Search

Trimethoprim (TMP) is a commonly used BCS class II antibiotic, the water solubility of which needs to be improved to overcome the limitations in oral administration. Herein, we combined TMP with pharmaceutically acceptable coformers (CCFs) by co-crystallization method and reported five new salts (two solvated salts and three unsolvated salts) of TMP. The crystal structures of the salts were determined by single-crystal X-ray diffraction. The supramolecular interactions in the crystal structures were theoretically investigated via molecular electrostatic potential surface (MEPs), Hirshfeld surface, and atoms-in-molecules (AIM) analyses of TMP and CCFs. The properties of the salts were experimentally characterized by using TGA, DSC, hot stage microscopy, and Fourier transform infrared (FTIR) spectroscopy. Furthermore, the equilibrium solubility and intrinsic dissolution rate (IDR) of the salts were determined and compared to that of bare TMP in buffers of pH 1.2 and 6.8. It was found that the equilibrium solubility and IDR of TMP–GA–2M (GA: gallic acid; M: methanol), TMP–SYA–0.16H (SYA: syringic acid; H: H2O), and TMP–OA (OA: orotic acid) dramatically increased in both buffers. The antibacterial activity measurement of these three salts against Escherichia coli revealed that the bacteriostasis rate of the three salts all increased by more than 40% and the minimum inhibitory concentrations (MICs) of salts all decreased compared to that of TMP. There may be drug synergism between the API and the CCF. Based on food safety considerations, TMP–SYA–0.16H and TMP–OA will have more potential prospects in further application of TMP. [ABSTRACT FROM AUTHOR]

Abstract Fuzzy rule-based models have attracted attention because of their modular architectures, well-developed design methodologies and practices as well as interpretability aspects. Methods exploiting factors of randomness offer significant efficiency and implementation simplicity that are essential in numerous application areas. In this study, we propose an original development of fuzzy rule-based models established with the aid of concepts of randomization algorithms. Several design strategies involving different random prototypes generation and basis functions approximation are studied. We investigate performance aspects of randomized rule-base and look at the performance versus the key components of the models such as the number of rules and the use of the randomized algorithms in the development. Furthermore, a comparative study is offered to quantify the efficiency of randomized algorithms. Experimental studies are reported for a series of publicly available data sets to illustrate the effectiveness of the proposed method and discuss its main features. [ABSTRACT FROM AUTHOR]

Fuzzy classifiers have been studied in the area of fuzzy sets for a long time resulting in a number of architectures. In this study, we thoroughly investigate and critically assess fuzzy rule-based classifiers. A topology of the classifier is discussed along with a discussion of the role of fuzzy set technology in the construction of condition and conclusion parts of the classification rules. Some optimization mechanisms utilized in the adjustment of information granules forming the rules are presented. Performance of the fuzzy classifiers is quantified in terms of their accuracy and an area under curve ( AUC ) determined for the receiver operating characteristics ( ROC ). The performance of the classifier is evaluated vis-à-vis a collection of triangular norms used in the construction of the fuzzy classifiers. Experimental studies involve synthetic and publicly available data. Furthermore, comparative studies include the experiments with the commonly used non-fuzzy classifiers. [ABSTRACT FROM AUTHOR]

It becomes apparent that there are no ideal numeric models. Bringing a concept of information granularity to the original numeric model makes it well aligned with the experimental data and helps deliver a better insight into the credibility of the results provided by the model. Information granularity is regarded as a crucial design asset being optimally allocated across the numeric parameters of the originally constructed model. The underlying objective of this study is to propose a concept of a granular output space and develop an optimization process of allocation of information granularity across this space. The optimization is carried out by optimizing output information granules produced by the granular model by considering a product of the essential criteria describing information granules, namely specificity and coverage. The detailed optimization procedure involving Particle Swarm Optimization (PSO) is presented. We stress a generality of the approach that cuts across a variety of classes of models. A collection of experimental studies involving interval information granules is reported demonstrating the main features of the proposed approach. [ABSTRACT FROM AUTHOR]

