Facile dissolution of cellulose by superbase-derived ionic liquid using organic solvents as co-solvents at mild temperatures.

Dissolving cellulose at low temperatures is a key step in its efficient utilization as a renewable resource to produce high-value-added platform chemicals and high-performance materials. Here, the potential of four aprotic organic solvents was investigated for use as co-solvents with a sustainable DBU-derived ionic liquid (SIL) for the low-temperature dissolution and regeneration of cellulose. Combined experiments, density functional theory calculations, and molecular dynamic simulations were performed. The type and amount of co-solvent were found to have a significant impact on the solubility of cellulose, the dissolution process, and the structure of regenerated cellulose. The addition of organic solvents can significantly reduce the cellulose dissolution temperature and increase the solubility. Among the solvents assessed, 40 wt% DMSO exhibited the most effective synergistic interaction with SIL, where the solubility of cellulose was 14.6 wt% at 75 °C. Subsequently, the effects of the different types and amounts of co-solvents on the microscopic morphology and chemical structure of regenerated cellulose were thoroughly explored. The results showed that different types of organic solvents had different effects on the microstructure of regenerated cellulose. The results may guide the manufacturing specifications of high-performance regenerated fiber materials.

Hearing intervention for decreasing risk of developing dementia in elders with mild cognitive impairment: study protocol of a multicenter randomized controlled trial for Chinese Hearing Solution for Improvement of Cognition in Elders (CHOICE).

BACKGROUND: Age-related hearing loss (ARHL) signifies the bilateral, symmetrical, sensorineural hearing loss that commonly occurs in elderly individuals. Several studies have suggested a higher risk of dementia among patients diagnosed with ARHL. Although the precise causal association between ARHL and cognitive decline remains unclear, ARHL has been recognized as one of the most significant factors that can be modified to reduce the risk of developing dementia potentially. Mild cognitive impairment (MCI) typically serves as the initial stage in the transition from normal cognitive function to dementia. Consequently, the objective of our randomized controlled trial (RCT) is to further investigate whether the use of hearing aids can enhance cognitive function in older adults diagnosed with ARHL and MCI. METHODS AND DESIGN: This study is a parallel-arm, randomized controlled trial conducted at multiple centers in Shanghai, China. We aim to enlist a total of 688 older adults (age ≥ 60) diagnosed with moderate-to-severe ARHL and MCI from our four research centers. Participants will be assigned randomly to either the hearing aid fitting group or the health education group using block randomization with varying block sizes. Audiometry, cognitive function assessments, and other relevant data will be collected at baseline, as well as at 6, 12, and 24 months post-intervention by audiologists and trained researchers. The primary outcome of our study is the rate of progression to dementia among the two groups of participants. Additionally, various evaluations will be conducted to measure hearing improvement and changes in cognitive function. Apart from the final study results, we also plan to conduct an interim analysis using data from 12-month follow-up. DISCUSSION: In recent years, there has been a notable lack of randomized controlled trials (RCTs) investigating the possible causal relationship between hearing fitting and the improvement of cognitive function. Our findings may demonstrate that hearing rehabilitation can be a valuable tool in managing ARHL and preventing cognitive decline, which will contribute to the development of a comprehensive framework for the prevention and control of cognitive decline. TRIAL REGISTRATION: Chinese Clinical Trial Registry chictr.org.cn ChiCTR2000036139. Registered on 21 August 2020.

The effect of steamed potato-wheat bread intake on weight, lipids, glucose, and urinary Na+/K+: A randomized controlled trial in Chinese adults.

