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Due to the low activity and poor selectivity of current artificial nitrogen fixation catalysts, there is an urgent need to develop efficient and environmentally friendly Electrochemical ammonia synthesis (EAS) electrocatalysts. Electrochemical ammonia synthesis is considered an environmentally friendly and sustainable method for artificial nitrogen fixation. Herein, Fe2O3 nanoparticles assembled on MoSe2 (Fe2O3/MoSe2) were first developed and regarded as an efficient electrocatalytic nitrogen fixation catalyst with high electroactive. The Fe2O3/MoSe2 composites exhibited excellent NRR activity with an NH3 yield of 55.52 µg∙h− 1∙mg− 1 at -0.5 V and Faradaic efficiency of 9.6% at -0.6 V vs. RHE. Notably, the Fe2O3/MoSe2 composites exhibited excellent stability and durability in recycling tests. Density functional theory (DFT) calculations revealed that the interfacial charge transport from Fe2O3 to MoSe2 could significantly enhance the Electrochemical nitrogen reduction reaction (NRR) activity of Fe2O3/MoSe2 by promoting the conductivity of Fe2O3/MoSe2 and reducing the free energy barrier for the rate-determining of *N2 to *N2H formation step. This work provides a promising avenue for the green synthesis of NH3.

Background Dexmedetomidine (Dex) is a highly selective agonist of the α2 adrenergic receptor and a common sedative; however, its anti-inflammatory effect has been studied. In this study, the inhibitory effect of Dex on inflammation in dental pulp cells was assessed. For this, the effect of Dex on inflammation induced by carrageenan (Car) in human dental pulp cells (hDPCs) was evaluated. Car incubation induced a robust inflammatory response in hDPCs as well as activation of PKA–STAT3 and PKC–nuclear factor kappa B (NF-κB) signaling pathways. Results Dex reduced the expression of inflammatory cytokines in a dose-dependent manner. Meanwhile, the phosphorylation of PKA, PKC, STAT3, and NF-κB as well as the nuclear accumulation of STAT3 and NF-κB were significantly increased in Dex-treated Car-induced hDPCs. Western blotting results also showed that the phosphorylation level of transient receptor potential cation channel subfamily V member 1 (TRPV1) was downregulated as a result of Dex treatment. Furthermore, we found that administration of the TRPV1 agonist capsaicin (Cap) reversed the effects of Dex on proinflammatory cytokines; however, the expression and activation of PKA–STAT3 and PKC–NF-κB signals were not altered owing to Cap administration. Conclusions These results indicate that Dex plays a defensive role in dental pulp inflammation by regulating the TRPV1 channel and can be used as a potential target for human dental pulp inflammation intervention.

In molecular dynamics simulations, compared with popular all-atom force field approaches, coarse-grained (CG) methods are frequently used for the rapid investigations of long time- and length-scale processes in many important biological and soft matter studies. The typical task in coarse-graining is to derive interaction force functions between different CG site types in terms of their distance, bond angle or dihedral angle. In this paper, an L1-regularized least squares model is applied to form the force functions, which makes additional use of the B-spline wavelet frame transform in order to preserve the important features of force functions. The B-spline tight frames system has a simple explicit expression which is useful for representing our force functions. Moreover, the redundancy of the system offers more resilience to the effects of noise and is useful in the case of lossy data. Numerical results for molecular systems involving pairwise non-bonded, three and four-body bonded interactions are obtained to demonstrate the effectiveness of our approach. MOE (Min. of Education, S’pore) Accepted version

Additional file 1: Fig. S1. Ropivacaine and bupivacaine arrest cell cycle via increasing G2/M percentage in esophageal carcinoma cells. After 24-h drug treatment, the cell cycle was assessed by staining cells with Propidium iodide (PI) and followed by flow cytometry. *P

