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BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting over 300,000 people worldwide. It is characterized by the progressive decline of the nervous system that leads to the weakening of muscles which impacts physical function. Approximately, 15% of individuals diagnosed with ALS have a known genetic variant that contributes to their disease. As therapies that slow or prevent symptoms continue to develop, such as antisense oligonucleotides, it is important to discover novel genes that could be targets for treatment. Additionally, as cohorts continue to grow, performing analyses in ALS subtypes, such as primary lateral sclerosis (PLS), becomes possible due to an increase in power. These analyses could highlight novel pathways in disease manifestation. METHODS: Building on our previous discoveries using rare variant association analyses, we conducted rare variant burden testing on a substantially larger multi-ethnic cohort of 6,970 ALS patients, 166 PLS patients, and 22,524 controls. We used intolerant domain percentiles based on sub-region Residual Variation Intolerance Score (subRVIS) that have been described previously in conjunction with gene based collapsing approaches to conduct burden testing to identify genes that associate with ALS and PLS. RESULTS: A gene based collapsing model showed significant associations with SOD1, TARDBP, and TBK1 (OR = 19.18, p = 3.67 × 10-39; OR = 4.73, p = 2 × 10-10; OR = 2.3, p = 7.49 × 10-9, respectively). These genes have been previously associated with ALS. Additionally, a significant novel control enriched gene, ALKBH3 (p = 4.88 × 10-7), was protective for ALS in this model. An intolerant domain-based collapsing model showed a significant improvement in identifying regions in TARDBP that associated with ALS (OR = 10.08, p = 3.62 × 10-16). Our PLS protein truncating variant collapsing analysis demonstrated significant case enrichment in ANTXR2 (p = 8.38 × 10-6). CONCLUSIONS: In a large multi-ethnic cohort of 6,970 ALS patients, collapsing analyses validated known ALS genes and identified a novel potentially protective gene, ALKBH3. A first-ever analysis in 166 patients with PLS found a candidate association with loss-of-function mutations in ANTXR2.

With the rapid development of data collection techniques in recent years, multiple types of data have emerged, including scalar data, functional data (curve-like), and compositional data (pie-like). While existing studies propose predictive models for multiple-type of data, few address the issue of variable selection. The challenge lies in the fact that different data types originate from different vector spaces, making it difficult to conduct variable selection at the variable level instead of selection at their sub-component level. This study leverages the group selection ability of gPLS (group Partial Least Squares) and gsPLS (group sparse Partial Least Squares) by regarding the functional and compositional variables as natural groups and proposes two variable selection approaches, named MD-gPLS and MD-gsPLS, after building a vector space for multiple types of data. Numerical studies and real-world examples verify the effectiveness of the proposed approaches. This study broadens the statistical modeling tools of multiple types of data analysis in terms of variable selection and also contributes to the literature by introducing the vector space of multiple types of data. [ABSTRACT FROM AUTHOR]

Childhood is the most sensitive and influential period of every person's life because different physical and psychological dimensions of the child are forming and interacting in childhood and adolescence. Therefore, maintaining and ensuring the child's growth and physical and mental health is of great importance. This is even though when these children are subjected to violence, it will have negative effects on the child and society. In the meantime, sexual violence against children by pedophiles in different ways is a serious issue for all countries that require consideration at the national and transnational levels. Therefore, in this study, identifying the characteristics of pedophile people and underlying factors in becoming a pedophile and becoming a victim of this phenomenon is the main subject of this study. The statistical population of this study includes all experts and professionals active in the field of psychiatry and law who specialize in topics related to sexual violence of pedophiles, and 235 of them were randomly selected. The research tool is a researcher-made questionnaire based on 47 questions, and the desired model is fitted based on PLS. The obtained results indicate that factors such as personality disorder, depression, suicide, alcohol and drug use, family problems, mental-psychological problems, and medical history are among the common characteristics of pedophiles. Educational and control measures, educational factors and criminal punishment are the proposed solutions for prevention and economic, social and cultural factors are among the underlying factors in becoming a pedophile. [ABSTRACT FROM AUTHOR]

