Nanomaterials offer the potential for positive technological impact in a variety of industries. The major breakthrough is in neurological therapeutic applications as their physical and chemical properties allow them to penetrate the blood-brain barrier (BBB). However, questions concerning its safety have arisen as a result of its permeability and the broad application of nanomaterials especially the engineered nanomaterial (ENMs). Due to the large spectrum of ENM properties, pinpointing individual features that caused toxicity is difficult. It is therefore urgent to capitalise on these new developments in ENM safety evaluation. Indeed, novel risk assessment and risk management techniques for humans and the environment across the whole life-cycle of nanomaterial products have emerged in recent years, including systems biology approaches and high-throughput screening platforms. Moreover, the new toxicology technology should practically reduce the number of animal samples required for testing and allow both in vitro and in vivo cell studies. Unlike traditional cytotoxicity, which limits the analysis effect to a single experiment, hazardous risk assessment by integrated omics technologies using high-throughput technologies provides robustness of systemic functional analysis towards ENM, allowing the discovery of biomarkers and functional pathways affecting ENM safety application.
Audiograms are used to show the hearing capability of a person at different frequencies. The filter bank in a hearing aid is designed to match the shape of patients’ audiograms. Configuring the hearing aid is done by modifying the designed filters’ gains to match the patient’s audiogram. There are few problems faced in achieving this objective successfully. There is a shortage in the number of audiologists; the filter bank hearing aid designs are complex; and, the hearing aid fitting process is tiring. In this work, a machine learning solution is introduced to classify the audiograms according to the shapes based on unsupervised spectral clustering. The features used to build the ML model are peculiar and describe the audiograms better. Different normalization methods are applied and studied statistically to improve the training data set. The proposed Machine Learning (ML) algorithm outperformed the current existing models, where, the accuracy, precision, recall, specificity, and F-score values are higher. The reason for the better performance is the use of multi-stage feature selection to describe the audiograms precisely. This work introduces a novel ML technique to classify audiograms according to the shape, which, can be integrated to the future and existing studies to change the existing practices in classifying audiograms.
This paper examines the impact of non-pharmaceutical intervention by government on stock market return as well as volatility. Using daily Malaysian equity data from January 28, 2020 to May 31, 2022, the regression analysis with bootstrapping technique reveals that the government's response in combating the deadly virus through Stringency index has shown a positive direct effect on both stock market returns and volatility, and indirect negative effect on stock market returns. The study revealed that international travel restriction and cancelling public events are the major contributors to the growth of volatility when estimated for Malaysia stock market index. On the one hand, heterogenous impact is expected from the perspective of different sectors when the individual social distancing measures were taken into account in determining stock return and volatility. Apart from that, the robustness check for the main findings remains intact in majority of the regression models after incorporating daily COVID-19 death rate, log (daily vaccination) and day-of-the-week effect as additional control variable in alternative.
Background. Polio supplementary immunization activities (SIAs) are one of the polio eradication pillars in the Global Polio Eradication Initiative (GPEI) that increased the immunization coverage and made progress towards polio eradication. However, socioecological challenges faced during SIAs contribute to suboptimal campaign quality. Te aim of this review is to identify the reported challenges during polio supplementary immunization activities (SIAs) and associated improvement strategies based on the socioecological model (SEM). Methods. Articles were searched from three databases which were WOS, Scopus, and PubMed. Te systemic review identifed the primary articles related to SIA that focused on the impact of immunization coverage, challenges, and improvement strategies. Te inclusion criteria were open access English articles that were published between 2012 and 2021 and conducted in the Asia region. Results. Tere are nine articles described and explained regarding some form of supplementary immunization activities (SIAs) in their fndings across Asia region. Te majority of studies selected reported on post vaccination coverage and revealed a multifaceted challenge faced during SIAs which are widely diverse range from the microlevel of in-terpersonal aspects up to the macrolevel of government policy. Upon further analysis, the intervention at community level was the most dominant strategies reported during the SIA program. Conclusions. An efective SIAs program provides the opportunity to increase the national capacity of the polio immunization program, reducing inequities in service delivery and ofering additional public health benefts in controlling polio outbreaks in both endemic and nonendemic countries. Strengthening routine immunization (RI) programmes is also important for the sustainability of SIA's programs. Despite the challenges and hurdles, many Asian countries exhibited great political willingness to boost polio immunization coverage through SIA eforts.
