Enhancing Electrocatalytic Kinetics via Synergy of Co Nanoparticles and Co/Ni-N4-C- and N-Doped Porous Carbon.

Transition-metal species embedded in carbon have sparked intense interest in the fields of oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). However, improvement of the electrocatalytic kinetics remains a challenge caused by the synergistic assembly. Here, we propose a biochemical strategy to fabricate the Co nanoparticles (NPs) and Co/Ni-N4-C co-embedded N-doped porous carbon (CoNPs&Co/Ni-N4-C@NC) catalysts via constructing the zeolitic imidazolate framework (ZIF)@yeast precursor. The rich amino groups provide the possibility for the anchorage of Co2+/Ni2+ ions as well as the construction of Co/Ni-ZIF@yeast through the yeast cell biomineralization effect. The functional design induces the formation of CoNPs and Co/Ni-N4-C sites in N-doped carbon as well as regulates the porosity for exposing such sites. Synergy of CoNPs, Co/Ni-N4-C, and porous N-doped carbon delivered excellent electrocatalytic kinetics (the ORR Tafel slope of 76.3 mV dec-1 and the OER Tafel slope of 80.4 mV dec-1) and a high voltage of 1.15 V at 10 mA cm-2 for the discharge process in zinc air batteries. It provides an effective strategy to fabricate high-performance catalysts.

Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa).

Selection for more nutritious crop plants is an important goal of plant breeding to improve food quality and contribute to human health outcomes. While there are efforts to integrate genomic prediction to accelerate breeding progress, an ongoing challenge is identifying strategies to improve accuracy when predicting within biparental populations in breeding programs. We tested multiple genomic prediction methods for 12 seed fatty acid content traits in oat (Avena sativa L.), as unsaturated fatty acids are a key nutritional trait in oat. Using two well-characterized oat germplasm panels and other biparental families as training populations, we predicted family mean and individual values within families. Genomic prediction of family mean exceeded a mean accuracy of 0.40 and 0.80 using an unrelated and related germplasm panel, respectively, where the related germplasm panel outperformed prediction based on phenotypic means (0.54). Within family prediction accuracy was more variable: training on the related germplasm had higher accuracy than the unrelated panel (0.14-0.16 and 0.05-0.07, respectively), but variability between families was not easily predicted by parent relatedness, segregation of a locus detected by a genome-wide association study in the panel, or other characteristics. When using other families as training populations, prediction accuracies were comparable to the related germplasm panel (0.11-0.23), and families that had half-sib families in the training set had higher prediction accuracy than those that did not. Overall, this work provides an example of genomic prediction of family means and within biparental families for an important nutritional trait and suggests that using related germplasm panels as training populations can be effective.

Investigation on the Influence of Fiber Bundle Undulating Architecture on Tensile Behavior of Filament Wound Composite Laminates.

In filament wound composites, fiber bundles cross each other and form an undulating architecture, which may significantly affect the mechanical behavior of composites. In this study, the tensile mechanical behavior of filament wound laminates was studied experimentally and numerically, and the influences of the bundle thickness and winding angle on the mechanical behavior of the filament wound plates were also explored. In the experiments, tensile tests were carried out on filament wound plates and laminated plates. It was found that, compared to laminated plates, filament wound plates had lower stiffness, greater failure displacement, similar failure loads, and more obvious strain concentration areas. In numerical analysis, mesoscale finite element models, which take into account the fiber bundles' undulating morphology, were created. The numerical predictions correlated well with the experimental ones. Further numerical studies have shown that the stiffness reduction coefficient of filament wound plates with a winding angle of ±55° decreased from 0.78 to 0.74 as the bundle thickness increased from 0.4 mm to 0.8 mm. The stiffness reduction coefficients of filament wound plates with wound angles of ±15°, ±25°, and ±45° were 0.86, 0.83, and 0.8, respectively.

Effect of Flame Treatment on Bonding Performance of GF/EP Pultrusion Sheets Used for VARI Process.

