Assessment of heavy metal levels in polyculture fish farms and their aquatic ecosystems: an integrative study addressing environmental and human health risks associated with dam water usage.

This study examines the levels of heavy metals in polyculture fish (Labeo rohita, Cyprinus carpio, and Catla catla), water, and sediment in Tanda Dam, Kohat, Pakistan, aiming to understand environmental and health risks. Samples of fish, water, and sediment were collected from 3 fish farms, and heavy metal concentrations were measured using a Flame Atomic Absorption Spectrophotometer (AAS). Results reveal that C. catla exhibited significantly higher (p < 0.05) levels of Zn than other fish species. Conversely, C. carpio showed significantly higher (p < 0.05) concentrations of Pb, Cd, Cr, Mn, Cu, As, and Ni than other species. The heavy metal hierarchy in C. carpio was found to be Zn > Cu > Pb > Cr > Cd > Mn > As > Ni. While heavy metal levels in L. rohita and C. catla generally fell within reference ranges, exceptions were noted for Zn, Pb, and Cd. Conversely, in C. carpio, all metals exceeded reference ranges except for Cu and Ni. Principal Component Analysis (PCA) indicated a close relationship between water and sediment. Additionally, cluster analysis suggested that C. catla formed a distinct cluster from L. rohita and C. carpio, implying different responses to the environment. Despite concerns raised by the Geoaccumulation Index (Igeo) and Contamination Factor (CF), particularly for Cd, which exhibited a high CF. Furthermore, Hazard Index (HI) values for all three fish species were below 1, suggesting low health risks. However, elevated Igeo and CF values for Cd suggest significant pollution originating from anthropogenic sources. This study underscores the importance of monitoring heavy metals in water for both environmental preservation and human health protection. Future research efforts should prioritize pollution control measures to ensure ecosystem and public health safety.

A bayesian spatio-temporal dynamic analysis of food security in Africa.

Exploring the factors influencing Food Security and Nutrition (FSN) and understanding its dynamics is crucial for planning and management. This understanding plays a pivotal role in supporting Africa's food security efforts to achieve various Sustainable Development Goals (SDGs). Utilizing Principal Component Analysis (PCA) on data from the FAO website, spanning from 2000 to 2019, informative components are derived for dynamic spatio-temporal modeling of Africa's FSN Given the dynamic and evolving nature of the factors impacting FSN, despite numerous efforts to understand and mitigate food insecurity, existing models often fail to capture this dynamic nature. This study employs a Bayesian dynamic spatio-temporal approach to explore the interconnected dynamics of food security and its components in Africa. The results reveal a consistent pattern of elevated FSN levels, showcasing notable stability in the initial and middle-to-late stages, followed by a significant acceleration in the late stage of the study period. The Democratic Republic of Congo and Ethiopia exhibited particularly noteworthy high levels of FSN dynamicity. In particular, child care factors and undernourishment factors showed significant dynamicity on FSN. This insight suggests establishing regional task forces or forums for coordinated responses to FSN challenges based on dynamicity patterns to prevent or mitigate the impact of potential food security crises.

Comparison of Yamuna (India) and Mississippi River (United States of America) bacterial communities reveals greater diversity below the Yamunotri Glacier.

The Yamuna River in India and the Mississippi River in the United States hold significant commercial, cultural, and ecological importance. This preliminary survey compares the bacterial communities sampled in surface waters at 11 sites (Yamuna headwaters, Mississippi headwaters, Yamuna River Yamunotri Town, Mississippi River at Winona, Tons River, Yamuna River at Paonta Sahib, Yamuna River Delhi-1, Yamuna River Delhi-2, Yamuna River before Sangam, Sangam, Ganga River before Sangam). Bacterial 16S rDNA analyses demonstrate dominance of Proteobacteria and Bacteroidetes phyla. Actinobacteria were also dominant at sites near Sangam in India and sites in Minnesota. A dominance of Epsilonbacteraeota were found in Delhi, India. Principal component analysis (PCA) using unique operational taxonomic units (OTUs) resulted in the identification of 3 groups that included the Yamuna River locations in Delhi (Delhi locations), Yamuna headwaters and Yamuna River at Yamunotri (Yamuna River locations below the Glacier) and Mississippi, Ganga, Tons, and other Yamuna River locations. Diversity indices were significantly higher at the Yamuna River locations below the Glacier (Simpson D = 0.986 and Shannon H = 5.06) as compared (p value <0.001) to the Delhi locations (D = 0.951 and H = 4.23) and as compared (p value < 0.001) to Mississippi, Ganga, Tons, and other Yamuna River locations (D = 0.943 and H = 3.96). To our knowledge, this is the first survey to compare Mississippi and Yamuna River bacterial communities. We demonstrate higher diversity in the bacterial communities below the Yamunotri glacier in India.