The COVID-19 pandemic has significantly impacted food security for the people due to the imposition of stringent measures to halt the spread of COVID-19 transmission. This study aimed to measure the community’s perception of the level of COVID-19 impacts on their food security and to identify the community’s participation in forest management around the Forest Management Unit (FMU) in Sook, Keningau District of Sabah, to improve their livelihood. A mixed-method approach was conducted where a total of 122 respondents were sampled using a questionnaire survey, focus group discussion with communities, and expert interviews to gather more valuable data. The result showed that the communities were primarily involved in forest management through employment, empowerment, capacity building, and decision-making, which could indirectly contribute to their food security. Meanwhile, the impacts of the COVID-19 transmission were found to moderately affect the people who live inside or adjacent to the forest. The impacts could be explained based on eight themes as the outcome of Principal Component Analysis (PCA): market access, food storage and safety, resource availability, adequate nutrition, food aid, affordability, continuous food supply, and food adaptation to shock. Communities were mainly involved in agricultural practices and could obtain resources from the forest to supplement their daily need. The communities raise a prominent issue regarding land tenure that needs to be resolved; thus, it is suggested that imperative action be considered to create a balance between conservation, economy, and social responsibilities.
The high market demand for collagen as a biomaterial has prompted additional studies into improved methods of collagen extraction without sacrificing the overall quality of collagen. The use of clean technology for sustainable collagen extraction has recently gained popularity due to its efficiency and minimal waste production. This review focuses on improving collagen processing based on three essential steps: pre‐treatment (fermentation, high shear mechanical homogenization, ultrasound), extraction (ultrasound‐assisted extraction, microwave‐assisted extraction, physical‐aided extraction, and supercritical fluid technology), and recovery (ultrafiltration). This review also summarises the benefits and drawbacks of the emerging green technologies used in each step to obtain collagen. This study shows that these clean technologies eliminate the need for excessive chemicals and shortens processing time. However, each collagen‐processing step must be carefully controlled to preserve structural integrity, which influences collagen's properties and hence its uses for further applications. This article is protected by copyright. All rights reserved.
Background Cellulose extraction from gloss art paper (GAP) waste is a recycling strategy for the abundance of gloss art paper waste. Here, a study was conducted on the impact of ultrasonic homogenization for cellulose extraction from GAP waste to improve the particle size, crystallinity, and thermal stability. Results At treatment temperature of 75.8 °C, ultrasonic power level of 70.3% and 1.4 h duration, cellulose with properties of 516.4 nm particle size, 71.5% crystallinity, and thermal stability of 355.2 °C were extracted. Surface modification of cellulose GAP waste with H 3 PO 4 hydrolysis and 2,2,6,6-tetramethylpiperidine-1-oxyl radical (TEMPO) oxidation was done followed by starch reinforcement. Surface hydrophobicity and mechanical strength were increased for H 3 PO 4 hydrolysis and TEMPO oxidation starch–cellulose. No reduction of thermal properties observed during the treatment, while increment of crystallinity index up to 47.65–59.6% was shown. Neat starch film was more transparent, followed by starch–TEMPO film and starch–H 3 PO 4 film, due to better homogeneity. Conclusions The cellulose GAP reinforced starch film shows potential in developing packaging materials and simultaneously provide an alternative solution of GAP waste recycling. Graphical Abstract
