Microscopy and Chemical Composition of Healthy and Resinous Wood from the Agarwood-Producing Species, Aqualaria Beccariana

Aquilaria beccariana is a vulnerable Malaysian agarwood-producing species due to illegal harvesting and indiscriminate deforestation. Despite its current conservation status, the chemical profiling of this valuable species is seemingly non-existent. The current study aimed to evaluate the morphological characteristics of A. beccariana wood and identify the volatile chemical compounds of its wood and essential oil. The field emission scanning electron microscope (FESEM) was used to study the wood morphology, while gas chromatography with flame ionization detection and gas chromatography coupled with mass spectrometry was used. The FESEM analysis revealed that vessel pits were distinct in the healthy wood samples but hardly visible in the resinous wood sample. The monoterpene, sesquiterpenes, and sesquiterpenoid were detected, whereby 35 constituents were from the resinous wood sample, which consisted of 15.29% sesquiterpenes and 50.68% sesquiterpenoid. The major compounds were kessane, α-longipinene, α-curcumene, eudesmol, and epi-α-bisabolol. Approximately 32 compounds were identified in the essential oil sample, comprising 36.69% of sesquiterpenes and 49.58% of sesquiterpenoids. The principal compounds were 7-epi-γ-eudesmol, γcadinene, allo-aromadendrene, kessane, and nor-ketoagarofuran. This study provides valuable information on the volatile chemical compound profiles of A. beccariana; thus, it would further contribute to the search for potential chemical markers for species detection and agarwood classification efforts

Financial and Mental Health Concerns of Impoverished Urban-dwelling Bangladeshi People During COVID-19

Background: The COVID-19 pandemic has impacted the physical, mental and financial health of many individuals. Individuals living in impoverished crowded settings may be particularly vulnerable to COVID-19-related stressors. How substantially marginalized groups like impoverished urban-dwelling individuals have been impacted during this pandemic is poorly understood. The present study aimed to investigate the associated factors of financial concerns and symptoms of depression and posttraumatic stress disorder (PTSD) during the COVID-19 pandemic among impoverished urban-dwelling individuals residing in Dhaka, Bangladesh. Methods: A cross-sectional survey was conducted between August and September 2020 using face-to-face interviews in six disadvantaged neighborhoods (“slums”) in Dhaka. Individuals were interviewed using a semi-structured questionnaire consisting of questions assessing socio-demographics, lifestyle, financial well-being relating to the COVID-19 pandemic, depression, and PTSD. Results: Four-hundred-and-thirty-five individuals (male = 54.7%; mean age = 45.0 ± 12.0 years; age range = 18–85 years) participated. Most (96.3%) reported that their household income decreased due to the COVID-19 pandemic. Factors associated with decreased household incomes included female gender, primary education, joblessness, food scarcity and depression. Depression symptoms were linked to female gender, joblessness, divorce, living in a joint family, excessive sleep and smoking. Low incomes, excessive sleep, joblessness and food scarcity were positively associated with PTSD symptoms. In contrast, less sleep appeared protective against PTSD. Conclusions: Public health initiatives, in particular mental health services that target stress and biocentric approaches that consider how humans interact with multiple facets of nature, should be introduced to mitigate against potential financial and psychological effects of the pandemic on impoverished urban-dwelling individuals in Bangladesh

Analysis of algorithms variation in multilayer perceptron neural network for agarwood oil qualities classification

This study investigates the performance of the Multilayer Perceptron (MLP) classifier in discriminating the qualities of agarwood oil significant compounds by different qualities based on three training algorithms namely Scaled Conjugate Gradient (SCG), Levernbergh-Marquardt (LM) and Resilient Backpropagation (RP) Neural Network by using Matlab version 2013a. The dataset used in this study were obtained at Forest Research Institute Malaysia (FRIM) and University Malaysia Pahang (UMP). Further, the areas (abundances, %) of chemical compounds is set as an input and the quality represented (high or low) as an output. The MLP performance was examined with different number of hidden neurons which is in the ranged of 1 to 10. Their performances were observed to accurately found the best technique of optimization to apply to the model. It was found that the LM is effective in reducing the error by enhancing the number of hidden neurons during the network development. The MSE of LM is the smallest among SCG and RP. Besides that, the accuracy of training, validation and testing of LM performed the best accuracy (100%)

Pattern classifier of chemical compounds in different qualities of agarwood oil parameter using scale conjugate gradient algorithm in MLP

This paper presents the modelling of agarwood oil (AO) significant compounds by different qualities using Scaled Conjugate Gradient (SCG) algorithm. This technique involved of data collection from Gas Chromatography-Mass Spectrometry (GC-MS) for compound extraction. The development of Multilayer perceptron (MLP) is used to discriminate the qualities of AO chemical compounds to the high and low quality. The input and output data was transferred to the MATLAB version R2013a for extended analysis. The input is the abundances of significant compounds (%) and the output is the oil quality either high or low. This involved of identification, selection and optimization of a MLP as classifiers to identify and classify the agarwood oil quality. The result showed that MLP as pattern classifier is successful classify agarwood oil quality using SCG algorithm with 100% accuracy. This finding is important in agarwood oil area especially in grading system

A Study on Contamination of Nitrite in Edible Bird's Nest (Swiftlets)

