Multivariate Statistical Models Research Articles

Influence of geological and technological Parameters on the Efficiency of acid treatments in carbonate reservoirs: Experimental and statistical study

Acid treatment technology has become widespread in the development of carbonate layers in oil fields. Virtually every well in the history of its production has been acid treated downhole, ultimately providing millions of tons of incremental oil production globally. Despite the considerable amount of theoretical and practical research devoted to this technology, the problem of controlling the efficiency of acid treatments remains topical. In this connection, the purpose of this paper is to study the influence of known and available in field conditions geological and technological parameters on the efficiency of acid stimulation in carbonate reservoirs. Laboratory studies (in free volume, filtration and X-ray tomography tests) using two hydrochloric acid compositions and carbonate rock samples of the same geologic age from oil fields of Perm krai to determine the dependencies were performed. Acid treatments were modeled by varying technological parameters (volume, rate and pressure of injection of acid compositions, reaction time of acid with rock) under thermodynamic conditions corresponding to reservoir properties. Statistical processing of empirically obtained data allowed us to note the predominant role of technological parameters to achieve a successful result of acid stimulation. Multivariate statistical models describing the process of formation of acid treatment efficiency in carbonate reservoirs have been developed. It was found that the reservoir stimulation efficiency varies in direct proportion to the increase in the injection pressure of the acid composition and the volume of agent used due to the development of wormholes. The efficiency of acid treatment at dolomite content in the rock is significantly lower, which requires increasing the rate of composition injection and reaction time to achieve the highest stimulation result. Thus, the paper provides a basis for further development of the process of controlling the effectiveness of acid treatments in carbonate reservoirs of oil fields.

Effects of Seasonal Freezing and Thawing on Soil Moisture and Salinity in the Farmland Shelterbelt System in the Hetao Irrigation District

Water resources are scarce, and secondary soil salinization is severe in the Hetao Irrigation District. Farmland shelterbelt systems (FSS) play a critical role in regulating soil water and salt dynamics within the irrigation district. However, the understanding of soil water and salt migration within FSS during the freeze–thaw period remains unclear due to the complex and multifaceted interactions between water and salt. This study focused on a typical FSS and conducted comprehensive monitoring of soil moisture, salinity, temperature, and meteorological parameters during the freeze–thaw period. The results revealed consistent trends in air temperature and soil temperature overall. Soil freezing durations exceeded thawing durations, and both decreased with an increasing soil depth. At the three critical freeze–thaw nodes, the soil moisture content at a 0–20 cm depth was significantly lower than at a 40–100 cm depth (p < 0.05). The soil water content increased with time and depth at varying distances from the shelterbelt, with an average increase of 7.63% after freezing and thawing. The surface water content at the forest edge (0.3H, 4H) was lower than inside the farmland (1H, 2H, 3H). Soil salt accumulation occurred during both freezing stable periods and melting–thawing periods in the 0–100 cm soil layer near the forest edge (0.3H, 4H), with the highest soil salinity reaching 0.62 g·kg−1. After the freeze–thaw period, the soil salt content in each layer increased by 11.41–47.26% compared to before the freeze–thaw period. Salt accumulation in farmland soil near the shelterbelt was stronger than in the far shelterbelt. The multivariate statistical model demonstrated goodness of fit for soil water and salt as 0.94 and 0.72, respectively, while the BP neural network model showed goodness of fit for soil water and salt as 0.82 and 0.85, respectively. Our results provide an efficient theoretical basis for FSS construction and agricultural water management practices.

Spatial colocalization and combined survival benefit of natural killer and CD8 T cells despite profound MHC class I loss in non-small cell lung cancer.