Patients with early psychosis (EP) (within 3 years after psychosis onset) show significant variability, which makes predicting outcomes challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, which limits the development of early interventions. A data-driven approach, partial least squares correlation, was used across 2 independent datasets to examine multivariate relationships between white matter properties and symptomatology and to identify stable and generalizable signatures in EP. The primary cohort included patients with EP from the Human Connectome Project for Early Psychosis (n = 124). The replication cohort included patients with EP from the Feinstein Institute for Medical Research (n = 78) as part of the MEND (Multimodal Evaluation of Neural Disorders) Project. Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders. In both cohorts, a significant latent component corresponded to a symptom profile that combined negative symptoms, primarily diminished expression, with specific somatic symptoms. Both latent components captured comprehensive features of white matter disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the partial least squares model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use. This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural white matter alterations in EP across diagnoses and datasets, showing strong covariance of these alterations with a unique profile of negative and somatic symptoms. These findings suggest the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings. [ABSTRACT FROM AUTHOR]

Rapid changes in the business environment and advances in information technology encourage companies to maximize the potential of human resources (HR). This study aims to examine the influence of transformational leadership, transactional leadership, and organizational commitment on employee performance at PT Salim Ivomas Pratama Tbk. The research method uses a quantitative approach with the distribution of questionnaires to 90 employees as a sample from a population of 167 employees. The data was analyzed using the Partial Least Square (PLS) technique to test the relationship between latent variables. The results showed that transformational leadership had a significant and positive influence on employee performance, while transactional leadership did not show a significant influence. In addition, organizational commitment has also been proven to significantly affect employee performance. In conclusion, transformational leadership and organizational commitment are important factors in improving employee performance. The implications of this research provide insights for companies to focus more on transformational leadership development and strategies to increase organizational commitment to achieve optimal performance. [ABSTRACT FROM AUTHOR]

This article gives large sample theory for the one component partial least squares estimator, including some hypothesis tests for high dimensional data, under much weaker conditions than those in the literature. Simple theory is also given for some data splitting estimators and the marginal maximum likelihood estimators. It is shown that lasso, one component partial least squares, and ordinary least squares often estimate different population multiple linear regression models. The article also proves that there are often many valid population models for regression methods such as binary regression. [ABSTRACT FROM AUTHOR]

Garlic is a plant with numerous benefits, including its use as a culinary ingredient and for medicinal purposes. However, the rising demand does not align with the availability of garlic in Indonesia, resulting in high garlic prices. Excessively high prices may potentially lead to garlic powder adulteration. The objective of this study is to determine whether there are garlic powder products adulterated with maltodextrin in the Marketplace. A total of 10 garlic powder samples were purchased on Marketplace. The study utilized a combination of FTIR spectroscopy with chemometrics. The chemometrics employed in this study were OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis) for sample grouping, as well as PLS (Partial Least Squares) for multivariate calibration. The findings revealed that OPLS-DA could effectively group the samples. Additionally, the best multivariate calibration model (PLS) was obtained at the wavenumber range of 679-440 cm-1 with a calibration R2 value of 0.9981, RMSEC value of 1.11, validation R2 value of 0.9926, and RMSEP value of 2.12. Based on the research results, it was found that three products were not pure garlic powder, with details indicating two products containing maltodextrin as an adulterant and one product containing a non-maltodextrin adulterant.

Optimizing forage quality is vital for enhancing animal performance and supporting the global animal production industry. Near-infrared (NIR) spectroscopy offers a rapid, non-destructive alternative to traditional, time-intensive laboratory analyses, enabling the on-site assessment of forage properties with significant advantages in cost, speed, and environmental impact. This review traces the development of NIR spectroscopy, outlines its core principles, and highlights its applications in animal nutrition. Additionally, it discusses the current technological state, challenges, and future prospects, emphasizing NIR’s growing role in promoting more sustainable and efficient animal production systems.

ABSTRACT Raman spectroscopy, in combination with multivariate analysis, is a powerful analytical tool for solving regression and classification problems in various fields—from materials science to clinical practice. However, in practical applications, experimental studies and the implementation of Raman spectroscopy present numerous challenges, including multicollinearity in spectral data and the ‘black box’ problem of complex analytical models. To avoid these problems, the proposed classification and regression models require proper interpretation. This study makes use of a comparative analysis of explanation methods based on the SP‐LIME (local interpretable model‐agnostic explanations with submodular pick) algorithm of a bilinear model (projection onto latent structures [PLS]) and a nonlinear model (one‐dimensional convolutional neural network [CNN]). The models to be interpreted are trained to solve the regression task of the blood serum Raman characteristics and the urea levels. Effective SP‐LIME evaluation of the blood Raman spectra revealed that in urea analysis for both PLS and CNN models, the important band is at 1003 cm−1. This approach is based on the value of the root mean square error estimation only when a single Raman band is analyzed. The aim of this paper is to develop an approach to explain the operation of the analytical models and provides the way to reveal the exact Raman bands with the biggest impact on the model performance. [ABSTRACT FROM AUTHOR]

