Synergistic Engineering of CoO/MnO Heterostructures Integrated with Nitrogen-Doped Carbon Nanofibers for Lithium-Ion Batteries.

The integration of heterostructures within electrode materials is pivotal for enhancing electron and Li-ion diffusion kinetics. In this study, we synthesized CoO/MnO heterostructures to enhance the electrochemical performance of MnO using a straightforward electrostatic spinning technique followed by a meticulously controlled carbonization process, which results in embedding heterostructured CoO/MnO nanoparticles within porous nitrogen-doped carbon nanofibers (CoO/MnO/NC). As confirmed by density functional theory calculations and experimental results, CoO/MnO heterostructures play a significant role in promoting Li+ ion and charge transfer, improving electronic conductivity, and reducing the adsorption energy. The accelerated electron and Li-ion diffusion kinetics, coupled with the porous nitrogen-doped carbon nanofiber structure, contribute to the exceptional electrochemical performance of the CoO/MnO/NC electrode. Specifically, the as-prepared CoO/MnO/NC exhibits a high reversible specific capacity of 936 mA h g-1 at 0.1 A g-1 after 200 cycles and an excellent high-rate capacity of 560 mA h g-1 at 5 A g-1, positioning it as a competitive anode material for lithium-ion batteries. This study underscores the critical role of electronic and Li-ion regulation facilitated by heterostructures, offering a promising pathway for designing transition metal oxide-based anode materials with high performances for lithium-ion batteries.

Automated chemoenzymatic modular synthesis of human milk oligosaccharides on a digital microfluidic platform.

Glycans, along with proteins, nucleic acids, and lipids, constitute the four fundamental classes of biomacromolecules found in living organisms. Generally, glycans are attached to proteins or lipids to form glycoconjugates that perform critical roles in various biological processes. Automatic synthesis of glycans is essential for investigation into structure-function relationships of glycans. In this study, we presented a method that integrated magnetic bead-based manipulation and modular chemoenzymatic synthesis of human milk oligosaccharides (HMOs), on a DMF (Digital Microfluidics) platform. On the DMF platform, enzymatic modular reactions were conducted in solution, and purification of products or intermediates was achieved by using DEAE magnetic beads, circumventing the intricate steps required for traditional solid-phase synthesis. With this approach, we have successfully synthesized eleven HMOs with highest yields of up to >90% on the DMF platform. This study would not only lay the foundation for OPME synthesis of glycans on the DMF platform, but also set the stage for developing automated enzymatic glycan synthesizers based on the DMF platform.

KSNP: a fast de Bruijn graph-based haplotyping tool approaching data-in time cost.

Long reads that cover more variants per read raise opportunities for accurate haplotype construction, whereas the genotype errors of single nucleotide polymorphisms pose great computational challenges for haplotyping tools. Here we introduce KSNP, an efficient haplotype construction tool based on the de Bruijn graph (DBG). KSNP leverages the ability of DBG in handling high-throughput erroneous reads to tackle the challenges. Compared to other notable tools in this field, KSNP achieves at least 5-fold speedup while producing comparable haplotype results. The time required for assembling human haplotypes is reduced to nearly the data-in time.

Vortexing-generated high throughput single-cell droplets for facile analysis of multiplexed microRNA dynamic secretion.

Discriminating secretory phenotypes provides a direct, intact, and dynamic way to evaluate the heterogeneity in cell states and activation, which is significant for dissecting non-genetic heterogeneity for human health studies and disease diagnostics. In particular, secreted microRNAs, soluble signaling molecules released by various cells, are increasingly recognized as a critical mediator for cell-cell communication and the circulating biomarkers for disease diagnosis. However, single-cell analysis of secreted miRNAs is still lacking due to the limited available tools. Herein, we realized three-plexed miRNA secretion analysis over four time points from single cells encapsulated in picoliter droplets with extreme simplicity, coupling vortexing-generated single-cell droplets with multiplexed molecular beacons. Notably, our platform only requires pipetting and vortexing steps to finish the assay setup within 5 min with minimal training, and customized software was developed for automatic data quantification. Applying the platform to human cancer cell lines and primary cells revealed previously undifferentiated heterogeneity and paracrine signaling underlying miRNA secretion. This platform can be used to dissect secretion heterogeneity and cell-cell interactions and has the potential to become a widely used tool in biomedical research.

