Skin cells undergo asynthetic fission to expand body surfaces in zebrafish.

As an animal's surface area expands during development, skin cell populations must quickly respond to maintain sufficient epithelial coverage. Despite much progress in understanding of skin cell behaviours in vivo1,2, it remains unclear how cells collectively act to satisfy coverage demands at an organismic level. Here we created a multicolour cell membrane tagging system, palmskin, to monitor the entire population of superficial epithelial cells (SECs) in developing zebrafish larvae. Using time-lapse imaging, we found that many SECs readily divide on the animal body surface; during a specific developmental window, a single SEC can produce a maximum of four progeny cells over its lifetime on the surface of the animal. Remarkably, EdU assays, DNA staining and hydroxyurea treatment showed that these terminally differentiated skin cells continue splitting despite an absence of DNA replication, causing up to 50% of SECs to exhibit reduced genome size. On the basis of a simple mathematical model and quantitative analyses of cell volumes and apical surface areas, we propose that 'asynthetic fission' is used as an efficient mechanism for expanding epithelial coverage during rapid growth. Furthermore, global or local manipulation of body surface growth affects the extent and mode of SEC division, presumably through tension-mediated activation of stretch-activated ion channels. We speculate that this frugal yet flexible mode of cell proliferation might also occur in contexts other than zebrafish skin expansion.

Predicting Selectivity with a Bifurcating Surface: Inaccurate Model or Inaccurate Statistics of Dynamics?

Reactions on post-transition-state bifurcation (PTSB) energy surfaces are an important class of reaction in which classical rate theories, such as the transition state theory, fail to account for the selectivity. Quasiclassical trajectory molecular dynamic (QCT-MD) simulation is an important computational approach to understanding reactions mechanisms, especially for reactions that cannot be predicted from conventional rate theories. However, the applicability of direct dynamic simulations is hampered by huge computational costs for collecting a statistically meaningful set of trajectories, making it difficult to compare simulation results with theoretical or physical insights-based predictions (non-MD predictions). In this work, we examine the PTSB of Schmidt-Aubé reactions studied by Tantillo and co-workers. With machine-learning using kernel-ridge regression (KRR) to predict atomic forces, statistical reliability was enhanced by significantly increasing the number of trajectories. With KRR, the bottleneck of simulating dynamics (atomic forces in QCT-MD with density functional theory) was accelerated more than 100-fold. We found that this KRR-aided QCT-MD approach is successful in predicting branching ratios with a much larger number of trajectories. With our approach, statistical errors are greatly reduced, and hypothetical non-MD models for predicting selectivity are tested with much higher confidence. By comparison with non-MD models, dynamical properties that affect branching ratios become more clearly described.

Two-Step Machine Learning Approach for Charge-Transfer Coupling with Structurally Diverse Data.

Electronic coupling is important in determining charge-transfer rates and dynamics. Coupling strength is sensitive to both intermolecular, e.g., orientation or distance, and intramolecular degrees of freedom. Hence, it is challenging to build an accurate machine learning model to predict electronic coupling of molecular pairs, especially for those derived from the amorphous phase, for which intermolecular configurations are much more diverse than those derived from crystals. In this work, we devise a new prediction algorithm that employs two consecutive KRR models. The first model predicts molecular orbitals (MOs) from structural variation for each fragment, and coupling is further predicted by using the overlap integral included in a second model. With our two-step procedure, we achieved mean absolute errors of 0.27 meV for an ethylene dimer and 1.99 meV for a naphthalene pair, much improved accuracy amounting to 14-fold and 3-fold error reductions, respectively. In addition, MOs from the first model can also be the starting point to obtain other quantum chemical properties from atomistic structures. This approach is also compatible with a MO predictor with sufficient accuracy.

Machine-learned dynamic disorder of electron transfer coupling.

