Untargeted metabolomics and machine learning unveil quality and authenticity interactions in grated Parmigiano Reggiano PDO cheese

The chemical composition of Parmigiano Reggiano (PR) hard cheese can be significantly affected by different factors across the dairy supply chain, including ripening, altimetric zone, and rind inclusion levels in grated hard cheeses. The present study proposes an untargeted metabolomics approach combined with machine learning chemometrics to evaluate the combined effect of these three critical parameters. Specifically, ripening was found to exert a pivotal role in defining the signature of PR cheeses, with amino acids and lipid derivatives that exhibited their role as key discriminant compounds. In parallel, a random forest classifier was used to predict the rind inclusion levels (> 18%) in grated cheeses and to authenticate the specific effect of altimetry dairy production, achieving a high prediction ability in both model performances (i.e., ∼60% and > 90%, respectively). Overall, these results open a novel perspective to identifying quality and authenticity markers metabolites in cheese.

Optimizing BP neural network algorithm for Pericarpium Citri Reticulatae (Chenpi) origin traceability based on computer vision and ultra-fast gas-phase electronic nose data fusion

This study utilized computer vision to extract color and texture features of Pericarpium Citri Reticulatae (PCR). The ultra-fast gas-phase electronic nose (UF-GC-E-nose) technique successfully identified 98 volatile components, including olefins, alcohols, and esters, which significantly contribute to the flavor profile of PCR. Multivariate statistical Analysis was applied to the appearance traits of PCR, identifying 57 potential marker-trait factors (VIP > 1 and P < 0.05) from the 118 trait factors that can distinguish PCR from different origins. These factors include color, texture, and odor traits. By integrating multivariate statistical Analysis with the BP neural network algorithm, a novel artificial intelligence algorithm was developed and optimized for traceability of PCR origin. This algorithm achieved a 100% discrimination rate in differentiating PCR samples from various origins. This study offers a valuable reference and data support for developing intelligent algorithms that utilize data fusion from multiple intelligent sensory technologies to achieve rapid traceability of food origins.

An integrative multi-omics approach aimed to gain insight on the effect of composition, style, yeast, and wheat species on wheat craft beer flavour

This study was aimed to unravel the effect of raw materials (barley and wheat), wheat concentration (0, 25, 40, and 100 %), wheat species (common and durum), beer style (Blanche and Weiss), and yeast (US-05 and WB-06) on the chemical composition, volatiles, and sensory profile of wheat craft beers by using a multivariate statistical approach. Beer samples were analysed for their composition, volatiles and sensory profile and data were processed using unsupervised multivariate analyses, PLS regression and a multi-omics approach using multi-block PLS-DA. Multi-block variable sparsification was used as an embedded dimension reduction step. The adopted multi-omics approach permitted to correctly classify beers with different styles and wheat concentration, and to accurate classify (95 % accuracy) beers according to yeast type. Wheat species was of lower importance since it permitted a classification with 49 % accuracy which increased to 74 % in Blanche beers, thus suggesting that malting flattened differences determined by wheat species.

Trace the origin of yak meat in Xizang based on stable isotope combined with multivariate statistics

In this study, the feasibility of tracing the origin of yak meat in Xizang Autonomous Region based on stable isotope combined with multivariable statistics was researched. The δ13C, δ15N, δ2H and δ18O in yak meat were determined by stable isotope ratio mass spectrometry, and the data were analyzed by analysis of variance, fisher discriminant analysis (FDA), back propagation (BP) neural network and orthogonal partial least squares discrimination analysis (OPLS-DA). The results showed that the δ13C, δ15N, δ2H and δ18O had significant differences among different origins (P < 0.05). The overall original correct discrimination rate of fisher discriminant analysis was 89.7 %, and the correct discrimination rate of cross validation was 88.2 %. The correct classification rate of BP neural network based on training set was 93.38 %, and the correct classification rate of BP neural network based on test set was 89.83 %. The OPLS-DA model interpretation rate parameter R2Y was 0.67, the model prediction rate parameter Q2 was 0.409, which could distinguish yak meat from seven different producing areas in Xizang Autonomous Region. The results showed that the origin of yak meat in Xizang Autonomous Region can be traced based on stable isotope combined with multivariate statistics.

An analytical strategy for challenging members of the microplastic family: Particles from anti-corrosion coatings

Potentially hazardous particles from paints and functional coatings are an overlooked fraction of microplastic (MP) pollution since their accurate identification and quantification in environmental samples remains difficult. We have applied the most relevant techniques from the field of microplastic analysis for their suitability to chemically characterize anti-corrosion coatings containing a variety of polymer binders (LDIR, Raman and FTIR spectroscopy, Py-GC/MS) and inorganic additives (ICP-MS/MS). We present the basis of a possible toolbox to study the release and fate of coating particles in the (marine) environment. Our results indicate that, due to material properties, spectroscopic methods alone appear to be unsuitable for quantification of coating/paint particles and underestimate their environmental abundance. ICP-MS/MS and an optimized Py-GC/MS approach in combination with multivariate statistics enables a straightforward comparison of the multi-elemental and organic additive fingerprints of paint particles. The approach can improve the identification of unknown particles in environmental samples by an assignment to different typically used coating types. In future, this approach may facilitate allocation of emission sources of different environmental paint/coating particles. Indeed, future work will be required to tackle various remaining analytical challenges, such as optimized particle extraction/separation of environmental coating particles.

Graphical, statistical and index-based techniques integrated for identifying the hydrochemical fingerprints and groundwater quality of In Salah, Algerian Sahara

Groundwater, a predominant reservoir of freshwater, plays a critical role in providing a sustainable potable water and water for agricultural and industry uses in the In Salah desert region of Algeria. This research collected 82 underground water samples from Albian aquifers to assess water quality and identify hydrogeochemical processes influencing mineralization. To achieve this objective, various methods were employed to evaluate water quality based on its intended uses. The drinking water quality index utilized revealed the water potability status, while the indicators of irrigation potability were employed to evaluate its quality for agricultural purposes. Additionally, an assessment of groundwater susceptibility to corrosion and scaling in an industrial context was conducted using several indices, e.g., Langelier index, Larson-Skold index, Ryznar index, chloride-sulfate mass ratio, Puckorius index, aggressiveness index, and the Revelle index. The findings of this study revealed that the groundwater quality for consumption fell into four categories: good (2.44%), fair (29.27%), poor (65.85%), and non-potable (2.44%). Concerning agricultural irrigation, the indexical results indicated that 15.85% of the waters exhibited adequate quality, while 84.15% were questionable for irrigation. Calculations based on various corrosion and scaling evaluation indices showed that most wells were prone to corrosion, with a tendency for calcium bicarbonate deposit formation. Furthermore, the hydrochemical study identified three water types: Na-Cl (53.66%), Ca-Mg-Cl (37.80%), and Ca-Cl (8.54%) waters. Analyses of correlation matrices, R-type clustering, factor loadings, Gibbs diagrams, scatterplots, and chloro-alkaline indices highlighted that the chemistry of the Albian groundwater is fundamentally impacted by a number of processes such as silicate weathering, evaporite dissolution, ionic exchange, and anthropogenic inputs, that played impactful role in the aquifer's water chemistry.

Detection of fragrances on the skin and study of their interaction using infrared and Raman spectroscopy

Nowadays, fragrances belong to the widely used cosmetics. Their composition is designed in a way that it evolves and changes over time. In this work, the effect of fragrances on the skin was studied - the interactions between pig skin samples and fragrances and the possibility of their detection and mutual differentiation. Non-invasive techniques of vibrational spectroscopy were used to obtain the data, namely FT-IR spectroscopy with attenuated total reflection accessory and Raman microspectroscopy. Vibrational spectra were measured within 8 h with different time intervals and after 22 h from the application of fragrance for FT-IR and Raman measurements, respectively. The obtained spectra were pre‑processed and subsequently evaluated by multivariate statistical methods. The study showed that skin treated by fragrances is well distinguishable from untreated skin, even after 22 h. In addition, it is possible to differentiate individual fragrances from each other; therefore, the use of spectroscopical techniques could be a potential tool for forensic analysis of fragrances.

Exploring geochemical distribution of potentially toxic elements (PTEs) in wetland and agricultural soils and associated health risks

This study is carried out to understand the degree of soil pollution, transport mechanism, and distribution pattern of potentially toxic elements (PTEs), including the exposure effects on human health. Towards this, topsoil samples were collected from the Saman wetland and surrounding agricultural fields in the Gangetic plain, India. The results show that the mean concentration of Cu, Hg, Zn, Pb, Th, As, U, and Cd of both soil types exceed the natural background values. The multivariate analysis suggests the soils are moderately contaminated with As, Cd, Zn, Pb, and Hg (possibly from anthropogenic sources) and heavily contaminated with Th and U, likely ascended from geogenic sources. The GIS-based geostatistical plots coupled with principal component analysis (PCA) and hierarchical cluster analysis (HCA) apportion the sources of these toxic elements, which vary greatly and are closely correlated to the geogenic processes and local anthropogenic sources like pesticides and agrochemicals. The health risk assessment revealed that the cumulative hazard index (HI) values of PTEs are lower than the safe level, suggesting no significant noncarcinogenic effect for adults and children. However, excess cancer risk (ECR) values exceed the permissible limit (1 × 10-6), signifying that exposure to the toxic element concentration may cause cancer in the exposed population, most probably in the children subpopulation. Thus, this study highlights the importance of local compliance, ensuring the quality checks and management policies in using pesticides and other agrochemicals containing PTEs to control the imposed cancer risks.

