This study aimed to investigate the effect of a surfactant on the liquid-liquid phase separation, dissolution, diffusion, and the oral bioavailability of a weakly basic drug (l-tetrahydropalmatine; ...l-THP) from an amorphous solid dispersion (ASD). The carrier used in the ASD was optimized by the application of casting film, solvent shift, and pH shift methods. The interaction between the optimized carrier (HPMCP) and l-THP was then evaluated by Fourier transform-infrared spectroscopy and powder X-ray diffraction. The impact of the surfactant on ASD prepared by the spray-drying method was evaluated by both in vitro and in vivo studies. The results of in vitro studies, including liquid-liquid phase separation, drug diffusion, and pH-shift dissolution, indicated that the addition of a surfactant at a certain concentration below critical micelle concentration to ASD caused the precipitation of and a reduction in the membrane diffusion of l-THP in pH 6.8. This observation was confirmed in an in vivo study in which the drug concentration of l-THP in rabbit plasma was determined by the LC-MS/MS analysis method. Then the absolute and relative bioavailability of l-THP was calculated from the obtained pharmacokinetic parameters. Specifically, the addition of 1.5% surfactant (Poloxamer 188) to the binary ASD decreased the relative bioavailability of l-THP by approximately 2.4 times compared with the original binary ASD. Besides, the study proved that l-THP had low absolute bioavailability (around 1.24%), and the application of binary ASD was meaningful in enhancing the oral bioavailability of l-THP by around 334.77% compared to the raw material. The study is expected to provide a better understanding of how different dosage forms influence the bioavailability of l-THP, thereby allowing the selection of the optimal approach for this weakly basic drug.
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The study first aimed to apply a design of experiment (DoE) approach to investigate the influences of excipients on the properties of liquid self-microemulsifying drug delivery system ...(SMEDDS) and SMEDDS loaded in the pellet (pellet-SMEDDS) containing l-tetrahydropalmatine (l-THP). Another aim of the study was to compare the bioavailability of l-THP suspension, liquid SMEDDS and pellet-SMEDDS in the rabbit model. By using Central Composite Face design (CCF), the optimum ratio of Capryol 90, and Smix `(Cremophor RH 40: Transcutol HP) in the formulation of SMEDDS was determined. This optimum SMEDDS was absorbed on the solid carrier (Avicel or Aerosil) for the preparation of pellet-SMEDDS by extrusion and spheronization method. The ANOVA table indicated that Avicel was more effective than Aerosil, the traditional solid carrier, in both terms of preservation of dissolution rate of l-THP from the original SMEDDS and pelletization yield. Results obtained from scanning electron microscopy (SEM) indicated that the existence of liquid SMEDDS droplets on the surface of pellet-SMEDDS was due to the absorption on Avicel. The powder X-ray diffractometry proved the amorphous state of l-THP in pellet-SMEDDS. Pharmacokinetic study in the rabbit model using liquid chromatography tandem mass spectrometry showed that the SMEDDS improved the oral bioavailability of l-THP by 198.63% compared to l-THP suspension. Besides, pharmacokinetics study also proved that the mean relative bioavailability (AUC) and mean maximum concentration (Cmax) of pellet-SMEDDS were not significantly different from the original liquid SMEDDS (p > 0.05).
In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model ...reconciliation, has been proposed as a way to bring the model of the human user closer to the agent’s model. To do so, the agent provides an explanation that can be used to update the model of human such that the agent’s plan is feasible or optimal to the human user. Existing approaches to solve this problem have been based on automated planning methods and have been limited to classical planning problems only.
In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning, and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes. In particular, we propose a logic-based framework for explanation generation, where given a knowledge base KBa (of an agent) and a knowledge base KBh (of a human user), each encoding their knowledge of a planning problem, and that KBa entails a query q (e.g., that a proposed plan of the agent is valid), the goal is to identify an explanation ε ⊆ KBa such that when it is used to update KBh, then the updated KBh also entails q. More specifically, we make the following contributions in this paper: (1) We formally define the notion of logic-based explanations in the context of model reconciliation problems; (2) We introduce a number of cost functions that can be used to reflect preferences between explanations; (3) We present algorithms to compute explanations for both classical planning and hybrid systems planning problems; and (4) We empirically evaluate their performance on such problems. Our empirical results demonstrate that, on classical planning problems, our approach is faster than the state of the art when the explanations are long or when the size of the knowledge base is small (e.g., the plans to be explained are short). They also demonstrate that our approach is efficient for hybrid systems planning problems.