IMU Data-Driven and PCA-Based Approach to Establish Quantifiable and Practically Applicable Measures for V2 Technique Elements in Cross-Country Skiing.

Quantifying movement coordination in cross-country (XC) skiing, specifically the technique with its elemental forms, is challenging. Particularly, this applies when trying to establish a bidirectional transfer between scientific theory and practical experts' knowledge as expressed, for example, in ski instruction curricula. The objective of this study was to translate 14 curricula-informed distinct elements of the V2 ski-skating technique (horizontal and vertical posture, lateral tilt, head position, upper body rotation, arm swing, shoulder abduction, elbow flexion, hand and leg distance, plantar flexion, ski set-down, leg push-off, and gliding phase) into plausible, valid and applicable measures to make the technique training process more quantifiable and scientifically grounded. Inertial measurement unit (IMU) data of 10 highly experienced XC skiers who demonstrated the technique elements by two extreme forms each (e.g., anterior versus posterior positioning for the horizontal posture) were recorded. Element-specific principal component analyses (PCAs)-driven by the variance produced by the technique extremes-resulted in movement components that express quantifiable measures of the underlying technique elements. Ten measures were found to be sensitive in distinguishing between the inputted extreme variations using statistical parametric mapping (SPM), whereas for four elements the SPM did not detect differences (lateral tilt, plantar flexion, ski set-down, and leg push-off). Applicability of the established technique measures was determined based on quantifying individual techniques through them. The study introduces a novel approach to quantitatively assess V2 ski-skating technique, which might help to enhance technique feedback and bridge the communication gap that often exists between practitioners and scientists.

Differences in whole blood before and after hemodialysis session of subjects with chronic kidney disease measured by Raman spectroscopy.

This study aimed to identify differences in the composition of whole blood of patients with chronic kidney disease (CKD), before and after a hemodialysis session (HDS), and possible differences in blood composition between stages and between genders using Raman spectroscopy and principal component analysis (PCA). Whole blood samples were collected from 40 patients (20 women and 20 men), before and after a HDS. Raman spectra were obtained and the spectra were evaluated by PCA and partial least squares (PLS) regression. Mean spectra and difference spectrum between the groups were calculated: stages Before and After HDS, and gender Women and Men, which had their most intense peaks identified. Stage: mean spectra and difference spectrum indicated positive peaks that could be assigned to red blood cells, hemoglobin and deoxi-hemoglobin in the group Before HDS. There was no statistically significant difference by PCA. Gender: mean spectra and difference spectrum Before HDS indicated positive peaks that could be assigned to red blood cells, hemoglobin and deoxi-hemoglobin with greater intensity in the group Women, and negative peaks to white blood cells and serum, with greater intensity in the group Men. There was statistically significant difference by PCA, which also identified the peaks assigned to white blood cells, serum and porphyrin for Women and red blood cells and amino acids (tryptophan) for Men. PLS model was able to classify the spectra of the gender with 83.7% accuracy considering the classification per patient. The Raman technique highlighted gender differences in pacients with CKD.

Research on the relation between hydro-chemical and geological characteristics in karst area: Case study in Zhong Liang Mountain, Southwest China.

Karst groundwater, which is one of most important drinking water sources, is vulnerable to be polluted as its closed hydraulic relation with surface water. Thus, it is very important to identify the groundwater source to control groundwater pollution. The Pearson correlation coefficient among major ions (Na + K+, Ca2+, Mg2+, HCO3 -, SO4 2-, and Cl-) was employed to deduce the groundwater types in Zhong Liang Mountain, Southwest China. Then, the combined method of principal component analysis and cluster analysis were employed to identify the groundwater sources in a typical karst region of southwest China. The results shown that (1) the high positive correlation between cations and anions indicated the water-rock reaction of Ca-HCO3, Ca-SO4, (Na + K)-Cl, and Mg-SO4. (2) The major two principal components that would represent water-rock reaction of CaSO4 and Ca-HCO3 would, respectively, explain 60.41% and 31.80% of groundwater information. (3) Based on the two principal components, 33 groundwater samples were clustered into eight groups through hierarchical clustering, each group has similar water-rock reaction. The findings would be employed to forecast the surge water, that was an important work for tunnel construction and operation. PRACTITIONER POINTS: The components of groundwater was highly correlated with water-rock reaction. The principal component analysis screens the types of groundwater. The cluster analysis identifies the groundwater sources.

