Pressure-Dependent CO2 Electroreduction to Methane over Asymmetric Cu-N2 Single-Atom Sites.

Single-atom catalysts (SACs) with unitary active sites hold great promise for realizing high selectivity toward a single product in the CO2 electroreduction reaction (CO2RR). However, achieving high Faradaic efficiency (FE) of multielectron products like methane on SACs is still challenging. Herein, we report a pressure-regulating strategy that achieves 83.5 ± 4% FE for the CO2-to-CH4 conversion on the asymmetric Cu-N2 sites, representing one of the best CO2-to-CH4 performances. Elevated CO2 pressure was demonstrated as an efficient way to inhibit the hydrogen evolution reaction via promoting the competing adsorption of reactant CO2, regardless of the nature of the active sites. Meanwhile, the asymmetric Cu-N2 structure could endow the Cu sites with stronger electronic coupling with *CO, thus suppressing the desorption of *CO and facilitating the following hydrogenation of *CO to *CHO. This work provides a synergetic strategy of the pressure-induced reaction environment regulating and the electronic structure modulating for selective CO2RR toward targeted products.

Anodic Reconstructed p+-GaAs/a-InAsN for Stable and Efficient Photoelectrochemical Hydrogen Evolution.

The extremely poor solution stability and massive carrier recombination have seriously prevented III-V semiconductor nanomaterials from efficient and stable hydrogen production. In this work, an anodic reconstruction strategy based on group III-V active semiconductors is proposed for the first time, resulting in 19-times photo-gain. What matters most is that the device after anodic reconstruction shows very superior stability under the protracted photoelectrochemical (PEC) test over 8100 s, while the final photocurrent density does not decrease but rather increases by 63.15%. Using the experiment and DFT theoretical calculation, the anodic reconstruction mechanism is elucidated: through the oxidation of indium clusters and the migration of arsenic atoms, the reconstruction formed p+-GaAs/a-InAsN. The hole concentration of the former is increased by 10 times (5.64 × 1018 cm-1 increases up to 5.95 × 1019 cm-1) and the band gap of the latter one is reduced to a semi-metallic state, greatly strengthening the driving force of PEC water splitting. This work turns waste into treasure, transferring the solution instability into better efficiency.

Chemprop: A Machine Learning Package for Chemical Property Prediction.

Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by nonexperts. Among the current approaches, directed message-passing neural networks (D-MPNNs) have proven to perform well on a variety of property prediction tasks. The software package Chemprop implements the D-MPNN architecture and offers simple, easy, and fast access to machine-learned molecular properties. Compared to its initial version, we present a multitude of new Chemprop functionalities such as the support of multimolecule properties, reactions, atom/bond-level properties, and spectra. Further, we incorporate various uncertainty quantification and calibration methods along with related metrics as well as pretraining and transfer learning workflows, improved hyperparameter optimization, and other customization options concerning loss functions or atom/bond features. We benchmark D-MPNN models trained using Chemprop with the new reaction, atom-level, and spectra functionality on a variety of property prediction data sets, including MoleculeNet and SAMPL, and observe state-of-the-art performance on the prediction of water-octanol partition coefficients, reaction barrier heights, atomic partial charges, and absorption spectra. Chemprop enables out-of-the-box training of D-MPNN models for a variety of problem settings in fast, user-friendly, and open-source software.

Toward Accurate Quantum Mechanical Thermochemistry: (1) Extensible Implementation and Comparison of Bond Additivity Corrections and Isodesmic Reactions.

