Sampling Variability Research Articles

Review of plasma exchange and rituximab for prevention of recurrent focal segmental glomerulosclerosis after a prior graft loss.

Focal segmental glomerulosclerosis (FSGS) often recurs after transplantation, leading to graft dysfunction and graft loss. Patients who have lost prior grafts due to recurrence are at particularly high risk of re-recurrence in subsequent grafts. Rituximab and plasma exchange have been used pre-emptively to prevent post-transplant recurrence. However, the efficacy of such preventative measures remains unclear. To investigate the outcomes of preventative rituximab and plasma exchange for recurrent FSGS in transplant recipients after prior graft loss. We conducted a systematic review of 11 studies with 32 patients who had experienced prior graft loss due to post-transplant FSGS recurrence and were treated with either pre-emptive plasma exchange alone, rituximab alone, or a combination of both. Overall, 47% of the 32 patients experienced recurrence despite prophylactic treatment. Re-recurrence was seen in 25% (1/4) with pre-emptive rituximab alone, and 45% recurrence (9/20) with plasma exchange alone. Re-recurrence was noted in 63% with the use of combined plasma exchange and rituximab. There is a paucity of available evidence in the literature to draw clear conclusions on the benefits of pre-emptive measures to prevent FSGS re-recurrence. The small sample sizes and variations in protocols call for larger and controlled studies to serve this patient population at high risk of recurrence and graft loss.

Robustness assessment of regressions using cluster analysis typologies: a bootstrap procedure with application in state sequence analysis

BackgroundIn standard Sequence Analysis, similar trajectories are clustered together to create a typology of trajectories, which is then often used to evaluate the association between sequence patterns and covariates inside regression models. The sampling uncertainty, which affects both the derivation of the typology and the associated regressions, is typically ignored in this analysis, an oversight that may lead to wrong statistical conclusions. We propose utilising sampling variation to derive new estimates that further inform on the association of interest.MethodsWe introduce a novel procedure to assess the robustness of regression results obtained from the standard analysis. Bootstrap samples are drawn from the data, and for each bootstrap, a new typology replicating the original one is constructed, followed by the estimation of the corresponding regression models. The bootstrap estimates are then combined using a multilevel modelling framework that mimics a meta-analysis. The fitted values from this multilevel model allow to account for the sampling uncertainty in the inferential analysis. We illustrate the methodology by applying it to the study of healthcare utilisation trajectories in a Swiss cohort of diabetic patients.ResultsThe procedure provides robust estimates for an association of interest, along with 95% prediction intervals, representing the range of expected values if the clustering and associated regressions were performed on a new sample from the same underlying distribution. It also identifies central and borderline trajectories within each cluster. Regarding the illustrative application, while there was evidence of an association between regular lipid testing and subsequent healthcare utilisation patterns in the original analysis, this is not supported in the robustness assessment.ConclusionsInvestigating the relationship between trajectory patterns and covariates is of interest in many situations. However, it is a challenging task with potential pitfalls. Our Robustness Assessment of Regression using Cluster Analysis Typologies (RARCAT) may assist in ensuring the robustness of such association studies. The method is applicable wherever clustering is combined with regression analysis, so its relevance goes beyond State Sequence Analysis.

Measurement of 1,25-dihydroxyvitamin D in serum by LC-MS/MS compared to immunoassay reveals inconsistent agreement in paediatric samples.

Automated immunoassays for 1,25-dihydroxyvitamin D (1,25(OH)2D) have increased the use of serum measurements in clinical and research settings, but disagreement with LC-MS/MS methods remains an issue. In this study, we examined this problem using samples obtained from healthy young adults, n=80, mean age 21.7 (18-32) years, and a large cohort of paediatric samples, n=422, mean age 7.3 (0-17) years. We compared serum concentrations of 1,25(OH)2D3/D2 produced by the DiaSorin LIAISON® XL immunoassay against an LC-MS/MS method with immunoaffinity enrichment and DAPTAD derivation. Both assays showed intra/inter-assay imprecision of≤9.4 % across their respective assay range. DEQAS between April 2020 to Jan 2024 (n=80) showed mean bias (SD, 95 %CI) for DiaSorin-0.6 % (6.2,-12.8 to 11.6) and LC-MS/MS of +1.3 % (7.4,-13.3 to 15.8) against their respective method group means. Comparison of measurements in the adult samples showed a strong correlation (r2=0.9331) and concordance (CCC=0.959) between the two methods. LC-MS/MS values were lower than DiaSorin by an overall mean (±SD, 95 %CI) of-1.6 (±14.3,-29.6 to 26.5) pmol/L with an increased negative bias at higher concentrations. In the paediatric samples, weaker correlation (r2=0.6536) and concordance (CCC=0.782) were observed, with greater bias mean (±SD, 95 %CI) of-9.8 (±23.4,-55.7 to 35.9) pmol/L. The variability in the paediatric samples was not associated with concentration or participant age. There was an increase in the correlation and concordance when 1,25(OH)2D2 was included in the analysis. It is likely that the metabolites of vitamin D present in the paediatric population contributed to the measurement of 1,25(OH)2D. The inconsistent agreement highlights the need for better assay harmonisation and paediatric reference intervals using LC-MS/MS method.