Electron transfer (ET) is a fundamental process in chemistry and biochemistry, and electronic coupling is an important determinant of the rate of ET. However, the electronic coupling is sensitive to many nuclear degrees of freedom, particularly those involved in intermolecular movements, making its characterization challenging. As a result, dynamic disorder in electron transfer coupling has rarely been investigated, hindering our understanding of charge transport dynamics in complex chemical and biological systems. In this work, we employed molecular dynamic simulations and machine-learning models to study dynamic disorder in the coupling of hole transfer between neighboring ethylene and naphthalene dimer. Our results reveal that low-frequency modes dominate these dynamics, resulting primarily from intermolecular movements such as rotation and translation. Interestingly, we observed an increasing contribution of translational motion as temperature increased. Moreover, we found that coupling is sub-Ohmic in its spectral density character, with cut-off frequencies in the range of 102 cm-1. Machine-learning models allow direct study of dynamics of electronic coupling in charge transport with sufficient ensemble trajectories, providing further new insights into charge transporting dynamics.

Reformulation of thermally assisted-occupation density functional theory in the Kohn-Sham framework.

We reformulate the thermally assisted-occupation density functional theory (TAO-DFT) into the Kohn-Sham single-determinant framework and construct two new post-self-consistent field (post-SCF) static correlation correction schemes, named rTAO and rTAO-1. In contrast to the original TAO-DFT with the density in an ensemble form, in which each orbital density is weighted with a fractional occupation number, the ground-state density is given by a single-determinant wavefunction, a regular Kohn-Sham (KS) density, and total ground state energy is expressed in the normal KS form with a static correlation energy formulated in terms of the KS orbitals. In post-SCF calculations with rTAO functionals, an efficient energy scanning to quantitatively determine θ is also proposed. The rTAOs provide a promising method to simulate systems with strong static correlation as original TAO, but simpler and more efficient. We show that both rTAO and rTAO-1 is capable of reproducing most results from TAO-DFT without the additional functional Eθ used in TAO-DFT. Furthermore, our numerical results support that, without the functional Eθ, both rTAO and rTAO-1 can capture correct static correlation profiles in various systems.

Basal leakage in oscillation: Coupled transcriptional and translational control using feed-forward loops.

The circadian clock is a complex system that plays many important roles in most organisms. Previously, many mathematical models have been used to sharpen our understanding of the Arabidopsis clock, which brought to light the roles of each transcriptional and post-translational regulations. However, the presence of both regulations, instead of either transcription or post-translation, raised curiosity of whether the combination of these two regulations is important for the clock's system. In this study, we built a series of simplified oscillators with different regulations to study the importance of post-translational regulation (specifically, 26S proteasome degradation) in the clock system. We found that a simple transcriptional-based oscillator can already generate sustained oscillation, but the oscillation can be easily destroyed in the presence of transcriptional leakage. Coupling post-translational control with transcriptional-based oscillator in a feed-forward loop will greatly improve the robustness of the oscillator in the presence of basal leakage. Using these general models, we were able to replicate the increased variability observed in the E3 ligase mutant for both plant and mammalian clocks. With this insight, we also predict a plausible regulator of several E3 ligase genes in the plant's clock. Thus, our results provide insights into and the plausible importance in coupling transcription and post-translation controls in the clock system.

Molecular and nano structures of chiral PEDOT derivatives influence the enantiorecognition of biomolecules. In silico analysis of chiral recognition.

In this study we investigated the synergistic effects of the chirality (molecular structure) and surface morphology (nanostructure) of a newly designed sensing platform for the stereoselective recognition of biomolecules. We synthesized 3,4-ethylenedioxythiophene monomers presenting an OH functional group on the side chain (EDOT-OH) with either R or S chirality and then electropolymerized them in a template-free manner to engineer poly(EDOT-OH) nanotubes and smooth films with R or S chirality. We used a quartz crystal microbalance (QCM) to examine the differential binding of fetal bovine serum, RGD peptide, insulin, and (R)- and (S)-mandelic acid (MA) on these chiral polymeric platforms. All of these biomolecules bound stereoselectively and with greater affinity toward the nanotubes than to the smooth films. The sensitive chiral recognition of (S)- and (R)-MA on the (R)-poly(EDOT-OH) nanotube surface occurred with the highest chiral discrepancy ratio of 1.80. In vitro experiments revealed a greater degree of protein deposition from MCF-7 cells on the chiral nanotube surfaces. We employed ab initio molecular dynamics simulations and density functional theory calculations to investigate the mechanism underlying the sensitive chiral recognition between the chiral sensing platforms and the chiral analyte molecules.

Reorganization energies and spectral densities for electron transfer problems in charge transport materials.

