Principal Component Analysis Research Articles

Performance comparison of GPM IMERG V07 with its predecessor V06 and its application in extreme precipitation clustering over Türkiye

Defining regions with similar characteristics for extreme precipitation is crucial for understanding the impacts of climate change, planning and managing water resources, and designing hydraulic structures. However, studies on the regionalization of extreme precipitation for Türkiye are limited, and regional extreme precipitation characteristics are not well defined. In this study, motivated by the need to contribute to this field, homogenous regions for extreme precipitation across Türkiye were determined using the latest version (V07) of Integrated Multi-satellitE Retrievals for GPM (IMERG). We initially validated IMERG V07 estimates using data from 214 ground-based stations and compared the results with its predecessor V06. The results revealed that IMERG showed some notable improvements from V06 to V07 for all seasons, especially in winter. During this season, the correlation coefficient increased from 0.57 to 0.64, the mean absolute bias decreased from 78.22 % to 69.27 %, and the RMSE decreased from 11.10 mm/day to 9.70 mm/day. In V07, while the trend of decreasing accuracy with increasing elevation observed in V06 continues, it has been shown that some notable improvements were achieved in continuous and categorical metrics. We then applied widely used non-hierarchical (K-means) and hierarchical (Ward's method) clustering techniques. To perform this, we first applied Principal Component Analysis (PCA) to reduce the number of variables related to extreme precipitation (e.g. amount, frequency, standard deviation, and seasonality) and geographic characteristics to identify the most significant variables for analysis. The K-means method delineated Türkiye into eight extreme precipitation regions, while the Ward's method resulted in six distinct extreme precipitation regions. We evaluated the results based on the existing extreme precipitation climatology literature for Türkiye and by associating them to known precipitation dynamics, and as a result, we recommended eight precipitation regions determined by the K-means. The identified precipitation regions are expected to contribute to future studies analyzing the effects of climate change and to regional studies on natural disasters resulting from extreme precipitation.

An expert system for species discrimination and grade identification of fish maw

Fish maw, derived from dried swim bladders of fish, is valued for its nutritional and medicinal properties, which has led to an increased market demand. However, price variability based on species and grades has results in unethical practices such as counterfeiting and mislabeling, highlighting the need for reliable quality authentication. To address this, an expert system using MATLAB software has been developed. This system employs nuclear magnetic resonance (NMR) technology and pattern recognition methods to identify fish maw species and classify their grade. The study analyzed ten species across three grades of fish maw, identifying 43 nutritional components from NMR spectra, including sugars, amino acids, fatty acids, organic acids, and vitamins. Univariate statistical analysis was integrated with multivariate statistical analyses, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares (OPLS-DA) using SIMCA software, to develop models for species and grades identification. A four-dimensional volcano map was constructed to highlight characteristic components of various fish maws, resulting in an NMR database for common fish maws. The expert system’s accuracy was validated with new samples, achieving 92.6% for species identification and 90.0% for grade classification. This study provides a valuable tool for the quality evaluation of fish maw and a scientific basis for market regulation.

Powder dosing within continuous manufacturing: A lean approach for gravimetric dosing configuration and equipment selection

This work presents a novel approach to select gravimetric dosing configurations for continuous unit operations in pharmaceutical processing. It optimizes material, time, and personnel use, and allows selections for configurations of materials not included in the model by robust material attribute-based interpolation. The approach does not apply Principal Component Analysis (PCA) and Partial Least Squares (PLS). Instead, a reduced set of material attributes that require measurement was determined from existing material attribute libraries found in literature. Only those identified 17 attributes were measured for 13 materials and employed in this study. The established model proved to be predictive for an even further reduced attribute set of 12 attributes. The model introduces a simplified method for selecting feeding equipment combining precision metrics (relative mean intra-bin variability) and model parameters (fmax, fmin, beta). The reduction of Material Attributes (MAs) in this short-cut method enables fast evaluation and enhances resource efficiency by incorporating only a few selected MAs into the model and therefore introduces a different line of thinking. It was also possible to compare feeding of different hopper geometries, showing that flat bottom-hoppers have higher dosing precision than round bottom-hoppers. In conclusion, this work introduces a smart and innovative model that simplifies the equipment selection process and brings objectivity through a comprehensive numerical description of the discharge characteristics, and provides new options for continuous dosing in pharma through combining precision and curve fitting parameters.