In recent years, granular fuzzy models have become an intensively studied category of fuzzy models. Granular fuzzy models help elevate the existing models to the higher level of abstraction subsequently making the resulting constructs in a better rapport with real-world systems. In contrast to numeric models, granular models produce results in the form of information granules (such as intervals, fuzzy sets, rough sets and alike). A number of studies have been focused on efficient designing granular fuzzy models where the information granularity has been formalized in the form of intervals. In this study, we propose a novel conceptual and algorithmic approach by elevating existing fuzzy models to granular fuzzy models in the form of fuzzy sets. Concentrating on Takagi-Sugeno fuzzy rule-based models (being commonly used in the literature), we establish a way of an optimal allocation of information granularity across the parameters of the original model and making them fuzzy sets (fuzzy numbers). As a result, the outputs of the granular fuzzy model become fuzzy sets. In the process of allocation of information granularity, we resort ourselves to some population-based meta-heuristic optimization techniques such as particle swarm optimization and differential evolution. To evaluate the performance of granular fuzzy models, we engage the principle of justifiable information granularity leading to the assessment and optimization of the coverage and specificity of the resulting fuzzy set. Besides, we involve the defuzzification (decoding) process to evaluate the impact of granular parameters on the quality of the numeric manifestation of the model. Comprehensive experimental studies involving synthetic and publicly available data sets are reported to demonstrate the performance of the granular fuzzy models. [ABSTRACT FROM AUTHOR]

Processing realized in fuzzy rule-based models is associated with matching individual rules with the existing input data and aggregation of the levels of matching and conclusions of the rules. Rules are processed as individual entities. In this study, we introduce an augmentation of fuzzy models by facilitating interaction among the rules leading to more flexible type membership functions of fuzzy sets forming conditions of the rules (thus resulting in substantially advanced topology of the partition of the input space). Different ways of realizing interaction among the rules are studied. In the sequel, we develop a granular fuzzy model implied by the rule-based model showing how granular parameters of the original rule-based model enhance its quality expressed in terms of coverage of experimental data. The two evaluation criteria of the constructed granular model, namely coverage and specificity are studied. Experimental results are reported for a series of publicly available data. [ABSTRACT FROM AUTHOR]

Granular Computing (GrC) is a human-centric way to discover the fundamental structure of data sets. The resulting information granules can be efficiently exploited to organize knowledge and reveal data descriptions, which can play a pivotal role in the classification problems. Furthermore, information granules are abstract collections of data entities and exhibit flexibility and tolerance when it comes to the representation of incomplete data. However, most of the existing methods focused on the data imputation and classification separately. They also require better interpretability. The crux of this study is to develop a novel information granule-based classification method for incomplete data and a way of representing missing entities and regarding them as information granules in a unified framework. The first aspect focuses on revealing the structural backbone of multiple labeled subspaces of data by fuzzy clustering of missing values. It emerges a classifier with interpretable "IF-THEN" rules by the refinement of fuzzy prototypes in a supervised mode to capture the critical relationship of the multi-class incomplete data. The second aspect concerns the construction of some information granules to impute and represent missing values according to the refined prototypes and classification findings. The experimental studies involved synthetic and publicly available datasets in quantifying the advantages of the classification and representation abilities of the proposed methods on incomplete data. • We proposed a novel information granule-based classifier to reveal the structural of the subspaces of data, which is easier to be interpreted. • We considered incomplete data among classification modeling, so that this classifier can deal with the incomplete data straightforwardly. • We present a refinement mechanism for the information granule-based classifier to optimize the prototypes of the classification rules. • As a byproduct of the classification, the imputed information granules are distinguished from present data and have more tolerance to the imputation error. [ABSTRACT FROM AUTHOR]