High-resolution performance of pillar[6]arene functionalized with imidazolium ionic liquids for gas chromatography.

Pillar[n]arenes (P[n]A, n = 5-10) have attracted much attention because of their highly symmetric pillar-shaped architecture with π-electron rich cavity. Nevertheless, the use of ionic liquid functionalized P[n]A in chromatography has not been reported up to data. This work reports the investigation of the imidazolium ionic liquids functionalized pillar[6]arene (P6A-C10-IM-C8[NTf2]) as the stationary phase for gas chromatography (GC). The statically coated P6A-C10-IM-C8[NTf2] column (0.25 mm i.d.) showed moderate polarity and high column efficiency of 4733 plates/m determined by n-dodecane at 120 °C (k = 2.29). Owing to its unique amphiphilic conformation, the P6A-C10-IM-C8[NTf2] showed good column inertness and resolving capability for a wide range of analytes and isomers. Particularly, the P6A-C10-IM-C8[NTf2] column exhibited distinctly advantageous performance for the challenging isomers of halogenated benzenes, benzaldehydes, phenols and anilines over the common commercial columns, namely 5% phenyl methyl polysiloxane (HP-5) and 35% phenyl methyl polysiloxane (HP-35). In addition, it exhibited good column repeatability and reproducibility with RSD values on the retention times less than 0.05% for run-to-run, 0.38% for day-to-day and 2.94% for column-to-column, respectively. This work demonstrates the promising future of ionic liquid P[n]A stationary phases for chromatographic separations.

Image Deblurring by Exploring In-Depth Properties of Transformer.

Image deblurring continues to achieve impressive performance with the development of generative models. Nonetheless, there still remains a displeasing problem if one wants to improve perceptual quality and quantitative scores of recovered image at the same time. In this study, drawing inspiration from the research of transformer properties, we introduce the pretrained transformers to address this problem. In particular, we leverage deep features extracted from a pretrained vision transformer (ViT) to encourage recovered images to be sharp without sacrificing the performance measured by the quantitative metrics. The pretrained transformer can capture the global topological relations (i.e., self-similarity) of image, and we observe that the captured topological relationships about the sharp image will change when blur occurs. By comparing the transformer features between recovered image and target one, the pretrained transformer provides high-resolution blur-sensitive semantic information, which is critical in measuring the sharpness of the deblurred image. On the basis of the advantages, we present two types of novel perceptual losses to guide image deblurring. One regards the features as vectors and computes the discrepancy between representations extracted from recovered image and target one in Euclidean space. The other type considers the features extracted from an image as a distribution and compares the distribution discrepancy between recovered image and target one. We demonstrate the effectiveness of transformer properties in improving the perceptual quality while not sacrificing the quantitative scores peak signal-to-noise ratio (PSNR) over the most competitive models, such as Uformer, Restormer, and NAFNet, on defocus deblurring and motion deblurring tasks. The code is available at https://github. com/erfect2020/TransformerPerceptualLoss.

A compressive seeding algorithm in conjunction with reordering-based compression.

MOTIVATION: Seeding is a rate-limiting stage in sequence alignment for next-generation sequencing reads. The existing optimization algorithms typically utilize hardware and machine-learning techniques to accelerate seeding. However, an efficient solution provided by professional next-generation sequencing compressors has been largely overlooked by far. In addition to achieving remarkable compression ratios by reordering reads, these compressors provide valuable insights for downstream alignment that reveal the repetitive computations accounting for more than 50% of seeding procedure in commonly used short read aligner BWA-MEM at typical sequencing coverage. Nevertheless, the exploited redundancy information is not fully realized or utilized. RESULTS: In this study, we present a compressive seeding algorithm, named CompSeed, to fill the gap. CompSeed, in collaboration with the existing reordering-based compression tools, finishes the BWA-MEM seeding process in about half the time by caching all intermediate seeding results in compact trie structures to directly answer repetitive inquiries that frequently cause random memory accesses. Furthermore, CompSeed demonstrates better performance as sequencing coverage increases, as it focuses solely on the small informative portion of sequencing reads after compression. The innovative strategy highlights the promising potential of integrating sequence compression and alignment to tackle the ever-growing volume of sequencing data. AVAILABILITY AND IMPLEMENTATION: CompSeed is available at https://github.com/i-xiaohu/CompSeed.

