Parallel Analysis Research Articles

Insight into the mechanism of alkali-thermal pretreatment of food-waste solid residue through fluorescence spectroscopy coupled with parallel factor analysis

Food-waste solid residue is the remaining solid after food waste treatment, with high yield, high solid content, high protein and fiber content. Effective pretreatment is necessary to improve the efficiency of hydrolysis and acidification for anaerobic digestion of food-waste solid residue. In this study, fluorescence spectroscopy coupled with parallel factor analysis were used to insight into the mechanism of food-waste solid residue during three pretreatments (alkali, thermal and alkali-thermal). Pretreatments increased the solubility of lignocellulosic substrate and destroyed structure of starch, while lignocellulosic analogs were effectively cracked, changing the composition and improving the degradability. Soluble chemical oxygen demand, soluble protein and soluble polysaccharide concentrations were increased by 144.60%, 350.57% and 138.72% after pretreatment under the condition of 120 °C + 2% CaO, respectively. Three-dimensional fluorescence spectra showed the region of maximum fluorescence intensity under alkali-thermal pretreatments, indicating chemical bonds (such as OC–C) were easier broken and the solubility of organic substances were increased. Three main fluorescence components were obtained by parallel factor analysis, which were humic acid-like, lignocellulose-like and protein-like, respectively, while the lignocellulose-like had the maximum Fmax value. The fluorescence intensity of samples under alkali-thermal pretreatment varied in the range from 59.48 × 105 to 13.18 × 106, which was an increase of 174.27%–507.74% over the control (21.68 × 105), indicating that alkali-thermal pretreatment observably accelerated the breaking of chemical bonds, and thus promoted the dissolution of organic matter. This study deeply revealed the mechanism of alkali-thermal pretreatment of food-waste solid residue, which is of great significance for efficient resource utilization of food waste and food-waste solid residue.

Impacts of an intensive traditional mariculture model on offshore environments as evidenced by dissolved organic matter and bacterial communities

Mariculture is a key protein source and economic driver but traditional methods pollute coastal waters, hindering sustainable development. Current research on the ecological impacts of mariculture mainly focuses on specific pollutants and lacks an assessment of the biogeochemical impacts of mariculture discharge. This study examined Xiangshan Bay, a representative intensive mariculture area in China, to explore the impact of mariculture tailwater discharge on water quality, focusing on dissolved organic matter (DOM) and bacterial communities. Based on the fluorescence excitation-emission matrix combined with parallel factor analysis, a characteristic fluorescence component C4 was identified in mariculture tailwater (intensity of 40 ± 4 %, n = 8), while higher C4 components (47 ± 3 %, n = 15) were found throughout the bay. Humic-like components from riverine input (C1 + C2: 32 ± 3 %, n = 15) and protein-like components from domestic sewage (C5: 2 ± 1 %, n = 15) were significantly lower in the seawater samples, indicating the strong influence of mariculture tailwater on the entire DOM structure of the bay. The bacterial community structure showed a response consistent with DOM, as revealed by nonmetric multidimensional scaling analysis. This showed that bacterial communities in mariculture tailwater and bay water samples clustered together, independent of riverine input. The SourceTracker model indicated that mariculture tailwater (91 ± 5 %) predominantly contributed to the bacterial community, with minimal contributions from riverine input (< 5 %). Co-occurrence network analysis further showed that under long-term high-intensity mariculture discharge, the C4 component became the core DOM and was closely associated with the bacterial community. The results here demonstrate the profound impact of traditional mariculture on coastal water quality and show that riverine input is not the primary pollution source in this region, providing clear directions for coastal environmental restoration efforts.

Hospital Heart Failure Medical Therapy Score and Associated Clinical Outcomes and Costs.