Background : Dexmedetomidine (Dex) is a highly selective agonist of the α2 adrenergic receptor and a common sedative; however, its anti-inflammatory effect has been studied. In this study, the inhibitory effect of Dex on inflammation in dental pulp cells was assessed. To solve this problem, the effect of Dex on inflammation induced by carrageenan (Car) in human dental pulp cells (hDPCs) was studied. Car incubation induced a robust inflammatory response in the hDPCs as well as activation of PKA–STAT3 and PKC–nuclear factor kappa B (NFκB) signaling pathways. Results : Dex could reduce the expression of inflammatory cytokines in a dose-dependent manner. Meanwhile, the phosphorylation of PKA, PKC, STAT3, and NFκB as well as the nuclear accumulation of STAT3 and NFκB were significantly increased in Dex-treated Car-induced hDPCs. Western blotting results also showed that the phosphorylation level of transient receptor potential cation channel subfamily V member 1 (TRPV1) was downregulated owing to Dex treatment. Further, we found that the administration of the TRPV1 agonist capsaicin (Cap) reversed the effects of Dex on proinflammatory cytokines; however, the expression and activation of PKA–STAT3 and PKC–NFκB signals were not altered owing to Cap administration. Conclusions : These results indicate that Dex exerts a defensive role in dental pulp inflammation by regulating the TRPV1 channel and can be used as a potential target for human dental pulp inflammation intervention.

Our understanding of mutations in noncoding DNA is still nascent across most cancer types. Similarly, the potential clinical utility of noncoding DNA in cancer liquid biopsy assays remains mostly unexplored. Here, we identify high-frequency noncoding mutation hotspots in whole genomes from hundreds of gastrointestinal tumors and show that these mutations can be detected in liquid biopsy samples from cancer patients. We demonstrate how profiling of noncoding mutation hotspots can significantly increase the accuracy of ctDNA burden estimation, with minimal impact on sequencing cost, when paired with existing targeted cfDNA assays. Our results suggest that targeted NGS liquid biopsy assays should target both protein-coding and noncoding mutations when estimation and monitoring of ctDNA burden is a key clinical endpoint. Citation Format: Guo Yu, Zhong Wee Poh, Guanhua Zhu, Iain Tan, Sarah Ng, Patrick Tan, Anders Skanderup. Monitoring of ctDNA burden from noncoding DNA [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 44.

Zika virus (ZIKV) has become a great public health emergency. Its non-structural protein 3 (NS3) is a key enzyme in viral replication and has been considered as a potential therapeutic target. A conformational characterization of ZIKV NS3 is critical for a comprehensive understanding of its molecular interactions and functions. However, the high conformational flexibility of solution NS3 obstacles the structural characterization of NS3 solely from the experimental observable that averages over its heterogeneous conformations. Here, we employed replica exchange with solute tempering (REST) method to simulate the di-domain protein ZIKV NS3. Three independent MD simulations identified a conserved conformational ensemble of NS3, consisting of a major conformational state and several minor states from compact to loose conformations. The major state agrees well with the scattering profile from small-angle X-ray scattering (SAXS) experiments. Moreover, the simulated ensemble is supported by a direct data-fitting result that requires both short- and long-range structural contacts to recover the experimental data. We discussed the interplay between simulation and experiment in ensemble construction of flexible biomolecules and shed light on the physically derived conformational ensembles. Ministry of Education (MOE) This research is supported by the Tier 3 grant (MOE2012-T3-1- 008) from the Ministry of Education, Singapore.

The structural variations of multidomain proteins with flexible parts mediate many biological processes, and a structure ensemble can be determined by selecting a weighted combination of representative structures from a simulated structure pool, producing the best fit to experimental constraints such as interatomic distance. In this study, a hybrid structure‐based and physics‐based atomistic force field with an efficient sampling strategy is adopted to simulate a model di‐domain protein against experimental paramagnetic relaxation enhancement (PRE) data that correspond to distance constraints. The molecular dynamics simulations produce a wide range of conformations depicted on a protein energy landscape. Subsequently, a conformational ensemble recovered with low‐energy structures and the minimum‐size restraint is identified in good agreement with experimental PRE rates, and the result is also supported by chemical shift perturbations and small‐angle X‐ray scattering data. It is illustrated that the regularizations of energy and ensemble‐size prevent an arbitrary interpretation of protein conformations. Moreover, energy is found to serve as a critical control to refine the structure pool and prevent data overfitting, because the absence of energy regularization exposes ensemble construction to the noise from high‐energy structures and causes a more ambiguous representation of protein conformations. Finally, we perform structure‐ensemble optimizations with a topology‐based structure pool, to enhance the understanding on the ensemble results from different sources of pool candidates. MOE (Min. of Education, S’pore) Accepted version