In describing the dynamics of electron transfer or charge transport, the reorganization energy and the spectral density function describe the influence of nuclei motion to the transporting electron. The spectral density can be obtained using various theoretical approaches: from a model of dielectric response, or calculated with various computational means. With the vast advancement of modern computational techniques, many details in vibronic coupling can be obtained, including those described in the early literature. In this work, we provide a comprehensive understanding of the nature of vibronic coupling in light of some early literature. The theoretical connection among different quantities for vibronic coupling is discussed, followed by a brief review of the spectral density function. Various approaches and some of the results for the spectral density function are also reviewed. The importance of low-frequency bands in nonpolar systems that can be overlooked is also discussed, for both Holstein and Peierls types of electron-phonon couplings.

Artificial neural networks for predicting charge transfer coupling.

Quantum chemistry calculations have been very useful in providing many key detailed properties and enhancing our understanding of molecular systems. However, such calculation, especially with ab initio models, can be time-consuming. For example, in the prediction of charge-transfer properties, it is often necessary to work with an ensemble of different thermally populated structures. A possible alternative to such calculations is to use a machine-learning based approach. In this work, we show that the general prediction of electronic coupling, a property that is very sensitive to intermolecular degrees of freedom, can be obtained with artificial neural networks, with improved performance as compared to the popular kernel ridge regression method. We propose strategies for optimizing the learning rate and batch size, improving model performance, and further evaluating models to ensure that the physical signatures of charge-transfer coupling are well reproduced. We also address the effect of feature representation as well as statistical insights obtained from the loss function and the data structure. Our results pave the way for designing a general strategy for training such neural-network models for accurate prediction.

Excitation energies from thermally assisted-occupation density functional theory: Theory and computational implementation.

The time-dependent density functional theory (TDDFT) has been broadly used to investigate the excited-state properties of various molecular systems. However, the current TDDFT heavily relies on outcomes from the corresponding ground-state DFT calculations, which may be prone to errors due to the lack of proper treatment in the non-dynamical correlation effects. Recently, thermally assisted-occupation DFT (TAO-DFT) [J.-D. Chai, J. Chem. Phys. 136, 154104 (2012)], a DFT with fractional orbital occupations, was proposed, explicitly incorporating the non-dynamical correlation effects in the ground-state calculations with low computational complexity. In this work, we develop TDTAO-DFT, which is a TD, linear-response theory for excited states within the framework of TAO-DFT. With tests on the excited states of H2, the first triplet excited state (13Σu +) was described well, with non-imaginary excitation energies. TDTAO-DFT also yields zero singlet-triplet gap in the dissociation limit for the ground singlet (11Σg +) and the first triplet state (13Σu +). In addition, as compared to traditional TDDFT, the overall excited-state potential energy surfaces obtained from TDTAO-DFT are generally improved and better agree with results from the equation-of-motion coupled-cluster singles and doubles.

Machine Learning for Predicting Electron Transfer Coupling.

Electron transfer coupling is a critical factor in determining electron transfer rates. This coupling strength can be sensitive to details in molecular geometries, especially intermolecular configurations. Thus, studying charge transporting behavior with a full first-principle approach demands a large amount of computation resources in quantum chemistry (QC) calculation. To address this issue, we developed a machine learning (ML) approach to evaluate electronic coupling. A prototypical ML model for an ethylene system was built by kernel ridge regression with Coulomb matrix representation. Since the performance of the ML models highly dependent on their building strategies, we systematically investigated the generality of the ML models, the choice of features and target labels. The best ML model trained with 40 000 samples achieved a mean absolute error of 3.5 meV and greater than 98% accuracy in predicting phases. The distance and orientation dependence of electronic coupling was successfully captured. Bypassing QC calculation, the ML model saved 10-104 times the computation cost. With the help of ML, reliable charge transport models and mechanisms can be further developed.

Establishment of Guidelines for the Control of Glycosylation Reactions and Intermediates by Quantitative Assessment of Reactivity.

Stereocontrolled chemical glycosylation remains a major challenge despite vast efforts reported over many decades and so far still mainly relies on trial and error. Now it is shown that the relative reactivity value (RRV) of thioglycosides is an indicator for revealing stereoselectivities according to four types of acceptors. Mechanistic studies show that the reaction is dominated by two distinct intermediates: glycosyl triflates and glycosyl halides from N-halosuccinimide (NXS)/TfOH. The formation of glycosyl halide is highly correlated with the production of α-glycoside. These findings enable glycosylation reactions to be foreseen by using RRVs as an α/ß-selectivity indicator and guidelines and rules to be developed for stereocontrolled glycosylation.

