Rheological properties and structural features of coconut milk emulsions stabilized with maize kernels and starch

peer-reviewedIn this study, maize kernels and starch with different amylose contents at the same concentration were added to coconut milk. The nonionic composite surfactants were used to prepare various types of coconut milk beverages with optimal stability, and their fluid properties were studied. The steady and dynamic rheological property tests show that the loss modulus (G″) of coconut milk is larger than the storage modulus (G′), which is suitable for the pseudoplastic fluid model and has a shear thinning effect. As the droplet size of the coconut milk fluid changed by the addition of maize kernels and starch, the color intensity, ζ-potential, interfacial tension and stability of the sample significantly improved. The addition of the maize kernels significantly reduced the size of the droplets (p < 0.05). The potential values of zeta (ζ) and the surface tension of the coconut milk increased. Based on the differential scanning calorimetry (DSC) measurement, the addition of maize kernels leads to an increase in the transition temperature, especially in samples with a high amylose content. The higher transition temperature can be attributed to the formation of some starches and lipids and the partial denaturation of proteins in coconut milk, but phase separation occurs. These results may be helpful for determining the properties of maize kernels in food-containing emulsions (such as sauces, condiments, and beverages) that achieve the goal of physical stability

CIDO, a community-based ontology for coronavirus disease knowledge and data integration, sharing, and analysis

Ontologies, as the term is used in informatics, are structured vocabularies comprised of human- and computer-interpretable terms and relations that represent entities and relationships. Within informatics fields, ontologies play an important role in knowledge and data standardization, representation, integra- tion, sharing and analysis. They have also become a foundation of artificial intelligence (AI) research. In what follows, we outline the Coronavirus Infectious Disease Ontology (CIDO), which covers multiple areas in the domain of coronavirus diseases, including etiology, transmission, epidemiology, pathogenesis, diagnosis, prevention, and treatment. We emphasize CIDO development relevant to COVID-19

Classification of temporal lobe epilepsy based on neuropsychological tests and exploration of its underlying neurobiology

ObjectiveTo assist improving long-term postoperative seizure-free rate, we aimed to use machine learning algorithms based on neuropsychological data to differentiate temporal lobe epilepsy (TLE) from extratemporal lobe epilepsy (extraTLE), as well as explore the relationship between magnetic resonance imaging (MRI) and neuropsychological tests.MethodsTwenty-three patients with TLE and 23 patients with extraTLE underwent neuropsychological tests and MRI scans before surgery. The least absolute shrinkage and selection operator were firstly employed for feature selection, and a machine learning approach with neuropsychological tests was employed to classify TLE using leave-one-out cross-validation. A generalized linear model was used to analyze the relationship between brain alterations and neuropsychological tests.ResultsWe found that logistic regression with the selected neuropsychological tests generated classification accuracies of 87.0%, with an area under the receiver operating characteristic curve (AUC) of 0.89. Three neuropsychological tests were acquired as significant neuropsychological signatures for the diagnosis of TLE. We also found that the Right-Left Orientation Test difference was related to the superior temporal and the banks of the superior temporal sulcus (bankssts). The Conditional Association Learning Test (CALT) was associated with the cortical thickness difference in the lateral orbitofrontal area between the two groups, and the Component Verbal Fluency Test was associated with the cortical thickness difference in the lateral occipital cortex between the two groups.ConclusionThese results showed that machine learning-based classification with the selected neuropsychological data can successfully classify TLE with high accuracy compared to previous studies, which could provide kind of warning sign for surgery candidate of TLE patients. In addition, understanding the mechanism of cognitive behavior by neuroimaging information could assist doctors in the presurgical evaluation of TLE

