Chinese rice wine (CRW), with its high nutritional value and unique flavor, has been popular in China for thousands of years. The traditional steps of immersion and steam cooking during the brewing ...of rice wine have several drawbacks, including the long production time, large energy consumption, generation of wastewater and the large production area it occupies. Extrusion puffing has the potential to overcome these drawbacks. In this study, the physicochemical properties of extruded-puffed brown rice obtained under different conditions were analyzed. The puffed extrudates exhibited low bulk density, high water solubility index and higher surface area than the steam-cooked brown rice. Extrusion puffing operates at high temperatures, high pressure and provides mechanical shearing forces, resulting in the puffed extrudates having a higher degree of gelatinization, as determined by X-ray diffractometer (XRD), Fourier transform infrared spectroscopy with attenuated total reflection (FTIR-ATR) and differential scanning calorimetry (DSC), revealed the disruption of the native crystalline structure. The effect of extrusion on enzymatic hydrolysis was evaluated using amylase digestion. The results indicated that puffed extrudates significantly improved the enzymatic hydrolysis rate and yield. Moreover, the catalytic efficiency was evaluated by kinetic constants, with extrusion puffing treatment demonstrating an efficiency18-fold higher than steam-cooked brown rice. Finally, extruded-puffed blown rice was used for Chinese rice wine fermentation. The extruded-puffed blown rice improved alcohol yield during the fermentation process, which increased the wine yield by 12.4% compared to traditional cooking methods.
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•Brown rice had significant changes in physicochemical properties after extrusion.•Structure features analyzed by XRD and FTIR-ATR.•Extrusion puffing highly enhanced the efficiency of saccharification.•Extruded-puffed brown rice used for Chinese rice wine fermentation.•Extrusion puffing showed potential to replace immersion and cooking techniques.
Catalytic transformations of α-amino C–H bonds to afford valuable enantiomerically enriched α-substituted amines, entities that are prevalent in pharmaceuticals and bioactive natural products, have ...been developed. Typically, such processes are carried out under oxidative conditions and require precious metal-based catalysts. Here, we disclose a strategy for an enantioselective union of N-alkylamines and α,β-unsaturated compounds, performed under redox-neutral conditions, and promoted through concerted action of seemingly competitive Lewis acids, B(C6F5)3, and a chiral Mg–PyBOX complex. Thus, a wide variety of β-amino carbonyl compounds may be synthesized, with complete atom economy, through stereoselective reaction of an in situ-generated enantiomerically enriched Mg-enolate and an appropriate electrophile.
This paper aims at providing an in-depth overview of designing interpretable fuzzy inference models from data within a unified framework. The objective of complex system modelling is to develop ...reliable and understandable models for human being to get insights into complex real-world systems whose first-principle models are unknown. Because system behaviour can be described naturally as a series of linguistic rules, data-driven fuzzy modelling becomes an attractive and widely used paradigm for this purpose. However, fuzzy models constructed from data by adaptive learning algorithms usually suffer from the loss of model interpretability. Model accuracy and interpretability are two conflicting objectives, so interpretation preservation during adaptation in data-driven fuzzy system modelling is a challenging task, which has received much attention in fuzzy system modelling community. In order to clearly discriminate the different roles of fuzzy sets, input variables, and other components in achieving an interpretable fuzzy model, a taxonomy of fuzzy model interpretability is first proposed in terms of
low-level interpretability and
high-level interpretability in this paper. The low-level interpretability of fuzzy models refers to fuzzy model interpretability achieved by optimizing the membership functions in terms of semantic criteria on fuzzy set level, while the high-level interpretability refers to fuzzy model interpretability obtained by dealing with the coverage, completeness, and consistency of the rules in terms of the criteria on fuzzy rule level. Some criteria for low-level interpretability and high-level interpretability are identified, respectively. Different data-driven fuzzy modelling techniques in the literature focusing on the interpretability issues are reviewed and discussed from the perspective of low-level interpretability and high-level interpretability. Furthermore, some open problems about interpretable fuzzy models are identified and some potential new research directions on fuzzy model interpretability are also suggested.
The direct ortho‐trifluoromethylation of arenes, including heteroarenes, with TMSCF3 has been accomplished by a copper(II)‐promoted CH activation reaction which completes within 30 minutes. ...Mechanistic investigations are consistent with the involvement of CH activation, rather than a simple electrophilic aromatic substitution (SEAr), as the key step.
CF3 installation: The direct ortho‐trifluoromethylation of arenes including heteroarenes with TMSCF3 has been accomplished by a copper(II)‐promoted CH activation reaction. Mechanistic investigations are consistent with the involvement of CH activation rather than a simple electrophilic aromatic substitution as the key step. DG=directing group, TMS=trimethylsilyl.
The Ru(II)-catalyzed ortho-C–H amination directed by a weakly coordinating amide auxiliary with O-benzoyl hydroxylamines at room temperature has been achieved. This reaction is compatible with ...heterocycles including pyrazole, thiophene, benzothiophene, furan, benzofuran, and indole.
