Browse publications by year: 2024

Fulltext Molecular mechanisms underlying the regulation of tumour suppressor genes in lung cancer

Lee JY, Bhandare RR, Boddu SHS, Shaik AB, Saktivel LP, Gupta G, et al.

Biomed Pharmacother, 2024 Apr;173:116275.
PMID: 38394846 DOI: 10.1016/j.biopha.2024.116275

Tumour suppressor genes play a cardinal role in the development of a large array of human cancers, including lung cancer, which is one of the most frequently diagnosed cancers worldwide. Therefore, extensive studies have been committed to deciphering the underlying mechanisms of alterations of tumour suppressor genes in governing tumourigenesis, as well as resistance to cancer therapies. In spite of the encouraging clinical outcomes demonstrated by lung cancer patients on initial treatment, the subsequent unresponsiveness to first-line treatments manifested by virtually all the patients is inherently a contentious issue. In light of the aforementioned concerns, this review compiles the current knowledge on the molecular mechanisms of some of the tumour suppressor genes implicated in lung cancer that are either frequently mutated and/or are located on the chromosomal arms having high LOH rates (1p, 3p, 9p, 10q, 13q, and 17p). Our study identifies specific genomic loci prone to LOH, revealing a recurrent pattern in lung cancer cases. These loci, including 3p14.2 (FHIT), 9p21.3 (p16INK4a), 10q23 (PTEN), 17p13 (TP53), exhibit a higher susceptibility to LOH due to environmental factors such as exposure to DNA-damaging agents (carcinogens in cigarette smoke) and genetic factors such as chromosomal instability, genetic mutations, DNA replication errors, and genetic predisposition. Furthermore, this review summarizes the current treatment landscape and advancements for lung cancers, including the challenges and endeavours to overcome it. This review envisages inspired researchers to embark on a journey of discovery to add to the list of what was known in hopes of prompting the development of effective therapeutic strategies for lung cancer.

MeSH terms: Cell Transformation, Neoplastic/genetics; Humans; Mutation/genetics; Genes, Tumor Suppressor; Loss of Heterozygosity
Artificial neural network and convolutional neural network for prediction of dental caries

Basri KN, Yazid F, Mohd Zain MN, Md Yusof Z, Abdul Rani R, Zoolfakar AS

Spectrochim Acta A Mol Biomol Spectrosc, 2024 May 05;312:124063.
PMID: 38394882 DOI: 10.1016/j.saa.2024.124063

Dental caries has high prevalence among kids and adults thus it has become one of the global health concerns. The current modern dentistry focused on the preventives measures to reduce the number of dental caries cases. The employment of machine learning coupled with UV spectroscopy plays a crucial role to detect the early stage of caries. Artificial neural network with hyperparameter tuning was employed to train spectral data for the classification based on the International Caries Detection and Assesment System (ICDAS). Spectra preprocessing namely mean center (MC), autoscale (AS) and Savitzky Golay smoothing (SG) were applied on the data for spectra correction. The best performance of ANN model obtained has accuracy of 0.85 with precision of 1.00. Convolutional neural network (CNN) combined with Savitzky Golay smoothing performed on the spectral data has accuracy, precision, sensitivity and specificity for validation data of 1.00 respectively. The result obtained shows that the application of ANN and CNN capable to produce robust model to be used as an early screening of dental caries.

MeSH terms: Machine Learning; Humans; Sensitivity and Specificity; Neural Networks (Computer)
Air pollutant emissions caused by receiving international industrial transfer in Southeast Asian developing countries from 1990 to 2018

Liu L, Wang Y, Zhao Y

Sci Total Environ, 2024 Feb 22;921:171110.
PMID: 38395172 DOI: 10.1016/j.scitotenv.2024.171110

Receiving international industrial transfer (mainly foreign direct investment, FDI) is extremely important for economic development but also brings negative environmental impacts for Southeast Asian developing countries (SEADCs). Due to relatively low labor costs and large market potential, SEADCs have become an attractive destination for industrial transfer after China, while studies were far from sufficient on the associated air pollutant emissions that would worsen air quality and threaten human health. We develop an exploratory framework to estimate the long-term trends of relevant air pollutant emissions in eight major SEADCs, including Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Thailand, and Vietnam. During 1990-2018, the emissions generally show a fluctuating upward trend and increased significantly in Cambodia, Laos, Philippines, and Vietnam. The total emissions of CO, NMVOC, SO2, NOX, PM2.5, and NH3 from the eight SEADCs increased from 19.0, 4.3, 3.6, 1.5, 0.5, and 0.4 kilotons (kt) to 391.6, 260.9, 271.1, 182.4, 48.4, and 12.2 kt, respectively. The emission growth in almost all SEADCs accelerated after 2008 and faster than FDI growth. The disparities in emissions among SEADCs basically grew first and then declined to a level lower than that of 1990, but generally exceeded the disparities in FDI. Productivity gain and emission intensity decrease primarily caused the emission growth and reduction, respectively. Relatively small reductions in emission intensity are found for NOX and SO2. In general, most SEADCs have utilized FDI for economic development without sufficient efforts on air pollutant emission controls. Our outcomes can inform the formulation and optimization of relevant policies reconciling economic development and air quality improvement in SEADCs.
Fulltext Natural bioactive compounds targeting DNA methyltransferase enzymes in cancer: Mechanisms insights and efficiencies

Aanniz T, Bouyahya A, Balahbib A, El Kadri K, Khalid A, Makeen HA, et al.

Chem Biol Interact, 2024 Apr 01;392:110907.
PMID: 38395253 DOI: 10.1016/j.cbi.2024.110907

The regulation of gene expression is fundamental to health and life and is essentially carried out at the promoter region of the DNA of each gene. Depending on the molecular context, this region may be accessible or non-accessible (possibility of integration of RNA polymerase or not at this region). Among enzymes that control this process, DNA methyltransferase enzymes (DNMTs), are responsible for DNA demethylation at the CpG islands, particularly at the promoter regions, to regulate transcription. The aberrant activity of these enzymes, i.e. their abnormal expression or activity, can result in the repression or overactivation of gene expression. Consequently, this can generate cellular dysregulation leading to instability and tumor development. Several reports highlighted the involvement of DNMTs in human cancers. The inhibition or activation of DNMTs is a promising therapeutic approach in many human cancers. In the present work, we provide a comprehensive and critical summary of natural bioactive molecules as primary inhibitors of DNMTs in human cancers. The active compounds hold the potential to be developed as anti-cancer epidrugs targeting DNMTs.

MeSH terms: Humans; CpG Islands; DNA Methylation; Epigenesis, Genetic
Exploring the binding mechanisms of thermally and ultrasonically induced molten globule-like β-lactoglobulin with heptanal as revealed by multi-spectroscopic techniques and molecular simulation

Han C, Zheng Y, Huang S, Xu L, Zhou C, Sun Y, et al.

Int J Biol Macromol, 2024 Apr;263(Pt 1):130300.
PMID: 38395276 DOI: 10.1016/j.ijbiomac.2024.130300

This work employed the model protein β-lactoglobulin (BLG) to investigate the contribution of microstructural changes to regulating the interaction patterns between protein and flavor compounds through employing computer simulation and multi-spectroscopic techniques. The formation of molten globule (MG) state-like protein during the conformational evolution of BLG, in response to ultrasonic (UC) and heat (HT) treatments, was revealed through multi-spectroscopic characterization. Differential MG structures were distinguished by variations in surface hydrophobicity and the microenvironment of tryptophan residues. Fluorescence quenching measurements indicated that the formation of MG enhanced the binding affinity of heptanal to protein. LC-MS/MS and NMR revealed the covalent bonding between heptanal and BLG formed by Michael addition and Schiff-base reactions, and MG-like BLG exhibited fewer chemical shift residues. Molecular docking and molecular dynamics simulation confirmed the synergistic involvement of hydrophobic interactions and hydrogen bonds in shaping BLG-heptanal complexes thus promoting the stability of BLG structures. These findings indicated that the production of BLG-heptanal complexes was driven synergistically by non-covalent and covalent bonds, and their interaction processes were influenced by processes-induced formation of MG potentially tuning the release and retention behaviors of flavor compounds.

MeSH terms: Aldehydes*; Chromatography, Liquid; Tandem Mass Spectrometry*; Molecular Dynamics Simulation; Molecular Docking Simulation
Fulltext Single-cell profile reveals the landscape of cardiac immunity and identifies a cardio-protective Ym-1hi neutrophil in myocardial ischemia-reperfusion injury

Dong Y, Kang Z, Zhang Z, Zhang Y, Zhou H, Liu Y, et al.

Sci Bull (Beijing), 2024 Apr 15;69(7):949-967.
PMID: 38395651 DOI: 10.1016/j.scib.2024.02.003

Myocardial ischemia-reperfusion injury (MIRI) is a major hindrance to the success of cardiac reperfusion therapy. Although increased neutrophil infiltration is a hallmark of MIRI, the subtypes and alterations of neutrophils in this process remain unclear. Here, we performed single-cell sequencing of cardiac CD45+ cells isolated from the murine myocardium subjected to MIRI at six-time points. We identified diverse types of infiltrating immune cells and their dynamic changes during MIRI. Cardiac neutrophils showed the most immediate response and largest changes and featured with functionally heterogeneous subpopulations, including Ccl3hi Neu and Ym-1hi Neu, which were increased at 6 h and 1 d after reperfusion, respectively. Ym-1hi Neu selectively expressed genes with protective effects and was, therefore, identified as a novel specific type of cardiac cell in the injured heart. Further analysis indicated that neutrophils and their subtypes orchestrated subsequent immune responses in the cardiac tissues, especially instructing the response of macrophages. The abundance of Ym-1hi Neu was closely correlated with the therapeutic efficacy of MIRI when neutrophils were specifically targeted by anti-Lymphocyte antigen 6 complex locus G6D (Ly6G) or anti-Intercellular cell adhesion molecule-1 (ICAM-1) neutralizing antibodies. In addition, a neutrophil subtype with the same phenotype as Ym-1hi Neu was detected in clinical samples and correlated with prognosis. Ym-1 inhibition exacerbated myocardial injury, whereas Ym-1 supplementation significantly ameliorated injury in MIRI mice, which was attributed to the tilt of Ym-1 on the polarization of macrophages toward the repair phenotype in myocardial tissue. Overall, our findings reveal the anti-inflammatory phenotype of Ym-1hi Neu and highlight its critical role in myocardial protection during the early stages of MIRI.

MeSH terms: Animals; Myocardium; Neutrophils; Intercellular Adhesion Molecule-1/genetics; Mice
How the social media impact women's psychological well-being in the patriarchal structure? The moderating effect of social capital

He L, Firdaus A, Gong J, Dharejo N, Aksar IA

BMC Public Health, 2024 Feb 23;24(1):581.
PMID: 38395820 DOI: 10.1186/s12889-024-18013-y

BACKGROUND: Despite technological, political and economic progress, Pakistan is still a traditionally patriarchal society, and cultural norms curb women's freedom of socialization, which contributes to poor mental health. The digital technology spaces are rampant with male dominance, and offline cultural behaviours are replicated. Therefore, the current research in Pakistan intends to focus solely on women, their social media uses and the consequent impact on their psychological well-being. Furthermore, the mediation role of social capital is explored, which is linked to women's socialization. In virtual communication, women can expand their connection or remain limited to known people.
METHODS: An online survey collected 240 responses from women social media users. The questionnaire was divided into demographics, social media use patterns like access, online time, frequency of use, social media uses, online social capital and psychological well-being. The obtained responses were statistically analyzed using Smart PLS.
RESULTS: Pakistani women use social media extensively; however, their uses are culturally influenced. The women use social media and socialize online but do not openly disclose their personalities and emotions to extend the connection. They seek information only from acquaintances and do not trust newly developed online contacts. Therefore, the mediation role of bonding social capital is significant, referring to the importance of close ties and trust in psychological well-being. Though virtual spaces provide an opportunity for bridging social capital, women use social media for socialization; however, it doesn't contribute to women's psychological well-being.
CONCLUSION: Despite the higher penetration of digital technologies, cultural power still rules in developing countries like Pakistan. Social media uses are gender- and culturally specific, contributing to psychological well-being and developing social capital. The results from Pakistani society recommend ensuring a secure digital experience for women to get maximum benefits from social media and enhance their psychological well-being.

MeSH terms: Female; Gender Identity; Humans; Male; Social Behavior; Social Media*; Social Capital*
Correction: Utilizing decision tree machine learning model to map dental students' preferred learning styles with suitable instructional strategies

Shoaib LA, Safii SH, Idris N, Hussin R, Sazali MAH

BMC Med Educ, 2024 Feb 23;24(1):177.
PMID: 38395881 DOI: 10.1186/s12909-024-05162-2
Fulltext Chromosome-level genome assembly of the silver pomfret Pampus argenteus

Wei J, Xiao Y, Liu J, Herrera-Ulloa A, Loh KH, Xu K

Sci Data, 2024 Feb 23;11(1):234.
PMID: 38395996 DOI: 10.1038/s41597-024-03070-0

Pampus argenteus (Euphrasen, 1788) is one of the major fishery species in coastal China. Pampus argenteus has a highly specialized morphology, and its declining fishery resources have encouraged massive research efforts on its aquacultural biology. In this study, we reported the first high-quality chromosome-level genome of P. argenteus obtained by integrating Illumina, PacBio HiFi, and Hi-C sequencing techniques. The final size of the genome was 518.06 Mb, with contig and scaffold N50 values of 20.47 and 22.86 Mb, respectively. The sequences were anchored and oriented onto 24 pseudochromosomes based on Hi-C data corresponding to the 24-chromatid karyotype of P. argenteus. A colinear relationship was observed between the P. argenteus genome and that of a closely related species (Scomber japonicus). A total of 24,696 protein-coding genes were identified from the genome, 98.9% of which were complete BUSCOs. This report represents the first case of high-quality chromosome-level genome assembly for P. argenteus and can provide valuable information for future evolutionary, conservation, and aquacultural research.

MeSH terms: Animals; Chromosomes/genetics; Phylogeny; Genome*; Sequence Analysis, DNA
Fulltext The Nutritional Quality of Food Donated to a Western Australian Food Bank

Mossenson S, Giglia R, Pulker CE, Dhaliwal SS, Chester M, Bigwood R, et al.

Nutrients, 2024 Feb 11;16(4).
PMID: 38398833 DOI: 10.3390/nu16040509

Food banks provide an indispensable service to people experiencing severe food insecurity. Food banks source donations from across the food system; however, the food redistributed to clients across the developed world is nutritionally poor. This, together with the increasing prevalence of diet-related diseases and food insecurity, has prompted a focus on nutritional quality. Despite more food being distributed via food banks in Australia, the nutritional quality of donated food remains unreported. This study analyzed all food (84,996 kg (1216 products)) donated to Foodbank WA over a 5-day period using diet-, food-, and nutrient-based nutrition classification schemes (NCSs). A total of 42% (27% of total weight) of donated food products were deemed 'unsuitable' and 19% (23% by weight) were 'suitable' according to all NCSs. There was no agreement on 39% of products (50% by weight). Overall, NOVA and the Healthy Eating Research Nutrition Guidelines (HERNG) (κ = 0.521) had the highest level of agreement and the ADGs and HERNGs the lowest (κ = 0.329). The findings confirm the poor nutritional quality of food donated to food banks and the need to work with donors to improve the food they donate. Fit-for-purpose nutrition guidelines are urgently needed for Australian food banks to support them in providing nutritious food to their vulnerable clients.

MeSH terms: Australia; Diet*; Food*; Food Supply; Humans; Nutritive Value; Western Australia
Fulltext A TRIZ Approach for Designing a Smart Lighting and Control System for Classrooms Based on Counter Application with Dual PIR Sensors

Chong PL, Ismail D, Ng PK, Kong FY, Basir Khan MR, Thirugnanam S

Sensors (Basel), 2024 Feb 10;24(4).
PMID: 38400335 DOI: 10.3390/s24041177

Electrical energy is often wasted through human negligence when people do not switch off electrical appliances such as lighting after leaving a place. Such a scenario often happens in a classroom when the last person leaves the class and forgets to switch off the electrical appliances. Such wastage may not be able to be afforded by schools that are limited financially. Therefore, this research proposed a simple and cost-effective system that can analyze whether there is or is not a human presence in the classroom by applying a counter to count the total number of people entering and leaving the classroom based on the sensing signals of a set of dual PIR sensors only and then correlating this to automatically turn on or off the electrical appliances mentioned. The total number of people identified in the classroom is also displayed on an LCD screen. A TRIZ approach is used to support the ideation of the system. The system can switch on several electrical output loads simultaneously when the presence of people is detected and switch them off when there are no people in the classroom. The proposed system can be expanded to be used in homes, offices, and buildings to prevent the high cost of electricity consumption caused by the negligence of people. This enables smarter control of electricity consumption.

MeSH terms: Electricity; Humans; Lighting*; Schools*
Fulltext UWB Antenna with Enhanced Directivity for Applications in Microwave Medical Imaging

Awan D, Bashir S, Khan S, Al-Bawri SS, Dalarsson M

Sensors (Basel), 2024 Feb 18;24(4).
PMID: 38400473 DOI: 10.3390/s24041315

Microwave medical imaging (MMI) is experiencing a surge in research interest, with antenna performance emerging as a key area for improvement. This work addresses this need by enhancing the directivity of a compact UWB antenna using a Yagi-Uda-inspired reflector antenna. The proposed reflector-loaded antenna (RLA) exhibited significant gain and directivity improvements compared to a non-directional reference antenna. When analyzed for MMI applications, the RLA showed a maximum increase of 4 dBi in the realized gain and of 14.26 dB in the transmitted field strength within a human breast model. Moreover, it preserved the shape of time-domain input signals with a high correlation factor of 94.86%. To further validate our approach, another non-directional antenna with proven head imaging capabilities was modified with a reflector, achieving similar directivity enhancements. The combined results demonstrate the feasibility of RLAs for improved performance in MMI systems.

MeSH terms: Diagnostic Imaging; Humans; Microwaves*; Radiography
Age at autism spectrum disorder diagnosis and its association with child and family characteristics in a tertiary care hospital in Malaysia

Singh JKA, Ling WH, Sivanesom RS, Huay CCS, Lan ACS

Autism Res, 2024 Mar;17(3):637-649.
PMID: 38400514 DOI: 10.1002/aur.3106

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder. Despite the absence of a cure, early diagnosis and intensive early intervention can improve the outcomes. However, little is known about the median age at ASD diagnosis in Malaysia or the child/family characteristics associated with early diagnosis. Therefore, this study aimed to determine the median age at ASD diagnosis among Malaysian children presenting to the country's largest public tertiary neurodevelopmental center and to investigate the possible demographic, child, and family characteristics associated with an early age at diagnosis. Data were collected between February 2017 and February 2019 from a database maintained by the child development unit of the country's largest publicly funded tertiary hospital, containing data from an ethnically diverse population. Among Malaysian children attending the clinic, the median age at ASD diagnosis was 48 months. Early autism diagnosis (<36 months of age) was associated with increased severity of social communication and interaction impairments, coexisting intellectual impairment, children from high socioeconomic status families, and children who receive joint care from their families and a maid or babysitter. The study findings highlight the socioeconomic inequalities in the country, a lack of parental awareness of early ASD signs, and the presence of cultural influences on the age at diagnosis of ASD.

MeSH terms: Child; Child Development; Child, Preschool; Humans; Malaysia/epidemiology; Tertiary Care Centers
Endoscopy for Crohn's disease-related strictures: Can we finally replace the surgeons?

Nik Affendi NA, Hilmi I

J Gastroenterol Hepatol, 2024 May;39(5):779-780.
PMID: 38400711 DOI: 10.1111/jgh.16522
MeSH terms: Constriction, Pathologic; Humans; Endoscopy, Gastrointestinal
Zinc oxide nanoparticles functionalized with cinnamic acid for targeting dental pathogens receptor and modulating apoptotic genes in human oral epidermal carcinoma KB cells

Ravikumar OV, Marunganathan V, Kumar MSK, Mohan M, Shaik MR, Shaik B, et al.

Mol Biol Rep, 2024 Feb 24;51(1):352.
PMID: 38400866 DOI: 10.1007/s11033-024-09289-9

BACKGROUND: Oral diseases are often attributed to dental pathogens such as S. aureus, S. mutans, E. faecalis, and C. albicans. In this research work, a novel approach was employed to combat these pathogens by preparing zinc oxide nanoparticles (ZnO NPs) capped with cinnamic acid (CA) plant compounds.
METHODS: The synthesized ZnO-CA NPs were characterized using SEM, FTIR, and XRD to validate their composition and structural features. The antioxidant activity of ZnO-CA NPs was confirmed using DPPH and ABTS free radical scavenging assays. The antimicrobial effects of ZnO-CA NPs were validated using a zone of inhibition assay against dental pathogens. Autodock tool was used to identify the interaction of cinnamic acid with dental pathogen receptors.
RESULTS: ZnO-CA NPs exhibited potent antioxidant activity in both DPPH and ABTS assays, suggesting their potential as powerful antioxidants. The minimal inhibitory concentration of ZnO-CA NPs against dental pathogens was found 25 µg/mL, indicating their effective antimicrobial properties. Further, ZnO-CA NPs showed better binding affinity and amino acid interaction with dental pathogen receptors. Also, the ZnO-CA NPs exhibited dose-dependent (5 µg/mL, 15 µg/mL, 25 µg/mL, and 50 µg/mL) anticancer activity against Human Oral Epidermal Carcinoma KB cells. The mechanism of action of apoptotic activity of ZnO-CA NPs on the KB cells was identified through the upregulation of BCL-2, BAX, and P53 genes.
CONCLUSIONS: This research establishes the potential utility of ZnO-CA NPs as a promising candidate for dental applications. The potent antioxidant, anticancer, and effective antimicrobial properties of ZnO-CA NPs make them a valuable option for combating dental pathogens.

MeSH terms: Anti-Bacterial Agents/pharmacology; Anti-Bacterial Agents/chemistry; Antioxidants/pharmacology; Carcinoma*; Cinnamates*; Humans; KB Cells; Staphylococcus aureus; Sulfonic Acids*; Benzothiazoles*
Geopolitical risk, financial development, and renewable energy consumption: empirical evidence from selected industrial economies

Ben Abdallah A, Becha H, Sharif A, Bashir MF

Environ Sci Pollut Res Int, 2024 Mar;31(14):21935-21946.
PMID: 38400971 DOI: 10.1007/s11356-024-32565-x

The rapid rise in climate and ecological challenges have allowed policymakers to introduce stringent environmental policies. In addition, financial limitations may pose challenges for countries looking to green energy investments as energy transition is associated with geopolitical risks that could create uncertainty and dissuade green energy investments. The current study uses PTR and PSTR as econometric strategy to investigate how geopolitical risks and financial development indicators influence energy transition in selected industrial economies. Our findings indicate a non-linear DCPB-RE relationship with a threshold equal to 39.361 in PTR model and 35.605 and 122.35 in PSTR model. Additionally, when the threshold was estimated above, financial development indicators and geopolitical risk positively impacts renewable energy. This confirms that these economies operate within a geopolitical context, with the objective of investing more in clean energy. We report novel policy suggestion to encourage policymakers promoting energy transition and advance the sustainable financing development and ecological sustainability.

MeSH terms: Carbon Dioxide; Climate*; Industry; Investments*; Economic Development; Environmental Policy; Renewable Energy
Revolutionizing crop disease detection with computational deep learning: a comprehensive review

Ngugi HN, Ezugwu AE, Akinyelu AA, Abualigah L

Environ Monit Assess, 2024 Feb 24;196(3):302.
PMID: 38401024 DOI: 10.1007/s10661-024-12454-z

Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted plants, into valuable insights, like the identification of specific crop diseases. This innovation has spurred the development of cutting-edge techniques for early detection and diagnosis of crop diseases, leveraging tools such as convolutional neural networks (CNN), K-nearest neighbour (KNN), support vector machines (SVM), and artificial neural networks (ANN). This paper offers an all-encompassing exploration of the contemporary literature on methods for diagnosing, categorizing, and gauging the severity of crop diseases. The review examines the performance analysis of the latest machine learning (ML) and DL techniques outlined in these studies. It also scrutinizes the methodologies and datasets and outlines the prevalent recommendations and identified gaps within different research investigations. As a conclusion, the review offers insights into potential solutions and outlines the direction for future research in this field. The review underscores that while most studies have concentrated on traditional ML algorithms and CNN, there has been a noticeable dearth of focus on emerging DL algorithms like capsule neural networks and vision transformers. Furthermore, it sheds light on the fact that several datasets employed for training and evaluating DL models have been tailored to suit specific crop types, emphasizing the pressing need for a comprehensive and expansive image dataset encompassing a wider array of crop varieties. Moreover, the survey draws attention to the prevailing trend where the majority of research endeavours have concentrated on individual plant diseases, ML, or DL algorithms. In light of this, it advocates for the development of a unified framework that harnesses an ensemble of ML and DL algorithms to address the complexities of multiple plant diseases effectively.

MeSH terms: Machine Learning; Algorithms; Environmental Monitoring; Neural Networks (Computer)
Improving the efficiency of induction motor drive by flux and torque control: A hybrid LSE-RERNN approach

Sivaraju SS, Senthilkumar T, Sankar R, Anuradha T, Usha S, Bin Musirin I

ISA Trans, 2024 Apr;147:215-226.
PMID: 38402102 DOI: 10.1016/j.isatra.2024.01.034

A hybrid technique is proposed in this manuscript for the optimal design of an induction motor (IM) drive for the dynamic load profiles during torque and flux control. The proposed hybrid method combines a Ladder-Spherical-Evolution-Search-Algorithm (LSE) and a recalling-enhanced recurrent-neural network (RERNN), which is called an LSE-RERNN technique. The major objective of the proposed method is to minimize IM losses while maintaining control over speed and torque. The proposed method effectively tunes the gain parameter of the PI controller for flux and torque regulation. The LSE methodgenerates a set of gain parameters optimally predicted by RERNN. The method reduces losses without prior knowledge of load profiles, achieving energy savings for steady-state optimum flux. The performance of the proposed technique is done in the MATLAB and is compared with different existing techniques. The value of the proposed method for the mean is 0.328, the standard deviation (SD) is 0.00334, and the median is 0.4173. The loss of the proposed method is much less than 0.3 W while compared to different existing approaches. Moreover, the computation time of the proposed approach is lesser than the existing techniques.
Fulltext A novel neural network-based framework to estimate oil and gas pipelines life with missing input parameters

Shaik NB, Jongkittinarukorn K, Benjapolakul W, Bingi K

Sci Rep, 2024 Feb 24;14(1):4511.
PMID: 38402261 DOI: 10.1038/s41598-024-54964-3

Dry gas pipelines can encounter various operational, technical, and environmental issues, such as corrosion, leaks, spills, restrictions, and cyber threats. To address these difficulties, proactive maintenance and management and a new technological strategy are needed to increase safety, reliability, and efficiency. A novel neural network model for forecasting the life of a dry gas pipeline system and detecting the metal loss dimension class that is exposed to a harsh environment is presented in this study to handle the missing data. The proposed strategy blends the strength of deep learning techniques with industry-specific expertise. The main advantage of this study is to predict the pipeline life with a significant advantage of predicting the dimension classification of metal loss simultaneously employing a Bayesian regularization-based neural network framework when there are missing inputs in the datasets. The proposed intelligent model, trained on four pipeline datasets of a dry gas pipeline system, can predict the health condition of pipelines with high accuracy, even if there are missing parameters in the dataset. The proposed model using neural network technology generated satisfactory results in terms of numerical performance, with MSE and R2 values closer to 0 and 1, respectively. A few cases with missing input data are carried out, and the missing data is forecasted for each case. Then, a model is developed to predict the life condition of pipelines with the predicted missing input variables. The findings reveal that the model has the potential for real-world applications in the oil and gas sector for estimating the health condition of pipelines, even if there are missing input parameters. Additionally, multi-model comparative analysis and sensitivity analysis are incorporated, offering an extensive comprehension of multi-model prediction abilities and beneficial insights into the impact of various input variables on model outputs, thereby improving the interpretability and reliability of our results. The proposed framework could help business plans by lowering the chance of severe accidents and environmental harm with better safety and reliability.
Fulltext Enhancing the potential of rapeseed cake as protein-source food by γ-irradiation

Xiong C, Zou X, Phan CW, Huang W, Zhu Y

Biosci Rep, 2024 Mar 29;44(3).
PMID: 38391133 DOI: 10.1042/BSR20231807

Rapeseed cake serves as a by-product in the oil extraction industry, characterized by its elevated protein content. However, the presence of antinutritional factors limits the utilization of rapeseed cake as a viable protein source. In this study, different doses of γ-irradiation were used to irradiate rapeseed cake and rapeseed protein isolate was extracted through a modified alkaline solution and acid precipitation method from rapeseed cake. The chemical composition and in vivo acute toxicity of rapeseed protein isolate were determined. The protein recovery rate of rapeseed protein isolate was 39.08 ± 3.01% after irradiation, while the content of antinutritional factors was significantly reduced. Moreover, γ-irradiation did not have any experimentally related effects on clinical observations or clinicopathology in mice. Overall, the reduced antinutrients and increased functional properties suggest that the irradiation of rapeseed cake (<9 kGy) could be utilized as a pre-treatment in the development of rapeseed cake-based value-added protein products.

MeSH terms: Animals; Mice

External Links