Multiview Classification Through Learning From Interval-Valued Data.

The classification problem concerning crisp-valued data has been well resolved. However, interval-valued data, where all of the observations' features are described by intervals, are also a common data type in real-world scenarios. For example, the data extracted by many measuring devices are not exact numbers but intervals. In this article, we focus on a highly challenging problem called learning from interval-valued data (LIND), where we aim to learn a classifier with high performance on interval-valued observations. First, we obtain the estimation error bound of the LIND problem based on the Rademacher complexity. Then, we give the theoretical analysis to show the strengths of multiview learning on classification problems, which inspires us to construct a new algorithm called multiview interval information extraction (Mv-IIE) approach for improving classification accuracy on interval-valued data. The experiment comparisons with several baselines on both synthetic and real-world datasets illustrate the superiority of the proposed framework in handling interval-valued data. Moreover, we describe an application of Mv-IIE that we can prevent data privacy leakage by transforming crisp-valued (raw) data into interval-valued data.

Epidemiological Characteristics and Trends of Scarlet Fever in Zhejiang Province of China: Population-Based Surveillance during 2004-2022.

Background: Over the past two decades, scarlet fever has resurged in some countries or areas. Nationwide nonpharmaceutical interventions changed the patterns of other infectious diseases, but its effects on the spread of scarlet fever were rarely studied. This study aimed to evaluate the changes in scarlet fever incidence in Zhejiang Province, China, before and during the COVID-19 pandemic periods and to provide references for scarlet fever prevention and control. Methods: Scarlet fever surveillance data in Zhejiang, China (2004-2022), were analyzed in three stages. Two-sample z test, ANOVA, and Tukey's test were used to compare and analyze the characteristics of disease spread at different stages. The ARIMA model was used to predict the overall trend. The data were obtained from the National Infectious Disease Reporting Information System. Results: A total of 28,652 cases of scarlet fever were reported across Zhejiang Province during the study period, with the lowest average monthly incidences in 2020 (0.111/100,000). The predominant areas affected were the northern and central regions of Zhejiang, and all regions of Zhejiang experienced a decrease in incidence in 2020. The steepest decline in incidence in 2020 was found in children aged 0-4 years (67.3% decrease from 23.8/100,000 to 7.8/100,000). The seasonal pattern changed, with peak occurrences in April to June and November to January during 2004-2019 and 2021 and a peak in January in 2020. The median duration from diagnosis to confirmation was highest before COVID-19 (4 days); however, it decreased to 1 day in 2020-2022, matching the other two medians. Conclusions: In 2020, Zhejiang experienced an unprecedented decrease in scarlet fever, with the lowest incidence in nearly 18 years, but it rebounded in 2021 and 2022. The seasonal epidemiologic characteristics of scarlet fever also changed with the COVID-19 outbreaks. This suggested that nationwide nonpharmaceutical interventions greatly depressed the spread of scarlet fever. With the relaxation of non-pharmaceutical intervention restrictions, scarlet fever may reappear. Government policymakers should prioritize the control of future scarlet fever outbreaks for public health.

Construction of a Membrane Yeast Two-Hybrid Library and Screening of MsPYR1-Like Interacting Proteins in Malus sieversii.

To investigate the biological effects of the ABA receptor pyrabactin resistance 1-like (PYR1-like) in Malus sieversii seeds, the proteins interacting with MsPYR1-like were screened by the membrane yeast two-hybrid library based on the split-ubiquitin system, and to construct the bait vector pBT3-SUC-PYR1 for Malus sieversii cDNA library, which had no self-activating effect on the yeast cells of the pPR3-N membrane yeast two-hybrid library. The library titer assay showed that it could meet the requirements for membrane yeast two-hybrid library screening. After sequencing, GenBank database blast, and yeast rotary validation, 28 candidate proteins interacting with MsPYR1-like were obtained, including ribosomal proteins, late embryogenesis abundant proteins, F-actin-capping proteins, phytochrome-interacting proteins, low-temperature-inducible 65 kDa protein-like, senescence-associated, PP2C and SnRK2 family members, and unknown proteins. Gene ontology analysis of the interaction proteins was related to plant hormone response and negative regulation of seed germination, overexpression of MsPYR1-like in Arabidopsis negatively regulates seed germination, and the study of the biological roles of MsPYR1-like interacting proteins lays the foundation for revealing the lifting of seed dormancy in Malus sieversii.

