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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]

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]

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]

The pyrolysis behaviors and gaseous products of coal slime (CS) and moso bamboo (MB) mixture were investigated by Thermogravimetric analyses, Thermogravimetric-Fourier transform infrared spectrometer (TG-FTIR), and Gas chromatogram/mass spectrum (GC/MS). Data showed that the synergistic effect of co-pyrolysis between CS and MB was more prominent above 200 °C. The release of alcohols and esters was inhibited after adding MB to CS. Dominant reactions and pyrolysis stages of CS and MB co-pyrolysis were determined based on principal component analysis. The average apparent activation energy (E α) of a sample with 70% MB ratio was at its lowest and 29.7% lower than CS pyrolysis alone. Reaction mechanisms of co-pyrolysis varied with MB blending ratio, and the diffusion model was used to describe the mechanism of mixtures except CS. The E α prediction model had high accuracy, the variation curve of E α with conversion rate and blending ratio was obtained using backpropagation neural network models. [Display omitted] • MB incorporation improved the comprehensive pyrolysis performance of CS. • Dominant reactions and pyrolysis stage were determined based on PCA. • Adding MB to CS inhibited the formation of alcohols and esters. • Reaction mechanism of samples except CS could be depicted by diffusion model. • The change curve of E α with α and blending ratio was acquired. [ABSTRACT FROM AUTHOR]

The acoustic emission (AE) technique is commonly utilized for identifying source mechanisms and material damage. In applications requiring numerous sensors and limited detection areas, achieving significant cost savings, weight reduction, and miniaturization of AE sensors is crucial. This prevents excessive weight burdens on structures while minimizing interference with structural integrity. Thin Piezoelectric Wafer Active Sensors (PWAS), compared to conventional commercially available sensors, offer a miniature, lightweight, and affordable alternative. The low signal-to-noise ratio (SNR) of PWAS sensors and their limited effectiveness in monitoring thick structures result in the decreased reliability of a single classical PWAS sensor for damage detection. This research aims to enhance the functionality of PWAS in AE applications by employing multiple thin PWAS and performing a data-level fusion of their outputs. To achieve this, as a first step, the selection of the optimal PWAS is performed and a configuration is designed for multiple sensors. Pencil break lead (PBL) tests were performed to investigate the compatibility between selected PWAS and traditional WSα and R15α sensors. The responses of all sensors from different AE sources were compared in both the time and frequency domains. After that, convolutional neural networks (CNNs) combined with principal component analysis (PCA) are proposed for signal processing and data fusion. The signals generated by the PBL tests were used for network training and evaluation. This approach, developed by the authors, fuses the signals from multiple PWAS and reconstructs the signals obtained from conventional bulky AE sensors for damage detection. Three CNNs with different architectures were built and tested to optimize the network. It is found that the proposed methodology can effectively reconstruct and identify the PBL signals with high precision. The results demonstrate the feasibility of using a deep-learning-based method for AE monitoring using PWAS for real engineering structures. [ABSTRACT FROM AUTHOR]

Drought adversely affects the growth and performance of plants. By contrast, the application of organic modifiers can improve plant growth by supplying nutrients and water. The influence of foliar application of organic fertilizer under water deficit conditions on growth traits, chemical composition, and fruit quality of tomato (Lycopersicon esculentum Mill., var. Maya) were investigated in greenhouse conditions based on bi-plot and principal component analysis (PCA). Plants which were cultivated in soil under greenhouse conditions were subjected to four levels foliar spraying of Zargreen liquid organic fertilizer, ZLOF (0, 2.5, 5, and 7.5 L 1000−1, shown as Z0, Z2.5, Z5, and Z7.5, respectively), and three levels of soil water, SW (100, 75, and 50% of field capacity (FC), shown W100, W75, and W50, respectively). The results of biplot analysis using the different treatments representing 42.9% and 38.3%, 60.3% and 28.8%, and 63.1% and 22.4% of the variance attributed to the first two principal components (PCs) for the PC1 and PC2, under 100, 75, and 50% FC conditions, respectively. Water deficit induced a reduction of fruit dry, and fresh weights. Application of 2.5, 5, and 7.5 L 1000−1 of the organic fertilizer significantly increased fruit fresh weight by 16, 20, and 22% and fruit dry weight by 13, 20, and 20% as compared to that of control, respectively. Vitamin C content of fruit significantly increased by 16 and 33% when respectively 5 and 7.5 L 1000−1 of the organic fertilizer was foliar sprayed. Besides, fruit iron (Fe), sodium (Na (and potassium (K) concentrations increased with the application of the organic fertilizer at different levels of water deficit. Furthermore, the highest fruit zinc (Zn) concentration was obtained at the highest level of both applied organic fertilizer and water deficit. The best treatments were selected with increased PC1 and decreased PC2 for different water conditions. The W100Z7.5, W75Z7.5, and W50Z5 treatments with the higher PC1 and the lower PC2, also exhibited higher scores for fruit dry weight, and Na and K concentrations under W100; vitamin C, number of fruits, fruit fresh weight, and fruit Fe concentration under W75; citric acid, and fruit Fe, Zn, Na, K, and Cu concentrations under W50 treatment. The addition of the organic fertilizer was effective in enhancing the plant growth traits under water deficit conditions. Therefore, it can be concluded that organic fertilizer addition is an effective management strategy to mitigate the adverse effects of drought and improve the quantity and quality of tomato fruit. [ABSTRACT FROM AUTHOR]