This paper presents an easy and low-cost flame treatment method to improve the bonding performance of GF/EP (Glass Fiber-Reinforced Epoxy) pultrusion plates, which are using widely for large size wind blades. In order to explore the effect of flame treatment on the bonding performance of the precast GF/EP pultruded sheet vs. the infusion plate, the GF/EP pultruded sheets were treated with different flame treatment cycles and were embedded in the fiber fabrics during the vacuum-assisted resin infusion process (VARI). The bonding shear strengths were measured by tensile shear tests. It is found that after 1, 3, 5, and 7 flame treatments, the tensile shear strength between the GF/EP pultrusion plate and infusion plate increased by 8.0%, 13.3%, 22.44%, and -2.1%, respectively. This indicates that the maximum tensile shear strength can be obtained after five times of flame treatment. In addition, DCB and ENF tests were also adopted to characterize the fracture toughness of the bonding interface with the optimal flame treatment. It is found that the optimal treatment gives increments of 21.84% and 78.36% for G I C and G II C, respectively. Finally, the surficial topography of the flame-treated GF/EP pultruded sheets were characterized by optical microscopy, SEM, contact angle test, FTIR, and XPS. The results show that flame treatment plays an impact on the interfacial performance through the combination of physical meshing locking and chemical bonding mechanism. Proper flame treatment would remove the weak boundary layer and mold release agent on the surface of the GF/EP pultruded sheet, etch the bonding surface and improve the oxygen-containing polar groups, such as C-O and O-C=O, to improve the surface roughness and surface tension coefficient of pultruded sheet to enhance the bonding performance. Excessive flame treatment destroys the integrity of epoxy matrix on bonding surface which results into the exposure of the glass fiber, and the carbonization of release agent and resin on the surface loosen the surficial structure, which reduces the bonding properties.

Multi-Trait Genomic Prediction Improves Accuracy of Selection among Doubled Haploid Lines in Maize.

Recent advances in maize doubled haploid (DH) technology have enabled the development of large numbers of DH lines quickly and efficiently. However, testing all possible hybrid crosses among DH lines is a challenge. Phenotyping haploid progenitors created during the DH process could accelerate the selection of DH lines. Based on phenotypic and genotypic data of a DH population and its corresponding haploids, we compared phenotypes and estimated genetic correlations between the two populations, compared genomic prediction accuracy of multi-trait models against conventional univariate models within the DH population, and evaluated whether incorporating phenotypic data from haploid lines into a multi-trait model could better predict performance of DH lines. We found significant phenotypic differences between DH and haploid lines for nearly all traits; however, their genetic correlations between populations were moderate to strong. Furthermore, a multi-trait model taking into account genetic correlations between traits in the single-environment trial or genetic covariances in multi-environment trials can significantly increase genomic prediction accuracy. However, integrating information of haploid lines did not further improve our prediction. Our findings highlight the superiority of multi-trait models in predicting performance of DH lines in maize breeding, but do not support the routine phenotyping and selection on haploid progenitors of DH lines.

Modeling Progressive Damage and Failure of Single-Lap Thin-Ply-Laminated Composite-Bolted Joint Using LaRC Failure Criterion.

Thin-ply composite failure modes also significantly differ from conventional ply composite failure modes, with the final failure mechanism switching from irregular progressive failure to direct fracture characterized by a uniform fracture with the reduction of the ply thickness. When open holes and bolt joints are involved, thin-ply-laminated composites exhibit more complex stress states, damage evolution, and failure modes. Compared to the experimental study of thin-ply-laminated composite-bolted joints, there are few reports about numerical analysis. In order to understand the damage evolution and failure mechanism of thin-ply-laminated composites jointed by single-lap bolt, a progressive damage model based on three-dimensional (3D) LaRC failure criterion combined with cohesive element is constructed. Through an energy-based damage evolution method, this model can capture some significant mechanical characteristics in thin-ply-laminated structures, such as the in situ effect, delamination inhibition, and fiber compressive kinking failure. The comparisons between the numerical predictions and experimental observations are made to verify the accuracy of the proposed model. It is found that the predicted stress-displacement curves, failure modes, damage morphologies, etc., are consistent with the experimental results, indicating that the presented progressive damage analysis method displays excellent accuracy. The predicted stress at the onset of delamination is 50% higher than that of the conventional thick materials, which is also consistent with experimental results. Moreover, the numerical model provides evidence that the microstructure of thin-ply-laminated composite performs better in uniformity, which is more conducive to inhibiting the intra-layer damage and the expansion of delamination damage between layers. This study on the damage inhibition mechanism of thin-ply provides a potential analytical tool for evaluating damage tolerance and bearing capabilities in thin-ply-laminated composite-bolted joints.