Introduction: With the advancement of high-throughput studies, an increasing wealth of high-dimensional multi-omics data is being collected from the same patient cohort. However, leveraging this multi-omics data to predict survival outcomes poses a significant challenge due to its complex structure. Methods: In this article, we present a novel approach, the Adaptive Sparse Multi-Block Partial Least Squares (asmbPLS) Regression model, which introduces a dynamic assignment of penalty factors to distinct blocks within various PLS components, facilitating effective feature selection and prediction. Results: We compared the proposed method with several state-of-the-art algorithms encompassing prediction performance, feature selection and computation efficiency. We conducted comprehensive evaluations using both simulated data with various scenarios and a real dataset from the melanoma patients to validate the effectiveness and efficiency of the asmbPLS method. Additionally, we applied the lung squamous cell carcinoma (LUSC) dataset from The Cancer Genome Atlas (TCGA) to further assess the feature selection capability of asmbPLS. Discussion: The inherent nature of asmbPLS imparts it with higher sensitivity in feature selection compared to other methods. Furthermore, an R package called asmbPLS implementing this method is made publicly available. [ABSTRACT FROM AUTHOR]

In this study, an innovative hybrid process was proposed and examined as an alternative for nickel/cobalt separation from a synthetic pregnant leach solution. The first step was carried out by employing electrodeionization of a feed solution containing 950 ppm of nickel and 100 ppm of cobalt after adding EDTA to form (Ni-EDTA)−2 complex to suppress migration of nickel cations toward cathodic compartment. This process increased nickel percentage from 90.47 to 97 in the diluted feed solution. This solution was then subjected to cation exchanging process to adsorb the remaining cobalt cations, leading to a nickel purity of 99.7 wt% with an overall recovery 71.1%. On the other hand, the cathode rinse solution of EDI process which contained a major portion of the cobalt cations underwent three stages of extraction process using cyanex 272, resulting in recovery of 65.5 wt% of cobalt with a purity of 100%. It was seen that six stages of solvent extraction process were needed to reach the same purity of cobalt cations by the traditional extraction method. These findings highlight the superior performance of the proposed hybrid process, which not only yielded a relatively high recovery ratio but also ensured excellent purity of nickel and cobalt. [ABSTRACT FROM AUTHOR]

The present study proposes the use of Near-Infrared (NIR) spectroscopy as a rapid method for estimating the growth of Spirulina platensis cultures, avoiding any sample manipulation or pretreatment. NIR spectroscopy in diffuse reflectance mode was used on culture volumes as received, with Principal Component Analysis (PCA) and Partial Least Squares (PLS) linear regression, for developing the calibration model in the wavelength range of 1000–2500 nm, in order to choose the appropriate wavelength to estimate the growth of the microalga. The local reflectance maximum at 1062.6 nm, connected with reduced water absorption and scattering effects by the microalga, was identified from PCA as the positive peak in the first loading plot, correlating diffuse reflectance with dilution levels. The calibration curve of diffuse reflectance at 1062.6 nm in response to dilution presented strong linearity, supported by a coefficient of determination (R2) of 0.995. Cross-validation of NIR spectra with a S. platensis culture confirmed the method's reliability, showing that the growth follows an exponential pattern. The study shows that diffuse reflectance NIR spectroscopy can be used for the rapid monitoring of Spirulina platensis growth. [ABSTRACT FROM AUTHOR]

Optimizing forage quality is vital for enhancing animal performance and supporting the global animal production industry. Near-infrared (NIR) spectroscopy offers a rapid, non-destructive alternative to traditional, time-intensive laboratory analyses, enabling the on-site assessment of forage properties with significant advantages in cost, speed, and environmental impact. This review traces the development of NIR spectroscopy, outlines its core principles, and highlights its applications in animal nutrition. Additionally, it discusses the current technological state, challenges, and future prospects, emphasizing NIR's growing role in promoting more sustainable and efficient animal production systems. [ABSTRACT FROM AUTHOR]

