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Objective: Post-treatment cartilage morphometry in the FORWARD study was performed without blinding to MRI acquisition order, involving potential reader bias. Here we obtained unbiased estimates of cartilage change post-treatment, reading year (Y)2 and Y5 MRIs with blinding to time point. We studied whether post-treatment cartilage thickness change differed between sprifermin- and placebo-treated knees. Methods: FORWARD was a 5-year randomized control trial in 549 knee osteoarthritis patients. Here, Y2/Y5 images were analyzed with blinding to relative temporal order and treatment group. Cartilage change during Y2→Y5 was obtained in 337 participants: n ​= ​57 treated with placebo intra-articular injections every 6 months (q6M); n ​= ​69 with 30 ​μg sprifermin every 12 months (q12 ​M), n ​= ​67 with 30 ​μg q6M, n ​= ​73 with 100 ​μg q12 ​M, and n ​= ​71 with 100 ​μg q6M between baseline (BL) and 18 ​M. Total femorotibial joint (TFTJ) cartilage thickness was the primary analytic focus. Results: TFTJ cartilage thickness change during Y2→Y5 was −26μm (SD64; 95%CI -32,-19) across the cohort; no statistically significant difference (p ​= ​0.80) was observed between Sprifermin treated or placebo arms (one-way ANOVA). All groups lost cartilage, but the treatment-related difference in cartilage thickness in Sprifermin arms relative to placebo at Y2 was maintained until Y5. Annualized cartilage change in placebo participants was −8.2 ​μm (SD21; 95%CI -14,-2.5) during Y2→Y5 vs. −5.4 ​μm (SD27; 95%CI -13,1.8) during BL→Y2; no significant difference was identified (t-test). Conclusion: FORWARD is the first study evaluating post-treatment benefits of a potential disease modifying osteoarthritis drug. Cartilage thickness gained with 100 ​μg sprifermin at Y2 is maintained to Y5 and thus appears viable and sustainable.This is a post-hoc analysis of the FORWARD trial: ClinicalTrials.gov Identifier: NCT01919164.

IntroductionGastric schwannoma is a rare benign tumor accounting for only 1–2% of alimentary tract mesenchymal tumors. Owing to their low incidence rate, most cases are misdiagnosed as gastrointestinal stromal tumors (GISTs), especially tumors with a diameter of less than 5 cm. Therefore, this study aimed to develop and validate a diagnostic nomogram based on computed tomography (CT) imaging features for the preoperative prediction of gastric schwannomas and GISTs (diameters = 2–5 cm).MethodsGastric schwannomas in 47 patients and GISTs in 230 patients were confirmed by surgical pathology. Thirty-four patients with gastric schwannomas and 167 with GISTs admitted between June 2009 and August 2022 at Hospital 1 were retrospectively analyzed as the test and training sets, respectively. Seventy-six patients (13 with gastric schwannomas and 63 with GISTs) were included in the external validation set (June 2017 to September 2022 at Hospital 2). The independent factors for differentiating gastric schwannomas from GISTs were obtained by multivariate logistic regression analysis, and a corresponding nomogram model was established. The accuracy of the nomogram was evaluated using receiver operating characteristic and calibration curves.ResultsLogistic regression analysis showed that the growth pattern (odds ratio [OR] 3.626; 95% confidence interval [CI] 1.105–11.900), absence of necrosis (OR 4.752; 95% CI 1.464–15.424), presence of tumor-associated lymph nodes (OR 23.978; 95% CI 6.499–88.466), the difference between CT values during the portal and arterial phases (OR 1.117; 95% CI 1.042–1.198), and the difference between CT values during the delayed and portal phases (OR 1.159; 95% CI 1.080–1.245) were independent factors in differentiating gastric schwannoma from GIST. The resulting individualized prediction nomogram showed good discrimination in the training (area under the curve [AUC], 0.937; 95% CI, 0.900–0.973) and validation (AUC, 0.921; 95% CI, 0.830–1.000) datasets. The calibration curve showed that the probability of gastric schwannomas predicted using the nomogram agreed well with the actual value.ConclusionThe proposed nomogram model based on CT imaging features can be used to differentiate gastric schwannoma from GIST before surgery.

A PLS-based quality prediction method is proposed for batch processes using two-dimensional extended windows. To realize the adoption of information in the directions of sampling time and batch, a newly defined region of support (ROS), called the k-i-back-extended region of support (KIBROS), is proposed; it establishes an extended window by adding two regions of data to the traditional ROS to include all possible important data for quality prediction. Based on the new ROS, extended windows are established, and different models are proposed using the extended windows for batch process quality prediction. Furthermore, using the typical injection molding batch process as an example, the proposed quality prediction method is experimentally verified, proving that the proposed methods have higher prediction accuracy than the traditional method and that the prediction stability is also improved.