The impact of support electronegativity on the electrochemical properties of platinum.

Modulating the electronic structure of platinum (Pt) through a support is an important strategy for enhancing its electrocatalytic properties. In this work, to explore the impact of support electronegativity on Pt's catalytic activity for hydrogen evolution, we chose diverse metals with varying electronegativities that are stable in acidic solutions, such as titanium (Ti), molybdenum (Mo), and tungsten (W), as supports. Ti is the optimal support according to density functional theory (DFT) calculations. As expected, the Pt@Ti catalyst demonstrated remarkable efficiency in the hydrogen evolution reaction (HER), displaying a minimal overpotential of 13 mV at -10 mA cm-2, a Tafel slope of 34.5 mV dec-1, and sustained durability over 110 h in a 0.5 M H2SO4 solution. To unravel the metal-support interaction (MSI) between Pt and Ti, a comprehensive exploration encompassing both experimental investigations and DFT calculations was undertaken. The results elucidate that the outstanding HER performance of Pt@Ti stems from robust synergies forged between Pt and Ti atoms within the Ti support. This work not only furnishes a technique for producing electrocatalysts with superior efficiency and stability but also streamlines the process of choosing the most appropriate metal support. Moreover, it enhances comprehension of the interaction between Pt and the metal support.

Deep Lossy Plus Residual Coding for Lossless and Near-Lossless Image Compression.

Lossless and near-lossless image compression is of paramount importance to professional users in many technical fields, such as medicine, remote sensing, precision engineering and scientific research. But despite rapidly growing research interests in learning-based image compression, no published method offers both lossless and near-lossless modes. In this paper, we propose a unified and powerful deep lossy plus residual (DLPR) coding framework for both lossless and near-lossless image compression. In the lossless mode, the DLPR coding system first performs lossy compression and then lossless coding of residuals. We solve the joint lossy and residual compression problem in the approach of VAEs, and add autoregressive context modeling of the residuals to enhance lossless compression performance. In the near-lossless mode, we quantize the original residuals to satisfy a given ℓ∞ error bound, and propose a scalable near-lossless compression scheme that works for variable ℓ∞ bounds instead of training multiple networks. To expedite the DLPR coding, we increase the degree of algorithm parallelization by a novel design of coding context, and accelerate the entropy coding with adaptive residual interval. Experimental results demonstrate that the DLPR coding system achieves both the state-of-the-art lossless and near-lossless image compression performance with competitive coding speed.

Ester-functionalized pillar[6]arene as the gas chromatographic stationary phase with high-resolution performance towards the challenging isomers of xylenes, diethylbenzenes, and ethyltoluenes.

This work presents the first example of the utilization of polar ester group functionalized pillar[6]arene (P6A-C10-OAc) as a stationary phase for capillary gas chromatographic (GC) separations. The statically coated P6A-C10-OAc column showed a high column efficiency of 5393 plates/m and moderate polar nature. Its resolving capability and retention behaviors were investigated for a mixture of 20 analytes and more than a dozen isomers from apolar to polar in nature. As evidenced, the P6A-C10-OAc column achieved high-resolution separations of all the analytes and good inertness. Importantly, it exhibited distinctly advantageous performance for high resolution of the challenging isomers of xylenes, diethylbenzenes, ethyltoluenes, and halobenzenes over the commercial HP-5 (5% phenyl dimethyl polysiloxane), HP-35 (25% phenyl dimethyl polysiloxane), and PEG-20M (polyethylene glycol) columns.