It is imperative to measure the chemical composition of Sungkai leaf herbal tea in order to produce high-quality goods that promote human health. The moisture and ash content of Sungkai leaf herbal tea are critical parameters for assessing the quality of herbal tea. This study aimed to evaluate an NIR spectroscopy method for quickly determining the moisture and ash content of Sungkai leaf herbal tea. Sungkai leaf herbal tea has a moisture content between 3.93% and 7.59%, and an ash content between 3.94% and 5.51%. We developed a calibration model using partial least squares (PLS) with several pretreatment methods. We split the data into calibration and prediction sets and performed an internal random cross-validation. A PLS calibration model with Rp² = 0.86, a root means square error of prediction (RMSEP) of 0.30 (%), and a residual predictive deviation (RPD) of 2.76, performed exceptionally well at predicting the moisture content when the standard normal variate (SNV) pre-treatment was applied to the NIR spectra. The Savitzky-Golay derivative (a 9-point smoothing window, second-order polynomial, dg2) pretreatment method also generated the best PLS calibration model for ash content determination, with Rp² = 0.70, RMSEP = 0.16 (%), and RPD = 1.86. NIR spectroscopy can quickly determine the moisture and ash content of Sungkai leaf herbal tea, as suggested by these findings. [ABSTRACT FROM AUTHOR]

AbstractPapaya leaf stalks (PLS) were utilized in the current research to eliminate Amaranth (AM) dye from an aqueous medium. PLS was characterized through various techniques, including BET/BJH, pHPZC, XRD, FTIR, FESEM, and EDX. The FTIR analysis unveiled that -OH (hydroxyl), -C=O (carbonyl), and -COOH (carboxylic) groups were involved in binding AM to the surface of PLS. Batch mode adsorption tests were conducted, and various operational variables like pH, the mass of PLS, AM dye concentration, contact duration, stirring speed, temperature, and regeneration of the PLS were investigated. The maximal AM removal efficiency of 91.1 ± 2.601% was observed at pH 2.0. The pHPZC of the PLS was determined to be 5.5. The experiment results were analyzed using isotherm models (Freundlich, Temkin and Langmuir) and kinetic models (pseudo-second-order and pseudo-first-order). The pseudo-second-order kinetic and Langmuir isotherm models provided the best fit. The maximal sorption uptake of AM dye on PLS was achieved at 121.3 ± 1.493 mg/g at 298 K. The thermodynamic variables indicated that the sorption process was spontaneous (ΔGo < 0), exothermic (ΔHo > 0), and feasible (ΔSo > 0) at different temperatures. The AM adsorption on PLS was mainly due to electrostatic interactions. Desorption tests were also carried out to investigate the feasibility of regenerating PLS, and 89.2 ± 1.184% of the adsorbed AM was recovered using 0.2 M-NaOH. The reusability results indicated that 76.9 ± 2.204% of the adsorption efficiency could still be maintained even after six cycles. These results demonstrate that PLS is an environmentally friendly and suitable material for removing AM from wastewater. [ABSTRACT FROM AUTHOR]

We report a patient who presented clinically with progressive supranuclear palsy (PSP) but was pathologically diagnosed as having primary lateral sclerosis (PLS) with magnetic resonance imaging (MRI) with a quantitative susceptibility mapping (QSM) protocol. A 70‐year‐old man was clinically diagnosed with PSP due to early falls and unresponsiveness to levodopa therapy. Postmortem pathological examination revealed mild loss of Betz cells, gliosis, and transactive response DNA binding protein of 43 kDa (TDP‐43)‐positive inclusions in the motor cortex, leading to the pathological diagnosis of PLS. To explore methods for differentiating PLS from PSP, ante‐mortem QSM images were visually and quantitatively assessed for abnormal increases in magnetic susceptibility in the motor cortex. Prussian blue and Luxol fast blue combined with periodic acid‐Schiff staining were also performed to understand the source of the susceptibility increases. QSM showed clear hyperintense signals in the motor cortex. Magnetic susceptibility in the motor cortex was higher in the PLS patient (Z = 4.7, p < 0.001) compared to normal controls and pathologically diagnosed PSP patients. Pathological examination of the region showed intracortical myelin loss, as well as iron deposition. Underlying pathological processes for the increased magnetic susceptibility include not only iron deposition but also intracortical myelin. Our case suggests that QSM is a potential tool to differentiate PLS from PSP, providing insights for accurate diagnosis and enhancing clinical decision‐making. [ABSTRACT FROM AUTHOR]