Peony seeds, an important oil resource, have been attracting much attention because of α-linolenic acid. Oil bodies (OBs), naturally pre-emulsified oils, have great potential applications in the food industry. This study investigated the effects of extraction pH and Quillaja saponin (QS) on the physicochemical properties of peony oil body (POB) emulsions. POBs were extracted from raw peony milk at pH 4.0, 5.0, 6.0, and 7.0 (named pH 4.0-, 5.0-, 6.0-, and 7.0-POBs). All POBs contained extrinsic proteins and oleosins. The extrinsic proteins of pH 4.0- and pH 5.0-POB were 23 kDa and 38 kDa glycoproteins, the unknown proteins were 48 kDa and 60 kDa, while the 48 kDa and 38 kDa proteins were completely removed under the extraction condition of pH 6.0 and 7.0. The percentage of extrinsic proteins gradually decreased from 78.4% at pH 4.0-POB to 33.88% at pH 7.0-POB, while oleosin contents increased. The particle size and zeta potential of the POB emulsions decreased, whereas the oxidative stability, storage stability, and pI increased with the increasing extraction pH. QS (0.05~0.3%) increased the negative charges of all the POB emulsions, and 0.1% QS significantly improved the dispersion, storage, and the oxidative stability of the POB emulsions. This study provides guidance for selecting the proper conditions for the aqueous extraction of POBs and improving the stability of OB emulsions.

Global changes in nutrient deposition rates and habitat fragmentation are likely to have profound effects on plant communities, particularly in the nutrient-limited systems of the tropics and subtropics. However, it remains unclear how increased phosphorus (P) supply affects seedling growth in P-deficient subtropical fragmented forests. To explore this, we applied P to 11 islands in a subtropical Chinese archipelago and examined the results in combination with a contemporary greenhouse experiment to test the influence of P addition on seedling growth and survival. We measured the growth (i.e., base area) and mortality rate of seedlings for one arbuscular mycorrhizal (AM) and one ectomycorrhizal (EcM) tree species separately and calculated their relative growth rate and mortality when compared with P addition and control treatment on each island. We also measured three functional traits and the biomass of seedlings in the greenhouse experiment. Results showed that P addition significantly increased the mortality of AM and EcM seedlings and reduced the growth rate of EcM seedlings. The relative growth rate of AM seedlings, but not EcM seedlings, significantly decreased as the island area decreased, suggesting that P addition could promote the relative growth rate of AM seedlings on larger islands. The greenhouse experiment showed that P addition could reduce the specific root length of AM and EcM seedlings and reduce the aboveground and total biomass of seedlings, indicating that P addition may affect the resource acquisition of seedlings, thereby affecting their survival and growth. Our study reveals the synergistic influence of habitat fragmentation and P deposition, which may affect the regeneration of forest communities and biodiversity maintenance in fragmented habitats.

Soybean oil body (SOB) emulsions were prepared using OBs extracted at pH 11.0 and pH 7.0. The pH 11.0-SOB comprised oleosins, whereas pH 7.0-SOB comprised extrinsic proteins and oleosins. All SOB emulsions were heated at 60–100 °C for 15 min. Heating may lead to the release of extrinsic proteins from the surface of pH 7.0-SOB due to heat-induced denaturation. The total proportion of α-helix and β-sheets gradually decreased from 77 (unheated) to 36.2% (100 °C). During stomach digestion, the extrinsic protein hydrolysis of heated pH 7.0-SOB emulsions was fast between 60 and 80 °C, and it then slowed between 90 and 100 °C; heating inhibited the oleosin hydrolysis of pH 7.0- and 11.0-SOBs. Heat treatment promoted aggregation and coalescence, and it resulted in increased particle sizes for all emulsions. Larger aggregates were found in heated pH 7.0-SOB emulsions, and larger oil droplets were found in heated pH 11.0-SOB emulsions. After intestinal digestion, the droplets of all SOB emulsions gradually dispersed, and particle sizes decreased. Different heating temperatures had lesser effects on particle sizes and microstructures. Lipolysis was affected by the extraction pH and heating. For pH 11.0-SOB emulsions, the FFA release tendency was greatly affected by the heating temperature, and heating to 80 °C resulted in the highest FFA release (74%). However, all pH 7.0-SOB emulsions had similar total FFA releases. In addition, the droplet charges of heated pH 7.0-SOB emulsions were lower than those of unheated pH 7.0-SOB emulsions in both the intestine and stomach phases; however, the charge changes in different pH 11.0-SOB emulsions showed the opposite tendency. This study will offer guidance regarding the application of SOB emulsions in food.