Obtaining accurate enthalpies of formation of chemical species, ΔHf, often requires empirical corrections that connect the results of quantum mechanical (QM) calculations with the experimental enthalpies of elements in their standard state. One approach is to use atomization energy corrections followed by bond additivity corrections (BACs), such as those defined by Petersson et al. or Anantharaman and Melius. Another approach is to utilize isodesmic reactions (IDRs) as shown by Buerger et al. We implement both approaches in Arkane, an open-source software that can calculate species thermochemistry using results from various QM software packages. In this work, we collect 421 reference species from the literature to derive ΔHf corrections and fit atomization energy corrections and BACs for 15 commonly used model chemistries. We find that both types of BACs yield similar accuracy, although Anantharaman- and Melius-type BACs appear to generalize better. Furthermore, BACs tend to achieve better accuracy than IDRs for commonly used model chemistries, and IDRs can be less robust because of the sensitivity to the chosen reference species and reactions. Overall, Anantharaman- and Melius-type BACs are our recommended approach for achieving accurate QM corrections for enthalpies.

Facet Engineering and Pore Design Boost Dynamic Fe Exchange in Oxygen Evolution Catalysis to Break the Activity-Stability Trade-Off.

The oxygen evolution reaction (OER) plays a vital role in renewable energy technologies, including in fuel cells, metal-air batteries, and water splitting; however, the currently available catalysts still suffer from unsatisfactory performance due to the sluggish OER kinetics. Herein, we developed a new catalyst with high efficiency in which the dynamic exchange mechanism of active Fe sites in the OER was regulated by crystal plane engineering and pore structure design. High-density nanoholes were created on cobalt hydroxide as the catalyst host, and then Fe species were filled inside the nanoholes. During the OER, the dynamic Fe was selectively and strongly adsorbed by the (101̅0) sites on the nanohole walls rather than the (0001) basal plane, and at the same time the space-confining effect of the nanohole slowed down the Fe diffusion from catalyst to electrolyte. As a result, a local high-flux Fe dynamic equilibrium inside the nanoholes for OER was achieved, as demonstrated by the Fe57 isotope labeled mass spectrometry, thereby delivering a high OER activity. The catalyst showed a remarkably low overpotential of 228 mV at a current density of 10 mA cm-2, which is among the best cobalt-based catalysts reported so far. This special protection strategy for Fe also greatly improved the catalytic stability, reducing the Fe leaching amount by 2 orders of magnitude compared with the pure Fe hydroxide catalyst and thus delivering a long-term stability of 130 h. An assembled Zn-air battery was stably cycled for 170 h with a low discharge/charge voltage difference of 0.72 V.

Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning.

Knowledge of critical properties, such as critical temperature, pressure, density, as well as acentric factor, is essential to calculate thermo-physical properties of chemical compounds. Experiments to determine critical properties and acentric factors are expensive and time intensive; therefore, we developed a machine learning (ML) model that can predict these molecular properties given the SMILES representation of a chemical species. We explored directed message passing neural network (D-MPNN) and graph attention network as ML architecture choices. Additionally, we investigated featurization with additional atomic and molecular features, multitask training, and pretraining using estimated data to optimize model performance. Our final model utilizes a D-MPNN layer to learn the molecular representation and is supplemented by Abraham parameters. A multitask training scheme was used to train a single model to predict all the critical properties and acentric factors along with boiling point, melting point, enthalpy of vaporization, and enthalpy of fusion. The model was evaluated on both random and scaffold splits where it shows state-of-the-art accuracies. The extensive data set of critical properties and acentric factors contains 1144 chemical compounds and is made available in the public domain together with the source code that can be used for further exploration.

Tormentic acid, a triterpenoid isolated from the fruits of Chaenomeles speciose, protected indomethacin-induced gastric mucosal lesion via modulating miR-139 and the CXCR4/CXCL12/PLC/PKC/Rho a/MLC pathway.