Diagnosis of Plasmodium infections using artificial intelligence techniques versus standard microscopy in a reference laboratory.

Diagnosing malaria using standard techniques is time-consuming. With limited staffing in many laboratories, this may lead to delays in reporting. Innovative technologies are changing the diagnostic landscape and may help alleviate staffing shortages. The miLab MAL, an automated artificial intelligence-driven instrument was compared with standard microscopy at LabCorp reference laboratories. Four hundred eight samples submitted for parasitic examination were prepared with thick and thin smears and Noul's malaria platform miLab MAL, and evaluated for positivity, negativity, percent positivity, and species identification. Of 408 samples, 399 samples were manually negative, while 397 were negative by miLab MAL. Two samples initially classified as negative manually were found positive by miLab MAL. In all nine cases, Plasmodium falciparum was identified by both methods. Percentage parasitemia was higher in the manually calculated method, especially when >1%. miLab MAL was accurate in identifying the absence of Plasmodium falciparum and exhibited higher sensitivity than the manual method. All positive samples detected by microscopy were also identified with miLab MAL. All positive Plasmodium cases were correctly identified by miLab MAL. However, the number of positive samples was limited to only Plasmodium falciparum. Although parasitemia by the manual method was on average six times higher than with miLab MAL, this may be due to sampling variability. The findings show that miLab MAL can be used to screen out negative Plasmodium falciparum samples. Further studies assessing parasitemia between methods and identification of non-falciparum samples are necessary to assess the reliability of this new technology.

DNA markers for early detection of somaclonal variation in Berangan banana

Banana is Malaysia’s second most cultivated fruit, with 50% of the cultivation focusing on Berangan and Cavendish types. Bananas have been identified as one of the important fruits in the National Agrofood Policy (2011-2020) due to their demand in the domestic and export markets. Thus, FGV is committed to achieving a 50% market share of local banana production through tissue culture propagation techniques. This technique enables the mass production of banana planting material in a short period. However, one downside of this technique is the random occurrence of somaclonal variation, which has been reported for various crops. FGV’s clonal propagation aims to produce true-to-type banana planting materials, therefore, somaclonal variants are undesired. Hence, we aimed to establish a marker-based screening method for early detection and elimination of Berangan somaclonal variants prior to field planting. Berangan leaf samples with and without somaclonal variation were collected from different sites. The samples’ DNA was extracted using an in-house cetyltrimethylammonium bromide (CTAB) extraction method and screened using 145 Random Amplified Polymorphic DNA (RAPD) markers. RAPD markers that could distinguish somaclonal variation in the Berangan samples were developed into Sequence Characterized Amplified Region (SCAR) markers for routine screening. The SCAR markers were validated for their ability to reproduce the specific bands from the RAPD results. Of the 145 RAPD markers, three markers do not produce any bands, 47 RAPD markers produce monomorphic bands and 95 RAPD markers produce polymorphic bands. Three out of the 95 polymorphic RAPD markers were able to selectively distinguish the somaclonal variation in samples from similar sources. The three RAPD markers were developed into SCAR markers, and they successfully reproduced the specific bands from their respective RAPD results. Hence, the developed SCAR markers will be utilized for the early detection and elimination of Berangan somaclonal variants from selected sources.

When Composite Likelihood meets Stochastic Approximation

A composite likelihood is an inference function derived by multiplying a set of likelihood components. This approach provides a flexible framework for drawing inferences when the likelihood function of a statistical model is computationally intractable. While composite likelihood has computational advantages, it can still be demanding when dealing with numerous likelihood components and a large sample size. This paper tackles this challenge by employing an approximation of the conventional composite likelihood estimator based on a stochastic optimization procedure. This novel estimator is shown to be asymptotically normally distributed around the true parameter. In particular, based on the relative divergent rate of the sample size and the number of iterations of the optimization, the variance of the limiting distribution is shown to compound for two sources of uncertainty: the sampling variability of the data and the optimization noise, with the latter depending on the sampling distribution used to construct the stochastic gradients. The advantages of the proposed framework are illustrated through simulation studies on two working examples: an Ising model for binary data and a gamma frailty model for count data. Finally, a real-data application is presented, showing its effectiveness in a large-scale mental health survey.