Mach number, as an important index to judge the system performance in wind tunnel tests, its stability determines the quality of the flow field of this wind tunnel and needs to be controlled precisely. Due to the complex process in the wind tunnel test, it is difficult to ensure the smooth operation of the Mach number by the traditional control strategy, therefore, a Mach number prediction model based on a nonlinear autoregressive exogenous model (NARX)-Elman is proposed in this paper. Firstly, the NARX model is adopted as the basic framework of this model, the false nearest neighbor (FNN) is used for model order solving, and the Elman network is used for dynamic nonlinear fitting of the model. Secondly, considering the wind tunnel system with multiple conditions and the high cost of conducting complete data experiments, a new model migration method, the input-output slope/bias correction-genetic algorithm (IOSBC-GA), is proposed, which uses IOSBC method as the basic framework to construct the migration model based on the historical condition model and a small amount of data of the new condition, and uses GA to find the slope of deviation in the new model and correct the input-output relationship between the old and new conditions, so as to establish the model for the new condition. By comparing the model respectively with the traditional algorithm and the model built without migration, the root mean square error (RMSE) and the maximum deviation (MD) of this model are less than 0.001, indicating that the model has high prediction accuracy.

Hot-pressed peanut meal protein hydrolysates are rich in Arg residue, but there is a lack of research on their α-glucosidase inhibitory activity. In this study, different proteases were used to produce hot-pressed peanut meal protein hydrolysates (PMHs) to evaluate the α-glucosidase inhibitory activity. All PMHs showed good α-glucosidase inhibitory activity with the best inhibition effect coming from the dual enzyme system of Alcalase and Neutrase with an IC50 of 5.63 ± 0.19 mg/mL. The fractions with the highest inhibition effect were separated and purified using ultrafiltration and cation exchange chromatography. Four novel α-glucosidase inhibitory peptides (FYNPAAGR, PGVLPVAS, FFVPPSQQ, and FSYNPQAG) were identified by nano-HPLC-MS/MS and molecular docking. Molecular docking showed that peptides could occupy the active pocket of α-glucosidase through hydrogen bonding, hydrophobic interaction, salt bridges, and π-stacking, thus preventing the formation of complexes between α-glucosidase and the substrate. In addition, the α-glucosidase inhibitory activity of PMHs was stable against hot, pH treatment and in vitro gastrointestinal digestion. The study demonstrated that PMHs might be used as a natural anti-diabetic material with the potential to inhibit α-glucosidase.

The process of hydrometallurgical has the characteristics of many sub-processes with complicated reaction mechanism and long process flow. How to keep the hydrometallurgical process running in the state of optimal economic efficiency is the difficulty task. In this paper, a method based on industrial big data is proposed to compensate the production index of the hydrometallurgical process. Based on the current production index, the just-in-time learning (JITL) idea is used to establish the model that describes the relationship between the compensation value and the economic benefit increment. Then, the compensation value of the current production index is calculated, and the result is applied to the production process. The simulation and offline experiment results verify the effectiveness of the proposed method.

This paper proposed a quality-related online monitoring strategy based on time and batch two-dimensional evolution information for batch processes. In the direction of time, considering the difference between each phase and the steady part and the transition part in the same phase, the change trend of the regression coefficient of the PLS model is used to divide each batch into phases, and each phase into parts. The phases, the steady parts, and the transition parts are finally distinguished and dealt with separately in the subsequent modeling process. In the batch direction, considering the slow time-varying characteristics of batch evolution, sliding windows are used to perform mode division by analyzing the evolution trend of the score matrix T in the PLS model on the base of phase division and within-phase part division. Finally, an online monitoring model that comprehensively considers the evolution information of time and batch is obtained. In a typical batch operation process, injection molding is used as an example for experimental analysis. The results show that the proposed algorithm takes advantage of mixing the time-batch two-dimensional evolution information. Compared with the traditional methods, the proposed method can overcome the shortcomings caused by the single dimension analysis and has better monitoring results.

