Diesel exhaust and house dust mite allergen lead to common changes in the airway methylome and hydroxymethylome.

Exposures to diesel exhaust particles (DEP) from traffic and house dust mite (HDM) allergens significantly increase risks of airway diseases, including asthma. This negative impact of DEP and HDM may in part be mediated by epigenetic mechanisms. Beyond functioning as a mechanical barrier, airway epithelial cells provide the first line of immune defense towards DEP and HDM exposures. To understand the epigenetic responses of airway epithelial cells to these exposures, we exposed human bronchial epithelial cells to DEP and HDM and studied genome-wide 5-methyl-cytosine (5mC) and 5-hydroxy-methylcytosine (5hmC) at base resolution. We found that exposures to DEP and HDM result in elevated TET1 and DNMT1 expression, associated with 5mC and 5hmC changes. Interestingly, over 20% of CpG sites are responsive to both exposures and changes in 5mC at these sites negatively correlated with gene expression differences. These 5mC and 5hmC changes are located in genes and pathways related to oxidative stress responses, epithelial function and immune cell responses and are enriched for binding sites of transcription factors (TFs) involved in these pathways. Histone marks associated with promoters, enhancers and actively transcribed gene bodies were associated with exposure-induced DNA methylation changes. Collectively, our data suggest that exposures to DEP and HDM alter 5mC and 5hmC levels at regulatory regions bound by TFs, which coordinate with histone marks to regulate gene networks of oxidative stress responses, epithelial function and immune cell responses. These observations provide novel insights into the epigenetic mechanisms that mediate the epithelial responses to DEP and HDM in airways

AllHands: Ask Me Anything on Large-scale Verbatim Feedback via Large Language Models

Verbatim feedback constitutes a valuable repository of user experiences, opinions, and requirements essential for software development. Effectively and efficiently extracting valuable insights from such data poses a challenging task. This paper introduces Allhands , an innovative analytic framework designed for large-scale feedback analysis through a natural language interface, leveraging large language models (LLMs). Allhands adheres to a conventional feedback analytic workflow, initially conducting classification and topic modeling on the feedback to convert them into a structurally augmented format, incorporating LLMs to enhance accuracy, robustness, generalization, and user-friendliness. Subsequently, an LLM agent is employed to interpret users' diverse questions in natural language on feedback, translating them into Python code for execution, and delivering comprehensive multi-modal responses, including text, code, tables, and images. We evaluate Allhands across three diverse feedback datasets. The experiments demonstrate that Allhands achieves superior efficacy at all stages of analysis, including classification and topic modeling, eventually providing users with an "ask me anything" experience with comprehensive, correct and human-readable response. To the best of our knowledge, Allhands stands as the first comprehensive feedback analysis framework that supports diverse and customized requirements for insight extraction through a natural language interface

Aptamer nucleotide analog drug conjugates in the targeting therapy of cancers

Aptamers are short single-strand oligonucleotides that can form secondary and tertiary structures, fitting targets with high affinity and specificity. They are so-called “chemical antibodies” and can target specific biomarkers in both diagnostic and therapeutic applications. Systematic evolution of ligands by exponential enrichment (SELEX) is usually used for the enrichment and selection of aptamers, and the targets could be metal ions, small molecules, nucleotides, proteins, cells, or even tissues or organs. Due to the high specificity and distinctive binding affinity of aptamers, aptamer–drug conjugates (ApDCs) have demonstrated their potential role in drug delivery for cancer-targeting therapies. Compared with antibodies which are produced by a cell-based bioreactor, aptamers are chemically synthesized molecules that can be easily conjugated to drugs and modified; however, the conventional ApDCs conjugate the aptamer with an active drug using a linker which may add more concerns to the stability of the ApDC, the drug-releasing efficiency, and the drug-loading capacity. The function of aptamer in conventional ApDC is just as a targeting moiety which could not fully perform the advantages of aptamers. To address these drawbacks, scientists have started using active nucleotide analogs as the cargoes of ApDCs, such as clofarabine, ara-guanosine, gemcitabine, and floxuridine, to replace all or part of the natural nucleotides in aptamer sequences. In turn, these new types of ApDCs, aptamer nucleotide analog drug conjugates, show the strength for targeting efficacy but avoid the complex drug linker designation and improve the synthetic efficiency. More importantly, these classic nucleotide analog drugs have been used for many years, and aptamer nucleotide analog drug conjugates would not increase any unknown druggability risk but improve the target tumor accumulation. In this review, we mainly summarized aptamer-conjugated nucleotide analog drugs in cancer-targeting therapies

Polydopamine functionalized dendritic fibrous silica nanoparticles as a generic platform for nucleic acid-based biosensing