An ensemble-modeling scheme incorporating coarse-grained simulations with experimental small-angle X-ray scattering (SAXS) data is applied to dengue virus 2 (DENV2) nonstructural protein 5 (NS5). NS5 serves a key role in viral replication through its two domains that are connected by a 10-residue polypeptide segment. A set of representative structures is generated from a simulated structure pool using SAXS data fitting by the non-negativity least squares (NNLS) or standard ensemble optimization method (EOM) based on a genetic algorithm (GA). It is found that a proper low-energy threshold of the structure pool is necessary to produce a conformational ensemble of two representative structures by both NNLS and GA that agrees well with the experimental SAXS profile. The stability of the constructed ensemble is validated also by molecular dynamics simulations with an all-atom force field. The constructed ensemble successfully revealed the domain–domain orientation and domain-contacting interface of DENV2 NS5. Using experimental data fitting and additional investigations with synthesized data, it is found that energy restraint on the conformational pool is necessary to avoid overinterpretation of experimental data by spurious conformational representations. MOE (Min. of Education, S’pore) Accepted version

The structural variation of multi-domain proteins with flexible components mediates many biological processes. Their structure determination is crucial for understanding the mechanism of their interactions and functions, but it remains a challenge to derive the structure ensemble of flexible biomolecules solely from experimental data. Computational methods assist in the interpretation of the time- and spatial-averaged experimental data for a correct structural characterization. A conformational ensemble can be determined by selecting a weighted combination of representative structures from a given structure pool, yielding the best fit to experimental measurements. However, a raw numerical data fitting causes the over-interpretation of the experimental data, which would recover the structure ensemble of wrong conformations. In our study, we have employed the idea of hybrid models to simulate multi-domain proteins. The intra-domain structures are modeled with the structure-based potential and the inter-domain interactions are characterized with the physics-based energies. For ensemble construction with experimental data, structure energy is proposed as a physics-based regularization to minimize the problem of data over-fitting. The absence of energy regularization exposes ensemble construction to the noise from high-energy structures. We have demonstrated the importance of the physics-based regularization to interpret experimental data to avoid arbitrary and spurious conformational representations. We have implemented our computational model and ensemble optimization to two systems of multi-domain proteins on the coarse-grained and atomistic levels respectively. Firstly, we simulate the dengue virus 2 (DENV2) non-structural protein 5 (NS5) at the coarse-grained level and construct its conformational ensemble with experimental small-angle X-ray scattering (SAXS) data. The energy regularization assists to recover a conformational ensemble from the SAXS profile, which successfully reveals the domain-domain orientation and domain contacting interface of NS5. Moreover, we applied the computational modeling scheme to a di-domain complex of a poly(U)-binding protein (Pub1), followed by ensemble construction with its experimental paramagnetic relaxation enhancement (PRE) data that correspond to inter-atomic distance restraints. A conformational ensemble of Pub1 has been recovered from the low-energy structures and with the ensemble-size restraint. This ensemble is in good agreement with experimental PRE rates and also supported by the experimental data of chemical shift perturbations. ​Doctor of Philosophy (SBS)

It is an important way for segmentations of CT images to extract contours of objects slice-by-slice. For such a way, an important idea is analogy. That is to say, correct the contour in current slice (current contour) according to the contour in previous slice (previous contour). The key to properly correct the current contour is the ability to recognize occlusions (or say leaking parts) in the current contour. We present a curve-based curve parameterization method to recognize occlusions. The previous contour is evolved to the current contour using line projections. In the process of evolution, the parameterization is realized, which includes two types of information for every point in the evolved contour: the arc length parameter on the previous contour, and distance moved from the initial position to the present position. Using these two parameters, we are able to recognize occlusions in the current contour. Many experiments indicate that the method can recognize all of the occlusions in a given contour. Consequently, the method is robust and can be used as a part of an algorithm to automatically extract contours for CT images.