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We have performed a data science study of Monte Carlo (MC) simulation trajectories to understand factors that can accelerate the formation of zeolite nanoporous crystals, a process that can take days or even weeks. In previous work, MC simulations predicted and experiments confirmed that using a secondary organic structure-directing agent (OSDA) accelerates the crystallization of all-silica LTA zeolite, with experiments finding a three-fold speedup [Bores et al., Phys. Chem. Chem. Phys. 24, 142–148 (2022)]. However, it remains unclear what physical factors cause the speed-up. Here, we apply data science to analyze the simulation trajectories to discover what drives accelerated zeolite crystallization in MC simulations going from a one-OSDA synthesis (1OSDA) to a two-OSDA version (2OSDA). We encoded simulation snapshots using the smooth overlap of atomic positions approach, which represents all two- and three-body correlations within a given cutoff distance. Principal component analyses failed to discriminate datasets of structures from 1OSDA and 2OSDA simulations, while the Support Vector Machine (SVM) approach succeeded at classifying such structures with an area-under-curve (AUC) score of 0.99 (where AUC = 1 is a perfect classification) with all three-body correlations and as high as 0.94 with only two-body correlations. SVM decision functions reveal relatively broad/narrow histograms for 1OSDA/2OSDA datasets, suggesting that the two simulations differ strongly in information heterogeneity. Informed by these results, we performed pair (2-body) entropy calculations during crystallization, resulting in entropy differences that semi-quantitatively account for the speedup observed in the previous MC simulations. We conclude that altering synthesis conditions in ways that substantially change the entropy of labile silica networks may accelerate zeolite crystallization, and we discuss possible approaches for achieving such acceleration. [ABSTRACT FROM AUTHOR]

Machine learning methods provide an advanced means for understanding inherent patterns within large and complex datasets. Here, we employ the principal component analysis (PCA) and the diffusion map (DM) techniques to evaluate the glass transition temperature (Tg) from low-dimensional representations of all-atom molecular dynamic simulations of polylactide (PLA) and poly(3-hydroxybutyrate) (PHB). Four molecular descriptors were considered: radial distribution functions (RDFs), mean square displacements (MSDs), relative square displacements (RSDs), and dihedral angles (DAs). By applying Gaussian Mixture Models (GMMs) to analyze the PCA and DM projections and by quantifying their log-likelihoods as a density-based metric, a distinct separation into two populations corresponding to melt and glass states was revealed. This separation enabled the Tg evaluation from a cooling-induced sharp increase in the overlap between log-likelihood distributions at different temperatures. Tg values derived from the RDF and MSD descriptors using DM closely matched the standard computer simulation-based dilatometric and dynamic Tg values for both PLA and PHB models. This was not the case for PCA. The DM-transformed DA and RSD data resulted in Tg values in agreement with experimental ones. Overall, the fusion of atomistic simulations and DMs complemented with the GMMs presents a promising framework for computing Tg and studying the glass transition in a unified way across various molecular descriptors for glass-forming materials. [ABSTRACT FROM AUTHOR]

Processes or products are typically complex systems with numerous interrelated procedures and interdependent components. This results in complex relationships between responses and input factors, as well as complex nonlinear correlations among multiple responses. If the two types of complex correlations in the quality design cannot be properly dealt with, it will affect the prediction accuracy of the response surface model, as well as the accuracy and reliability of the recommended optimal solutions. In this paper, we combine kernel trick-based kernel principal component analysis, spline-based Bayesian semiparametric additive model, and normal boundary intersection-based evolutionary algorithm to address these two types of complex correlations. The effectiveness of the proposed method in modeling and optimisation is validated through a simulation study and a case study. The results show that the proposed Bayesian semiparametric additive model can better describe the process relationships compared to least squares regression, random forest regression, and support vector basis regression, and the proposed multi-objective optimisation method performs well on several indicators mentioned in the paper. [ABSTRACT FROM AUTHOR]

In the semiconductor industry, virtual metrology (VM) is a cost-effective and efficient technique for monitoring the processes from one wafer to another. This technique is implemented by generating a predictive model that uses real-time data from equipment sensors in conjunction with measured wafer quality characteristics. Before establishing a prediction model for the VM system, appropriate selection of relevant input variables should be performed to maintain the efficiency of subsequent analyses considering the large dimensionality of the sensor data inputs. However, wafer production processes usually employ multiple sensors, which leads to cost escalations. Herein, we propose a variant of the sparse principal component analysis (PCA) called true sparse PCA (TSPCA). The proposed method uses a small number of input variables in the first few principal components. The main contribution of the proposed TSPCA is reducing the number of essential sensors. Our experimental results demonstrate that compared to the existing sparse PCA methods, the proposed approach can reduce the number of sensors required while explaining an approximately equivalent amount of variance. [ABSTRACT FROM AUTHOR]

We rely on a total of 23 (cluster size, 8 structural, and 14 connectivity) descriptors to investigate structural patterns and connectivity motifs associated with water cluster aggregation. In addition to the cluster size n (number of molecules), the 8 structural descriptors can be further categorized into (i) one-body (intramolecular): covalent OH bond length (rOH) and HOH bond angle (θHOH), (ii) two-body: OO distance (rOO), OHO angle (θOHO), and HOOX dihedral angle (ϕHOOX), where X lies on the bisector of the HOH angle, (iii) three-body: OOO angle (θOOO), and (iv) many-body: modified tetrahedral order parameter (q) to account for two-, three-, four-, five-coordinated molecules (qm, m = 2, 3, 4, 5) and radius of gyration (Rg). The 14 connectivity descriptors are all many-body in nature and consist of the AD, AAD, ADD, AADD, AAAD, AAADD adjacencies [number of hydrogen bonds accepted (A) and donated (D) by each water molecule], Wiener index, Average Shortest Path Length, hydrogen bond saturation (% HB), and number of non-short-circuited three-membered cycles, four-membered cycles, five-membered cycles, six-membered cycles, and seven-membered cycles. We mined a previously reported database of 4 948 959 water cluster minima for (H2O)n, n = 3–25 to analyze the evolution and correlation of these descriptors for the clusters within 5 kcal/mol of the putative minima. It was found that rOH and % HB correlated strongly with cluster size n, which was identified as the strongest predictor of energetic stability. Marked changes in the adjacencies and cycle count were observed, lending insight into changes in the hydrogen bond network upon aggregation. A Principal Component Analysis (PCA) was employed to identify descriptor dependencies and group clusters into specific structural patterns across different cluster sizes. The results of this study inform our understanding of how water clusters evolve in size and what appropriate descriptors of their structural and connectivity patterns are with respect to system size, stability, and similarity. The approach described in this study is general and can be easily extended to other hydrogen-bonded systems. [ABSTRACT FROM AUTHOR]

