H-151 attenuates lipopolysaccharide-induced acute kidney injury by inhibiting the STING-TBK1 pathway.

Sepsis is a severe systemic infectious disease that often leads to multi-organ dysfunction. One of the common and serious complications of sepsis is renal injury. In this study, we aimed to investigate the potential mechanistic role of a novel compound called H-151 in septic kidney injury. We also examined its impact on renal function and mouse survival rates. Initially, we confirmed abnormal activation of the STING-TBK1 signaling pathway in the kidneys of septic mice. Subsequently, we treated the mice with H-151 and observed significant improvement in sepsis-induced renal dysfunction. This was evidenced by reductions in blood creatinine and urea nitrogen levels, as well as a marked decrease in inflammatory cytokine levels. Furthermore, H-151 substantially improved the seven-day survival rate of septic mice, indicating its therapeutic potential. Importantly, H-151 also exhibited an inhibitory effect on renal apoptosis levels, further highlighting its mechanism of protecting against septic kidney injury. These study findings not only offer new insights into the treatment of septic renal injury but also provide crucial clues for further investigations into the regulatory mechanisms of the STING-TBK1 signaling pathway and potential drug targets.

Non-Canonical STING-PERK Pathway Modulation of Cellular Senescence and Therapeutic Response in Sepsis-Associated Acute Kidney Injury.

Abstract-This study explored the role of the non-canonical STING-PERK signaling pathway in sepsis-associated acute kidney injury (SA-AKI). Gene expression data from the GEO database and serum STING protein levels in patients with SA-AKI were analyzed. An LPS-induced mouse model and an in vitro model using HK-2 cells were used to investigate the role of STING in SA-AKI. STING expression was suppressed using shRNA silencing technology and the STING inhibitor C176. Kidney function, inflammatory markers, apoptosis, and senescence were measured. The role of the STING-PERK pathway was investigated by silencing PERK in HK-2 cells and administering the PERK inhibitor GSK2606414. STING mRNA expression and serum STING protein levels were significantly higher in patients with SA-AKI. Suppressing STING expression improved kidney function, reduced inflammation, and inhibited apoptosis and senescence. Silencing PERK or administering GSK2606414 suppressed the inflammatory response, cell apoptosis, and senescence, suggesting that PERK is a downstream effector in the STING signaling pathway. The STING-PERK signaling pathway exacerbates cell senescence and apoptosis in SA-AKI. Inhibiting this pathway could provide potential therapeutic targets for SA-AKI treatment.

Expression of endogenous UDP-glucosyltransferase in endophyte Phomopsis liquidambaris reduces deoxynivalenol contamination in wheat.

Fusarium head blight is a devastating disease that causes severe yield loses and mycotoxin contamination in wheat grain. Additionally, balancing the trade-off between wheat production and disease resistance has proved challenging. This study aimed to expand the genetic tools of the endophyte Phomopsis liquidambaris against Fusarium graminearum. Specifically, we engineered a UDP-glucosyltransferase-expressing P. liquidambaris strain (PL-UGT) using ADE1 as a selection marker and obtained a deletion mutant using an inducible promoter that drives Cas9 expression. Our PL-UGT strain converted deoxynivalenol (DON) into DON-3-G in vitro at a rate of 71.4 % after 36 h. DON inactivation can be used to confer tolerance in planta. Wheat seedlings inoculated with endophytic strain PL-UGT showed improved growth compared with those inoculated with wildtype P. liquidambaris. Strain PL-UGT inhibited the growth of Fusarium graminearum and reduced infection rate to 15.7 %. Consistent with this finding, DON levels in wheat grains decreased from 14.25 to 0.56 µg/g when the flowers were pre-inoculated with PL-UGT and then infected with F. graminearum. The expression of UGT in P. liquidambaris was nontoxic and did not inhibit plant growth. Endophytes do not enter the seeds nor induce plant disease, thereby representing a novel approach to fungal disease control.

Potential effects of soil petroleum contamination on decomposition of Artemisia annua plant litter.