Multi-layered physical factors govern mercury release from soil: Implications for predicting the environmental fate of mercury

Despite the recognised risks of human exposure to mercury (Hg), the drivers of gaseous elemental mercury (GEM) emissions from the soil remain understudied. In this study, we aimed to identify the environmental parameters that affect the GEM flux from soil and derive the correlations between environmental parameters and GEM flux. Principal component analysis (PCA), factor analysis (FA), and structural equation modelling (SEM) were performed on samples from forest and non-forest sites. The associated results revealed the impact of each environmental parameter on GEM flux, either due to the interaction between the parameters or as a coherent set of parameters. An introductory correlation matrix examining the relationship between two components showed a negative correlation between GEM flux and atmospheric pressure at the two sites, as well as strong correlations between atmospheric pressure and soil temperature. In cases of non-forest open sites with no trees, the PCA and FA results were consistent, indicating that atmospheric pressure, solar irradiance, and soil moisture-defined as primary causality-are largely independent drivers of GEM flux. In contrast, the PCA and FA results for the forest areas with high humidity, tree coverage, and shade were inconsistent, confirming the hypothesis that primary causality affects GEM flux rather than consequent parameters driven by primary causality, such as air and soil temperature and atmospheric humidity. The SEM results provided further evidence for primary and consequent causality as crucial drivers of the GEM flux. This study demonstrates the importance of key primary parameters, such as atmospheric pressure, solar irradiance, and soil moisture content, that can be used to predict mercury release from soils, as well as the importance of consequent parameters, such as air and soil temperature and atmospheric humidity. Monitoring the magnitude of these environmental parameters alone may facilitate the estimation of mercury release from soils and be useful for detailed modelling of soil-air Hg exchange.

Monitoring network optimization and impact of fish farming upon water quality in the Três Marias Hydroelectric Reservoir, Brazil

Hydroelectric power is the main source of electrical energy in Brazil. Electrical energy providers have the duty to monitor water quality in reservoirs to preserve water quality and support best management practices that enable multiple water uses, including fish production. In this context, the objectives of this study were (i) to perform a historical evaluation of water quality in Três Marias Reservoir, (ii) to present an optimization of the water quality monitoring network, and (iii) to evaluate the evolution and impact of fish farming upon surface water quality by using secondary data measured in situ and remote sensing. A systematic approach was applied to analyze historical water quality data. Principal component analysis (PCA) and cluster analysis (CA) were applied to identify the most important parameters and monitoring points. Images obtained from Sentinel 2 were treated by contrast to quantify simple and weighted densities of fish farming activities in the region while regression analysis was performed to verify correlations between these densities and water quality parameters. Results showed that the pH and total suspended solids were the most important parameters for characterizing water quality, especially near tributaries, and that monitoring points could be grouped into three clusters (upstream, central, and downstream regions) with distinct water quality conditions. The PCA indicated that there is no redundance among parameters nor monitoring stations and that areas near tributaries must be prioritized for monitoring as these are important sources of suspended solids. Remote sensing images showed that the area occupied by fish farms has increased in the reservoir from 2016 to 2022 and the methodology used for this purpose in this study may be applied to other bodies of water. Chlorophyll-a showed a direct relationship with the density of fish farms indicating a possible influence of nutrient input to the reservoir by this activity. These results provide valuable information to support decision-making related to water management in the reservoir.

A lateralized alpha-band marker of the interference of exogenous attention over endogenous attention

Current theories of attention differentiate exogenous from endogenous orienting of visuospatial attention. While both forms of attention orienting engage different functional systems, endogenous and exogenous attention are thought to share resources, as shown by empirical evidence of their functional interactions. The present study aims to uncover the neurobiological basis of how salient events that drive exogenous attention disrupts endogenous attention processes. We hypothesize that interference from exogenous attention over endogenous attention involves alpha-band activity, a neural marker of visuospatial attention. To test this hypothesis, we contrast the effects of endogenous attention across two experimental tasks while we recorded electroencephalography (n = 32, both sexes): a single cueing task where endogenous attention is engaged in isolation, and a double cueing task where endogenous attention is concurrently engaged with exogenous attention. Our results confirm that the concurrent engagement of exogenous attention interferes with endogenous attention processes. We also found that changes in alpha-band activity mediate the relationship between endogenous attention and its effect on task performance, and that the interference of exogenous attention on endogenous attention occurs via the moderation of this indirect effect. Altogether, our results substantiate a model of attention, whereby endogenous and exogenous attentional processes involve the same neurophysiological resources.

SIGNIFICANCE STATEMENT

Scientists differentiate top-down from bottom-up visuospatial attention processes. While bottom-up attention is rapidly engaged by emerging demands from the environment, top-down attention in contrast reflects slow voluntary shifts of spatial attention. Several lines of research substantiate the idea that top-down and bottom-up attentional processes involve distinct functional systems. An increasing number of studies, however, argue that both attention systems share brain processing resources. The current study examines how salient visual events that engage bottom-up processes interfere with top-down attentional processes. Using neurophysiological recordings and multivariate pattern classification techniques, the authors show that these patterns of interference occur within the alpha-band of neural activity (8-12 Hz), which implies that bottom-up and top-down attention processes share this narrow-band frequency brain resource. The results further demonstrate that patterns of alpha-band activity explains, in part, the interference between top-down and bottom-up attention at the behavioral level.

Elemental distribution patterns in rock samples from Egypt using neutron activation and complementary X-ray fluorescence analyses

The study utilized instrumental neutron activation analysis (INAA) and X-ray fluorescence (XRF) to accurately analyze the elemental composition of 28 felsite (rhyolite), rock samples. Statistical approaches, including bivariate and multivariate analysis, were employed to characterize the rocks and determine their origin. Major findings include significantly high levels of silicon (297000 ± 4000) mg/kg and low levels were noticed for gold (0.10 ± 0.01) mg/kg. The dominant major elements in the rocks were ranked as follows: silicon > aluminum > potassium > sodium > zirconium > calcium > zinc > manganese. A comparison with the upper continental crust (UCC) revealed higher levels for most elements, except for a few. The study also identified substantial amounts of uranium and thorium. Variations in elemental composition were observed both between different profiles and within felsite (rhyolite) rock samples, indicating heterogeneity and varying origins of the rocks. The findings contribute valuable baseline data for the area and highlight its economic significance for Egypt. Additionally, the study addresses the integration of results from different analytical methods, providing a comprehensive answer to this issue.

Statistics in identifying factors that control the geochemical distribution of potentially polluting elements over a tailings pond surface: a case study

The study shows how the statistical approach can provide information on the factors and processes that control the geochemical distribution of elements at the surface of an abandoned tailings pond. In this regard, the case study of a waste deposit resulting from the ore processing plant of Fundu Moldovei was carried out. The facility was concentrating Cu, Pb, and Zn from the polymetallic sulfide ores of the Fundu Moldovei-Leșu Ursului mining district (Romania). The statistics indicate three types of waste, showing specific properties: (i) Waste of the beach, rich in soluble fraction (14.4%) and secondary minerals (e.g., jarosite, ferricopiapite, magnesiocopiapite, pickeringite, and clay minerals). The latter and the related high contents of Al, K, Fe, Co, Ni, Cu, Pb, and Zn are controlled by the water evaporation and subsequent transient pH (2.6-3.5) of the leachates accumulated as puddles. The lower pH and scarce soluble fraction favor a rise in the Cu and Zn contents, while Al, K, Fe, and Co are noticeable at a higher pH when the soluble fraction is abundant. (ii) Waste of the upper dam slope, marked by intense oxidation and a meager occurrence of secondary minerals precipitated from highly acidic pore leachates (average pH of 2.55), namely, jarosite, ferricopiapite, magnesiocopiapite, and coquimbite. The surface waste contains more pyrite and is coarser because of the fine particle removal during rainfall. Unlike the beach waste, in the upper dam tailings, Al, K, Fe, Co, Cu, Pb, and Zn seem to relate mainly to the primary minerals (muscovite, chlorite, and pyrite). (iii) Downslope dam waste is less acidic (average pH of 3.75) than that of the upper slope; it contains secondary minerals stable at a higher pH (e.g., gypsum, apjohnite, dietrichite, clay minerals, and schwertmannite). Calcium, Mn, and Cd are more abundant in the dam waste. They originate from both primary and secondary minerals (e.g., muscovite, chlorite, gypsum, ferricopiapite, and magnesiocopiapite) and correlate with the coarser waste.