Accurate and rapid detection of nucleic acid sequences is of utmost importance in various fields, including disease monitoring, clinical treatment, gene analysis and drug discovery. In this study, we developed a "turn-on" fluorescence biosensor that enables simple and highly efficient detection of nucleic acid biomarkers. Our approach involves the utilization of 6-carboxyfluorescein modified single-stranded DNA (FAM-ssDNA) as molecular recognition element, along with polydopamine-functionalized dendritic fibrous nanosilica (DFNS). FAM-ssDNA serves as both specific molecular recognition element for target analyte and reporter capable of transducing a detectable signal through Watson-Crick base pairing. The polydopamine-functionalized DFNS (DFNS@DA) exhibits strong binding to FAM-ssDNA via polyvalent metal mediated coordination leading to effective quenching by fluorescence resonance energy transfer. In the presence of a complementary target sequence, FAM-ssDNA forms hybridized structure and detaches from DFNS@DA, which causes an increased fluorescence emission. The analytical system based on FAM-ssDNA and DFNS@DA demonstrates exceptional sensitivity, selectivity, and rapid response for the detection of nucleic acid sequences, leveraging the high adsorption and quenching properties of DFNS@DA. For the first proof of concept, we demonstrated the successful detection of microRNA (miR-21) in cancer cells using the FAM-ssDNA/DFNS@DA system. Our results highlight the promising capabilities of DFNS@DA and nucleic acid-based biosensors, offering a generic and cost-effective solution for the detection of nucleic acid-related biomarkers

Recyclable nanoparticles based on a boronic acid–diol complex for the real-time monitoring of imprinting, molecular recognition and copper ion detection

Molecularly imprinted polymers (MIPs) have now become one of the most remarkable materials in the field of molecular recognition. Although many efforts have been made to study the process and mechanism of molecular imprinting, it has not been possible to monitor the interactions between the template and the growing polymer chains under real-time experimental conditions. The behavior of the template–monomer complex during the whole polymerization process has remained largely unknown. In this work, we introduce a fluorescence technique that allows monitoring of the template–functional monomer complex during an actual imprinting process, as well as the real-time signaling of template binding and dissociation from the imprinted polymer. For the first proof-of-principle, we select Alizarin Red S (ARS) and 4-vinylphenylboronic acid as the template and functional monomer, respectively, to synthesize MIP particles via precipitation polymerization. As the formation of the template–functional monomer complex leads to strong fluorescence emission, it allows the status of the template binding to be monitored throughout the whole reaction process in real time. Using the same fluorescence technique, the kinetics of template binding and dissociation can be studied directly without particle separation. The hydrophilic MIP particles can be used as a scavenger to remove ARS from water. In addition, the MIP particles can be used as a recyclable sensor to detect Cu ions. As the Cu ion forms a stable complex with ARS, it causes ARS to dissociate from the MIP nanoparticles, leading to effective fluorescence quenching. The non-separation analytical method based on fluorescence measurement provides a convenient means to study molecular imprinting reactions and the kinetics of molecular recognition using imprinted polymers. The recyclable nanoparticle sensor allows toxic Cu ions to be detected directly in water in the range of 0.1–100 μM with a recovery of 84–95%

Ancient Stone Inscription Image Denoising and Inpainting Methods Based on Deep Neural Networks

Chinese ancient stone inscriptions contain Chinese traditional calligraphy culture and art information. However, due to the long history of the ancient stone inscriptions, natural erosion, and poor early protection measures, there are a lot of noise in the existing ancient stone inscriptions, which has adverse effects on reading these stone inscriptions and their aesthetic appreciation. At present, digital technologies have played important roles in the protection of cultural relics. For ancient stone inscriptions, we should obtain more perfect digital results without multiple types of noise, while there are few deep learning methods designed for processing stone inscription images. Therefore, we propose a basic framework for image denoising and inpainting of stone inscriptions based on deep learning methods. Firstly, we collect as many images of stone inscriptions as possible and preprocess these images to establish an inscriptions image dataset for image denoising and inpainting. In addition, an improved GAN with a denoiser is used for generating more virtual stone inscription images to expand the dataset. On the basis of these collected and generated images, we designed a stone inscription image denoising model based on multiscale feature fusion and introduced Charbonnier loss function to improve this image denoising model. To further improve the denoising results, an image inpainting model with the coherent semantic attention mechanism is introduced to recover some effective information removed by the former denoising model as much as possible. The experimental results show that our image denoising model achieves better results on PSNR, SSIM, and CEI. The final results have obvious visual improvement compared with the original stone inscription images