Fuzzy rule-based models, due to modular architecture, have attracted attention and resulted in some practical implications because of their nonlinear characteristics and substantial interpretability. Data-driven fuzzy modeling is one of the most prevailing approaches, and the performance of such fuzzy models has been directly affected by a fundamental bias–variance dilemma. The concept and ensuing topologies of the ensemble strategy (bagging and boosting) offer an efficient method for constructing models to address this dilemma and to achieve a sound tradeoff. In this study, we design an ensemble fuzzy rule-based model in the setting of random forest and boosting mechanisms. To demonstrate the feasibility of the proposed method, we focus on the regression type of models. First, we design a method for assembling fuzzy rule-based models to improve the prediction accuracy. Second, we quantify the performance of the ensemble mechanism. To illustrate the effectiveness and discuss the main features of the proposed method, a series of publicly available datasets are considered in the experimental studies. • We design bagging and boosting mechanisms of assembling fuzzy rule-based models. • We quantify and analyze the performance of the ensemble mechanism. • We thoroughly study the predominant parameters of resulting ensemble models. [ABSTRACT FROM AUTHOR]

Missing data are frequently encountered in reality, which inevitably poses great challenges to data mining techniques devoted to data structure identification. In view of the fact that missing data generally exhibits high uncertainty, this paper first introduces the concept of information granule, and performs granular imputation on missing data in a more abstract and inclusive way. With the tolerant nature of the information granule to uncertainty, the error of data imputation and the adverse effects on the subsequent research caused by the low-quality data can be largely reduced. Second, the initial data structure (including granular cluster centers and numeric partition matrix) of the data set with missing values is identified by performing fuzzy clustering on the mixed data set (including both numeric values and information granules) formed by imputation. Third, the bounds of granular cluster centers are further optimized by using the principle of justifiable granularity, and a more robust and reliable granular partition matrix is formed subsequently. Finally, by constructing a reconstruction criterion for mixed data, clustering performance and the optimization of some critical parameters (e.g., the cluster number) used in the proposed method could be investigated. This paper conducts comprehensive experimental studies on both synthetic and publicly available data sets to show the feasibility and effectiveness of the proposed data structure exploration method. [ABSTRACT FROM AUTHOR]

Currently, there are only a few cases of both mechanical stimulation-induced and seeding-triggered fluorescence conversion in which the mechanism of phase transition remains less well investigated. Herein, the first report of a luminescent switch based on mechano-/seeding-triggered crystal-crystal phase transition in a cocrystal polymorphic system is present. Based on the structure analysis and theoretical calculations, the luminescent switching can be attributed to the orbital overlap of the chromophores and exciton coupling. Furthermore, a mechanism of the phase transition was proposed, in which the synthons undergo movements including slipping and twisting during the phase transition. The effects of heating, local mechanical stimulus, and seed contact on the transition were discussed. It was proposed that contacting seeds can lower the energy barrier of the phase transition by inducing the formation of intermediate transition states of synthons via molecular recognition. The energy barrier was found to be the key factor in such mechano-/seeding-triggered phase transition. Green-to-orange luminescent switching triggered by mechanical force and seed contacting in a cocrystal polymorphic system is demonstrated. Local seed contacting can arise domino-like phase transition, which may follow a mechanism of synthon transformation and energy barrier decreasing via molecular recognition. [Display omitted] • The first report of mechano-/seed-triggered luminescent switch in cocrystals is present. • Luminescent switch is based on the crystal-crystal transition involving large packing changes. • A mechanism is proposed to interpret the phase transition on both mechanical stimulation and seed contacting. • The work provides a strategy to design new mechanochromic luminescent materials. [ABSTRACT FROM AUTHOR]

An extensive economic growth mode leads to resource depletion and environmental degradation. Green development is the best way to solve this problem. We analyzed the sample data of 41 cities in the Yangtze River Delta of China from 2007 to 2019. We used the spatial Durbin model and interaction term model to study the effects of regional financial agglomeration (FA) on green development and the moderating role of green technology innovation. The results show that FA promotes local green development but inhibits the green development of neighboring areas. Because the polarization effect of the growth pole is greater than its dispersion effect, cities with high levels of FA have a strong "siphon effect" on the surrounding areas. Green technology innovation positively moderates the promoting effect of FA on green development; the higher the level of green technology innovation and the degree of emphasis on it, the stronger the promoting effect. The government should encourage green finance to promote green technology innovation and promote the green development of the regional economy. These findings provide new insights for developing countries to achieve sustainable development under environmental constraints. [ABSTRACT FROM AUTHOR]