A composite score for guideline-directed medical therapy (GDMT) for patients with heart failure (HF) is associated with increased survival. Whether hospital performance according to a GDMT score is associated with a broader array of clinical outcomes at lower costs is unknown. To evaluate hospital variability in GDMT score at discharge, 90-day risk-standardized clinical outcomes and costs, and associations between hospital GDMT score and clinical outcomes and costs. This was a retrospective cohort study conducted from January 2015 to September 2019. Included for analysis were patients hospitalized for HF with reduced ejection fraction (HFrEF) in the Get With the Guidelines-Heart Failure Registry, a national hospital-based quality improvement registry. Study data were analyzed from July 2022 to April 2023. GDMT score at discharge. Hospital variability in GDMT score, a weighted index from 0 to 1 of GDMT prescribed divided by the number of medications eligible, at discharge was evaluated using a generalized linear mixed model using the hospital as a random effect and quantified with the adjusted median odds ratio (AMOR). Parallel analyses centering on 90-day mortality, HF rehospitalization, mortality or HF rehospitalization, home time, and costs were performed. Costs were assessed from the perspective of the Centers of Medicare & Medicaid Services. Associations between hospital GDMT score and clinical outcomes and costs were evaluated using Spearman coefficients. Among 41 161 patients (median [IQR] age, 78 [71-85] years; 25 546 male [62.1%]) across 360 hospitals, there was significant hospital variability in GDMT score at discharge (AMOR, 1.23; 95% CI, 1.21-1.26), clinical outcomes (mortality AMOR, 1.17; 95% CI, 1.14-1.24; HF rehospitalization AMOR, 1.22; 95% CI, 1.18-1.27; mortality or HF rehospitalization AMOR, 1.21; 95% CI, 1.18-1.26; home time AMOR, 1.07; 95% CI, 1.06-1.10) and costs (AMOR, 1.23; 95% CI, 1.21-1.26). Higher hospital GDMT score was associated with lower hospital mortality (Spearman ρ, -0.22; 95% CI, -0.32 to -0.12; P < .001), lower mortality or HF rehospitalization (Spearman ρ, -0.17; 95% CI, -0.26 to -0.06; P = .002), more home time (Spearman ρ, 0.14; 95% CI, 0.03-0.24; P = .01), and lower cost (Spearman ρ, -0.11; 95% CI, -0.21 to 0; P = .047) but not with HF rehospitalization (Spearman ρ, -0.10; 95% CI, -0.20 to 0; P = .06). Results of this cohort study reveal that hospital variability in GDMT score, clinical outcomes, and costs was significant. Higher GDMT score at discharge was associated with lower mortality, lower mortality or hospitalization, more home time, and lower cost. Efforts to increase health care value should include GDMT optimization.

A New Chinese Manichaean Painting: Auspicious Signs Heralding Mānī’s Birth

This paper introduces and analyzes a new Chinese Manichaean painting entitled ‘Auspicious Signs Heralding Mānī’s Birth’. Zhang Peng 张鹏, a doctoral candidate at the Central Academy of Fine Arts (Zhongyang Meishu Xueyuan 中央美术学院), was the first to notice the Manichaean affiliation of this painting, which was auctioned by the Beijing Poly International Auction Co., Ltd (Beijing Baoli Guoji Paimai Youxian Gongsi 北京保利國際拍賣有限公司) in 2021. This study employs the methods of iconographic comparison and analysis of textual parallels to argue that the recently identified hagiographic painting shares striking stylistic similarities with at least three other previously known Manichaean paintings, and that it can be directly related to one of them.

Characterization of sediment organic matter in the outer Yangtze River Estuary using stable isotopes, optical techniques, and FT-ICR-MS: Implications for the carbon burial mechanism

