ABSTRACT
Multiplex, digital nucleic acid detections have important biomedical applications, but the multiplexity of existing methods is predominantly achieved using fluorescent dyes or probes, making the detection complicated and costly. Here, we present the StratoLAMP for label-free, multiplex digital loop-mediated isothermal amplification based on visual stratification of the precipitate byproduct. The StratoLAMP designates two sets of primers with different concentrations to achieve different precipitate yields when amplifying different nucleic acid targets. In the detection, deep learning image analysis is used to stratify the precipitate within each droplet and determine the encapsulated targets for nucleic acid quantification. We investigated the effect of the amplification reagents and process on the precipitate generation and optimized the assay conditions. We then implemented a deep-learning image analysis pipeline for droplet detection, achieving an overall accuracy of 94.3%. In the application, the StratoLAMP successfully achieved the simultaneous quantification of two nucleic acid targets with high accuracy. By eliminating the need for fluorescence, StratoLAMP represents a unique concept toward label-free, multiplex nucleic acid assays and an analytical tool with great cost-effectiveness.
Subject(s)
Nucleic Acid Amplification Techniques , Nucleic Acids , Nucleic Acid Amplification Techniques/methods , Molecular Diagnostic Techniques/methods , DNA Primers , Sensitivity and SpecificityABSTRACT
The conversion of lignocellulosic biomass into various high-value chemicals has been a rapid expanding research topic in industry and agriculture. Among them, alkaline removal and utilization of lignin are important for the accelerated degradation of biomass. Modern biorefinery has been focusing the vision on the advancement of economical, green, and environmentally friendly processes. Therefore, it is indispensable to develop cost-effective and simple biomass conversion technologies to obtain high-value products. In this study, the black liquor (BL) obtained from the alkaline pretreatment of biomass was added to polyvinyl alcohol (PVA) solution and used to prepare degradable ultraviolet (UV) shielding films, achieving direct and efficient utilization of the aqueous phase from alkaline pretreatment. This method avoids the extraction step of lignin fraction from black liquor, which can be directly utilized as the raw materials of films preparation. In addition, the direct use of alkaline BL results in films with similar UV-shielding properties, higher physical strength, and similar thermal stability compared with films made by commercial alkaline lignin. Therefore, this strategy is proposed for alkaline-pretreated biorefineries as a simple way to convert waste BL into valuable products and partially recover unconsumed sodium hydroxide to achieve as much integration of biomass and near zero-waste biorefineries as possible.
ABSTRACT
Objective and Impact Statement. This study developed and validated a deep semantic segmentation feature-based radiomics (DSFR) model based on preoperative contrast-enhanced computed tomography (CECT) combined with clinical information to predict early recurrence (ER) of single hepatocellular carcinoma (HCC) after curative resection. ER prediction is of great significance to the therapeutic decision-making and surveillance strategy of HCC. Introduction. ER prediction is important for HCC. However, it cannot currently be adequately determined. Methods. Totally, 208 patients with single HCC after curative resection were retrospectively recruited into a model-development cohort (n=180) and an independent validation cohort (n=28). DSFR models based on different CT phases were developed. The optimal DSFR model was incorporated with clinical information to establish a DSFR-C model. An integrated nomogram based on the Cox regression was established. The DSFR signature was used to stratify high- and low-risk ER groups. Results. A portal phase-based DSFR model was selected as the optimal model (area under receiver operating characteristic curve (AUC): development cohort, 0.740; validation cohort, 0.717). The DSFR-C model achieved AUCs of 0.782 and 0.744 in the development and validation cohorts, respectively. In the development and validation cohorts, the integrated nomogram achieved C-index of 0.748 and 0.741 and time-dependent AUCs of 0.823 and 0.822, respectively, for recurrence-free survival (RFS) prediction. The RFS difference between the risk groups was statistically significant (P<0.0001 and P=0.045 in the development and validation cohorts, respectively). Conclusion. CECT-based DSFR can predict ER in single HCC after curative resection, and its combination with clinical information further improved the performance for ER prediction.
ABSTRACT
A novel ternary solvent system for organosolv fractionation of lignocellulosic biomass, named APW process, which is composed of acetone, phenoxyethanol and water with the advantages of monophasic deconstruction and biphasic separation of components was developed. Through fractionation of amorpha as a case study, a monophasic APW solution (acetone/phenoxyethanol/water = 5:11:4, volume ratio) with the best lignin affinity was constructed based on Hansen solubility parameters. According to Taguchi experimental design, the optimal conditions were 130 °C, 70 min, 0.15 M sulfuric acid and 20 LSR. Under optimal conditions, removal of lignin and hemicellulose reached 95.60% and 98.39%, respectively. While 80.48% of cellulose was retained in residue and its digestibility was 80.36%. Then, 83.74% of hemicellulose was recovered from aqueous as sugars, and 35.64% of lignin was recovered by precipitation. Moreover, APW process also have effective fractionation of sugarcane bagasse, corn cob and pine, cellulose and hemicellulose recovery were both over 80%.
Subject(s)
Acetone , Water , Biomass , Chemical Fractionation , Ethylene Glycols , Hydrolysis , Lignin , SolventsABSTRACT
Typically, the Electromechanical Impedance (EMI) technique does not use an analytical model for basic damage identification. However, an accurate model is necessary for getting more information about any damage. In this paper, an EMI model is presented for predicting the electromechanical impedance of a cracked beam structure quantitatively. A coupled system of a cracked Timoshenko beam with a pair of PZT patches bonded on the top and bottom surfaces has been considered, where the bonding layers are assumed as a Kelvin-Voigt material. The shear lag model is introduced to describe the load transfer between the PZT patches and the beam structure. The beam crack is simulated as a massless torsional spring; the dynamic equations of the coupled system are derived, which include the crack information and the inertial forces of both PZT patches and adhesive layers. According to the boundary conditions and continuity conditions, the analytical expression of the admittance of PZT patch is obtained. In the case study, the influences of crack and the inertial forces of PZT patches are analyzed. The results show that: (1) the inertial forces affects significantly in high frequency band; and (2) the use of appropriate frequency range can improve the accuracy of damage identification.