The taxonomy of Sahelanthropus tchadensis from a craniometric perspective.

Sahelanthropus tchadensis has raised much debate since its initial discovery in Chad in 2001, given its controversial classification as the earliest representative of the hominin lineage. This debate extends beyond the phylogenetic position of the species, and includes several aspects of its habitual behavior, especially in what regards its locomotion. The combination of ancestral and derived traits observed in the fossils associated with the species has been used to defend different hypotheses related to its relationship to hominins. Here, the cranial morphology of Sahelanthropus tchadensis was assessed through 16 linear craniometric measurements, and compared to great apes and hominins through Principal Component Analysis based on size and shape and shape information alone. The results show that S. tchadensis share stronger morphological affinities with hominins than with apes for both the analysis that include size information and the one that evaluates shape alone. Since TM 266-01-060-1 shows a strong morphological affinity with the remaining hominins represented in the analysis, our results support the initial interpretations that S. tchadensis represents an early specimen of our lineage or a stem basal lineage more closely related to hominins than to Panini.

Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews.

This work proposes a functional data analysis approach for morphometrics in classifying three shrew species (S. murinus, C. monticola, and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 89 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis and linear discriminant analysis were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear model) using predicted PC scores obtained from both methods (a combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species.

Estimation for Raw Material Plants of a Henna Product Using LC-High Resolution MS and Multivariate Analysis.

Henna is a plant-based dye obtained from the powdered leaf of the pigmented plant Lawsonia inermis, and has often been used for grey hair dyeing, treatment, and body painting. As a henna product, the leaves of Indigofera tinctoria and Cassia auriculata can be blended to produce different colour variations. Although allergy from henna products attributed to p-phenylenediamine, which is added to enhance the dye, is reported occasionally, raw material plants of henna products could also contribute to the allergy. In this study, we reported that raw material plants of commercial henna products distributed in Japan can be estimated by LC-high resolution MS (LC-HRMS) and multivariate analysis. Principal Component Analysis (PCA) score plot clearly separated 17 samples into three groups [I; henna, II; blended henna primarily comprising Indigofera tinctoria, III; Cassia auriculata]. This grouping was consistent with the ingredient lists of products except that one sample listed as henna was classified as Group III, indicating that its ingredient label may differ from the actual formulation. The ingredients characteristic to Groups I, II, and III by PCA were lawsone (1), indirubin (2), and rutin (3), respectively, which were reported to be contained in each plant as ingredients. Therefore, henna products can be considered to have been manufactured from these plants. This study is the first to estimate raw material plants used in commercial plant-based dye by LC-HRMS and multivariate analysis.

Evaluating the quality of sediments in streams draining contrasting land-use areas in Osogbo metropolis, southwestern Nigeria.

The attendant effects of urbanization on the environment and human health are evaluable by measuring the potentially harmful element (PHE) concentrations in environmental media such as stream sediments. To evaluate the effect of urbanization in Osogbo Metropolis, the quality of stream sediments from a densely-populated area with commercial/industrial activities was contrasted with sediments from a sparsely-populated area with minimal anthropogenic input.Forty samples were obtained: 29 from Okoko stream draining a Residential/Commercial Area (RCA, n = 14) and an Industrial Area (IA, n = 15), and 11 from Omu stream draining a sparsely-populated area (SPA). The samples were air-dried, sieved to < 75 micron fraction, and analysed for PHEs using inductively-coupled plasma atomic emission spectrometry (ICP-AES). Index of geoaccumulation (Igeo), pollution index (PI), ecological risk factor (Er) and index (ERI) were used for assessment. Inter-elemental relationships and source identification were done using Pearson's correlation matrix and principal component analysis (PCA).PHE concentrations in the stream sediments were RCA: Zn > Pb > Cu > Cr > Sr > Ni > Co, IA: Zn > Cr > Ni > Co > Pb > Cu > Sr and SPA: Zn > Co > Cr > Cu > Sr > Ni > Pb. Igeo calculations revealed moderate-heavy contamination of Cu, Pb and Zn in parts of RCA, moderate-heavy contamination of Zn in IA while SPA had moderate contamination of Co and Zn. PI values revealed that stream sediments of RCA are extremely polluted, while those of IA and SPA are moderately and slightly polluted, respectively.The pollution of the stream sediments in RCA and IA is adduced to anthropogenic activities like vehicular traffic, automobile repairs/painting, blacksmithing/welding and metal scraping. In SPA however, the contamination resulted from the application of herbicides/fertilizers for agricultural purposes.