Allele-specific Expression Reveals Multiple Paths to Highland Adaptation in Maize.

Maize is a staple food of smallholder farmers living in highland regions up to 4,000 m above sea level worldwide. Mexican and South American highlands are two major highland maize growing regions, and population genetic data suggest the maize's adaptation to these regions occurred largely independently, providing a case study for convergent evolution. To better understand the mechanistic basis of highland adaptation, we crossed maize landraces from 108 highland and lowland sites of Mexico and South America with the inbred line B73 to produce F1 hybrids and grew them in both highland and lowland sites in Mexico. We identified thousands of genes with divergent expression between highland and lowland populations. Hundreds of these genes show patterns of convergent evolution between Mexico and South America. To dissect the genetic architecture of the divergent gene expression, we developed a novel allele-specific expression analysis pipeline to detect genes with divergent functional cis-regulatory variation between highland and lowland populations. We identified hundreds of genes with divergent cis-regulation between highland and lowland landrace alleles, with 20 in common between regions, further suggesting convergence in the genes underlying highland adaptation. Further analyses suggest multiple mechanisms contribute to this convergence in gene regulation. Although the vast majority of evolutionary changes associated with highland adaptation were region specific, our findings highlight an important role for convergence at the gene expression and gene regulation levels as well.

Design of Boron Carbonitrides-Polyaniline (BCN-PANI) assembled supercapacitor with high voltage window.

Boron carbonitrides (BCN) have been widely concerned in the field of energy storage and conversion. However, the energy storage mechanism of electrical double-layer behavior and their stacked-layer structure severely limit the improvement of capacitance, thereby hindering their further development in energy storage. Therefore, an ultrasonic-ball milling method was first chosen to obtain BCN nanosheets, together with a feasible way of polyaniline (PANI) modification performed to boost the capacitive reaction of BCN nanosheets. For the first time, a BCN-PANI-based symmetric supercapacitor device can reach a high voltage window of 3.0 V when 1 M Et4N·BF4 was chosen as the electrolyte. The working voltage of 3.0 V is three times that of a device with pure PANI with the ultrahigh energy density of 67.1 W h kg-1, superior to most of the reported PANI-based devices. The eminent electrochemical performance provides a promising strategy to pave the way for configuring carbon-based multiple composite electrodes for other energy storage devices.

Generalizable approaches for genomic prediction of metabolites in plants.

Plant metabolites are important traits for plant breeders seeking to improve nutrition and agronomic performance yet integrating selection for metabolomic traits can be limited by phenotyping expense and degree of genetic characterization, especially of uncommon metabolites. As such, developing generalizable genomic selection methods based on biochemical pathway biology for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for >600 metabolites measured by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite genome-wide association study (mGWAS) and selected loci to use in multikernel models that encompassed metabolome-wide mGWAS results or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We ranked model performance by metabolite and found that metabolites with similar polarity had consistent rankings of models. Overall, testing biological rationales for developing kernels for genomic prediction across populations contributes to developing frameworks for plant breeding for metabolite traits.

Selection for seed size has uneven effects on specialized metabolite abundance in oat (Avena sativa L.).

Plant breeding strategies to optimize metabolite profiles are necessary to develop health-promoting food crops. In oats (Avena sativa L.), seed metabolites are of interest for their antioxidant properties, yet have not been a direct target of selection in breeding. In a diverse oat germplasm panel spanning a century of breeding, we investigated the degree of variation of these specialized metabolites and how it has been molded by selection for other traits, like yield components. We also ask if these patterns of variation persist in modern breeding pools. Integrating genomic, transcriptomic, metabolomic, and phenotypic analyses for three types of seed specialized metabolites-avenanthramides, avenacins, and avenacosides-we found reduced heritable genetic variation in modern germplasm compared with diverse germplasm, in part due to increased seed size associated with more intensive breeding. Specifically, we found that abundance of avenanthramides increases with seed size, but additional variation is attributable to expression of biosynthetic enzymes. In contrast, avenacoside abundance decreases with seed size and plant breeding intensity. In addition, these different specialized metabolites do not share large-effect loci. Overall, we show that increased seed size associated with intensive plant breeding has uneven effects on the oat seed metabolome, but variation also exists independently of seed size to use in plant breeding. This work broadly contributes to our understanding of how plant breeding has influenced plant traits and tradeoffs between traits (like growth and defense) and the genetic bases of these shifts.