The burial of sediment organic matter (SOM) in the estuary and shelf plays an important role in the global carbon cycle. However, it is challenging to determine the source, composition, and burial of SOM in the coastal sea, especially at the molecular level. This was explored in the coastal area outside the largest Yangtze River of China with multiple techniques including elemental and stable isotopic analysis, absorption spectroscopy, fluorescence excitation-emission matrices coupled with parallel factor analysis (EEMs-PARAFAC), and ultra-high resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). The end-member mixing analysis based on δ13C and δ15N showed a dominance of marine contribution (up to 70%) at most stations while the terrestrial contribution increased to >55% nearshore in summer at a high fluvial sediment flux. This was consistent with the offshore decreasing humic-like C1 and C2, humification index (HIX), %lignin-like compounds, and %CHO but increasing tryptophan-like C3, biological index (BIX), %protein-like compounds, and %CHOS from EEMs-PARAFAC and FT-ICR-MS analysis. The %clay correlated positively with SOM content, HIX, %lignin-like compounds, O/C, and modified aromaticity index (AImod) but correlated negatively with %C3, H/C, and the relative abundance of labile formulas (MLBL), while %silt showed contrasting correlations. These results indicated the fine clay sediments adsorbed more humified, aromatic, oxygenated, and terrestrial compounds that were probably more resistant to biodegradation and thus had a higher burial efficiency than those on the silty sediments. Principal component analysis based on SOM indices further revealed different characteristics of SOM in the nearshore, northern offshore, and southern offshore regions, which were probably dependent on the delivery by local current systems. Overall, these findings contributed to unraveling the source and molecular composition of SOM associated with different grain size sediments and local current delivery, which are fundamental for understanding the factors underlying carbon burial in the complex coastal environment.

Identification and Quantification of Multiphase U(VI) Speciation on Gibbsite with pH Using TRLFS and PARAFAC of Excitation Emission Matrices.

The significant abundance of uranium in radioactive waste inventories worldwide necessitates a thorough understanding of its behavior. In this work, the speciation of uranyl(VI), (UO22+) in a gibbsite system under ambient conditions has been determined as a function of pH by deconvolution and analysis of luminescence spectroscopic data. Uniquely, a combined experimental and statistical approach utilizing time-resolved luminescence spectroscopy and parallel factor analysis (PARAFAC) of excitation emission matrices has been successfully utilized to identify four separate luminescent U(VI) species in the uranyl-gibbsite system for the first time. The speciation of all luminescent U(VI) species in an environmentally relevant system over a pH range of 6-11 is discerned through the analysis of emission fingerprints at low temperature (20 K). Comparison of the deconvoluted luminescence spectra with mineral standards and geochemical models of the system allows the assignment of the luminescent chemical species as metaschoepite, Na-compreignacite, surface adsorbed ≡AlO2-UO2(OH) and ≡AlO2-UO2(CO3)24- complexes, with assignments supported by fitting of extended X-ray absorption fine structure data. The combined spectroscopic techniques in this study show that assignment and quantification of uranyl(VI) species in a sorption system over a large pH range can be accurately achieved using PARAFAC to deconvolute a three way emission spectroscopic data set.

Parallel 3-RSR Wrist Rehabilitation Mechanism and Motion Performance Analysis

In order to help patients with wrist injuries to carry out recovery training, a simple and portable parallel 3-RSR wrist rehabilitation mechanism was introduced in order to achieve the expected therapeutic effect. Firstly, the parallel 3-RSR wrist rehabilitation mechanism is described. Taking the parallel mechanism as the research object, the degree of freedom is calculated and the pose is solved by analytical method, which verifies the rationality of the mechanism motion. Then, based on the boundary search method, the reachable workspace of the parallel wrist rehabilitation mechanism is analyzed, and the relevant workspace map is drawn. Finally, considering the reasonable optimization of the structural size of the workspace, the results show that the optimized reachable attitude workspace is significantly larger than the initial workspace. This study provides a theoretical reference for the ontology design of rehabilitation institutions.

A Screw Theory Approach for Instantaneous Kinematic Analysis of Parallel–Serial Manipulators

Abstract The paper presents an approach to perform an instantaneous kinematic analysis of parallel–serial (hybrid) manipulators using screw theory. In this study, we focus on non-kinematically redundant manipulators that include a single parallel mechanism. The proposed systematic procedure allows deriving Jacobian matrices for such manipulators, which provide mathematical relations between the end-effector velocities and speeds in the actuated joints. A generalized structure of the obtained matrices also reflects the constrained motions of the end-effector and the parallel mechanism. To illustrate the developed techniques, we consider three examples where we analyze three well-known parallel–serial manipulators with six, five, and four degrees-of-freedom. Following the proposed method, we determine Jacobian matrices for each manipulator. Next, we apply the presented approach for velocity analysis of a novel parallel–serial manipulator with five degrees-of-freedom. Numerical simulations validate the proposed theoretical techniques. The suggested approach represents the basis for subsequent singularity and performance analysis, and it can be adapted to hybrid manipulators with other architectures.