Steamed potato bread has received much attention from nutritionists and agriculturalists since it became a staple food of China in 2015. Epidemiological studies have indicated that potatoes may cause diabetes and hypertension, but few trials have evaluated this effect. Through a clinical trial, we evaluated the effect of steamed potato bread intake on adults. In total, 49 and 30 individuals were assigned to the intervention and control groups, respectively. Potato-wheat bread (raw wheat flour and cooked potato flour in the ratio 3:7) and steamed wheat bread (100% raw wheat flour) were provided to the intervention and control groups, respectively, once a day for 4 weeks. Compared with the control group, the intervention group showed significant net changes in weight (-0.6 kg; 95% confidence interval [CI]: -1.2, -0.1; p = 0.016), body mass index (BMI, -0.2 kg/m2; 95% CI: -0.4, -0.1; p = 0.020), low-density lipoprotein cholesterol (LDL-c, -0.22 mmol/L; 95% CI: -0.49, -0.01; p = 0.035), and the urinary level of Na+/K+ (-2.4; 95% CI: -4.1, -0.7; p = 0.007). In conclusion, the steamed potato-wheat bread intake for 4 weeks resulted in decreases in weight, BMI, LDL-c, and the urinary Na+/K+ level among Chinese adults.

Identification and validation of a novel prognostic model of inflammation-related gene signature of lung adenocarcinoma.

Previous literatures have suggested the importance of inflammatory response during lung adenocarcinoma (LUAD) development. This study aimed at exploring the inflammation-related genes and developing a prognostic signature for predicting the prognosis of LUAD. Survival­associated inflammation-related genes were identified by univariate Cox regression analysis in the dataset of The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) penalized Cox regression model was used to derive a risk signature which is significantly negatively correlated with OS and divide samples into high-, medium- and low-risk group. Univariate and multivariate Cox analyses suggested that the level of risk group was an independent prognostic factor of the overall survival (OS). Time-dependent receiver operating characteristic (ROC) curve indicated the AUC of 1-, 3- and 5-years of the risk signature was 0.715, 0.719, 0.699 respectively. A prognostic nomogram was constructed by integrating risk group and clinical features. The independent dataset GSE30219 of Gene Expression Omnibus (GEO) was used for verification. We further explored the differences among risk groups in Gene set enrichment analysis (GSEA), tumor mutation and tumor microenvironment. Furthermore, Single Sample Gene Set Enrichment Analysis (ssGSEA) and the results of Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) suggested the status of immune cell infiltration was highly associated with risk groups. We demonstrated the prediction effect of CTLA-4 and PD-1/PD-L1 inhibitors in the low-risk group was better than that in the high-risk group using two methods of immune score include immunophenoscore from The Cancer Immunome Atlas (TCIA) and TIDE score from Tumor Immune Dysfunction and Exclusion (TIDE). In addition, partial targeted drugs and chemotherapy drugs for lung cancer had higher drug sensitivity in the high-risk group. Our findings provide a foundation for future research targeting inflammation-related genes to predictive prognosis and some reference significance for the selection of immunotherapy and drug regimen for lung adenocarcinoma.

Biallelic p.V37I variant in GJB2 is associated with increasing incidence of hearing loss with age.

PURPOSE: This study aimed to quantitatively assess the incidence of hearing loss in relation to age in individuals with biallelic p.V37I variant in GJB2. METHODS: Population screening of the biallelic p.V37I variant was performed in 30,122 individuals aged between 0 and 97 years in Shanghai. Hearing thresholds of the biallelic p.V37I individuals and the controls were determined by click auditory brainstem response or pure tone audiometry. RESULTS: Biallelic p.V37I was detected in 0.528% (159/30,122) of the subjects. Of the biallelic p.V37I newborns, 43.91% (18/41) passed their distortion-product otoacoustic emissions-based newborn hearing screening or had hearing thresholds lower than 20 decible above normal hearing level. The older newborns had elevated hearing thresholds, with increasing incidence of 9.52%, 23.08%, 59.38%, and 80.00% for moderate or higher grade of hearing loss in age groups of 7 to 15 years, 20 to 40 years, 40 to 60 years, and 60 to 85 years, respectively. Their hearing deteriorated at a rate of 0.40 dB hearing level per year on average; males were more susceptible, and deterioration occurred preferentially at higher sound frequencies. CONCLUSION: The biallelic p.V37I variant is associated with steadily progressive hearing loss with increasing incidence over the course of life. Most of the biallelic p.V37I individuals may develop significant hearing loss in adulthood and, can benefit from early diagnosis and intervention through wide-spread genetic screening.