Background: Recent studies have suggested that pregnancy-associated plasma protein-A (PAPP-A) is involved in the pathogenesis of atherosclerosis. This study aim is to investigate the role and ...mechanisms of PAPP-A in reverse cholesterol transport (RCT) and inflammation during the development of atherosclerosis. Methods and Results: PAPP-A was silenced in apolipoprotein E (apoE−/−) mice with administration of PAPP-A shRNA. Oil Red O staining of the whole aorta root revealed that PAPP-A knockdown reduced lipid accumulation in aortas. Oil Red O, hematoxylin and eosin (HE) and Masson staining of aortic sinus further showed that PAPP-A knockdown alleviated the formation of atherosclerotic lesions. It was found that PAPP-A knockdown reduced the insulin-like growth factor 1 (IGF-1) levels and repressed the PI3K/Akt pathway in both aorta and peritoneal macrophages. The expression levels of LXRα, ABCA1, ABCG1, and SR-B1 were increased in the aorta and peritoneal macrophages from apoE−/−mice administered with PAPP-A shRNA. Furthermore, PAPP-A knockdown promoted RCT from macrophages to plasma, the liver, and feces in apoE−/−mice. In addition, PAPP-A knockdown elevated the expression and secretion of monocyte chemoattractant protein-1 (MCP-1), interleukin-6 (IL-6), tumor necrosis factor-α, and interleukin-1β through the nuclear factor kappa-B (NF-κB) pathway. Conclusions: The present study results suggest that PAPP-A promotes the development of atherosclerosis in apoE−/−mice through reducing RCT capacity and activating an inflammatory response.
1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition ...in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs.
This study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. The proposed method was then extensively validated on an independent dataset, and compared for performance with two existing deterministic algorithms for RA which had been developed using expert clinical knowledge.
Primary care EHRs were available for 2,238,360 patients over the age of 16 and of these 20,667 were also linked in the secondary care rheumatology clinical system. In the linked dataset, 900 predictors (out of a total of 43,100 variables) in the primary care record were discovered more frequently in those with versus those without RA. These variables were reduced to 37 groups of related clinical codes, which were used to develop a decision tree model. The final algorithm identified 8 predictors related to diagnostic codes for RA, medication codes, such as those for disease modifying anti-rheumatic drugs, and absence of alternative diagnoses such as psoriatic arthritis. The proposed data-driven method performed as well as the expert clinical knowledge based methods.
Data-driven scheme, such as ensemble machine learning methods, has the potential of identifying the most informative predictors in a cost-effective and rapid way to accurately and reliably classify rheumatoid arthritis or other complex medical conditions in primary care EHRs.
Learning-based hashing is a leading approach of approximate nearest neighbor search for large-scale image retrieval. In this paper, we develop a deep supervised hashing method for multi-label image ...retrieval, in which we propose to learn a binary "mask" map that can identify the approximate locations of objects in an image, so that we use this binary "mask" map to obtain length-limited hash codes which mainly focus on an image's objects but ignore the background. The proposed deep architecture consists of four parts: 1) a convolutional sub-network to generate effective image features; 2) a binary "mask" sub-network to identify image objects' approximate locations; 3) a weighted average pooling operation based on the binary "mask" to obtain feature representations and hash codes that pay most attention to foreground objects but ignore the background; and 4) the combination of a triplet ranking loss designed to preserve relative similarities among images and a cross entropy loss defined on image labels. We conduct comprehensive evaluations on four multi-label image data sets. The results indicate that the proposed hashing method achieves superior performance gains over the state-of-the-art supervised or unsupervised hashing baselines.
Abstract
Pulmonary function testing (PFT) allows for quantitative analysis of lung function. However, as a result of the coronavirus disease 2019 (COVID-19) pandemic, a majority of international ...medical societies have postponed PFTs in an effort to mitigate disease transmission, complicating the continuity of care in high-risk patients diagnosed with COVID-19 or preexisting lung pathologies. Here, we describe the development of a non-contact wearable pulmonary sensor for pulmonary waveform analysis, pulmonary volume quantification, and crude thoracic imaging using the eddy current (EC) phenomenon. Statistical regression analysis is performed to confirm the predictive validity of the sensor, and all data are continuously and digitally stored with a sampling rate of 6,660 samples/second. Wearable pulmonary function sensors may facilitate rapid point-of-care monitoring for high-risk individuals, especially during the COVID-19 pandemic, and easily interface with patient hospital records or telehealth services.
Smoothed particle hydrodynamics (SPH) is a Lagrangian, meshfree particle method and has been widely applied to different areas in engineering and science. Since its original extension to modeling ...free surface flows by Monaghan in 1994, SPH has been gradually developed into an attractive approach for modeling viscous incompressible fluid flows. This paper presents an overview on the recent progresses of SPH in modeling viscous incompressible flows in four major aspects which are closely related to the computational accuracy of SPH simulations. The advantages and disadvantages of different SPH particle approximation schemes, pressure field solution approaches, solid boundary treatment algorithms and particle adapting algorithms are described and analyzed. Some new perspectives and future trends in SPH modeling of viscous incompressible flows are discussed.
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