Assessment of the water quality using multivariate statistics and the water quality index: a case study of the Yağlıdere Stream (Giresun) in the Eastern Black Sea region, Turkey

In this study, the water quality of the Yağlıdere Stream passing through Espiye (Giresun-NE Turkey) and Yağlıdere districts, where old and new mining activities are present, was evaluated, and characterized by using the water quality index (WQI), multivariate statistical, and GIS techniques. The downstream aquifer of the Yağlıdere Stream, which originates from Erimez Mountains and reaches the sea from the west of Espiye district, meets the domestic and drinking water needs of Yağlıdere and Espiye districts. In addition, activities such as energy production and fish farming are carried out along the stream. Therefore, it is of great importance to investigate the water quality of the stream. In order to evaluate the water quality, 50 water samples were taken from 10 sampling points (5 periods) along the flow direction of the stream. Parameters other than T, DO, Mg, F, NH3, CN, and HS show significant spatial variations indicating the influence of geogenic and anthropogenic activities. Some of the investigated parameters (T, DO, Ca2+, Mg2+, SO42-, NH3, CN, Fe, Se) exhibit important seasonal variations due to high seasonality in water temperature and water flow. Principal component analysis/factor analysis reveals that the parameters responsible for water quality changes in the Yağlıdere Stream are mainly related to the geogenic structure, mining wastes, agricultural activities, and domestic wastes. According to the WQI values, the water samples taken from the upstream and middle part of the stream are in the water quality between "Excellent" and "Good", on the other hand, in the downstream regions where anthropogenic and geogenic effects are dominant, the water samples are in the quality of "Poor water" and "Unsuitable for drinking". The most effective water quality parameters on WQI are Al, Fe, and Mn and the water quality in the basin is negatively affected by geogenic and anthropogenic effects.

A geochemical and mineralogical characterization of soils associated with podoconiosis

Podoconiosis is a disease that causes swelling and disfiguration of the lower legs found in several developing countries where shoes are not regularly worn. The current model for the etiology of the disease proposes that mineralogical agents enter the lymph system through the skin leading to inflammation that causes swelling of the feet and legs. We collected 125 soil samples from 21 towns associated with podoconiosis, 8 towns unassociated with Podoconiosis as controls, and 3 towns of unknown status. Data collected for each soil sample included color, particle size, mineralogy, and geochemistry to distinguish unique components within the podoconiosis-associated soils. Our results indicate podoconiosis-associated soils are more highly weathered than non-podoconiosis associated soils. The enrichment of kaolinite and gibbsite suggests that these minerals, their surface chemistry, and trace elements associated with them should be prioritized in future podoconiosis research. In addition, we found that color may be a valuable tool to identify soils at greater risk for inducing podoconiosis.

Data-driven multivariate identification of gyrification patterns in a transdiagnostic patient cohort: A cluster analysis approach

BACKGROUND

Multivariate data-driven statistical approaches offer the opportunity to study multi-dimensional interdependences between a large set of biological parameters, such as high-dimensional brain imaging data. For gyrification, a putative marker of early neurodevelopment, direct comparisons of patterns among multiple psychiatric disorders and investigations of potential heterogeneity of gyrification within one disorder and a transdiagnostic characterization of neuroanatomical features are lacking.

METHODS

In this study we used a data-driven, multivariate statistical approach to analyze cortical gyrification in a large cohort of N = 1028 patients with major psychiatric disorders (Major depressive disorder: n = 783, bipolar disorder: n = 129, schizoaffective disorder: n = 44, schizophrenia: n = 72) to identify cluster patterns of gyrification beyond diagnostic categories.

RESULTS

Cluster analysis applied on gyrification data of 68 brain regions (DK-40 atlas) identified three clusters showing difference in overall (global) gyrification and minor regional variation (regions). Newly, data-driven subgroups are further discriminative in cognition and transdiagnostic disease risk factors.

CONCLUSIONS

Results indicate that gyrification is associated with transdiagnostic risk factors rather than diagnostic categories and further imply a more global role of gyrification related to mental health than a disorder specific one. Our findings support previous studies highlighting the importance of association cortices involved in psychopathology. Explorative, data-driven approaches like ours can help to elucidate if the brain imaging data on hand and its a priori applied grouping actually has the potential to find meaningful effects or if previous hypotheses about the phenotype as well as its grouping have to be revisited.

The hidden impact of seafood processing on coastal aquifers: Hydrogeochemistry and water quality assessment

Seafood processing industries play a crucial role in meeting global food demands; however, their consequences on groundwater quality are a growing concern. The study fills the existing knowledge gap among the shallow aquifers of the Southwest coast of India by conducting a thorough assessment of the hydrogeochemical alterations caused by the release of wastewater and waste by-products related to seafood processing operations. The methodology involves a multi-faceted approach, encompassing field sampling, hydro-chemical modelling, and statistical validation. Depleted DO (0.9-2.4 mg/L), elevated TDS (687-2325 mg/L), EC (890-3120 μS/cm), TH (332-700 mg/L), BOD(2-6.73 mg/L), COD(9-34.6 mg/L), NH3 (1.51-8.14 mg/L), Fe2+ (1.8-7.6 mg/L) and SO42- (8.41-61.6 mg/L) were inferred. Various quality indices of water suggested its unsuitability for drinking and suitability for agricultural purposes. However, the water is corrosive to be used for industries only after proper treatment. Hazard-Index indicates the area is not prone to carcinogenic risk. The study explains the hydrogeochemistry and groundwater suitability with respect to over-exploitation and seafood industrial effluent discharges into nearby water bodies and drains.

The Comet Assay, a Sensitive Biomarker of Water Quality Improvement Following Adoption of Beneficial Agricultural Practices?

Numerous actions have been undertaken by farmers to attenuate the impact of agricultural activities on aquatic ecosystems. The identification of biomarkers that respond quickly to water quality improvement could facilitate the assessment of adopted alternative practices and help maintain mobilization among stakeholders. We evaluated the potential of the comet assay, a biomarker of genotoxic effects, using a freshwater mussel, Elliptio complanata, as a model animal. The frequency of DNA damage was assessed in hemocytes of mussels collected from a pristine habitat and caged for 8 weeks in the Pot au Beurre River, a tributary of the fluvial Lake St.-Pierre (Quebec, Canada) impacted by agricultural activities. We found that the level of DNA damage naturally induced in mussel hemocytes was low and showed very limited variations over time. Compared with these baseline levels and to laboratory controls, we observed a doubling in DNA alterations in mussels exposed to agricultural runoff in the third branch of the Pot au Beurre River. The genotoxic response was significantly lower in mussels caged in the first branch of the Pot au Beurre River, where longer stretches of shoreline have been restored as buffer strips. Glyphosate, mesotrione, imazethapyr, and metolachlor were the main discriminant pesticides between these two branches. Metolachlor was found in sufficient concentrations to induce DNA damage, but it is more likely that the observed genotoxicity was the result of a "cocktail effect," that is, the cumulative contribution of coexisting genotoxicants including the above-mentioned herbicides and ingredients in their formulation. Our findings suggest that the comet assay is a sensitive tool for the early detection of changes in water toxicity following the adoption of agricultural beneficial practices. Environ Toxicol Chem 2023;42:2201-2214. © 2023 Crown copyright and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. This article is published with the permission of the Controller of HMSO and the King's Printer for Scotland.

Heavy metals in five commonly consumed fish species from River Swat, Pakistan, and their implications for human health using multiple risk assessment approaches

This study analyzed the levels of heavy metals bioaccumulation in commonly consumed riverine fish species, including G. cavia, T. macrolepis, G. gotyla, S. plagiostomus, and M. armatus from River Swat in Pakistan, and quantify their potential risk to children and adults in general and fisherfolk communities using multiple pollution and risk assessment approaches. The highest metal detected by inductive coupled plasma mass spectrometry (ICP-MS) was Zn, which ranged from 49.61 to 116.83 mg/kg, followed by Fe (19.25-101.33 mg/kg) > Mn (5.25-40.35 mg/kg) > Cr (3.05-14.59 mg/kg) > Ni (4.26-11.80 mg/kg) > Al (1.59-12.25 mg/kg) > Cu (1.24-8.59 mg/kg) > Pb (0.29-1.95 mg/kg) > Co (0.08-0.46 mg/kg) > Cd (0.01-0.29 mg/kg), demonstrating consistent fluctuation with the safe recommendations of global regulatory bodies. The average bioaccumulation factor (BAF) values in the examined fish species were high (BAF > 5000) for Pb, Zn, Mn, Cu, Cr, Ni, and Cd, bioaccumulate (1000 > BAF < 5000) for Co, and probable accumulative (BAF <1000) for Fe, and Al, while the overall ∑heavy metals pollution index (MPI) values were greater than one (MPI > 1) indicating sever heavy metals toxicity in G. cavia, followed by S. plagiostomus, M. armatus, G. gotyla, and T. macrolepis. The multivariate Pearson's correlation analysis identified the correlation coefficients between heavy metal pairs (NiCr, CuCr, PbCr, AlCo, CuNi, and PbNi), the hierarchical cluster analysis (CA) determined the origin by categorizing heavy metal accumulation into Cluster-A, Cluster-B, and Cluster-C, and the principal component analysis (PCA) discerned nearby weathering, mining, industrial, municipal, and agricultural activities as the potential sources of heavy metals bioaccumulation in riverine fish. As per human risk perspective, S.plagiostomus contributed significantly to the estimated daily intake (EDI) of heavy metals, followed by G.cavia > M.armatus > G.gotyla > T.macrolepis in dependent children and adults of the fisherfolk followed by the general population. The non-carcinogenic target hazard quotient (THQ) and hazard index (HI) values for heavy metal intake through fish exposure were < 1, while the carcinogenic risk (CR) for individual metal intake and the total carcinogenic risk (TCR) for cumulative Cr, Cd, and Pb intake were within the risk threshold of 10-6-10-4, suggesting an acceptable to high non-carcinogenic and carcinogenic risk for both children and adults in the fisherfolk, followed by the general population.