CONTEXT: Tormentic acid (TA), an effective triterpenoid isolated from Chaenomeles speciosa (Sweet) Nakai (Rosaceae) fruits, exerts an effective treatment for gastric damage. OBJECTIVE: To investigate the gastroprotective effect of TA on indomethacin (IND) damaged GES-1 cells and rats, and explore potential mechanisms. MATERIALS AND METHODS: TA concentrations of 1.563-25 µM were used. Cell proliferation, apoptosis and migration were performed using MTT, colony formation, wound healing, migration, Hoechst staining assays. SD rats were divided into control, IND, TA (1, 2 and 4 mg/kg) + IND groups, once a day for 21 continuous days. Twenty-four hours after the last administration, all groups except the control group were given IND (100 mg/kg) by gavage. Gastric juice parameters, gastric ulcer, gastric blood flow (GBF), blood biochemical parameters and cytokine analysis and gastric mucosal histopathology were detected for 2 h and 6 h after IND oral administration. The mRNA and protein expression of miR-139 and the CXCR4/CXCL12/PLC/PKC/Rho A/MLC pathway were analyzed in the IND-damaged GES-1 cells and gastric tissue of rats. RESULTS: TA might ameliorate the gastric mucosal injury by accelerating the IND-damaged GES-1 cell proliferation and migration, ameliorating GBF, ulcer area and pathologic changes, the redox system and cytokine levels, the gastric juice parameters, elevating the gastric pH in IND damaged rats; suppressed miR-139 mRNA expression, elevated CXCR4 and CXCL12 mRNA and protein expression, p-PLC, p-PKC, Rho A, MLCK and p-MLC protein expression. DISCUSSION AND CONCLUSIONS: TA may have potential use as a clinical drug candidate for gastric mucosal lesion treatment.

S and O Co-Coordinated Mo Single Sites in Hierarchically Porous Tubes from Sulfur-Enamine Copolymerization for Oxygen Reduction and Evolution.

The highly efficient bifunctional catalyst for the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER) is the key to achieving high-performance rechargeable Zn-air batteries. Non-precious-metal single-atom catalysts (SACs) have attracted intense interest due to their low cost and very high metal atomic utilization; however, high-activity bifunctional non-precious-metal SACs are still rare. Herein, we develop a new nanospace-confined sulfur-enamine copolymerization strategy to prepare a new type of bifunctional Mo SACs with O/S co-coordination (Mo-O2S2-C) supported on the multilayered, hierarchically porous hollow tubes. The as-prepared catalyst can not only expose more active sites and facilitate mass transfer due to their combined micropores, mesopores, and macropores but also have the S/O co-coordination structure for optimizing the adsorption energies of the ORR intermediates. Its ORR activity is among the highest, and it shows a low overpotential of 324 mV for the OER at 10 mA cm-2 in all of the reported Mo-based catalysts. When assembled in a Zn-air battery, it exhibits a high maximal power density of 197.3 mW cm-2 and a long service life of 50 hours, superior to those of Zn-air batteries using commercial Pt/C+IrO2.

Coronavirus Porcine Epidemic Diarrhea Virus Nucleocapsid Protein Interacts with p53 To Induce Cell Cycle Arrest in S-Phase and Promotes Viral Replication.