CO2 Adsorption Performance and Kinetics of Ionic Liquid-Modified Calcined Magnesite.

CO2 is a major contributor to global warming, and considerable efforts have been undertaken to capture and utilise it. Herein, a nanomaterial based on ionic liquid (IL)-modified calcined magnesites was investigated for CO2 capture. The synthesised nanomaterial (magnesite modified using [APMIM]Br) exhibited the best adsorption performance of 1.34 mmol/g at 30% IL loading amount, 50 °C, 0.4 MPa and 150 mL/min. In particular, the obtained nanomaterial could be regenerated at a low temperature of 90 °C for 3 h, and its CO2 adsorption capacity of 0.81 mmol/g was retained after eight cycles. FT-IR results showed that the imidazole ring and C-N group are directly related to CO2 adsorption capacity. Moreover, improving the conjugative effect of the imidazole ring enhanced the adsorption performance. Further, CO2 was adsorbed on the adsorbent surface and incomplete desorption decreased the BET surface area and CO2 adsorption capacity. Additionally, four models were selected to fit the adsorption kinetics. The results show that the adsorption mechanism fits the pseudo-first-order model well.

Studying the Evaluation Model of the Nutritional Quality of Edible Vegetable Oil Based on Dietary Nutrient Reference Intake.

Edible vegetable oils can provide most of the fatty acids, vitamin E, and certain phytochemicals necessary in the daily human diet to facilitate the required physiological activities. However, there are many types of edible vegetable oils on the market, and evaluating their nutritional quality is a matter of significant interest to consumers and producers. Most of the existing research studies that comparatively analyze and qualitatively describe the type, content, and proportion of nutrients in edible vegetable oil lack a comprehensive method for evaluating the nutritional quality of edible vegetable oil. Based on the physical and chemical analysis of fatty acids, vitamins, and phytochemicals in edible vegetable oil, this study aims to establish a model for a comprehensive evaluation of the nutritional quality of edible vegetable oils. The characteristic nutrients in edible vegetable oil were screened as the evaluation index, while the 2013 China Dietary Reference Intake and French Population Reference Intakes For Fatty Acids was considered the evaluation threshold. When each evaluation index in the edible vegetable oil reached the range stipulated by the reference intake of dietary nutrients, the index will get 1 point. The total score of each index was accumulated to evaluate the nutritional quality of the edible vegetable oils comprehensively. In this study, 13 edible vegetable oils, including low erucic acid rapeseed oil (in America, people usually call it canola oil), soybean oil, peanut oil, sunflower seed oil, flaxseed oil, edible blend oil, olive oil, palm oil, corn oil, camellia oil, peony seed oil, sacha inchi oil, and sesame oil, were selected as the evaluation objects because they are very common in China. Seven evaluation indexes were found for total saturated fatty acids (SFAs), atherogenic fatty acids (SFAs with 12, 14, and 16 carbon chains), monounsaturated fatty acids, polyunsaturated fatty acids like linoleic acid and α-linolenic acid, vitamin E, and phytosterol. When the evaluation index met the evaluation threshold, it was scored 1 point. Scores ranged from 2 to 6. The highest scores were obtained from peony seed oil, flaxseed oil, low erucic acid rapeseed oil, and edible blend oil all of which were 6 points. The lowest score belonged to palm oil at 2 points. The higher the score, the higher the degree of satisfaction between the various nutrients in the edible vegetable oil and the dietary reference intake of this model is. This paper establishes a new method for the nutritional evaluation of edible vegetable oils, which is convenient for comparing the overall nutritional quality of different kinds of edible vegetable oils while providing a new technique for the extensive evaluation of edible vegetable oil.

The bright fluorescence of non-aromatic molecules in aqueous solution originates from pH-induced CTE behavior.