Background: Orofacial clefts are the most common craniofacial anomalies that include a variety of conditions affecting the lips and oral cavity. They remain a significant global public health challenge. Despite this, the quality of care for orofacial clefts has not been investigated at global and country levels. Objective: We aimed to measure the quality-of-care index (QCI) for orofacial clefts worldwide. Methods: We used the 2019 Global Burden of Disease data to create a multifactorial index (QCI) to assess orofacial clefts globally and nationally. By utilizing data on incidence, prevalence, years of life lost, and years lived with disability, we defined four ratios to indirectly reflect the quality of healthcare. Subsequently, we conducted a principal component analysis to identify the most critical variables that could account for the observed variability. The outcome of this analysis was defined as the QCI for orofacial clefts. Following this, we tracked the QCI trends among males and females worldwide across various regions and countries, considering factors such as the socio-demographic index and World Bank classifications. Results: Globally, the QCI for orofacial clefts exhibited a consistent upward trend from 1990 to 2019 (66.4 to 90.2) overall and for females (82.9 to 94.3) and males (72.8 to 93.6). In the year 2019, the top five countries with the highest QCI scores were as follows: Norway (QCI = 99.9), Ireland (99.4), France (99.4), Germany (99.3), the Netherlands (99.3), and Malta (99.3). Conversely, the five countries with the lowest QCI scores on a global scale in 2019 were Somalia (59.1), Niger (67.6), Burkina Faso (72.6), Ethiopia (73.0), and Mali (74.4). Gender difference showed a converging trend from 1990 to 2019 (optimal gender disparity ratio (GDR): 123 vs. 163 countries), and the GDR showed a move toward optimization (between 0.95 and 1.05) in the better and worse parts of the world. Conclusion: Despite the positive results regarding the QCI for orofacial clefts worldwide, some countries showed a slight negative trend. [ABSTRACT FROM AUTHOR]

One of the diseases that affect the vision of human is diabetic retinopathy (DR). Numerous techniques were evolved for the identification of DR, but they are not easily accessible and unknown for many of the patients and this prevents them from getting help at the right time. If diabetes mellitus (DM) is left untreated for a long time, it results vision loss, thus the early identification of this disease is a necessary process. To identify and classify the DR from the datasets machine learning algorithms are commonly used. These algorithms do not contain proper preprocessing and feature extraction techniques, which leads to inaccurate detection and classification. Therefore, for the effective classification of DR, this paper proposes a novel Ensemble Random Extreme Gradient-based Puzzle Search (ERXG-PS) algorithm. Initially, data preprocessing is done in this paper using techniques like normalization, noise elimination, and grayscale conversion. Then feature extraction is done using modified principal component analysis (MPCA). The extracted images are fed into the classification model for the classification of DR. Finally, the extracted features are provided for classification, where the classification phase uses a novel ERXG-PS algorithm to classify DR and healthy images from diabetic retinopathy dataset, Diabetic Retinopathy MessidorEye_Pac_Pre-processed dataset, and IDRiD datasets. Then, the classified images are post-processed for the segmentation of the optic disc, blood vessels, and exudates of retinal images. The proposed technique attains an accuracy of 93.67% in the experiments conducted and it proves to be fit for practical implementation in the medical field. [ABSTRACT FROM AUTHOR]

ObjectivesMethodsResultsConclusionsScientific evidence suggests an association between diet quality and the prevalence of common mental disorders (CMD) in women. Thus, this study aimed to investigate the association between a healthy dietary pattern and CMD among women.A cross-sectional population-based study was conducted on a representative sample of 1128 women, aged 20–69 years, residing in the urban area of São Leopoldo, RS, Brazil. A validated food frequency questionnaire was used to assess dietary intake. A healthy dietary pattern, primarily consisting of fruits and vegetables, was identified using principal component analysis. CMD were evaluated using the Self-Reporting Questionnaire (SRQ-20: score ≥ 8). Prevalence ratios (PR) with 95% confidence intervals (CI) were estimated using multivariate Poisson regression with robust variance.The overall prevalence of CMD was 33.2% (95% CI: 30.6–36.1). After adjusting for potential confounding factors, a statistically significant inverse relationship between a healthy dietary pattern and CMD was observed. High adherence to a healthy dietary pattern was associated with a lower prevalence of CMD (PR = 0.74; 95% CI: 0.59–0.95; p = 0.017). Women with a higher score on the healthy dietary pattern were 26% less likely to have CMD.This study highlights a significant inverse association between a healthy dietary pattern and CMD in women. A high prevalence of CMD was also observed in this population group. These findings underscore the importance of promoting healthy dietary intake to prevent psychiatric disorders. [ABSTRACT FROM AUTHOR]

AbstractWe introduce a multivariate functional principal component analysis (MFPCA) residual control chart for multivariate functional data. Our method utilizes the vine copula technique and is applied to high-frequency financial data. We employ functional eigenfunctions to uncover hidden dependence structures and explain variations in sparse multivariate longitudinal data through MFPCA. With these functional eigenfunctions, we create a vine copula-based residual control chart for sparse multivariate longitudinal data. To handle sparse multivariate longitudinal data in this context, we employ predictive mean matching imputation. As part of real-world applications, we conduct analysis on high-frequency time series data for five technology stocks listed on the Nasdaq exchange, as well as high-frequency air quality data obtained from a significantly polluted area within an Italian city. [ABSTRACT FROM AUTHOR]