Hardness of thin films is a noteworthy property in the electronic and mechanical industry and is generally observed to be dependent on the degree of roughening facilitated from surface heights' surface spatial heterogeneity at the mesoscopic observation scale. Nonetheless, owing to enhanced scale fluctuations and higher-order central moments, conventional parameters provide limitations and errors in capturing the spatial heterogeneity of surfaces. Herein, we have utilized scale-independent fractal parameters to analyze the spatial heterogeneity of surface heights in Ti1−xSixN ternary hard films deposited with varying Si doping concentrations using sputtering technique. The fractal dimension, lacunarity coefficient, Moran index, surface entropy, Otsu's separability, and fractal succolarity were computed to provide an overarching understanding of the surface heights' spatial heterogeneity. Principal component analysis was employed on the data sets to identify the parameter(s) accounting for the maximum variance and accordingly, the structure–property relation between spatial heterogeneity of surface and hardness is analyzed and discussed in the context of the fractal dimension of surface heights. The results indicate the possibility of mesoscopic surface engineering and, consequently, tuning of hardness and modulus of elasticity in Ti1−xSixN hard films by mere changing of surface spatial heterogeneity facilitated by the fractal dimension of surface heights. [ABSTRACT FROM AUTHOR]

We have reanalyzed the rich plethora of ground state configurations of the asymmetric Wigner bilayer system that we had recently published in a related diagram of states [Antlanger et al., Phys. Rev. Lett. 117, 118002 (2016)], comprising roughly 60 000 state points in the phase space spanned by the distance between the plates and the charge asymmetry parameter of the system. In contrast to this preceding contribution where the classification of the emerging structures was carried out "by hand," we have used for the present contribution machine learning concepts, notably based on a principal component analysis and a k-means clustering approach: using a 30-dimensional feature vector for each emerging structure (containing relevant information, such as the composition of the configuration as well as the most relevant order parameters), we were able to reanalyze these ground state configurations in a considerably more systematic and comprehensive manner than we could possibly do in the previously published classification scheme. Indeed, we were now able to identify new structures in previously unclassified regions of the parameter space and could considerably refine the previous classification scheme, thereby identifying a rich wealth of new emerging ground state configurations. Thorough consistency checks confirm the validity of the newly defined diagram of states. [ABSTRACT FROM AUTHOR]

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

BACKGROUND: Spearmint and peppermint teas are widely consumed around the world for their flavor and therapeutic properties. Dynamic headspace sampling (HS) coupled to gas chromatography/mass spectrometry (GC–MS) with principal component analysis (PCA) of 'fingerprint' volatile profiles were used to investigate 27 spearmint and peppermint teas. Additionally, comparisons between mint teas were undertaken with an electronic nose (enose). RESULTS: Twenty compounds, all previously known in the literature, were identified using HS–GC–MS. PCA found distinct differences between the fingerprint volatile profiles of spearmint, peppermint and spearmint/peppermint combination teas. HS–GC–MS analysis performed with an achiral column allowed faster processing time and yielded tighter clustering of PCA tea groups than the analysis which used a chiral column. Two spearmint outliers were detected. One showed a high degree of variation in volatile composition and a second wholly overlapped with the peppermint PCA grouping. Enose analysis separated all treatments with no overlaps. CONCLUSION: Characterizing the volatile fingerprints of mint teas is critical to quality control for this valuable agricultural product. The results of this study show that fingerprint volatile profiles and enose analysis of mint teas are distinctive and could be used to rapidly identify unknown samples. With specific volatile profiles identified for each tea, samples could be tested in the laboratory, or potentially on a farm or along the supply chain, to confirm the provenance and authenticity of mint food or beverage commodities. © 2024 Society of Chemical Industry. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. [ABSTRACT FROM AUTHOR]

BACKGROUND: The present study focuses on the aroma and secondary metabolites profiling of three Pelargonium graveolens cultivars, baladi (GRB), sondos (GRS) and shish (GRSH), grown in Egypt. Utilizing a multiplex approach combining high resolution‐ultraperformance liquid chromatography (HR‐UPLC)/tandem mass spectrometry (MS/MS) and gas chromatography (GC)‐MS coupled with chemometrics, the study aims to identify and profile various secondary metabolites and aroma compounds in these cultivars. RESULTS: HR‐UPLC/MS/MS analysis led to the annotation of 111 secondary metabolites, including phenolics, flavonoids, terpenes and fatty acids, with several compounds being reported for the first time in geranium. Multivariate data analysis identified vinylanisole, dimethoxy‐flavonol, and eicosadienoic acid as discriminatory metabolites among the cultivars, particularly distinguishing the GRS cultivar in its phenolics profile. In total, 34 aroma compounds were detected using headspace solid‐phase microextraction coupled with GC‐MS, including alcohols, esters, ketones, ethers and monoterpene hydrocarbons. The major metabolites contributing to aroma discrimination among the cultivars were β‐citronellol in GRB, α‐farnesene in GRS and isomenthone in GRSH. CONCLUSION: The study provides a comprehensive profiling of the secondary metabolites and aroma compounds in the three Pelargonium graveolens cultivars. The GRS cultivar was identified as particularly distinct in both its phenolics and aroma profiles, suggesting its potential as a premium variety for cultivation and use. Future studies should focus on isolating and investigating the newly detected metabolites and exploring the biological effects of these compounds in food applications and other uses. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Background: Pitaya fruit (Hylocereus spp.) is rich in bioactive compounds such as betanin. This study aimed to extract betanin‐rich pitaya fruit and encapsulate it in electrospun nanofibers produced with sweet potato starch. The influence of different concentrations of this bioactive compound on the morphology, functional groups, hydrophilicity, load capacity, color, thermal properties, and contact angle of the electrospun nanofibers with water and milk was assessed. The potential antioxidant and stability of nanofibers during gastrointestinal digestion in vitro were demonstrated. Results: The nanofibers presented average diameters ranging from 134 to 204 nm and displayed homogeneous morphology. The load capacity of the extract in the nanofibers was 43% to 83%. The encapsulation increased the thermal resistance of betanins (197–297 °C). The static contact angle with water and milk showed that these materials presented greater affinity with milk. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) showed that the nanofibers with 5%, 25%, and 45% pitaya extract presented unique characteristics. They showed resistance in delivering betanins to the stomach, with 12% inhibition of the 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH˙) radical. However, only the 45% concentration reached the intestine with 9.83% inhibition of the DPPH˙ radical. Conclusions: Pattern recognition from multivariate analyses indicated that nanofibers containing 5%, 25%, and 45% of the extract presented distinct characteristics, with the ability to preserve betanins against thermal degradation and perform the controlled delivery of these bioactives in the stomach and intestine to produce antioxidant activity. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Here we present the first use of principal component analysis of the full spectrum of a single europium complex to differentiate between structurally-similar analytes. We demonstrate that it can be used to distinguish between the nucleoside phosphate guests AMP, ADP, and ATP. [ABSTRACT FROM AUTHOR]

