A novel MCGDM technique based on correlation coefficients under probabilistic hesitant fuzzy environment and its application in clinical comprehensive evaluation of orphan drugs.

Probabilistic hesitant fuzzy sets (PHFSs) are superior to hesitant fuzzy sets (HFSs) in avoiding the problem of preference information loss among decision makers (DMs). Owing to this benefit, PHFSs have been extensively investigated. In probabilistic hesitant fuzzy environments, the correlation coefficients have become a focal point of research. As research progresses, we discovered that there are still a few unresolved issues concerning the correlation coefficients of PHFSs. To overcome the limitations of existing correlation coefficients for PHFSs, we propose new correlation coefficients in this study. In addition, we present a multi-criteria group decision-making (MCGDM) method under unknown weights based on the newly proposed correlation coefficients. In addition, considering the limitations of DMs' propensity to use language variables for expression in the evaluation process, we propose a method for transforming the evaluation information of the DMs' linguistic variables into probabilistic hesitant fuzzy information in the newly proposed MCGDM method. To demonstrate the applicability of the proposed correlation coefficients and MCGDM method, we applied them to a comprehensive clinical evaluation of orphan drugs. Finally, the reliability, feasibility and efficacy of the newly proposed correlation coefficients and MCGDM method were validated.

MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics.

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation.

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.

To standardize metabolomics data analysis and facilitate future computational developments, it is essential is have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

Robust small area estimation for unit level model with density power divergence.

Unit level model is one of the classical models in small area estimation, which plays an important role with unit information data. Empirical Bayesian(EB) estimation, as the optimal estimation under normal assumption, is the most commonly used parameter estimation method in unit level model. However, this kind of method is sensitive to outliers, and EB estimation will lead to considerable inflation of the mean square error(MSE) when there are outliers in the responses yij. In this study, we propose a robust estimation method for the unit-level model with outliers based on the minimum density power divergence. Firstly, by introducing the minimum density power divergence function, we give the estimation equation of the parameters of the unit level model, and obtain the asymptotic distribution of the robust parameters. Considering the existence of tuning parameters in the robust estimator, an optimal parameter selection algorithm is proposed. Secondly, empirical Bayesian predictors of unit and area mean in finite populations are given, and the MSE of the proposed robust estimators of small area means is given by bootstrap method. Finally, we verify the superior performance of our proposed method through simulation data and real data. Through comparison, our proposed method can can solve the outlier situation better.

MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data.

Microbiome studies have become routine in biomedical, agricultural and environmental sciences with diverse aims, including diversity profiling, functional characterization, and translational applications. The resulting complex, often multi-omics datasets demand powerful, yet user-friendly bioinformatics tools to reveal key patterns, important biomarkers, and potential activities. Here we introduce MicrobiomeAnalyst 2.0 to support comprehensive statistics, visualization, functional interpretation, and integrative analysis of data outputs commonly generated from microbiome studies. Compared to the previous version, MicrobiomeAnalyst 2.0 features three new modules: (i) a Raw Data Processing module for amplicon data processing and taxonomy annotation that connects directly with the Marker Data Profiling module for downstream statistical analysis; (ii) a Microbiome Metabolomics Profiling module to help dissect associations between community compositions and metabolic activities through joint analysis of paired microbiome and metabolomics datasets; and (iii) a Statistical Meta-Analysis module to help identify consistent signatures by integrating datasets across multiple studies. Other important improvements include added support for multi-factor differential analysis and interactive visualizations for popular graphical outputs, updated methods for functional prediction and correlation analysis, and expanded taxon set libraries based on the latest literature. These new features are demonstrated using a multi-omics dataset from a recent type 1 diabetes study. MicrobiomeAnalyst 2.0 is freely available at microbiomeanalyst.ca.

ExpressAnalyst: A unified platform for RNA-sequencing analysis in non-model species.