The accumulation of petroleum contaminants in phytoremediating plants can significantly impact the decomposition of their litter. However, the mechanisms underlying these effects and the potential influence of the contaminant concentration remain unclear. In this study, litter from Artemisia annua plants grown in soil with varying concentrations of petroleum (0, 15, 30, and 45 g kg-1) was collected. The litter samples were then inoculated with soil microorganisms and subjected to an indoor simulation of decomposition under controlled temperature and humidity conditions. Changes in the chemical properties, activities of decomposition-related enzymes in the litter, and decomposition rates were measured. Additionally, structural equation modeling was employed to analyze the mechanism through which soil petroleum contamination affects litter decomposition. The findings revealed several key points: (1) increasing soil petroleum contamination tended to reduce the concentration of carbon and nitrogen in litter while increasing those of lignin and total petroleum hydrocarbons (TPH). (2) Soil petroleum contamination tended to increase the activities of both total lignocellulases and total nutrient cycling-related enzymes in litter. (3) Soil petroleum contamination might indirectly inhibit the activity of lignocellulases by increasing the concentration of lignin and TPH in litter. However, it might also directly accelerate the activity of these enzymes, resulting in contradictory effects on litter decomposition. (4) Finally, A. annua litter produced in soil contaminated with 15 and 30 g kg-1 of petroleum exhibited significantly lower decomposition rates than that from uncontaminated soil.

Biomimetic Electrochemical Sensor Based on Single-Atom Nickel Laccase Nanoenzyme for Quercetin Detection.

Laccase, a member of the copper oxidase family, has been used as a green catalyst in the environmental and biochemical industries. However, laccase nanoenzymes are limited to materials with copper as the active site, and noncopper laccase nanoenzymes have been scarcely reported. In this study, inspired by the multiple copper active sites of natural laccase and the redox Cu2+/Cu+ electron transfer pathway, a novel nitrogen/nickel single-atom nanoenzyme (N/Ni SAE) with high laccase-like activity was prepared by inducing Ni and dopamine precipitation through a controllable water/ethanol interface reaction. Compared with that of laccase, the laccase activity simulated by N/Ni SAE exhibited excellent stability and reusability. The N/Ni SAE exhibited a higher efficiency toward the degradation of 2,4-dichlorophenol, hydroquinone, bisphenol A, and p-aminobenzene. In addition, a sensitive electrochemical biosensor was constructed by leveraging the laccase-like activity of N/Ni SAE; this sensor offered unique advantages in terms of catalytic activity, selectivity, stability, and repeatability. Its detection ranges for quercetin were 0.01-0.1 and 1.0-100 µM, and the detection limit was 3.4 nM. It was also successfully used for the quantitative detection of quercetin in fruit juices. Therefore, the single-atom biomimetic nanoenzymes prepared in this study promote the development of a new electrochemical strategy for the detection of various bioactive molecules and show great potential for practical applications.

Efficient Approximations for Matrix-Based Rényi's Entropy on Sequential Data.

The matrix-based Rényi's entropy (MBRE) has recently been introduced as a substitute for the original Rényi's entropy that could be directly obtained from data samples, avoiding the expensive intermediate step of density estimation. Despite its remarkable success in a broad of information-related tasks, the computational cost of MBRE, however, becomes a bottleneck for large-scale applications. The challenge, when facing sequential data, is further amplified due to the requirement of large-scale eigenvalue decomposition on multiple dense kernel matrices constructed by sliding windows in the region of interest, resulting in O(mn3) overall time complexity, where m and n denote the number and the size of windows, respectively. To overcome this issue, we adopt the static MBRE estimator together with a variance reduction criterion to develop randomized approximations for the target entropy, leading to high accuracy with substantially lower query complexity by utilizing the historical estimation results. Specifically, assuming that the changes of adjacent sliding windows are bounded by ß << 1 , which is a trivial case in domains, e.g., time-series analysis, we lower the complexity by a factor of √{ß} . Polynomial approximation techniques are further adopted to support arbitrary α orders. In general, our algorithms achieve O(mn2√{ß}st) total computational complexity, where s, t << n denote the number of vector queries and the polynomial degrees, respectively. Theoretical upper and lower bounds are established in terms of the convergence rate for both s and t , and large-scale experiments on both simulation and real-world data are conducted to validate the effectiveness of our algorithms. The results show that our methods achieve promising speedup with only a trivial loss in performance.