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations.

KEY MESSAGE: Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices.

The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait.

Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.).

Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional "omics" data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.

Mechanical Properties of Thin-Ply Composites Based on Acoustic Emission Technology.

Compared with standard-ply composites, thin-ply composites exhibit a superior mechanical performance under various operating conditions due to their positive size effects. Thin-ply laminate failure modes, including matrix initial damage (MID), matrix failure (MF), and fiber failure (FF), have been distinguished through a systematic acoustic emission (AE) signals analysis combined with scanning electron microscopy (SEM). First, the characteristic frequencies of various failure modes are identified based on unidirectional laminates ([90] 68 and [0] 68). Then, according to the identified frequencies corresponding to distinctive damage modes, four lay-up sequences (02[[90m/0m]ns]02, m = 1, 2, 4, 8, n × m = 16) with a constant total thickness are designed, and the effects of the number of identical plies in the laminate thickness on the damage evolution characteristics and the damage process under uniaxial tension loads are dynamically monitored. The obtained results indicate that the characteristic frequency ranges for MID, MF, and FF are identified as 0-85 kHz, 165-260 kHz, and 261-304 kHz, respectively. The thickness of identical plies has a significant effect on onset damage. With the decrease of the number of identical plies (i.e., m in the stacking sequences), the thin-ply laminates exhibit the initiation of damage suppression effects and crack propagation resistance.

Heritable temporal gene expression patterns correlate with metabolomic seed content in developing hexaploid oat seed.

Oat ranks sixth in world cereal production and has a higher content of health-promoting compounds compared with other cereals. However, there is neither a robust oat reference genome nor transcriptome. Using deeply sequenced full-length mRNA libraries of oat cultivar Ogle-C, a de novo high-quality and comprehensive oat seed transcriptome was assembled. With this reference transcriptome and QuantSeq 3' mRNA sequencing, gene expression was quantified during seed development from 22 diverse lines across six time points. Transcript expression showed higher correlations between adjacent time points. Based on differentially expressed genes, we identified 22 major temporal co-expression (TCoE) patterns of gene expression and revealed enriched gene ontology biological processes. Within each TCoE set, highly correlated transcripts, putatively commonly affected by genetic background, were clustered and termed genetic co-expression (GCoE) sets. Seventeen of the 22 TCoE sets had GCoE sets with median heritabilities higher than 0.50, and these heritability estimates were much higher than that estimated from permutation analysis, with no divergence observed in cluster sizes between permutation and non-permutation analyses. Linear regression between 634 metabolites from mature seeds and the PC1 score of each of the GCoE sets showed significantly lower p-values than permutation analysis. Temporal expression patterns of oat avenanthramides and lipid biosynthetic genes were concordant with previous studies of avenanthramide biosynthetic enzyme activity and lipid accumulation. This study expands our understanding of physiological processes that occur during oat seed maturation and provides plant breeders the means to change oat seed composition through targeted manipulation of key pathways.

A Simplified Computational Strategy Focused on Resin Damage to Study Matrix Cracking of The Cross-Ply Laminates Under Uniaxial Tension Load.

Transverse cracking is probably the first and most dominant mode of damage in composite materials. In this paper, transverse cracking of cross-ply [02/90n]s (n = 2,3,4) laminates under uniaxial tension load was studied by means of experimental and numerical methods. In the numerical simulations, a simplified computational strategy only focusing on the damage of the resin was proposed and the mechanical response of the cracking cross-ply laminates was studied by finite element analysis of multi-scale representative volume elements (RVEs). In the RVEs, the longitudinal 0° plies were represented by macro-scale, homogeneous, orthotropic elastic solids while the 90° plies were modeled by the discrete fibers and the surrounding matrix resin in micro-scale. Based on researching the critical longitudinal mechanical strain ε x which initiates the cracks, the in-situ transverse ply strength and the stiffness degradation of the transverse plies, the simplified computational strategy proposed was proven correct. In addition, the crack initiation is sensitive to residual stress. Higher process-induced residual stress levels are dangerous to laminates, leading to early crack initiation.