Understanding how stars form, evolve, and impact molecular clouds is key to understanding why star formation is such an inefficient process globally. In this paper, we use the infrared bright fraction, |$f_\text{IRB}$| (the fraction of a given molecular cloud that appears bright against the 8 |$\mu$| m Milky Way background) as a proxy for time evolution to test how cloud properties change as star formation evolves. We apply this metric to 12 000 high-mass star-forming molecular clouds we identify using the Herschel –Hi-GAL survey between |$|\ell |{\lt }70^{\circ }$| on the Milky Way plane. We find clouds are not static while forming stars. Instead, molecular clouds continuously gain mass while star formation progresses. By performing principal component analysis on the cloud properties, we find that they evolve down two paths distinguished by their mass gain. Most clouds (80 per cent) gain four times more mass as a function of |$f_\text{IRB}$|⁠. The remaining 20 per cent experience an extreme period of growth, growing in mass by a factor of 150 on average and during this period, they initially gain mass fast enough to outpace their star formation. For all clouds, it is only after half their area becomes star forming that mass-loss occurs. We expect stellar feedback and potentially galactic shear is responsible. By analysing cloud positions, we suggest that the rate of mass growth may be linked to the larger galactic environment. Altogether, these results have strong implications on how we assess star-forming ability on cloud scales when assuming molecular cloud masses are fixed in time. [ABSTRACT FROM AUTHOR]

This work aimed to evaluate the use of Visible and Near-infrared Spectroscopy (Vis-NIRS) as a tool in the classification of bovine carcasses. A total of 133 animals (77 females, 29 males surgically castrated and 27 males immunologically castrated) were used. Vis-NIRS spectra were collected in a chilling room 24 h postmortem directly on the hanging carcasses over the longissimus thoracis between the surface of the 5th and 6th ribs. The data were evaluated by principal component analysis (PCA) and the partial least squares regression (PLSR) method. For the prediction of sex, the best model was the Standard Normal Variate (SNV) because it presented a relatively high coefficient of determination for prediction, presenting a percentage of correctness of 75.51% and an error of 24.49%. Regarding age, none of the models were able to differentiate the samples through Vis-NIRS. The findings confirm that Vis-NIRS prediction models are a valuable tool for differentiating carcasses based on sex. To further enhance the precision of these predictions, we recommend using Vis-NIRS equipment with the full infrared wavelength range to collect and predict sex and age in intact beef samples. [ABSTRACT FROM AUTHOR]

Background: Analyzing free-living physical activity (PA) data presents challenges due to variability in daily routines and the lack of activity labels. Traditional approaches often rely on summary statistics, which may not capture the nuances of individual activity patterns. To address these limitations and advance our understanding of the relationship between PA patterns and health outcomes, we propose a novel motif clustering algorithm that identifies and characterizes specific PA patterns. Methods: This paper proposes an elastic distance-based motif clustering algorithm for identifying specific PA patterns (motifs) in free-living PA data. The algorithm segments long-term PA curves into short-term segments and utilizes elastic shape analysis to measure the similarity between activity segments. This enables the discovery of recurring motifs through pattern clustering. Then, functional principal component analysis (FPCA) is then used to extract digital biomarkers from each motif. These digital biomarkers can subsequently be used to explore the relationship between PA and health outcomes of interest. Results: We demonstrate the efficacy of our method through three real-world applications. Results show that digital biomarkers derived from these motifs effectively capture the association between PA patterns and disease outcomes, improving the accuracy of patient classification. Conclusions: This study introduced a novel approach to analyzing free-living PA data by identifying and characterizing specific activity patterns (motifs). The derived digital biomarkers provide a more nuanced understanding of PA and its impact on health, with potential applications in personalized health assessment and disease detection, offering a promising future for healthcare. [ABSTRACT FROM AUTHOR]

The budding yeast Xrn1 protein shuttles between the nucleus, where it stimulates transcription, and the cytoplasm, where it executes the major cytoplasmic mRNA decay. In the cytoplasm, apart from catalyzing 5'→3' decay onto non translated mRNAs, Xrn1 can follow the last translating ribosome to degrade the decapped mRNA template, a process known as "cotranslational mRNA decay". We have previously observed that the import of Xrn1 to the nucleus is required for efficient cytoplasmic mRNA decay. Here by using an Xrn1 mutant that cannot enter the nucleus, but is otherwise functional in ribonuclease activity, we show that nuclear import is necessary for proper global cotranslational decay of mRNAs along coding regions and also affects degradation in the of 5' region of a large group of mRNAs, which comprise about 20% of the transcriptome. Furthermore, a principal component analysis of the genomic datasets of this mutant and other Xrn1 mutants also shows that lack of a cytoplasmic 5'→3' exoribonuclease is the primary cause of the physiological defects seen in a xrn1Δ mutant, but also suggests that Xrn1 import into the nucleus is necessary for its full in vivo functions. [ABSTRACT FROM AUTHOR]

This research freshly investigates the effect of business competition on national welfare in Indonesia. This research uses business competition index data obtained from the Indonesia Competition Commission (KPPU) and welfare indicators data collected from the Indonesian Bureau of Central Statistics (BPS) at the economic sectoral and provincial levels for the period 2018–2020. The relationship between business competition and welfare is estimated using pooled regression model combining year, economic sector, and province. This research also uses business competition index applying both same weights for all dimensions of competition and the weights derived from principal component analysis. This research finds that business competition has a positive effect on welfare as represented by gross regional domestic product, regional productivity, productivity growth, wages, and human development index. Furthermore, the regions with high scores on the competition index mostly come from Java Island. Therefore, Indonesian government must encourage the mainstreaming of business competition in all provinces in Indonesia. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a method based on the adaptive Hopf central pattern generator (CPG) to describe and measure the coordination laws of human lower limb movements during walking. Six young, healthy individuals (equal number of males and females) were asked to walk to a metronome beat while we collected angle data of their main joints during normal and two abnormal gaits. We used the Hopf-CPG network to characterize joint angles as amplitude and phase parameters. By examining the lower limb gait characteristics based on these parameters, we revealed the constraint relationship between lower-limb joints. We then used principal component analysis (PCA) to obtain a reduced dimensional description of the coordination characteristics of lower-limb gait, which suggested the existence of coordination characteristics shared by lower limbs during normal human walking at different step frequencies. Finally, we proposed a quantitative scoring method to measure gait coordination based on the coordination constraint law of lower limb joints, which yielded scores ranging from 0 to 1. Our results showed that the mean coordination scores for normal gait (greater than 0.93) were higher than those for toe walking (0.78) and knee-locked walking (0.51), which indicates that our method effectively quantifies lower limb coordination. [ABSTRACT FROM AUTHOR]