Multipurpose new gas chromatography column based on pillararenes functionalized with imidazolium ionic liquids.

BACKGROUND: Gas chromatography is worldwide recognized as one of the most important analytical techniques, due to its high versatility and reliability. The heart of a gas chromatograph is the column, that allows analyte peak separations and, consequently, accurate qualitative and qualitative analyses. New and more efficient columns are always requested to satisfy new and challenging analytical needs. RESULTS: In this work, imidazolium ionic liquids functionalized pillar [5] arenes have been used for the first time as gas chromatographic stationary phases, considering their highly symmetric pillar-shaped architecture with cavities rich in π-electrons. Four imidazolium ionic liquids functionalized pillar [5] arenes have been tested as stationary phases with numerous analytes and isomers. In particular, one of these showed superior performances if compared to commercial columns, enabling challenging isomeric separations of halogenated benzenes, aromatic aldehydes, and aromatic anilines. SIGNIFICANCE AND NOVELTY: To our knowledge, this is the first report on the use of the ionic liquid P[n]A as a stationary phase in chromatography, either in GC or liquid chromatography (LC) separations. This work demonstrates the promising potential of ionic liquid P[n]A stationary phases for chromatographic separations.

Hydroxyl-functionalized pillar[5]arene with high separation performance for gas chromatography.

Here we report the first example of employing hydroxyl-functionalized pillar[5]arene (P5A-C10-OH) as stationary phase for capillary gas chromatographic (GC) separations. The statically coated P5A-C10-OH capillary column possessed moderate polarity and column efficiency of 3233 plates per m determined by n-dodecane. As a result, the P5A-C10-OH column exhibited high-resolution capability for the mixture of 17 analytes from apolar to polar nature. Importantly, it exhibited advantageous performance for high resolution of the challenging isomers of bromonitrobenzene, chloroaniline, bromoaniline, iodoaniline and dimethylaniline with good peak shapes over the P5A-C10 and commercial HP-35 columns. In addition, eight cis-/trans-isomers with diverse types were baseline separated on the P5A-C10-OH column. And the application of detecting isomeric impurities in real samples gave strong evidence of its potential and feasibility for the viable GC analysis.

Upregulation of tumor suppressor PIAS3 by Honokiol promotes tumor cell apoptosis via selective inhibition of STAT3 tyrosine 705 phosphorylation.

The natural product Honokiol exhibits robust antitumor activity against a range of cancers, and it has also received approval to undergo phase I clinical trial testing. We confrmed that honokiol can promote the apoptotic death of tumor cells through cell experiments. Then siRNA constructs specific for PIAS3, PIAS3 overexpression plasmid and the mutation of the STAT3 Tyr705 residue were used to confirm the mechanism of Honokiol-induced apoptosis. Finally, we confrmed that honokiol can promote PIAS3 upregulation, in turn suppressing STAT3 Tyr705 phosphorylation through the in vivo and in vitro experiments. Honokiol was ultimately found to reduce tumor cell viability by promoting apoptosis through a mechanism dependent on the ability of Honokiol to promote PIAS3 upregulation and the selective inhibition of p-STAT3 (Tyr705) without affecting p-STAT3 (Ser727) or p-STAT1 (Tyr701) levels. PIAS3 knockdown and overexpression in tumor cells altered STAT3 activation and associated DNA binding activity through the control of Tyr705 phosphorylation via PIAS3-STAT3 complex formation, ultimately shaping Honokiol-induced tumor cell apoptosis. Honokiol was also confirmed to significantly prolong the survival of mice bearing xenograft tumors in a PIAS3-dependent fashion. Together, these findings highlight a novel pathway through which Honokiol can promote PIAS3 upregulation, in turn suppressing STAT3 Tyr705 phosphorylation and promoting the apoptotic death of tumor cells.

Constructing Abundant Oxygen-Containing Functional Groups in Hard Carbon Derived from Anthracite for High-Performance Sodium-Ion Batteries.