Deployment of entropy information theory in the Indian Sundarban region using hydrogeochemical parameters and GIS for assessment of irrigation suitability

The evaluation of irrigation suitability plays a crucial role for the socio-economic development of the society, especially in the region of Sundarban. For sustainable agricultural practices, groundwater quality must be suitable for irrigation; otherwise, it can degrade soil and diminish crop yield. The entropy information theory, several irrigational indices, multivariate statistics, GIS, and geostatistics are used in this work to evaluate the geographical distribution and quality of groundwater in the Indian Sundarban region. In total, 33 groundwater samples were collected in 2018 (April and May), and they were evaluated for major cations, anions, as well as other parameters like electrical conductivity (EC), soluble sodium percentage (SSP), potential salinity (PS), total dissolved solids (TDS), Kelly ratio (KR), sodium absorption ratio (SAR), permeability index (PI), residual sodium carbonate (RSC), magnesium hazard (MH), and residual sodium bicarbonate (RSBC). The overall trend of the principal cations and anions is in the sequence of Na+  ≥ Mg2+  ≥ Ca2+  ≥ K2+ and HCO3-  ≥ Cl-  ≥ NO3-  ≥ SO42-  ≥ F-, respectively, whereas the spatial variation of %Na, SAR, RSBC, and MH demonstrate very poor irrigation water quality, and spatial variation of KR, RSC, SSP, PI, and PS signifies that the irrigation water quality is excellent to good. In order to identify the specific association and potential source of the dissolved chemical in the groundwater, statistical techniques like correlation and principal component analysis were also employed. The hydrochemical facies indicates that mixed type makes up the bulk (51.51%) of the water samples. Following the Wilcox plot, more than 75% of the water samples are good to doubtful; however, by the US salinity hazard map, roughly 60.60% of the samples had high salinity (C3-S1 zone). The EWQII reports that no samples fall into the very good (no restriction) category, whereas 30.30%, 30.30%, and 39.40% of the sample wells record good (low restriction), average (moderate restriction), and poor (severe restriction) irrigation water quality, respectively. Based on this study, the bulk of the groundwater samples taken from the study area are unsuitable for cultivation. The findings of this study will also help decision-makers develop adequate future plans for irrigation and groundwater resource management.

Paleoenvironmental multiproxy dataset of the Quaternary abandoned channel in Tövises bed, Great Hungarian Plain

To obtain the multiproxy paleoenvironmental dataset from southeast Great Hungarian Plain (GHP), 345 sediment samples were collected at one cm intervals from the cores retrieved from Tovisies bed paleochannel, and six samples were analyzed for 14C dates. The obtained radiocarbon dates were calibrated to calendar ages using the IntCal20 calibration curve. Bayesian statistics within the R bacon 2.5.8 age-depth modeling package were used to establish the age-depth model and it represented the time frame for the entire dataset. The obtained polymodal grain size distribution (GSD) data was unmixed into four EMs using the AnalySize v.1.2.0 algorithm, employing the built-in General Weibull function which helped explain the dynamicity of the endmembers' sedimentation process. To understand the alluviation history, the endmember abundances were correlated with LOI55, LOI950, and magnetic susceptibility. The dataset presented in this article could be of potential reuse for studying the spatial-temporal environmental changes and in geoarchaeological research, providing insights into how human societies adapted to environmental shifts across the southeast GHP.

Did COVID-19 enlarge spatial disparities in population dynamics? A comparative, multivariate approach for Italy

A short-term issue that has been occasionally investigated in the current literature is if (and, eventually, how) population dynamics (directly or indirectly) driven by COVID-19 pandemic have contributed to enlarge regional divides in specific demographic processes and dimensions. To verify this assumption, our study run an exploratory multivariate analysis of ten indicators representative of different demographic phenomena (fertility, mortality, nuptiality, internal and international migration) and the related population outcomes (natural balance, migration balance, total growth). We developed a descriptive analysis of the statistical distribution of the ten demographic indicators using eight metrics that assess formation (and consolidation) of spatial divides, controlling for shifts over time in both central tendency, dispersion, and distributional shape regimes. All indicators were made available over 20 years (2002-2021) at a relatively detailed spatial scale (107 NUTS-3 provinces) in Italy. COVID-19 pandemic exerted an impact on Italian population because of intrinsic (e.g. a particularly older population age structure compared with other advanced economies) and extrinsic (e.g. the early start of the pandemic spread compared with the neighboring European countries) factors. For such reasons, Italy may represent a sort of 'worst' demographic scenario for other countries affected by COVID-19 and the results of this empirical study can be informative when delineating policy measures (with both economic and social impact) able to mitigate the effect of pandemics on demographic balance and improve the adaptation capacity of local societies to future pandemic's crises.

Contamination and health risk assessment of groundwater along the Red Sea coast, Northwest Saudi Arabia

Coastal groundwater is the major source of freshwater in coastal areas, due to shortage of the water resource in coastal zones. Groundwater samples were collected from 48 sites along the Saudi Red Sea coast between Rabigh and Yanbu to document the hydrogeochemical characteristics, water quality, and the health risks of nitrate, fluoride, nickel, copper, and zinc on adults and children. Groundwater chemistry indicated neutral to slightly alkaline nature, and the total dissolved salts (TDS) concentrations mainly increase towards the coastal plain. Major cations, major anions, and heavy metals (HMs) were in the order of Cl^- > Na⁺ > SO₄^2- > Ca²⁺ > HCO₃^- > NO₃^- > Mg²⁺ > K⁺, F^- > Zn > Cu > Ni. Na-Cl, mixed Ca-Mg-Cl and Na-K-HCO₃, CaCl, and Mg-HCO₃ were the principal hydrochemical water types. Results of heavy metal pollution index (HPI), and water quality index (WQI) showed moderate to heavy pollution and unsuitable groundwater for human consumption mostly in the western side along the coastal plain, which might be influenced by the groundwater salinization. Principal component analysis (PCA) generated four components, which indicated the various sources of contamination. Hazard index (HI) of nitrate and fluoride were above the safety limit of 1, suggesting increase non-cancer health risk issues in both children and adults.

Geochemical characterisation and geostatistical evaluation of groundwater suitability: a case study in Perambalur District, Tamil Nadu, India

Groundwater is a naturally occurring potential source for drinking, irrigation, agricultural and industrial purposes. The population growth and accelerated development of industries and agriculture activity degrade groundwater quality. The groundwater quality of an area was determined by the physical and chemical parameters, influenced by geology, soil, land use, land cover and anthropogenic activities. Perambalur district in Tamil Nadu has been selected as a study area with a total geographical area of around 1757 km². In the study area, groundwater quality decreases due to the usage of chemical fertilisers and pesticides in agricultural land and mining activities. So, the hydrogeochemical assessment will help to determine the groundwater suitability for drinking. Forty-eight groundwater samples were collected from the study area during the pre-monsoon (July 2021) and post-monsoon season (January 2022). Samples were analysed using the standard methods prescribed by the American Public Health Association for pH, electrical conductivity (EC), total dissolved solids (TDS), calcium, magnesium, sodium, potassium, carbonate, bicarbonate, chloride, sulphate, nitrate and fluoride. The spatial distribution of major physiochemical parameters is mapped using the inverse distance weighted (IDW) interpolation technique. The evaluation of hydrochemical facies from piper plots revealed that the major cation and anion were in the order of Ca²⁺  > Mg²⁺  > Na⁺  > K⁺ and Cl^-  > HCO₃^-  > SO₄^2-  > NO₃^- in both seasons, respectively. Further, the plot explains the presence of both permanent and temporary hardness in the groundwater. The evaluation of hydrochemical facies from the piper plot emphasises that the reverse ion exchange controls groundwater chemistry. The assessment of chloro-alkaline indices reveals that the sodium and potassium in groundwater get substituted with magnesium and calcium in the parent rock, which determines the groundwater composition. The values of saturation indices reveal that calcite and dolomite are supersaturated and tend to precipitate. From principal component analysis, the principal components have an eigenvalue of more than 1, containing 79.8% and 79.2% in the total variance in pre-monsoon and post-monsoon, respectively. Most physiochemical parameters like TDS, EC, Na⁺, Mg²⁺, Cl^- and SO₄² - have strong positive loading and are responsible for the changes in groundwater chemistry. Finally, the calculation of the water quality index identified that groundwater quality in post-monsoon tends to decline compared to pre-monsoon.

Evolution of social mood in Spain throughout the COVID-19 vaccination process: a machine learning approach to tweets analysis

OBJECTIVES

This paper presents a new approach based on the combination of machine learning techniques, in particular, sentiment analysis using lexicons, and multivariate statistical methods to assess the evolution of social mood through the COVID-19 vaccination process in Spain.

METHODS

Analysing 41,669 Spanish tweets posted between 27 February 2020 and 31 December 2021, different sentiments were assessed using a list of Spanish words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy and disgust) and three valences (neutral, negative and positive). How the different subjective emotions were distributed across the tweets was determined using several descriptive statistics; a trajectory plot representing the emotional valence vs narrative time was also included.