In this study, thalli nitrogen (N) and metal ions contents in the two fruticose lichens (Ramalina intermedia and Usnea cornuta) were quantified, which were collected from a pristine Himalayan forest of Shey Phoksundo National Park of western Nepal. Besides, the probable impacts of N and metal ions in physicochemical responses such as electrical conductivity, chlorophyll contents, and chlorophyll degradation were studied. Our initial hypothesis was there are considerable amounts of thallus N and metal ions in lichens from the study area, and it has substantial impacts on physicochemical responses. In comparison, the thalli N concentration was observed to be the greatest in R. intermedia thalli (0.24%–1.15%). However, comparable amounts of metal ions in both lichen species were observed. Regression analysis revealed the least or no impacts of N and metal ions in physicochemical responses. The principal component analysis suggests the contribution of different environmental variable and their correlation with lichen's variables. This study provides evidence of the least concentration of N and metal ions and only minor impacts on the physicochemical integrity of the two sensitive lichens in our study area. This study is likely to contribute as field‐based reference information for further studies on the species‐specific N and metal ions accumulation in lichens from different Himalayan forest systems to monitor ecosystem health. [ABSTRACT FROM AUTHOR]

ABSTRACT Recently, optically pumped magnetometers (OPMs), which use the spin polarization of alkali metal atoms to measure magnetic fields, have attracted much attention. However, in order to realize high‐sensitivity biomagnetic field measurements using OPMs, it is necessary to reduce environmental magnetic noise and system noise. In this study, we investigate the effectiveness of the noise reduction of OPM signals when principal component analysis, independent component analysis, and empirical mode decomposition are used for signal processing. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSION The relationship between oat grain composition and physical attributes as influenced by oat genotype and Canadian growing environments was investigated. Thirty Canadian oat (Avena sativa L.) genotypes, grown in three Canadian growing locations (Brandon, Manitoba; Portage la Prairie, Manitoba; and Lacombe, Alberta) over 2 consecutive years (2020–2021), were analyzed.Analysis of variance showed that the protein, total starch, and amylose content were significantly affected by genotype, environment, and their interaction. A principal component analysis bi‐plot illustrated that protein and total starch had an inverse relationship and were more affected by growing year, whereas amylose content had a negligible influence. The majority of genotypes were stable across environments but some genotypes, like CDC Morrison, were more influenced by different environments. Correlation analysis suggested that drought‐like conditions early in the growing season generated oat genotypes that favored the accumulation of protein, β‐glucan, and oil.The results provide detailed information regarding the relationship between important oat chemical and physical traits and different growing environments, which can assist breeders to improve characteristics to obtain high‐quality oat grains and thus high‐quality end products. © 2025 His Majesty the King in Right of Canada and The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. Reproduced with the permission of the Minister of Agriculture and Agri‐Food Canada. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC), one of the most common and heterogeneous malignancies, is the second leading cause of cancer death worldwide and is closely related to dietary habits. Fatty acid is one of the main nutrients of human beings, which is closely related to diabetes, hypertension and other diseases. However, the correlation between fatty acid metabolism and the development and progression of GC remains largely unknown. Here, we profiled the genetic alterations of fatty acid anabolism-related genes (FARGs) in gastric cancer samples from the TCGA cohort and GEO database to evaluate the possible relationships and their internal regulatory mechanism. Through consistent clustering and functional enrichment analysis, three distinct fatty acid anabolism clusters and three gene subtypes were identified to participate in different biological pathways, and correlated with the characteristics of immune cell infiltration and clinical prognosis. Importantly, a distinctive FA-score was constructed through the principal component analysis to quantify the characteristics of fatty acid anabolism in each GC patient. Further analysis showed patients grouped in the high FA-score group were characterized with greater tumor mutational burden (TMB) and higher microsatellite stability (MSI-H), which may be more aeschynomenous to immunotherapy and had a favorable prognosis. Altogether, our bioinformatics analysis based on FARGs uncovered the potential roles of fatty acid metabolism in GC, and may provide newly prognostic information and novel approaches for promoting individualized immunotherapy in patients with GC. [ABSTRACT FROM AUTHOR]

AbstractThis study utilized SERS to characterize and identify Klebsiella pneumonia, Staphylococcus aureus, and Pseudomonas aeruginosa which were collected from pus and confirmed using 16S rRNA sequencing. These bacteria are typically cultured and isolated from human wounds. SERS peaks at 575, 629, 930, 1008, 1038, 1099, 1134, 1221, 1283, 1294, 1374, 1589, and 1707 cm−1 were shown to be distinguishing features of these strains. Principal component analysis (PCA) and partial least squares – discriminant analysis (PLS-DA) was applied to SERS spectral datasets. This study demonstrates that combining SERS with PCA and PLS-DA is effective for recognizing and distinguishing these bacterial isolates. [ABSTRACT FROM AUTHOR]