Introduction: Current research on occupational gender stereotypes in language has indicated that gender bias is influenced by various aspects, including social knowledge about roles associated with either men or women as well as linguistic information. This study focuses on gender perception of language users of Estonian and Russian. The former is a grammatically genderless Finno-Ugric language, while the latter has grammatical gender. Based on previous studies, we investigate whether occupational gender stereotypes in these languages are evoked by social beliefs, stereotypes and other extralinguistic factors and/or by language. Additionally, we examine whether the extent of gender bias varies across these languages. Methods: Two separate web-based Likert scale surveys were conducted, one in Estonian and the other in Russian. The surveys included sentences featuring 36 occupational titles in Estonian and 34 in Russian. Data were analyzed using R software, employing principal component analysis and binomial logistic regression models. Results: A total of 581 Estonian-speaking as well as 326 Russian-speaking participants took part in the study. Analyses revealed that biased responses were primarily influenced by social knowledge, followed by the influence of language. In Russian, results indicated that stereotypical information often overrides linguistic cues. Discussion: The results suggest that gender perceptions are shaped by social knowledge and stereotypes, which work in cooperation with language. Based on these results, we propose that Estonian, while grammatically genderless and thus seemingly gender neutral, evokes as much or even more bias than grammatically gendered Russian. [ABSTRACT FROM AUTHOR]

Introduction: Sorghum bicolor: widely cultivated in Asia and Africa, faces increasing challenges from climate change, specifically from abiotic stresses like drought and salinity. This study evaluates how different sorghum genotypes respond to separate and combined stresses of drought and salinity. Methods: Carried out with three replications using a randomized complete block design, the experiment measured biochemical and physiological parameters, including stomatal conductance, chlorophyll content, and antioxidant enzyme activities. Molecular analysis focused on stress-responsive gene expression. Results: Results indicated enhanced stress responses under combined conditions, with significant variation in antioxidant enzymatic activities among genotypes. Genotype-specific osmotic adjustments were observed through proline and glycine betaine accumulation. Physiological parameters such as chlorophyll content, cell membrane stability, stomatal conductance, and water potential were critical indicators of stress tolerance. Gene expression analysis revealed upregulation of stress-responsive genes, particularly under combined stress conditions. Discussion: Correlation and principal component analysis analyses highlighted the interdependencies among traits, emphasizing their roles in oxidative stress mitigation. Samsorg-17 exhibited the highest resilience due to consistently high levels of catalase, superoxide dismutase, and glycine betaine, alongside superior physiological attributes. CRS-01 showed moderate resilience with the highest Na/K ratio and notable photosynthesis rate and relative water content, but was less consistent in biochemical markers under stress. Samsorg-42 demonstrated resilience under specific conditions but was generally less robust than Samsorg-17 across most indicators. These findings emphasize the importance of developing stress-resilient sorghum cultivars through targeted breeding programs to enhance tolerance to drought and salinity in sustainable agriculture. [ABSTRACT FROM AUTHOR]

Climate change-associated disturbances such as storms, wildfires, and pest outbreaks increasingly destabilize forest systems, threatening their ecological, economic, and social functions. These disruptions impact the forest value chain (FVC) by causing fluctuations in timber supply, from a quantity and quality perspective. This study employed the operational resilience framework (ORF) to assess FVC resilience in five European case studies (CZ, HR, DE, FIN, and ESP), focusing on timber supply as a key system variable. A resilience assessment was conducted using resilience thresholds, considering sustainability from both ecological and economic perspectives. Principal component analysis (PCA) identified three predictor groups that influenced FVC resilience: wood production (WP), harvesting systems (HS), and management and silviculture (MS). Findings revealed that regions with proactive management and sufficient processing capacities (CZ, HR, and ESP) maintained relative stability despite natural disturbances, while others (DE and FIN) experienced prolonged instability due to market-driven logging practices and limited adaptive measures. The study highlighted the frequent breaching of resilience thresholds, particularly during high-volume salvage logging following disturbances such as bark beetle outbreaks, windstorms, and wildfires. The results emphasized the importance of integrating adaptive and proactive strategies to mitigate these impacts. The ORF demonstrated potential for operationalizing FVC resilience and provided guidance for improving preparedness against future disturbances. [ABSTRACT FROM AUTHOR]

Stony corals possess major components of the circadian system, which oscillate in response to light-dark cycles in aquatic environments. However, the extent to which the circadian system influences physiological processes remains unknown. This study investigated the role of circadian genes (cry1 , cry2 , cry3 , clock , cycle , and slmb) in modulating the transcription of photoreceptor (opsin1 , opsin2 , and opsin3), calcification/metabolism-related (ca , pmca , sglt , and ppp1r), and homeostasis/stress-related (hif1α , egln , sod , and hsp70) genes in Acropora digitifera , a stony coral inhabiting shallow water. Nubbins of A. digitifera were reared under light-dark (LD) and constant darkness (DD) conditions and sampled at 4-h intervals. Quantitative PCR analysis revealed that cry1 , cry2 , cry3 , and clock expression increased during the daytime under LD conditions and attenuated during the subjective daytime under DD conditions, suggesting that these genes are light-responsive. In contrast, cycle and slmb exhibited similar expression profiles with increases during the daytime/subjective daytime under both LD and DD conditions, implying robust roles in the circadian system. The abundance of opsin1 showed minimal change under LD and DD conditions, whereas the abundances of opsin2 and opsin3 increased during daytime/subjective daytime under both conditions, indicating circadian regulation. Some genes tested by qPCR significantly fluctuated with light (pmca , sglt , ppp1r , egln , sod , and hsp70) and time (ca , pmca , sglt , ppp1r , hif1α , egln , sod , and sod). Principal component analysis revealed significant correlations of circadian genes (cycle or slmb) with calcification/metabolism-related (pmca), oxygen homeostasis (hif1α), and stress indicator (sod) genes under both LD and DD conditions. Therefore, some physiological responses in A. digitifera exhibit daily changes and are partially regulated by the circadian system. [ABSTRACT FROM AUTHOR]

The use of submillimeter resolution functional magnetic resonance imaging (fMRI) is increasing in popularity due to the prospect of studying human brain activation non-invasively at the scale of cortical layers and columns. This method, known as laminar fMRI, is inherently signal-to-noise ratio (SNR)-limited, especially at lower field strengths, with the dominant noise source being of thermal origin. Furthermore, laminar fMRI is challenged with signal displacements due to draining vein effects in conventional gradient-echo blood oxygen level-dependent (BOLD) imaging contrasts. fMRI contrasts such as cerebral blood volume (CBV)-sensitive vascular space occupancy (VASO) sequences have the potential to mitigate draining vein effects. However, VASO comes along with another reduction in detection sensitivity. NOise Reduction with DIstribution Corrected (NORDIC) PCA (principal component analysis) is a denoising technique specifically aimed at suppressing thermal noise, which has proven useful for increasing the SNR of high-resolution functional data. While NORDIC has been examined for BOLD acquisitions, its application to VASO data has been limited, which was the focus of the present study. We present a preliminary analysis to evaluate NORDIC's capability to suppress thermal noise while preserving the VASO signal across a wide parameter space at 3T. For the data presented here, with a proper set of parameters, NORDIC reduced thermal noise with minimal bias on the underlying signal and preserved spatial resolution. Denoising performance was found to vary with different implementation strategies and parameter choices, for which we provide recommendations. We conclude that when applied properly, NORDIC has the potential to overcome the sensitivity limitations of laminar-specific VASO fMRI. Since very few groups currently have 3T VASO data, by sharing our analysis and code, we can compile and compare the effects of NORDIC across a broader range of acquisition parameters and study designs. Such a communal effort will help develop robust recommendations that will increase the utility of laminar fMRI at lower field strengths. [ABSTRACT FROM AUTHOR]