RESULTS

The results achieved are highly illustrative of the social mood of citizens, registering the different emerging opinion clusters, gauging public states of mind via the collective valence, and detecting the prevalence of different emotions in the successive phases of the vaccination process.

CONCLUSIONS

The present combination in formal models of objective and subjective information would therefore provide a more accurate vision of social reality, in this case regarding the COVID-19 vaccination process in Spain, which will enable a more effective resolution of problems.

Discrimination of leukemias and non-leukemic cancers in blood serum samples of children and adolescents using a Raman spectral model

This study aimed to identify the differences presented in the Raman spectrum of blood serum from normal subjects compared to leukemic and non-leukemic subjects and the differences between the leukemics and non-leukemics, correlating the spectral differences with the biomolecules. Serum samples from children and adolescents were subjected to Raman spectroscopy (830 nm, laser power 350 mW; n = 566 spectra, being 72 controls, 269 leukemics, and 225 non-leukemics). Exploratory analysis based on principal component analysis (PCA) of the serum sample's spectra was performed. Classification models based on partial least squares discriminant analysis (PLS-DA) were developed to classify the spectra into normal, leukemic, and non-leukemic, as well as to discriminate spectra of leukemic from non-leukemic. The exploratory analysis showed principal components with peaks related to amino acids, proteins, lipids, and carotenoids. The spectral differences between normal, leukemic, and non-leukemic showed features assigned to proteins (serum features), amino acids, and carotenoids. The PLS-DA model classified the spectra of the normal group versus leukemic and non-leukemic groups with accuracy of 66%, sensitivity of 99%, and specificity of 57%. The PLS-DA discriminated the spectra of the leukemic and non-leukemic groups with accuracy of 67%, sensitivity of 72%, and specificity of 60%. The study showed that Raman spectroscopy is a technique that may be used for the biochemical differentiation of leukemias and other types of cancer in serum samples of children and adolescents. Nevertheless, building an extensive data library of Raman spectra from serum samples of controls, leukemics, and non-leukemics of different age groups is necessary to understand the findings better.

Technology-critical elements in Rhine sediments - A case study on occurrence and spatial distribution

Despite their presence in almost every technical device, little is known about the occurrence, distribution, and fate of technology-critical elements (TCEs) within the environment. Due to high economic demands and short product lifespans as well as low recycling rates, many TCEs appear to become emerging contaminants. Within the scope of this work, 57 sediment samples from the German part of the Rhine river, as well as various tributaries, were collected to study the occurrence and distribution of TCEs. This specific catchment area has consistently been subjected to strong anthropogenic influences over the last century. Hierarchical cluster analysis, as well as principal component analysis were used to gain first insights into the spatial distribution and possible sources of TCEs along the Rhine. Obtained mass fractions in conjunction with corresponding geoaccumulation indices (I_geo) provide first indications of a possible enrichment along the Rhine for the TCEs of interest (Ga, Ge, Nb, In, Te, rare earth elements, and Ta). Especially the mass fractions of Zn, Ge, In, La, Sm, and Gd exhibit significant anthropogenic inputs. For stations characterized by high Ge and In mass fractions, element fingerprints imply possible atmospheric deposition stemming from e.g. combustion processes. Distinct anomalies of La and Sm most likely originate from discharges located at the city of Worms into the Upper Rhine. Statistical analysis of all analyzed 55 elemental mass fractions revealed similar behavior of TCEs compared to classical heavy metals. Diffuse as well as point sources of TCEs are likely. As a result, this study provides further insight into the role of TCEs as potential emerging contaminants in the environment.

The community ecology perspective of omics data

The measurement of uncharacterized pools of biological molecules through techniques such as metabarcoding, metagenomics, metatranscriptomics, metabolomics, and metaproteomics produces large, multivariate datasets. Analyses of these datasets have successfully been borrowed from community ecology to characterize the molecular diversity of samples (ɑ-diversity) and to assess how these profiles change in response to experimental treatments or across gradients (β-diversity). However, sample preparation and data collection methods generate biases and noise which confound molecular diversity estimates and require special attention. Here, we examine how technical biases and noise that are introduced into multivariate molecular data affect the estimation of the components of diversity (i.e., total number of different molecular species, or entities; total number of molecules; and the abundance distribution of molecular entities). We then explore under which conditions these biases affect the measurement of ɑ- and β-diversity and highlight how novel methods commonly used in community ecology can be adopted to improve the interpretation and integration of multivariate molecular data. Video Abstract.

Water quality assessment of the Karasu River (Turkey) using various indices, multivariate statistics and APCS-MLR model

Determining the water quality status of a river and accurately identifying potential pollution sources threatening the river are pillars in effective control of pollution and sustainable water management. In this study, water quality indices, multivariate statistics and absolute principal component score-multiple linear regression (APCS-MLR) were applied to evaluate the water quality of the Karasu River, the main tributary of the Euphrates River (Turkey). For this, 19 water quality variables were monitored monthly at eight stations along the river during one year. Based on the mean dissolved oxygen (DO), electrical conductivity (EC), nitrate-nitrogen (NO₃-N), orthophosphate-phosphorus (PO₄-P), total phosphorus (TP), ammonium-nitrogen (NH4-N), chemical oxygen demand (COD) and total nitrogen (TN) levels, most stations of the river had "very good" water status according to surface water quality criteria. Spatial cluster analysis (CA) divided eight stations into three regions as clean region, moderate clean region and very clean region. The mean values of Nutrient Pollution Index indicated that the river was "no polluted". Similarly, Water Quality Index and Organic Pollution Index values indicated that the river water quality was between "good" and "excellent". A minimum water quality index (WQI_min) consisted of ten crucial parameters was not significantly different with the WQI based on all the 17 parameters. Discriminant analysis (DA) results showed that water temperature (WT), EC, chlorophyll-a (Chl-a), potassium (K), calcium (Ca), NO₃-N and COD are the variables responsible for temporal changes, while WT, total dissolves solids (TDS), Chl-a, K, magnesium (Mg), Ca, NH₄-N and COD are the variables responsible for spatial changes in the river water quality. Principal component analysis/factor analysis (PCA/FA) identified four potential sources, including anthropogenic, natural, seasonal and phytoplankton. Source apportionment in the APCS-MLR model revealed that seasonal and anthropogenic sources contributed 35.2% and 25.5% to river water quality parameters, respectively, followed by phytoplankton (21.4%) and natural sources (17.9%).

Salinization of groundwater during 20 years of agricultural irrigation, Luxor, Egypt

Groundwater salinization is a global problem accounting for 11-30% of the world's irrigated areas. Luxor region in Upper Egypt is one of such areas affected by salinity. Multivariate statistics indicate that groundwater is affected by intermixed processes; mineralization (salinization), fertilization, domestic wastes, and meteoric recharge. Temporal change in salinity and hydrochemical facies during 1997-2017 revealed aquifer salinization, due to the dissolution of salts from overlain saline soil and marine deposits underneath as well as up-coning of deep saline water. Increasing salinity over time was statistically documented, exhibited temporally high significant differences (P < 0.05), where salinization consumed a quarter of the aquifer during 20 years. Evolution of water facies from less mineralized Ca-Cl, Mg-Cl to highly mineralized Na-Cl species explains the salinization process over time. Elevated content of Na and Cl is associated with the dissolution of marine sediments and saltwater intrusion. The shift from silicate weathering into evaporation dominance confirmed the saltwater intrusion. As a result, groundwater has a high degree of salinity, is not suitable for domestic and other uses. On other hand, fertilization and domestic sewage are probably responsible for the high NO₃^- and Cd content. Over 80% of Cd exists in mobile species facilitates Cd-plant uptake indicating an alarming environmental situation. Cd mobility is closely related to elevated salinity and chlorinity, allowing competition with major ions and forming of soluble complexes. The present approach will improve the uncertainties of environmental interpretation, as an initial step for aquifers management in reclaimed lands.

Comparative assessment of groundwater quality indices of Kannur District, Kerala, India using multivariate statistical approaches and GIS

The aim of the study was to determine the groundwater characteristics of rural and industrial zones in the Kannur region. In 2011, 25 groundwater data were collected from the centre for water resource development management (CWRDM), and in 2019, 25 groundwater samples from rural and near-industrial areas were collected and analysed for major anions (HCO₃-, CO₃^2-, Cl^-, NO₃- and SO₄^2-), and cations (TH, Ca²⁺, Mg²⁺, Na⁺, K⁺ and Fe²⁺) using APHA standards. To better understand the link between water quality parameters, multivariate statistical analysis approaches such as principal component analysis (PCA), hierarchical cluster analysis (HCA), correlation matrix analysis (CMA), and Pearson correlation bivariate one-tailed analysis (PCBOTA) were used to analyse the inter-relationship of data. The Inverse Distance Weighed (IDW) method was used to generate the spatial distribution of the groundwater quality index (GWQI). In 2011, the water quality index (WQI) value of groundwater samples was excellent at 24.42% and good at 54.14%, which were used for drinking purposes and moderate at 17.22% and poor at 4.22% for irrigation purposes in this study area. In 2019, excellent 21.62%, good 51.56% were used for drinking purpose, and moderate at 18.14%, and poor at 8.68% for irrigation purposes. By comparing the data with BIS and WHO standards, it is clear that groundwater in Kannur district is of good quality. In groundwater samples, the PCA eigen values were reported in 2011 (84.7%) and 2019 (73.4%) for statistical approaches. This study uses HCA and PCBOTA to analyse the elements, resulting in a better understanding of groundwater quality development. GIS based WQI maps were obtained and utilised to gain a better knowledge of the study area's past and present water quality status. We observed that the quality of groundwater in the study region's north-western portion is insufficient for drinking water.