This study focuses on identifying distinct precipitation zones across Europe, Spain and Catalonia, and second, examining how various large‐ and small‐scale climatic patterns affect the precipitation in these zones. Previous research has focused primarily on the relationships between individual climatic indices and precipitation in specific regions but has often overlooked the combined influence of multiple climate signals on precipitation variability. To address these issues, this study proposes the use of principal component analysis (PCA) as a multivariate analysis framework to investigate the complex relationships amongst multiannual precipitation patterns at different spatial scales, specifically in Europe, Spain and Catalonia. Distinct correlations amongst total annual precipitation occur in European countries, Spanish provinces and small Catalonian regions. Europe and Spain have five precipitation zones, whereas Catalonia has four. The calculated trends indicate a total precipitation reduction in the Iberian Peninsula, western Mediterranean and southwestern Europe, with a projected further decrease. Conversely, northern and central Europe anticipate normal to high precipitation tendencies. A second PCA application explores time and spatial correlations between precipitation zones and local/global climatic indices. The Southern Annular Mode, key Pacific teleconnections (PNA, TNA, WHWP, PACWARM and BEST) and confirmed Atlantic patterns (EA, NAO and AO) emerged as influential. The WeMO and MO indices showed the expected relevance at local spatial resolutions. Multivariate data analysis methods for two‐ or multidimensional datasets, which span multiple years and various spatial units (countries/provinces/regions), can extend the use of multivariate data analysis tools for correlation analysis over time in diverse geographical areas, including other continents, with varying spatial and temporal resolutions. The inclusion of monthly average precipitation data as an additional dimension in datasets analysed by multivariate statistical methods, such as PCA, will improve the knowledge of spatiotemporal climate variability. [ABSTRACT FROM AUTHOR]

Objectives: Toona sinensis, commonly known as Chinese toon, is a perennial woody plant with significant economic and ecological importance. This study employed whole-genome resequencing of 180 T. sinensis samples collected from Shandong to analyze genetic variation and diversity, ultimately identifying 18,231 high-quality SNPs after rigorous quality control and linkage disequilibrium pruning. This comprehensive genomic resource provides novel insights into the genetic architecture of T. sinensis, facilitating the elucidation of population structure and supporting future breeding programs. Data description: We performed whole-genome resequencing on 180 Toona sinensis samples, generating 1170.26 Gbp of clean data with a Q30 percentage of 93.69%. The average alignment rate to the reference genome was 96.72%, with an average coverage depth of 8 × and a genome coverage of 88.71%. Following data quality control and alignment, we performed SNP calling and filtering to identify high-quality SNPs across all samples. Population structure analyses were then conducted using the identified SNPs, including principal component analysis (PCA), structure analysis, and phylogenetic tree construction. These comprehensive analyses provide a foundation for understanding the genetic diversity and evolutionary dynamics of T. sinensis. [ABSTRACT FROM AUTHOR]

Purpose: The development of modern Artificial Intelligence (AI) based models for the early diagnosis of Parkinson's disease (PD) has been gaining deep attention by researchers recently. In particular, the use of different types of datasets (voice, hand movements, gait, etc.) increases the variety of up-to-date models. Movement disorders and tremors are also among the most prominent symptoms of PD. The usage of drawings in the detection of PD can be a crucial decision-support approach that doctors can benefit from. Methods: A dataset was created by asking 40 PD and 40 Healthy Controls (HC) to draw spirals with and without templates using a special tablet. The patient-healthy distinction was achieved by classifying drawings of individuals using Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) algorithms. Prior to classification, the data were normalized by applying the min-max normalization method. Moreover, Leave-One-Subject-Out (LOSO) Cross-Validation (CV) approach was utilized to eliminate possible overfitting scenarios. To further improve the performances of classifiers, Principal Component Analysis (PCA) dimension reduction technique were also applied to the raw data and the results were compared accordingly. Results: The highest accuracy among machine learning based classifiers was obtained as 90% with SVM classifier using non-template drawings with PCA application. Conclusion: The model can be used as a pre-evaluation system in the clinic as a non-invasive method that also minimizes environmental and educational level differences by using simple hand gestures such as hand drawing, writing numbers, words, and syllables. As a result of our study, preliminary preparation has been made so that hand drawing analysis can be used as an auxiliary system that can save time for health professionals. We plan to work on more comprehensive data in the future. [ABSTRACT FROM AUTHOR]

Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n = 32) and serum (n = 68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk. Differential expression analysis, feature reduction, multi-omic data integration, unsupervised hierarchical clustering, principal component analysis, spearman correlations and leave-one-out cross-validation were performed using RStudio and CombiROC software. An 11-feature multi-omic panel in PCF [PIGR, S100A8, REG1A, LGALS3, TCN1, LCN2, PRSS8, MUC6, SNORA66, miR-216a-5p, miR-216b-5p] generated an AUC = 0.806. A 13-feature multi-omic panel in serum [SHROOM3, IGHV3-72, IGJ, IGHA1, PPBP, APOD, SFN, IGHG1, miR-197-5p, miR-6741-5p, miR-3180, miR-3180-3p, miR-6782-5p] produced an AUC = 0.824. Integration of the strongest performing biomarkers generated a 10-feature cross-biofluid multi-omic panel [S100A8, LGALS3, SNORA66, miR-216b-5p, IGHV3-72, IGJ, IGHA1, PPBP, miR-3180, miR-3180-3p] with an AUC = 0.970. Multi-omic profiling provides an abundance of potential biomarkers. Integration of data from different omic compartments, and across biofluids, produced a biomarker panel that performs with high accuracy, showing promise for the risk stratification of patients with pancreatic cystic lesions. [ABSTRACT FROM AUTHOR]