3D Printing of Thermally Responsive Shape Memory Liquid Crystalline Epoxy Networks.

A two-component liquid crystalline epoxy network (LCEN) with shape memory behavior was developed and evaluated as a candidate material for 3D printing. The cure kinetics of the uncured material and the shape memory properties of the cured LCEN were investigated by using parallel plate rheology and dynamic mechanical analysis, respectively. A commercially available fumed silica additive was introduced to the neat, uncured material to improve the rheological properties for 3D printing. The addition of fumed silica was found to increase the yield stress, shear-thinning behavior, and toughness of the uncured epoxy ink. Polarized light microscopy, differential scanning calorimetry, and wide-angle X-ray scattering measurements between the neat and additive-modified LCEN suggested a reduction in liquid crystalline alignment in the modified LCEN, owing to interactions between crystalline domains and fumed silica, which in turn influenced the mechanical behavior. Overall, the additive was found to be successful in preserving the shape memory properties of LCEN while improving its printability.

Carbon emission right circulation and corporate green innovation: Evidence from a quasi-natural experiment in China

This paper studies how carbon emissions right circulation affects corporate green innovation performance. Using the carbon emissions trading policy in China as a quasi-natural experiment, we find that carbon emission right circulation has a significant positive impact on corporate green innovation performance. This finding is robust to various checks, including the parallel trend analysis, placebo test, the instrumental variable approach, and alternative measures of green innovation. Mechanism tests show that the carbon emission right circulation policy promotes corporate green innovation performance by reducing environmental uncertainty, alleviating financing constraints, and improving external monitoring. In addition, cross-sectional analyses reveal that the positive impact of carbon emission right circulation on corporate green innovation is more significant in firms without political connections and lower levels of capacity utilization.

Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling.

To understand psychological data, it is crucial to examine the structure and dimensions of variables. In this study, we examined alternative estimation algorithms to the conventional GLASSO-based exploratory graph analysis (EGA) in network psychometric models to assess the dimensionality structure of the data. The study applied Bayesian conjugate or Jeffreys' priors to estimate the graphical structure and then used the Louvain community detection algorithm to partition and identify groups of nodes, which allowed the detection of the multi- and unidimensional factor structures. Monte Carlo simulations suggested that the two alternative Bayesian estimation algorithms had comparable or better performance when compared with the GLASSO-based EGA and conventional parallel analysis (PA). When estimating the multidimensional factor structure, the analytically based method (i.e., EGA.analytical) showed the best balance between accuracy and mean biased/absolute errors, with the highest accuracy tied with EGA but with the smallest errors. The sampling-based approach (EGA.sampling) yielded higher accuracy and smaller errors than PA; lower accuracy but also lower errors than EGA. Techniques from the two algorithms had more stable performance than EGA and PA across different data conditions. When estimating the unidimensional structure, the PA technique performed the best, followed closely by EGA, and then EGA.analytical and EGA.sampling. Furthermore, the study explored four full Bayesian techniques to assess dimensionality in network psychometrics. The results demonstrated superior performance when using Bayesian hypothesis testing or deriving posterior samples of graph structures under small sample sizes. The study recommends using the EGA.analytical technique as an alternative tool for assessing dimensionality and advocates for the usefulness of the EGA.sampling method as a valuable alternate technique. The findings also indicated encouraging results for extending the regularization-based network modeling EGA method to the Bayesian framework and discussed future directions in this line of work. The study illustrated the practical application of the techniques to two empirical examples in R.