Quality by Design steered Development of Niclosamide Loaded Liposomal Thermogel for Melanoma: In vitro and Ex vivo Evaluation

Melanoma is the most malignant form of skin cancer across the globe. Conventional therapies are currently ineffective which could be attributed to the rampant chemo-resistance, metastasis, inability to cross the skin barriers and accumulate within the tumor microenvironment. This advent brings in the principles of drug repurposing by repositioning Niclosamide (NIC), an anthelmintic drug for skin cancer. Incorporation into the liposomes facilitated enhanced melanoma cell uptake and apoptosis. Cytotoxicity studies revealed 1.756-fold enhancement in SK-MEL-28 cytotoxicity by NIC-loaded liposomes compared to free drug. Qualitative and quantitative cell internalization indicated greater drug uptake within the melanoma cells illustrating the efficacy of liposomes as efficient carrier systems. Nuclear staining showed blebbing and membrane shrinkage. Elevated ROS levels and apoptosis shown by DCFDA and acridine orange-ethidium bromide staining revealed greater melanoma cell death by liposomes compared to free drug. Incorporating NIC liposomes into the thermogel system restricted the liposomes as a depot onto the upper skin layers. Sustained zero order release up to 48 h with liposomes and 23.58-fold increase in viscosity led to the sol-to-gel transition at 33℃ was observed with liposomal thermogel. Ex vivo gel permeation studies revealed that C-6 loaded liposomes incorporated within the thermogel successfully formed a depot over the upper skin layer for 6 h to prevent transdermal delivery and systemic adverse effects. Thus, it could be concluded that NIC loaded liposomal thermogel system could be an efficacious therapeutic alternative for the management of melanoma.

Multi-geochemical background comparison and the identification of the best normalizer for the estimation of PTE contamination in agricultural soil

Identifying a suitable geochemical background level (GBL) and an appropriate normalizer is imperative for ensuring soil quality, health, and security. The objective of this study was to identify the appropriate normalizer and suitable GBL for determining PTE enrichment levels in agricultural soils and investigate if there are any statistical differences due to the GBL [World Average Value (WAV) European Average Value (EAV)] used. Forty-nine topsoil samples were obtained from seven agricultural communities in the Frdek-Mstek District (Czech Republic). Portable X-ray fluorescence was used to determine the total PTEs (Cr, Ni, Cu, Y, Ba, Th, As, Pb, and Zn) concentration levels in the soil. Correlation matrix analysis was used to determine the metallic relationship between the PTEs and the normalizers (Al, Fe, Ti, Zr, Sr and Rb). Pollution indices such as contamination factor (CF), geoaccumulation index (Igeo) and enrichment factor (EF) analysis were used to determine the most suitable GBL. Al, Fe, Sr, Ti and Rb strongly correlated with the CF, Igeo and EF, whereas WAV performed better than the other geochemical background (EAV). The results indicated that Rb was the suitable normalizer and WAV was the appropriate GBL for agricultural soil and provided a foundation for evaluating and surveilling soil quality and health in agricultural soil.

Feasibility study of separation and purification of bile acid derivatives by HPLC on C18 and F5 columns

Organic synthesis could be very demanding, usually due to difficulties related to the separation of main reaction products from by-products. Steroidal compounds could have similar lipophilicity, which is mostly based on the lipophilicity of the steroidal core. This causes many problems during purification, i.e. in obtaining a pure single steroidal compound. In this research, a group of bile acid derivatives were subjected to HPLC analysis using four experimental systems, which presented combinations of C18 and F5 columns with methanol-water and acetonitrile-water as mobile phases. Retention parameters and retention order of the compounds were established and indicated that all experimental systems could be applicable in order to separate and/or purify some individual compounds or a mixture of a few compounds. However, the only experimental system that could separate a mixture of all investigated derivatives proved to be a C18 column with acetonitrile-water as a mobile phase. Since complex interactions between F5 column and the analytes exist, molecular surface polarity (MSP) was tested as a lipophilicity parameter, and also compared with logP using multivariate statistics. Retention parameters obtained on F5 column were used as descriptors, both with MSP and with logP, concluding that logP has shown to be a better lipophilicity descriptor.

Identifying contamination of heavy metals in soils of Peruvian Amazon plain: use of multivariate statistical techniques

The Peruvian Amazon plain has abundant natural resources and is home to great biodiversity, which makes it an area with high economic potential. However, the use of its resources through various activities has contributed to the release of heavy metals (HMs) into its soils, generating severe pollution problems which have mainly affected the health of local populations and their ecosystems. Currently, there are no comprehensive studies that have identified the specific sources of contamination by HMs in the soils of this part of the Peruvian territory. In this sense, this research aims to identify the possible sources of contamination by HMs in the soils of the Peruvian Amazon plain to focus efforts on the establishment of adequate measures for the protection of the health of people and the ecosystem. In the present study, samples of topsoils (0-20 cm depth) and subsoils (100-150 cm depth) were collected for the analysis of 11 HMs (Co, Cr, Cu, Fe, Mn, Ni, Pb, V, Zn, Be, and Hg) in 48 sites located in four regions of the Peruvian Amazon plain (Loreto, Amazonas, San Martín, and Ucayali), over the year 2019. The enrichment factor and geoaccumulation index were applied to assess contamination levels of HMs. The results indicated that topsoils and subsoils presented a greater enrichment by the elements Be and Pb, and were classified as moderately contaminated. Likewise, the integral analysis of these indexes together with principal component analysis, hierarchical cluster analysis, correlation analysis, and coefficient of variation allowed the identification of potential sources of contamination by HMs. As a result, Fe, Co, Zn, Ni, V, and Cr were associated with natural or lithogenic sources (parent material, crude oil deposits, and organic matter decomposition). Hg was attributed to anthropogenic sources (illegal gold mining, atmospheric deposition, and vehicle emissions). Be, Pb, Cu, and Mn originated from natural sources (parent material, crude oil deposits, decomposition of organic matter, and forest fires) and anthropogenic (areas degraded by solid waste, illegal gold mining, agriculture, and hydrocarbons). These findings provide essential information to establish regulations and prevent and control HM contamination in soils of the Peruvian Amazon plain.

Multielement and chemometric analysis for the traceability of the Pachino Protected Geographical Indication (PGI) cherry tomatoes

To prevent PGI (Protected Geographical Indication) cherry tomato of Pachino (Sicily, Italy) from frauds, an alternative method, which includes chemometric treatments, was proposed. The content of 32 inorganic elements (macro-micronutrients and lanthanides) present in 16 PGI and 24 not PGI cherry tomato samples cv Naomy, and in 16 PGI and 8 not PGI soil samples, was determined by Inductively Coupled Plasma - Mass Spectrometer (ICP-MS). To identify the elements able to differentiate PGI and not PGI cherry tomato samples, Principal Components Analysis (PCA) and Canonical discriminant analysis (CDA) were performed. The first two principal components (PC1-PC2) explain a total variance of 71,41% between PGI and not PGI group, whereas CDA showed Zn, Cd, Mn and Ca as inorganic markers able to correctly classify the 100% of samples. Furthermore, with a translocation factor (K), evaluated in soil/plant chain, the comparison of absorption trends for PGI and not PGI samples was realized.

Impact of land use and cover on shallow groundwater quality in Songyuan city, China: A multivariate statistical analysis

The utilization and development of land resources is an important process in which human activities affect groundwater quality. However, the impact of land use on groundwater chemical composition has complex multiple relationships, and is affected by the scale of the buffer zone. Based on these problems, this study used correlation analysis (CA) and principal component analysis (PCA) to discuss the mechanism of the effect of land use/land cover (LULC) on the hydrochemical composition of groundwater in Songyuan City. Samples were divided into two groups, i.e., quaternary unconfined aquifer (0-30 m) and quaternary confined aquifer (30-100 m). By comparing the variation trends of the correlation coefficient and cumulative variance interpretation rate of PCA in different buffer ranges, it was found that the optimal buffer range was 3000 m. Cropland had the greatest impact on groundwater hydrochemistry in the city. The transformation of natural landscapes (such as saline‒alkaline alkali land and grassland) to cropland inhibited salt accumulation in groundwater. This finding is noteworthy since few studies have involved areas where saline‒alkaline land is widely distributed. Compared with CA results, PCA results emphasized the deterioration of groundwater quality by agricultural pollution. Moreover, agricultural pollutants such as NO₃^- and K⁺ were accumulated in areas where cropland transitioned to natural landscapes such as grassland and water bodies. Considering that wide lakes and rivers provide the drainage area for irrigation water in the study area, the groundwater quality in the surrounding area was affected by the contaminated surface water. The multiple interaction relationship between LULC and hydrochemistry was further confirmed by the combination of principal component scores.