The plasma electrolysis method using N2 and O2 injection is an effective and environmentally friendly solution for nitrogen fixation into nitrate and ammonia. The reaction pathway, the effect of the N2 and O2 gas injection composition are important parameters in understanding the mechanism and effectiveness of these processes. This study aims to determine the formation pathway of nitrate and ammonia by observing the formation and role of reactive species as well as intermediate compounds. Two reaction pathways of NOx and ammonia formation have been observed. The NOx compound formed in the solution was oxidized by ∙OH to NO2, followed by the production of a stable nitrate compound. The ammonium produced from the ammonia pathway was generated from nitrogen reacting with ∙H from H2O. The amount of NH3 formed was lesser compared to the NOx compounds in the liquid and gas phases. This indicates that the NOx pathway is more dominant than that of ammonia. The gas injection test with a ratio of N2/O2 = 79/21 was the most effective for nitrate formation compared to another ratio. The results of the emission intensity measurement test show that the reactive species ∙N, ∙N2*, ∙N2⁺, ∙OH, and ∙O have a significant role in the nitrate formation through the NOx pathway, while the reactive species ∙N and ∙H lead to the formation of NH3. The highest nitrate product was obtained at a ratio of N2/O2: 79/21 by 1889 mg L⁻¹, while the highest ammonia product reached 31.5 mg L⁻¹ at 100% N2 injection. Graphic Abstract
Spices are indigenous plants flourishing well in Indonesia contain lots of bioactive compounds. It is potential to be developed as functional food products. The aims of this study are to examine the physicochemical properties of spice syrups composed of different ratios of cardamom and clove. This study employed an experimental design by identifying the effects of different ratios of cardamom and clove in spice syrup colored by teleng flowers (Clitoria ternatea). The collected data were then analyzed using the Independent Sample T-Test. Results showed that syrup with a higher ratio of cloves had a higher a- value (redness). Meanwhile, the L and b+ values were not different. The higher ratio of cloves used was also affected in accordance with chemical analysis results of beta-carotene, gallic acid, kaempferol, and quercetin that showed a higher value of syrup parameter. It can be summarized that using the ratio of cardamom and cloves has a significant effect on the physicochemical properties of spice syrup.
Atherosclerosis threatens human health by developing cardiovascular diseases, the deadliest disease world widely. The major mechanism contributing to the formation of atherosclerosis is mainly due to vascular endothelial cell (VECs) senescence. We have shown that 17β-estradiol (17β-E2) may protect VECs from senescence by upregulating autophagy. However, little is known about how 17β-E2 activates the autophagy pathway to alleviate cellular senescence. Therefore, the aim of this study is to determine the role of estrogen receptor (ER) α and β in the effects of 17β-E2 on vascular autophagy and aging through in vitro and in vivo models. Hydrogen peroxide (H2O2) was used to establish Human Umbilical Vein Endothelial Cells (HUVECs) senescence. Autophagy activity was measured through immunofluorescence and immunohistochemistry staining of light chain 3 (LC3) expression. Inhibition of ER activity was established using shRNA gene silencing and ER antagonist. Compared with ER-β knockdown, we found that knockdown of ER-α resulted in a significant increase in the extent of HUVEC senescence and senescence-associated secretory phenotype (SASP) secretion. ER-α-specific shRNA was found to reduce 17β-E2-induced autophagy, promote HUVEC senescence, disrupt the morphology of HUVECs, and increase the expression of Rb dephosphorylation and SASP. These in vitro findings were found consistent with the in vivo results. In conclusion, our data suggest that 17β-E2 activates the activity of ER-α and then increases the formation of autophagosomes (LC3 high expression) and decreases the fusion of lysosomes with autophagic vesicles (P62 low expression), which in turn serves to decrease the secretion of SASP caused by H2O2 and consequently inhibit H2O2-induced senescence in HUVEC cells.