Non-aromatic fluorescent materials with inherent visible light emission have received widespread attention. In this work, a biomimetic fluorescent molecule CA-AEP with a dipeptide structure is introduced. CA-AEP will emit bright biomimetic fluorescence in aqueous solutions by adjusting the pH, which has never been reported. This unique luminescent characteristic can be rationalized by the clustering-triggered emission (CTE) mechanism. In addition, CA-AEP can be used to monitor the maximum dynamic pH in the alkaline range of aqueous systems. Finally, the cytotoxicity assay to A549 cells showed that CA-AEP was non-toxic. Therefore, this work provides a new type of luminogen, which has potential application prospects in the field of environmental monitoring and cell biology.

Highly sensitive and selective detection of PCB 77 using an aptamer-catalytic hairpin assembly in an aquatic environment.

Polychlorinated biphenyls (PCBs) are synthetic organic compounds that are extremely difficult to break down in water and can accumulate in human fat and organisms. However, methods that can be used to detect large amounts of PCBs remain unsatisfactory, as they are generally overly sensitive and involve complex operations. An aptamer-based catalytic hairpin assembly (aptamer-CHA) reaction for the selective detection of 3,3',4,4'-tetrachlorobiphenyl (PCB 77) was developed. It combines the advantages of aptamers and signal amplification reactions. The aptamer selectivity recognizes the target, PCB 77, which triggers the sensitive CHA reaction to produce a fluorescence signal. CHA is a sensitive enzyme-free signal amplification method suitable for on-site detection. Therefore, the identification aptamer is the basis for the quantitative detection of PCB 77, with a detection range of 0.01 µg L-1 to 500 µg L-1 and a detection limit of 0.01 µg L-1. In this study, the aptamer was used to improve the selectivity of the reaction, and the CHA reaction improved the sensitivity of the detection system. Such high-sensitivity PCB detection capabilities with simplified procedures may be useful for real-time field detection and other monitoring tasks. This method can be used as a rapid fluorescence detection strategy for other targets in aquatic environments.

Effects of granular activated carbon and Fe-modified granular activated carbon on anammox process start-up.

In this study, granular activated carbon (GAC) and Fe-modified granular activated carbon (FeGAC) prepared by ultrasonic impregnation method were added into respective up-flow anaerobic sludge blanket (UASB) reactors to explore their effects on the anammox process start-up. The results showed that the time of anammox system start-up could be reduced from 108 d in R1 (control group) to 94 d in R2 (GAC reactor) and to 83 d in R3 (FeGAC reactor). After 120 days of operation, the nitrogen removal rates (NRR) of all reactors could reach more than 0.8 kg-N m-3 d-1. Extracellular polymeric substance (EPS) amount, heme c content and the anammox bacterial functional gene copy numbers gradually increased in all reactors with the passage of culture time, and manifested the superiority in R3 especially. High throughput sequencing revealed that Candidatus Kuenenia was the dominant species in all reactors in the end. It was also demonstrated that FeGAC markedly strengthened the growth and aggregation of anammox bacteria, which is promising for the practical application of the anammox process.

Effect of Steamed Potato Bread Intake on Glucose, Lipids, and Urinary Na+ and K+: A Randomized Controlled Trial with Adolescents.

Although potatoes are highly nutritious, many epidemiological studies have connected their consumption with abnormal lipids, diabetes, and hypertension. Steamed potato bread has recently become one of China's staple foods. A randomized controlled trial was designed to evaluate the effect of steamed potato bread consumption on Chinese adolescents. Four classes from a high school were randomly selected and assigned to the intervention group (two classes) or control group (two classes). The steamed wheat bread (100% raw wheat flour) and potato bread (raw wheat flour to cooked potato flour ratio of 3:7) were provided to the control group and intervention group as staple food once a school day for 8 weeks, respectively. Compared with the control group, the intervention group had significant net changes in systolic blood pressure (4.6 mmHg, p = 0.010), insulin (-4.35 mIU/L, p < 0.001), total cholesterol (-0.13 mmol/L, p = 0.032), and high-density lipoproteins cholesterol (-0.07 mmol/L, p = 0.010). The urinary level of Na+/K+ did not differ between the groups. In conclusion, the intake of steamed potato bread for 8 weeks resulted in positive effects on the total cholesterol and insulin profiles but a negative effect on the systolic blood pressure and high-density lipoproteins cholesterol of adolescents.