Multivariate statistics explaining groundwater chemistry, Asyut, Egypt

Groundwater is an important source for domestic and irrigation purposes in Asyut area. Water quality varied widely due to complex geochemical processes and pollution sources. Understanding the processes controlling groundwater chemistry is necessary to overcome related problems. Multivariate statistics revealed that groundwater is affected by anthropogenic recharge (agricultural/organic pollution), mineralization, and redox processes. Contributions from natural vs. anthropogenic sources explain the variance in hydrochemical data. Shallow wells are relatively higher in bicarbonate content due to oxidation of organic pollutants. Shallow wells anomaly high with iron and organically polluted are most probably owing to pipe corrosion in residential areas. N fertilization impact on natural weathering has been demonstrated. Groundwater is getting more mineralized toward desert fringes due to lithological and hydrogeological characteristics under unconfined conditions. Evaporation factor enhances groundwater salinity under aridity. Fe and Mn contents are relatively higher as the redox potential is getting more reducing. The current study will help in building suitable management plan to protect the aquifer.

Groundwater quality and human health risk assessment in selected coastal and floodplain areas of Bangladesh

Groundwater aquifers are a common source of drinking water in Bangladesh. However, groundwater contamination is a major public health concern across the country. This research aims to examine the groundwater quality and health concerns using a random sampling process. Multivariate statistical and health risk analyses of elements were performed to determine the source of contaminants and their effects on human health. A total of 24 parameters were analyzed, where Na⁺, NH₄⁺, K⁺, Mg²⁺, F^-, NO₃^-, Mn, Fe, Se, U, and As concentrations were found to be high in different sampling points compared to the Department of Environment of Bangladesh (DoE), and the World Health Organization (WHO) groundwater quality standards. Principal Component Analysis (PCA) and Cluster Analysis (CA) identified the dominant and potential sources of contaminants in the groundwater aquifer, including geogenic, salinity intrusion, industrial, and agricultural. The results of the degree of contamination level (C_d) and the heavy metal pollution index (HPI) showed that 28% and 12% of the sampling points had high levels of heavy metal contamination, indicating a high risk for human health issues. Cr concentrations were found to have a higher carcinogenic (cancer) risk than As and Cd concentrations. Hazard quotient (HQ) and hazard index (HI) scores expressed the hazardous status and possible chronic effects in the context of individual sampling points. For both child and adults, 44% and 36% of the sampling points had a high HI score, indicating the possibility of long-term health risks for local populations.

Hydro-geochemical characteristics and quality appraisal of aquifers using multivariate statistics and associated risk assessment in Tarn-Taran district, Punjab, India

Monitoring of groundwater is essential in the alluvial region of Tarn-Taran district, western Punjab, India where this freshwater source is being overexploited causing quality deterioration, groundwater depletion and posing serious threats to inhabitants. The present integrated study was conducted to appraise quality and suitability of groundwater for drinking/irrigation purposes, hydro-geochemical characteristics, source identification and associated health risks. In this study, 96% and 51% samples were detected with arsenic (As) and uranium (U), respectively higher than their acceptable limits posing high cancerous risks to local inhabitants via ingestion. Further, the quality indices revealed that groundwater of the study region is appropriate for irrigation but not suitable for drinking purposes. Hydro-geochemical studies showed that 83% of samples belonged to Ca2+-Mg2+-HCO3- type with major contribution of natural geogenic processes like rock-water interactions, silicate and carbonate dissolution along with reverse ion-exchange mechanisms in aquifer chemistry. Multivariate statistics revealed that along with geogenic sources, contribution of anthropogenic activities such as injudicious application of agrochemicals and domestic waste discharge was also very significant. Hazard quotient values for As were found to be 2.119 and 2.743 for children and adults, respectively representing both population groups prone to non-cancerous health risks due to As intake. Children were found to be more vulnerable than adults. This study draws an attention of public and local government about the current status of groundwater pollution in Tarn-Taran district, so that proper remediation steps can be taken to ensure the availability of good quality water.

Heavy metal pollution in the soil of a riverine basin: distribution, source, and potential hazards

Soil pollution with heavy metals (HMs) has become a world environmental problem. This study focuses on surface soil contamination with Cr, Mn, Co, Ni, Cu, Zn, Cd, Hg, Pb, Fe, and Al, their sources, and potential hazards along the basin of River Swat, Pakistan. The average concentrations (mg/kg) of HMs were the most abundant for Al (24,730.19) followed by Fe (22,419.41) > Mn (386.78) > Zn (57.75) > Cr (38.07) > Ni (32.46) > Cu (23.43) > Pb (19.59) > Co (10.77) > Cd (3.18) > Hg (0.12). The concentrations of Cr and Mn in 5.45% each, Co in 10.90%, Zn in 27.27%, Cu in 36.36%, Ni in 41.81%, and Hg in 92.72% of the total soil samples exceeded their respective background values. The geostatistical approaches determined the distribution patterns of HM pollution along the basin, whereas the statistics of principal component analysis exposed the likely sources of HM contamination in the area. Pollution indices evaluated the overall HM distribution and pollution status in the area. Contamination factor showed a high degree of HM contamination in 82% of the total sampling sites, while the geo-accumulation index designated low to moderate contamination with Cr, Mn, Co, Ni, Cu, Zn, Hg, and Pb, and moderate to extreme contamination with Cd, Fe, and Al. The trend of ecological toxicity showed potential ups and downs along with the sites from low to considerable hazard (< 95 < PEHI < 190), whereas the human carcinogenic hazard was within the USEPA acceptable limits (1 × 10^-7-1 × 10^-4), but the non-carcinogenic hazard was higher than the threshold (HI > 1) for children because they are more exposed than adults.

Metazoan parasite diversity of the endemic South African intertidal klipfish, Clinus superciliosus: Factors influencing parasite community composition

The current trend in marine parasitology research, particularly in South Africa, is to focus on a specific parasite taxon and not on the total parasite community of a specific fish host. However, these records do not always reveal the ecological role of parasites in ecosystems. Thus, the present study aimed to determine which factors influence the parasite community composition of the endemic southern African intertidal klipfish, Clinus superciliosus (n = 75). Metazoan parasites were sampled from four localities (two commercial harbours - west coast; and two relatively pristine localities - southeast coast) along the South African coast. A total of 75 klipfish were examined for parasites, where 30 distinct taxa, representing seven taxonomic groups were found: Acanthocephala (4 taxa), Cestoda (2 taxa), Crustacea (5 taxa), Digenea (11 taxa), Hirudinea (2 taxa), Monogenea (1 taxon) and Nematoda (5 taxa). Results indicated that the main driver of diversity was locality, with the highest diversity on the southeast coast, most likely due to higher water temperatures and upwelling compared to the west coast. The parasite community composition of the klipfish was significantly influenced by water temperature and parasite life cycle. These results emphasise the importance of parasitological surveys including all parasite taxa in hosts from multiple localities and seasons, to better comprehend their ecological role.

Endemic-epidemic models to understand COVID-19 spatio-temporal evolution

We propose an endemic-epidemic model: a negative binomial space-time autoregression, which can be employed to monitor the contagion dynamics of the COVID-19 pandemic, both in time and in space. The model is exemplified through an empirical analysis of the provinces of northern Italy, heavily affected by the pandemic and characterized by similar non-pharmaceutical policy interventions.

Trace metals pollution, distribution and associated health risks in the arid coastal aquifer, Hada Al-Sham and its vicinities, Saudi Arabia

Trace metals pollution, distribution and associated health risk were evaluated in the arid coastal aquifer, Hada Al-Sham, Western Saudi Arabia using an integrated approach namely heavy metal pollution index (HPI), contamination index (C_d), health risk assessment (HRA) model and multivariate statistical analysis. Groundwater samples (n = 47) were analysed for EC, pH, TDS, Ag, Al, B, Ba, Co, Cd, Cr, Cu, Fe, Mo, Ni, Pb, V and Zn. Groundwater is mostly alkaline (72%) with high salinity (TDS >1500 mg/l, 77%). Average trace metals concentrations are in the dominance order of B > Cu > Al > Mo > Pb > V > Ba > Zn > Ni > Cr > Fe > Ag. Groundwater (100% wells) is unsafe for drinking based on V, Mo, Al concentrations followed by the Pb (96%), B (91%), Ni (72%), Cr (23%), Cu (17%) and Ag (6%), which exceeded the WHO and USEPA prescribed limits. Results of HPI and C_d also implied the high contamination and enhanced level of metals in the groundwater. Chronic daily intake (CDI_oral), hazard quotient (HQ) and total hazard quotient (THQ) were employed for health risk assessment. HQ values reveal that 100% (Al, Mo), 85% (Cu), 21% (B), 13% (Cr) and 6% (Pb) of samples exceeded the recommended limit (>1), which can cause an adverse health risk to adult and children. Pearson correlation, principal components analyses, and Hierarchical cluster analysis justified that aluminium silicates weathering and Fe oxides/hydroxides dissolution (Pb, Cr, Ni, Fe, Ag, Al, Mo and V), evaporation, anthropogenic input (Cu and B) and dissolution of carbonate and sulphate minerals (Ba and Zn) controlled the water chemistry in this aquifer. This study recommends a proper treatment of the groundwater to be safe for various uses. The comprehensive approach, employed here, is applicable to any arid aquifers worldwide.