Molecular Spectroscopy for the Biochemical Composition Analysis of Patient-Derived Pancreatic Cancer Organoids.

Pancreatic ductal adenocarcinoma (PDAC) continues to pose profound challenges within the field of oncology due to its notorious resistance to existing therapies and constant high mortality rates. The recent emergence of three-dimensional patient-derived organoid (PDO) models marks a significant advancement, opening new avenues for exploring cancer biology and assessing therapeutic approaches. The aim of this study focuses on the innovative use of Fourier-transform infrared (FT-IR) spectroscopy to analyze PDAC organoids, thus illuminating their biochemical intricacies. In this study, PDAC organoids, cultivated from specimens sourced from cancer patients, were subjected to FT-IR spectroscopic imaging. By examining the spectral data within the critical fingerprint region (950-1800 cm-1), and employing principal component analysis (PCA), biochemical disparities were detected and analyzed. The results revealed distinct spectral profiles corresponding to different sample preparation techniques, which in turn highlighted variations in protein content and structure. PCA revealed a high homogeneity within classes and minimal passage number influence on spectral profiles, with variations in lipid content and protein profiles. Significantly, the biochemical fingerprint of these PDOs closely mirrored that of the original human tissue samples. This investigation underscores the efficacy of molecular spectroscopy as a non-invasive method for profound characterization of PDAC organoids, enhancing our comprehension of tumor biochemistry. The capacity for swift and precise biochemical profiling of PDOs via molecular spectroscopy heralds a promising future for this technique in the realms of cancer diagnostics and personalized medicine.

Arbuscular mycorrhizal fungi and plant growth-promoting rhizobacteria promoted changes in plant metabolism and the volatile profile of Piper callosum Ruiz & Pav

Piper callosum Ruiz & Pav. is known in the Amazon as a paregoric elixir and exists as a shrub native to the Neotropics. In traditional medicine, the tea from its leaves is used to treat pain in the digestive tract, rheumatism, and muscular aches. This study aimed to evaluate the influence of arbuscular mycorrhizal fungi (AMF) and plant growth-promoting rhizobacteria (PGPR) inoculation on the secondary metabolism of P. callosum. The plants were inoculated after 30 days of cultivation and kept in a greenhouse. Plant development parameters were monitored at 30-, 60-, and 90-days after inoculation (DAI) and indicated that AMF and PGPR favored plant growth throughout all stages of development. The leaf volatiles extracted by simultaneous distillation and extraction were analyzed by gas chromatography coupled with mass spectrometry and showed that phenylpropanoids and monoterpene hydrocarbons were predominant in all essential oils, revealing distinct abundance patterns across the different sampling points. The major volatile compounds identified were safrole, methyl eugenol, and β-pinene, and a reduction trend in their content was observed for safrole and methyl eugenol, especially at 90 DAI in both treatments. Principal component analysis revealed that germacrene D-4-ol and limonene were among the most influential in both treatments. Finally, the total phenolic content and the enzymatic activity of phenylalanine ammonia-lyase revealed different effect patterns for inoculation and time of evaluation on each treatment. All the data presented indicate that symbiotic inoculation can optimize the production of essential oil, which can improve the quality and productivity of P. callosum plants.

Enhancing Predicted Visual Acuity After SmartSight Lenticule Extraction: Identifying Key Factors With Machine Learning.

To develop a predictive model aimed at assessing the likelihood of improvement in corrected distance visual acuity (CDVA) for patients undergoing lenticule extraction using the SmartSight system from SCHWIND eye-tech-solutions. This model evaluates the effectiveness and weight of various clinical and procedural parameters in predicting enhancements in visual acuity. Data from 1,262 eyes treated with the SmartSight system, encompassing 86 features, were analyzed. Regression and classification techniques were employed to estimate the probability of CDVA gain, ensuring robust results by comparing different methods. The dataset was divided into training (70%, 883 treatments) and testing (30%, 379 treatments) subsets to ensure comprehensive model evaluation using machine learning methods. Feature significance was determined via analysis of variance and principal components analysis to identify impactful parameters. Three principal components accounting for more than 70% of the data variance were identified. The first component was primarily linked to system settings, whereas the second and third components were associated with patient gender and laterality. Factors significantly influencing CDVA improvement included a higher spot-to-track distance ratio, tighter track distance, lower pulse energy, lower average laser power, larger spot distance, greater cap thickness, and lower dosage. These variables were ranked based on their impact on CDVA enhancement. Using low-energy asymmetric spacing for lenticule extraction with the SmartSight system is safe and effective. This approach improves visual outcomes for patients undergoing treatment for myopic astigmatism, offering a reliable method for predicting CDVA improvements. [J Refract Surg. 2024;40(12):e974-e984.].