Intrinsic Properties of Macroscopically Tuned Gallium Nitride Single-Crystalline Facets for Electrocatalytic Hydrogen Evolution.

The anisotropy of crystalline materials results in different physical and chemical properties on different facets, which warrants an in-depth investigation. Macroscopically facet-tuned, high-purity gallium nitride (GaN) single crystals were synthesised and machined, and the electrocatalytic hydrogen evolution reaction (HER) was used as the model reaction to show the differences among the facets. DFT calculations revealed that the Ga and N sites of GaN (100) had a considerably smaller ΔGH* value than those of the metal Ga site of GaN (001) or N site of GaN (00-1), thereby indicating that GaN (100) should be more catalytically active for the HER on account of its nonpolar facet. Subsequent experiments testified that the electrocatalytic performance of GaN (100) was considerably more efficient than that of other facets for both acidic and alkaline HERs. Moreover, the GaN crystal with a preferentially (100) active facet had an excellently durable alkaline electrocatalytic HER for more than 10 days. This work provides fundamental insights into the exploration of the intrinsic properties of materials and designing advanced materials for physicochemical applications.

High quality self-separated GaN crystal grown on a novel nanoporous template by HVPE.

In this study, a novel nanoporous template was obtained by a two-step etching process from MOCVD-GaN/Al2O3 (MGA) with electrochemical etching sequentially followed by chemical wet etching. The twice-etched MOCVD-GaN/Al2O3 (TEMGA) templates were utilized to grow GaN crystals by hydride vapor phase epitaxy (HVPE) method. The GaN crystals were separated spontaneously from the TEMGA template with the assistance of voids formed by the etched nanopores. Several techniques were utilized to characterize the quality of the free-standing GaN crystals obtained from the TEMGA template. Results showed that the quality of the as-obtained GaN crystals was improved obviously compared with those grown on the MGA. This convenient technique can be applied to grow high-quality free-standing GaN crystals.

Clinically Significant Differences in Ki-67 Proliferation Index Between Primary and Metastases in Resected Pancreatic Neuroendocrine Tumors.

OBJECTIVE: Pancreatic neuroendocrine tumors (NETs) (pNETs) have a varied prognosis according to their grade. The European Neuroendocrine Tumor Society grading system uses assessment of the proliferation index via Ki-67 immunohistochemistry to aid prognosis. There is evidence that the proliferation index can vary significantly within a single tumor, but it is not fully understood to what extent heterogeneity occurs between the primary and metastatic sites and how this may affect the grade. The aim of this study is to determine whether the grade assigned to a pNET varies depending on which site is selected for Ki-67 immunolabeling. METHODS: Patients were selected from our institution's NET database. Patients were included if they had a confirmed pNETs, had multiple resection specimens, and had consented to research being performed on their specimens. Ki-67 immunohistochemistry was performed on all resected specimens meeting the inclusion criteria. RESULTS: Pancreatic neuroendocrine tumors specimens resected from 16 patients were analyzed. There was no trend to higher Ki-67 in metastatic than primary disease. Ki-67 was on average 3% higher in liver metastases than lymph node metastases (P < 0.001). CONCLUSIONS: The grade of pNETs varies according to the tumor selected for Ki-67 immunolabeling. Useful information can be gained by performing Ki-67 PI on liver metastases.

One-step fabrication of porous GaN crystal membrane and its application in energy storage.

Single-crystal gallium nitride (GaN) membranes have great potential for a variety of applications. However, fabrication of single-crystalline GaN membranes remains a challenge owing to its chemical inertness and mechanical hardness. This study prepares large-area, free-standing, and single-crystalline porous GaN membranes using a one-step high-temperature annealing technique for the first time. A promising separation model is proposed through a comprehensive study that combines thermodynamic theories analysis and experiments. Porous GaN crystal membrane is processed into supercapacitors, which exhibit stable cycling life, high-rate capability, and ultrahigh power density, to complete proof-of-concept demonstration of new energy storage application. Our results contribute to the study of GaN crystal membranes into a new stage related to the elelctrochemical energy storage application.