Subversion of the host cell cycle to facilitate viral replication is a common feature of coronavirus infections. Coronavirus nucleocapsid (N) protein can modulate the host cell cycle, but the mechanistic details remain largely unknown. Here, we investigated the effects of manipulation of porcine epidemic diarrhea virus (PEDV) N protein on the cell cycle and the influence on viral replication. Results indicated that PEDV N induced Vero E6 cell cycle arrest at S-phase, which promoted viral replication (P < 0.05). S-phase arrest was dependent on the N protein nuclear localization signal S71NWHFYYLGTGPHADLRYRT90 and the interaction between N protein and p53. In the nucleus, the binding of N protein to p53 maintained consistently high-level expression of p53, which activated the p53-DREAM pathway. The key domain of the N protein interacting with p53 was revealed to be S171RGNSQNRGNNQGRGASQNRGGNN194 (NS171-N194), in which G183RG185 are core residues. NS171-N194 and G183RG185 were essential for N-induced S-phase arrest. Moreover, small molecular drugs targeting the NS171-N194 domain of the PEDV N protein were screened through molecular docking. Hyperoside could antagonize N protein-induced S-phase arrest by interfering with interaction between N protein and p53 and inhibit viral replication (P < 0.05). The above-described experiments were also validated in porcine intestinal cells, and data were in line with results in Vero E6 cells. Therefore, these results reveal the PEDV N protein interacts with p53 to activate the p53-DREAM pathway, and subsequently induces S-phase arrest to create a favorable environment for virus replication. These findings provide new insight into the PEDV-host interaction and the design of novel antiviral strategies against PEDV. IMPORTANCE Many viruses subvert the host cell cycle to create a cellular environment that promotes viral growth. PEDV, an emerging and reemerging coronavirus, has led to substantial economic loss in the global swine industry. Our study is the first to demonstrate that PEDV N-induced cell cycle arrest during the S-phase promotes viral replication. We identified a novel mechanism of PEDV N-induced S-phase arrest, where the binding of PEDV N protein to p53 maintains consistently high levels of p53 expression in the nucleus to mediate S-phase arrest by activating the p53-DREAM pathway. Furthermore, a small molecular compound, hyperoside, targeted the PEDV N protein, interfering with the interaction between the N protein and p53 and, importantly, inhibited PEDV replication by antagonizing cell cycle arrest. This study reveals a new mechanism of PEDV-host interaction and also provides a novel antiviral strategy for PEDV. These data provide a foundation for further research into coronavirus-host interactions.

Kinetic Modeling of API Oxidation: (2) Imipramine Stress Testing.

Gauging the chemical stability of active pharmaceutical ingredients (APIs) is critical at various stages of pharmaceutical development to identify potential risks from drug degradation and ensure the quality and safety of the drug product. Stress testing has been the major experimental method to study API stability, but this analytical approach is time-consuming, resource-intensive, and limited by API availability, especially during the early stages of drug development. Novel computational chemistry methods may assist in screening for API chemical stability prior to synthesis and augment contemporary API stress testing studies, with the potential to significantly accelerate drug development and reduce costs. In this work, we leverage quantum chemical calculations and automated reaction mechanism generation to provide new insights into API degradation studies. In the continuation of part one in this series of studies [Grinberg Dana et al., Mol. Pharm. 2021 18 (8), 3037-3049], we have generated the first ab initio predictive chemical kinetic model of free-radical oxidative degradation for API stress testing. We focused on imipramine oxidation in an azobis(isobutyronitrile) (AIBN)/H2O/CH3OH solution and compared the model's predictions with concurrent experimental observations. We analytically determined iminodibenzyl and desimipramine as imipramine's two major degradation products under industry-standard AIBN stress testing conditions, and our ab initio kinetic model successfully identified both of them in its prediction for the top three degradation products. This work shows the potential and utility of predictive chemical kinetic modeling and quantum chemical computations to elucidate API chemical stability issues. Further, we envision an automated digital workflow that integrates first-principle models with data-driven methods that, when actively and iteratively combined with high-throughput experiments, can substantially accelerate and transform future API chemical stability studies.

Diversification of the phytophagous lineages of true bugs (Insecta: Hemiptera: Heteroptera) shortly after that of the flowering plants.