Bacterial community and dissolved organic matter networks in urban river: The role of human influence

Human activities have significantly altered the biogeochemical cycles of carbon, nitrogen, and sulfur in aquatic ecosystems, leading to ecological problems.This study utilized 16S rRNA gene high-throughput sequencing and excitation-emission matrix parallel factor analysis (EEM-PARAFAC) to evaluate the bacterial community composition and dissolved organic matter structure in the upstream (less impacted) and downstream (severely impacted) sections of the river, with a focus on the interactions between bacterial diversity and dissolved organic matter (DOM) characteristics.Results indicated significant spatial diversity in bacterial communities, with a higher α-diversity upstream compared to the more polluted downstream sections. Environmental parameters, particularly total phosphorus (TP) and dissolved oxygen (DO), were found to significantly influence the distribution and composition of bacterial phyla through redundancy analysis. The pattern of bacterial community assembly has shifted from predominantly deterministic to predominantly stochastic as a result of human activities. The analysis of DOM through EEM-PARAFAC identified three main fluorescent components, reflecting varied sources and interactions with bacterial communities. Upstream, microbial activities predominantly contributed to autochthonous DOM, while downstream, increased inputs of allochthonous DOM from human activities were evident. Furthermore, the study revealed that through the introduction of various organic pollutants and nutrient loads that shift microbial metabolic functions towards increased degradation and transformation of complex organic compounds downstream. Structural equation modeling (SEM) revealed that upstream human activities primarily affected bacterial communities indirectly by altering DOM properties. In contrast, downstream activities had both direct and indirect effects due to higher pollutant loads and more complex environmental conditions. These interactions underline the profound effect of anthropogenic factors on riverine ecosystems and emphasize the importance of managing human impacts to preserve microbial biodiversity and water quality.

Abstract C023: PDAC tumors from patients treated with combination MEK and autophagy inhibition reveal a treatment sensitive epithelial subtype that is enriched in murine models

Abstract Combining MEK and autophagy inhibitors in preclinical PDAC mouse models resulted in tumor regressions and improved rodent survival. These findings provided the rationale for clinical investigation of this combination. The phase 1 MEKiAUTO trial evaluated combination cobimetinib, HCQ, with or without atezolizumab in 14 patients (pts) with KRAS-mutated pancreatic adenocarcinoma (PDAC). The study was closed due to a lack of preliminary efficacy and poor tolerability; however, three patients experienced progression free survival (PFS) beyond 12 weeks (two with KRAS G12R , one with KRAS G12D ). Correlative analyses on twenty-one biopsies (8-paired FFPE and 6-paired fresh frozen specimens) with multiplex immunofluorescence (mIF) demonstrated decreased phosphorylation of ERK and accumulation of p62 indicating a pharmacodynamic effect. Reverse phase protein array (RPPA) also confirmed MAPK pathway inhibition, with a decrease in pERK, and autophagy inhibition, with decreased autophagy signature score, within the tumor compartment of on-treatment biopsies compared to baseline. Single nucleus RNAseq with protein expression activity extrapolated using ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) and VIPER (Virtual Inference of Protein-activity by Enriched Regulon) pipelines identified five distinct tumor subpopulations. Comparing prevalence of tumor clusters before and after treatment identified differential responses to treatment. Gene set enrichment analysis revealed that the most sensitive tumor cluster, tumor cluster 2, was enriched in autophagy and MAPK signaling. Pre- treatment frequency of tumor cluster 2 was higher in patients who experienced a clinical benefit and independently correlated with PFS. Among the non-tumor cell clusters identified, patients with clinical benefit also had increased immune cell infiltration. To explain the discordance in efficacy with this combination in preclinical models and humans, we interrogated the preclinical tumor model systems for tumor heterogeneity. Single cell RNA-Seq analysis of tumors from Kras LSL.G12D/+ ; p53 LSL.R172H/+ ; Pdx1-Cre tg/+ (KPC) mice treated with the combination confirmed presence of similar treatment-sensitive and treatment-resistant clusters, with the former being enriched for MAPK and autophagy signatures. Parallel analysis of cell lines previously shown to be sensitive to combination MEK and autophagy inhibition, including MiaPACA2 and PANC1, showed they were mostly enriched in the treatment-sensitive human cluster 2 phenotype but not treatment-resistant clusters. Our results suggest that some preclinical models may overrepresent subtypes which are sensitive to MEK and autophagy inhibitors and thus do not recapitulate the extent of intertumoral heterogeneity in human PDAC. Observed preclinical responses likely represented elimination of treatment-sensitive cluster(s), with the remaining clusters responsible for maintained resistance. Citation Format: Brian W Labadie, Aleksandar Z Obradovic, Sarah Sta Ana, Michael May, Naomi Sender, Isabelle V Ross, Monica Rodriguez-Mendez, Urszula Wasko, Lorenzo Tomassoni, Li Li, Manan Vij, Sinan Abuzaid, Julia Wulfkuhle, Winston Wong, Ilenia Pellicciotta, Benjamin Izar, Emanuel F Petricoin, Aik Choon Tan, Kenneth P Olive, Alex G Raufi, Gulam A Manji. PDAC tumors from patients treated with combination MEK and autophagy inhibition reveal a treatment sensitive epithelial subtype that is enriched in murine models [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C023.