Edible bird's nest (EBN) derived from the hardened saliva of swallows and considered as high-end health food with organic nutrients. Excessive of nitrite were found in EBN imported from Malaysia to China in past few years and still an issue today. Therefore the purpose of this study is to investigate the source of EBN contamination. EBN were collected in Aspa Cottage, Kuantan and divide into two treatment (a) treated EBN (tEBN), which exposed to bird soil; (b) untreated EBN (uEBN). The nitrite (NO2) and nitrate (NO3) level were analyzed using ion chromatography for 14 days. Result showed the level of NO2 and NO3 level for treated EBN reported significantly more than untreated EBN. The source of NO2 and NO3 could have been derived mainly from the bird soil. It is suspected also the color of EBN change from yellowish and brownish simultaneously as level of NO2 and NO3 of EBN increased. Thus the bird soil could be the source of the NO2 contamination in EBN

Statistical Analysis of Agarwood Oil Compounds In Discriminating the Quality of Agarwood Oil

Agarwood oil is one of the plant’s primary metabolites. Products produced by agarwood oil are very valuable and have a great demand in many fragrance as well as aromatic industries. Enhancing and improving the discrimination technique is the main aim to determine or grade the good quality of agarwood oil. In this paper, all statistical works were performed via SPSS software. Two parameters involved are abundance of compound (%) and quality of the agarwood oil either low or high quality. The result showed that, there is clear significant normality test depends on the parameters through the boxplot and Quantile-Quantile (Q-Q) plot for data distribution. Then, Kolmogorov-Smirnov (K-S), non-parameter test and hypothesis were covered in normality test. The techniques proved their capabilities in statistical analysis for agarwood oil compounds and confirmed that the data is suitable for further work especially for classification

Molecular sieve application in the recovery of Gaharu active marker compound from water mixture

The essential oil of agarwood, which is known as Gaharu in Malaysia, is highly prized for its value for luxury fragrances and incense. However, the complexities of the chemical composition of agarwood are the primary challenge facing the establishment of an effective recovery method that can ensure uniform qualities and standards for each batch of essential-oil production. Although agarwood oil can be easily retrieved using the present hydro-distillation method, the high water solubility properties of a number of their key markers are the limiting factor for a proficient retrieval process. Regarding this problem, an elementary chemistry resolution study was performed on commercial agarwood essential oil-water mixture with the application of high performance liquid chromatography (HPLC) and fourier transforms infrared spectroscopy (FTIR). Interpretation of the results leads to a theoretical postulation that the agarwood water mixture consists of agarospirol, jinkohol, jinkoh eremol and kusenol. This study serves to identify the chemical characteristics of water-soluble agarwood compounds and to provide insight for researchers to develop a more strategic technique to improve their extraction process. In addition, this study is the groundwork on the ability of nano-sieve technique in the recovery of water soluble agarwood marker compounds from agarwood hydrosol. To achieve this purpose, a polypiperazine (PPA) and polyethlenesulfone (PES) based nanofiltration (NF) membrane were used. The effectiveness of this membrane on the separation of agarwood marker molecules was analysed and FTIR results showed that most of marker compounds (i.e. agarospirol, jinkohol, jinkoh eremol and khusenol) has been successfully separated from the aqueous agarwood. However, the performance of the membrane in terms of flux and permeability is quite low, which is 23.30 L/m2.h, with 6.76 L/m2.h.bar, respectively. The recovery of gaharu marker compound using membrane would help the local gaharu industry to improve the quality of gaharu essential oil

Psychological responses during the COVID-19 outbreak among university students in Bangladesh

Mental health problems in students are considered a public health challenge. We assessed the prevalence of depression, anxiety, and stress (DAS) with the DASS-21, as well as associated factors, among university students in Bangladesh early in the COVID-19 outbreak. We hypothesized high levels of DAS and associations between DAS and factors previously reported (poor sleep, lack of exercise, heavy internet use) and those linked to disadvantage (e.g., low monthly family income). We also enquired about participants’ satisfaction with their pursuit of their academic studies while living under COVID-19 restrictions. An internet-based survey was conducted during the month of April 2020, involving 3,122 Bangladeshi university students aged 18 to 29 years (59.5% males; mean age 21.4±2 years). Prevalence estimates of depression, anxiety and stress were, respectively, 76.1%, 71.5% and 70.1% for at least mild symptoms, 62.9%, 63.6% and 58.6% for at least moderate symptoms, 35.2%, 40.3%, and 37.7% for at least severe symptoms and 19.7%, 27.5% and 16.5% for at least very severe symptoms. There was a suggestion that these rates were higher than in previous, pre-COVID-19 studies among Bangladeshi students. Regression analyses with DASS-21-score as a dependent variable revealed associations with factors mostly as hypothesized. The largest effect size on DAS symptoms was related to students’ satisfaction with their academic studies during the pandemic. As this survey used cross-sectional and self-reported methods, causality cannot be inferred. Mental health monitoring of students attempting to cope with the impacts of the COVID-19 outbreak may be useful and feasible

Agarwood Oil Quality Classifier Using Machine Learning

Agarwood Oil is known as one of the most expensive and precious oils being traded. It is widely used in traditional ceremonies, and religious prayers. It’s quality plays an important role on the market price that it can be traded. This paper proposes on a proper classification method of the agarwood oil quality using machine learning model k-nearest neighbour (k-NN). The chemical compounds of the agarwood oil from high and low quality are used to train and build the k-NN classifier model. Correlation-based feature selection was used to reduce the dimension of the data before it is being fed into the model. The results show a very high accuracy (100%) model trained and can be used to classify the agarwood oil quality accurately