KHGCN: Knowledge-Enhanced Recommendation with Hierarchical Graph Capsule Network.

Knowledge graphs as external information has become one of the mainstream directions of current recommendation systems. Various knowledge-graph-representation methods have been proposed to promote the development of knowledge graphs in related fields. Knowledge-graph-embedding methods can learn entity information and complex relationships between the entities in knowledge graphs. Furthermore, recently proposed graph neural networks can learn higher-order representations of entities and relationships in knowledge graphs. Therefore, the complete presentation in the knowledge graph enriches the item information and alleviates the cold start of the recommendation process and too-sparse data. However, the knowledge graph's entire entity and relation representation in personalized recommendation tasks will introduce unnecessary noise information for different users. To learn the entity-relationship presentation in the knowledge graph while effectively removing noise information, we innovatively propose a model named knowledge-enhanced hierarchical graph capsule network (KHGCN), which can extract node embeddings in graphs while learning the hierarchical structure of graphs. Our model eliminates noisy entities and relationship representations in the knowledge graph by the entity disentangling for the recommendation and introduces the attentive mechanism to strengthen the knowledge-graph aggregation. Our model learns the presentation of entity relationships by an original graph capsule network. The capsule neural networks represent the structured information between the entities more completely. We validate the proposed model on real-world datasets, and the validation results demonstrate the model's effectiveness.

Applying correlation analysis to electrode optimization in source domain.

In brain computer interface-based neurorehabilitation system, a large number of electrodes may increase the difficulty of signal acquisition and the time consumption of decoding algorithm for motor imagery EEG (MI-EEG). The traditional electrode optimization methods were limited by the low spatial resolution of scalp EEG. EEG source imaging (ESI) was further applied to reduce the number of electrodes, in which either the electrodes covering activated cortical areas were selected, or the reconstructed electrodes of EEGs with higher Fisher scores were retained. However, the activated dipoles do not all contribute equally to decoding, and the Fisher score cannot represent the correlations between electrodes and dipoles. In this paper, based on ESI and correlation analysis, a novel electrode optimization method, denoted ECCEO, was developed. The scalp MI-EEG was mapped to cortical regions by ESI, and the dipoles with larger amplitudes were chosen to designate a region of interest (ROI). Then, Pearson correlation coefficients between each dipole of the ROI and the corresponding electrode were calculated, averaged, and ranked to obtain two average correlation coefficient sequences. A small but important group of electrodes for each class were alternately added to the predetermined basic electrode set to form a candidate electrode set. Their features were extracted and evaluated to determine the optimal electrode set. Experiments were conducted on two public datasets, the average decoding accuracies achieved 95.99% and 88.30%, and the reduction of computational cost were 65% and 56%, respectively; statistical significance was examined as well.

Dual Attention and Patient Similarity Network for drug recommendation.