The increasing application of RNA sequencing to study non-model species demands easy-to-use and efficient bioinformatics tools to help researchers quickly uncover biological and functional insights. We developed ExpressAnalyst ( www.expressanalyst.ca ), a web-based platform for processing, analyzing, and interpreting RNA-sequencing data from any eukaryotic species. ExpressAnalyst contains a series of modules that cover from processing and annotation of FASTQ files to statistical and functional analysis of count tables or gene lists. All modules are integrated with EcoOmicsDB, an ortholog database that enables comprehensive analysis for species without a reference transcriptome. By coupling ultra-fast read mapping algorithms with high-resolution ortholog databases through a user-friendly web interface, ExpressAnalyst allows researchers to obtain global expression profiles and gene-level insights from raw RNA-sequencing reads within 24 h. Here, we present ExpressAnalyst and demonstrate its utility with a case study of RNA-sequencing data from multiple non-model salamander species, including two that do not have a reference transcriptome.

Preparation of Cellulose Nanocrystals by Synergistic Action of Ionic Liquid and Recyclable Solid Acid under Mild Conditions.

Cellulose nanocrystals (CNCs) are nanoscale particles made from cellulose. They have many unique properties such as being lightweight, stiff, and renewable, making them promising for a variety of applications in a wide range of industries, including materials science, energy storage, and biomedicine. In this paper, a two-stage (swelling-SA-catalyzed) method including IL pretreatment and solid acid hydrolysis process was developed to extract CNCs with high purity and good thermal stability from microcrystalline cellulose (MCC). In the first stage, the swelling of MCC in ionic liquid was studied with the assistance of ultrasonication, and it was found that the amorphous regions became more disordered while the crystalline areas were selectively retained under the conditions of 30 min of reaction time, 45 °C of temperature, 2% of ionic liquid water content and 1:4 mass ratio of cellulose to ionic liquid. CNCs were extracted using solid acid hydrolysis, with a 45 wt% solid acid to cellulose ratio and a 5.0 h hydrolysis process at 45 °C. The morphology, crystallinity, surface characteristics and thermo stability of the sample were characterized by atomic force microscopy (AFM), X-ray diffraction (XRD) and thermogravimetric analysis (TGA), respectively. Results demonstrated the highly thermostable CNCs were successful extracted with rodlike shape of 300 ± 100 nm in length and 20 ± 10 nm in width. Solid acid recovery and reuse were also studied, revealing a promising candidate that can reduce the environmental impact associated with chemical products.

Contactless Sensing-Aided Respiration Signal Acquisition Using Improved Empirical Wavelet Transform for Rhythm Detection.

Respiration is one of the most important vital signs indicating physical condition, while the signal detection is challenging due to the complex rhythm and effort in practical scenarios. In this paper, we propose a contactless sensing-aided respiration signal acquisition technique, which can adaptively extract the desired signal under time-varying respiration rhythms within a wide range. To be specific, respiration is perceived by piezoelectric ceramics sensors along with ballistocardiography and other interference in a contactless manner, and the proposed improved empirical wavelet transform (IEWT) performs spectrum division and recognition based on upper envelop and principal component criteria, respectively, to adaptively extract the respiration spectrum for signal reconstruction. For validations, we extracted respiration signals from 8 healthy individuals in lab breathing at specified rhythms from 0.2 Hz to 0.6 Hz as well as 38 in-patients suffering from sleep-disordered-breathing with reference of polysomnogram in practical clinic scenario. The results showed that the detected respiration rhythms perfectly fitted the ones in experimental lab dataset with a correlation coefficient of 0.98, which validated the effectiveness of the respiration spectrum extraction of the proposed IEWT method. Besides, in practical clinical dataset, the proposed IEWT method could yield mean absolute and relative errors of respiration intervals of 0.4 and 0.05 seconds, respectively, achieving significant improvement in comparison with conventional ones. Meanwhile, the performance of IEWT was robust to rhythm variation, individual difference and breathing cycle detection techniques, which demonstrated the feasibility and superiority of the proposed IEWT method for practical respiration monitoring.

Microbiota-mediated nitrogen fixation and microhabitat homeostasis in aerial root-mucilage.

