A Sharp upper bound for the spectral radius of a nonnegative matrix and applications

In this paper, we obtain a sharp upper bound for the spectral radius of a nonnegative matrix. This result is used to present upper bounds for the adjacency spectral radius, the Laplacian spectral radius, the signless Laplacian spectral radius, the distance spectral radius, the distance Laplacian spectral radius, the distance signless Laplacian spectral radius of a graph or a digraph. These results are new or generalize some known results.Comment: 16 pages in Czechoslovak Math. J., 2016. arXiv admin note: text overlap with arXiv:1507.0705

Competent Overall Water-Splitting Electrocatalysts derived from ZIF-67 Grown on Carbon Cloth

The design of nonprecious, bifunctional, and highly competent electrocatalysts for both H2 and O2evolution reactions (HER and OER) has attracted increasing interest recently. Herein, we report a cobalt-based electrocatalyst derived from ZIF-67 grown on carbon cloth (Co–P/NC/CC) for overall water splitting electrocatalysis. The as-prepared Co–P/NC/CC catalyst exhibited remarkable catalytic performance in 1 M KOH with Tafel slopes of 52 and 61 mV dec−1 for HER and OER, respectively. When serving as catalysts for both the cathode and anode, our Co–P/NC/CC demonstrated high efficiency and strong robustness. A thorough comparison with other control samples and detailed characterization results revealed that the superior activity and excellent stability of Co–N–C likely originated from the highly porous, self-supported, and binder-free nature of the electrocatalyst, as well as the high conductivity of carbon cloth. Hence, direct decoration of metal organic frameworks on conductive substrates represents an effective approach for the development of electrocatalysts not only promising for water splitting but also for many other applications

Testing alternative theories of gravity with space-based gravitational wave detectors

We use gravitational waves (GWs) from binary black holes (BBHs) and neutron stars inspiraling into intermediate-mass black holes to evaluate how accurately the future space-based GW detectors such as LISA, Taiji and TianQin and their combined networks can determine source parameters and constrain alternative theories of gravity. We find that, compared with single detector, the detector network can greatly improve the estimation errors of source parameters, especially the sky localization, but the improvement of the constraint on the graviton mass $m_g$ and the Brans-Dicke coupling constant $\omega_{BD}$ is small. We also consider possible scalar modes existed in alternative theories of gravity and we find the inclusion of the scalar mode has little effect on the constraints on source parameters, $m_g$, and $\omega_{BD}$ and the parametrized amplitude $A_B$ of scalar modes are small. For the constraint on the graviton mass, we consider both the effects in the GW phase and the transfer function due to the mass of graviton. With the network of LISA, Taiji and TianQin, we get the lower bound on the graviton Compton wavelength $\lambda_g\gtrsim 1.24 \times 10^{20}$ m for BBHs with masses $(10^6+10^7)M_\odot$, and $A_B< 5.7\times 10^{-4}$ for BBHs with masses $(1+2)\times 10^5M_\odot$; $\omega_{BD}>6.11\times10^{6}$ for neutron star-black hole binary with masses $(1.4+400)M_{\odot}$.Comment: 21 pages, 3 figures, 4 tables. Typos corrected and references updated. Published in PR

Universal Surface Engineering of Transition Metals for Superior Electrocatalytic Hydrogen Evolution in Neutral Water

The development of low-cost hybrid water splitting-biosynthetic systems that mimic natural photosynthesis to achieve solar-to-chemical conversion is of great promise for future energy demands, but often limited by the kinetically sluggish hydrogen evolution reaction (HER) on the surface of nonprecious transition metal catalysts in neutral media. It is thus highly desirable to rationally tailor the reaction interface to boost the neutral HER catalytic kinetics. Herein, we report a general surface nitrogen modification of diverse transition metals (e.g. iron, cobalt, nickel, copper, and nickel-cobalt alloy), accomplished by a facile low-temperature ammonium carbonate treatment, for significantly improved hydrogen generation from neutral water. Various physicochemical characterization techniques including synchroton X-ray absorption spectroscopy (XAS) and theory modeling demonstrate that the surface nitrogen modification does not change the chemical composition of the underlying transition metals. Notably, the resulting nitrogen-modified nickel framework (N-Ni) exhibits an extremely low overpotential of 64 mV at 10 mA cm-2, which is, to our knowledge, the best among those nonprecious electrocatalysts reported for hydrogen evolution at pH 7. Out combined experimental results and density functional theory (DFT) calculations reveal that the surface electron-rich nitrogen simultaneously facilitates the initial adsorption of water via the electron-deficient H atom and the subsequent dissociation of the electron-rich HO-H bond via H transfer to N on the nickel surface, beneficial to the overall hydrogen evolution process

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates

Epigenetic dynamics shaping melanophore and iridophore cell fate in zebrafish

BACKGROUND: Zebrafish pigment cell differentiation provides an attractive model for studying cell fate progression as a neural crest progenitor engenders diverse cell types, including two morphologically distinct pigment cells: black melanophores and reflective iridophores. Nontrivial classical genetic and transcriptomic approaches have revealed essential molecular mechanisms and gene regulatory circuits that drive neural crest-derived cell fate decisions. However, how the epigenetic landscape contributes to pigment cell differentiation, especially in the context of iridophore cell fate, is poorly understood. RESULTS: We chart the global changes in the epigenetic landscape, including DNA methylation and chromatin accessibility, during neural crest differentiation into melanophores and iridophores to identify epigenetic determinants shaping cell type-specific gene expression. Motif enrichment in the epigenetically dynamic regions reveals putative transcription factors that might be responsible for driving pigment cell identity. Through this effort, in the relatively uncharacterized iridophores, we validate alx4a as a necessary and sufficient transcription factor for iridophore differentiation and present evidence on alx4a\u27s potential regulatory role in guanine synthesis pathway. CONCLUSIONS: Pigment cell fate is marked by substantial DNA demethylation events coupled with dynamic chromatin accessibility to potentiate gene regulation through cis-regulatory control. Here, we provide a multi-omic resource for neural crest differentiation into melanophores and iridophores. This work led to the discovery and validation of iridophore-specific alx4a transcription factor

Smart membranes for oil/water emulsions separation: a review

Oily wastewater poses a significant impact on both environments and human societies. Especially, the treatment of oil/water emulsions for separating oil from water is challenging due to the high stability of oil/water emulsions. Smart membranes, known as stimuli-responsive membranes, are one of the emerging technologies that have been paid wide attention for separating oil/water emulsions in recent years. Smart membranes possess the unique features of switchable wettability between hydrophilicity and hydrophobicity after being triggered by external stimuli and have desired anti-fouling properties. This review summarizes the development of smart membranes for oil/water emulsions separation during the past five years (2018 – present). It was found that solvent stimuli-responsive membranes are the most popular type of smart membranes for oil/water emulsions separation. For multi-stimuli-responsive membranes that can respond to more than one stimulus, future research should focus on developing appropriate fabrication strategies to increase the separation and anti-fouling performances of the membranes. Additionally, surface coating, surface grafting, and copolymer blending are the most popular methods for smart membranes fabrication. However, these methods might not be universally applicable to the different types of stimuli-responsive membranes