MOTIVATION: Artificially making clinical decisions for patients with multi-morbidity has long been considered a thorny problem due to the complexity of the disease. Drug recommendations can assist doctors in automatically providing effective and safe drug combinations conducive to treatment and reducing adverse reactions. However, the existing drug recommendation works ignored two critical information. (i) Different types of medical information and their interrelationships in the patient's visit history can be used to construct a comprehensive patient representation. (ii) Patients with similar disease characteristics and their corresponding medication information can be used as a reference for predicting drug combinations. RESULTS: To address these limitations, we propose DAPSNet, which encodes multi-type medical codes into patient representations through code- and visit-level attention mechanisms, while integrating drug information corresponding to similar patient states to improve the performance of drug recommendation. Specifically, our DAPSNet is enlightened by the decision-making process of human doctors. Given a patient, DAPSNet first learns the importance of patient history records between diagnosis, procedure and drug in different visits, then retrieves the drug information corresponding to similar patient disease states for assisting drug combination prediction. Moreover, in the training stage, we introduce a novel information constraint loss function based on the information bottleneck principle to constrain the learned representation and enhance the robustness of DAPSNet. We evaluate the proposed DAPSNet on the public MIMIC-III dataset, our model achieves relative improvements of 1.33%, 1.20% and 2.03% in Jaccard, F1 and PR-AUC scores, respectively, compared to state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: The source code is available at the github repository: https://github.com/andylun96/DAPSNet.

Water-soluble dual lysosome/mitochondria-targeted fluorescent probe for detection of SO2 in water, food, herb, and live cells.

In this paper, we present a new donor-π bridge-acceptor type fluorescent probe, MIB, which bears two organelle-targeted groups, namely positively charged benzothiazole group for mitochondria and morpholine moiety for lysosomes. In aqueous solution, the nucleophilic addition of HSO3- (as SO2 donor) to MIB blocked its long-range π-conjugation and ICT process and resulted in significant optical signal changes (blue-shifted UV absorbance and fluorescence), which enabled colorimetric and ratiometric fluorescent detection of HSO3- with high selectivity and sensitivity (detection limit of 63.15 nM). MIB offers obvious advantages of good water-solubility, fast response time (within 1 min), unique dual lysosome/mitochondria targeting capability and has been applied to the sensing of endogenous and exogenous SO2 in live cells through fluorescent imaging. In addition, the proposed probe has been utilized for the determination of bisulfite in real water, food and herbal medicine samples, showing good recovery (91.45 % - 109.3 %) and precision.

HistoML, a markup language for representation and exchange of histopathological features in pathology images.

The study of histopathological phenotypes is vital for cancer research and medicine as it links molecular mechanisms to disease prognosis. It typically involves integration of heterogenous histopathological features in whole-slide images (WSI) to objectively characterize a histopathological phenotype. However, the large-scale implementation of phenotype characterization has been hindered by the fragmentation of histopathological features, resulting from the lack of a standardized format and a controlled vocabulary for structured and unambiguous representation of semantics in WSIs. To fill this gap, we propose the Histopathology Markup Language (HistoML), a representation language along with a controlled vocabulary (Histopathology Ontology) based on Semantic Web technologies. Multiscale features within a WSI, from single-cell features to mesoscopic features, could be represented using HistoML which is a crucial step towards the goal of making WSIs findable, accessible, interoperable and reusable (FAIR). We pilot HistoML in representing WSIs of kidney cancer as well as thyroid carcinoma and exemplify the uses of HistoML representations in semantic queries to demonstrate the potential of HistoML-powered applications for phenotype characterization.

Markov Subsampling Based on Huber Criterion.

Subsampling is an important technique to tackle the computational challenges brought by big data. Many subsampling procedures fall within the framework of importance sampling, which assigns high sampling probabilities to the samples appearing to have big impacts. When the noise level is high, those sampling procedures tend to pick many outliers and thus often do not perform satisfactorily in practice. To tackle this issue, we design a new Markov subsampling strategy based on Huber criterion (HMS) to construct an informative subset from the noisy full data; the constructed subset then serves as refined working data for efficient processing. HMS is built upon a Metropolis-Hasting procedure, where the inclusion probability of each sampling unit is determined using the Huber criterion to prevent over scoring the outliers. Under mild conditions, we show that the estimator based on the subsamples selected by HMS is statistically consistent with a sub-Gaussian deviation bound. The promising performance of HMS is demonstrated by extensive studies on large-scale simulations and real data examples.

Use of Quantitative Metagenomics Next-Generation Sequencing to Confirm Fever of Unknown Origin and Infectious Disease.