This study aimed to assess the validity and reliability of a questionnaire on patient acceptance of orthodontic retainers. The original questionnaire was forward- and backward-translated, followed by four validity tests (content validity, face validity, construct validity, criterion validity) and two reliability tests (test-retest reliability, internal consistency). Content validity was assessed by nine orthodontists who appraised the questionnaire's representativeness, relevance, clarity, and necessity. Face validity was established through semi-structured in-depth interviews with 35 English-literate participants currently wearing orthodontic retainers. Construct validity was established through Exploratory Factor Analysis (EFA). For criterion validity, 107 participants concurrently answered the questionnaire and the Retainer-modified Malaysian Oral Health Impact Profile questionnaire. Test-retest reliability was verified by 34 subjects who responded to the questionnaire again after a two-week interval. Six revised items passed the threshold value of 0.78 for Item-Content Validity Index and Content Validity Ratio and were revised based on findings from the face validity test. Principal Component Analysis of EFA extracted information on only one component, and all items were positively correlated with the component matrix. Spearman's rho value (rs = 0.490 and rs = 0.416) indicated a moderate correlation between the two questionnaires for criterion validity. Intraclass Correlation Coefficient ranged from 0.687 to 0.913, indicating moderate to excellent test-retest reliability. Cronbach's alpha ranged from 0.687 to 0.913 indicating that none of the questionnaire items showed unacceptable or poor internal consistency. The questionnaire on patient acceptance of orthodontic retainers has been validated and can be used in both clinical and research settings. [ABSTRACT FROM AUTHOR]

Background: Sweetpotato is a vegetatively propagated crop cultivated worldwide, predominantly in developing countries, valued for its adaptability, short growth cycle, and high productivity per unit land area. In most sub-Saharan African (SSA) countries, it is widely grown by smallholder farmers. Niger, Nigeria, and Benin have a huge diversity of sweetpotato accessions whose potential has not fully been explored to date. Diversity Arrays Technology (DArTseq), a Genotyping by Sequencing (GBS) method, has been developed and enables genotyping with high-density single nucleotide polymorphisms (SNPs) in different crop species. The aim of this study was to assess the genetic diversity and population structure of the West African sweetpotato collection using Diversity Arrays Technology through Genotyping by Sequencing (GBS). Results: 29,523 Diversity Arrays Technology (DArTseq) single nucleotide polymorphism markers were used to genotype 271 sweetpotato accessions. Genetic diversity analysis revealed an average polymorphic information content (PIC) value of 0.39, a minor allele frequency of 0.26, and an observed heterozygosity of 10%. The highest value of polymorphic information content (PIC) (0.41) was observed in chromosomes 4, while the highest proportion of heterozygous (He) (0.18) was observed in chromosomes 11. Molecular diversity revealed high values of polymorphic sites (Ps), theta (θ), and nucleotide diversity (π) with 0.973, 0.158, and 0.086, respectively, which indicated high genetic variation. The pairs of genetic distances revealed a range from 0.08 to 0.47 with an overall average of 0.34. Population structure analysis divided the 271 accessions into four populations (population 1 was characterised by a mixture of accessions from all countries; population 2, mostly comprised of Nigerian breeding lines; population 3 contained exclusively landraces from Benin; and population 4 was composed by only landraces from West African countries) at K = 4, and analysis of molecular variance (AMOVA) based on PhiPT values showed that most of the variation was explained when accessions were categorized based on population structure at K = 4 (25.25%) and based on cluster analysis (19.43%). Genetic distance showed that group 4 (which constituted by landraces of Niger and Benin) was genetically distant (0.428) from groups 2 (formed by 75% of breeding lines of Nigeria), while group 1 was the closest (0.182) to group 2. Conclusions: This study employed 7,591 DArTseq-based SNP markers, revealing extensive polymorphism and variation within and between populations. Variability among countries of origin (11.42%) exceeded that based on biological status (9.13%) and storage root flesh colour (7.90%), emphasizing the impact of migration on genetic diversity. Population structure analysis using principal component analysis (PCA), Neighbor-Joining (NJ) tree, and STRUCTURE at K = 4 grouped 271 accessions into distinct clusters, irrespective of their geographic origins, indicating widespread genetic exchange. Group 4, dominated by landraces (95%), showed significant genetic differentiation (Nei's Gst = 0.428) from Group 2, mainly comprising breeding lines, suggesting their potential as heterotic groups for breeding initiatives like HEBS or ABS. [ABSTRACT FROM AUTHOR]

The increasing cultivation of perennial C4 grass known as Miscanthus spp. for biomass production holds promise as a sustainable source of renewable energy. Unlike the sterile triploid hybrid of M. × giganteus, which cannot reproduce through seeds, M. sinensis possesses attributes that could potentially address these limitations by effectively establishing itself through seed propagation. This study aimed to investigate how 18 genotypes of M. sinensis respond to chilling stress and subsequent recovery. Various traits were measured, including growth and biomass yield, the rate of leaf elongation, and a variety of chlorophyll fluorescence parameters, as well as chlorophyll content estimated using the SPAD method. Principal Component Analysis revealed unique genotype responses to chilling stress, with distinct clusters emerging during the recovery phase. Strong, positive correlations were identified between biomass content and yield-related traits, particularly leaf length. Leaf growth analysis delineated two subsets of genotypes: those maintaining growth and those exhibiting significant reductions under chilling conditions. The Comprehensive Total Chill Stress Response Index (SRI) pinpointed highly tolerant genotypes such as Ms16, Ms14, and Ms9, while Ms12 showed relatively lower tolerance. [ABSTRACT FROM AUTHOR]