Introduction: In order to elucidate the physiological mechanism of post-flowering assimilate transport regulating the formation of yields in arid regions and to provide technological support for further water-saving and high yields in the wheat region in Xinjiang, we conducted a study on the effects of different fertility periods and different degrees of drought and re-watering on the post-flowering dry matter accumulation and transport of spring wheat and the characteristics of grain filling. Methods: In two spring wheat growing seasons in 2023 and 2024, a split-zone design was used, with the drought-sensitive variety Xinchun 22 (XC22) and drought-tolerant variety Xinchun 6 (XC6) as the main zones and a fully irrigated control during the reproductive period [CK, 75%~80% field capacity (FC)], with mild drought at the tillering stage (T1, 60%~65% FC), moderate drought at the tillering stage (T2, 45%~50% FC), mild drought at the jointing stage (J1, 60%~65% FC), and mild drought at the jointing stage (J2, 45%~50% FC) as the sub-zones. Results: The dry matter accumulation of the aboveground parts of wheat (stem sheaths, leaves, and spikes), the transfer rate and contribution rate of nutrient organs, the maximum filling rate (Vmax), and the mean filling rate (Vmean) increased significantly after re-watering in the T1 treatment, and decreased with the deepening of the degree of water stress. The 13C isotope tracer results also showed that the T1 treatment increased the distribution rate of 13C assimilates in the grain at maturity. Correlation and principal component analyses showed that grain weight was highly significantly and positively correlated with stem sheath, leaf, and spike dry matter accumulation, amount of nutrient organ post-flowering transports, transport rate, contribution rate, the onset and the termination time of the rapid growth period, Vmax, and Vmean, and stem sheath and spike dry matter accumulation had a direct effect on grain weight. While the two varieties performed differently among the treatments, both exhibited optimal performance in the T1 treatment. Discussion: In conclusion, mild drought at the tillering stage (60%-65% FC) was the best model for water conservation and high yield of wheat under the conditions of this trial. [ABSTRACT FROM AUTHOR]

AbstractGraphical models are popular tools for exploring relationships among a set of variables. The Gaussian graphical model (GGM) is an important class of graphical models, where the conditional dependence among variables is represented by nodes and edges in a graph. In many real applications, we are interested in detecting hubs in graphical models, which refer to nodes with a significant higher degree of connectivity compared to non-hub nodes. A typical strategy for hub detection consists of estimating the graphical model, and then using the estimated graph to identify hubs. Despite its simplicity, the success of this strategy relies on the accuracy of the estimated graph. In this paper, we directly target on the estimation of hubs, without the need of estimating the graph. We establish a novel connection between the presence of hubs in a graphical model, and the spectral decomposition of the underlying covariance matrix. Based on this connection, we propose the method of inverse principal components for hub detection (IPC-HD). Both consistency and convergence rates are established for IPC-HD. Our simulation study demonstrates the superior performance and fast computation of the proposed method compared to existing methods in the literature in terms of hub detection. Our application to a prostate cancer gene expression dataset detects several hub genes with close connections to tumor development. [ABSTRACT FROM AUTHOR]

Introduction: The availability of large-scale multi-omic data has revolution-ized the study of cellular machinery, enabling a systematic understanding of biological processes. However, the integration of these datasets into Genome-Scale Models of Metabolism (GEMs) re-mains underexplored. Existing methods often link transcriptome and proteome data independently to reaction boundaries, providing models with estimated maximum reaction rates based on individual datasets. This independent approach, however, introduces uncertainties and inaccuracies. Methods: To address these challenges, we applied a principal component analysis (PCA)-based approach to integrate transcriptome and proteome data. This method facilitates the reconstruction of context-specific models grounded in multi-omics data, enhancing their biological relevance and predictive capacity. Results: Using this approach, we successfully reconstructed an astrocyte GEM with improved prediction capabilities compared to state-of-the-art models available in the literature. Discussion: These advancements underscore the potential of multi-omic inte-gration to refine metabolic modeling and its critical role in studying neurodegeneration and developing effective therapies. [ABSTRACT FROM AUTHOR]

As an agricultural planting practice, preceding cropping can not only enhance soil fertility and reduce pests and diseases but also boost crop yield and quality. In this study, SZS samples from different preceding cropping areas were selected as research subjects. Phenolic compounds were analyzed using high-performance liquid chromatography (HPLC), and antioxidant activities were assessed based on free radical scavenging effects. Variety differences were explored through chemical pattern recognition, and the spectrum-effect relationship between the fingerprint spectra of SZS and antioxidant activity was investigated using Pearson correlation analysis, grey relational analysis, and other methods. A total of 17 peaks were observed, among which 4 peaks were identified. They are gallic acid, catechin, spinosin, and scutellarin. The 22 SZS samples could be categorized into 3 groups, with cluster analysis and principal component analysis results being largely consistent. Spinosin, a marker compound of SZS, is a crucial contributor to the total antioxidant activity. In conclusion, the spectrum-effect relationship between phenolic compounds and the antioxidant activity of SZS was established, and the main characteristic components affecting antioxidant activity were identified, providing a reference for the quality evaluation of SZS and the development of its products. [ABSTRACT FROM AUTHOR]

Cardiovascular signals exhibit self-similarity characteristics, which are influenced by changes in autonomic nervous system (ANS) regulation caused by shift work. This study aims to assess the self-similarity properties of inter-beat interval (IBI) in healthy female shift workers and non-shift workers in different sleep stages to detect alterations in heartbeat fluctuations due to shift work. Short- and long-term self-similarity properties of the IBI signal (α1 and α2 scaling exponents, respectively) were analyzed using Detrended Fluctuation Analysis. Time and frequency indices were also calculated. In addition, Principal Component Analysis (PCA) was employed to reduce dimensionality and evaluate group separability based on the obtained features. Most indices showed similar values in the different sleep stages for both groups, but α1 during light sleep and sympathovagal balance during REM sleep showed a significant decrease in shift workers compared to non-shift workers (p<0.016). In addition, PCA was able to separate shift workers from non-shift workers and differentiate between nighttime and daytime sleep of workers. This analysis aids in identifying cardiovascular impairment associated with shift work and suggests a loss of ANS self-similarity in shift workers, indicating reduced adaptive capacity. Such alterations in ANS behavior could lead to serious health consequences related to cardiovascular disease. [ABSTRACT FROM AUTHOR]

This research reduces the number of attributes to describe the varied risks in the homeland security domain using Principal Component Analysis (PCA). Reducing the dimensions of homeland security risks to a smaller, more manageable set of characteristics can enhance policy-making processes, especially given the broad spectrum of consequence and non-consequence attributes and the varying frequencies of such events. PCA was used to reduce the larger set of risk attributes to five – representing health, economic, societal, dread and the unknown – or two – representing consequence and perceptual characteristics. While the five-component approach describes the data more completely, the two-component approach also explains a large proportion of the variance of the dataset but does so with a lower cognitive load. Either approach can provide composite variables that describe the homeland security risks in a more efficient fashion without losing excessive information on the risks. [ABSTRACT FROM AUTHOR]