MHC class I (MHC-I) loss is frequent in non-small cell lung cancer (NSCLC) rendering tumor cells resistant to T cell lysis. NK cells kill MHC-I-deficient tumor cells, and although previous work indicated their presence at NSCLC margins, they were functionally impaired. Within, we evaluated whether NK cell and CD8 T cell infiltration and activation vary with MHC-I expression. We used single-stain immunohistochemistry (IHC) and Kaplan-Meier analysis to test the effect of NK cell and CD8 T cell infiltration on overall and disease-free survival. To delineate immune covariates of MHC-I-disparate lung cancers, we used multiplexed immunofluorescence (mIF) imaging followed by multivariate statistical modeling. To identify differences in infiltration and intercellular communication between IFNγ-activated and non-activated lymphocytes, we developed a computational pipeline to enumerate single cell neighborhoods from mIF images followed by multivariate discriminant analysis. Spatial quantitation of tumor cell MHC-I expression revealed intra- and inter-tumoral heterogeneity, which was associated with the local lymphocyte landscape. IHC analysis revealed that high CD56+ cell numbers in patient tumors were positively associated with disease-free survival (DFS) (HR=0.58, p=0.064) and overall survival (OS) (HR=0.496, p=0.041). The OS association strengthened with high counts of both CD56+ and CD8+ cells (HR=0.199, p<1×10-3). mIF imaging and multivariate discriminant analysis revealed enrichment of both CD3+CD8+ T cells and CD3-CD56+ NK cells in MHC-I-bearing tumors (p<0.05). To infer associations of functional cell states and local cell-cell communication, we analyzed spatial single cell neighborhood profiles to delineate the cellular environments of IFNγ+/- NK cells and T cells. We discovered that both IFNγ+ NK and CD8 T cells were more frequently associated with other IFNγ+ lymphocytes in comparison to IFNγ- NK cells and CD8 T cells (p<1×10-30). Moreover, IFNγ+ lymphocytes were most often found clustered near MHC-I+ tumor cells. Tumor-infiltrating NK cells and CD8 T cells jointly affected control of NSCLC tumor progression. Co-association of NK and CD8 T cells was most evident in MHC-I-bearing tumors, especially in the presence of IFNγ. Frequent co-localization of IFNγ+ NK cells with other IFNγ+ lymphocytes in near-neighbor analysis suggests NSCLC lymphocyte activation is coordinately regulated.

Managing Seismic Risk Associated to Development Blasting Using Random Forests Predictive Models Based on Geologic and Structural Rockmass Properties

As mining activities are expected to develop at greater depths, seismic responses to the blasting of development drift segments are expected to increase and present a greater hazard. A database of 379 development blasts was created for a mining site, recording seismic responses related to these blasts and rock mass structural and geologic properties associated with the drift segment. A random forest, multivariate statistical predictive model was developed with 75% of the drift segments. The model's performance was validated by analyzing 100 drift segments that were not used to create the model. The improved understanding of the variation in the intensity of seismic responses to development blasting through the sum of the seismic moment of the events is a clear benefit of random forest model development for the case study. In addition, the development of the predictive random forest model provides a tool for decision-makers to select performance criteria thresholds that they deem acceptable. The threshold selected would depend on the risk appetite of the decision-makers. The proposed approach provides quantitative data on the distribution of seismic hazards associated with development blasting which managers can rely on. Combining the proposed approach with current seismic protocols used at different mine sites could improve our management of seismic risk associated with development blasting. Using the predictive model for the sector and period studied has shown a potential to increase the accuracy, sensitivity, and precision for anticipating a high-intensity seismic response to a development blast.

Spatial analysis of socio-economic and demographic factors influencing urban flood vulnerability

Rapid urbanization and climate change require a thorough understanding of flood vulnerability in order to assure urban safety and resilience. Understanding the factors that contribute to flood vulnerability, allows us to develop effective initiatives that could mitigate the destructive consequences of flooding, while also protecting communities. The objective of this research is to identify and model the socio-economic and demographic factors that significantly influence flood vulnerability in the floodplains of Jackson, Mississippi, and Birmingham, Alabama, USA. First we analyzed the correlation between socio-economic and demographic factors then employed Principal Component Analysis (PCA) to address multicollinearity, a common challenge in multivariate statistical modeling. Subsequently, PCs-based global regression (PCR) and geographically weighted regression (PCGWR) analysis are used to identify key drivers of flood vulnerability. The findings demonstrate that a significant proportion of the variance (>80%) of these factors can be captured by first two to three Principal Components (PCs). Consistent with existing research, African American, poverty, seniors, and the number of less educated people positively correlate with flood vulnerability, while income and housing prices exhibit a negative correlation. Additionally, PCGWR outperformed the Principal Component Regression (PCR) in most cases, highlighting the spatial heterogeneity of flood vulnerability. This study focuses on two U.S. cities, and the methodology is applicable to other cities with similar characteristics. The identified factors align with global research on flood vulnerability, making the proposed research and findings valuable worldwide. The findings of this research are useful for local governments, policymakers, and urban developers to make detailed location specific flood vulnerability plan to reduce impact of flood and improve urban resilience.