Boronic Acid Functionalized Nanosilica for Binding Guest Molecules

Dendritic fibrous nanosilica (DFNS) has very high surface area and well-defined nanochannels; therefore, it is very useful as supporting material for numerous applications including catalysis, sensing, and bioseparation. Due to the highly restricted space, addition of molecular ligands to DFNS is very challenging. This work studies how ligand conjugation in nanoscale pores in DFNS can be achieved through copper-catalyzed click reaction, using an optional, in situ synthesized, temperature-responsive polymer intermediate. A clickable boronic acid is used as a model to investigate the ligand immobilization and the molecular binding characteristics of the functionalized DFNS. The morphology, composition, nanoscale pores, and specific surface area of the boronic acid functionalized nanosilica were characterized by electron microscopy, thermogravimetric and elemental analysis, Fourier transform infrared spectroscopy, and nitrogen adsorption-desorption measurements. The numbers of boronic acid molecules on the modified DFNS with and without the polymer were determined to be 0.08 and 0.68 mmol of ligand/g of DFNS, respectively. We also studied the binding of small cis-diol molecules in the nanoscale pores of DFNS. The boronic acid modified DFNS with the polymer intermediate exhibits higher binding capacity for Alizarin Red S and nicotinamide adenine dinucleotide than the polymer-free DFNS. The two types of boronic acid modified DFNS can bind small cis-diol molecules in the presence of large glycoproteins, due in large part to the effect of size exclusion provided by the nanochannels in the DFNS

Electro-Fenton mineralization of real textile wastewater by micron-sized ZVI powder anode

The diverse compositions and complex nature of the textile wastewater make it imperative to find an economical and suitable degradation pathway. The degradation of real textile wastewater on a novel heterogeneous electro-Fenton system was carried out with a composite anode of magnetically fixed micron ZVI coupling with a Ti/RuO2-IrO2 sheet. The influences of different variables such as mZVI dosage, H2O2 amount, applied voltage and pH value on both total organic carbon and chemical oxygen demand removal efficiencies and energy consumption were investigated. The optimized parameters were simultaneously verified by using electrochemical workstation Tafel curves and Nyquist plots. The optimal operating conditions for evaluating the wastewater treatment were H2O2 dosage of 0.10 mol·L−1, applied voltage of 5.0 V, mZVI amount of 1.0 g·L−1 and initial pH value of 3.0. The high TOC and COD removal efficiencies of 92.44 and 82.84% could be achieved simultaneously in 60 min, respectively. XRD, XPS and SEM-EDS were used to investigate the interaction between the pollutant and the mZVI. GC-MS analysis was performed on untreated and treated wastewater to determine the degradation of pollutants in dyeing wastewater during the electro-Fenton process and to effectively propose a suitable degradation mechanism for this system. HIGHLIGHTS A heterogeneous electro-Fenton process was performed on mZVI anode.; This study is performed on the real textile wastewater.; High COD and TOC removal efficiencies were achieved by the heterogeneous E-Fenton process.; The performance of electro-Fenton for contaminant removal was evaluated at different parameters.; The mechanisms were proposed based on the physiochemical and electrochemical properties of the anode.

Differential analysis of landscape patterns of land cover products in tropical marine climate zones – A case study in Malaysia

Land cover in tropical marine climate zones is important for global climate change. The existing analysis of land cover product consistency mainly focuses on a continental or national scale and rarely takes different geographical zones (such as tropical marine climate zones) as examples to carry out micro-interpretation from the perspective of ecology from the grid scale. In fact, some types of land cover under different zones have poor accuracy due to the standard of cognition and the complexity of the spatial pattern of ground objects. In addition, land cover and its change in tropical Marine climate zones will affect the greenhouse effect, energy balance, water transport, and so on, thus affecting climate change on a regional or even global scale. Therefore, this article presents an evaluation based on GLOBCOVER, CCI LC, and MCD12Q1 data using Malaysia as a case study, through area composition similarity, field sample point validation, and landscape indices. The results showed that (1) the area correlation coefficient between GLOBCOVER and CCI LC is the highest at 0.998. (2) The CCI LC had the highest OA and kappa coefficient of 59.01% and 0.4957, while the GLOBCOVER product had the lowest OA and kappa coefficient of 49.24% and 0.3614, respectively. (3) The consistency of the water landscape index is high between the CCI LC and GLOBCOVE data, the consistency of the artificial surfaces landscape index is high between the CCI LC and MCD12Q1 products, and the consistency of the grassland/shrubland landscape index is high between the GLOBCOVE and MCD12Q1 products. The results of microscopic landscape patterns show that the three product landscape patterns are generally more consistent in East Malaysia than in West Malaysia. The low accuracy of grassland, bareland, and shrubland is the key reason for the wide variation in landscape patterns between the three products