Rooting for Success: The Role of Microorganisms in Promoting Growth and Resilience in Black Alder Seedlings.

Polycyclic aromatic hydrocarbons (PAHs) pose a global environmental risk, impacting human health. Enhancing phytoremediation with microbial-plant interactions could help mitigate these pollutants. However, tree responses to PAHs are unclear, necessitating controlled studies before field experiments. This study examined how PAH-degrading microbes affect black alder (Alnus glutinosa L.) seedlings grown hydroponically, hypothesizing that specific microbes improve growth and stress tolerance. Two half-sib families (41-65-7 K, 13-99-1 K) were inoculated with Rhodotorula sphaerocarpa (R.s.), Pseudomonas putida (P.p.), and Sphingobium yanoikuyae (S.y.). Results showed family-dependent and microbe-specific effects, with family 41-65-7 K showing enhanced shoot growth (threefold increase by R.s.) and higher carotenoid levels. Antioxidant enzyme activities varied: R.s. elevated superoxide dismutase activity by 4.8-fold in 13-99-1 K, while catalase activity increased but decreased in 41-65-7 K. Principal component analysis revealed distinct phytochemical clustering based on microbial treatment, highlighting genotype-specific modulations. Each microorganism had unique plant growth-promoting traits, with P.p. producing the most phytohormone and S.y. fixing nitrogen. These findings support targeted microbial inoculation for effective remediation of PAH-contaminated environments.

European anchovy's abundance, more affected by climatic conditions than fishing activities in the northwest African waters.

The marine waters off the coast of northwest Africa are known for being highly productive upwelling regions in the Eastern Atlantic Ocean. The present study evaluated the combined effects of climate change and sustainable fishing levels on the long-term sustainability of European anchovy (Engraulis encrasicolus), a target pelagic species found along the West African coastal waters. The present study used survey biomass time series from survey vessels and species catch time series from commercial fisheries operating in the region. Hence, we investigated the relationship between fishing dynamics and environmental factors. The status of anchovy stock was determined by analyzing changes in different initial depletion rates using a state-space Bayesian surplus production model. We used Principal component analysis to identify the essential environmental data set, while Generalized Additive Models (GAM) were employed to determine the influence of the environment on fisheries. The stock assessment model revealed that fishing intensity did not affect the abundance of anchovy in the region even when tested under different biomass depletion scenarios. However, by employing GAMs, the research assessed the impact of environmental variables on fish biomass, indicating that sea surface temperature (SST) significantly influences anchovy abundance in the region. The latitudinal gradient also had a significant influence on this species' abundance. These findings underscore the vulnerability of the small-scale fisheries sector in the tropical upwelling zone to climate change. The study concludes that even in the face of potential negative impacts from environmental factors, optimizing fishing methods can contribute to the preservation of pelagic resources in the region. It emphasizes the importance of managing fishery resources in a sustainable, economically viable, and socially acceptable way.

Chemical composition and antioxidant activity of 'Nufar' basil (Ocimum basilicum L.) essential oil from three municipalities of Tolima, Colombia

Essential oils are natural compounds with great potential to be used as products with high antioxidant activity. The aim of this research was to determine the chemical composition and antioxidant activity of the Nufar variety basil essential oil from three municipalities in Tolima, Colombia. The essential oil extracted from the leaves of this basil variety using microwave-assisted extraction predominantly comprises linalool (37.9–41.1%), estragole (24.5–33.6%), and 1,8-cineole (5.2–7.6%). The quantities of these compounds are influenced by the plant's place of origin. Regarding antioxidant activity, the basil essential oil cultivated in the municipality of Honda shows higher antioxidant activity in the DPPH and ABTS·+ tests (29.89 ± 4.03 and 9.60 ± 1.74 mg/mL, respectively), compared to the values from the municipalities of Mariquita and Espinal. The Principal Component Analysis applied to the data generated biplots that explain 99.8% of the total variation. These indicate that the essential oils from basil grown in the municipality of Espinal are characterized by having a high IC50 (mg/mL) in the DPPH test. On the other hand, the essential oils of basil cultivated in the municipalities of Honda and Mariquita are distinguished by their high content of monoterpenes and total terpenes. The study concludes that the geographical location significantly impacts the chemical composition and antioxidant activity of basil plants.