Swallowing characterization of adult-onset Niemann-Pick, type C1 patients

BackgroundNiemann-Pick disease, type C1 (NPC1) is a rare lysosomal disorder with progressive neurological manifestations, historically recognized as a pediatric disease. However, awareness of the adult-onset (AO) subtype is increasing, often with non-specific symptoms leading to delayed and misdiagnosis. Dysphagia, commonly recognized as a clinical morbidity in NPC1, raises concerns for swallowing safety and aspiration risk. This study aims to characterize swallowing function in AO NPC1, addressing the gap in understanding and clinical management.MethodsFourteen AO NPC1 individuals in a prospective natural history study (NCT00344331) underwent comprehensive assessments, including history and physical examinations utilizing the NPC1 severity rating scale, videofluoroscopic swallowing studies with summary interpretive analysis, and cerebrospinal fluid (CSF) collection for biomarker evaluation at baseline visit. Descriptive statistics and multivariate statistical modeling were employed to analyze NPC1 disease covariates, along with the American Speech-Language-Hearing Association National Outcome Measure (ASHA-NOMS) and the NIH Penetration Aspiration Scale (NIH-PAS).ResultsOur cohort, comprised of 14 predominately female (n = 11, 78.6%) individuals, had an average age of 43.1 ± 16.7 years at the initial visit. Overall, our AO patients were able to swallow independently with no/minimal cueing, with 6 (43%) avoiding specific food items or requiring more time. Upon risk analysis of aspiration, the cohort demonstrated no obvious aspiration risk or laryngeal aspiration in 8 (57%), minimal risk with intermittent laryngeal penetration and retrograde excursion in 5(36%), and moderate risk (7%) in only one. Dietary modifications were recommended in 7 (50%), particularly for liquid viscosities (n = 6, 43%) rather than solids (n = 3, 21%). No significant correlations were identified between swallowing outcomes and NPC1-related parameters or CSF biomarkers.ConclusionDespite the heterogeneity in NPC1 presentation, the AO cohort displayed functional swallowing abilities with low aspiration risk with some participants still requiring some level of dietary modifications. This study emphasizes the importance of regular swallowing evaluations and management in AO NPC1 to address potential morbidities associated with dysphagia such as aspiration. These findings provide clinical recommendations for the assessment and management of the AO cohort, contributing to improved care for these individuals.

Fractionated radiotherapy after gross-total resection of spinal chordoma: a systematic review of survival outcomes using individualized patient data.

Spinal chordoma treatment guidelines recommend resection. However, in patients in whom gross-total resection (GTR) is achieved, the benefits of radiation therapy (RT) are unclear. Therefore, the authors performed a systematic review to determine if RT is associated with postoperative progression-free survival (PFS) or overall survival (OS) after achieving GTR of spinal chordoma. The PubMed database was searched for studies including individualized data of patients undergoing GTR with or without RT for spinal chordoma. Patients < 18 years of age or those who underwent stereotactic body RT were excluded. Qualitative assessment was performed using Newcastle-Ottawa Scale guidelines. Log-rank tests for time-to-event data and a Cox proportional-hazards model were generated for a multivariable statistical model. Complete data of 132 patients were retrieved, with 37 (28%) patients receiving adjuvant RT and 95 (72%) not receiving adjuvant RT. The mean follow-up was not statistically significantly different between those undergoing RT and not undergoing RT (54.02 months and 65.43 months, respectively). Patients were more likely not to undergo RT if their disease was located in the sacrum versus the mobile spine (p < 0.001). When controlling for age ≥ 65 years, male sex, disease location, and treatment year ≥ 2010, patients undergoing RT had similar PFS and OS when compared with those not undergoing RT on multivariable survival analysis (HR 0.935 [95% CI 0.703-2.340], p = 0.844 and HR 2.078 [95% CI 0.848-5.090], p = 0.110, respectively). However, age ≥ 65 years was associated with poorer OS in adjusted analyses (HR 2.761 [95% CI 1.185-6.432], p = 0.018) relative to patients < 65 years of age. After achieving GTR of spinal chordoma, the utility of RT on PFS and OS remains unclear. Age ≥ 65 years appears to be associated with OS in spinal chordoma patients. Additional multicenter prospective studies are needed to determine the utility of RT in this patient population.

