Multivariate Statistical Models Research Articles

Integrated approaches for flash flood susceptibility mapping: spatial modeling and comparative analysis of statistical and machine learning models. A case study of the Rheraya watershed, Morocco

ABSTRACT Flash floods are highly destructive disasters, posing severe threats to lives and infrastructure. In this study, we conducted a comparative analysis of bivariate and multivariate statistical models and machine learning to predict flash flood susceptibility in the flood-prone Rheraya watershed. Six models were utilized, including frequency ratio (FR), logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), K-nearest neighbors (KNN), and naïve Bayes (NB). We considered 12 flash flood conditioning variables, such as slope, elevation, distance to the river, and others, as independent variables and 246 flash flood inventory points recorded over the past 40 years as dependent variables in the modeling process. The area under the curve (AUC) of the receiver operating characteristic was used to validate and compare the performance of the models. The results indicated that distance to the river was the most contributing factor to flash floods in the study area. Moreover, the RF outperformed all the other models, achieving an AUC of 0.86, followed by XGBoost (AUC = 0.85), LR (AUC = 0.83), NB (AUC = 0.76), KNN (AUC = 0.75), and FR (AUC = 0.72). The RF model effectively pinpoints highly susceptible zones, which is critical for establishing precise flash flood mitigation strategies within the region.

Prognostic Factors in Idiopathic Sudden Sensorineural Hearing Loss: The Experience of Two Audiology Tertiary Referral Centres

Background and Objectives: Although different hypotheses have been proposed over time, there is a dearth of information on factors able to predict the response to treatment for idiopathic sudden sensorineural hearing loss (ISSNHL) and hearing recovery. The aim of this study was to apply univariate and multivariate statistical models in a retrospective clinical setting of patients given therapy for ISSNHL at our tertiary academic audiological centers to investigate the prognostic value of clinical signs, symptoms, and comorbidities in relation to hearing recovery. Materials and Methods: The inclusion criteria were: history of ISSNHL diagnosed and treated at the Padova or Modena tertiary academic audiological centers; age ≥ 18 years; availability of clinical and audiological outcome data. The exclusion criteria were: hearing loss in acoustic schwannoma, endolymphatic hydrops, meningitis, trauma (head trauma, temporal bone fracture, acoustic trauma), barotrauma, perilymphatic fistula; exposure to noise levels ≥ 80 dB in the work environment; any unilateral or bilateral hearing loss (except for presbycusis) prior to ISSNHL diagnosis; any disorders affecting the external or middle ear; any previous ear surgery; refusal to make medical data available for research purposes. Eighty-six consecutive patients (38 females, 48 males; median age: 58 years; interquartile range: 47.00–69.00 years) were included. A systemic steroid therapy was administered to all patients, either orally with prednisone or intravenously with methylprednisolone. Second-line therapy included intratympanic steroid injections and/or hyperbaric oxygen therapy. Results: A multivariate logistic regression model was used, including the non-multicollinear clinical and audiological variables, which showed a p-value < 0.10 at the univariate analyses (namely age at diagnosis, time to diagnosis, oral steroid dose, and PTA on the affected side). Only PTA on the affected side retained its statistical significance (OR: 1.0615, 95% CI: 1.0185–1.1063, p = 0.005). Conclusions: The analysis of our data showed an association between the hearing threshold before treatment and the recovery from ISSNHL. Further studies on larger cohorts (especially in a prospective setting) are needed to shed more light on the prognostic role of clinical parameters in patients with ISSNHL. In a correct counseling setting, with regard to the patient’s concern about not being able to recover hearing, it is important to offer perspectives of appropriate hearing rehabilitation approaches.

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.

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.

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.

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.

Childhood Trauma and Self-harm in Youths with Bipolar Disorders.

Bipolar disorders (BD) in youth are associated with a high risk of self-harm behaviors. Childhood trauma (CT) is a relevant environmental stressor that is related to both BD diagnosis and self-harm in adulthood. It is not yet established whether CT may impact self-harm risk in youth. Therefore, the aim of this study was to investigate the distribution patterns of CT in youth BD with and without self-harm. We assessed 273 participants (aged 13-25 years), 96 youths with BD according to DSM-5 criteria and 177 healthy controls (HC). History of CT was obtained using the Childhood Trauma Questionnaire (CTQ). The association between CT and self-harm was tested using multivariate statistical models. Over 45% of participants with BD reported lifetime self-harm. The BD Self-harm group reported more emotional abuse, emotional neglect, sexual abuse, and physical abuse than HC. The BD No-Self-harm group reported more emotional abuse than HC. The BD Self-harm group reported more emotional abuse and neglect than the BD No-Self-harm group. The BD Self-harm group also reported separated parents, hospitalizations, smoking, use of antiepileptics, antipsychotics and lithium. Emotional abuse was an independent predictor of self-harm in youths with BD. Findings support the importance of assessing CT, in particular emotional abuse, in youth with BD at risk for self-harm.

Effects of the 2018 and 2019 floods in Kerala, India on the existing multivariate statistical models

Effects of the 2018 and 2019 floods in Kerala, India on the existing multivariate statistical models