Hard carbon is regarded as one of the greatest potential anode materials for sodium-ion batteries (SIBs) because of its affordable price and large layer spacing. However, its poor initial coulombic efficiency (ICE) and low specific capacity severely restrict its practical commercialization in SIBs. In this work, we successfully constructed abundant oxygen-containing functional groups in hard carbon by using pre-oxidation anthracite as the precursor combined with controlling the carbonization temperature. The oxygen-containing functional groups in hard carbon can increase the reversible Na+ adsorption in the slope region, and the closed micropores can be conducive to Na+ storage in the low-voltage platform region. As a result, the optimal sample exhibits a high initial reversible sodium storage capacity of 304 mAh g-1 at 0.03 A g-1, with an ICE of 67.29% and high capacitance retention of 95.17% after 100 cycles. This synergistic strategy can provide ideas for the design of high-performance SIB anode materials with the intent to regulate the oxygen content in the precursor.

RNF43 mutation as a predictor of immunotherapeutic efficacy in colorectal cancer.

RNF43 is a tumor suppressor for various cancers and is considered to drive carcinogenesis when mutated. However, the correlation between RNF43 mutation and colorectal cancer (CRC) immunotherapy remains unreported. We evaluated the role of RNF43 using publicly available data from the Cancer Genome Atlas (TCGA) and Memorial Sloan Kettering Cancer Center (MSKCC). In addition, further analysis was performed on an internal validation cohort (hcohort). The mutant profiles of RNF43 were analyzed in 873 Chinese CRC patients. The relationship between clinical pathologic features and RNF43 were analyzed using the two-sided chi-squared test or the Fisher exact test. Clinicopathologic characteristics were associated with overall survival using Cox regression and the Kaplan-Meier method. We found that RNF43 mutation was significantly associated with high TMB and high MSI score (all p-values < 0.05) in the MSKCC cohort. Additionally, RNF43 mutation was found to be enriched in MSI instability. Kaplan-Meier survival analysis revealed that patients with RNF43 mutation had better OS compared to RNF43 wild-type (not reached vs. 13 months, HR, 0.12; 95% CI 0.03 to 0.49; P = 0.0034). However, no association was observed between RNF43 and OS in the TCGA cohort (HR, 1.83; 95% CI 0.66 to 5.07; P = 0.2479). Our CRC hcohort confirmed the significance of RNF43 mutation in predicting better clinical outcomes, including ORR (45% vs. 21%, P = 0.0468). RNF43 mutation correlated with a high tumor mutation burden (P < 0.001). The mutation frequency of RNF43 in CRC patients was 8.4% (73/873); RNF43 G659Vfs*41 was found to be the most frequent mutation site. In patients with RNF43 mutations, TP53, KRAS, and TGFBR2 were genes with a high frequency of mutations. Compared with RNF43 wild-type patients, those with RNF43 mutations had a higher TMB score and a greater proportion of MSI-H, but no difference in PD-L1 expression. Moreover, the content of immune-related B cells, CD8+ T cells, neutrophils, and dendritic cells was higher in the RNF43 mutant group than in the wild-type group. Our results suggest that RNF43 mutation may correlate with better OS in CRC patients receiving PD-1/PD-L1 inhibitors. The exact mechanisms underlying RNF43 require further investigation.

Temporal and spatial differences in the vaginal microbiome of Chinese healthy women.

Background: Up the reproductive tract, there are large differences in the composition of vaginal microbes. Throughout the menstrual cycle, the structure of the vaginal microbiome shifts. Few studies have examined both in combination. Our study was designed to explore trends in the microbiome of different parts of the vagina in healthy women over the menstrual cycle. Methods: We performed metagenomic sequencing to characterize the microbiome differences between the cervical orifice and mid-vagina throughout the menstrual cycle. Results: Our results showed the vaginal microbiome of healthy women in the cervical orifice and the mid-vagina was similar during the periovulatory and luteal phases, with Lactobacillus being the dominant bacteria. In the follicular phase, Acinetobacter was detected in the cervical orifice. From the follicular phase to the luteal phase, the community state types (all five community status types were defined as CSTs) in samples No. 10 and No. 11 changed from CST III to CST I. In addition, the composition of the vaginal microbiome in healthy women from different regions of China was significantly different. We also detected viruses including Human alphaherpesvirus 1 (HSV-1) during periovulatory phase. Conclusion: This study is valuable for understanding whether the microbial composition of the vagina is consistent in different parts of the menstrual cycle.