CIDO: The Community-Based Coronavirus Infectious Disease Ontology

Current COVID-19 pandemic and previous SARS/MERS outbreaks have caused a series of major crises to global public health. We must integrate the large and exponentially growing amount of heterogeneous coronavirus data to better understand coronaviruses and associated disease mechanisms, in the interest of developing effective and safe vaccines and drugs. Ontologies have emerged to play an important role in standard knowledge and data representation, integration, sharing, and analysis. We have initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO). As an Open Biomedical Ontology (OBO) library ontology, CIDO is an open source and interoperable with other existing OBO ontologies. In this article, the general architecture and the design patterns of the CIDO are introduced, CIDO representation of coronaviruses, phenotypes, anti-coronavirus drugs and medical devices (e.g. ventilators) are illustrated, and an application of CIDO implemented to identify repurposable drug candidates for effective and safe COVID-19 treatment is presented

A new framework for host-pathogen interaction research

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed

A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology

The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology in early 2020. As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications

The Effect of Recommended Product on Purchase Decision

학위논문(석사)--서울대학교 대학원 :경영대학 경영학과,2020. 2. 김병도.The composition of a choice set is known to influence purchase decisions. Nowadays, as recommender systems becomes more and more important, researchers have begun to review how the effect of recommendation set on consumer behavior and decision making. Alternatives that are brought to consumers by recommender systems have been proved to influence purchase decisions (Cosley et al. 2003; Adomavicius et al. 2017), but whether and how the type of alternatives affects the target options are still under explored. In this paper, we explicitly examined whether and how the type of recommended products (complementary vs. substitutable vs. unrelated) influence purchase intention for the focal option, as well as total purchase. In study 1, we find that consumers express higher purchase intention for a focal product when recommended with complementary products and lower purchase intention when recommended with unrelated products, compared to substitutable recommendation. In study 2, we find that total purchase is the highest in complementary recommendation, while there is no significant difference between substitutable and unrelated recommendation. The results are important for theoreticians and practitioners who desire to develop efficient recommender systems to generate the maximum sales.선택상품군의 구성은 구매결정에 영향을 끼친다. 최근 추천 시스템이 점점 중요해지면서 연구자들은 추천상품군이 소비자 행동과 의사 결정에 미치는 영향을 검토하기 시작했다. 추천 시스템을 통해 소비자에게 제공되는 대안들은 구매 결정에 영향을 미치는 것으로 입증되었다 (Cosley et al. 2003; Adomavicius et al. 2017). 그러나 대안 유형이 목표 옵션에 영향을 미치는지 여부와 어떻게 영향을 미치는지에 대해서는 아직 탐구 중이다. 이 논문에서, 추천 상품의 유형(보완재 대 대체재 대 무관한 상품)이 초점 옵션과 전체 구매에 대한 구매 의도에 영향을 미치는지 여부와 어떻게 미치는지를 명시적으로 검토한다. 첫 번째 연구에서, 대체재를 추천 받을 때에 비해서, 소비자들이 보완재를 추천 받을 때 초점제품에 대한 구매의지를 더 높게 표현하고, 무관한 상품으로 추천 받을 때 구매의도를 낮춘다는 것을 발견됐다. 두 번째 연구에서, 전체 구매는 보완재를 추천 받은 사항 중 가장 높은 것이지만, 대체재를 추천 받은 사항과 무관한 상품을 추천 받은 사항 사이에는 유의한 차이가 없다. 이런 결과는 효율적인 추천 시스템을 개발하여 최대 매출을 창출하고자 하는 학자 및 실무자들에게 중요하다.1. Introduction 1 2. Literature Review 3 2.1 Complementarity and Substitutability 3 2.2 Choice Sets 4 3. Study 9 3.1 Stimuli 9 3.2 Study 1A 10 3.2.1 Method 10 3.2.2 Result and Discussion 11 3.3 Study 1B 14 3.4. Study 2A 16 3.4.1 Method 17 3.4.2 Result and Discussion 18 3.5 Study 2B 21 4. General Discussion 23 4.1 Key Findings 23 4.2 Theoretical Contribution and Managerial Implication 25 4.3 Limitation and Future Research 28 5. Conclusion 29 Reference 30Maste