More than 95% of phytophagous true bug (Hemiptera: Heteroptera) species belong to four superfamilies: Miroidea (Cimicomorpha), Pentatomoidea, Coreoidea, and Lygaeoidea (all Pentatomomorpha). These iconic groups of highly diverse, overwhelmingly phytophagous insects include several economically prominent agricultural and silvicultural pest species, though their evolutionary history has not yet been well resolved. In particular, superfamily- and family-level phylogenetic relationships of these four lineages have remained controversial, and the divergence times of some crucial nodes for phytophagous true bugs have hitherto been little known, which hampers a better understanding of the evolutionary processes and patterns of phytophagous insects. In the present study, we used 150 species and concatenated nuclear and mitochondrial protein-coding genes and rRNA genes to infer the phylogenetic relationships within the Terheteroptera (Cimicomorpha + Pentatomomorpha) and estimated their divergence times. Our results support the monophyly of Cimicomorpha, Pentatomomorpha, Miroidea, Pentatomoidea, Pyrrhocoroidea, Coreoidea, and Lygaeoidea. The phylogenetic relationships across phytophagous lineages are largely congruent at deep nodes across the analyses based on different datasets and tree-reconstructing methods with just a few exceptions. Estimated divergence times and ancestral state reconstructions for feeding habit indicate that phytophagous true bugs explosively radiated in the Early Cretaceous-shortly after the angiosperm radiation-with the subsequent diversification of the most speciose clades (Mirinae, Pentatomidae, Coreinae, and Rhyparochromidae) in the Late Cretaceous.

Enhancement of the NIR Emission of Cr3+-Yb3+ Co-doped La3GaGe5O16 Phosphors by Doping Nd3+ Ions via Efficient Energy Transfer for NIR Spectroscopy Regulation.

The efficient energy transfer in La3GaGe5O16:Cr3+, Yb3+/Nd3+ and La3GaGe5O16:Cr3+, Yb3+, Nd3+ was investigated in detail. In this phosphor, Cr3+ acts as the energy absorber (250-700 nm) to sensitize Yb3+/Nd3+ in La3GaGe5O16. Under excitation at 418 nm, La3GaGe5O16:Cr3+, Yb3+ phosphors exhibited a broad emission band in the near-infrared (NIR) region located at 976 nm (La3GaGe5O16:Cr3+, Nd3+ at 1056 nm), which was attributed to the 2F5/2-2F7/2 transition of the Yb3+ ions (2F3/2 → 4I11/2 transition of Nd3+). Moreover, a Nd3+ ion was introduced into La3GaGe5O16:Cr3+, Yb3+. The analysis of excitation spectra and decay time proves that Nd3+ acts as a bridging ion in the system. This can be used as a new strategy to enhance the energy transfer in Cr3+, Yb3+ co-doped phosphors, and these phosphors have potential applications in NIR spectroscopy regulation.

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy.

We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed group contribution method uses atom-centered functional groups with corrections for ring and polycyclic strain while the machine learning models adopt a directed message passing neural network. The solute parameters predicted from SoluteGC and SoluteML are used to calculate solvation energy and enthalpy via linear free energy relationships. Extensive data sets containing 8366 solute parameters, 20,253 solvation free energies, and 6322 solvation enthalpies are compiled in this work to train the models. The three models are each evaluated on the same test sets using both random and substructure-based solute splits for solvation energy and enthalpy predictions. The results show that the DirectML model is superior to the SoluteML and SoluteGC models for both predictions and can provide accuracy comparable to that of advanced quantum chemistry methods. Yet, even though the DirectML model performs better in general, all three models are useful for various purposes. Uncertain predicted values can be identified by comparing the three models, and when the 3 models are combined together, they can provide even more accurate predictions than any one of them individually. Finally, we present our compiled solute parameter, solvation energy, and solvation enthalpy databases (SoluteDB, dGsolvDBx, dHsolvDB) and provide public access to our final prediction models through a simple web-based tool, software packages, and source code.

Kinetic Modeling of API Oxidation: (1) The AIBN/H2O/CH3OH Radical "Soup".