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction.

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.

A Flexible Supercapacitor Based on Niobium Carbide MXene and Sodium Anthraquinone-2-Sulfonate Composite Electrode.

MXene-based composites have been widely used in electric energy storage device. As a member of MXene, niobium carbide (Nb2C) is a good electrode candidate for energy storage because of its high specific surface area and electronic conductivity. However, a pure Nb2C MXene electrode exhibits limited supercapacitive performance due to its easy stacking. Herein, sodium anthraquinone-2-sulfonate (AQS) with high redox reactivity was employed as a tailor to enhance the accessibility of ions and electrolyte and enhance the capacitance performance of Nb2C MXene. The resulting Nb2C-AQS composite had three-dimensional porous layered structures. The supercapacitors (SCs) based on the Nb2C-AQS composite exhibited a considerably higher electrochemical capacitance (36.3 mF cm-2) than the pure Nb2C electrode (16.8 mF cm-2) at a scan rate of 20 mV s-1. The SCs also exhibited excellent flexibility as deduced from the almost unchanged capacitance values after being subjected to bending. A capacitance retention of 99.5% after 600 cycles was observed for the resulting SCs, indicating their good cycling stability. This work proposes a surface modification method for Nb2C MXene and facilitates the development of high-performance SCs.

Mechanical response of composite materials prepared with polyurethane elastomers and polyvinyl chloride films.

This study describes a new method for the preparation of composite materials using polyvinyl chloride (PVC) films and polyurethane (PU) foam elastomers. This new preparation method was applied to composite materials used for sound and thermal insulation in the automotive and aerospace industries, and it was found to be effective in reducing debonding and fracture defects. This feature was achieved via the formation of through-holes in the surface material and the substrate prior to lamination, which led to the increase in the flow of air and adhesive and allowed for better compatibility between the material layers. The composite material shows a tensile strength of up to 37.6 Kg⋅cm-2 and can achieve a tensile fracture strength of up to 281.3 N, if woven or biomaterials are used. This can be useful in solving challenges in the aerospace and automotive industries and may also act as a potential coating material for other applications in the future.

Ultra-high sensitivity microwave-photonic sapphire fiber Fabry-Perot interferometry based on the Vernier effect.

The wavelength of microwave is longer than that of lightwave, causing the interferometric optical path difference induced by the measurand changes to be relatively smaller than that of microwave, which results in the lower sensitivity of sapphire fiber Fabry-Perot interferometer (FPI) sensor in microwave band. To improve sensitivity, a parallel FPI sensing system is constructed, in which a section of sapphire fiber connected to a single-mode transmission fiber is used as a sensing FPI, and a single-mode fiber (SMF) with a slightly different optical path from a sensing FPI is utilized as a reference FPI. By connecting two FPIs in parallel, Vernier effect is formed to improve sensitivity. The influence of relationship between the optical path difference of the reference FPI and the sensing FPI on the sensitivity amplification factor is analyzed based on the microwave interference spectrum of the parallel FPI. A section of sapphire fiber with the length of 8 cm is used as temperature sensor to construct high-temperature sensing system. The results demonstrate that the temperature sensitivity reaches about 2338.68kHz/°C, which is approximately 130 times higher than that of the sensing FPI alone. Furthermore, when the difference of optical path between the sensing FPI and the reference FPI is kept constant while the sensing FPI is unchanged, the amplification factor of the temperature sensitivity is approximately 2.64 times higher with longer length of the reference FPI compared to the situation with shorter length of the reference FPI.