Differential Comparison of Chemical Compositions Between Two Wutou Decoctions Based on Ultrahigh-Performance Liquid Chromatography Coupled With Quadrupole Time-of-Flight Tandem Mass Spectrometry Combined With Chemometric Analysis.

Wutou decoction is an ancient compound preparation composed of five herbal medicines, which has good therapeutic effects on rheumatoid arthritis. There are two classic preparation methods of Wutou decoction using honey and water as decocting excipients, respectively. However, the differences of chemical compositions between these two Wutou decoctions yet remain unclear. Therefore, honey-decocting Wutou decoction was prepared according to the record written in Synopsis of Golden Chamber, and water-decocting Wutou decoction was prepared by the process commonly used in clinics nowadays. Wutou decoction database was also established for the identification of their chemical compositions. Ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry technique combined with chemometric analysis was further established and applied to analyze their chemical compositions. A total of 128 components were identified in honey-decocting Wutou decoction, whereas 126 components in water-decocting Wutou decoction. Isoguanosine and aromadendrin were found to exist only in honey-decocting Wutou decoction, but not in water-decocting Wutou decoction. Principal component analysis results showed that there were obvious differences between honey-decocting Wutou decoction and water-decocting Wutou decoction. Orthogonal partial least squares discriminant analysis further verified these differences, of which 30 potential differential components were found by calculation of their variable importance in the projection values. Different chemical compositions will inevitably affect the toxicity and safety of Wutou decoction, and it is imperative to calibrate its preparation process.

Applicability of soil health assessment dominated by biological indicators in facility agriculture

The minimum data set (MDS) is widely used for soil health index (SHI) assessment, with the selection of indicators being crucial. However, biological indicators are less frequently used compared to physical and chemical indicators. This study aimed to establish a biologically-centered MDS for assessing soil health in tomato facility agriculture in Ningbo. Using biochemical, microbiological, and nematode data from 60 continuous cropping soils, we identified key indicators for the MDS. Principal component analysis revealed a strong correlation between the MDS and the total data set (TDS), with the final MDS including available phosphorus, electrical conductivity, β-glucosidase, urease, bacterial Chao1 index, bacterial Shannon index, Ralstonia solanacearum, nematode Shannon index, structure index, and channel index. Random forest modeling showed that the predicted SHI had a high fit with the MDS-SHI, with bacterial and nematode Shannon indices being the most influential predictors, emphasizing the critical role of biological communities in maintaining soil health. Furthermore, the study identified three distinct phases in soil health during long-term continuous cropping: healthy (0–10 years), sub-healthy (11–15 years), and recovery (16–20 years), demonstrating the soil's potential for self-recovery. By incorporating biodiversity as a core component, this biologically-focused MDS provides a more comprehensive and precise method for evaluating soil health in facility agriculture. The findings suggest that this MDS could inform future research and applications in similar agricultural systems. Moreover, the results highlight the vital role of biodiversity in agroecosystem stability, suggesting that future soil health assessments should place greater emphasis on biological indicators to better capture the complexity of soil functions.

Carbonic anhydrase activity and metabolite variation of different microalgae species at alkaline pHs

A significant increase in atmospheric greenhouse gases over the last century has led to the development of several methods and technologies to remove carbon dioxide (CO2). Microorganisms produce carbonate minerals through the natural mineralization of CO2; however, the feasibility of this process remains in research. This work aimed to study the cultivation of different microalgae under alkaline pH to maintain their potential for carbon mitigation. According to the results, the highest carbonic anhydrase activity has been reached (4.64 mg/g) for Chlamydomonas reinhardtii at a pH of 10. C. reinhardtii, at a pH of 9.5, yielded the highest chlorophyll content (23.58 mg/g), while Spirulina at a pH of 8.5 produced the highest biomass (882.9 mg/L). Also, a positive correlation existed between pH and lipid content for C. reinhardtii. Spirulina, however, exhibits the opposite effect. According to a principal component analysis, there is an opposite relationship between pH and carbonic anhydrase (CA) activity for C. reinhardtii and filamentous-type cyanobacteria from Salda Lake. The following order of the suitability of the microalgae species for high carbon capture is determined by the Analytic hierarchy process method: Spirulina>C. reinhardtii > Chlorella vulgaris > Coccus-type cyanobacteria > Filamentous-type cyanobacteria from Salda Lake. Additionally, this study provided important results regarding the cyanobacteria species isolated from an alkaline lake, Lake Salda. This would contribute to future studies of carbon capture and carbon mitigation mechanisms.