In this paper, focusing on the slow time-varying characteristics, a series of works have been conducted to implement an accurate quality prediction for batch processes. To deal with the time-varying characteristics along the batch direction, sliding windows can be constructed. Then, the start-up process is identified and the whole process is divided into two modes according to the steady-state identification. In the most important mode, the process data matrix, used to establish the regression model of the current batch, is expanded to involve the process data of previous batches, which is called batch augmentation. Thus, the process data of previous batches, which have an important influence on the quality of the current batch, will be identified and form a new batch augmentation matrix for modeling using the partial least squares (PLS) method. Moreover, considering the multiphase characteristic, batch augmentation analysis and modeling is conducted within each phase. Finally, the proposed method is applied to a typical batch process, the injection molding process. The quality prediction results are compared with those of the traditional quality prediction method based on PLS and the ridge regression method under the proposed batch augmentation analysis framework. The conclusion is obtained that the proposed method based on the batch augmentation analysis is superior.

The authors wish to make the following correction to this paper [...]

Invasive breast cancer is highly regulated by tumor-derived cytokines in tumor microenvironment. The development of drugs that specifically target cytokines are promising in breast cancer treatment. In this study, we reported that arctigenin, a bioactive compound from Arctium lappa L., could decrease tumor-promoting cytokines GM-CSF, MMP-3, MMP-9 and TSLP in breast cancer cells. Arctigenin not only inhibited the proliferation, but also the invasion and stemness of breast cancer cells via decreasing GM-CSF and TSLP. Mechanistically, arctigenin decreased the promoter activities of GM-CSF and TSLP via reducing the nuclear translocation of NF-κB p65 which is crucial for the transcription of GM-CSF and TSLP. Furthermore, arctigenin-induced depletion of GM-CSF and TSLP inhibited STAT3 phosphorylation and β-catenin signaling resulting in decreased proliferation, invasion and stemness of breast cancer cells in vitro and in vivo. Our findings provide new insights into the mechanism by which tumor-promoting cytokines regulate breast cancer progression and suggest that arctigenin is a promising candidate for cytokine-targeted breast cancer therapy.

Carboxymethyl-quaternary ammonium oligochitosan (CM-QAOC) exhibited high inhibition to scaling and microbial formation and also remarkable fluorescence. In this paper its fluorescent properties and application as a fluorescent tracing chemical for industrial water treatment were studied in detail. The fluorescence intensities of CM-QAOC were in good linear agreement with its content in the concentration range of 5 to 500 mg/L and in the range of pH 7 to 9, which shows CM-QAOC can trace itself directly. The results showed the fluorescence would not be influenced by common phosphoruscontaining organic and inorganic water treatment chemicals and N-dodecyl-N,N-dimethylbenzenemethanaminium chloride. This means CM-QAOC is compatible with those chemicals. The metal ions Ca2+, Mg2+, Fe3+ and Cu2+ from raw water or corrosion products could cause obvious enhancement in fluorescence intensities and sometimes blue-shifts in the fluorescence maxima, which demonstrated CM-QAOC could also be used as tracer to monitor damages like corrosion and scaling in water systems, by varying changes of fluorescence intensities and maximum emission wavelength. The fluorescence of CM-QAOC may be influenced by NaClO, and be quenched by sunshine slightly. Its ratio of biochemical oxygen demand to chemical oxygen demandwas 0.53, which indicates CM-QAOC is a biodegradable chemical. Therefore, CM-QAOC can be applied as a tracer and environmental-friendly chemical for industrial cooling water treatment. [ABSTRACT FROM AUTHOR]

Soybean soluble polysaccharides (SSPS) were extracted under acidic condition from the okara of producing soybean protein isolate. The methyl esterificated carboxylic group of galacturonic acid (GalA) in SSPS could be demethoxylated by alkali treatment when heated, which had effects on SSPS properties. In this study, okara was treated in six selected alkali conditions, and then SSPS were extracted under the acidic condition (115 °C, 100 min, pH 4.5). The obtained SSPS were determined to have degree of esterification (DE) of 49, 55, 60, 66, 74 and 83%, respectively. Violent alkali treatment produced SSPS with not only lower DE but also lower molecular weight (MW) compared to mild alkali treatment. MW was increased from 396 to 489 kDa with increasing DE. It was found that SSPS with lower DE possessed lower aqueous viscosity, more negative charges, better emulsifying activity in oil-in-water emulsion and stabilizing activity in acidic milk drink. When DE changed from 49 to 83%, aqueous viscosity (10% (w/w) SSPS), average particle size of fresh oil-in-water emulsion, and centrifugation precipitating rate (CPR) of acidic milk drink (pH 4.5; two-stage 30 MPa homogeneous pressure) were increased from 25.8 to 108.2 mPa s, 1.5 to 4.0 μm, and 0.20 to 0.47%, respectively; and zeta potential (pH 7) was decreased from 17 to 10 mV. These results suggested that SSPS obtained from different alkali treatment had different functional properties. [ABSTRACT FROM AUTHOR]

