Uncialamycin as a novel payload for antibody drug conjugate (ADC) based targeted cancer therapy

Uncialamycin analogs were evaluated as potential cytotoxic agents in an antibody-drug conjugate (ADC) approach to treating human cancer. These analogs were synthesized using Hauser annulations of substituted phthalides as a key step. A highly potent uncialamycin analog 3c with a valine-citrulline dipeptide linker was conjugated to an anti-mesothelin monoclonal antibody (mAb) through lysines to generate a meso-13 conjugate. This conjugate demonstrated subnanomolar potency (IC50?=?0.88?nM, H226 cell line) in in vitro cytotoxicity experiments with good immunological specificity to mesothelin-positive lung cancer cell lines. The potency and mechanism of action of this uncialamycin class of enediyne antitumor antibiotics make them attractive payloads in ADC-based cancer therapy.     

Fasting-Mimicking Diet Modulates Microbiota and Promotes Intestinal Regeneration to Reduce Inflammatory Bowel Disease Pathology

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Evolution of an intermediate C4 photosynthesis in the non-foliar tissues of the Poaceae

Photosynthesis Research - Carbon concentrating mechanisms (CCMs) in plants are abaptive features that have evolved to sustain plant growth in unfavorable environments, especially at low atmospheric...     

Library-based numerical reduction of the Hodgkin–Huxley neuron for network simulation

We present an efficient library-based numerical method for simulating the Hodgkin–Huxley (HH) neuronal networks. The key components in our numerical method involve (i) a pre-computed high resolution data library which contains typical neuronal trajectories (i.e., the time-courses of membrane potential and gating variables) during the interval of an action potential (spike), thus allowing us to avoid resolving the spikes in detail and to use large numerical time steps for evolving the HH neuron equations; (ii) an algorithm of spike-spike corrections within the groups of strongly coupled neurons to account for spike-spike interactions in a single large time step. By using the library method, we can evolve the HH networks using time steps one order of magnitude larger than the typical time steps used for resolving the trajectories without the library, while achieving comparable resolution in statistical quantifications of the network activity, such as average firing rate, interspike interval distribution, power spectra of voltage traces. Moreover, our large time steps using the library method can break the stability requirement of standard methods (such as Runge–Kutta (RK) methods) for the original dynamics. We compare our library-based method with RK methods, and find that our method can capture very well phase-locked, synchronous, and chaotic dynamics of HH neuronal networks. It is important to point out that, in essence, our library-based HH neuron solver can be viewed as a numerical reduction of the HH neuron to an integrate-and-fire (I&F) neuronal representation that does not sacrifice the gating dynamics (as normally done in the analytical reduction to an I&F neuron).     

Distribution of correlated spiking events in a population-based approach for Integrate-and-Fire networks

Randomly connected populations of spiking neurons display a rich variety of dynamics. However, much of the current modeling and theoretical work has focused on two dynamical extremes: on one hand homogeneous dynamics characterized by weak correlations between neurons, and on the other hand total synchrony characterized by large populations firing in unison. In this paper we address the conceptual issue of how to mathematically characterize the partially synchronous “multiple firing events” (MFEs) which manifest in between these two dynamical extremes. We further develop a geometric method for obtaining the distribution of magnitudes of these MFEs by recasting the cascading firing event process as a first-passage time problem, and deriving an analytical approximation of the first passage time density valid for large neuron populations. Thus, we establish a direct link between the voltage distributions of excitatory and inhibitory neurons and the number of neurons firing in an MFE that can be easily integrated into population–based computational methods, thereby bridging the gap between homogeneous firing regimes and total synchrony.     

Changes in the Chlorophyll Content and Cytokinin Levels in the Top Three Leaves of New Plant Type Rice During Grain Filling

This paper reports the ways that the differences in leaf senescence are related to grain filling, grain yield, and the dynamics of cytokinins (CKs) in the top three leaves of four field-grown new plant type (NPT) rice, a tropical japonica developed at the International Rice Research Institute, Philippines, to increase the yield potential of rice. The chlorophyll content in leaves decreased from flowering to maturity in all the NPT lines, whereas the grain filling percentage was higher in the fast-senescing NPT line than in slow-senescing NPT line. Grain yield was positively correlated with senescence in the flag leaf. Rapid changes in the CK levels were recorded in the leaves of the fast-senescing line, whereas the CK levels were relatively stable in leaves of the slow-senescing line, suggesting that the dynamics of CKs in the fast-senescing line are vital for fast senescence. There were no significant changes in bioactive CKs, CK O-glucosides (storage CKs), and cis-zeatin derivatives in different leaves of the slow-senescing NPT line between 0 and 3 weeks after flowering, suggesting that the content of these CKs is relatively stable during grain filling. A progressive increase in levels of bioactive CKs was positively correlated with gradual accumulation of CK N-glucosides (inactive CKs) in the top three leaves of the slow-senescing NPT line, whereas the decrease of bioactive CKs in the flag leaf of the fast-senescing line was accompanied by accumulation of CK O-glucosides. These results suggest that there is a higher rate of biosynthesis and/or import of bioactive CKs as well as their turnover which may favor delay of leaf senescence in the slow-senescing NPT line.