Effect of jujube pulp on the quality of cream cheese product during storage.

Cheese is favored by foreign consumers for its remarkable nutritional profile and unique taste. However, Chinese consumers find it difficult to accept its strong flavor profile. Therefore, it is imperative to develop a cheese product that meets the flavor preferences of Chinese consumers. In this study, jujube pulp was used to partially substitute cow milk (0%, 5%, 10%, 15%, 20%; wt/wt) to develop cream cheese products. The effect of different amounts of jujube pulp on the composition, texture, rheology, microstructure and sensory properties of cream cheese products during storage was investigated. The results showed that at the same storage time, the moisture content and yield of the product increased as the amount of jujube pulp increased, while both protein content and fat content decreased. In addition, hardness and storage modulus decreased, protein cross-linking was disrupted, serum channels were enlarged, and jujube flavor and smoothness scores increased while cream flavor scores decreased. At the same amount of jujube pulp, the soluble nitrogen content of the product increased during storage, while the hardness and storage modulus decreased, and the sensory score peaked at 7 d. Principal component analysis revealed that there were positive relationships between protein content, fat content, hardness and storage modulus of the product, whereas these parameters showed negative relationships with moisture content and jujube flavor. The cream cheese products with 10% and 15% jujube pulp showed the best sensory quality. This study will provide a theoretical basis for the quality control of jujube flavored cream cheese products and technical support for the development of cheese products tailored to the preferences of Chinese consumers.

Effect of γ-irradiation combined with enzymatic modification on the physicochemical properties of defatted rice bran dietary fiber

This study comprehensively examines how combining γ-irradiation and enzymatic modification influences the microstructure and physicochemical properties of dietary fiber (DF) obtained from defatted rice bran. The resulting yields of soluble dietary fiber (SDF) and insoluble dietary fiber (IDF) were measured at 13.38 ± 0.40 g/100 g and 52.19 ± 0.97 g/100 g, respectively. The modifications led to a diminish in particle size, an increase in specific surface area, and an improvement in water-holding capacity, oil-holding capacity, swelling capacity, glucose adsorption capacity, and cholesterol adsorption capacity. Furthermore, the modified DF exhibited enhanced anti-digestive properties and probiotic activity. Cluster and principal component analysis results revealed that the modified SDF exhibited superior functional properties. Correlation analysis indicated a noticeable relationship between the monosaccharide composition of DF and its functional characteristics. These findings suggest that γ-irradiation combined with enzymatic modification represents a viable approach for enhancing the quality of rice bran DF.

Advanced characterisation of groundwater contamination at a dumpsite: Methodology and assessment - Case study of a municipal solid waste dumpsite in India.

Groundwater (GW) contamination due to municipal solid waste (MSW) disposal in open dumpsites is a pressing global issue. Traditional GW assessment studies are limited to single-depth sampling from nearby wells/handpumps, providing limited insights into subsurface soil characteristics and are prone to cross-contamination. The present study introduces an innovative methodology integrating advanced techniques: Cone Penetration Testing (CPT), Hydraulic Profiling Tool (HPT), Continuous Soil Sampling, and Discrete GW Sampling. Conducted at an operational dumpsite in New Delhi, India, from January to February 2023, the site investigation program covered seven distinct locations to incorporate the entire dumpsite area. The investigation proceeded in stages, starting with subsurface soil characterisation using CPT and HPT, followed by extracting soil and GW samples using CPT and HPT data. Due to restrictions in the northeast direction, GW samples were directly extracted from borewells. The results revealed maximum and minimum concentrations of 171 items/l and 26 items/l of MPs, while ionic concentration reached 13,200ppm for Cl- and 4437ppm for SO4-2. A maximum of 0.721ppm, 0.663ppm and 0.948ppm concentration was observed for Ni, Cu and Mo in GW samples. Spearman correlation and principal component analysis underscore the influence of Ec, TDS, Na+1 and Cl-1 on GW quality. This integrated approach effectively identifies high-permeability layers, which are crucial for understanding contaminant dispersion, and ensures precise sampling at various depths with minimal cross-contamination. This research demonstrates the proposed methodology's effectiveness in providing more profound and precise insights into GW contamination dynamics and suggests its utility in forming the basis for more effective remediation and regulatory strategies.