Effect of sulfate removal in a high sulfate volumetric loading micro-aerobic bio-reactor and study of subsequent bio-sulfur adsorption by iron-modified activated carbon.

Removal of sulfide from a micro-aerobic bio-reactor was studied at 10 000 mg L-1 chemical oxygen demand (COD) of inlet water, with the sulfate volumetric loading 0.75, 1.0, 1.5 and 2.0 kg (m-3 d-1), respectively. Tentatively, activated carbon (AC) as an adsorbent was modified in positively charged iron to adsorb bio-sulfur through electrostatic interaction. At an O2/S molar ratio of 8-10, the reactor was sufficient to decrease the sulfide in the effluent and biogas to low levels at the sulfate volumetric loading of 2 kg (m-3 d-1). After iron-modified, the specific surface area of AC was form 32.4 m2 g-1 to 65.0 m2 g-1, and the zeta potential was 25.3 mV at pH 7.0. The XRD pattern of the iron-modified activated carbon (FeAC) explained that the metal species of iron was Fe3O4. It could be clearly seen that there was Fe3O4 on the surface of the FeAC, and sulfur particles with a large particle size were adsorbed by the FeAC on the SEM figures. And the XRD pattern of the bio-sulfur explained that the bio-sulfur was made up of S8 (91.444%), C3H4N2OS (1.491%) and CH5N3S (7.075%). The zeta potential of bio-sulfur was -25 mV and the particle size was mainly distributed at the average diameter of 1935 nm at pH 7.0.

Synergetic promotion of direct interspecies electron transfer for syntrophic metabolism of propionate and butyrate with graphite felt in anaerobic digestion.

Direct interspecies electron transfer (DIET) was considered to replace interspecies H2 transfer to enhance syntrophic metabolism during the anaerobic digestion. The graphite felt with excellent electrical conductivity was added into a semi-continuous reactor as a conductive material, strengthening DIET in enhancing the degradation of propionate and butyrate. The final average CH4 production rate increased by 19.1% and 16.7%, respectively, during the propionate and butyrate degradation. Microbial community analysis revealed that the relative abundance of the microflora that could participate in DIET, like Geobacter, Methanosaeta and Methanosarcina species, was greatly improved in the presence of graphite felt. Further experiments showed that the high H2 partial pressure had no obvious effect on the degradation of propionate and butyrate in the graphite felt-supplement reactors.

Efficient removal of H2S at high temperature using the ionic liquid solutions of [C4mim]3PMo12O40-An organic polyoxometalate.

An innovative approach to H2S capture and sulfur recovery via liquid redox at high temperature has been developed using [C4mim]3PMo12O40 at temperatures ranging from 80 to 180°C, which is superior to the conventional water-based system with an upper limit of working temperature normally below 60°C. The ionic liquids used as solvents include [C4mim]Cl, [C4mim]BF4, [C4mim]PF6 and [C4mim]NTf2. Microscopic observation and turbidity measurement were used to investigate the dissolution of [C4mim]3PMo12O40 in the ionic liquids. Stabilization energy between H2S and the anion of ionic liquid as well as H2O was calculated to illustrate the interaction between H2S and the solvents. The cavity theory can be adopted to illustrate the mechanism for H2S absorption: the Cl- ion with small radius can be incorporated into the cavities of [C4mim]3PMo12O40, and interact with H2S strongly. The underlying mechanism for sulfur formation is the redox reaction between H2S and PMo12O403-. H2S can be oxidized to elemental sulfur and Mo6+ is partly reduced during absorption, according to UV-vis and FTIR spectra. The [C4mim]3PMo12O40-[C4mim]Cl after reaction can be readily regenerated by air and thus enabling its efficient and repeatitive use. The absorbent of [C4mim]3PMo12O40-ionic liquid system provides a new approach for wet oxidation desulfurization at high temperature.

Another look at statistical learning theory and regularization.