A body temperature >38.3°C that lasts ≥3 weeks and lacks a clear diagnosis after 1 week of standard hospital examination and treatment is called "fever of unknown origin" (FUO). The main causes of FUO are infections, hematological diseases, autoimmune diseases, and other non-infectious inflammatory diseases. In recent years, quantitative metagenomics next-generation sequencing (Q-mNGS) has been used widely to detect pathogenic microorganisms, especially in the contribution of rare or new (e.g., severe acute respiratory syndrome-coronavirus-2) pathogens. This review addresses the undetermined cause of fever and its evaluation by Q-mNGS.

A representation model for biological entities by fusing structured axioms with unstructured texts.

MOTIVATION: Structured semantic resources, for example, biological knowledge bases and ontologies, formally define biological concepts, entities and their semantic relationships, manifested as structured axioms and unstructured texts (e.g. textual definitions). The resources contain accurate expressions of biological reality and have been used by machine-learning models to assist intelligent applications like knowledge discovery. The current methods use both the axioms and definitions as plain texts in representation learning (RL). However, since the axioms are machine-readable while the natural language is human-understandable, difference in meaning of token and structure impedes the representations to encode desirable biological knowledge. RESULTS: We propose ERBK, a RL model of bio-entities. Instead of using the axioms and definitions as a textual corpus, our method uses knowledge graph embedding method and deep convolutional neural models to encode the axioms and definitions respectively. The representations could not only encode more underlying biological knowledge but also be further applied to zero-shot circumstance where existing approaches fall short. Experimental evaluations show that ERBK outperforms the existing methods for predicting protein-protein interactions and gene-disease associations. Moreover, it shows that ERBK still maintains promising performance under the zero-shot circumstance. We believe the representations and the method have certain generality and could extend to other types of bio-relation. AVAILABILITY AND IMPLEMENTATION: The source code is available at the gitlab repository https://gitlab.com/BioAI/erbk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

LibMI: An Open Source Library for Efficient Histopathological Image Processing.

BACKGROUND: Whole-slide images (WSIs) as a kind of image data are rapidly growing in the digital pathology domain. With unusual high resolution, these images make them hard to be supported by conventional tools or file formats. Thus, it obstructs data sharing and automated analysis. Here, we propose a library, LibMI, along with its open and standardized image file format. They can be used together to efficiently read, write, modify, and annotate large images. MATERIALS AND METHODS: LibMI utilizes the concept of pyramid image structure and lazy propagation from a segment tree algorithm to support reading and modifying and to guarantee that both operations have linear time complexity. Further, a cache mechanism was introduced to speed up the program. RESULTS: LibMI is an open and efficient library for histopathological image processing. To demonstrate its functions, we applied it to several tasks including image thresholding, microscopic color correction, and storing pixel-wise information on WSIs. The result shows that libMI is particularly suitable for modifying large images. Furthermore, compared with congeneric libraries and file formats, libMI and modifiable multiscale image (MMSI) run 18.237 times faster on read-only tasks. CONCLUSIONS: The combination of libMI library and MMSI file format enables developers to efficiently read and modify WSIs, thus can assist in pixel-wise image processing on extremely large images to promote building image processing pipeline. The library together with the data schema is freely available on GitLab: https://gitlab.com/BioAI/libMI.

Ultrathin Ruddlesden-Popper Perovskite Heterojunction for Sensitive Photodetection.

Thanks to their unique optical and electric properties, 2D materials have attracted a lot of interest for optoelectronic applications. Here, the emerging 2D materials, organic-inorganic hybrid perovskites with van der Waals interlayer interaction (Ruddlesden-Popper perovskites), are synthesized and characterized. Photodetectors based on the few-layer Ruddlesden-Popper perovskite show good photoresponsivity as well as good detectivity. In order to further improve the photoresponse performance, 2D MoS2 is chosen to construct the perovskite-MoS2 heterojunction. The performance of the hybrid photodetector is largely improved with 6 and 2 orders of magnitude enhancement for photoresponsivity (104 A W-1 ) and detectivity (4 × 1010 Jones), respectively, which demonstrates the facile charge separation at the interface between perovskite and MoS2 . Furthermore, the contribution of back gate tuning is proved with a greatly reduced dark current. The results demonstrated here will open up a new field for the investigation of 2D perovskites for optoelectronic applications.