• It is found that CMC can change the habit of NAU·H2O crystals from plate-like to block-like. • The face indices of crystals with different habits were determined by experiments. • The synergistic effect of acid and CMC was analyzed by molecular dynamics simulation. • The electrostatic force is the main factor affecting the adsorption of CMC on crystal face (0 0 1). 6-amino-1,3-dimethyl-5-nitrosouracil (NAU) is an important intermediate for the synthesis of caffeine and theophylline, usually in the form of monohydrate. However, NAU·H 2 O is aggregated and has a plate-like habit, which is very unfavorable for downstream process such as filtration and transportation. In this work, carboxymethylcellulose (CMC), a very common polymer, was selected as an additive to manipulate the habit of NAU·H 2 O. It was found that under acidic conditions, the addition of CMC can change the habit from plate-like to block-like and can significantly improve the aggregation. On the contrary, the additive cannot change the habit under neutral or weakly alkaline conditions. The underlying mechanism was analyzed by experiment and molecular dynamics simulation. It is found that, under neutral or alkaline conditions, CMC will dissociate into negatively charged polymer chains, which have a stronger interaction with water. But CMC will combine with hydrogen ions under acidic conditions, weakening the interaction with water, allowing it to adhere to the (0 0 1) face and inhibiting the growth of this face. The results of this study should be helpful in the selection and mechanism research of polymer additives for crystal habit. [ABSTRACT FROM AUTHOR]

The notions of multi-granulation and multi-scale are two important issues for the granular computing, both of which can describe the granular structure in certain ways. In this paper, we investigate the belief structure and the plausibility structure by defining belief and plausibility functions from the multi-granulation viewpoint, and discuss how to construct multi-granulation rough set (MGRS) models in multi-scale information systems (MSISs). Based on the MGRS in MSISs, the optimal scale selection methods with various requirements are studied in two aspects of optimistic and pessimistic multi-granulation for a multi-scale decision information system (MSDIS). To interpret and understand the proposed theories, some important properties of optimistic and pessimistic multi-granulation optimal scale selection are analyzed for the MSDIS, which could indicate the inner relationships among the different selection methods of the optimal scales. Furthermore, an example is provided to illustrate and verify these investigated properties. • Multigranulation-based belief and plausibility functions and structures are proposed. • Multigranulation rough sets in multi-scale information systems are presented. • Multigranulation-based optimal scale selection for multi-scale information systems are discussed. • Important properties of multigranulation-based optimal scale selection are analyzed. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to compare primary efficacy indicators of a low-cost, electrospun, nanoscale P(LLA-CL)/fibrinogen patch with a porcine small intestine submucosa patch for hernia repair.Study Design: A randomized, single-blind, controlled multicenter trial was performed in 3 hospitals in Shanghai. Eligible patients (20 to 75 years old) with primary unilateral, reducible groin hernias were randomly assigned (1:1) to electrospun nanoscale P(LLA-CL)/fibrinogen patch (experimental group) or porcine small intestine submucosa (control group) patch groups. Patients were treated with the Lichtenstein technique, and the primary endpoint was hernia recurrence at 33 months after surgery. The secondary endpoints were postoperative complications including groin pain and operative site infections.Results: Between July 2014 and February 2016, 172 patients were assigned to experimental (n = 86) and control (n = 86) groups. At 6-month follow-up, postoperative complications occurred in 5 patients (5 of 86, 5.81%) and 2 (2 of 86, 2.35%) patients in the control and experimental groups, respectively (p < 0.05). At 33-month follow-up, recurrence was observed in 2 patients (2 of 79, 2.53%) in the control group vs none in the experimental group (0 of 78) (the 95% CI difference between the experimental and control groups was -0.93% to 6.00% and within the preset noninferior margin of Δ10%). No significant differences were found in the degree of chronic pain and complications 33 months after surgery between the 2 groups.Conclusions: Because the recurrence rates and postoperative complications after 33 months were not inferior in the experimental group, we believe that the P(LLA-CL)/fibrinogen patch, as a low cost alternative, has prospects for widespread clinical use. [ABSTRACT FROM AUTHOR]