The paper reviews and highlights distinctions between function-approximation (FA) and VC theory and methodology, mainly within the setting of regression problems and a squared-error loss function, and illustrates empirically the differences between the two when data is sparse and/or input distribution is non-uniform. In FA theory, the goal is to estimate an unknown true dependency (or 'target' function) in regression problems, or posterior probability P(y/x) in classification problems. In VC theory, the goal is to 'imitate' unknown target function, in the sense of minimization of prediction risk or good 'generalization'. That is, the result of VC learning depends on (unknown) input distribution, while that of FA does not. This distinction is important because regularization theory originally introduced under clearly stated FA setting [Tikhonov, N. (1963). On solving ill-posed problem and method of regularization. Doklady Akademii Nauk USSR, 153, 501-504; Tikhonov, N., & V. Y. Arsenin (1977). Solution of ill-posed problems. Washington, DC: W. H. Winston], has been later used under risk-minimization or VC setting. More recently, several authors [Evgeniou, T., Pontil, M., & Poggio, T. (2000). Regularization networks and support vector machines. Advances in Computational Mathematics, 13, 1-50; Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference and prediction. Springer; Poggio, T. and Smale, S., (2003). The mathematics of learning: Dealing with data. Notices of the AMS, 50 (5), 537-544] applied constructive methodology based on regularization framework to learning dependencies from data (under VC-theoretical setting). However, such regularization-based learning is usually presented as a purely constructive methodology (with no clearly stated problem setting). This paper compares FA/regularization and VC/risk minimization methodologies in terms of underlying theoretical assumptions. The control of model complexity, using regularization and using the concept of margin in SVMs, is contrasted in the FA and VC formulations.

Multiple model regression estimation.

This paper presents a new learning formulation for multiple model estimation (MME). Under this formulation, training data samples are generated by several (unknown) statistical models. Hence, most existing learning methods (for classification or regression) based on a single model formulation are no longer applicable. We describe a general framework for MME. Then we introduce a constructive support vector machine (SVM)-based methodology for multiple regression estimation. Several empirical comparisons using synthetic and real-life data sets are presented to illustrate the proposed approach for multiple model regression formulation.

Practical selection of SVM parameters and noise estimation for SVM regression.

We investigate practical selection of hyper-parameters for support vector machines (SVM) regression (that is, epsilon-insensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than re-sampling approaches commonly used in SVM applications. In particular, we describe a new analytical prescription for setting the value of insensitive zone epsilon, as a function of training sample size. Good generalization performance of the proposed parameter selection is demonstrated empirically using several low- and high-dimensional regression problems. Further, we point out the importance of Vapnik's epsilon-insensitive loss for regression problems with finite samples. To this end, we compare generalization performance of SVM regression (using proposed selection of epsilon-values) with regression using 'least-modulus' loss (epsilon=0) and standard squared loss. These comparisons indicate superior generalization performance of SVM regression under sparse sample settings, for various types of additive noise.

Comparison of model selection for regression.

We discuss empirical comparison of analytical methods for model selection. Currently, there is no consensus on the best method for finite-sample estimation problems, even for the simple case of linear estimators. This article presents empirical comparisons between classical statistical methods - Akaike information criterion (AIC) and Bayesian information criterion (BIC) - and the structural risk minimization (SRM) method, based on Vapnik-Chervonenkis (VC) theory, for regression problems. Our study is motivated by empirical comparisons in Hastie, Tibshirani, and Friedman (2001), which claims that the SRM method performs poorly for model selection and suggests that AIC yields superior predictive performance. Hence, we present empirical comparisons for various data sets and different types of estimators (linear, subset selection, and k-nearest neighbor regression). Our results demonstrate the practical advantages of VC-based model selection; it consistently outperforms AIC for all data sets. In our study, SRM and BIC methods show similar predictive performance. This discrepancy (between empirical results obtained using the same data) is caused by methodological drawbacks in Hastie et al. (2001), especially in their loose interpretation and application of SRM method. Hence, we discuss methodological issues important for meaningful comparisons and practical application of SRM method. We also point out the importance of accurate estimation of model complexity (VC-dimension) for empirical comparisons and propose a new practical estimate of model complexity for k-nearest neighbors regression.