Fluorescence spectrometric analysis for diagnosing compositional variations in effluent organic matter by chlorination and ozonation

Analyzing the reactivity of organic matter to oxidants such as chlorination and ozonation is crucial for evaluating the effectiveness of water treatment systems and their potential impacts on environmental safety and human health. This study explored the changes in organic substances, specifically bovine serum albumin (BSA), humic acid sodium salt (HA), and effluent organic matter (EfOM) from a wastewater treatment facility during chlorination and ozonation. Four spectrometric techniques were employed: ultraviolet absorbance at 254 nm (UVA254), fluorescent excitation-emission matrix (EEM), synchronous fluorescence two-dimensional correlation spectroscopy (SF-2DCOS), and EEM-parallel factor integrated 2DCOS (EEM-PARAFAC-2DCOS). The findings revealed that ozone possesses superior oxidizing properties compared to chlorine, as evidenced by UVA254 and EEM analyses, resulting in more diverse structural modifications in EfOM. SF-2DCOS and EEM-PARAFAC-2DCOS provided comprehensive details on the direction and sequence of these changes, with EEM-PARAFAC-2DCOS delivering clear and intuitive insights. Protein-like and fulvic-like substances were susceptible to chlorination and ozonation, exhibiting different reaction sequences with each oxidant. Furthermore, variations in protein-like and humic-like components in actual EfOM samples may not align precisely with those in model substances, emphasizing the importance of considering specific organic matter variations in real EfOM samples compared to model substances. This research offered a deeper understanding of the reactivity and transformation of organic matter in wastewater treatment processes through simple and rapid spectroscopic methods, potentially improving the management and mitigation of undesired byproducts.

Uncertainty Analysis in Ecological Risk Assessment: Sensitivity Analysis of Different Exposure Models

Ecological risk assessments are used for decision making regarding releasing new products to market as well as remedial action required to address contamination at legacy sites. Uncertainty analysis is considered a key part of the process (USEPA 1997) but assessments seldom address uncertainty quantitatively. Key sources of uncertainty include: Sampling and analytical variability (soil/ sediment matrix, lab error), Choice of “Indicator” Species as Target Receptors for Different Exposure Pathways, Sample size. Bioaccumulation and Food Chain Modeling involve assumptions and uncertainty including: Linear assumption of bioaccumulation and selection of bioaccumulation factors; Other model inputs such as: Home range size, Dietary percentages, Body Weights, Ingestion Rate; Literature data on effects, Use of No Observed Adverse Effects Level (NOAEL) or Lowest Observed Adverse Effects Level (LOAEL) as a decision point. Toxicity testing where employed brings its own set of assumptions and interpretation that are subject to uncertainty. These include: Use of single organism, Correlation (or lack thereof) with Contaminants of Concern, Contaminant mixtures, and Reference area selection. Keywords: Ecotoxicology, Risk Assessment, Ecology

GlycoPCT: Pressure Cycling Technology-Based Quantitative Glycoproteomics Reveals Distinctive N-Glycosylation in Human Liver Biopsy Samples of Nonalcoholic Fatty Liver Disease.

Protein N-glycosylation is vital in the human liver and influences functions such as lipid metabolism, apoptosis, and inflammation. However, site-specific N-glycosylation patterns and variations in liver biopsy samples between healthy individuals and those with nonalcoholic fatty liver disease (NAFLD) remain incompletely characterized, primarily due to the limitations of current clinical glycoproteomic methods, including a large demand for clinical samples, low efficiency of tissue protein extraction, and a low recovery rate of intact N-glycopeptides (IGPs). To address this issue, we developed GlycoPCT, a quantitative glycoproteomic method based on pressure cycling technology. It enables efficient recovery of IGPs and accurate analysis of trace liver biopsy samples. Our research revealed a total of 4,459 unique IGPs and 361 glycans from 758 glycoproteins. High-mannose type, complex type, fucosylation type, and sialylation type N-glycans were significantly upregulated in the NAFLD group (p < 0.001, t test). Notably, we also identified 182 upregulated IGPs from 67 proteins (p < 0.05, FC > 1.50) and 108 downregulated IGPs from 44 proteins (p < 0.05, FC < 0.67) in the NAFLD group. Furthermore, we highlighted an essential acute phase glycoprotein, alpha-1-acid glycoprotein 1 (A1TA), which is synthesized in the liver and plays a significant role in NAFLD progression. These novel glyco-signatures provide crucial clues for the diagnosis and pathogenesis of NAFLD.