A Novel Approach for the Detection and Severity Grading of Chronic Obstructive Pulmonary Disease Based on Transformed Volumetric Capnography.

Chronic Obstructive Pulmonary Disease (COPD), as the third leading cause of death worldwide, is a major global health issue. The early detection and grading of COPD are pivotal for effective treatment. Traditional spirometry tests, requiring considerable physical effort and strict adherence to quality standards, pose challenges in COPD diagnosis. Volumetric capnography (VCap), which can be performed during natural breathing without requiring additional compliance, presents a promising alternative tool. In this study, the dataset comprised 279 subjects with normal pulmonary function and 148 patients diagnosed with COPD. We introduced a novel quantitative analysis method for VCap. Volumetric capnograms were converted into two-dimensional grayscale images through the application of Gramian Angular Field (GAF) transformation. Subsequently, a multi-scale convolutional neural network, CapnoNet, was conducted to extract features and facilitate classification. To improve CapnoNet's performance, two data augmentation techniques were implemented. The proposed model exhibited a detection accuracy for COPD of 95.83%, with precision, recall, and F1 measures of 95.21%, 95.70%, and 95.45%, respectively. In the task of grading the severity of COPD, the model attained an accuracy of 96.36%, complemented by precision, recall, and F1 scores of 88.49%, 89.99%, and 89.15%, respectively. This work provides a new perspective for the quantitative analysis of volumetric capnography and demonstrates the strong performance of the proposed CapnoNet in the diagnosis and grading of COPD. It offers direction and an effective solution for the clinical application of capnography.

Perspective on Agricultural Industrialization: Modification Strategies for Enhancing the Catalytic Capacity of Keratinase.

Keratinases is a special hydrolytic enzyme produced by microorganisms, which has the ability to catalyze the degradation of keratin. Currently, keratinases show great potential for application in many agricultural and industrial fields, such as biofermented feed, leather tanning, hair removal, and fertilizer production. However, these potentials have not yet been fully unleashed on an industrial scale. This paper reviews the sources, properties, and catalytic mechanisms of keratinases. Strategies for the molecular modification of keratinases are summarized and discussed in terms of improving the substrate specificity, thermostability, and pH tolerance of keratinases. The modification strategies are also enriched by the introduction of immobilized enzymes and directed evolution. In addition, the selection of modification strategies when facing specific industrial applications is discussed and prospects are provided. We believe that this review serves as a reference for the future quest to extend the application of keratinases from the laboratory to industry.

Carboxymethylated Lignin Reinforcement of SPI Adhesive: Enhancing Strength, Antimicrobial, and Flame-Retardant Properties without Excessive Alkali Introduction.

To address the challenges associated with formaldehyde emissions in engineered wood adhesives and simultaneously enhance adhesive properties related to water resistance, fire resistance, and mold resistance, a novel environmentally sustainable biomass-based adhesive was formulated. In this work, kraft lignin was carboxymethylated and then blended with the soy protein isolate (SPI)-based adhesive, the dry and wet shear strength of the plywood bonded by the resultant adhesive was enhanced from 1.10 and 0.63 MPa to 1.73 and 1.23 MPa, respectively, resulting in improvements of 157% and 195%. Carboxymethylated lignin (CML) significantly improved the mold resistance and flame-resistance residual rate of the adhesive and decreased the water absorption rate from 190% to 108%. Furthermore, the adhesive exhibits outstanding flame-retardancy, with self-extinguishing capability rendering it suitable for industrial production. In addition, we also evaluated the performances of resulting adhesives cured with different diepoxides and triepoxides, and the comparisons of the adhesive in this work to commercial urea glue and soy protein-based adhesives were conducted. To our delight, the SPI-10CML adhesive presented comparable or even improved performances, showing its promising practical applications such as for fire doors.

A biocompatible electrode/exoelectrogens interface augments bidirectional electron transfer and bioelectrochemical reactions.