Finally, we evaluate the real-world efficacy of explanations generated by our algorithms through a controlled human user study, where we develop a proof-of-concept visualization system and use it as a medium for explanation communication.
Antibiotic residues and antimicrobial resistance in surface water are issues of global concern, especially in developing countries. In this study, the occurrence of seven antibiotics and one ...antiparasitic agent was determined in surface water samples collected from four rivers running through Hanoi urban area in the Red River Delta, northern Vietnam. The pharmaceuticals in water samples were analyzed by solid-phase extraction combined with liquid chromatography–tandem mass spectrometry method. The concentrations of pharmaceuticals in our samples ranged from 3050 to 16,700 (median 7800) ng/L, which were generally higher than levels found in river water from many other locations in the world. Amoxicillin, oxfendazole, and lincomycin were the most dominant and frequently detected compounds (detection rate 100%), which together accounted for 76 ± 14% of total concentrations. Sulfacetamide and sulfamethoxazole were detected at moderate concentrations in more than two-thirds of the analyzed samples. The remaining antibiotics (i.e., azithromycin, ciprofloxacin, and ofloxacin) were found at lower detection frequency and concentrations. Antibiotic concentrations in the water samples were not significantly different between the investigated rivers. Meanwhile, levels of pharmaceuticals in the samples collected in February 2020 were higher than those found in the remaining samples, largely due to the sharp decrease in sulfamethoxazole and azithromycin concentrations of the samples collected in March and April. Considerable ecological risks of antibiotics in surface water were estimated for some compounds such as amoxicillin, ciprofloxacin, and ofloxacin.
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The objective of this study was to prepare and evaluate some physiochemical and biopharmaceutical properties of bitter taste masking microparticles containing azithromycin loaded in ...dispersible tablets. In the first stage of the study, the bitter taste masking microparticles were prepared by solvent evaporation and spray drying method. When compared to the bitter threshold (32.43µg/ml) of azithromycin (AZI), the microparticles using AZI:Eudragit L100=1:4 and having a size distribution of 45–212µm did significantly mask the bitter taste of AZI. Fourier transform infrared spectroscopy (FTIR), and proton nuclear magnetic resonance spectroscopy (1H NMR) proved that the taste masking of microparticles resulted from the intermolecular interaction of the amine group in AZI and the carbonyl group in Eudragit L100. Differential scanning calorimeter (DSC) analysis was used to display the amorphous state of AZI in microparticles. Images obtaining from optical microscopy and scanning electron microscopy (SEM) indicated the existence of microparticles in regular cube shape with many layers. In the second stage, dispersible tablets containing microparticles (DTs-MP) were prepared by direct compression technique. Stability study was conducted to screen pH modulators for DTs-MP, and a combination of alkali agents (CaCO3:NaH2PO4, 2:1) was added into DTs-MP to create microenvironment pH of 5.0–6.0 for the tablets. The disintegration time of optimum DTs-MP was 53±5.29s and strongly depended on the kinds of lubricant and diluent. The pharmacokinetic study in the rabbit model using liquid chromatography tandem mass spectrometry showed that the mean relative bioavailability (AUC) and mean maximum concentration (Cmax) of DTs-MP were improved by 2.19 and 2.02 times, respectively, compared to the reference product (Zithromax®, Pfizer).
An action language for multi-agent domains Baral, Chitta; Gelfond, Gregory; Pontelli, Enrico ...