Generation of equilateral-polygon-like flat-top focus by tightly focusing radially polarized beams superposed with off-axis vortex arrays.

In this paper, the general formula for tightly focusing radially polarized beams (RPB) superposed with off-axis vortex arrays is derived based on Richard-Wolf vector diffraction theory. The off-axis vortex breaks the rotational symmetry of the energy flow along the axial direction and leads to the spatial redistribution of intensity within the focal plane. The dependence of the consequent focal intensity redistribution on the off-axis distance of vortices as well as the numerical aperture of the lens is theoretically studied. Based on this intriguing feature, generation of equilateral-polygon-like flat-top focus (EPFF) with a flat-top area on the level of sub-λ2 is realized. The demonstrated method provides new opportunities for focus shaping and holds great potentials in optical manipulation and laser fabrication.

The effects of nitrogen fertilization on N2O emissions from a rubber plantation.

To gain the effects of N fertilizer applications on N2O emissions and local climate change in fertilized rubber (Hevea brasiliensis) plantations in the tropics, we measured N2O fluxes from fertilized (75 kg N ha(-1) yr(-1)) and unfertilized rubber plantations at Xishuangbanna in southwest China over a 2-year period. The N2O emissions from the fertilized and unfertilized plots were 4.0 and 2.5 kg N ha(-1) yr(-1), respectively, and the N2O emission factor was 1.96%. Soil moisture, soil temperature, and the area weighted mean ammoniacal nitrogen (NH4(+)-N) content controlled the variations in N2O flux from the fertilized and unfertilized rubber plantations. NH4(+)-N did not influence temporal changes in N2O emissions from the trench, slope, or terrace plots, but controlled spatial variations in N2O emissions among the treatments. On a unit area basis, the 100-year carbon dioxide equivalence of the fertilized rubber plantation N2O offsets 5.8% and 31.5% of carbon sink of the rubber plantation and local tropical rainforest, respectively. When entire land area in Xishuangbanna is considered, N2O emissions from fertilized rubber plantations offset 17.1% of the tropical rainforest's carbon sink. The results show that if tropical rainforests are converted to fertilized rubber plantations, regional N2O emissions may enhance local climate warming.

Collaborative Filtering Recommendation on Users' Interest Sequences.

As an important factor for improving recommendations, time information has been introduced to model users' dynamic preferences in many papers. However, the sequence of users' behaviour is rarely studied in recommender systems. Due to the users' unique behavior evolution patterns and personalized interest transitions among items, users' similarity in sequential dimension should be introduced to further distinguish users' preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users' interest sequences (IS) that rank users' ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users' longest common sub-IS (LCSIS) and the count of users' total common sub-IS (ACSIS). Then, these semantics are utilized to obtain users' IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users' preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction.

Characterization of critical reagents in ligand-binding assays: enabling robust bioanalytical methods and lifecycle management.

The effective management of validated ligand-binding assays used for PK, PD and immunogenicity assessments of biotherapeutics is vital to ensuring robust and consistent assay performance throughout the lifetime of the method. The structural integrity and functional quality of critical reagents is often linked to ligand-binding assay performance; therefore, physicochemical and biophysical characterization coupled with assessment of assay performance can enable the highest degree of reagent quality. The implementation of a systematic characterization process for monitoring critical reagent attributes, utilizing detailed analytical techniques such as LC-MS, can expedite assay troubleshooting and identify deleterious trends. In addition, this minimizes the potential for costly delays in drug development due to reagent instability or batch-to-batch variability. This article provides our perspectives on a proactive critical reagent QC process. Case studies highlight the analytical techniques used to identify chemical and molecular factors and the interdependencies that can contribute to protein heterogeneity and integrity.