The use of the self-organizing map (SOM) methodology to study the dissolved organic matter (DOM) in different water body types.

This study investigated the characteristics of dissolved organic matter (DOM) in two distinct water bodies, through the utilization of three-dimensional fluorescence spectroscopy coupled with self-organizing map (SOM) methodology. Specifically, this analysis concentrated on neurons 3, 14, and 17 within the SOM model, identifying notable differences in the DOM compositions of a coal subsidence water body (TX) and the MaChang Reservoir (MC). The humic substance content of DOM TX exceeded that of MC. The origin of DOM in TX was primarily linked to agricultural inputs and rainfall runoff, whereas the DOM in MC was associated with human activities, displaying distinctive autochthonous features and heightened biological activity. Principal component analysis revealed that humic substances dominated the DOM in TX, while the natural DOM in MC was primarily autochthonous. Furthermore, a multiple linear regression model (MLR) determined that external pollution was responsible for 99.11% of variation in the humification index (HIX) of water bodies.

Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction.

Nasopharyngeal cancer (NPC) is a malignant tumor with high prevalence in Southeast Asia and highly invasive and metastatic characteristics. Radiotherapy is the primary strategy for NPC treatment, however there is still lack of effect method for predicting the radioresistance that is the main reason for treatment failure. Herein, the molecular profiles of patient plasma from NPC with radiotherapy sensitivity and resistance groups as well as healthy group, respectively, were explored by label-free surface enhanced Raman spectroscopy (SERS) based on surface plasmon resonance for the first time. Especially, the components with different molecular weight sizes were analyzed via the separation process, helping to avoid the possible missing of diagnostic information due to the competitive adsorption. Following that, robust machine learning algorithm based on principal component analysis and linear discriminant analysis (PCA-LDA) was employed to extract the feature of blood-SERS data and establish an effective predictive model with the accuracy of 96.7% for identifying the radiotherapy resistance subjects from sensitivity ones, and 100% for identifying the NPC subjects from healthy ones. This work demonstrates the potential of molecular separation-assisted label-free SERS combined with machine learning for NPC screening and treatment strategy guidance in clinical scenario.

Effects of wet-dry alternation on organic phosphorus dynamics and sediment characteristics in the intertidal zone of Nansi Lake.

The study of the fractions and distribution characteristics of organic phosphorus in the sediment of the water level fluctuating zone of Nansi Lake is conducive to revealing the transformation of phosphorus in the lake, and has important scientific significance for controlling the eutrophication of Nansi Lake. Based on the sediment of the water level fluctuation zone of Nansi Lake. The improved Hedley continuous grading extraction, ultraviolet-visible spectroscopy and three-dimensional fluorescence spectroscope were used to characterize the structural characteristics and stability of organic molecules in the sediment, and to reflect the differences in the structure and stability of organophosphate in the water level fluctuating zone. Principal component analysis (PCA), Redundancy analysis (RDA) and correlation heat map analysis were used to analyze the correlation between phosphorus and physicochemical index. The results showed that the alternation between wet-dry conditions was more favorable for the release of phosphorus from sediment, compared to continuous inundation conditions. Moreover, the higher the frequency of wet-dry alternations, the greater the release of phosphorus in different forms from the sediment. Wet-dry alternation resulted in a reduction of substituent on the aromatic rings of sediment DOM (dissolved organic matter), and the continuous drying would increase the molecular weight and humidification degree of DOM in the sediment. Correlation analysis showed that NaOH-Po content in sediment was significantly negatively correlated with TP, IP, OP and various organophosphorus forms, indicating a close transformation relationship between phosphorus forms in sediment. The results can provide a scientific basis for controlling the release of endogenous phosphorus and the risk of eutrophication in Nansi Lake.

Evaluation of ecological and health risks of trace elements in soils of olive orchards and apportionment of their sources using the APCS-MLR receptor model.