BACKGROUND: Plants sustain intimate relationships with diverse microbes. It is well-recognized that these plant-associated microbiota shape individual performance and fitness of host plants, but much remains to be explored regarding how they exert their function and maintain their homeostasis. RESULTS: Here, using pink lady (Heterotis rotundifolia) as a study plant, we investigated the phenomenon of microbiota-mediated nitrogen fixation and elucidated how this process is steadily maintained in the root mucilage microhabitat. Metabolite and microbiota profiling showed that the aerial root mucilage is enriched in carbohydrates and diazotrophic bacteria. Nitrogen isotope-labeling experiments, 15N natural abundance, and gene expression analysis indicated that the aerial root-mucilage microbiota could fix atmospheric nitrogen to support plant growth. While the aerial root mucilage is a hotspot of nutrients, we did not observe high abundance of other environmental and pathogenic microbes inside. We further identified a fungus isolate in mucilage that has shown broad-spectrum antimicrobial activities, but solely allows the growth of diazotrophic bacteria. This "friendly" fungus may be the key driver to maintain nitrogen fixation function in the mucilage microhabitat. Video Abstract CONCLUSION: The discovery of new biological function and mucilage-habitat friendly fungi provides insights into microbial homeostasis maintenance of microenvironmental function and rhizosphere ecology.

Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data.

BACKGROUND: Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices. RESULTS: We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.

Sleep Staging Using Noncontact-Measured Vital Signs.

As a physiological phenomenon, sleep takes up approximately 30% of human life and significantly affects people's quality of life. To assess the quality of night sleep, polysomnography (PSG) has been recognized as the gold standard for sleep staging. The drawbacks of such a clinical device, however, are obvious, since PSG limits the patient's mobility during the night, which is inconvenient for in-home monitoring. In this paper, a noncontact vital signs monitoring system using the piezoelectric sensors is deployed. Using the so-designed noncontact sensing system, heartbeat interval (HI), respiratory interval (RI), and body movements (BM) are separated and recorded, from which a new dimension of vital signs, referred to as the coordination of heartbeat interval and respiratory interval (CHR), is obtained. By extracting both the independent features of HI, RI, and BM and the coordinated features of CHR in different timescales, Wake-REM-NREM sleep staging is performed, and a postprocessing of staging fusion algorithm is proposed to refine the accuracy of classification. A total of 17 all-night recordings of noncontact measurement simultaneous with PSG from 10 healthy subjects were examined, and the leave-one-out cross-validation was adopted to assess the performance of Wake-REM-NREM sleep staging. Taking the gold standard of PSG as reference, numerical results show that the proposed sleep staging achieves an averaged accuracy and Cohen's Kappa index of 82.42% and 0.63, respectively, and performs robust to subjects suffering from sleep-disordered breathing.

Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data.

Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has become a workhorse in global metabolomics studies with growing applications across biomedical and environmental sciences. However, outstanding bioinformatics challenges in terms of data processing, statistical analysis and functional interpretation remain critical barriers to the wider adoption of this technology. To help the user community overcome these barriers, we have made major updates to the well-established MetaboAnalyst platform ( www.metaboanalyst.ca ). This protocol extends the previous 2011 Nature Protocol by providing stepwise instructions on how to use MetaboAnalyst 5.0 to: optimize parameters for LC-HRMS spectra processing; obtain functional insights from peak list data; integrate metabolomics data with transcriptomics data or combine multiple metabolomics datasets; conduct exploratory statistical analysis with complex metadata. Parameter optimization may take ~2 h to complete depending on the server load, and the remaining three stages may be executed in ~60 min.

OmicsNet 2.0: a web-based platform for multi-omics integration and network visual analytics.

Researchers are increasingly seeking to interpret molecular data within a multi-omics context to gain a more comprehensive picture of their study system. OmicsNet (www.omicsnet.ca) is a web-based tool developed to allow users to easily build, visualize, and analyze multi-omics networks to study rich relationships among lists of 'omics features of interest. Three major improvements have been introduced in OmicsNet 2.0, which include: (i) enhanced network visual analytics with eleven 2D graph layout options and a novel 3D module layout; (ii) support for three new 'omics types: single nucleotide polymorphism (SNP) list from genetic variation studies; taxon list from microbiome profiling studies, as well as liquid chromatography-mass spectrometry (LC-MS) peaks from untargeted metabolomics; and (iii) measures to improve research reproducibility by coupling R command history with the release of the companion OmicsNetR package, and generation of persistent links to share interactive network views. We performed a case study using the multi-omics data obtained from a recent large-scale investigation on inflammatory bowel disease (IBD) and demonstrated that OmicsNet was able to quickly create meaningful multi-omics context to facilitate hypothesis generation and mechanistic insights.

Non-Contact Heartbeat Detection Based on Ballistocardiogram Using UNet and Bidirectional Long Short-Term Memory.