Bidirectional electron transfer is about that exoelectrogens produce bioelectricity via extracellular electron transfer at anode and drive cytoplasmic biochemical reactions via extracellular electron uptake at cathode. The key factor to determine above bioelectrochemical performances is the electron transfer efficiency under biocompatible abiotic/biotic interface. Here, a graphene/polyaniline (GO/PANI) nanocomposite electrode specially interfacing exoelectrogens (Shewanella loihica) and augmenting bidirectional electron transfer was conducted by in-situ electrochemical modification on carbon paper (CP). Impressively, the GO/PANI@CP electrode tremendously improved the performance of exoelectrogens at anode for wastewater treatment and bioelectricity generation (about 54 folds increase of power density compared to blank CP electrode). The bacteria on electrode surface not only showed fast electron release but also exhibited high electricity density of extracellular electron uptake through the proposed direct electron transfer pathway. Thus, the cathode applications of microbial electrosynthesis and bio-denitrification were developed via GO/PANI@CP electrode, which assisted the close contact between microbial outer-membrane cytochromes and nanocomposite electrode for efficient nitrate removal (0.333 mM/h). Overall, nanocomposite modified electrode with biocompatible interfaces has great potential to enhance bioelectrochemical reactions with exoelectrogens.

Thermodynamic modeling of freeze pretreatment in the destruction of rice straw structure combined with alkaline-hydrothermal method for enzymatic hydrolysis.

Freeze pretreatment combined with alkaline-hydrothermal method of rice straw for enzymatic hydrolysis was studied. Crystallization stress in the rice stem pores caused by water freezing at -20- -40 °C was modeled to illustrate the destruction mechanism. The stress was calculated as 22.5-38.3 MPa that were higher than the tensile yield stress of untreated stems (3.0 MPa), indicating ice formation damaging pore structure. After freeze at -20 °C, rice straw was further hydrothermally treated at 190 °C with 0.4 M Na2CO3, achieving 72.0 % lignin removal and 97.2 % cellulose recovery. Glucose yield rose to 91.1 % by 4.3 times after 24 h hydrolysis at 10 FPU loading of Cellic®CTec2 cellulase. The specific surface area of rice straw was 2.6 m2/g increased by 1.2 times after freeze. Freeze combined with alkaline-hydrothermal treatment is a green and energy-efficient method for improving enzymatic hydrolysis.

Bacillus subtilis: current and future modification strategies as a protein secreting factory.

Bacillus subtilis is regarded as a promising microbial expression system in bioengineering due to its high stress resistance, nontoxic, low codon preference and grow fast. The strain has a relatively efficient expression system, as it has at least three protein secretion pathways and abundant molecular chaperones, which guarantee its expression ability and compatibility. Currently, many proteins are expressed in Bacillus subtilis, and their application prospects are broad. Although Bacillus subtilis has great advantages compared with other prokaryotes related to protein expression and secretion, it still faces deficiencies, such as low wild-type expression, low product activity, and easy gene loss, which limit its large-scale application. Over the years, many researchers have achieved abundant results in the modification of Bacillus subtilis expression systems, especially the optimization of promoters, expression vectors, signal peptides, transport pathways and molecular chaperones. An optimal vector with a suitable promoter strength and other regulatory elements could increase protein synthesis and secretion, increasing industrial profits. This review highlights the research status of optimization strategies related to the expression system of Bacillus subtilis. Moreover, research progress on its application as a food-grade expression system is also presented, along with some future modification and application directions.

Two new sesterterpenoids from Atractylodes japonica Koidz. ex Kitam.

Two new sesterterpenoids, atractylodes japonica terpenoid acid I (1) and atractylodes japonica terpenoid aldehyde I (2), were isolated from the rhizomes of Atractylodes japonica Koidz. ex Kitam together with ten known compounds (3-12). Their structures were elucidated on the basis of comprehensive spectroscopic analysis (1D/2D NMR, HRESIMS and IR). In addition, all of these isolated compounds were evaluated for their cytotoxic activities against human gastric cancer cell MGC-803 and human hepatocellular cancer cell HepG-2. Most of them exhibited moderate to weak inhibitory effects with IC50 values in the range of 25.15-88.85 µM except for 9-12.

Formulating compatible non-flammable electrolyte for lithium-ion batteries with ethoxy (pentafluoro) cyclotriphosphazene.