Improved Bayesian model updating of geomaterial parameters for slope reliability assessment considering spatial variability

In engineering practice, Bayesian model updating using field data is often conducted to reduce the substantial inherent epistemic uncertainties in geomaterial properties resulting from complex geological processes. The Bayesian Updating with Subset simulation (BUS) method is commonly employed for this purpose. However, the wealth of field data available for engineers to interpret can lead to challenges associated with the “curse of dimensionality”. Specifically, the value of the likelihood function in the BUS method can become extremely small as the volume of field data increases, potentially falling below the accuracy threshold of computer floating-point operations. This undermines both the computational efficiency and accuracy of Bayesian model updating. To effectively address this technical challenge, this paper proposes an improved BUS method developed based on parallel system reliability analysis. Leveraging the Cholesky decomposition-based midpoint method, the total failure domain in the original BUS method, which involves a low acceptance rate, is subdivided into several sub-failure domains with a high acceptance rate. Facilitated with an improved Metropolis-Hastings algorithm, the improved BUS method enables the consideration of a large volume of field data and spatial variability of geomaterial properties in the probabilistic back analysis. The results of an illustrative soil slope, involving spatially variable undrained shear strength, demonstrate that the improved BUS method is effective in simultaneously incorporating a substantial volume of field measurements and observations in the model updating process. Through a comparison with the original BUS method, the improved BUS method is shown to be useful for Bayesian model updating of high-dimensional spatially variable geomaterial properties and slope reliability assessment.

Variability of Bile Baseline Excitation-emission Fluorescence of Two Tropical Freshwater Fish Species.

The quantification of pollutant metabolites in fish bile is an efficient approach to xenobiotic pollution monitoring in freshwaters since these measurements directly address exposure. Fluorescence excitation-emission matrix spectroscopy (EEMS) has demonstrated to be a highly specific and cost-effective technique for polycyclic aromatic hydrocarbon (PAH) and PAH-metabolite identification and quantification. EEMS ability to quantify these compounds strongly depends on the intensity and variability of the bile baseline fluorescence (BBF). We found large differences in BBF among Aequidens metae (AME) individuals and of these with Piaractus orinoquensis (PIO). Moreover, BBF was large enough that solvent dilutions of over 1:400 were needed to avoid inner filter effects. We used parallel factor analysis (PARAFAC) to model the intra- and inter-species BBF variability. PARAFAC successfully decomposed the EEMS set into three fluorophores present in all samples, although in concentrations spreading over ~ 3 orders of magnitude. One of the factors was identified as tryptophan. Tryptophan and Factor 2 were covariant and much more abundant in AME than in PIO, while Factor 3 was ~ 6 times more abundant in PIO than in AME. Also, tryptophan was ~ 10x more abundant in AME specimens immediately caught in rivers than in their laboratory-adapted peers. The PARAFAC decomposition effectiveness was confirmed by the positive proportionality of scores to dilution ratios. A large inner filter indicates that Factor 2 is as strong a light absorber as tryptophan. Our results stress the need to include bile matrix variable components for the detection and quantification of pollutant metabolites using PARAFAC.

Validation of the HIV/AIDS-Targeted Quality of Life (HAT-QOL) for Evaluation of Health-related Quality of Life in People Living with HIV/AIDS in Brazil.