Developing a Fluvial and Pluvial Stochastic Flood Model of Southeast Asia

AbstractFlood event set generation, as employed in catastrophe risk models, relies on gauge information that is not available in data‐scarce regions. To overcome this limitation, we develop a stochastic fluvial and pluvial flood model of Southeast Asia, using freely and globally available discharge data from the global hydrological model GloFAS and rainfall from the ERA5 reanalysis. We use a conditional multivariate statistical model to produce a synthetic catalog of 10,000 years of flood events. We calculate the flood population exposure associated with each flood event using freely available population data from WorldPop and generate exposure probability exceedance curves. We validate the population exposure curves against observed flood disaster data from EM‐DAT, showing that our methodology provides exposure estimates that are in line with historical observations. We find that there is a 1% probability that more than 30 million people will be exposed to flooding in a given year according to our event set. This number is roughly half the population living in the 100‐year return period flood zone of Fathom's hazard maps, suggesting most studies based on static flood maps overestimate exposure. This analysis provides significant progress over previous non‐stochastic studies which are only able to compute total or average exposure within a given floodplain area and demonstrates that a reanalysis‐based stochastic flood model can be designed to generate reliable estimates of population exposure probability exceedance. This study is a step toward a fully global catastrophe model for floods capable of providing exposure and loss estimates worldwide.

Hydrogeochemical evolution and processes based on multivariable statistical and inverse simulation modeling: A coal mine in the Northern Coalfield, China

ABSTRACT Considering groundwater from the aquifers overlying the bedrock is an important water source for drinking purposes. As such, the investigation of its property is essential. Based on the spatial structure of aquifers in the study area, the aquifers in the Cenozoic strata are divided into three groups. The multivariate statistical approaches are employed to identify the hydrogeochemical processes and hydro-chemical types, and hydrogeochemical inverse modeling is applied to further validate and elucidate the hydrogeochemical process and water–rock interaction. The results are as follows: (1) The hydro-chemical type of the upper aquifer is dominated by the K + Na-HCO3 type, while others have similar water quality types, which are dominated by the K + Na-Cl type and the K + Na-SO4 type. (2) The saturation index of anhydrite, gypsum, halite, and CO2(g) is below zero in three aquifers, indicating that they are unsaturated. While aragonite, calcite, and dolomite in the middle aquifer remain in the unsaturated–saturated state. (3) The cation exchange process accelerating the reduction of Ca2+ concentration and the increase of SO42− concentration occurs in three aquifers, and the dissolution of calcite and dolomite minerals occurs in most cases. This study supports the fundamental evidence for the hydrogeochemical processes and water resource utilization and has a certain practical significance.

Developing statistical tools to analyze historical stress-controlled fatigue data in additively manufactured austenitic stainless steel

The development of robust fatigue property data sets for additively manufactured 316L austenitic stainless steels is hindered by the small sample sizes, inconsistent reporting of material properties and processing conditions, and differences in testing procedures for current sources of testing data. By applying standardization and categorization protocols to account for these shortcomings, historical data sets were aggregated into a single large data set and analyzed in greater detail, providing new levels of insight into the stress-controlled fatigue behavior of additively manufactured materials. Using this single aggregated data set, scatter was quantified using an eigenvalue analysis, and a multi-variable statistical model for predicting the mean fatigue life was developed. Interactions between variables and the sensitivity to different loading conditions were then identified, with the scatter in the data being attributed to differences in testing and post-processing condition such as surface condition and heat treatment, and the model was used to identify the role that each variable plays in determining fatigue life. An analysis of the fatigue responses across the aggregated data set using clustering algorithms allowed two distinct trends to be identified and attributed to the differing roles of microstructure and porosity on the resulting fatigue lives across different equivalent stress amplitude ranges.