Lithium (Li) ion batteries have played a great role in modern society as being extensively used in commercial electronic products, electric vehicles, and energy storage systems. However, battery safety issues have gained growing concerns as there might be thermal runaway, fire or even explosion under external abuse. To tackle these safety issues, developing non-flammable electrolytes is a promising strategy. However, the balance between the flame-retarding effect and the electrochemical performance of electrolytes remains a great challenge. Herein, we evaluate the function of ethoxy (pentafluoro) cyclotriphosphazene (PFPN) as an effective flame-retarding additive for lithium-ion batteries. The flammability of electrolytes is greatly suppressed with the introduction of a small amount of PFPN. Moreover, PFPN exhibited excellent compatibility with LiFePO4 (LFP) cathode and graphite (Gr) anode, the electrochemical performances of LFP|Li and Gr|Li half cells are virtually unaffected. Scanning electron microscope (SEM) and X-ray photoelectron spectroscopy (XPS) reveal the stable interphase between PFPN-containing electrolyte and LFP and Gr electrodes. Fourier transform infrared spectroscopy (FT-IR), Raman and nuclear magnetic resonance (NMR) spectra demonstrate the introduction of PFPN only exhibits negligible influence on the solvation structure of electrolyte. Benefiting from these merits of PFPN, the LFP|Gr cell shows desirable long-term cycling performance, which demonstrates great potential for practical application.

The application of impulse oscillometry system based on machine learning algorithm in the diagnosis of chronic obstructive pulmonary disease.

Objective. Diagnosing chronic obstructive pulmonary disease (COPD) using impulse oscillometry (IOS) is challenging due to the high level of clinical expertise it demands from doctors, which limits the clinical application of IOS in screening. The primary aim of this study is to develop a COPD diagnostic model based on machine learning algorithms using IOS test results.Approach. Feature selection was conducted to identify the optimal subset of features from the original feature set, which significantly enhanced the classifier's performance. Additionally, secondary features area of reactance (AX) were derived from the original features based on clinical theory, further enhancing the performance of the classifier. The performance of the model was analyzed and validated using various classifiers and hyperparameter settings to identify the optimal classifier. We collected 528 clinical data examples from the China-Japan Friendship Hospital for training and validating the model.Main results. The proposed model achieved reasonably accurate diagnostic results in the clinical data (accuracy = 0.920, specificity = 0.941, precision = 0.875, recall = 0.875).Significance. The results of this study demonstrate that the proposed classifier model, feature selection method, and derived secondary feature AX provide significant auxiliary support in reducing the requirement for clinical experience in COPD diagnosis using IOS.

Bioelectricity and CO2-to-butyrate production using photobioelectrochemical cells with bio-hydrogel.

Bio-photoelectrochemical cell (BPEC) is an emerging technology that can convert the solar energy into electricity or chemicals. However, traditional BPEC depending on abiotic electrodes is challenging for microbial/enzymatic catalysis because of the inefficient electron exchange. Here, electroactive bacteria (Shewanella loihica PV-4) were used to reduce graphene oxide (rGO) nanosheets and produce co-assembled rGO/Shewanella biohydrogel as a basic electrode. By adsorbing chlorophyll contained thylakoid membrane, this biohydrogel was fabricated as a photoanode that delivered maximum photocurrent 126 µA/cm3 under visible light. Impressively, the biohydrogel could be served as a cathode in BPEC by forming coculture system with genetically edited Clostridium ljungdahlii. Under illumination, the BPEC with above photoanode and cathode yielded âˆ¼ 5.4 mM butyrate from CO2 reduction, 169 % increase compared to dark process. This work provided a new strategy (nanotechnology combined with synthetic biology) to achieve efficient bioelectricity and valuable chemical production in PBEC.

Artificial and Biosynthetic Nanoparticles Boost Bioelectrochemical Reactions via Efficient Bidirectional Electron Transfer of Shewanella loihica.