Benefiting from non-invasive sensing tech- nologies, heartbeat detection from ballistocardiogram (BCG) signals is of great significance for home-care applications, such as risk prediction of cardiovascular disease (CVD) and sleep staging, etc. In this paper, we propose an effective deep learning model for automatic heartbeat detection from BCG signals based on UNet and bidirectional long short-term memory (Bi-LSTM). The developed deep learning model provides an effective solution to the existing challenges in BCG-aided heartbeat detection, especially for BCG in low signal-to-noise ratio, in which the waveforms in BCG signals are irregular due to measured postures, rhythm and artifact motion. For validations, performance of the proposed detection is evaluated by BCG recordings from 43 subjects with different measured postures and heart rate ranges. The accuracy of the detected heartbeat intervals measured in different postures and signal qualities, in comparison with the R-R interval of ECG, is promising in terms of mean absolute error and mean relative error, respectively, which is superior to the state-of-the-art methods. Numerical results demonstrate that the proposed UNet-BiLSTM model performs robust to noise and perturbations (e.g. respiratory effort and artifact motion) in BCG signals, and provides a reliable solution to long term heart rate monitoring.

ResNet-BiLSTM: A Multiscale Deep Learning Model for Heartbeat Detection Using Ballistocardiogram Signals.

As the heartbeat detection from ballistocardiogram (BCG) signals using force sensors is interfered by respiratory effort and artifact motion, advanced signal processing algorithms are required to detect the J-peak of each BCG signal so that beat-to-beat interval can be identified. However, existing methods generally rely on rule-based detection of a fixed size, without considering the rhythm features in a large time scale covering multiple BCG signals. Methods. This paper develops a deep learning framework based on ResNet and bidirectional long short-term memory (BiLSTM) to conduct beat-to-beat detection of BCG signals. Unlike the existing methods, the proposed network takes multiscale features of BCG signals as the input and, thus, can enjoy the complementary advantages of both morphological features of one BCG signal and rhythm features of multiple BCG signals. Different time scales of multiscale features for the proposed model are validated and analyzed through experiments. Results. The BCG signals recorded from 21 healthy subjects are conducted to verify the performance of the proposed heartbeat detection scheme using leave-one-out cross-validation. The impact of different time scales on the detection performance and the performance of the proposed model for different sleep postures are examined. Numerical results demonstrate that the proposed multiscale model performs robust to sleep postures and achieves an averaged absolute error (E abs) and an averaged relative error (E rel) of the heartbeat interval relative to the R-R interval of 9.92 ms and 2.67 ms, respectively, which are superior to those of the state-of-the-art detection protocol. Conclusion. In this work, a multiscale deep-learning model for heartbeat detection using BCG signals is designed. We demonstrate through the experiment that the detection with multiscale features of BCG signals can provide a superior performance to the existing works. Further study will examine the ultimate performance of the multiscale model in practical scenarios, i.e., detection for patients suffering from cardiovascular disorders with night-sleep monitoring.

Multiomics Reveals the Effect of Root Rot on Polygonati Rhizome and Identifies Pathogens and Biocontrol Strain.