Rice (Oryza sativa L.) holds immense global significance, serving as a staple food crop that sustains billions of people and supports livelihoods across diverse cultures and economies, including Bangladesh. For food security in Bangladesh, there is a significant demand for high-yielding Aman rice varieties. We assessed genetic variability of 16 Aman rice genotypes, including 11 promising Aman lines and 5 established Aman cultivars in Bangladesh, with the aim of identifying high-yielding genotypes through a three-year field experiment. The examined genotypes displayed notable variations in both yield and agronomic traits contributing to yield, as evidenced by their greater range of coefficients of variance and genetic variability components. Traits such as effective tillers per hill (ET), filled grains per panicle (FG), plant height (PH) at 80 days after transplanting, and panicle length (PL) demonstrated notable correlations with grain yield (GY) throughout the three-year evaluation. Moreover, the higher broad-sense heritability (H2) observed in ET, FG, PH, and PL traits suggested a substantial influence of genetic factors on the variability of the Aman rice genotypes. Principal component analysis based biplot and heatmap analyses revealed that the advanced lines BU acc4 and BU acc5 had higher harvest index and relatively greater GY compared to the other Aman rice varieties. These promising Aman lines could prove to be valuable materials for future multi-location yield trials or ongoing and forthcoming breeding programs aimed at enhancing high-yielding Aman rice cultivars. [ABSTRACT FROM AUTHOR]

Background: Current functional assessment tools for orthopaedic oncology are long surveys that contribute to patients' survey fatigue and yet lack the ability to discern meaningful differences in a patient population that is often mobile but unable to perform strenuous activities. We sought to determine whether a shorter, novel tool based on existing, validated surveys could better capture differences in a sample of orthopaedic oncology patients. Questions/purposes: (1) Can a concise fixed-item functional tool derived from the 50 items in the Toronto Extremity Salvage Score for the lower extremity (TESS LE) and the Lower Extremity Functional Scale (LEFS) demonstrate similar responsiveness in terms of sensitivity and specificity? (2) What is the precision and accuracy of the concise tool compared with the TESS LE and LEFS? Methods: Functional outcome data were collected and maintained in a longitudinally maintained database at a single institution. Patients were included in the study if (1) they had undergone a tumor excision or a nononcologic orthopaedic procedure (for example, arthroplasty for osteoarthritis) for a bone or soft tissue tumor affecting lower extremity function, and (2) they had completed the LEFS, TESS LE, and Patient-Reported Outcomes Measurement Information System (PROMIS) global health tool on at least two clinic visits. Between September 2014 and April 2022, we treated 14,234 patients for primary bone or soft tissue sarcoma, metastatic disease to bone, or orthopaedic sequelae of chronic cancer care. Approximately 6% (854 of 14,234) were excluded due to the need of a language translator. Approximately 2% (278 of 13,380) refused or were unable to participate. Seventy-two percent (9433 of 13,102) of the patients had an operation on a lower extremity. Of these, 4% (339 of 9433) of the patients completed the TESS LE, LEFS, and Item 3 of the PROMIS global health tool on ≥ 2 clinic visits. Of the patients in the current study, 49% (167 of 339) were women, and 27% (93 of 339) had metastatic carcinoma. Twelve percent (41 of 339) of the patients died before the end of the study period. Spearman rank-order correlation, principal component analysis (PCA), and item response theory (IRT) modeling identified 14 highly discriminating items from the TESS LE and LEFS. Multiple linear stepwise regression (MLSR) was performed with the dependent variable being the summary score of the 14 items derived from the TESS LE and LEFS and standardized to a percentage of 100. The beta coefficient from the MLSR was used to derive a weight for each of the 14 items. Evaluation of the model with 10 to 17 variables was performed to ensure that the model with the 14 items met the most criteria for fit with the PCA, the receiver operating characteristic (ROC) curve, and the IRT modeling criteria. The responsiveness (sensitivity and specificity) of the change scores in the shortened 14-item survey, the 30-item TESS LE, and the 20-item LEFS as compared with the dichotomized changes in Item 3 of the PROMIS global health tool was evaluated using ROCs. The concordance (accuracy and precision) of the 14 items derived from the LEFS and TESS LE was evaluated. Results: The responsiveness (sensitivity and specificity) of the shortened 14-item survey, the TESS LE, and the LEFS to the criterion target of the PROMIS global health tool (Item 3) was similar, with areas under the curve (AUCs) ranging from 0.62 to 0.65 for the ROC curves. The responsiveness of the 14-item survey to the TESS LE showed sensitivity of 96% and specificity of 90%, with an AUC of 0.98 (p < 0.001). The responsiveness of the 14 items to the LEFS showed sensitivity of 95% and specificity of 86%, with an AUC of 0.96. The validity of the 14 items to the TESS LE was measured by concordance, with a precision of 0.98 and an accuracy of 0.97. Concordance of the 14 items to the LEFS showed a precision of 0.98 and accuracy of 0.83. Conclusion: The concise 14 items derived from patient-reported responses in the TESS LE and LEFS outcome measures showed similar responsiveness (sensitivity and specificity) as the original TESS LE and LEFS for cancer patients after lower extremity orthopaedic surgery performed for oncologic and nononcologic indications. The concise 14 items have a similar ability to the TESS LE and LEFS to tell the clinician or patient how they are functioning compared with other patients. These 14 items are shorter than the combined 50 items of the TESS LE and LEFS while retaining the capacity to describe a broad range of lower extremity function for orthopaedic oncology patients. We have named the 14-item survey the Lower Extremity Oncology Functional Assessment Tool (LEO). Level of Evidence Level II, diagnostic study. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Fluid overload (FO) in the intensive care unit (ICU) is common, serious, and may be preventable. Intravenous medications (including administered volume) are a primary cause for FO but are challenging to evaluate as a FO predictor given the high frequency and time‐dependency of their use and other factors affecting FO. We sought to employ unsupervised machine learning methods to uncover medication administration patterns correlating with FO.This retrospective cohort study included 927 adults admitted to an ICU for ≥72 h. FO was defined as a positive fluid balance ≥7% of admission body weight. After reviewing medication administration record data in 3‐h periods, medication exposure was categorized into clusters using principal component analysis (PCA) and Restricted Boltzmann Machine (RBM). Medication regimens of patients with and without FO were compared within clusters to assess their temporal association with FO.FO occurred in 127 (13.7%) of 927 included patients. Patients received a median (interquartile range) of 31(13–65) discrete intravenous medication administrations over the 72‐h period. Across all 47,803 intravenous medication administrations, 10 unique medication clusters, containing 121 to 130 medications per cluster, were identified. The mean number of Cluster 7 medications administered was significantly greater in the FO cohort compared with patients without FO (25.6 vs.10.9, p < 0.0001). A total of 51 (40.2%) of 127 unique Cluster 7 medications were administered in more than five different 3‐h periods during the 72‐h study window. The most common Cluster 7 medications included continuous infusions, antibiotics, and sedatives/analgesics. Addition of Cluster 7 medications to an FO prediction model including the Acute Physiologic and Chronic Health Evaluation (APACHE) II score and receipt of diuretics improved model predictiveness from an Area Under the Receiver Operation Characteristic (AUROC) curve of 0.719 to 0.741 (p = 0.027).Using machine learning approaches, a unique medication cluster was strongly associated with FO. Incorporation of this cluster improved the ability to predict FO compared to traditional prediction models. Integration of this approach into real‐time clinical applications may improve early detection of FO to facilitate timely intervention. [ABSTRACT FROM AUTHOR]