Cardio Vascular Disease (CVD) is one of the leading causes of mortality and it is estimated that 1 in 4 deaths happens due to it. The disease prevalence rate becomes higher since there is an inadequate system/model for predicting CVD at an earliest. Diabetic Retinopathy (DR) is a kind of eye disease was associated with increasing risk factors for all-causes of CVD events. The early diagnosis of DR plays a significant role in preventing CVD. However, there are many works have been carried out on classification of the disease but they focused less on feature selection and increasing the accuracy of the model. The proposed work introduces Improvised Deep Belief Network named I-DBN to resolve the above mentioned problems and mainly to concentrate on improving the entire performance of the model leading to the unbiased output. We used Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO) algorithm for feature extraction and selection respectively. Five performance metrics have been used to assess the proposed model. The results of I-DBN outperform other state-of-the-art methods. The result validation ensures that I-DBN can deliver trustworthy recommendations to doctors to treat the patients by enhancing the accuracy of CVD prediction up to 98.95%. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, notably delta and omicron, has significantly accelerated the global pandemic, worsening conditions worldwide. However, there is a lack of research concerning the molecular mechanisms related to immune responses and metabolism induced by these variants. Methods: Here, metabolomics combined with transcriptomics was performed to elucidate the immunometabolic changes in the lung of hamsters infected with delta and omicron variants. Results: Both variants caused acute inflammation and lung pathology in intranasally infected hamsters. Principal component analysis uncovered the delta variant significantly altered lung metabolite levels between the pre- and post-infection states. Additionally, metabolic pathways determined by assessment of metabolites and genes in lung revealed significant alterations in arginine biosynthesis, glutathione metabolism, and tryptophan metabolism upon infection with both variants and closely linked to inflammatory cytokines, indicating immune activation and oxidative stress in response to both variants. These metabolic changes were also evident in the serum, validating the presence of systemic alterations corresponding to those identified in lung. Notably, the delta variant induced a more robust metabolic regulation than the omicron variant. Discussion: The study suggests that multi-omics is a valuable approach for understanding immunometabolic responses to infectious diseases, and providing insights for effective treatment strategies. [ABSTRACT FROM AUTHOR]

The role of striatal pathways in cognitive processing is unclear. We studied dorsomedial striatal cognitive processing during interval timing, an elementary cognitive task that requires mice to estimate intervals of several seconds and involves working memory for temporal rules as well as attention to the passage of time. We harnessed optogenetic tagging to record from striatal D2-dopamine receptor-expressing medium spiny neurons (D2-MSNs) in the indirect pathway and from D1-dopamine receptor-expressing MSNs (D1-MSNs) in the direct pathway. We found that D2-MSNs and D1-MSNs exhibited distinct dynamics over temporal intervals as quantified by principal component analyses and trial-by-trial generalized linear models. MSN recordings helped construct and constrain a four-parameter drift-diffusion computational model in which MSN ensemble activity represented the accumulation of temporal evidence. This model predicted that disrupting either D2-MSNs or D1-MSNs would increase interval timing response times and alter MSN firing. In line with this prediction, we found that optogenetic inhibition or pharmacological disruption of either D2-MSNs or D1-MSNs increased interval timing response times. Pharmacologically disrupting D2-MSNs or D1-MSNs also changed MSN dynamics and degraded trial-by-trial temporal decoding. Together, our findings demonstrate that D2-MSNs and D1-MSNs had opposing dynamics yet played complementary cognitive roles, implying that striatal direct and indirect pathways work together to shape temporal control of action. These data provide novel insight into basal ganglia cognitive operations beyond movement and have implications for human striatal diseases and therapies targeting striatal pathways. [ABSTRACT FROM AUTHOR]

Remote sensing hyperspectral images (HSIs) are rich sources of information about land cover captured across hundreds of narrow, contiguous spectral wavelength bands. However, using the entire original HSI for practical applications can lead to suboptimal classification accuracy. To address this, band reduction techniques, categorized as feature extraction and feature selection methods, are employed to enhance classification results. One commonly used feature extraction approach for HSIs is Principal Component Analysis (PCA). However, PCA may fall short of capturing the local and specific characteristics present in the HSI data. In this paper, we introduce two novel feature extraction methods: Segmented Truncated Singular Value Decomposition (STSVD) and Spectrally Segmented Truncated Singular Value Decomposition (SSTSVD) to improve classification performance. Segmentation is carried out based on highly correlated bands' segments and spectral bands' segments within the HSI data. Our study evaluates and compares these newly proposed methods against classical feature extraction methods, including PCA, Incremental PCA, Sparse-PCA, Kernel PCA, Segmented-PCA (SPCA), and Truncated Singular Value Decomposition (TSVD). We perform this analysis on three distinct HSI datasets, namely the Indian Pines HSI, the Pavia University HSI, and the Kennedy Space Center HSI, using per-pixel Support Vector Machine (SVM) and Random Forest (RF) classification. The experimental results demonstrate the superiority of our proposed methods for all three datasets. The best-performing feature extraction methods when classification is performed using an SVM classifier are STSVD3 (89.03%), SSTSVD2 (95.55%), and STSVD3 (97.74%) for the Indian Pines, Pavia University, and Kennedy Space Center datasets, respectively. Similarly, for the RF classifier, the best-performing feature extraction methods are SSTSVD4 (88.98%), SSTSVD3 (96.04%), and SSTSVD4 (96.09%) for Indian Pines, Pavia University, and Kennedy Space Center datasets, respectively. [ABSTRACT FROM AUTHOR]

Background: Cancer immune responses are generated in secondary lymphoid organs, such as the lymph nodes and tonsils. In the current study, transcriptional profiles of peritumoral tonsillar tissues (PTTs) from oropharyngeal cancers (OPCs) were assessed and compared with those of inflammatory tonsils and regional lymph nodes (rLNs). Methods: RNA samples of PTTs and rLNs from 13 OPCs, and 4 inflammatory tonsils were subjected to microarray analysis, and differentially expressed genes (DEGs) identified from 730 nCounter Panel immune-related genes. Gene Set enrichment Analysis (GSEA) was used for DEG profiling of PTTs and rLNs between lymph node metastasis-negative and metastasis-positive cases. The top 20 genes, as ranked by GSEA metric scores, were extracted and subjected to principal component analysis (PCA). The correlation of each patient's PCA score with lymph node status was assessed by Receiver Operating Characteristics (ROC) analysis. Results: Comparing DEG analyses of PTTs with those of inflammatory tonsils and rLNs revealed 144 and 45 upregulated genes, respectively. ClueGO, a widely used Cytoscape plug-in, revealed activated pathways in PTTs, including lymphocyte proliferation (followed by T cell activation involved in the immune response) and positive regulation of leukocyte migration (followed by antimicrobial humoral immune response mediated by antimicrobial peptides) as the most significantly enriched immune system process functions in the gene ontology when comparing inflammatory tonsils and rLNs. The area under the ROC curves of PTTs and rLNs were 0.806 and 0.389, and were significant by DeLong's test (p = 0.025). Conclusion: PTTs exhibit unique immunological features distinguishing them from inflammatory tonsils and rLNs. Gene expression analysis of PTTs is useful for investigating the mechanism of OPC lymphatic spread, even compared with analysis of rLNs. [ABSTRACT FROM AUTHOR]

