4,013 research outputs found
Inhibition of TNF-α and IL-1 by compounds from selected plants for rheumatoid arthritis therapy: In vivo and in silico studies
Purpose: To investigate the inhibitory activities of herbal compounds from Curcuma longa, Sophora japonica and Camellia sinensis against tumor necrosis factor alpha (TNF-α) and interleukin-1 (IL-1) using in vivo and in silico tools.Methods: The extracts of the medicinal herbs (Curcuma longa, Sophora japonica and Camellia sinensis) were evaluated for immune-modulatory activities based using neutrophil oxidative burst assay. The compounds present in the medicinal herbs were screened for their inhibitory effects against TNF-α (PDB ID: 2AZ5) and IL-1 (PDB ID: 2L5X) using Molegro Virtual Docker 6.0 (MVD). The stabilities of the top docking poses were confirmed by Molecular Dynamics (MD) simulation run for 20 nanoseconds (ns).Results: The herbal compounds exerted strong inhibitory effects against TNF-α (PDB ID: 2AZ5) and IL- 1 (PDB ID: 2L5X), implying their therapeutic potential for use in rheumatoid arthritis (RA). Of the compounds, curcumin diglucoside and curcumin monoglucoside showed the strongest inhibitory effects on monocytes, with inhibitory levels of 82.75 and 81.34 %, respectively, while eugenin had the weakest inhibitory activity (11.12 %). In addition, molecular docking scores were consistent with the in vivo results, and revealed strong inhibitory effects of curcumin diglucoside and curcumin monoglucoside against TNF-α and IL-1.Conclusion: Herbal compounds present in Curcuma longa, Sophora japonica and Camellia sinensis possess strong inhibitory effects against the pro-inflammatory cytokines TNF-α and IL-1. Thus, these compounds have therapeutic potentials that can be exploited for the treatment of RA.Keywords: Curcuma longa, Sophora japonica, Camellia sinensis, Rheumatoid arthritis, Cytokines, TNF-α, IL-1, Immuno-modulation, Molecular dockin
Optimal pricing strategy for green products under salience theory
Environmental pressures and people’s demands for green consumption have prompted manufacturers to engage in the
research and development of green products. Manufacturers
need to consider the price and greenness of products when making production decisions. This paper analyzes the level of greenness and price competition of duopoly manufacturers in the
consumer market in which both green-sensitive consumers (salience to greenness) and price-sensitive consumers (salience to
price) exist simultaneously according to salience theory. We find
that the regular manufacturer will enter the green market when
all consumers’ average degree of price responsiveness is small or
in a moderate part of the region. In addition, this paper also discusses the influence of salience on manufacturers’ level of greenness and pricing strategy choice. We find that the degree of
salient thinking of consumers influences optimal pricing, optimal
greenness and profits under the uniform pricing and price discrimination mechanisms
A Comprehensive Indoor Environment Dataset from Single-family Houses in the US
The paper describes a dataset comprising indoor environmental factors such as
temperature, humidity, air quality, and noise levels. The data was collected
from 10 sensing devices installed in various locations within three
single-family houses in Virginia, USA. The objective of the data collection was
to study the indoor environmental conditions of the houses over time. The data
were collected at a frequency of one record per minute for a year, combining
over 2.5 million records. The paper provides actual floor plans with sensor
placements to aid researchers and practitioners in creating reliable building
performance models. The techniques used to collect and verify the data are also
explained in the paper. The resulting dataset can be employed to enhance models
for building energy consumption, occupant behavior, predictive maintenance, and
other relevant purposes
Machine learning approach in the development of building occupant personas
The user persona is a communication tool for designers to generate a mental
model that describes the archetype of users. Developing building occupant
personas is proven to be an effective method for human-centered smart building
design, which considers occupant comfort, behavior, and energy consumption.
Optimization of building energy consumption also requires a deep understanding
of occupants' preferences and behaviors. The current approaches to developing
building occupant personas face a major obstruction of manual data processing
and analysis. In this study, we propose and evaluate a machine learning-based
semi-automated approach to generate building occupant personas. We investigate
the 2015 Residential Energy Consumption Dataset with five machine learning
techniques - Linear Discriminant Analysis, K Nearest Neighbors, Decision Tree
(Random Forest), Support Vector Machine, and AdaBoost classifier - for the
prediction of 16 occupant characteristics, such as age, education, and, thermal
comfort. The models achieve an average accuracy of 61% and accuracy over 90%
for attributes including the number of occupants in the household, their age
group, and preferred usage of heating or cooling equipment. The results of the
study show the feasibility of using machine learning techniques for the
development of building occupant persona to minimize human effort.Comment: 12 pages, 4 figure
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