Izmir, Turkey's third most populous city, is in an important position in terms of both agriculture and industry. The province, which contributes 9.3% to the country's industrial production, also has an important potential in terms of olive cultivation. However, until now, no research has been undertaken to analyze the content of trace elements (TEs) in the soil of olive orchards in Izmir. This study was carried out to determine the pollution level and ecological risks of TEs in the olive orchards soils of Izmir province, to reveal their potential sources and to evaluate their health risks. Among the TEs, the average content of only Ni (37.9 mg/kg) exceeded the world soil average content (29 mg/kg), while the average content of only Cd (0.176 mg/kg) exceeded the upper continental crust content (0.09 mg/kg). Enrichment factor revealed that there was significant enrichment for Cd in 73.6%, Ni in 11.6% and Cr in 5.4% of olive orchards, respectively, due to polluted irrigation water and agrochemicals. Similarly, ecological risk factor indicated that there were moderate and considerable ecological risks for Cd in 48.8% and 23.3% of olive orchards, respectively. Absolute principal component scores-multiple linear regression (APCS-MLR) model showed that Ni and Cr in the study area are affected by agricultural sources, Al, Co, Cu, Fe, Mn, Pb and Zn originate from lithogenic sources, and Cd originates from mixed sources. Based on health risk evaluation methods, non-carcinogenic and carcinogenic effects would not be expected for residents. This study provides significant knowledge for evaluating soil TE pollution in olive orchards and serves a model for source apportionment and human health risk evaluation of TEs in other agricultural regions.

Compressed representation of brain genetic transcription.

The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. The established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorisation (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility across signalling, microstructural, and metabolic targets, drawn from large-scale open-source MRI and PET data. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain.

Tablet-based Rey-Osterrieth Complex Figure copy task: a novel application to assess spatial, procedural, and kinematic aspects of drawing in children.

The paper-and-pencil Rey-Osterrieth Complex Figure (ROCF) copy task has been extensively used to assess visuo-constructional skills in children and adults. The scoring systems utilized in clinical practice provide an integrated evaluation of the drawing process, without differentiating between its visuo-constructional, organizational, and motor components. Here, a tablet-based ROCF copy task capable of providing a quantitative assessment of the drawing process, differentiating between visuo-constructional, organizational, and motor skills, is trialed in 94 healthy children, between 7 and 11 years of age. Through previously validated algorithms, 12 indices of performance in the ROCF copy task were obtained for each child. Principal component analysis of the 12 indices identified spatial, procedural, and kinematic components as distinct dimensions of the drawing process. A composite score for each dimension was determined, and correlation analysis between composite scores and conventional paper-and-pencil measures of visuo-constructional, procedural, and motor skills performed. The results obtained confirmed that the constructional, organizational, and motor dimensions underlie complex figure drawing in children; and that each dimension can be measured by a unique composite score. In addition, the composite scores here obtained from children were compared with previsions results from adults, offering a novel insight into how the interplay between the three dimensions of drawing evolves with age.

[Effect of different initial processing treatments on commercial characters and chemical components of Fritillaria taipaiensis].

To investigate the effects of different initial processing methods on the quality of Fritillaria taipaiensis, this study explored the effects of anti-browning treatment, drying methods, and drying temperatures on the commercial characters, chromaticity values, and alkaloid and nucleoside components of Fritillariae Taipaiensis Bulbus. The results were comprehensively evaluated through correlation analysis(CA), principal component analysis(PCA), and hierarchical clustering analysis(HCA). Compared with those of the direct drying group(WD60), the chromaticity values(ΔE*) of the groups with scraped outer skin( FHB1) and mixed lime powder treatments(FHB2) were significantly reduced, indicating the inhibition of the browning process. The total alkaloid content of the group with mixed raw soil treatment(FHB3) and the FHB2 group showed no significant change, whereas that of the group with 5%Na Cl O solution rinse treatment(FHB4) was the lowest. Compared with air-blast dried(WD50) samples, the ΔE* values of freezedried(FS6) and vacuum-dried(FS5) samples were significantly decreased, with an increase in total alkaloid contents. Conversely,the ΔE* values of shade-dried(FS1) and sun-dried(FS2) samples were significantly increased, with severe browning and low total alkaloid contents. The total alkaloid contents of heat-pump-dried(FS4) samples showed no significant change, and their ΔE* value was significantly decreased, with a light degree of browning and favorable commercial characters. The total alkaloid content of air-blast dried samples initially increased and then decreased within the range of 40-80 ℃, and the highest content was recorded at 70 ℃. The ΔE* values of high-temperature air-blast dried samples(70-80 ℃) were smaller with a light degree of browning, whereas their texture was compact and lacked powder. CA revealed a significant relationship between the uracil content and chromaticity value of the samples(P< 0. 05). The clustering relationships among samples subjected to different treatments were visualized via PCA and HCA. The results showed that FHB2 and air-blast drying(50-60 ℃) were more suitable for large-scale production, and heat pump drying could be a promising direction for future development. This study provides a scientific basis for optimizing the initial processing methods of Fritillaria taipaiensis.