Assessment of potability of minewater pumped out from Jharia Coalfield, India: an integrated approach using integrated water quality index, heavy metal pollution index, and multivariate statistics

The dense and industrialized populace in the mining areas of Jharia Coalfield (JCF) is plagued by a severe shortage of water supply. The unutilized pumped out coal minewater discharges may be utilized to cater to the increasing water demand of the region but it runs the risk of getting contaminated from domestic and industrial effluents. The current study aimed to assess the suitability of augmenting underground minewater for potable purposes. For this purpose, ninety underground minewater samples collected from 15 locations across JCF for the hydrological year 2019-2020 were analysed to gain an insight on the physicochemical characteristics of the minewater using an integrated approach of standard hydrochemical methods, integrated water quality index (IWQI), heavy metal pollution index (HPI), and multivariate statistical analysis. For the minewater quality to be deemed suitable for potable purposes, both IWQI (lower than 2) and HPI (lower than 30) values were considered. IWQI values of the minewater samples from the study area ranged from 1.97 to 5.08, while the HPI values ranged from 18.40 to 53.05. The pH of the samples were found to be mildly acidic to alkaline (6.5 to 8.3) with varying total hardness (149 to 719 mg L^-1), total dissolved solids (341 to 953 mg L^-1), and electrical conductivity (568 to 1389 µS cm^-1), reflecting heterogeneity in underlying hydrosystems, variations in geological formations, and the influence of lithogenic and anthropogenic processes on the water chemistry of the region, which was corroborated by the principal component analysis (PCA) and hierarchical cluster analysis (HCA) of the minewater samples. Two major water types of the region were identified, viz., Ca-Mg-HCO₃ and Ca-Mg-Cl-SO₄. This multiparametric approach gives a holistically accurate assessment of the minewater quality, overcoming the limitations of traditional water quality indices and facilitating time-saving and effective water management practices, and sets the foundation for augmenting minewater for potable purposes to meet increasing demands.

Support vector machine and PCA for the exploratory analysis of Salvia officinalis samples treated with growth regulators based in the agronomic parameters and multielement composition

The objective of this work was to evaluate the influence of different growth regulators on the mineral and total phenolic contents of Salvia officinalis. The samples received the applications of salicylic acid (AS); gibberellic acid (GA₃); abscisic acid (ABA) and solution without regulators (control). The exploratory evaluation of the samples was carried out through the Principal Component Analysis (PCA). In addition, has been used supervised learning methods with support vector machine (SVM) algorithms to classify the samples. The phenolic and total flavonoid contents were higher in the plants treated with the regulators. The element found in the highest concentration in Salvia officinalis was N. Plants sprayed with ABA showed higher concentrations of N, K, and Mn; Fe and Al were higher with ABA and gibberellin application, while the application of AS provided the highest accumulation of P. The application of plant regulators improves the nutraceutical properties of Salvia officinalis.

Hydrogeochemistry, identification of hydrogeochemical evolution mechanisms, and assessment of groundwater quality in the southwestern Ordos Basin, China

Understanding the evolution process of hydrogeochemistry and groundwater quality is essential for water supply and health in the southwestern Ordos Basin, where groundwater is a vital source for drinking. This study systematically illustrates the hydrogeochemical characteristics and evolution mechanism based on the groundwater samples (n = 67) collected from Loess area by integrating multivariate statistical methods and hydrogeochemical methods. Furthermore, the entropy water quality index (EWQI) and water quality indices combined with spatial analysis were employed to evaluate the suitability of groundwater for drinking and irrigation purposes and analyze the spatial variation of water quality. The hierarchical cluster analysis and principal component analysis classified groundwater dataset into four clusters and four components which were examined using a Piper diagram and Gibbs diagram, representing different hydrogeochemical characteristics and controlling factors. Based on results, the groundwater chemistry was characterized by representative water types: freshwater (cluster 1, cluster 2), low salinity (half of cluster 3), high salinity (half of cluster 3, cluster 4), and the main controlling factors of hydrogeochemistry revealed by Gibbs diagram were evaporation crystallization (cluster 3, cluster 4) and water-rock interactions (cluster 1, cluster 2). Moreover, the Gaillardet diagram, chloro-alkaline indices, binary diagram, and saturation index further comprehensively illustrate that the silicate and evaporite weathering, ion exchange, dissolution of halite, gypsum, and anhydrite are responsible for hydrogeochemical process. Based on EWQI and ArcGIS, the groundwater quality is categorized as excellent (47.0%), good (31.8%), medium (4.5%), poor (6.1%), and extremely poor (10.6%) types, and the quality in the south of the study area is better than north. Additionally, the USSL diagram shows that most of samples belong to C3S1 (high-salinity hazard and low-sodium hazard) and C2S1 (medium-salinity hazard and low-sodium hazard), and Wilcox diagram shows that 77.2% of samples are suitable for irrigation.

A probabilistic approach towards source level inquiries for forensic soil examination based on mineral counts

Forensic soil examination has a well-established foundation in forensic science, this is in part due to the widely varied and complex nature of soil. Within this domain, mineral suite studies are a commonly utilized tool in soil examination. However, statistical or probabilistic approaches towards the interpretation of results from such analysis are lacking and this study aims to fill that gap. Soil samples from four different locations in the city of Lausanne, Switzerland were sampled and their mineral fractions, light and heavy of size between 90 and 180 µm, were studied utilizing microscopical methods. First, the light minerals were identified and counted by employing scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS). Second, the heavy minerals were identified and counted manually under a polarized light microscope (PLM). The resulting count data were subjected to various multivariate statistical treatments such as principal components analysis (PCA), hierarchical clustering analysis (HCA), and linear discriminant analysis (LDA). These methods assist in identifying pertinent variables and subsequently in building various classification models. The validities of these models were then tested and evaluated using blind tests. Finally, these methods demonstrate how a probabilistic approach can be taken in the interpretation of the results to answer source level questions.

Hydrogeochemistry and health hazards of fluoride-enriched groundwater in the Tarim Basin, China

Fluoride (F^-) enrichment reduces the availability of groundwater resources in the arid region, and it is thus important to investigate the hydrogeochemistry and health hazards of fluoride-enriched groundwater. Seventy-two groundwater samples (20 unconfined samples from the piedmont plain, 22 unconfined samples and 30 shallow confined samples from the alluvial plain) were collected in the Tarim Basin of China to illustrate the geochemical processes driving the F^- enrichment and the incidence of dental fluorosis. The patterns of average ions contents in groundwater are Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ and SO₄^2- > Cl^- > HCO₃^- > NO₃^- > F^-. The highest F^- concentration (average 2.16 mg/L) is observed in unconfined groundwater in the alluvial plain, while the lowest (average 0.63 mg/L) is recorded in unconfined groundwater in the piedmont plain. Approximately 5.0% of unconfined groundwater in the piedmont plain, 90.9% of unconfined groundwater and 33.3% of shallow confined groundwater in the alluvial plain contain F^- concentrations exceeding 1.0 mg/L (Chinese drinking water standard). Mineral dissolution, cation exchange, and evaporation play a significant role in the formation of solutes in groundwater. High-F^- groundwater is mostly associated with SO₄·Cl-Na·Ca, SO₄·Cl-Na·Mg, and SO₄·Cl-Na types water. Thermodynamic simulations reveal that the dissolution of F-bearing minerals (e.g., fluorite) significantly controls the F^- contents in groundwater. High concentrations of F^- are closely related to high HCO₃^-, high Na⁺, high salinity, cation exchange, and evaporation. This demonstrates that high F^- concentrations are caused by the increase in fluorite solubility due to high ionic strength, Ca²⁺ consumption and the desorption of F^- from solid surfaces under alkaline conditions. Mixing with the upper unconfined groundwater plays a vital role in the enrichment of F^- in shallow confined groundwater in the alluvial plain. The health risk assessment based on Dean's classification indicates that the percentage prevalence of fluorosis for boys aged 6 to 18 is 15.5% for Yecheng (YC), 18.4% for Zepu (ZP), 33.3% for Shache (SC), 29.8% for Maigaiti (MG), and 44.9% for Bachu (BC), while that for girls of the same age is 14.3% for YC, 24.3% for ZP, 42.2% for SC, 41.4% for MG, and 45.3% for BC. For male and female adults aged between 19 and 68, the percentage prevalence of fluorosis is: YC (11.5%, 12.0%), ZP (18.3%, 20.0%), SC (35.4%, 35.0%), MG (32.5%, 39.7%), and BC (42.4%, 44.3%). It is obvious that younger generation, especially girls, suffers from more severe dental fluorosis. This study has implications for the effective management of high-F^- groundwater in arid regions.

Prediction of two novel overlapping ORFs in the genome of SARS-CoV-2

Six candidate overlapping genes have been detected in SARS-CoV-2, yet current methods struggle to detect overlapping genes that recently originated. However, such genes might encode proteins beneficial to the virus, and provide a model system to understand gene birth. To complement existing detection methods, I first demonstrated that selection pressure to avoid stop codons in alternative reading frames is a driving force in the origin and retention of overlapping genes. I then built a detection method, CodScr, based on this selection pressure. Finally, I combined CodScr with methods that detect other properties of overlapping genes, such as a biased nucleotide and amino acid composition. I detected two novel ORFs (ORF-Sh and ORF-Mh), overlapping the spike and membrane genes respectively, which are under selection pressure and may be beneficial to SARS-CoV-2. ORF-Sh and ORF-Mh are present, as ORF uninterrupted by stop codons, in 100% and 95% of the SARS-CoV-2 genomes, respectively.