Summary Background Objectives Study design Methods Results Main limitations Conclusions Assuming that the ventricular myocardium of horses is subjected to exercise‐induced hypertrophy, we hypothesised that the mean electrical axis (MEA) of the heart would change.To define a longitudinal study to detect any changes in the direction of the MEA in Thoroughbred horses using ECG.ECGs were recorded on each horse in each training group at day 0 (T0), 1 month (T1) and 2 months (T2) of training.A total of 43 Thoroughbred horses in training in Italy were recruited. The horses were divided into three groups according to age. The ECGs were recorded by positioning the electrodes according to Dubois's method for measuring MEA in the frontal plane. Intervals with artefact‐free QRS complexes in both bipolar DI and augmented unipolar aVF leads were selected, and the vector obtained was identified as the MEA. The statistical analysis was performed via generalised linear mixed model (GLMM) and principal component analysis (PCA).A statistically significant effect of time passing between T0 and T2 (p < 0.001) and an interaction between time and sex on the MEA was found (p = 0.04). PCA revealed that the population studied had different patterns, with three horses showing higher variability in the MEA direction.There was no good sex balance in the age groups of the population studied, and there was no control group. The 1‐month sampling intervals of ECGs may have been too short. Confirmatory studies are needed.We believe that our results are the first to suggest that training may lead to changes in MEA orientation in horses. Sex and individuality were found to influence MEA orientation and may have contributed to the difficulty in detecting training‐dependent changes in MEA to date. [ABSTRACT FROM AUTHOR]

Last-mile logistics has emerged as a tool for responsiveness, however, requires dynamic business models and technology to integrate all the stakeholders. Blockchain is emerging as a platform for logistics and supply chain problems due to its prospective features, however, the adoption is slow due to unclear benefits and diverse challenges it brings. This study explores the relationship between last-mile logistics practices (LMLPs), blockchain adoption challenges, blockchain features and blockchain adoption benefits. Data are collected and principal component analysis is used to rank the practices. Finally, we measure the impact of blockchain benefits on LMLPs using regression analysis. The findings revealed that the most important practice is close partnership with customers, followed by a state-of-the-art cybersecurity system. Blockchain features such as transparency, real-time tracking, data-driven decision-making and secure information exchange offer key benefits, including lead-time reduction and enhanced flexibility in supply chains. However, policy uncertainty remains a major challenge to blockchain adoption. [ABSTRACT FROM AUTHOR]

