Trolleharper5339

The overall association evidence of a genetic variant with multiple traits can be evaluated by cross phenotype association analysis using summary statistics from genome wide association studies (GWAS). Further dissecting the association pathways from a variant to multiple traits is important to understand the biological causal relationships among complex traits.

Here we introduce a flexible and computationally efficient Iterative Mendelian Randomization and Pleiotropy (IMRP) approach to simultaneously search for horizontal pleiotropic variants and estimate causal effect. Extensive simulations and real data applications suggest that IMRP has similar or better performance than existing Mendelian Randomization methods for both causal effect estimation and pleiotropic variant detection. The developed pleiotropy test is further extended to detect colocalization for multiple variants at a locus. IMRP will greatly facilitate our understanding of causal relationships underlying complex traits, in particular, when a large number of genetic instrumental variables are used for evaluating multiple traits.

The software IMRP is available at https//github.com/XiaofengZhuCase/IMRP. The simulation codes can be downloaded at http//hal.case.edu/~xxz10/zhu-web/ under the link MR Simulations software.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

Machine-learning scoring functions have been found to outperform standard scoring functions for binding affinity prediction of protein-ligand complexes. A plethora of reports focus on the implementation of increasingly complex algorithms, while the chemical description of the system has not been fully exploited.

Herein, we introduce Extended Connectivity Interaction Features (ECIF) to describe protein-ligand complexes and build machine-learning scoring functions with improved predictions of binding affinity. ECIF are a set of protein-ligand atom-type pair counts that take into account each atom's connectivity to describe it and thus define the pair types. ECIF were used to build different machine-learning models to predict protein-ligand affinities (pKd / pKi). The models were evaluated in terms of "scoring power" on the Comparative Assessment of Scoring Functions 2016. The best models built on ECIF achieved Pearson correlation coefficients of 0.857 when used on its own, and 0.866 when used in combination with ligand descriptors, demonstrating ECIF descriptive power.

Data and code to reproduce all the results are freely available at https//github.com/DIFACQUIM/ECIF.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.The fluorescence imaging technique has attracted increasing attention in the detection of various biological molecules in situ and in real-time owing to its inherent advantages including high selectivity and sensitivity, outstanding spatiotemporal resolution and fast feedback. In the past few decades, a number of fluorescent probes have been developed for bioassays and imaging by exploiting different fluorophores. Among various fluorophores, resorufin exhibits a high fluorescence quantum yield, long excitation/emission wavelength and pronounced ability in both fluorescence and colorimetric analysis. This fluorophore has been widely utilized in the design of responsive probes specific for various bioactive species. In this review, we summarize the advances in the development of resorufin-based fluorescent probes for detecting various analytes, such as cations, anions, reactive (redox-active) sulfur species, small molecules and biological macromolecules. The chemical structures of probes, response mechanisms, detection limits and practical applications are investigated, which is followed by the discussion of recent challenges and future research perspectives. This review article is expected to promote the further development of resorufin-based responsive fluorescent probes and their biological applications.It is of great interest to investigate the evolution pattern of gold nanoclusters (Au NCs) due to its significance in understanding the growth mechanism and origin of Au NCs. Capture of metastable cluster intermediates is an effective way to meet this demand since they provide valuable information for understanding the conversion pathway of Au NCs. However, it is still challenging to obtain metastable Au NCs, especially thiol-protected Au NCs, and solve their structures. In this work, a metastable thiol-protected gold nanocluster, Au22(SAdm)16 (Au22), was synthesized and its structure was determined by single crystal X-ray diffraction. Au22 shows a close structure-evolution correlation with Au21(SAdm)15 (Au21). The symmetric Au10 kernel of Au21 is twisted by the insertion of an additional Au-SR unit on the motif during its structure evolution into Au22. The distortion in structures results in significantly distinguishing absorption and emission spectra between Au22 and Au21. Noteworthily, the structure correlation between Au22 and Au21 was also found experimentally that Au22 can spontaneously transform into Au21 due to the metastability of Au22 in solution. This size conversion process was monitored by time-dependent UV-vis spectroscopy and ESI-MS. Furthermore, the solvent effect on the size conversion process was also investigated. This transformation from Au22 to Au21 provides a unique platform for studies on the evolution pattern of gold nanoclusters at the single atom level.Nevadensin (NEV), a natural flavonoid compound derived from Lysionotus pauciflorus Maxim, has numerous biological activities. However, few researchers have examined its potential impact on alleviating allergies. In the present study, NEV was found to upregulate rectal temperature, suppress the development of diarrhea, and decrease the levels of serum specific immunoglobulin E, histamine and mouse MC protease-1 in ovalbumin-allergic mice. Moreover, NEV also alleviated passive cutaneous anaphylaxis reactions and inhibited the release of β-hexosaminidase and histamine in bone marrow-derived mast cells. learn more Furthermore, we provide the first demonstration that NEV decreases the expression of c-Kit and suppresses the proliferation of bone marrow-derived mast cells and accelerates their apoptosis. These findings indicated that L. pauciflorus-derived NEV might have the potential to alleviate food hypersensitivity.