Bioelectrochemical reactions using whole-cell biocatalysts are promising carbon-neutral approaches because of their easy operation, low cost, and sustainability. Bidirectional (outward or inward) electron transfer via exoelectrogens plays the main role in driving bioelectrochemical reactions. However, the low electron transfer efficiency seriously inhibits bioelectrochemical reaction kinetics. Here, a three dimensional and artificial nanoparticles-constituent inverse opal-indium tin oxide (IO-ITO) electrode is fabricated and employed to connect with exoelectrogens (Shewanella loihica PV-4). The above electrode collected 128-fold higher cell density and exhibited a maximum current output approaching 1.5 mA cm-2 within 24 h at anode mode. By changing the IO-ITO electrode to cathode mode, the exoelectrogens exhibited the attractive ability of extracellular electron uptake to reduce fumarate and 16 times higher reverse current than the commercial carbon electrode. Notably, Fe-containing oxide nanoparticles are biologically synthesized at both sides of the outer cell membrane and probably contributed to direct electron transfer with the transmembrane c-type cytochromes. Owing to the efficient electron exchange via artificial and biosynthetic nanoparticles, bioelectrochemical CO2 reduction is also realized at the cathode. This work not only explored the possibility of augmenting bidirectional electron transfer but also provided a new strategy to boost bioelectrochemical reactions by introducing biohybrid nanoparticles.

Reliable automatic sleep stage classification based on hybrid intelligence.

Sleep staging is a vital aspect of sleep assessment, serving as a critical tool for evaluating the quality of sleep and identifying sleep disorders. Manual sleep staging is a laborious process, while automatic sleep staging is seldom utilized in clinical practice due to issues related to the inadequate accuracy and interpretability of classification results in automatic sleep staging models. In this work, a hybrid intelligent model is presented for automatic sleep staging, which integrates data intelligence and knowledge intelligence, to attain a balance between accuracy, interpretability, and generalizability in the sleep stage classification. Specifically, it is built on any combination of typical electroencephalography (EEG) and electrooculography (EOG) channels, including a temporal fully convolutional network based on the U-Net architecture and a multi-task feature mapping structure. The experimental results show that, compared to current interpretable automatic sleep staging models, our model achieves a Macro-F1 score of 0.804 on the ISRUC dataset and 0.780 on the Sleep-EDFx dataset. Moreover, we use knowledge intelligence to address issues of excessive jumps and unreasonable sleep stage transitions in the coarse sleep graphs obtained by the model. We also explore the different ways knowledge intelligence affects coarse sleep graphs by combining different sleep graph correction methods. Our research can offer convenient support for sleep physicians, indicating its significant potential in improving the efficiency of clinical sleep staging.

Magnetic resonance imaging-based radiomics was used to evaluate the level of prognosis-related immune cell infiltration in breast cancer tumor microenvironment.

PURPOSE: The tumor immune microenvironment is a valuable source of information for predicting prognosis in breast cancer (BRCA) patients. To identify immune cells associated with BRCA patient prognosis from the Cancer Genetic Atlas (TCGA), we established an MRI-based radiomics model for evaluating the degree of immune cell infiltration in breast cancer patients. METHODS: CIBERSORT was utilized to evaluate the degree of infiltration of 22 immune cell types in breast cancer patients from the TCGA database, and both univariate and multivariate Cox regressions were employed to determine the prognostic significance of immune cell infiltration levels in BRCA patients. We identified independent prognostic factors for BRCA patients. Additionally, we obtained imaging features from the Cancer Imaging Archive (TCIA) database for 73 patients who underwent preoperative MRI procedures, and used the Least Absolute Shrinkage and Selection Operator (LASSO) to select the best imaging features for constructing an MRI-based radiomics model for evaluating immune cell infiltration levels in breast cancer patients. RESULTS: According to the results of Cox regression analysis, M2 macrophages were identified as an independent prognostic factor for BRCA patients (HR = 32.288, 95% CI: 3.100-357.478). A total of nine significant features were selected to calculate the radiomics-based score. We established an intratumoral model with AUCs (95% CI) of 0.662 (0.495-0.802) and 0.678 (0.438-0.901) in the training and testing cohorts, respectively. Additionally, a peritumoral model was created with AUCs (95% CI) of 0.826 (0.710-0.924) and 0.752 (0.525-0.957), and a combined model was established with AUCs (95% CI) of 0.843 (0.723-0.938) and 0.744 (0.491-0.965). The peritumoral model demonstrated the highest diagnostic efficacy, with an accuracy, sensitivity, and specificity of 0.773, 0.727, and 0.818, respectively, in its testing cohort. CONCLUSION: The MRI-based radiomics model has the potential to evaluate the degree of immune cell infiltration in breast cancer patients, offering a non-invasive imaging biomarker for assessing the tumor microenvironment in this disease.

Simulations of water pollutants in the Hangzhou Bay, China: Hydrodynamics, characteristics, and sources.

