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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Unsupervised Deep Exemplar Colorization via Pyramid Dual Non-Local Attention.

Exemplar-based colorization is a challenging task, which attempts to add colors to the target grayscale image with the aid of a reference color image, so as to keep the target semantic content while with the reference color style. In order to achieve visually plausible chromatic results, it is important to sufficiently exploit the global color style and the semantic color information of the reference color image. However, existing methods are either clumsy in exploiting the semantic color information, or lack of the dedicated fusion mechanism to decorate the target grayscale image with the reference semantic color information. Besides, these methods usually use a single-stage encoder-decoder architecture, which results in the loss of spatial details. To remedy these problems, we propose an effective exemplar colorization strategy based on pyramid dual non-local attention network to exploit the long-range dependency as well as multi-scale correlation. Specifically, two symmetrical branches of pyramid non-local attention block are tailored to achieve alignments from the target feature to the reference feature and from the reference feature to the target feature respectively. The bidirectional non-local fusion strategy is further applied to get a sufficient fusion feature that achieves full semantic consistency between multi-modal information. To train the network, we propose an unsupervised learning manner, which employs the hybrid supervision including the pseudo paired supervision from the reference color images and unpaired supervision from both the target grayscale and reference color images. Extensive experimental results are provided to demonstrate that our method achieves better photo-realistic colorization performance than the state-of-the-art methods.

Integrative genomic and transcriptomic analysis reveals genetic alterations associated with the early progression of follicular lymphoma.

Follicular lymphoma (FL), the most common indolent lymphoma, is a clinically and genetically heterogeneous disease. However, the prognostic value of driver gene mutations and copy number alterations has not been systematically assessed. Here, we analysed the clinical-biological features of 415 FL patients to identify variables associated with disease progression within 24 months of first-line therapy (POD24). Patients with B symptoms, elevated lactate dehydrogenase and ß2-microglobulin levels, unfavourable baseline haemoglobin levels, advanced stage, and high-risk FL International Prognostic Index (FLIPI) scores had an increased risk of POD24, with FLIPI being the most important factor in logistic regression. HIST1H1D, identified as a driver mutation, was correlated with POD24. Gains of 6p22.2 (HIST1H1D) and 18q21.33 (BCL2) and loss of 1p36.13 (NBPF1) predicted POD24 independent of FLIPI. Gene expression profiling of FL samples showed that the POD24 cohort was significantly enriched in the inflammatory response (mediated by interferon and tumour necrosis factor), cell cycle regulation (transcription, replication and proliferation) sets and PI3K-AKT-mTOR signalling. This result was further validated with transcriptome-wide information provided by RNA-seq at single-cell resolution. Our study, performed on a large cohort of FL patients, highlights the importance of distinctive genetic alterations and gene expression relevant to disease diagnosis and early progression.

Microemulsion-Based Keratin-Chitosan Gel for Improvement of Skin Permeation/Retention and Activity of Curcumin.

Curcumin (Cur) is a kind of polyphenol with a variety of topical pharmacological properties including antioxidant, analgesic and anti-inflammatory activities. However, its low water solubility and poor skin bioavailability limit its effectiveness. In the current study, we aimed to develop microemulsion-based keratin-chitosan gel for the improvement of the topical activity of Cur. The curcumin-loaded microemulsion (CME) was formulated and then loaded into the keratin-chitosan (KCS) gel to form the CME-KCS gel. The formulated CME-KCS gel was evaluated for its characterization, in vitro release, in vitro skin permeation and in vivo activity. The results showed that the developed CME-KCS gel had an orange-yellow and gel-like appearance. The particle size and zeta potential of the CME-KCS gel were 186.45 ± 0.75 nm and 9.42 ± 0.86 mV, respectively. The CME-KCS gel showed desirable viscoelasticity, spreadability, bioadhesion and controlled drug release, which was suitable for topical application. The in vitro skin permeation and retention study showed that the CME-KCS gel had better in vitro skin penetration than the Cur solution and achieved maximum skin drug retention (3.75 ± 0.24 µg/cm2). In vivo experimental results confirmed that the CME-KCS gel was more effective than curcumin-loaded microemulsion (Cur-ME) in analgesic and anti-inflammatory activities. In addition, the CME-KCS gel did not cause any erythema or edema based on a mice skin irritation test. These findings indicated that the developed CME-KCS gel could improve the skin penetration and retention of Cur and could become a promising formulation for topical delivery to treat local diseases.