The study aims to establish a graphical relationship between sediment concentration (C) and water discharge (Q) during flood events in the Isser catchment. Hysteresis, indicating a time lag between discharge flow (Q) and suspended sediment concentration (SSC) curves, varies based on sediment availability, event magnitude, and sequence. Based on the 2026 data pairs of water discharge and suspended sediment concentration (Q-SSC), we have selected 22 flood events. The most frequent hysteresis loops were complex (10 loops), with 08 clockwise loops, 02 figure-eight loops, and 02 anti-clockwise loops. Complex hysteresis loops accounted for 63% of solid loads and 37% of water discharge loads, while 50% of total water yield and 23% of total sediment yield were associated with clockwise loops. Principal Component Analysis (PCA) revealed that water discharge load, mean concentration, maximum concentration, and concentration at the flow discharge peak are key variables influencing hysteresis patterns. [ABSTRACT FROM AUTHOR]

The study inspects the status of school governance and school outcome at primary school level and set up roadmap for all the stakeholders to achieve the mandate of Goal 4 in SDGs within 2030, especially in the context of rural India. The status of school governance and school outcome are assessed under 4 dimensions and 16 parameters through a survey of 21 rural primary schools from rural Maharashtra. Two different indices have been constructed for school outcomes and school governance using multi-stage principal component analysis. Public and private-aided schools are compared according to the degree of accountability and transparency. It has been realized that there is an absolute need for strong school governance at ground level which is very poor across public schools in rural India. The study followed the Worldwide Governance Indicators (WGI) project and ASER (2014) and considered these baselines to find the present status of school governance and school outcome for the present study. [ABSTRACT FROM AUTHOR]

Fractures influence the mechanical strength of coal roof and floor, constraining the design of hydraulic fracturing for coalbed methane production. Currently, the predominant approach involves the integration of petrophysical logging with machine learning for fracture prediction. Nevertheless, challenges exist regarding the model's accuracy. In this study, we present a novel approach to predict fracture density. Our method optimises a back‐propagation (BP) neural network and utilises principal component analysis for feature extraction. We employ logging parameters (density, compensated neutron and acoustic time difference) obtained from Shouyang Block well SY‐1 and fracture density data from electrical imaging logging to construct the FVDC model's dataset. The BP neural network model is optimised using the Sparrow Search algorithm and Tent Chaotic Mapping. The results demonstrate a substantial enhancement over the BP neural network model, with reductions of 80.102% in mean absolute error, 94.182% in mean square error, 75.879% in root mean square error and 79.764% in mean absolute percentage error. When considering accuracy, the optimised model (97.098%) surpasses the support vector regression model (96.478%), the random forest model (94.404%) and the BP neural network model (85.657%). Scalability testing for the optimised model was conducted using data from well SY‐2, yielding a remarkable prediction accuracy of 96.775%. This performance exceeds that of the BP neural network (with an accuracy of 85.102%), as well as the random forest and support vector regression models (with accuracies of 91.234% and 90.384%, respectively). These results underscore the potential of well logging and machine learning in FVDC prediction. [ABSTRACT FROM AUTHOR]

Surface‐enhanced Raman spectroscopy (SERS) was utilized to measure low‐level fentanyl concentrations mixed in common cutting agents, cocaine, 3,4‐methylenedioxymethamphetamine (MDMA), methamphetamine, and caffeine. Mixtures were prepared with a fentanyl concentration range of 0–339 μM. Data was initially analyzed by plotting the area of a diagnostic peak (1026 cm−1) against concentration to generate a calibration model. This method was successful with fentanyl/MDMA samples (LOD 0.04 μM) but not for the other mixtures. A chemometric approach was then employed. The data was evaluated using principal component analysis (PCA), partial least squares (PLS1) regression, and linear discriminant analysis (LDA). The LDA model was used to classify samples into one of three designated concentration ranges, low = 0–0.4 mM, medium = 0.4–14 mM, or high >14 mM, with fentanyl concentrations correctly classified with greater than 85% accuracy. This model was then validated using a series of "blind" fentanyl mixtures and these unknown samples were assigned to the correct concentration range with an accuracy >95%. The PLS1 model failed to provide accurate quantitative assignments for the samples but did provide an accurate prediction for the presence or absence of fentanyl. The combination of the two models enabled accurate quantitative assignment of fentanyl in binary mixtures. This work establishes a proof of concept, indicating a larger sample size could generate a more accurate model. It demonstrates that samples, containing variable, low concentrations of fentanyl, can be accurately quantified, using SERS. [ABSTRACT FROM AUTHOR]

People have beliefs about the unacceptability of the expression and experience of negative emotions. These beliefs affect psychological health and can have a negative effect on the treatment and symptoms of people with mental disorders and medically unexplained symptoms. This study aimed to develop a Japanese version of the Beliefs about Emotions Scale (BES‐J) and evaluate its reliability and validity. In an online survey, participants with fibromyalgia (n = 226) and healthy controls (n = 184) completed the BES‐J and questionnaires concerning perfectionism, dysfunctional attitudes, depression, anxiety, pain, disability, well‐being, interdependent happiness, and loneliness. The results of the principal component analysis showed that the BES‐J comprised a one‐factor structure, identical to the original. The BES‐J had good internal consistency (.89) and showed a significant correlation with the questionnaires. The BES‐J showed good internal reliability, concurrent validity, and test–retest reliability. The present study suggests that the Japanese version of the BES is appropriate for use with Japanese speakers. [ABSTRACT FROM AUTHOR]