Any change in the composition or physicochemical parameters of honey outside the standardized intervals may be deemed fraud, irrespective of direct introduction of certain substances or feeding honeybees with syrups. Simple and rapid tools along with more sophisticated ones are required to monitor fraudulent practices in the honey trade. In this work, UV–Vis spectroscopy was used to identify and quantify six Romanian honey types (five monofloral and one polyfloral) mixed with commercially available corn syrup, corn syrup with plant extracts, inverted syrup, and fruit syrup at different concentrations (5%, 10%, 20%, 30%, 40%, and 50%). Relevant spectral features were used to develop a neural model, which was able to pinpoint adulteration, regardless of the honey and adulterant type. The proposed model was able to detect adulteration levels higher than 10%, thereby serving as a cost-effective and reliable tool to monitor honey quality. [ABSTRACT FROM AUTHOR]

Adulteration is performed by adding inferior products imperceptible by consumers. Consequently, the objective of this work was to develop and use analytical methods to detect adulterants in Evening Primrose Oil (EPO) through differential scanning calorimetry (DSC) with a controlled cooling/heating system and chemometrics. For such a purpose, binary mixtures were prepared using soybean oil as adulterant. It was weighed and prepared in a ratio ranging from 5 to 95% (m/m). Samples were submitted to DSC based on the following parameters: dynamic atmosphere of N2 (50 mL min−1); temperatures ranging from 283.15 to 198.15 K and from 198.15 to 283.15 K; cooling/heating rate of 275.15 K min−1; about 25 mg of sample in an aluminum crucible. Chemometric models were constructed from the DSC heating curves and normalized by the respective initial masses of samples. Data were pre-processed, normalized by their respective standard deviations and mean center. Results of multivariate analyzes were also compared with univariate calibration using Tonset data (referring to the EPO melting point). Chemometric models were successfully constructed to quantify the level of adulteration, showing RMSE (mean squared errors) of 2.23 and 3.27% m/m for PLS and iPLS (Partial Least Squares and interval Partial Least Squares), respectively. The univariate model demonstrated linearity ranging from 15 to 80% m/m and reached RMSE of 1.06% m/m, thus being an outstanding alternative for a preliminary analysis aimed to reveal the presence of soybean oil in EPO. Therefore, the strategy of coupling DSC with chemometrics for detecting soybean oil in EPO proved to be satisfactory. [ABSTRACT FROM AUTHOR]

The biopharmaceutical industry continually seeks advancements in the commercial manufacturing of therapeutic proteins, where mammalian cell culture plays a pivotal role. The current work presents a novel data‐driven predictive modeling application designed to enhance the efficiency and predictability of cell culture processes in biotherapeutic production. The capability of the cloud‐based digital data science application, developed using open‐source tools, is demonstrated with respect to predicting bioreactor potency from at‐line process parameters over a 5‐day horizon. The uncertainty in model's prediction is quantified, providing valuable insights for process control and decision‐making. Model validation on unseen data confirms the model's robust generalizability. An interactive dashboard, tailored to process scientist's requirements is also developed to streamline biopharmaceutical manufacturing processes, ultimately leading to enhanced productivity and product quality. [ABSTRACT FROM AUTHOR]

The current research was carried out with the aim of identifying the components of satisfaction from service re-creation with an emphasis on brand loyalty in the banking industry and is in the scope of developmental research. A mixed method was used in this research. The statistical population in the qualitative section is the experts and managers of Iran’s banking industry, of which 15 people were selected in a non-random way. The statistical population in the quantitative part includes all customers of the Agricultural Bank in Tehran (unlimited population). Based on this, a sample of 384 people is selected using Morgan’s table. The main tool for data collection is a semi-structured in-depth interview and a researcher-made questionnaire. Qualitative data analysis was done with the method of data base theory using MAXQDA software and in the quantitative part with partial least squares method. Based on the paradigm model obtained in this research, the components related to service recovery satisfaction in six categories of causal factors (customer-oriented, brand image, communication, evaluation of correct performance and growth and development of services), background conditions (brand culture), central phenomenon (satisfaction with re-creation), strategies (organization strategy, marketing, and advertising), intervening conditions (customer anger and environmental factors) and consequences (brand loyalty, brand trust, customer satisfaction and profit increase) were identified. The results of the quantitative part of the research showed that the model proposed in this research is valid. Based on the obtained results, it can be concluded that the level of satisfaction and loyalty of banking customers can be improved by focusing on the components identified in relation to service regeneration. [ABSTRACT FROM AUTHOR]