A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and Veterinary Drugs in Maize.

The contamination risks of plant-derived foods due to the co-existence of pesticides and veterinary drugs (P&VDs) have not been fully understood. With an increasing number of unexpected P&VDs illegally added to foods, it is essential to develop a non-targeted screening method for P&VDs for their comprehensive risk assessment. In this study, a modified support vector machine (SVM)-assisted metabolomics approach by screening eligible variables to represent marker compounds of 124 multi-class P&VDs in maize was developed based on the results of high-performance liquid chromatography-tandem mass spectrometry. Principal component analysis and orthogonal partial least squares discriminant analysis indicate the existence of variables with obvious inter-group differences, which were further investigated by S-plot plots, permutation tests, and variable importance in projection to obtain eligible variables. Meanwhile, SVM recursive feature elimination under the radial basis function was employed to obtain the weight-squared values of all the variables ranging from large to small for the screening of eligible variables as well. Pairwise t-tests and fold changes of concentration were further employed to confirm these eligible variables to represent marker compounds. The results indicate that 120 out of 124 P&VDs can be identified by the SVM-assisted metabolomics method, while only 109 P&VDs can be found by the metabolomics method alone, implying that SVM can promote the screening accuracy of the metabolomics method. In addition, the method's practicability was validated by the real contaminated maize samples, which provide a bright application prospect in non-targeted screening of contaminants. The limits of detection for 120 P&VDs in maize samples were calculated to be 0.3~1.5 µg/kg.

Label-Free Assessment of Neuronal Activity Using Raman Micro-Spectroscopy.

Given the pivotal role of neuronal populations in various biological processes, assessing their collective output is crucial for understanding the nervous system's complex functions. Building on our prior development of a spiral scanning mechanism for the rapid acquisition of Raman spectra from single cells and incorporating machine learning for label-free evaluation of cell states, we investigated whether the Paint Raman Express Spectroscopy System (PRESS) can assess neuronal activities. We tested this hypothesis by examining the chemical responses of glutamatergic neurons as individual neurons and autonomic neuron ganglia as neuronal populations derived from human-induced pluripotent stem cells. The PRESS successfully acquired Raman spectra from both individual neurons and ganglia within a few seconds, achieving a signal-to-noise ratio sufficient for detailed analysis. To evaluate the ligand responsiveness of the induced neurons and ganglia, the Raman spectra were subjected to principal component and partial least squares discriminant analyses. The PRESS detected neuronal activity in response to glutamate and nicotine, which were absent in the absence of calcium. Additionally, the PRESS induced dose-dependent neuronal activity changes. These findings underscore the capability of the PRESS to assess individual neuronal activity and elucidate neuronal population dynamics and pharmacological responses, heralding new opportunities for drug discovery and regenerative medicine advancement.

Widely Targeted Metabolomic Analysis Reveals the Improvement in Panax notoginseng Triterpenoids Triggered by Arbuscular Mycorrhizal Fungi via UPLC-ESI-MS/MS.

Panax notoginseng is a highly valued perennial medicinal herb in China and is widely used in clinical treatments. The main purpose of this study was to elucidate the changes in the composition of P. notoginseng saponins (PNSs), which are the main bioactive substances, triggered by arbuscular mycorrhizal fungi (AMF) via ultrahigh-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS). A total of 202 putative terpenoid metabolites were detected, of which 150 triterpene glycosides were identified, accounting for 74.26% of the total. Correlation analysis, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) of the metabolites revealed that the samples treated with AMF (group Ce) could be clearly separated from the CK samples. In total, 49 differential terpene metabolites were identified between the Ce and CK groups, of which 38 and 11 metabolites were upregulated and downregulated, respectively, and most of the upregulated differentially abundant metabolites were mainly triterpene glycosides. The relative abundances of the two major notoginsenosides (MNs), ginsenosides Rd and Re, and 13 rare notoginsenosides (RNs), significantly increased. The differential saponins, especially RNs, were more easily clustered into one branch and had a high positive correlation. It could be concluded that the biosynthesis and accumulation of some RNs share the same pathways as those triggered by AMF. This study provides a new way to obtain more notoginsenoside resources, particularly RNs, and sheds new light on the scientization and rationalization of the use of AMF agents in the ecological planting of medicinal plants.