Stress testing of active pharmaceutical ingredients (API) is an important tool used to gauge chemical stability and identify potential degradation products. While different flavors of API stress testing systems have been used in experimental investigations for decades, the detailed kinetics of such systems as well as the chemical composition of prominent reactive species, specifically reactive oxygen species, are unknown. As a first step toward understanding and modeling API oxidation in stress testing, we investigated a typical radical "soup" solution an API is subject to during stress testing. Here we applied ab initio electronic structure calculations to automatically generate and refine a detailed chemical kinetics model, taking a fresh look at API oxidation. We generated a detailed kinetic model for a representative azobis(isobutyronitrile) (AIBN)/H2O/CH3OH stress-testing system with a varied cosolvent ratio (50%/50%-99.5%/0.5% vol water/methanol) for 5.0 mM AIBN and representative pH values of 4-10 at 40 °C that was stirred and open to the atmosphere. At acidic conditions, hydroxymethyl alkoxyl is the dominant alkoxyl radical, and at basic conditions, for most studied initial methanol concentrations, cyanoisopropyl alkoxyl becomes the dominant alkoxyl radical, albeit at an overall lower concentration. At acidic conditions, the levels of cyanoisopropyl peroxyl, hydroxymethyl peroxyl, and hydroperoxyl radicals are relatively high and comparable, while, at both neutral and basic pH conditions, superoxide becomes the prominent radical in the system. The present work reveals the prominent species in a common model API stress testing system at various cosolvent and pH conditions, sets the stage for an in-depth quantitative API kinetic study, and demonstrates the usage of novel software tools for automated chemical kinetic model generation and ab initio refinement.

When did the ancestor of true bugs become stinky? Disentangling the phylogenomics of Hemiptera-Heteroptera.

The phylogeny of true bugs (Hemiptera: Heteroptera), one of the most diverse insect groups in terms of morphology and ecology, has been the focus of attention for decades with respect to several deep nodes between the suborders of Hemiptera and the infraorders of Heteroptera. Here, we assembled a phylogenomic data set of 53 taxa and 3102 orthologous genes to investigate the phylogeny of Hemiptera-Heteroptera, and both concatenation and coalescent methods were used. A binode-control approach for data filtering was introduced to reduce the incongruence between different genes, which can improve the performance of phylogenetic reconstruction. Both hypotheses (Coleorrhyncha + Heteroptera) and (Coleorrhyncha + Auchenorrhyncha) received support from various analyses, in which the former is more consistent with the morphological evidence. Based on a divergence time estimation performed on genes with a strong phylogenetic signal, the origin of true bugs was dated to 290-268 Ma in the Permian, the time in Earth's history with the highest concentration of atmospheric oxygen. During this time interval, at least 1007 apomorphic amino acids were retained in the common ancestor of the extant true bugs. These molecular apomorphies are located in 553 orthologous genes, which suggests the common ancestor of the extant true bugs may have experienced large-scale evolution at the genome level.

Phylogeny and the colourful history of jewel bugs (Insecta: Hemiptera: Scutelleridae).

Members of the family Scutelleridae (Heteroptera: Pentatomomorpha: Pentatomoidea) are also called shield bugs because of the greatly enlarged scutellum, or jewel bugs because of the brilliant colours of many species. All scutellerids are phytophagous, feeding on various parts of their host plants. Due to lack of obvious synapomorphies and the failure to apply rigorous phylogenetic methods, the higher classification of Scutelleridae has been disputed for more than 150 years. Here we reconstructed a phylogeny of Scutelleridae based on complete sequences of 18S and 28S nuclear rDNAs and all 13 protein-coding genes of the mitochondrial genome, with the sampled taxa covering all of the currently recognized subfamilies. The monophyly of Scutelleridae was confirmed by the congruence of the results of analyses conducted using Bayesian inference, maximum likelihood and maximum parsimony. The phylogenetic relationships among subfamilies were well resolved for the first time. Furthermore, time-divergence studies estimated that the time of origin of Scutelleridae was in the Early Cretaceous (142.1-122.8 Ma), after the origin of the angiosperms. The diversification between the extant subfamilies of Scutelleridae and within the subfamilies occurred from the late Palaeocene to the late Miocene, simultaneously with the rise of the major groups of angiosperms and other phytophagous insects.

Identifying Differential Gene Expression in Wing Polymorphism of Adult Males of the Largest Water Strider: De novo Transcriptome Assembly for Gigantometra gigas (Hemiptera: Gerridae).