Contribution of Organic Carbon, Moisture Content, Microbial Biomass-Carbon, and Basal Soil Respiration Affecting Microbial Population in Chronosequence Manganese Mine Spoil

The research was carried out to determine the potential effect of microbiota, organic carbon, percentage of moisture content, and microbial biomass concentration as an evaluator of variation in basal soil respiration rate. Relative distribution and composition of the microbial population were estimated from six different chronosequence manganese mine spoil (MBO0, MBO2, MBO4, MBO6, MBO8, MBO10) and forest soil (FS). The variation was seen in moisture content (6.494±0.210-11.535±0.072)%, organic carbon (0.126±0.001- 3.469± 0.099)%, MB-C (5.519±1.371- 646.969± 11.428) μg.g-1 of soil. A positive correlation was shown between OC with MB-C (r = 0.938; p< 0.01) and moisture content (MC) (r = 0.962; p< 0.01). Variation in the basal soil respiration (BSR) and microbial metabolic quotients (MMQ) was shown to range between 0.352 ± 0.007- 0.958 ±0.014μg CO2-C.g-1 and 6.5× 10-3 - 1.481×10-3 μg CO2-C.g-1 microbial-C.h-1 with BSR: OC from (2.793-0.276)% respectively. This result shows that there is a gradual increase in OC, MC, MB-C, and BSR across seven different sites due to progressive enhancement in soil fertility that leads to the initialization of succession. Stepwise multiple regression analysis further confirms the degree of variability added by microbial biomass C, moisture content, organic carbon, and microbial population on basal soil respiration in microbes. Principal component analysis enables the differentiation of seven different soil profiles into independent clusters based on cumulative variance given by physico-chemical and microbial attributes that indicate the level of degradation of land and act as an index to restore soil fertility.

Non-thermal shearing effect on gluten conformation for plant-based anisotropic structures

Wheat gluten manipulation to obtain fibrillar structures is a promising approach for elaborating meat analogs on large or small scales. Alternatively to extrusion, mild methods can save energy and avoid sensory attributes/protein degradation. This study aimed to evaluate low-temperature shearing to create gluten-based fibers, assessing the material structure as a function of processing intensity. The effect of the non-thermal shearing length (up to 10 min) and added mechanical energy (up to 565.5 J/g) applied on a highly hydrated wheat gluten-based matrix was investigated in terms of density, exudation, secondary protein structure (FTIR), protein network attributes (confocal laser scanning microscopy), elongation attributes, morphology (SEM), and anisotropicity of cooked matrices. The progression of the shearing process was associated with the development of β-sheet secondary structures (26 % to 50 % predominancy), higher elongation attributes, raw matrix density, and anisotropicity of cooked matrices. Higher protein vessel length and width were associated with the formation and stacking of β-sheets. Exudation, cooked matrix density, and endpoint rate were associated with higher α-helices content (lowered from 39 % to 11 % of predominancy) and lower shearing-added energies. A principal component analysis of the entire dataset confirmed these observations, and the morphology revealed the evolution of the matrix organization during the shearing process. These results underscore the potential of mild-temperature shearing of a highly hydrated gluten-enriched matrix to alter the protein conformation, opening possibilities for controlling fiber structure development, valid for new foods such as meat analogs.