Gut microbiome correlates of recurrent urinary tract infection: a longitudinal, multi-center study

Urinary tract infections (UTI) affect approximately 250 million people annually worldwide. Patients often experience a cycle of antimicrobial treatment and recurrent UTI (rUTI) that is thought to be facilitated by a gut reservoir of uropathogenic Escherichia coli (UPEC). 125 patients with UTI caused by an antibiotic-resistant organism (ARO) were enrolled from July 2016 to May 2019 in a longitudinal, multi-center cohort study. Multivariate statistical models were used to assess the relationship between uropathogen colonization and recurrent UTI (rUTI), controlling for clinical characteristics. 644 stool samples and 895 UPEC isolates were interrogated for taxonomic composition, antimicrobial resistance genes, and phenotypic resistance. Cohort UTI gut microbiome profiles were compared against published healthy and UTI reference microbiomes, as well as assessed within-cohort for timepoint- and recurrence-specific differences. Risk of rUTI was not independently associated with clinical characteristics. The UTI gut microbiome was distinct from healthy reference microbiomes in both taxonomic composition and antimicrobial resistance gene (ARG) burden, with 11 differentially abundant taxa at the genus level. rUTI and non-rUTI gut microbiomes in the cohort did not generally differ, but gut microbiomes from urinary tract colonized patients were elevated in E.coli abundance 7-14 days post-antimicrobial treatment. Corresponding UPEC gut isolates from urinary tract colonizing lineages showed elevated phenotypic resistance against 11 of 23 tested drugs compared to non-colonizing lineages. The gut microbiome is implicated in UPEC urinary tract colonization during rUTI, serving as an ARG-enriched reservoir for UPEC. UPEC can asymptomatically colonize the gut and urinary tract, and post-antimicrobial blooms of gut E.coli among urinary tract colonized patients suggest that cross-habitat migration of UPEC is an important mechanism of rUTI. Thus, treatment duration and UPEC populations in both the urinary and gastrointestinal tract should be considered in treating rUTI and developing novel therapeutics. This work was supported in part by awards from the U.S. Centers for Disease Control and Prevention Epicenter Prevention Program (grant U54CK000482; principal investigator, V.J.F.); to J.H.K. from the Longer Life Foundation (an RGA/Washington University partnership), the National Center for Advancing Translational Sciences (grants KL2TR002346 and UL1TR002345), and the National Institute of Allergy and Infectious Diseases (NIAID) (grant K23A1137321) of the National Institutes of Health (NIH); and to G.D. from NIAID (grant R01AI123394) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (grant R01HD092414) of NIH. R.T.'s research was funded by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation; grant 402733540). REDCap is Supported by Clinical and Translational Science Award (CTSA) Grant UL1 TR002345 and Siteman Comprehensive Cancer Center and NCI Cancer Center Support Grant P30 CA091842. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

Analysis of the Impact of ESG Performance on Financial Market Stability from the Perspective of Multivariate Statistical Models

The stability of the financial markets is impacted by environmental, social and governance (ESG) performance, which also lowers the risks associated with social injustice, climate change and poor corporate governance. Companies that prioritize ESG standards have a higher probability of being resilient to market shocks, attracting responsible investors and contributing to long-term sustainable economic growth. By the utilization of multivariate statistical models, we seek to execute thorough analysis in this study to assess the effect of ESG performance on financial market stability. The sample that was selected comprises various categories of companies. Financial in sequence from the stock market is retrieved; include balance sheets, cash flow statement and income statements. An ESG rating is also acquired for the inquiry. The collected data were evaluated using a multivariate regression analytical representation. Our empirical methodology ensures robustness in identifying the complicated relationships between ESG performance and financial market stability, providing expensive insight into the dynamics of Chinese A-share organizations. The result express that increasing the ESG rating reduces financing limitations such as financial insecurity, increases future risk resistance and improve the company’s standing for obtaining a commodities premium. The association determines that an advanced ESG rating leads to enhanced financial market stability, which in turn contributes to the stock market concert. This underscores the essential roles of ESG consideration in attractive market resilience and financial market stability.