Root (rhizome) rot of Polygonatum plants has received substantial attention because it threatens yield and sustainable utilization in the polygonati rhizome industry. However, the potential pathogens that cause rhizome rot as well as the direct and indirect (via root-associated microbes) strategies by which Polygonatum defends against pathogens remain largely unknown. Herein, we used integrated multiomics of plant-targeted metabolomics and transcriptomics, microbiome, and culture-based methods to systematically investigate the interactions between the Polygonatum cyrtonema Hua root-associated microbiota and pathogens. We found that root rot inhibited P. cyrtonema rhizome growth and that the fresh weight significantly decreased (P < 0.001). The transcriptomic and metabonomic results showed that the expression of differentially expressed genes (DEGs) related to specialized metabolic and systemic resistance pathways, such as glycolysis/gluconeogenesis and flavonoid biosynthesis, cycloartenol synthase activity (related to saponin synthesis), mitogen-activated protein kinase (MAPK) signaling, and plant hormone signal transduction, was particularly increased in diseased rhizomes. Consistently, the contents of lactose, d-fructose, sarsasapogenin, asperulosidic acid, botulin, myricadoil, and other saponins, which are functional medicinal compounds present in P. cyrtonema rhizomes, were also increased in diseased plants infected with rhizome rot. The microbiome sequencing and culture results showed that root rot disrupted the P. cyrtonema bacterial and fungal communities and reduced the microbial diversity in the rhizomes and rhizosphere soil. We further found that a clear enrichment of Streptomyces violascens XTBG45 (HJB-XTBG45) in the healthy rhizosphere could control the root rot caused by Fusarium oxysporum and Colletotrichum spaethianum. Taken together, our results indicate that P. cyrtonema can modulate the plant immune system and metabolic processes and enrich beneficial root microbiota to defend against pathogens. IMPORTANCE Root (rhizome or tuber) reproduction is the main method for the agricultural cultivation of many important cash crops, and infected crop plants rot, exhibit retarded growth, and experience yield losses. While many studies have investigated medicinal plants and their functional medicinal compounds, the occurrence of root (rhizome) rot of plant and soil microbiota has received little attention. Therefore, we used integrated multiomics and culture-based methods to systematically study rhizome rot on the famous Chinese medicine Polygonatum cyrtonema and identify pathogens and beneficial microbiota of rhizome rot. Rhizome rot disrupted the Polygonatum-associated microbiota and reduced microbial diversity, and rhizome transcription and metabolic processes significantly changed. Our work provides evidence that rhizome rot not only changes rhizome transcription and functional metabolite contents but also impacts the microbial community diversity, assembly, and function of the rhizome and rhizosphere. This study provides a new friendly strategy for medicinal plant breeding and agricultural utilization.

Efficiently conversion of raw lignocellulose to levulinic acid and lignin nano-spheres in acidic lithium bromide-water system by two-step process.

Herein, acidic concentrated lithium bromide-water system was efficiently carried out to synthesize levulinic acid (LA) from raw lignocellulose by two-step treatment. Saccharification was processed in 1st step, and 80.96 wt% glucose and 85.60 wt% xylose were yielded based on their theoretical yield from poplar at 110 °C for 20 min. The hydrolysate after solid residual lignin (SRL) separation was converted into LA and furfural by thermal treatment (130 °C) in the 2nd step, where 67.0 wt% LA and 48.0 wt% furfural were yielded. The SRL in 1st step, with high hydrophobicity and uniform dispersity, was used to prepare lignin nanoparticles (LNPs), which showed tailored size (100-200 nm diameters) and morphology in solid or hollow structure with single hole. Additionally, the residue in 2nd step was suggested as biochar. So far, this study offered a simple pathway for utilization of raw lignocellulose in water system, resulting in high yields of LA and LNPs.

Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals.

Purpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments. Methods: First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers. Results: The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF ≤ 49 % ). Conclusion: This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features.

A hydrotrope pretreatment for stabilized lignin extraction and high titer ethanol production.

The biomass pretreatment strategies using organic acids facilitate lignin removal and enhance the enzymatic digestion of cellulose. However, lignin always suffers a severe and irreversible condensation. The newly generated C-C bonds dramatically affect its further upgrading. In this study, we used a recyclable hydrotrope (p-Toluenessulfonic acid, p-TsOH) to dissolve lignin under mild condition and stabilized lignin with a quenching agent (formaldehyde, FA) during extraction, achieving both value-added lignin extraction and efficient enzymatic saccharification of cellulose. Approximately 63.7% of lignin was dissolved by 80% (wt. %) p-TsOH with 1.5% FA addition at 80 °C, 30 min. The obtained lignin was characterized by FTIR spectroscopy, TGA, 2D HSQC NMR spectroscopy, and GPC. The results indicated that the extracted lignin exhibited excellent properties, such as light color, a low molecular weight (Mw, 5371 g/mol), and a narrow polydispersity (Mw/Mn, 1.63). The pretreated substrate was converted to ethanol via a quasi-simultaneous saccharification and fermentation process (Q-SSF). After fermentation of 60 h, the ethanol concentration reached 38.7 ± 3.3 g/L which was equivalent to a theoretical ethanol yield of 82.9 ± 2.2% based on the glucan content, while the residual glucose concentration was only 4.69 ± 1.4 g/L. In short, this pretreatment strategy protected lignin to form new C-C linkages and improved the enzymatic saccharification of glucan for high-titer ethanol production.