H3 lysine 4 trimethylation (H3K4me3) modification and related regulators extensively regulate various crucial transcriptional courses in health and disease. However, the regulatory relationship between H3K4me3 modification and anti-tumor immunity has not been fully elucidated. We identified 72 independent prognostic genes of lung adenocarcinoma (LUAD) whose transcriptional expression were closely correlated with known 27 H3K4me3 regulators. We constructed three H3K4me3 modification patterns utilizing the expression profiles of the 72 genes, and patients classified in each pattern exhibited unique tumor immune infiltration characteristics. Using the principal component analysis (PCA) of H3K4me3-related patterns, we constructed a H3K4me3 risk score (H3K4me3-RS) system. The deep learning analysis using 12,159 cancer samples from 26 cancer types and 725 cancer samples from 5 immunotherapy cohorts revealed that H3K4me3-RS was significantly correlated with cancer immune tolerance and sensitivity. Importantly, this risk-score system showed satisfactory predictive performance for the ICB therapy responses of patients suffering from several cancer types, and we identified that SLAMF9 was one of the immunosuppressive phenotype and immunotherapy resistance-determined genes of H3K4me3-RS. The mice melanoma model showed Slamf9 knockdown remarkably restrained cancer progression and enhanced the efficacy of anti-CTLA-4 and anti-PD-L1 therapies by elevating CD8 + T cell infiltration. This study provided a new H3K4me3-associated biomarker system to predict tumor immunotherapy response and suggested the preclinical rationale for investigating the roles of SLAMF9 in cancer immunity regulation and treatment. [ABSTRACT FROM AUTHOR]

Background: Despite the growing use of social egg freezing (SEF), research focusing on its psychological aspects is lacking. This study aimed to investigate possible psychological predictors, reasons, and outcomes of SEF in German-speaking countries. Methods: The cross-sectional study included 1,131 women (average age 31 years) who had never used medical egg freezing. The participants were at different stages of SEF decision-making: women who cannot imagine using SEF (SEF-non-use), women who can imagine using SEF (SEF-possible-use), women who plan to use SEF (SEF-planned-use), women who have used SEF (SEF-use), and women who have used their oocytes frozen during SEF for assisted reproduction (SEF + ART-use). Data on sociodemographic and psychological characteristics, attitudes towards motherhood, well-being, and reasons for SEF were assessed. We used multinomial logistic regression to identify predictors of SEF decision-making stages, principal components analysis to examine motives for SEF, and multiple linear regression to analyze associations between motives and psychological variables. Results: The probability of belonging to the SEF-use group rather than SEF-non-use was higher among childless single women with tertiary education, high levels of employment, and high importance placed on the genetic relationship to the child, and rose with increasing age and importance of motherhood. The probability of belonging to the SEF-use group rather than SEF-planned-use was higher among childless women with a high importance placed on the genetic relationship to the child, and increased with age. The probability of belonging to the SEF + ART-use group rather than SEF-use depended mainly on the presence of infertility. The women froze eggs mainly to gain time to fulfill their desire for conventional parenthood (59%), including finding the right partner and enabling a genetic relationship to the child. Using SEF to actively shape one's life and family planning was rather associated with positive psychological outcomes, whereas relying on SEF in the hope of personal and societal changes (e.g. improving fertility) was associated with negative outcomes. Conclusion: SEF users might be characterized as mainly single, career-oriented, and greatly valuing genetic motherhood. As the motives for SEF, rather than its use per se, might be linked to psychological variables, these should be considered when counseling and supporting women. [ABSTRACT FROM AUTHOR]

AbstractThe formation and occurrence of acrylamide in carbohydrate-rich foods has been extensively studied over the course of the past few decades. However, the emergence of plant-based meat alternatives presents a new challenge in this field. The aim of this study was to evaluate the levels of acrylamide in commercially available plant-based meat alternatives before and after heat treatment. Trace levels of acrylamide were detected in all samples before heat-treatment, while the concentrations increased in 11 samples out of 16 after heat-treatment. The highest concentration of acrylamide increased from 65.7 ± 6.6 µg kg−1 before to 119 ± 12 µg kg−1 after heat-treatment. Principal component analysis (PCA) indicated that besides macronutrient composition, the use of additives and processing techniques have a strong influence on acrylamide formation in plant-based meat alternatives. The latter was supported by the analysis of self-made meat alternative models that were prepared using only the base ingredients. [ABSTRACT FROM AUTHOR]

In China, increasing breeding waste has caused environmental problems. This study explored the possibility of using breeding waste compost (BWC) instead of chemical fertilizer in vegetable cultivation. The experiment included no fertilizer (CK), 100% chemical fertilizer (CF), 10, 20, and 30% substitution of cow dung compost (CDC), goose dung compost (GDC), and duck dung compost (DDC) with chemical fertilizer (C01, C02, C03, G01, G02, G03, D01, D02, D03). The results showed that BWC, particularly GDC, promoted lettuce growth and development. Compared to CK, the leaf fresh and dry weight of G03 were the highest, increasing significantly by 6.60 and 7.29 times, and the root fresh and dry weight of G02 were the highest, increasing significantly by 12.72 and 6.00 times. Different BWC improved soluble sugar, soluble protein, and Vitamin C to varying degrees, and the nitrate contents of some BWC treatments were lower than that of CK and CF. Conversely, CF had the highest nitrate accumulation and limited effects on certain growth and quality parameters. The mineral elements in lettuce were also affected by the type and dosage of fertilizers. The total nitrogen of CF, total phosphorus of G03, total potassium of G02, Ca and Mg of D01, Fe of CF, and Zn of G02 were at the peak. The rapid increase of biomass in GDC treatments led to reductions in Ca and Fe. Applying fertilizers would affect heavy metals in lettuce to unequal degrees, but all were within the food safety scope. The principal component analysis revealed the comprehensive effect of GDC treatments was recommended. [ABSTRACT FROM AUTHOR]