Combined diagnostic ratio of trace elements and biomarkers with multivariate statistical analysis for the differentiation of crude oil origin

Crude oil serves as a crucial mineral resource in terms of both imports and exports, and therefore, traceability of crude oil origin is imperative. This study pioneered a novel approach to identifying crude oil origin that was specifically tailored to fraud testing in imports. The analysis included 27 representative crude oil samples from nine major exporting nations, utilizing gas chromatography–mass spectrometry (GC–MS) and inductively coupled plasma–mass spectrometry (ICP–MS). Based on these samples, a corresponding crude oil origin tracing model was established. The following significant findings were obtained: (1) There were differences in the content of trace elements, biomarkers, and diagnostic ratios among crude oil samples from different countries; (2) employing partial least squares discriminant analysis (PLS–DA), a multivariate statistical analysis model was constructed, incorporating 24 trace element and biomarker diagnostic ratios (DRs) and effectively determining crude oil origin; (3) eleven diagnostic rates with the highest discriminative power were extracted; (4) the model was tested by a confusion matrix, and the accuracy rate was 93.8%. This research method may have practical application value to better identify the origin of imported crude oil.

The impact of metastasectomy on survival of patients with synchronous metastatic renal cell cancer in Finland: A nationwide study.

Most of the studies on metastasectomy in renal cell cancer are based on metachronous, often oligometastatic disease. Prior data on the impact of metastasectomy in synchronous metastatic renal cell cancer (mRCC) is, however, very scarce. We aimed to investigate the role of complete and incomplete metastasectomy in a large, nationwide patient population. We analyzed nationwide data, including all synchronous mRCC cases in Finland diagnosed during a 6-year period identified from the Finnish Cancer Registry, and complemented with patient records from the treating hospitals. We only included the patients who underwent removal of the primary tumor by nephrectomy. We performed univariate and multivariable adjusted analysis to identify the effect of metastasectomy on overall survival (OS) and cancer-specific survival (CSS). We included 483 patients with synchronous mRCC. Overall, 57 patients underwent complete and 96 incomplete metastasectomy, while 330 patients had no metastasectomy. The median OS was 17.9 and CSS 17.2 months for all patients. The median OS and the median CSS were 59.3 and 60.8 months for the complete, 21.9 and 25.1 for the incomplete, and 14.5 and 14.8 months for the no metastasectomy groups (p < 0.001 for differences). In both applied multivariable statistical models, the OS and CSS benefit from complete metastasectomy remained significant (hazard ratios (HRs) varied between 0.42 and 0.54, p < 0.001) compared with the no metastasectomy group. However, there was no improvement in survival estimates in the incomplete metastasectomy group compared with the no metastasectomy group (HRs varied between 1.04 and 1.10, p > 0.40). Complete metastasectomy, when possible, can be considered as a treatment option for selected patients with synchronous mRCC who are fit for surgery. By contrast, we found no survival benefit from an incomplete metastasectomy suggesting that such procedures should not be performed for these patients.

An Empirical Study on Acceptance of Carpooling Service in Mirpur DOHS, Dhaka

Building a multivariate statistical model is the aim of this research for identifying the aspects that impact carpooling and acquisition in a residential area. Based on the stated attitudes and perceived risk of the users, a structured questionnaire is created with 152 feedbacks in total are gathered from residents of Mirpur DOHS. The research hypotheses are validated by means of structural equation modeling (SEM). The best SEM is chosen using statistical criteria and multiple trials. To validate the selected SEM, the feedbacks are further examined using the Relative Importance Index (RII) approach. The study shows that the “Safety &amp; Security” latent variable has more influence on the acceptance of the carpooling system among the two latent variables. Among the six observed variables underneath the “safety &amp; Security” latent variable, ‘Sharing Personal Information (y7)’ has the most significance. The service providers and policymakers will be able to execute the carpooling system in the Mirpur DOHS area with the aid of the identification of the significant factors in this.

Biotite chemistry as an indicator of hydrothermal deposit types and fluid sources: Insights from big data compilation, multivariate statistical analysis, and machine learning