HIV/AIDS-Targeted Quality of Life (HAT-QOL) is an instrument for evaluating health-related quality of life (HRQOL) in people living with HIV (PLWHIV). This has been adapted into Brazilian Portuguese, but its dimensional structure has not been analyzed. This study evaluated the psychometric properties of the Brazilian Portuguese version of the HAT-QOL, using a sample of 319 PLWHIV in Salvador, Brazil. The study performed Exploratory Factor Analysis (EFA) to assess the HAT-QOL dimensional structure. The analysis used a polychoric correlation matrix, Robust Diagonally Weighted Least Squares (RDWLS) as an extraction method, Parallel Analysis for factor retention, robust promin as oblique rotation, and Generalized H-index (G-H) for construct replicability of each factor. Model adequacy was assessed using the Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), and Tucker-Lewis Index (TLI). Concurrent validity was evaluated with the 36-item Short Form Health Survey, version 2 (SF-36v2). EFA identified a HAT-QOL six-factor solution: Financial Worries, Sexual Function, Medication Concerns, Life Satisfaction, Health Worries, and Overall Function. This solution showed high G-H indexes, concurrent validity, and satisfactory adequacy indexes (X2 = 231.345, df = 291, p < 0,001; RMSEA = 0.001, CFI = 0.999, TLI = 1.028). HIV Mastery, Disclosure Worries, and Provider Trust domains were not retained in EFA and did not have evidence of concurrent validity. This study proposed a HAT-QOL six-factor model for measuring HRQOL in the Brazilian PLWHIV. Future research could help identify another latent construct from not-included domains.

Assessing the Psychometric Properties of Quality Experience in Undergraduate Research Using Item Response Theory

AbstractUndergraduate research, recognized as one of the High-Impact Practices (HIPs), has demonstrated a positive association with diverse student learning outcomes. Understanding the pivotal quality factors essential for its efficacy is important for enhancing student success. This study evaluates the psychometric properties of survey items employed to gauge the quality of undergraduate research, including alignment with Kuh and O’Donnell’s (2013) eight HIP characteristics, alongside assessments of reliability, validity, and generalizability across demographic groups. The study assesses the validity and reliability of these measures at both the scale and item levels using data from the National Survey of Student Engagement’s (NSSE) HIP Quality Topical Module. The methodological approaches employed include Exploratory Factor Analysis, Parallel Analysis, Item Response Theory, and Differential Item Functioning (DIF). Our findings uncover a misalignment between NSSE’s HIP Quality module items and HIP characteristics, leading to the identification of seven subscales instead of eight. Nevertheless, four subscales—Reflective and Integrative Learning, Real-World Applications, Interactions with Others, and High-Performance Expectations—emerge as valid indicators of undergraduate research experiences. While specific items yield valuable insights at the item level, refinement is recommended for others. Despite the identification of two items exhibiting DIF, their negligible effect sizes suggest that major revisions are unwarranted solely on DIF grounds. This study offers recommendations for item refinement, including the incorporation of new items, wording updates, and tailored utilization of assessment tools within educational institutions. These recommendations are intended to empower educators and researchers to effectively capture the quality dimensions of students’ undergraduate research experiences, thereby fostering their academic success.

Unidimensional community detection: A monte carlo simulation, grid search, and comparison.

Unidimensionality is fundamental to psychometrics. Despite the recent focus on dimensionality assessment in network psychometrics, unidimensionality assessment remains a challenge. Community detection algorithms are the most common approach to estimate dimensionality in networks. Many community detection algorithms maximize an objective criterion called modularity. A limitation of modularity is that it penalizes unidimensional structures in networks, favoring two or more communities (dimensions). In this study, this penalization is discussed and a solution is offered. Then, a Monte Carlo simulation using one- and two-factor models is performed. Key to the simulation was the condition of model error or the misfit of the population factor model to the generated data. Based on previous simulation studies, several community detection algorithms that have performed well with unidimensional structures (Leading Eigenvalue, Leiden, Louvain, and Walktrap) were compared. A grid search was performed on the tunable parameters of these algorithms to determine the optimal trade-off between unidimensional and bidimensional recovery. The best-performing parameters for each algorithm were then compared against each other as well as maximum likelihood factor analysis and parallel analysis (PA) with mean and 95th percentile eigenvalues. Overall, the Leiden and Louvain algorithms and PA methods were the most accurate methods to recover unidimensional and bidimensional structures and were the most robust to model error. More nuanced method recommendations for specific unidimensional and bidimensional conditions are provided. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Characteristics of organic matter driven by Eichhornia crassipes during co-contamination with per(poly)fluoroalkyl substances (PFASs) and microplastics (MPs)