Mermithids are the most common parasites of black flies and are associated with host feminization and sterilization in infected hosts. However, information on the species / lineage of black fly mermithids in Southeast Asia, including Malaysia requires further elucidation. In this study, mermithids were obtained from black fly larvae collected from 138 freshwater stream sites across East and West Malaysia. A molecular approach based on nuclear-encoded 18S ribosomal RNA (18S rRNA) gene was used to identify the species identity / lineage of 77 nematodes successfully extracted and sequenced from the specimens collected. Maximum likelihood and neighbor-joining phylogenetic analyses demonstrated five distinct mermithid lineages. Four species delimitation analyses: automated simultaneous analysis phylogenetics (ASAP), maximum likelihood Poisson tree processes with Bayesian inferences (bPTP_ML), generalized mixed yule coalescent (GMYC) and single rate Poisson tree processes (PTP) were applied to delimit the species boundaries of mermithid lineages in this data set along with genetic distance analysis. Data analysis supports five distinct lineages or operational taxonomic units for mermithids in the present study, with two requiring further investigation as they may represent intraspecific variation or closely related taxa. One mermithid lineage was similar to that previously observed in Simulium nigrogilvum from Thailand. Co-infection with two mermithids of different lineages was observed in one larva of Simulium trangense. This study represents an important first step towards exploring other aspects of host - parasite interactions in black fly mermithids.
Sentiment Analysis is probably one of the best-known area in text mining. However, in recent years, as big data rose in popularity more areas of text classification are being explored. Perhaps the next task to catch on is emotion detection, the task of identifying emotions. This is because emotions are the finer grained information which could be extracted from opinions. So besides writer sentiments, writer emotion is also a valuable data. Emotion detection can be done using text, facial expressions, verbal communications and brain waves; however, the focus of this review is on text-based sentiment analysis and emotion detection. The internet has provided an avenue for the public to express their opinions easily. These expressions not only contain positive or negative sentiments, it contains emotions as well. These emotions can help in social behaviour analysis, decision and policy makings for companies and the country. Emotion detection can further support other tasks such as opinion mining and early depression detection. This review provides a comprehensive analysis of the shift in recent trends from text sentiment analysis to emotion detection and the challenges in these tasks. We summarize some of the recent works in the last five years and look at the methods they used. We also look at the models of emotion classes that are generally referenced. The trend of text-based emotion detection has shifted from the early keyword-based comparisons to machine learning and deep learning algorithms that provide more flexibility to the task and better performance.
Oxygen, an odorless and colorless gas constituent of the atmosphere, is a vital gas component for the Earth, as it makes up 21% of the composition of the air we breathe. Apart from the importance of oxygen for human breathing, its highly pure form is demanding for industrial applications. As such, several technologies have been established to increase the oxygen purity from 21% to somewhat higher than 95%. One of the competitive technologies for producing this high-purity oxygen from the air is through pressure swing adsorption (PSA), which has the advantages of low cost and energy while being highly efficient. Also, PSA is a simple and flexible system due to its ability to start up and shut down more rapidly since its operation occurs at ambient temperature, which is enabled through the use of adsorbents to bind and separate the air molecules. The enhancement of the PSA’s performances was reported through the modification of PSA step cycles and material (zeolite) tailoring. A simplified complete set of a mathematical model is included for modelling the PSA system, aiming to ease the experimental burden of the process design and optimization of an infinite modification of PSA step cycles. Finally, some technological importance of oxygen production via PSA, particularly for onboard oxygen generation system and oxy-enriched incineration of municipal solid waste, was discussed. Continuous development of PSA will make significant contributions to a wide range of chemical industries in the near future, be it for oxygen production or other gas separation applications.
Pulmonary artery thrombosis in-situ is a term used to describe a pulmonary embolism occurs in the absence of deep vein thrombosis in the lower extremities. Most cases occur in a patient who had a recent traumatic injury to the chest. Other risk factors include the presence of hypercoagulable conditions, including inflammatory state, hypoxia and vas-cular endothelial injury. Although it has been discussed extensively in the acute COVID-19 disease, pulmonary artery thrombosis in-situ that occur in the setting of Post-Acute COVID-19 syndrome is not commonly reported and poorly understood.