Wing polymorphism is common in a wide variety of insect species. However, few studies have reported on adaptations in the wing polymorphism of insects at molecular level, in particular for males. Thus, the adaptive mechanisms need to be explored. The remarkable variability in wing morphs of insects is well represented in the water striders (Hemiptera: Gerridae). Within this family, Gigantometra gigas (China, 1925), the largest water strider known worldwide, displays macropterous and apterous males. In the present study, we used de novo transcriptome assembly to obtain gene expression information and compared body and leg-component lengths of adult males in different wing morphs. The analyses in both gene expression and phenotype levels were used for exploring the adaptive mechanism in wing polymorphism of G. gigas. After checking, a series of highly expressed structural genes were found in macropterous morphs, which were related to the maintenance of flight muscles and the enhancement of flight capacity, whereas in the apterous morphs, the imaginal morphogenesis protein-Late 2 (Imp-L2), which might inhibit wing development and increase the body size of insects, was still highly expressed in the adult stage. Moreover, body and leg-component lengths were significantly larger in apterous than in macropterous morphs. The larger size of the apterous morphs and the differences in highly expressed genes between the two wing morphs consistently demonstrate the adaptive significance of wing polymorphism in G. gigas. These results shed light on the future loss-of-function research of wing polymorphism in G. gigas.

Enhanced light extraction from free-standing InGaN/GaN light emitters using bio-inspired backside surface structuring.

Light extraction from InGaN/GaN-based multiple-quantum-well (MQW) light emitters is enhanced using a simple, scalable, and reproducible method to create hexagonally close-packed conical nano- and micro-scale features on the backside outcoupling surface. Colloidal lithography via Langmuir-Blodgett dip-coating using silica masks (d = 170-2530 nm) and Cl2/N2-based plasma etching produced features with aspect ratios of 3:1 on devices grown on semipolar GaN substrates. InGaN/GaN MQW structures were optically pumped at 266 nm and light extraction enhancement was quantified using angle-resolved photoluminescence. A 4.8-fold overall enhancement in light extraction (9-fold at normal incidence) relative to a flat outcoupling surface was achieved using a feature pitch of 2530 nm. This performance is on par with current photoelectrochemical (PEC) nitrogen-face roughening methods, which positions the technique as a strong alternative for backside structuring of c-plane devices. Also, because colloidal lithography functions independently of GaN crystal orientation, it is applicable to semipolar and nonpolar GaN devices, for which PEC roughening is ineffective.

GPU MrBayes V3.1: MrBayes on Graphics Processing Units for Protein Sequence Data.

We present a modified GPU (graphics processing unit) version of MrBayes, called ta(MC)(3) (GPU MrBayes V3.1), for Bayesian phylogenetic inference on protein data sets. Our main contributions are 1) utilizing 64-bit variables, thereby enabling ta(MC)(3) to process larger data sets than MrBayes; and 2) to use Kahan summation to improve accuracy, convergence rates, and consequently runtime. Versus the current fastest software, we achieve a speedup of up to around 2.5 (and up to around 90 vs. serial MrBayes), and more on multi-GPU hardware. GPU MrBayes V3.1 is available from http://sourceforge.net/projects/mrbayes-gpu/.

One-pot diagnostic methods based on CRISPR/Cas and Argonaute nucleases: strategies and perspectives.

CRISPR/Cas and Argonaute (Ago) proteins, which target specific nucleic acid sequences, can be applied as diagnostic tools. Despite high specificity and efficiency, achieving sensitive detection often necessitates a preamplification step that involves opening the lid and multistep operation, which may elevate the risk of contamination and prove inadequate for point-of-care testing. Hence, various one-pot detection strategies have been developed that enable preamplification and sensing in a single operation. We outline the challenges of one-pot detection with Cas and Ago proteins, present several main implementation strategies, and discuss future prospects. This review offers comprehensive insights into this vital field and explores potential improvements to detection methods that will be beneficial for human health.