Forging a Sustainable Agricultural Future: Tradition and Innovation in Shaping Acceptance of Insect-Based Foods

This study investigates the potential for a sustainable future in agriculture, examining how the interplay between tradition and innovation influences the acceptance of insect-based foods. As global demand for alternative protein sources rises, it is important to understand how individuals’ attitudes toward traditional “disgusting” dishes and their sensitivity to environmental sustainability interact to shape attitudes toward new insect-derived products.The main research aim was to identify profiles of consumers possessing different attitudes toward traditional “disgusting” dishes and new insect-derived products. A survey of 1402 Italian participants was thus conducted to detect the relationships between i) opinions toward traditional “disgusting” food and insects-containing food, and ii) orientation toward both environmental issues and traditional food. A Cluster Analysis was performed on the basis of a previous Principal Component Analysis (PCA) in order to identify distinct consumers’ profiles based on their environmental concern and food system opinions. The four emerged clusters, - i.e. Earth-Balance responsibles, Green consumers, Indifferents and Selective eco-conscious consumers - differ for socio-demographic features and attitudes toward traditional “disgusting” food and insect-based alternatives, that were detected through a photo-choice task including 10 traditional Italian foods and 11 insect-based options. Correlation and Correspondence analyses verified the relationship between preferences for traditional foods and their insect-based counterparts within each cluster. Results show that environmental concern toward food systems influenced the acceptability of insect-based foods differently across the identified clusters. Additionally, socio-demographic features and ties to culinary traditions significantly influenced innovative food choices.In sum, this study provides insights for the sustainable integration of insect-based foods into mainstream dietary choices, emphasizing the crucial role of a harmonious blend of tradition and innovation in this evolving landscape. Furthermore, care for the visual aspects is suggested for product development within the emerging insect-based foods market.

Spatial and Temporal Bayesian Hierarchical Model Over Large Domains With Application to Holocene Sea Surface Temperature Reconstruction in the Equatorial Pacific

AbstractWe present a novel space‐time Bayesian hierarchical model (BHM) to reconstruct annual Sea Surface Temperature (SST) over a large domain based on SST at limited proxy (i.e., sediment core) locations. The model is tested in the equatorial Pacific. The BHM leverages Principal Component Analysis to identify dominant space‐time modes of contemporary variability of the SST field at the proxy locations and employs these modes in a Gaussian process framework to estimate SSTs across the entire domain. The BHM allows us to model the mean field and covariance, varying in space and time in the process layers of the hierarchy. Using the Markov Chain Monte Carlo (MCMC) method and suitable priors on the model parameters, posterior distributions of the model parameters and, consequently, posterior distributions of the SST fields and the attendant uncertainties are obtained for any desired year. The BHM is calibrated and validated in the contemporary period (1854–2014) and subsequently applied to reconstruct SST fields during the Holocene (0–10 ka). Results are consistent with prior inferences of La Niña‐like conditions during the Holocene. This modeling framework opens exciting prospects for modeling and reconstruction of other fields, such as precipitation, drought indices, and vegetation.

Lithological Mapping of the Neoproterozoic Basement Rocks in the Area East El-Sibai Core Complex (SCC), Central Eastern Desert of Egypt, Based on Remote Sensing Data and Field Observations

Spectral data are an active tool for mineral exploration, lithological, and structural mapping by spectral classification; due to its accuracy, ease of use, and low cost, especially in inaccessible areas. This study aims to integrate Landsat-8/OLI and ASTER images with extensive field survey and petrographical analysis to discriminate the Neoproterozoic basement rock units in the area east Gabal El-Sibai. In image enhancement techniques, we used the Correlation Coefficient and Optimum Index Factor for choosing the best-colored images to discriminate various rock units based on spectral signature curves. The exposed rock units consist of granitic rocks of variable compositions intruded directly into ophiolitic and island arc associations. The different rock units and structural elements in this study were mapped and discriminated using False Color Composites images (7-6-1 of Landsat-8 and 9-4-3 of ASTER), Minimum Noise Fraction (6-5-1), Principal Component Analysis (4-1-2 for Landsat-8 and 3-1-4 for ASTER), two newly proposed ratios images (3/4, 6/7, 5/6 for Landsat-8 and 4/1, 3/1, 8/9 for ASTER), with the Maximum Likelihood Classification and Support Vector Machine Classification. In conclusion; the overall accuracy and the Kappa coefficient show a good agreement with the Principal Component Analysis and Band Ratio results in the current geological map. Therefore, the suggested methodology shows that optical data have a strong potential for discriminating between lithological units. This approach provides accurate information to geologists, geophysical engineers, and land-use land-cover decision-makers. It can also improve and enhance the accuracy of geological maps in the Nubian Shield in Egypt and other arid environments worldwide.