modifications can be used to greatly improve the speed and potency of antimalarial action. Results: Investigation of 84 azithromycin analogues revealed nanomolar quick-killing potency directed against the very earliest stage of parasite development within red blood cells. Indeed, the best analogue exhibited 1600-fold higher potency than azithromycin with less than 48 hrs treatment in vitro. Analogues were effective against zoonotic Plasmodium knowlesi malaria parasites and against both multi-drug and artemisinin-resistant Plasmodium falciparum lines. Metabolomic profiles of azithromycin analogue-treated parasites suggested activity in the parasite food vacuole and mitochondria were disrupted. Moreover, unlike the food vacuole-targeting drug chloroquine, azithromycin and analogues were active across blood-stage development, including merozoite invasion, suggesting that these macrolides have a multi-factorial mechanism of quick-killing activity. The positioning of functional groups added to azithromycin and its quick-killing analogues altered their activity against bacterial-like ribosomes but had minimal change on 'quick-killing' activity. Apicoplast minus parasites remained susceptible to both azithromycin and its analogues, further demonstrating that quick-killing is independent of apicoplast-targeting, delayed-death activity. Conclusion: We show that azithromycin and analogues can rapidly kill malaria parasite asexual blood stages via a fast action mechanism. Development of azithromycin and analogues as antimalarials offers the possibility of targeting parasites through both a quick-killing and delayed-death mechanism of action in a single, multifactorial chemotype.Amy L. Burns, Brad E. Sleebs, Ghizal Siddiqui, Amanda E. De Paoli, Dovile Anderson, Benjamin Liffner, Richard Harvey, James G. Beeson, Darren J. Creek, Christopher D. Goodman, Geoffrey I. McFadden, and Danny W. Wilso

Observing the First Stars and Black Holes

The high sensitivity of JWST will open a new window on the end of the cosmological dark ages. Small stellar clusters, with a stellar mass of several 10^6 M_sun, and low-mass black holes (BHs), with a mass of several 10^5 M_sun should be directly detectable out to redshift z=10, and individual supernovae (SNe) and gamma ray burst (GRB) afterglows are bright enough to be visible beyond this redshift. Dense primordial gas, in the process of collapsing from large scales to form protogalaxies, may also be possible to image through diffuse recombination line emission, possibly even before stars or BHs are formed. In this article, I discuss the key physical processes that are expected to have determined the sizes of the first star-clusters and black holes, and the prospect of studying these objects by direct detections with JWST and with other instruments. The direct light emitted by the very first stellar clusters and intermediate-mass black holes at z>10 will likely fall below JWST's detection threshold. However, JWST could reveal a decline at the faint-end of the high-redshift luminosity function, and thereby shed light on radiative and other feedback effects that operate at these early epochs. JWST will also have the sensitivity to detect individual SNe from beyond z=10. In a dedicated survey lasting for several weeks, thousands of SNe could be detected at z>6, with a redshift distribution extending to the formation of the very first stars at z>15. Using these SNe as tracers may be the only method to map out the earliest stages of the cosmic star-formation history. Finally, we point out that studying the earliest objects at high redshift will also offer a new window on the primordial power spectrum, on 100 times smaller scales than probed by current large-scale structure data.Comment: Invited contribution to "Astrophysics in the Next Decade: JWST and Concurrent Facilities", Astrophysics & Space Science Library, Eds. H. Thronson, A. Tielens, M. Stiavelli, Springer: Dordrecht (2008

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Mixture of latent trait analyzers for model-based clustering of categorical data

Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. However, model-based clustering methods for categorical data are less standard. Latent class analysis is a commonly used method for model-based clustering of binary data and/or categorical data, but due to an assumed local independence structure there may not be a correspondence between the estimated latent classes and groups in the population of interest. The mixture of latent trait analyzers model extends latent class analysis by assuming a model for the categorical response variables that depends on both a categorical latent class and a continuous latent trait variable; the discrete latent class accommodates group structure and the continuous latent trait accommodates dependence within these groups. Fitting the mixture of latent trait analyzers model is potentially difficult because the likelihood function involves an integral that cannot be evaluated analytically. We develop a variational approach for fitting the mixture of latent trait models and this provides an efficient model fitting strategy. The mixture of latent trait analyzers model is demonstrated on the analysis of data from the National Long Term Care Survey (NLTCS) and voting in the U.S. Congress. The model is shown to yield intuitive clustering results and it gives a much better fit than either latent class analysis or latent trait analysis alone

Sulfonylpiperazine compounds prevent Plasmodium falciparum invasion of red blood cells through interference with actin-1/profilin dynamics

Published: April 13, 2023With emerging resistance to frontline treatments, it is vital that new antimalarial drugs are identified to target Plasmodium falciparum. We have recently described a compound, MMV020291, as a specific inhibitor of red blood cell (RBC) invasion, and have generated analogues with improved potency. Here, we generated resistance to MMV020291 and performed whole genome sequencing of 3 MMV020291-resistant populations. This revealed 3 nonsynonymous single nucleotide polymorphisms in 2 genes; 2 in profilin (N154Y, K124N) and a third one in actin-1 (M356L). Using CRISPR-Cas9, we engineered these mutations into wild-type parasites, which rendered them resistant to MMV020291. We demonstrate that MMV020291 reduces actin polymerisation that is required by the merozoite stage parasites to invade RBCs. Additionally, the series inhibits the actin-1-dependent process of apicoplast segregation, leading to a delayed death phenotype. In vitro cosedimentation experiments using recombinant P. falciparum proteins indicate that potent MMV020291 analogues disrupt the formation of filamentous actin in the presence of profilin. Altogether, this study identifies the first compound series interfering with the actin-1/profilin interaction in P. falciparum and paves the way for future antimalarial development against the highly dynamic process of actin polymerisation.Madeline G. Dans, Henni Piirainen, William Nguyen, Sachin Khurana, Somya Mehra, Zahra Razook, Niall D. Geoghegan, Aurelie T. Dawson, Sujaan Das, Molly Parkyn Schneider, Thorey K. Jonsdottir, Mikha Gabriela, Maria R. Gancheva, Christopher J. Tonkin, Vanessa Mollard, Christopher Dean Goodman, Geoffrey I. McFadden, Danny W. Wilson, Kelly L. Rogers, Alyssa E. Barry, Brendan S. Crabb, Tania F. de Koning-Ward, Brad E. Sleebs, Inari Kursula, Paul R. Gilso