Identification of a deposit type in an early stage of exploration can save money and time. However, despite several studies, there is a lack of simple deposit classifiers based exclusively on the major and trace element chemistry of minerals, which is relatively easier to measure. Furthermore, the identification of fluid source is crucial for any ore-genetic study, as it exerts a first order control on physicochemical characteristics of fluid, which ultimately control the alteration mineralogy, metal solubility and mobilization, and metal association in a deposit. The stable isotope composition (e.g., δ34S, δ13C, δ18O, δ11B, etc.) of rocks and minerals is conventionally used to constrain the source of ore-forming fluids, however, the potential of mineral chemistry for constraining fluid source is not well-explored. In this study, we compile the major element composition (wt% of oxides) of a large number (n = 1932) of hydrothermal biotite from various ore settings and execute multivariate statistical analysis and machine learning-based predictive modeling to explore the potential of using biotite chemistry as an indicator of deposit type and fluid sources. The atoms per formula units (i.e., apfu values), and calculated parameters (e.g., Xphl, chlorine intercept value, log(fH2O/fHCl)fluid) is used to perform principle component analysis (PCA) and partial least square discriminant analysis (PLS-DA). The results show that among the different ore settings, iron-oxide copper‑gold, Archean gold, porphyry, and magmatic-hydrothermal rare earth metals deposits and among the fluid sources, (meta-)evaporitic, magmatic-hydrothermal, and metamorphic fluid sources can be reliably discriminated using biotite chemistry. We also show that skarn, hydrothermal platinum group elements, and carbonatite-related rare earth elements deposits may not be discriminated using biotite chemistry. Similarly, mafic rock-derived, carbonatite-derived, and meteoric fluid sources may not be discriminated by biotite chemistry. We propose multiple binary and ternary classifier diagrams, which can be used as discriminator of deposit types and fluid sources. Furthermore, we have developed four different PLS-DA predictive modeling, two for deposit types and two for fluid sources, which can predict deposit types and fluid sources with good accuracy. We provide a Windows© based program that can be used to discriminate deposit types using our PLS-DA predictive modeling. The binary and ternary classifiers and the Windows© programs are tested with recently published biotite data, and it has been shown that these classifiers and programs can efficiently separate deposit types and fluid sources. We also discuss about the limitations of the classifiers.

Data-driven sensor delay estimation in industrial processes using multivariate projection methods

A key step in preparing industrial data for multivariate statistical modelling of (batch-)continuous processes is the estimation of sensor delays along the production line, to use as a correction for understanding relationships between them. Without such a correction, the measurements collected from the sensors do not all relate to the same portion of material travelling through the plant, which is where from a physical point of view the correlations are expected to arise from. Most methods to do these corrections currently reported in literature are limited in that they optimize only correlations bivariately between the different sensors, despite the eventual goal of being understood as a multivariate process model. This work proposes several methods for multivariate sensor delay estimation, based on multivariate projection methods, and critically investigates these using simulated and empirical process data from two industrial facilities. The objective is to provide more clarity on if, how, and why taking into account these multivariate correlations using such projection methods is beneficial. One of the proposed methods, named PLS-SEQ, was shown to outperform bivariate sensor delay estimation, which is regarded as benchmark method in this work, for all datasets. The average reduction in delay estimation error achieved by using PLS-SEQ is between 6% and 64%. The other newly proposed multivariate projection-based methods did not show such unanimous improvement in accuracy, but helped identifying the presence of sensor clusters in industrial data as a novel key challenge for sensor delay estimation. The presented results illustrate that projection-based multivariate methods, in particular the newly proposed method PLS-SEQ, show promising potential for sensor delay estimation, outperforming the bivariate sensor delay estimation methods used as benchmark. Ultimately, using these methods can improve the predictive and descriptive quality of statistical models for key industrial processes.

Cellulose Ethers from Banana (&lt;i&gt;Musa balbisiana&lt;/i&gt; Colla) Blossom Cellulose: Synthesis and Multivariate Optimization

Cellulose ethers are biocompatible polymers which have attracted considerable attention for various applications due to their physical and mechanical properties. The present work aims to find the optimum condition for synthesizing cellulose ethers from banana blossom cellulose (BBC) such as methylcellulose (MC), carboxymethyl cellulose (CMC) and hydroxypropyl cellulose (HPC). The ultrasonication-assisted method as an energy source is used to shorten the synthesis time at room temperature and obtain high yields. The influences of various parameters (NaOH concentration, etherification agents, and sonication time) were analyzed using a multivariate statistical modeling response surface methodology (RSM). The materials were characterized by FTIR, SEM, and TGA. The cellulose ethers obtained have the potential as food additives with DS values of 2.0, 0.7, and 0.86, respectively. MC was synthesized optimally with a yield of 96.52% using a composition of cellulose (0.4 g), 50% (w/v) NaOH (10 mL) and dichloromethane (6 mL). CMC was synthesized optimally with a yield of 98.26% using a composition of cellulose (0.4 g), 30% (w/v) NaOH (2 mL) and monochloroacetic acid (1 g). HPC was synthesized optimally with a yield of 97.51% using a composition of cellulose (0.4 g), 10% (w/v) NaOH (2 mL) and propylene oxide (1.5 mL).