Twenty-eight surface soil samples were collected in Santo Amaro, Brazil, to evaluate the current environmental impact caused by a lead smelter that operated in this city from 1960 to 1993. The smelter was deactivated after causing deaths and irreversible adverse effects on the health of the population due to contamination by lead and other elements. In this context, lead, cadmium, arsenic, chromium, copper, and zinc, six of the seven elements recommended by Hakanson in 1980 for ecological risk assessment, were determined using inductively coupled plasma – optical emission spectrometry (ICP OES). The contamination factor (CF), ecological risk index (Er), pollution load index (PLI), degree of contamination (mCdeg), and potential ecological risk index (PERI) were used to investigate the level of contamination and the ecological risk of the samples. The CF index demonstrated that the samples collected inside the smelter showed high contamination for lead, cadmium, and zinc, low contamination for chromium, and low to moderate contamination for arsenic and copper. In addition, the integrated PLI index demonstrated that all samples collected inside the foundry showed high pollution; however, of the other twenty-two samples investigated, only two showed pollution. The ecological risk index showed that the soil samples collected inside residences near the foundry denoted ecological risk due to cadmium contamination. The PERI demonstrated that samples collected on the city's access road and samples collected on streets close to the foundry denoted low ecological risk. The results obtained by applying the principal component analysis (PCA) and hierarchical cluster analysis (HCA) to data relating to the levels of chemical elements in the soil samples fully corroborate the results found using toxicological assessment indices. [ABSTRACT FROM AUTHOR]

We used biological and physical responses at 71 shallow waterbodies with contrasting nutrient levels undergoing recovery from eutrophication to predict potential changes in waterbird species abundance, an important component of lake ecosystems. These general predictions were tested using 28 years of breeding waterbird data from three Danish shallow eutrophic lakes, comparing species-specific responses to improved nutrient and water transparency in two lakes with a third where conditions remained constantly suitable for breeding waterbirds. We predicted positive responses to improved water quality from pursuit diving predators (three grebe species), a specialist zooplankton feeder (northern shoveler Anas clypeata) and waterbirds feeding on (common pochard Aythya ferina) or within (tufted duck A. fuligula) submerged macrophyte underwater canopies. These species were characterised by positive waterbird community composition changes (using Principal Components Analysis) associated with decreasing nutrient loading and increasing water transparency at two lakes, with no change in breeding waterbird community at the third. Secchi depth explained 73–95% of variance in both PC axes at both restored lakes, but not at the third, suggesting water transparency was the major factor driving waterbird community composition. These examples show predicting waterbird species-specific responses to management can usefully direct the use of breeding waterbirds as indicator species. [ABSTRACT FROM AUTHOR]

BACKGROUND: Baijiu is a traditional Chinese liquor produced from grains through fermentation, distillation, aging and blending. The flavor of Baijiu is influenced by factors such as raw materials, starter, processes and the environment, and since the relationship between these factors and the flavor of Baijiu is still being analyzed, the identification of different Baijiu is still somewhat difficult. In this paper, the volatile differential markers of 42 types of strong‐aroma Baijiu of different origin, alcohol content and grade were explored. RESULTS: A total of 24 volatile substances were detected by gas chromatography–electronic nose (GC‐E‐Nose) and 99 volatile substances were detected by gas chromatography–time‐of‐flight mass spectrometry (GC‐TOF MS). The peak areas of the substances obtained by GC‐E‐Nose were analyzed by the partial least squares (PLS) method, and the substances with variable importance in projection (VIP) >1 were screened out. Combined with the qualitative results of GC‐TOF MS, four substances (isobutyric acid, 2‐butanone, 2,3‐butanediol and 3‐methylbutyric acid) were selected as volatile differential markers for strong‐aroma Baijiu. An external standard curve was established to accurately quantify these four substances, and the Kruskal–Wallis test confirmed that the absolute contents of these four substances varied significantly among different samples (P < 0.01). Principal component analysis and hierarchical cluster analysis based on the absolute content of these four substances showed that different samples were prioritized for different alcohol contents. CONCLUSION: These four substances can be used as volatile differential markers of strong‐aroma Baijiu samples. This research provides theoretical support for the detection and improvement of Baijiu product quality. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) presents a wide spectrum of symptoms, the causes of which remain poorly understood. This study explored the associations between autoantibodies (AABs), particularly those targeting G protein-coupled receptors (GPCRs) and renin‒angiotensin system (RAS) molecules, and the clinical manifestations of COVID-19. Using a cross-sectional analysis of 244 individuals, we applied multivariate analysis of variance, principal component analysis, and multinomial regression to examine the relationships between AAB levels and key symptoms. Significant correlations were identified between specific AABs and symptoms such as fever, muscle aches, anosmia, and dysgeusia. Notably, anti-AGTR1 antibodies, which contribute to endothelial glycocalyx (eGC) degradation, a process reversed by losartan, have emerged as strong predictors of core symptoms. AAB levels increased with symptom accumulation, peaking in patients exhibiting all four key symptoms. These findings highlight the role of AABs, particularly anti-AGTR1 antibodies, in determining symptom severity and suggest their involvement in the pathophysiology of COVID-19, including vascular complications. [ABSTRACT FROM AUTHOR]

Drug-target interactions (DTIs) are pivotal in drug discovery and development, and their accurate identification can significantly expedite the process. Numerous DTI prediction methods have emerged, yet many fail to fully harness the feature information of drugs and targets or address the issue of feature redundancy. We aim to refine DTI prediction accuracy by eliminating redundant features and capitalizing on the node topological structure to enhance feature extraction. To achieve this, we introduce a PCA-augmented multi-layer heterogeneous graph-based network that concentrates on key features throughout the encoding-decoding phase. Our approach initiates with the construction of a heterogeneous graph from various similarity metrics, which is then encoded via a graph neural network. We concatenate and integrate the resultant representation vectors to merge multi-level information. Subsequently, principal component analysis is applied to distill the most informative features, with the random forest algorithm employed for the final decoding of the integrated data. Our method outperforms six baseline models in terms of accuracy, as demonstrated by extensive experimentation. Comprehensive ablation studies, visualization of results, and in-depth case analyses further validate our framework's efficacy and interpretability, providing a novel tool for drug discovery that integrates multimodal features. [ABSTRACT FROM AUTHOR]

The durability issue is the primary factor affecting the life and cost of Polymer Electrolyte Membrane Fuel Cell (PEMFC). This paper presents a novel State of health (SOH) prognosis method for PEMFC in different conditions using Principal Component Analysis (PCA) and Gaussian Process Regression (GPR). Firstly, the robust locally weighted smoothing method is used to preprocess the recorded PEMFC operation data for filtering measurement errors. Then, PCA is applied to extract the principal components of the time series of original multi-dimensional input variables for PEMFC, eliminating the correlation between the original variables and reducing the dimensionality of input variables. Finally, the degradation prognosis and Remaining Useful Life (RUL) prognosis are made by GPR. Two degradation experiments for PEMFC verify the proposed method in different conditions. The test result shows that PCA can effectively reduce the dimensionality of PEMFC operating conditions. Compared with traditional methods, PCA-GPR has higher SOH prognosis accuracy. PCA-GPR provides a 462-h RUL prognosis on a life duration of 1150 h, which is sufficient for maintaining the PEMFC. • PEMFC operating variables are reconstructed by the principal component analysis. • PEMFC degradation prognosis model is established by Gaussian process regression. • The proposed method provides a higher degradation prognosis accuracy for PEMFC. • Proposed method makes a long remaining useful life prognosis for PEMFC. [ABSTRACT FROM AUTHOR]