Co-contamination with MPs and PFASs has been recorded, particularly in surface-water environments. Floating macrophyte microcosms are an important part of the surface water ecosystem, and dissolved organic matter (DOM) driven by floating macrophytes (FMDDOM) is critical for maintaining material circulation. However, knowledge gaps remain regarding the impact of MPs and PFASs co-pollution on FMDDOM. An greenhouse simulation experiment was conducted in this study to investigate the effects of four PFASs, perfluorooctanoic acid (PFOA), perfluoro-octane-sulfonic acid (PFOS), perfluoro-2-methyl-3-oxahexanoic acid (Gen X), and potassium 9-chlorohexadecafluoro-3-oxanonane-1-sulfonate (F-53B), on FMDDOM sourced from Eichhornia crassipes (E. crassipes), a typical floating macrophyte, in the presence and absence of polystyrene (PS) MPs. Four PFASs increased FMDDOM release from E. crassipes, leading to a 32.52–77.49 % increase in dissolved organic carbon (DOC) levels. PS MPs further increased this, with results ranging from −21.28 % to 26.49 %. Based on the parallel factor analysis (PARAFAC), FMDDOM was classified into three types of fluorescent components: tryptophan-like, humic-like, and tyrosine-like compounds. Contaminants of MPs and PFASs modified the relative abundance of these three components. Protein secondary structure analysis showed that fluorocarbon bonds tended to accumulate on the α-helix of proteins in FMDDOM. The relative abundance of fluorescent and chromophorous FMDDOMs varied from 0.648 ± 0.044 to 0.964 ± 0.173, indicating that the photochemical structures of the FMDDOM were modified. FMDDOM exhibits decreased humification and increased aromaticity when contaminated with MPs and PFASs, which may be detrimental to the geochemical cycling of carbon. This study offers a theoretical basis for assessing the combined ecological risks of MPs and PFASs in floating macrophyte ecosystems.

Association of support group leader experience with Scleroderma Patient-centered Intervention Network Support group Leader EDucation Program outcomes: Secondary analysis of a two-arm parallel partially nested randomized controlled trial

Introduction/Objective: The Scleroderma Patient-centered Intervention Network Support group Leader EDucation Program was found in a randomized controlled trial to substantially improve leader self-efficacy. Whether the program is effective for leaders with different levels of experience, including candidate leaders with no prior experience and leaders with ⩽3 years experience or ⩾4 years experience, is not known. The objective of the present post hoc secondary analysis was to evaluate outcomes by leader experience, age, and education. Methods: The trial was a pragmatic, two-arm partially nested randomized controlled trial with 1:1 allocation to intervention or waitlist control. Eligible participants were existing or candidate support group leaders. The 13-session leader training was delivered in groups of five to six participants weekly via videoconference in 60- to 90-min sessions. The primary outcome was leader self-efficacy, measured by the Support Group Leader Self-efficacy Scale (SGLSS) immediately post-intervention. Secondary outcomes were Support Group Leader Self-efficacy Scale scores 3 months post-intervention and emotional distress, leader burnout, and volunteer satisfaction post-intervention and 3 months post-intervention. Leaders were classified as having no experience, ⩽3 years experience, or ⩾4 years experience. Results: A total of 148 participants were randomized to intervention (N = 74) or waitlist (N = 74). Compared to leaders with ⩾4 years of experience, Support Group Leader Self-efficacy Scale scores were non-statistically significantly higher post-intervention for leaders with 0–3 years experience and lower for leaders with no experience. The 3 months post-intervention Support Group Leader Self-efficacy Scale scores were significantly lower for leaders without experience and similar for leaders with 0–3 years to those with ⩾4 years experience. There were no differences by experience on other outcomes or by age and education on any outcomes. Conclusion: Support group leader education improved leader self-efficacy but was most effective for leaders with experience prior to initiating the program. Trial Registration: NCT03965780; registered on May 29, 2019.