Changing landscapes across the globe, but particularly in Southeast Asia, are pushing humans and animals closer together and may increase the likelihood of zoonotic spillover events. Malaysian Borneo is hypothesized to be at high risk of spillover events due to proximity between reservoir species and humans caused by recent deforestation in the region. However, the relationship between landscape and human-animal contact rates has yet to be quantified. An environmentally stratified cross-sectional survey was conducted in Sabah, Malaysia in 2015, collecting geolocated questionnaire data on potential risk factors for contact with animals for 10,100 individuals. 51% of individuals reported contact with poultry, 46% with NHPs, 30% with bats, and 2% with swine. Generalised linear mixed models identified occupational and demographic factors associated with increased contact with these species, which varied when comparing wildlife to domesticated animals. Reported contact rates with each animal group were integrated with remote sensing-derived environmental data within a Bayesian framework to identify regions with high probabilities of contact with animal reservoirs. We have identified high spatial heterogeneity of contact with animals and clear associations between agricultural practices and high animal rates. This approach will help inform public health campaigns in at-risk populations and can improve pathogen surveillance efforts on Malaysian Borneo. This method can additionally serve as a framework for researchers looking to identify targets for future pathogen detection in a chosen region of study.
Smart agriculture is the application of modern information and communication technologies (ICT) to agriculture, leading to what we might call a third green revolution. These include object detection and classification such as plants, leaves, weeds, fruits as well as animals and pests in the agricultural domain. Object detection, one of the most fundamental and difficult issues in computer vision has attracted a lot of attention lately. Its evolution over the previous two decades can be seen as the pinnacle of computer vision advancement. The detection of objects can be done via digital image processing. Machine learning has achieved significant advances in the field of digital image processing in current years, significantly outperforming previous techniques. One of the techniques that is popular is Few-Shot Learning (FSL). FSL is a type of meta-learning in which a learner is given practice on several related tasks during the meta-training phase to be able to generalize successfully to new but related activities with a limited number of instances during the meta-testing phase. Here, the application of FSL in smart agriculture, with particular in the detection and classification is reported. The aim is to review the state of the art of currently available FSL models, networks, classifications, and offer some insights into possible future avenues of research. It is found that FSL shows a higher accuracy of 99.48% in vegetable disease recognition on a limited dataset. It is also shown that FSL is reliable to use with very few instances and less training time.
Citation: Shahriar, S.A.; Husna, A.; Paul, T.T.; Eaty, M.N.K.; Quamruzzaman, M.; Siddique, A.B.; Rahim, M.A.; Ahmmed, A.N.F.; Uddain, J.; Siddiquee, S. Colletotrichum truncatum Causing Anthracnose of Tomato (Solanum lycopersicum L.) in Malaysia. Abstract: Tomato (Solanum lycopersicum L.) is a popular nutritious vegetable crop grown in Malaysia and other parts of the world. However, fungal diseases such as anthracnose pose significant threats to tomato production by reducing the fruit quality and food value of tomato, resulting in lower market prices of the crop globally. In the present study, the etiology of tomato anthracnose was investigated in commercial tomato farms in Sabah, Malaysia. A total of 22 fungal isolates were obtained from anthracnosed tomato fruits and identified as Colletotrichum species, using morphological characteristics. The phylogenetic relationships of multiple gene sequence alignments such as internal transcribed spacer (ITS), β-tubulin (tub2), glyceraldehyde 3-phosphate dehydrogenase (gapdh), actin (act), and calmodulin (cal), were adopted to accurately identify the Colletotrichum species as C. truncatum. The results of pathogenicity tests revealed that all C. truncatum isolates caused anthracnose disease symptoms on inoculated tomato fruits. To our knowledge, the present study is the first report of tomato anthracnose caused by C. truncatum in Malaysia. The findings of this study will be helpful in disease monitoring, and the development of strategies for effective control of anthracnose on tomato fruits.