With the proliferation of spatially oriented time-to-event data, spatial modeling in the survival context has received increased recent attention. A traditional way to capture a spatial pattern is to introduce frailty terms in the linear predictor of a semiparametric model, such as proportional hazards or accelerated failure time. We propose a new methodology to capture the spatial pattern by assuming a prior based on a mixture of spatially dependent Polya trees for the baseline survival in the proportional hazards model. Thanks to modern Markov chain Monte Carlo (MCMC) methods, this approach remains computationally feasible in a fully hierarchical Bayesian framework. We compare the spatially dependent mixture of Polya trees (MPT) approach to the traditional spatial frailty approach, and illustrate the usefulness of this method with an analysis of Iowan breast cancer survival data from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute. Our method provides better goodness of fit over the traditional alternatives as measured by log pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and full sample score (FSS) statistics. [ABSTRACT FROM AUTHOR]

Ligand-dependent activation of the Hedgehog (Hh) pathway in colorectal cancers (CRCs) is controversial, and the regulation mechanism of Hh ligands expression remains to be determined. In the present study, the mRNA expressions of Sonic hedgehog (SHH), Indian hedgehog (IHH), Patched, Smoothened, and Gli1 were examined in four cultured colon cancer cell lines by reverse transcription--PCR. Moreover, ligands mRNA expression (SHH and IHH) were examined by reverse transcription-PCR, and SHH protein expression by immunohistochemistry in 25 primary CRCs. The methylation status of SHH and IHH was also investigated by bisulfate sequencing or methylation specific PCR. IHH mRNA was completely absent in cell lines studied, and expressed at a very low level or not expressed at all in primary CRCs. Methylation analysis revealed that IHH promoter was hypermethylated in colon cancer cell lines. Absence of SHH mRNA expression and hypermethylation of its promoter was also observed in colon cancer cell lines. However, high level expression of SHH and hypomethylation of its promoter was detected in primary CRCs. In conclusion, ligand dependent activity of Hh pathway is inactive in cultured colon cancer cells correlating to ligands hypermethylation. In contrast, SHH overexpression, possible consequence of promoter hypomethylation, could play a role in the carcinogenesis of primary CRCs. [ABSTRACT FROM AUTHOR]

Presents a study conducted by researchers from the National University of Singapore, Singapore, on electroactive polymer patterns with metal incorporation on a polymeric substrate. Fabrication of electroactive polymer patterns on a polymeric substrate; Impact of argon plasma treatment on doping reaction between polyaniline and viologen; Effect of plasma polymerization on selective surface deposition of aniline polymer.

Mixtures of Polya trees offer a very flexible nonparametric approach for modelling time-to-event data. Many such settings also feature spatial association that requires further sophistication, either at the point level or at the lattice level. In this paper, we combine these two aspects within three competing survival models, obtaining a data analytic approach that remains computationally feasible in a fully hierarchical Bayesian framework using Markov chain Monte Carlo methods. We illustrate our proposed methods with an analysis of spatially oriented breast cancer survival data from the Surveillance, Epidemiology and End Results program of the National Cancer Institute. Our results indicate appreciable advantages for our approach over competing methods that impose unrealistic parametric assumptions, ignore spatial association or both. [ABSTRACT FROM AUTHOR]

Process operating performance assessment judges the optimal degree of the process performance and identifies the cause for non-optimality in real-time. Therefore, process operating performance assessment is of great significance in both the theoretical research and practical application. In this article, the multiple working modes, the plant-wide process characteristics, and the coexistence of both the quantitative and qualitative information are considered. To solve the above problems, a novel two-level multi-block hybrid model based operating performance assessment approach is proposed. Under each working mode, the two-level hybrid model is established. Due to the novel offline modeling method, the global performance grade is directly determined by the sub-block performance grades and the sub-blocks can be modeled separately with different modelling techniques. In this article, a quantitative information dominated sub-block is modeled by the quantitative method, while a qualitative information dominated sub-block is modeled by the qualitative approach. When any non-optimal operating performance occurs, a contribution rate based non-optimal cause identification technique is developed within the non-optimal sub-blocks. At the end, the developed assessment approach is applied to a gold hydrometallurgy process to illustrate its feasibility and validity. [ABSTRACT FROM AUTHOR]