Generalizations of ssss-supplemented modules

We introduce the concept of (strongly) $ss$-radical supplemented modules. We prove that if a submodule $N$ of $M$ is strongly $ss$-radical supplemented and $Rad(M/N)=M/N$, then $M$ is strongly $ss$-radical supplemented. For a left good ring $R$, we show that $Rad(R)\subseteq Soc(_{R}R)$ if and only if every left $R$-module is $ss$-radical supplemented. We characterize the rings over which all modules are strongly $ss$-radical supplemented. We also prove that over a left $WV$-ring every supplemented module is $ss$-supplemented

Structural motifs of pre-nucleation clusters

Structural motifs of pre-nucleation clusters prepared in single, optically levitated supersaturated aqueous aerosol microparticles containing CaBr2 as a model system are reported. Cluster formation is identified by means of X-ray absorption in the Br K-edge regime. The salt concentration beyond the saturation point is varied by controlling the humidity in the ambient atmosphere surrounding the 15–30 μm microdroplets. This leads to the formation of metastable supersaturated liquid particles. Distinct spectral shifts in near-edge spectra as a function of salt concentration are observed, in which the energy position of the Br K-edge is red-shifted by up to 7.1 ± 0.4 eV if the dilute solution is compared to the solid. The K-edge positions of supersaturated solutions are found between these limits. The changes in electronic structure are rationalized in terms of the formation of pre- nucleation clusters. This assumption is verified by spectral simulations using first-principle density functional theory and molecular dynamics calculations, in which structural motifs are considered, explaining the experimental results. These consist of solvated CaBr2 moieties, rather than building blocks forming calcium bromide hexahydrates, the crystal system that is formed by drying aqueous CaBr2 solutions

Flow cytometric maturity score as a novel prognostic parameter in patients with acute myeloid leukemia

The European LeukemiaNet (ELN) classification is widely accepted for risk stratification of patients with acute myeloid leukemia (AML). In order to establish immunophenotypic features that predict prognosis, the expression of single AML blast cell antigens has been evaluated with partly conflicting results; however, the influence of immunophenotypic blast maturity is largely unknown. In our study, 300 AML patients diagnosed at our institution between January 2003 and April 2012 were analyzed. A flow cytometric maturity score was developed in order to distinguish "mature" AML (AML-ma) from "immature" AML (AML-im) by quantitative expression levels of early progenitor cell antigens (CD34, CD117, and TdT). AML-ma showed significantly longer relapse-free survival (RFS) and overall survival (OS) than AML-im (p < 0.001). Interestingly, statistically significant differences in RFS and OS were maintained within the "intermediate-risk" group according to ELN (RFS, 7.0 years (AML-ma) vs. 3.3 years (AML-im); p = 0.002; OS, 5.1 years (AML-ma) vs. 3.0 years (AML-im); p = 0.022). Our novel flow cytometric score easily determines AML blast maturity and can predict clinical outcome. It remains to be clarified whether these results simply reflect an accumulation of favorable molecular phenotypes in the AML-ma subgroup or whether they rely on biological differences such as a higher proportion of leukemia stem cells and/or a higher degree of genetic instability within the AML-im subgroup

Continuous wavelet transform methods for the simultaneous determinations and dissolution profiles of valsartan and hydrochlorothiazide in tablets

ABSTRACT Continuous wavelet transform (CWT) was proposed for the simultaneous determination and dissolution profiles of valsartan (VAL) and hydrochlorothiazide (HCT) in tablets, without the use of a chemical separation procedure. The CWT approach was applied to the original UV spectra and their ratio spectra in the optimal wavelength ranges. After testing several wavelet families, Mexican hat function-CWT and Daubechies7-CWT (mexh-CWT and db7-CWT, respectively) were found to be suitable for the transformation of the original UV spectra. In the following procedure, mexh-CWT and Coiflets3-CWT (coif3-CWT) were found to be appropriate for the signal analysis of ratio spectra (RS) of VAL/HCT and HCT/VAL. Calibration graphs for VAL and HCT were obtained by measuring db7-CWT and mexh-CWT amplitudes in the transformation of the original absorption spectra and RS-coif-CWT and RS-mexh-CWT amplitudes in the transformation of the ratio spectra. The validity and applicability of the proposed CWT methods were evaluated through the analysis of an independent set of synthetic binary mixtures consisting of VAL and HCT. The proposed signal processing methods were then successfully applied to the simultaneous quantitative evaluation and simultaneous dissolution profiles of the related drugs in commercial tablets, with good agreement reported for the experimental results

Integration of Hi-C with short and long-read genome sequencing reveals the structure of germline rearranged genomes

Structural variants are a common cause of disease and contribute to a large extent to inter-individual variability, but their detection and interpretation remain a challenge. Here, we investigate 11 individuals with complex genomic rearrangements including germline chromothripsis by combining short- and long-read genome sequencing (GS) with Hi-C. Large-scale genomic rearrangements are identified in Hi-C interaction maps, allowing for an independent assessment of breakpoint calls derived from the GS methods, resulting in >300 genomic junctions. Based on a comprehensive breakpoint detection and Hi-C, we achieve a reconstruction of whole rearranged chromosomes. Integrating information on the three-dimensional organization of chromatin, we observe that breakpoints occur more frequently than expected in lamina-associated domains (LADs) and that a majority reshuffle topologically associating domains (TADs). By applying phased RNA-seq, we observe an enrichment of genes showing allelic imbalanced expression (AIG) within 100 kb around the breakpoints. Interestingly, the AIGs hit by a breakpoint (19/22) display both up- and downregulation, thereby suggesting different mechanisms at play, such as gene disruption and rearrangements of regulatory information. However, the majority of interpretable genes located 200 kb around a breakpoint do not show significant expression changes. Thus, there is an overall robustness in the genome towards large-scale chromosome rearrangements

A conserved long-distance telomeric silencing mechanism suppresses mTOR signaling in aging human fibroblasts

Telomeres are repetitive nucleotide sequences at the ends of each chromosome. It has been hypothesized that telomere attrition evolved as a tumor suppressor mechanism in large long-lived species. Long telomeres can silence genes millions of bases away through a looping mechanism called telomere position effect over long distances (TPE-OLD). The function of this silencing mechanism is unknown. We determined a set of 2322 genes with high positional conservation across replicatively aging species that includes known and candidate TPE-OLD genes that may mitigate potentially harmful effects of replicative aging. Notably, we identified PPP2R2C as a tumor suppressor gene, whose up-regulation by TPE-OLD in aged human fibroblasts leads to dephosphorylation of p70S6 kinase and mammalian target of rapamycin suppression. A mechanistic link between telomeres and a tumor suppressor mechanism supports the hypothesis that replicative aging fulfills a tumor suppressor function and motivates previously unknown antitumor and antiaging strategies

Masonry compressive strength prediction using artificial neural networks

The masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of masonry has been investigated. Specifically, back-propagation neural network models have been used for predicting the compressive strength of masonry prism based on experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of masonry walls in a reliable and robust manner.- (undefined

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts