Advanced Analysis Methods Research Articles

Greece since the 1960s: the mortality transition revisited: a joinpoint regression analysis

Mortality transition in Greece is a well-studied phenomenon in several of its aspects. It is characterised by an almost constant increase in life expectancy at birth and other ages and a parallel decrease in death probabilities. The scope of this paper is a comprehensive assessment of the mortality transition in Greece since 1961, in the light of holistic analysis. Within this paper, life tables by gender were calculated and the temporal trends of life expectancy at several ages were examined. Moreover, a cluster analysis was used in order to verify the temporal changes in the mortality patterns. The probabilities of death in large age classes are presented. Furthermore, the death distribution was analysed in relation to various parameters: the modal age at death, mode, left and right inflexion points and the length of the old age heap. Before that, a non-linear regression method, originating from the stochastic analysis, was applied. Additionally, the Gini coefficient, average inter-individual differences, and interquartile range of survival curves were examined. Finally, the standardised rates of the major causes of death are presented. All the analysis variables were scholastically examined for their temporal trends with the method of Joinpoint Regression analysis. Mortality transition in Greece after the year 1961 is asymmetrical with a gender and an age-specific component, leading to the elevation of life expectancy at birth over time. During this period, the older ages’ mortality decreases, but at a slower pace than that of the younger ones. The modal age at death, mode, the left and right inflexion points and the width of the old age heap denote the compression of mortality in the country. The old age death heap shifts towards older ages, while at the same time, the variability of ages at death decreases, being verified by the Gini Coefficient and average inter-individual differences. As a result, the rectangularization of survival curves is evident. These changes have a different pace of transition over time, especially after the emergence of the economic crisis. Finally, the major causes of death were the diseases of the circulatory system, neoplasms, diseases of the respiratory system and others. The temporal trends of these diseases differ according to the diseases and gender. Greece’s mortality transition is an asymmetrical stepwise process characterised by its gender and age-specific characteristics. This process, despite being a continuous one, is not linear. Instead, a combination of serious developments over time governs the country’s modern mortality regime. The evaluation of Greece’s mortality transition through the lens of more advanced analytical methods may provide new insights and methodological alternatives for assessing mortality transition in other countries of the world.

Kernel-based feature aggregation framework in point cloud networks

Various effective deep networks have been developed for analysis of 3D point clouds. One key step in these networks is to aggregate the features of orderless points into a compact representation for the cloud. As a typical order-invariant aggregation method, max-pooling has been widely applied. However, while enjoying simplicity and high efficiency, max-pooling does not fully exploit the feature information since it not only ignores the non-maximum elements in each feature dimension but also neglects the interactions between different dimensions. These drawbacks of max-pooling motivate us to explore advanced feature aggregation methods for 3D point cloud analysis. The desired advanced method should be capable of modeling richer information between the point features than max-pooling, and, at the same time, it can readily replace max-pooling without the need to modify other parts of the existing network architecture. To this end, this paper proposes a novel kernel-based feature aggregation framework for 3D point cloud analysis for the first time. The proposed method effectively considers all the elements in each dimension and models the nonlinear interactions between feature dimensions as complementary information to max-pooling. In addition, it is a plug-in module that can be integrated to many common networks as a replacement of max-pooling. Comprehensive experiments are conducted to demonstrate the consistently superior performance and high generality of the proposed method over max-pooling. Specifically, the proposed kernel-based feature aggregation framework consistently improves the max-pooling with three representative backbones of PointNet, DGCNN and PCT across four 3D point cloud based analysis tasks, including supervised 3D object classification, 3D part segmentation, indoor semantic segmentation and one additional unsupervised place retrieval task. Especially, it shows remarkable performance improvement over max-pooling in the unsupervised retrieval task, demonstrating its advantage in forming 3D point cloud representation.

The role of solar energy usage in environmental sustainability: Fresh evidence through time-frequency analyses

The most important challenge for both developed and developing countries is to ensure sustainability while struggling with environmental degradation. CO2 emissions as a proxy for environmental degradation can be considered an obstacle to sustainability. There exist several significant works in the literature on the effects of solar energy use on environmental degradation/sustainability. In this study, the effects of the use of solar energy within different time and frequency dimensions on CO2 emissions were examined with the methodology of the continuous wavelet transform. The paper investigated the association between solar energy consumption and total energy-related CO2 emissions in the USA through Morlet wavelet analysis, which is one of the most advanced time-frequency analysis methods for the period 1990:1–2022:6. In the wavelet coherency computations, geothermal energy consumption, hydroelectric energy consumption, industrial production, and manufacturing industry production variables were also included as control variables. Empirical findings demonstrate that solar energy consumption can have reducing effects on CO2 emissions at lower frequencies (longer-term cycles) and sub-time periods (2014:1–2022:1) in the USA. The findings can guide the energy and environmental policies of developed and developing countries that aim to struggle with global warming and/or climate change through the increase in solar energy usage.

Fault diagnosis analysis of combined cycle based on the structural theory of thermo‐economics

AbstractIn this paper, an improved thermo‐economic diagnosis model based on the structural theory of thermo‐economics combined with advanced exergy analysis is proposed in a combined cycle fault diagnosis model. Diagnosis model simulation of a 590 MW combined cycle unit is implemented by EBSILON Professional in terms of advanced exergy analysis method. The characteristic fault value (CFV) required for diagnosis is quantitatively presented and precisely located in the diagnosis model. The results show that with 3% performance deterioration fault of the compressor, gas turbine and intermediate pressure cylinder of the turbine, gas turbine failure leads to three times fuel consumption to the unit compared to the compressor. In contrast, multiple fault diagnosis results show that the fault value generated by each component under multiple faults is not a simple superposition of a single fault. The impact of some component faults is partially offset or increased. However, the increased fuel consumption of the whole unit under multiple faults or malfunctions must be greater than the sum of the increased fuel consumption under a single fault. CFV can effectively perform precise positioning and quantitative analysis on single and multiple fault scenarios of the combined cycle unit. In addition, CFV has strong sensitivity and changes linearly with the performance deterioration of the components, which paves the way for a reliable approach for early‐stage warning and diagnosis.

Designing Lightweight Stadium Roofing Structures Based on Advanced Analysis Methods

The current structural engineering practical standards are unable to offer an universal structural design standard for long-spanning lightweight stadium roofing structures. As such, the design procedure of a particular stadium roof is not replicable to another. This research aims to present a novel design procedure for lightweight stadium roofing structures considering the Lakhwiya stadium the Optus Stadium and the CommBank Stadium as experimental cases. Using the finite element analysis (FEA) software Strand7, the cases will be modelled and analysed. Varying load cases and combinations such as ultimate strength (ULS) and serviceability limit states (SLS) based on the Australian Standard AS1170.0:2002 will be calculated and subsequently applied. Linear static analysis will then be undertaken where critical members will be identified within the model. Based on this, preliminary member sizing and design feasibility checks will be conducted in order to ensure structural stability and compliance to the Australian Steel Structure code AS4100:2020. A linear buckling analysis is also conducted based on the selected sizes from the initial stage to determine critical loads. Advanced analysis including non-linear buckling computation is comprehensively managed. Some of the crucial parameters such as maximum displacement, maximum/minimum principal stresses, critical buckling loads, as well as load factors are examined. The main novelty of this study is to determine a clear road map to design stadium roofing systems subjected to a combination of different types of the loading.

Cell morphology as a biological fingerprint of chondrocyte phenotype in control and inflammatory conditions.

Little is known how inflammatory processes quantitatively affect chondrocyte morphology and how single cell morphometric data could be used as a biological fingerprint of phenotype. We investigated whether trainable high-throughput quantitative single cell morphology profiling combined with population-based gene expression analysis can be used to identify biological fingerprints that are discriminatory of control vs. inflammatory phenotypes. The shape of a large number of chondrocytes isolated from bovine healthy and human osteoarthritic (OA) cartilages was quantified under control and inflammatory (IL-1β) conditions using a trainable image analysis technique measuring a panel of cell shape descriptors (area, length, width, circularity, aspect ratio, roundness, solidity). The expression profiles of phenotypically relevant markers were quantified by ddPCR. Statistical analysis, multivariate data exploration, and projection-based modelling were used for identifying specific morphological fingerprints indicative of phenotype. Cell morphology was sensitive to both cell density and IL-1β. In both cell types, all shape descriptors correlated with expression of extracellular matrix (ECM)- and inflammatory-regulating genes. A hierarchical clustered image map revealed that individual samples sometimes responded differently in control or IL-1β conditions than the overall population. Despite these variances, discriminative projection-based modeling revealed distinct morphological fingerprints that discriminated between control and inflammatory chondrocyte phenotypes: the most essential morphological characteristics attributable to non-treated control cells was a higher cell aspect ratio in healthy bovine chondrocytes and roundness in OA human chondrocytes. In contrast, a higher circularity and width in healthy bovine chondrocytes and length and area in OA human chondrocytes indicated an inflammatory (IL-1β) phenotype. When comparing the two species/health conditions, bovine healthy and human OA chondrocytes exhibited comparable IL-1β-induced morphologies in roundness, a widely recognized marker of chondrocyte phenotype, and aspect ratio. Overall, cell morphology can be used as a biological fingerprint for describing chondrocyte phenotype. Quantitative single cell morphometry in conjunction with advanced methods for multivariate data analysis allows identifying morphological fingerprints that can discriminate between control and inflammatory chondrocyte phenotypes. This approach could be used to assess how culture conditions, inflammatory mediators, and therapeutic modulators regulate cell phenotype and function.

Why C-reactive protein is one of the most requested tests in clinical laboratories?

C-reactive protein (CRP) is an acute-phase protein which is synthesized by the liver in response to the secretion of several inflammatory cytokines including interleukin 6 (IL-6), IL-1 and tumor necrosis factor (TNF). CRP was the first acute-phase protein to be described and adopted in clinical laboratories as an exquisitely sensitive systemic marker of inflammation and tissue damage. The measurement of CRP is widely used for the diagnosis and monitoring of inflammatory conditions, including sepsis, trauma, and malignancies. In the last decades, impressive advances in analytical methods (from qualitative to high-sensitivity assays), automation and availability of results in a short time, not only translated in an increasing demand for the right management of systemic inflammatory diseases, but also in evaluating subclinical inflammatory processes underlying atherothrombotic events. CRP measurement is one of the most requested laboratory tests for both the wide range of clinical conditions in which it may assure a valuable information and some analytical advantages due to the evidence that it is a "robust biomarker". Even recently, the measurement of CRP received new interest, particularly as a biomarker of severity of Coronavirus disease 2019 (COVID-19), and it deserves further concern for improving demand appropriateness and result interpretation.

JACEP open annual report 2022.

JACEP open annual report 2022.

Assessing the Quality of Student-Generated Content at Scale: A Comparative Analysis of Peer-Review Models

Engaging students in creating learning resources has demonstrated pedagogical benefits. However, to effectively utilize a repository of student-generated content (SGC), a selection process is needed to separate high- from low-quality resources as some of the resources created by students can be ineffective, inappropriate, or incorrect. A common and scalable approach is to use a peer-review process where students are asked to assess the quality of resources authored by their peers. Given that judgments of students, as experts-in-training, cannot wholly be relied upon, a redundancy-based method is widely employed where the same assessment task is given to multiple students. However, this approach introduces a new challenge, referred to as the consensus problem: How can we assign a final quality to a resource given ratings by multiple students? To address this challenge, we investigate the predictive performance of 18 inference models across five well-established categories of consensus approaches for inferring the quality of SGC at scale. The analysis is based on the engagement of 2141 undergraduate students across five courses in creating 12 803 resources and 77 297 peer reviews. Results indicate that the quality of reviews is quite diverse, and students tend to overrate. Consequently, simple statistics such as mean and median fail to identify poor-quality resources. Findings further suggest that incorporating advanced probabilistic and text analysis methods to infer the reviewers' reliability and reviews' quality improves performance; however, there is still an evident need for instructor oversight and training of students to write compelling and reliable reviews.

The need for advanced analytical methods for big data in urology: A first application from the ReIMAGINE risk study

The need for advanced analytical methods for big data in urology: A first application from the ReIMAGINE risk study

Indistinct assessment of the quality of traditional Chinese medicine in precision medicine exampling as safflower

The quality of traditional Chinese medicine (TCM) guarantees its clinical efficacy. Although advanced analytical techniques and methods can accurately determine the content of chemical components in TCM, it is difficult to accurately determine its clinical efficacy. In addition, the current analytical methods and technologies are complex and have difficulty meeting the requirements of a rapid, accurate and convenient determination of TCM quality. In this study, we first propose the concept of “indistinct” evaluation of the quality of TCM, that is, combining biological potency with character evaluation, quantifying the character evaluation, and preparing the safflower quality grade evaluation card based on the character analysis, which provides research ideas and methods for the rapid and accurate evaluation of the quality of TCM. We determined the biological potency of different batches of safflower based on the in vitro antiplatelet aggregation model and divided the safflower samples into two grades based on the biological potency. We further collected the color information of different grades of safflower samples, quantified the color information of different grades of safflower, drew a quality grade evaluation card for the rapid judgment of safflower quality grade and verified its accuracy by pharmacodynamic evaluation. To further analyze the differences in the material basis of different grades of safflower, the LC-MS method was used to simultaneously determine the contents of 19 chemical components, such as myricetin, in different grades of safflower samples to analyze the differences in the material basis of different grades of safflower. The result shows that the different grades of safflower exhibited significant differences in color. The pharmacodynamic results show that the quality evaluation card prepared based on color information can accurately evaluate quality, and the effect of first-class safflower is significantly better than that of second-class safflower. The chemical analysis results of different grades of safflower show that there are also significant differences between them, among which hypericin, 6-hydroxyapin-6-O-glucose-7-O-glucuronide, 6-hydroxykaempferol-3,6-O-diglucoside-7-O-glucuronic acid glycoside, 6-hydroxykaempferol-3,6,7-tri-O-glucoside and hydroxysafflower yellow A exhibit significant differences, which may be the main differentiating components of different grades of safflower. This study preliminarily confirmed that the “indistinct” evaluation of the quality of TCM based on character analysis is accurate and scientific, and the quality evaluation card prepared can accurately judge the quality of TCM, providing a reference for the rapid application of TCM character evaluation.

Influence of Sports Training in Foothills on the Professional Athlete's Immunity.

Neuroplasticity and inflammation play important part in the body's adaptive reactions in response to prolonged physical activity. These processes are associated with the cross-interaction of the nervous and immune systems, which is realized through the transmission of signals from neurotransmitters and cytokines. Using the methods of flow cytometry and advanced biochemical analysis of blood humoral parameters, we showed that intense and prolonged physical activity at the anaerobic threshold, without nutritional and metabolic support, contributes to the development of exercise-induced immunosuppression in sportsmen. These athletes illustrate the following signs of a decreased immune status: fewer absolute indicators of the content of leukocytes, lowered values in the immunoregulatory index (CD4+/CD8+), and diminished indicators of humoral immunity (immunoglobulins A, M, and G, and IFN-γ). These factors characterize the functional state of cellular and humoral immunity and their reduction affects the prenosological risk criteria, indicative of the athletes' susceptibility to develop exercise-induced immunosuppression.

Big Data Analytics: A Literature Review Paper

In the age of information, decision-makers now have access to an enormous amount of data. The term "big data" refers to datasets that are not only large but also extremely diverse and fast, making it challenging to deal with them using conventional methods and tools. In order to handle and extract value and knowledge from these datasets, it is necessary to investigate and provide solutions due to the rapid growth of such data. In addition, decision-makers need to be able to draw useful conclusions from such a wide range of data that is constantly changing, such as data from daily transactions, customer interactions, and social network data. Using big data analytics, which is the application of advanced analytics methods to big data, such value can be provided. The purpose of this paper is to look at some of the various analytics tools and methods that can be used with big data, as well as the opportunities provided by using big data analytics in different decision domains.

All field emission experiments are noisy, … are any meaningful?

Representing field emission data on a Fowler–Nordheim plot is both very common and strongly not recommended. It leads to a spurious estimation of the emitter parameters despite a very good data fit. There is a lack of a reliable method of analysis and a proper estimation of the uncertainty in the extracted parameters. In this article, we show that the uncertainty in the estimation of the field enhancement factor or the emission area can be as high as ±50% even for a tungsten single emitter in good ultrahigh vacuum conditions analyzed by the Murphy–Good model. Moreover, the choice of the exact Murphy–Good method can have a noticeable impact. We found that advanced analysis methods, based on the measurement of the differential conductance of the emitter, are so demanding in terms of emitter stability that up to now its requirements are probably out of reach in any field emission laboratory.

The MUSEHubbleUltra Deep Field surveys: Data release II

We present the second data release of the MUSEHubbleUltra-Deep Field surveys, which includes the deepest spectroscopic survey ever performed. The MUSE data, with their 3D content, amazing depth, wide spectral range, and excellent spatial and medium spectral resolution, are rich in information. Their location in theHubbleultra-deep field area, which benefits from an exquisite collection of ancillary panchromatic information, is a major asset. This update of the first release incorporates a new 141-h adaptive-optics-assisted MUSE eXtremely Deep Field (MXDF; 1 arcmin diameter field of view) in addition to the reprocessed 10-h mosaic (3 × 3 arcmin2) and the single 31-h deep field (1 × 1 arcmin2). All three data sets were processed and analyzed homogeneously using advanced data reduction and analysis methods. The 3σpoint-source flux limit of an unresolved emission line reaches 3.1 × 10−19and 6.3 × 10−20 erg s−1 cm−2at 10- and 141-h depths, respectively. We have securely identified and measured the redshift of 2221 sources, an increase of 41% compared to the first release. With the exception of eight stars, the collected sample consists of 25 nearby galaxies (z < 0.25), 677 [O II] emitters (z= 0.25 − 1.5), 201 galaxies in the MUSE redshift desert range (z = 1.5 − 2.8), and 1308 Lyαemitters (z = 2.8 − 6.7). This represents an order of magnitude more redshifts than the collection of all spectroscopic redshifts obtained before MUSE in theHubbleultra-deep field area (i.e., 2221 versus 292). At high redshift (z > 3), the difference is even more striking, with a factor of 65 increase (1308 versus 20). We compared the measured redshifts against three published photometric redshift catalogs and find the photo-z accuracy to be lower than the constraints provided by photo-z fitting codes. Eighty percent of the galaxies in our final catalog have an HST counterpart. These galaxies are on average faint, with a median ABF775Wmagnitude of 25.7 and 28.7 for the [O II] and Lyαemitters, respectively. Fits of their spectral energy distribution show that these galaxies tend to be low-mass star-forming galaxies, with a median stellar mass of 6.2 × 108 M⊙and a median star-formation rate of 0.4 M⊙yr−1. We measured the completeness of our catalog with respect to HST and found that, in the deepest 141-h area, 50% completeness is achieved for an AB magnitude of 27.6 and 28.7 (F775W) atz= 0.8 − 1.6 andz = 3.2 − 4.5, respectively. Twenty percent of our catalog, or 424 galaxies, have no HST counterpart. The vast majority of these new sources are high equivalent-widthz > 2.8 Lyαemitters that are detected by MUSE thanks to their bright and asymmetric broad Lyαline. We release advanced data products, specific software, and a web interface to select and download data sets.

Characterisation of ash particles from co-combustion of bark and sludges from pulp and paper industry

Recycling phosphorus from waste streams for fertilization purposes could contribute to a sustainable society. The production in the pulp and paper industry results in several waste streams, among others nutrient-rich sludges in different forms. This study presents a detailed chemical and 3D characterization of ash from co-combustion of bark and two types of sludges from a paper mill; mixed sludge and biosludge. The combustion performance was investigated for these experiments and advanced analysis methods were used to characterise the ashes to correlate chemical and physical properties relevant for nutrient recycling. The elemental composition was determined by energy-dispersive X-ray spectroscopy; dominating crystalline phases by X-ray diffraction; and morphology, porosity, pore size distribution and active surface area of the slag were analysed with synchrotron-based X-ray micro-tomography and image analysis. Slag was formed in all combustion experiments to a large extent with increasing amounts with a higher proportion of sludge. Nutrient amounts indicate that slag particles from co-combustion of both biosludge and mixed sludge can be useful either as a soil improvement directly or for recovery processes. Slag from combustion of 30 wt% biosludge and 70 wt% bark contained the highest amount of phosphorus, 9 at% on a C and O free basis. Evaluation of tomography data showed that discrete and open pores could be distinguished on a micrometre scale. The porosity of the slag varied between the replicates and fuel mixtures, on average between 17 and 23 vol% for the bark and sludge mixtures. Open pore volume displayed large variations, on average 39–56 vol% of the pores were open pores connected to the surrounding volume. For all samples, 90 % of the pores were small, with an equivalent diameter under 30 μm, but the largest pore volume (80–90 %) consists of pores with an equivalent diameter over 75 μm. In soils, pores with a minimum equivalent diameter over 30 μm generally transmit water and the smaller pores store water. The slag particles have relatively thick walls, with few pore openings to the surroundings, indicating that the slag needs to be pre-treated by milling or crushing before application in the soil.

An approach for damage detection in noisy environments using DOG multi-scale space and fractal dimension

ABSTRACT The most prominent features of fractal dimension analysis are high sensitivity to damage and instant determination of damage location. However, an intrinsic deficiency is that it is difficult to identify damage in a structure while suppressing noise simultaneously. In addition, the vibration measurement with low-spatial-resolution is also the main factor restricting the accurate damage location. In this paper, a novel damage detection method based on fractal dimension is proposed, in order to provide the high-spatial-resolution vibration information, the phase-based optical flow is introduced, which is a vision-based measurement method that can calculate the structural vibration with high-spatial-resolution. Then, to detect damage in a structure while suppressing noise, an advanced signal analysis method is proposed, with this method, the fractal dimension (FRAC) is mapped into Difference of Gaussian (DOG) multi-scale space, producing the FRAC-DOG to reveal damage-induced singularities in mode shapes. Finally, a data fusion technique is applied to the proposed method to provide a reasonable damage detection result that maximises the elimination of uncertainty. The numerical and experimental results demonstrate that the proposed method can detect damage with high-precision in noisy environments by comparing it with the existing damage detection methods.

Exploring the interrelationship between the skin microbiome and skin volatiles: A pilot study

Unravelling the interplay between a human’s microbiome and physiology is a relevant task for understanding the principles underlying human health and disease. With regard to human chemical communication, it is of interest to elucidate the role of the microbiome in shaping or generating volatiles emitted from the human body. In this study, we characterized the microbiome and volatile organic compounds (VOCs) sampled from the neck and axilla of ten participants (five male, five female) on two sampling days, by applying different methodological approaches. Volatiles emitted from the respective skin site were collected for 20 min using textile sampling material and analyzed on two analytical columns with varying polarity of the stationary phase. Microbiome samples were analyzed by a culture approach coupled with MALDI-TOF-MS analysis and a 16S ribosomal RNA gene (16S RNA) sequencing approach. Statistical and advanced data analysis methods revealed that classification of body sites was possible by using VOC and microbiome data sets. Higher classification accuracy was achieved by combination of both data pools. Cutibacterium, Staphylococcus, Micrococcus, Streptococcus, Lawsonella, Anaerococcus, and Corynebacterium species were found to contribute to classification of the body sites by the microbiome. Alkanes, esters, ethers, ketones, aldehydes and cyclic structures were used by the classifier when VOC data were considered. The interdisciplinary methodological platform developed here will enable further investigations of skin microbiome and skin VOCs alterations in physiological and pathological conditions.

A Mini-Review On Emerging Trends and Recent Advances in Analytical Methods for Covid-19: Analytical Chemistry Perspective

In recent times SARS-CoV-2 pandemic, public health experts have slated testing, tracking infected people, as well as tracing their contacts as an efficient method to reduce the viral spread. Numerous diagnostic approaches are reported for detecting the coronavirus in public health, clinical and research laboratories. Most tests detect the infection directly by detecting the viral RNA while other test may indirectly detect the host antibodies in the course of detecting the infection. A diagnostic test during the pandemic should aid in making proper clinical decision within a short time frame. In this review, we describe the various techniques involved in sample collection from nasal, throat and sputum specimens, standard and developed methods for diagnosis of covid 19 such as the polymerase chain reaction (RT-PCR), CRISPR-based assays and Serological and immunological assays. General information on selected electroactive antiviral Covid-19 drugs have been highlighted including: Favipiravir (FAV), Remdesivir (REM), Lopinavir + Ritonavir (LOP + RIT), Hydroxychloroquine (HCQ) and Chloroquine (CQ), Ribavirin (RIB) and Sofosbuvir. This review presents the major standard, commercial and designed methods for detecting SARS-CoV-2 and their analytical performance. Also, chromatographic techniques have been highlighted as a rapid examination for SARS-CoV-2 identification, and some recent advances and emerging trends in analytical methods for covid-19 diagnosis have been addressed.

Applications of Metabolomics for the Elucidation of Abiotic Stress Tolerance in Plants: A Special Focus on Osmotic Stress and Heavy Metal Toxicity.

Plants undergo metabolic perturbations under various abiotic stress conditions; due to their sessile nature, the metabolic network of plants requires continuous reconfigurations in response to environmental stimuli to maintain homeostasis and combat stress. The comprehensive analysis of these metabolic features will thus give an overview of plant metabolic responses and strategies applied to mitigate the deleterious effects of stress conditions at a biochemical level. In recent years, the adoption of metabolomics studies has gained significant attention due to the growing technological advances in analytical biochemistry (plant metabolomics). The complexity of the plant biochemical landscape requires sophisticated, advanced analytical methods. As such, technological advancements in the field of metabolomics have been realized, aided much by the development and refinement of separatory techniques, including liquid and gas chromatography (LC and GC), often hyphenated to state-of-the-art detection instruments such as mass spectrometry (MS) or nuclear resonance magnetic (NMR) spectroscopy. Significant advances and developments in these techniques are briefly highlighted in this review. The enormous progress made thus far also comes with the dawn of the Internet of Things (IoT) and technology housed in machine learning (ML)-based computational tools for data acquisition, mining, and analysis in the 4IR era allowing for broader metabolic coverage and biological interpretation of the cellular status of plants under varying environmental conditions. Thus, scientists can paint a holistic and comprehensive roadmap and predictive models for metabolite-guided crop improvement. The current review outlines the application of metabolomics and related technological advances in elucidating plant responses to abiotic stress, mainly focusing on heavy metal toxicity and subsequent osmotic stress tolerance.