Image coding technology has become an indispensable technology in the field of modern information. With the vigorous development of the big data era, information security has received more attention. Image steganography is an important method of image encoding and hiding, and how to protect information security with this technology is worth studying. Using a basis of mathematical modeling, this paper makes innovations not only in improving the theoretical system of kernel function but also in constructing a random matrix to establish an information-hiding scheme. By using the random matrix as the reference matrix for secret-information steganography, due to the characteristics of the random matrix, the secret information set to be retrieved is very small, reducing the modification range of the steganography image and improving the steganography image quality and efficiency. This scheme can maintain the steganography image quality with a PSNR of 49.95 dB and steganography of 1.5 bits per pixel and can ensure that the steganography efficiency is improved by reducing the steganography set. In order to adapt to different steganography requirements and improve the steganography ability of the steganography schemes, this paper also proposes an adaptive large-capacity information-hiding scheme based on the random matrix. In this scheme, a method of expanding the random matrix is proposed, which can generate a corresponding random matrix according to different steganography capacity requirements to achieve the corresponding secret-information steganography. Two schemes are demonstrated through simulation experiments as well as an analysis of the steganography efficiency, steganography image quality, and steganography capacity and security. The experimental results show that the latter two schemes are better than the first two in terms of steganography capacity and steganography image quality.
Herein, we report the green synthesis of flower-like carrageenan-silver nanoparticles (c-AgNPs) through a facile hydrothermal reaction at 90 °C for 2 h. The reduction of silver nitrate (AgNO3) to c-AgNPs was evident by the colour change of the solution from colourless to dark brown and further confirmed by a UV-Vis surface plasmon resonance (SPR) peak at ~420 nm. The FTIR spectra showed that the abundance of functional groups present in the carrageenan were responsible for the reduction and stabilisation of the c-AgNPs. The XRD pattern confirmed the crystalline nature and face-centred cubic structure of the c-AgNPs, while the EDX analysis showed the presence of a high composition of elemental silver (85.87 wt%). Interestingly, the morphological characterisations by SEM and FE-SEM revealed the formation of flower-like c-AgNPs composed of intercrossed and random lamellar petals of approximately 50 nm in thickness. The growth mechanism of flower-like c-AgNPs were elucidated based on the TEM and AFM analyses. The c-AgNPs displayed promising antibacterial properties against E. coli and S. aureus, with zones of inhibition ranging from 8.0 ± 0.0 to 11.7 ± 0.6 mm and 7.3 ± 0.6 to 9.7 ± 0.6 mm, respectively, as the concentration of c-AgNPs increased from 0.1 to 4 mg/mL.
Blockchains are a new approach to creating distributed networks that were first introduced in 2008. It allows the formation of peer-to-peer networks based on consensus, forming chains from accepted blocks without requiring a central authority or centralized controller. A prominent application of this technology is its use in decentralized storage systems. Individuals in decentralized storage networks rent unused hardware storage space to other individuals. A decentralized network utilizing end-to-end encryption eliminates the risk of data loss associated with centralized data control by enabling clients to transmit their files securely. The storage providers must prove that they have kept unaltered files in this network for this time. Many studies have been conducted in this specific domain, most targeting storage capacity and efficiency, but a security, integrity and privacy loophole need to be addressed. This paper presents an overview of blockchain-based storage systems and how they work, followed by a comparison with cloud-based storage networks and a survey of various decentralized storage networks like SIA, File coin, and Storj available on the market. Next, we will discuss the advantages and disadvantages of blockchain-based storage. In our final discussion, we will examine the security problems of decentralized storage networks and explore potential solutions and research directions for the future.
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