Metabolomic insights into the therapeutic mechanisms of costus (Saussurea costus (Falc.) Lipsch.) root extract in propylthiouracil-induced hypothyroidism rat model

Ethnopharmacological relevanceSaussurea costus (Falc.) Lipschitz. is one of the most reputed medicinal plants as a traditional medicine in the Arab and Middle East regions in the treatment of thyroid disorders, however, more investigations are needed to fully understand its effectiveness and mechanism of action. Aim of the studyThe primary objective of the study was to assess the impact of Saussurea costus (COST) on the metabolic profiles of propylthiouracil (PTU)-induced hypothyroidism in rats. This involves a comprehensive examination of serum metabolites using UPLC/QqQ-MS analysis aiming to identify differential metabolites, elucidate underlying mechanisms, and evaluate the potential pharmacological effect of COST in restoring metabolic homeostasis. Materials and methodsHypothyroidism was induced in female Sprague-Dawley rats by oral administration of propylthiouracil (PTU). UPLC/QqQ MS analysis of serum samples from normal, PTU, and PTU + COST rats was utilized for annotation of intrinsic metabolites with the aid of online Human metabolome database (HMDB) and extensive literature surfing. Multivariate statistical analyses, including orthogonal partial least squares discriminant analysis (OPLS-DA), discerned variations between the different groups. Serum levels of T3, T4 and TSH in addition to arachidonic acid (ARA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) levels in thyroid gland tissues; Phospholipase A2 group IIA (PLA2G2A), and lipoprotein lipase (LPL) in liver tissues were assessed by specific ELISA kits. Gene expression for key proteins of the primary evolved pathwayswere quantified by one-step qRT-PCR technique. Histopathological evaluation of thyroid gland tissue was performed by an investigator blinded to the experimental group using light microscope. ResultsDistinct clustering in multivariate statistical analysis models indicated significant variations in serum chemical profiles among normal, disease, and treated groups. VIP values guided the selection of differential metabolites, revealing significant changes in metabolite concentrations. Subsequent to COST treatment, 43 differential intrinsic metabolites exhibited a notable tendency to revert towards normal levels. Annotated metabolites, such as lysophosphatidylcholine (LPC), L-acetylcarnitine, gamma-glutamylserine, and others, showed differential regulation in response to PTU and subsequent S. costus treatment. Notably, 21 metabolites were associated with polyunsaturated fatty acids (PUFAs) biosynthesis, arachidonic acid (ARA) metabolism, and glycerophospholipid metabolism exhibited significant changes on conducting metabolic pathway analysis. ConclusionsCOST improves PTU-induced hypothyroidism by regulating biosynthesis of PUFAs signified by n-3/n-6, ARA and glycerophospholipid metabolism. The study provides us a novel mechanism to explain the improvement of hypothyroidism and associated dyslipidemia by COST, depicts a metabolic profile of hypothyroidism, and gives us another point cut for further exploring the biomarkers and pathogenesis of hypothyroidism.

Effect of physical activity on the relationship between adiposity and cardiac autonomic modulation in female breast cancer survivors: a longitudinal study

ABSTRACT This longitudinal study aimed to analyze the influence of physical activity (PA) on the relationship between body adiposity and cardiac autonomic modulation (CAM) in women survivors of breast cancer. We collected body adiposity through electrical bioimpedance considering body fat percentage (BFP), CAM through heart rate variability (considering RMSSD, SDNN, PNN50, LF (m2), HF (m2), SD1 indexes and SD1/SD2 ratio) and PA through a questionnaire in 64 participants (58.0 ± 9.6 years), recruited through the local association of support for breast cancer and by direct indications from city mastologists. After insertion of PA into the multivariate statistical model, significant attenuation was observed in the relationship between body adiposity and CAM for the indices: SDNN (β = −0.94; 95 percent CI: −1.93; 0.04; p = .060) and SD1/SD2 (β = −0.01;95 percent CI = −0.02; 0.001; p = .065). In conclusion, it was observed that PA was able to mitigate the relationships between BFP and CAM (considering SDNN index and SD1/SD2 ratio) in breast cancer survivors.