Artificial intelligence,
January 2022, 2022-01-00, 20220101, Letnik:
302
Journal Article
Recenzirano
Odprti dostop
The goal of this paper is to investigate an action language, called mA⁎, for representing and reasoning about actions and change in multi-agent domains. The language, as designed, can also serve as a ...specification language for epistemic planning, thereby addressing an important issue in the development of multi-agent epistemic planning systems. The mA⁎ action language is a generalization of the single-agent action languages, extensively studied in the literature, to the case of multi-agent domains. The language allows the representation of different types of actions that an agent can perform in a domain where many other agents might be present—such as world-altering actions, sensing actions, and communication actions. The action language also allows the specification of agents' dynamic awareness of action occurrences—which has implications on what agents' know about the world and other agents' knowledge about the world. These features are embedded in a language that is simple, yet powerful enough to address a large variety of knowledge manipulation scenarios in multi-agent domains.
The semantics of mA⁎ relies on the notion of state, which is described by a pointed Kripke model and is used to encode the agents' knowledge1 and the real state of the world. The semantics is defined by a transition function that maps pairs of actions and states into sets of states. The paper presents a number of properties of the action theories and relates mA⁎ to other relevant formalisms in the area of reasoning about actions in multi-agent domains.
Abstract
With recent technological advances, Global Positioning Systems (GPS) and other tracking sensors can collect large amounts of data and transfer information to the cloud and then to producers ...to remotely monitor livestock health and well-being. Currently, supervised machine learning, such as random forest and linear or quadratic discriminant analysis techniques are used to develop algorithms to identify changes in animal behavior that may be associated with well-being concerns. Supervised machine learning requires behavior observation of monitored animals and may hinder normal expression. However, recording behavioral observations is time consuming and expensive. Our goal was to design a new unsupervised machine learning framework to identify animal behavior without the utilization of human observations. The framework contains two steps. The first step is to segment the tracking data of the animal using time series segmentation, and the second step is to group the segments into clusters where each cluster represents one type of behavior. To validate the applicability of our proposed framework, we utilize GPS tracking data collected at 2-minute intervals from eight cows from May 28 to June 22, 2018, in a 1096 ha rangeland pasture near Prescott, Arizona. After extensive experiments, our framework can partition the movement of the cow using the speed, direction, and distance of the cow from water. These segments were grouped into meaningful behavior clusters using clustering analysis. Speed was the most successful feature for clustering into behaviors. Results are similar to approaches based only on expert knowledge, that rely on speed for classification. Our study demonstrated that we can directly use unlabeled data to group animal behaviors from GPS tracking data. The proposed unsupervised two-step framework allows the analysis of cattle tracking data without direct human observation of behaviors. It is applicable for analyzing the immense amount of data that can be obtained from real-time tracking and sensor devices.
Abstract A plethora of approaches have been proposed for joint entity-relation (ER) extraction. Most of these methods largely depend on a large amount of manually annotated training data. However, ...manual data annotation is time-consuming, labor-intensive, and error-prone. Human beings learn using both data (through induction) and knowledge (through deduction). Answer Set Programming (ASP) has been a widely utilized approach for knowledge representation and reasoning that is elaboration tolerant and adept at reasoning with incomplete information. This paper proposes a new approach, ASP-enhanced Entity-Relation extraction (ASPER), to jointly recognize entities and relations by learning from both data and domain knowledge. In particular, ASPER takes advantage of the factual knowledge (represented as facts in ASP) and derived knowledge (represented as rules in ASP) in the learning process of neural network models. We have conducted experiments on two real datasets and compare our method with three baselines. The results show that our ASPER model consistently outperforms the baselines.
The paper introduces the notion of an epistemic argumentation framework (EAF) as a means to integrate the beliefs of a reasoner with argumentation. Intuitively, an EAF encodes the beliefs of an agent ...who reasons about arguments. Formally, an EAF is a pair of an argumentation framework and an epistemic constraint. The semantics of the EAF is defined by the notion of an ω-epistemic labelling set, where ω is complete, stable, grounded, or preferred, which is a set of ω-labellings that collectively satisfies the epistemic constraint of the EAF. The paper shows how EAF can represent different views of reasoners on the same argumentation framework. It also includes representing preferences in EAF and multi-agent argumentation. Finally, the paper discusses complexity issues and computation using epistemic logic programming.
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