The Twisted Benzo[ghi]-Perylenetriimide Dimer as a 3D Electron Acceptor for Fullerene-Free Organic Photovoltaics.

A series of twisted N,N-linked benzo[ghi]-perylenetriimide dimers (t-BPTI) with various lengths of the α-branched alkyl side chain at the six-membered imide ring position was designed, synthesized, and characterized. These compounds showed the low-lying LUMO energy level of -3.78 eV, which was similar to that of PC61 BM (-3.71 eV), but with intensive optical absorption in the range 350-500 nm. The twisted molecular geometry with two nearly perpendicular BPTI planes achieved a favorable nanoscale phase separation by relieving the self-aggregation of rigid BPTI units in blend films. The acceptor t-BPTI-3 unit with the longest alkyl side chains has been demonstrated to be an efficient electron acceptor in solution-processed bulk heterojunction organic photovoltaics (OPV), giving a power conversion efficiency of 3.68 % when using conjugated polymer PTB7-Th as the donor and without additional treatments.

One-Pot Tandem Photoredox and Cross-Coupling Catalysis with a Single Palladium Carbodicarbene Complex.

The combination of conventional transition-metal-catalyzed coupling (2 e- process) and photoredox catalysis (1 e- process) has emerged as a powerful approach to catalyze difficult cross-coupling reactions under mild reaction conditions. Reported is a palladium carbodicarbene (CDC) complex that mediates both a Suzuki-Miyaura coupling and photoredox catalysis for C-N bond formation upon visible-light irradiation. These two catalytic pathways can be combined to promote both conventional transition-metal-catalyzed coupling and photoredox catalysis to mediate C-H arylation under ambient conditions with a single catalyst in an efficient one-pot process.

Carbodicarbenes: Unexpected π-Accepting Ability during Reactivity with Small Molecules.

An investigation of carbodicarbenes, the less explored member of the carbenic complex/ligand family has yielded unexpected electronic features and concomitant reactivity. Observed 1,2-addition of E-H bonds (E = B, C, Si) across the carbone central carbon and that of the flanking N-heterocyclic carbene (NHC) fragment, combined with single-crystal X-ray studies of a model Pd complex strongly suggests a significant level of π-accepting ability at the central carbon of the NHC moiety. This feature is atypical of classic NHCs, which are strong σ-donors, with only nominal π-accepting ability. The unanticipated π-acidity is critical for engendering carbodicarbenes with reactivity more commonly observed with frustrated Lewis pairs (FLPs) rather than the more closely related NHCs and cyclic (alkyl)(amino)carbenes (CAACs).

Face-to-Face Packing of 2,3,9,10-Tetrasubstituted Pentacene Derivatives Revealed through a Solid State [4 + 4] Thermal Cycloaddition and Molecular Dynamic Simulation.

2,3,9,10-Substituted pentacene tetraesters and pentacene diester-dinitriles were synthesized. These pentacene derivatives underwent an unusual solid state [4 + 4] thermal dimerization with good efficiency and complete stereoselectivity. This observation indicates this series of pentacene derivatives adopt π-π stacking geometry with large mutual overlap in solid state. This notion was confirmed by molecualr dynamic simulation.

Synthesis and isolation of an acyclic tridentate bis(pyridine)carbodicarbene and studies on its structural implications and reactivities.

The simple synthetic development of acyclic pincer bis(pyridine)carbodicarbene is depicted herein. Presented is the first isolated structural pincer carbodicarbene with a C-C-C angle of 143°, larger than the monodentate framework. More importantly, theoretical analysis showed that this carbodicarbene embodies a more allene-like character. Palladium complexes supported by this pincer ligand are active catalysts for Heck-Mizoroki and Suzuki-Miyaura coupling reactions.

The elusive three-coordinate dicationic hydrido boron complex.

The formation of a hitherto unknown three-coordinate dicationic hydrido boron complex is described. Interestingly, supporting ligand carbodicarbene gave unprecedented reaction with BH3 without using more highly electrophilic Lewis acid precursors. Spectroscopic, crystallographic, and computational analysis was performed to understand the electronic features of these species.