China's coastal waters are confronting serious water quality problems, particularly the Hangzhou Bay in the Yangtze River Delta. To find out the underlying cause, we use the Regional Ocean Modeling System (ROMS) to simulate the hydrodynamic characteristics and the evolution of water pollutants. The results show that the hydrodynamic conditions are complicated and the semi-exchange time is 46 days, significantly hindering the dilution and diffusion of water pollutants. Concentrations of each typical pollutant as chemical oxygen demand (COD), dissolved inorganic nitrogen (DIN), and phosphate (PO4) decrease from west to east, showing an obvious enrichment in the coastal region. Source-oriented results show that the inland water pollution of the Yangtze River and the Qiantang River is the key contributor, and the sewage outfalls on the coast near the bay worsen the pollution. This suggests that the government needs to strengthen the management of sources that affect water security.

Interaction of human dendritic cell receptor DEC205/CD205 with keratins.

DEC205 (CD205) is one of the major endocytic receptors on dendritic cells and has been widely used as a receptor target in immune therapies. It has been shown that DEC205 can recognize dead cells through keratins in a pH-dependent manner. However, the mechanism underlying the interaction between DEC205 and keratins remains unclear. Here we determine the crystal structures of an N-terminal fragment of human DEC205 (CysR∼CTLD3). The structural data show that DEC205 shares similar overall features with the other mannose receptor family members such as the mannose receptor and Endo180, but the individual domains of DEC205 in the crystal structure exhibit distinct structural features that may lead to specific ligand binding properties of the molecule. Among them, CTLD3 of DEC205 adopts a unique fold of CTLD, which may correlate with the binding of keratins. Furthermore, we examine the interaction of DEC205 with keratins by mutagenesis and biochemical assays based on the structural information and identify an XGGGX motif on keratins that can be recognized by DEC205, thereby providing insights into the interaction between DEC205 and keratins. Overall, these findings not only improve the understanding of the diverse ligand specificities of the mannose receptor family members at the molecular level but may also give clues for the interactions of keratins with their binding partners in the corresponding pathways.

Enhancing Ni/Co Activity by Neighboring Pt Atoms in NiCoP/MXene Electrocatalyst for Alkaline Hydrogen Evolution.

Density functional theory (DFT) calculations demonstrate neighboring Pt atoms can enhance the metal activity of NiCoP for hydrogen evolution reaction (HER). However, it remains a great challenge to link Pt and NiCoP. Herein, we introduced curvature of bowl-like structure to construct Pt/NiCoP interface by adding a minimal 1 ‰-molar-ratio Pt. The as-prepared sample only requires an overpotential of 26.5 and 181.6 mV to accordingly achieve the current density of 10 and 500 mA cm-2 in 1 M KOH. The water dissociation energy barrier (Ea) has a ~43 % decrease compared with NiCoP counterpart. It also shows an ultrahigh stability with a small degradation rate of 10.6 µV h-1 at harsh conditions (500 mA cm-2 and 50 °C) after 3000 hrs. X-ray photoelectron spectroscopy (XPS), soft X-ray absorption spectroscopy (sXAS), and X-ray absorption fine structure (XAFS) verify the interface electron transfer lowers the valence state of Co/Ni and activates them. DFT calculations also confirm the catalytic transition step of NiCoP can change from Heyrovsky (2.71 eV) to Tafel step (0.51 eV) in the neighborhood of Pt, in accord with the result of the improved Hads at the interface disclosed by in situ electrochemical impedance spectroscopy (EIS) and scanning electrochemical microscopy (SECM) tests.

Designer protein compartments for microbial metabolic engineering.

Protein compartments are distinct structures assembled in living cells via self-assembly or phase separation of specific proteins. Significant efforts have been made to discover their molecular structures and formation mechanisms, as well as their fundamental roles in spatiotemporal control of cellular metabolism. Here, we review the design and construction of synthetic protein compartments for spatial organization of target metabolic pathways toward increased efficiency and specificity. In particular, we highlight the compartmentalization strategies and recent examples to speed up desirable metabolic reactions, to reduce the accumulation of toxic metabolic intermediates, and to switch competing metabolic pathways. We also identify the most important challenges that need to be addressed for exploitation of these designer compartments as a versatile toolkit in metabolic reprogramming.