Effectiveness of Non-Pharmacological Interventions in the Management of Pediatric Chronic Pain: A Systematic Review

(1) Background: Chronic pain in children remains an under-researched area, especially compared to acute pain. This systematic review aims to evaluate the effectiveness of non-pharmacological interventions in the management of pediatric chronic pain and their impact on the well-being of both children and their families. Given the growing interest in integrative treatments to reduce reliance on pharmacological solutions, this review addresses the need for alternative therapeutic approaches. (2) Methods: A systematic review was conducted following the PRISMA guidelines, covering studies published between 2019 and 2024 from PubMed, Cochrane Library, Web of Science, and Scopus. Eligible studies included children aged 2 to 18 years with chronic pain who received non-pharmacological interventions. Data were extracted on intervention types, participant characteristics, and outcomes. The risk of bias was assessed using RoB2 for randomized trials and ROBINS-I for non-randomized studies. (3) Results: A total of 11 studies involving 1739 children were included, assessing interventions such as cognitive behavioral therapy, personalized psychosocial follow-up programs, hypnotherapy, music therapy, and digital tools. The results demonstrated significant reductions in pain severity, improvements in emotional and physical well-being, and high patient satisfaction. However, the generalizability of findings was limited by the small sample sizes and variability in study designs. (4) Conclusions: Non-pharmacological interventions appear effective in managing pediatric chronic pain, offering improvements in pain reduction and quality of life. Further research is needed to optimize these approaches and confirm their long-term benefits across diverse populations. These interventions represent promising alternatives or complements to pharmacological treatments in pediatric pain management.

Leveraging the power of long reads for targeted sequencing.

Long-read sequencing technologies have improved the contiguity and, as a result, the quality of genome assemblies by generating reads long enough to span and resolve complex or repetitive regions of the genome. Several groups have shown the power of long reads in detecting thousands of genomic and epigenomic features that were previously missed by short-read sequencing approaches. While these studies demonstrate how long reads can help resolve repetitive and complex regions of the genome, they also highlight the throughput and coverage requirements needed to accurately resolve variant alleles across large populations using these platforms. At the time of this review, whole-genome long-read sequencing is more expensive than short-read sequencing on the highest throughput short-read instruments; thus, achieving sufficient coverage to detect low-frequency variants (such as somatic variation) in heterogenous samples remains challenging. Targeted sequencing, on the other hand, provides the depth necessary to detect these low-frequency variants in heterogeneous populations. Here, we review currently used and recently developed targeted sequencing strategies that leverage existing long-read technologies to increase the resolution with which we can look at nucleic acids in a variety of biological contexts.

Aromatic Serenity: How Lavender Eases Stress and Anxiety - A Literature Review

In today's fast-paced society, rising stress levels necessitate effective management strategies. While pharmacological treatments are common, their side effects have spurred interest in alternative therapies, particularly those rooted in natural remedies. Lavender (Lavandula angustifolia), long esteemed for its calming properties, has emerged as a promising non-pharmacological option. Historical use in herbal medicine for anxiety and stress-related disorders has inspired modern scientific research to explore its potential. This review synthesizes recent studies on lavender essential oil, examining its mechanisms—such as neurotransmitter modulation—and its effectiveness in diverse settings, from reducing stress among healthcare professionals to improving sleep quality. Despite promising results, limitations like small sample sizes and variability in application methods highlight the need for more rigorous research to establish lavender's efficacy and optimize its therapeutic use in stress management and mental health care.

Prediction of Salicornia europaea L. biomass using a computer vision system to distinguish different salt-tolerant populations

BackgroundSalicornia europaea L. is emerging as a versatile crop halophyte, requiring a low-cost, non-destructive method for salt tolerance classification to aid selective breeding. We propose using a computer vision system (CVS) with multivariate analysis to classify S. europaea based on morphometric and colour traits to predict plant biomass and the salinity in their substrate.ResultsA trial and validation set of 96 and 24 plants from 2 populations confirmed the efficacy. CVS and multivariate analysis evaluated the plants by morphometric traits and CIELab colour variability. Through Pearson analysis, the strongest correlations were between biomass fresh weight (FW) vs. projected area (PA) (0.91) and anatomical cross-section (ACS) vs. shoot diameter (Sd) (0.94). The PA and FW correlation retrieved different equation fits between lower and higher salt-tolerant populations (R2 = 0.93 for linear and 0.90 for 2nd-degree polynomial), respectively. The higher salt-tolerant reached a maximum biomass PA at 400 mM NaCl, while the lower salt-tolerant produced less under 200 and 400 mM. A second Pearson correlation and PCA described sample variability with 80% reliability using only morphometric-colour parameters. Multivariate discriminant analysis (MDA) demonstrated that the method correctly classifies plants (90%) depending on their salinity level and tolerance, which was validated with 100% effectiveness. Through multiple linear regression, a predictive model successfully estimated biomass production by PA, and a second model predicted the salinity substrate (Sal.s.) where the plants thrive. Plants' Sd and height influenced PA prediction, while Sd and colour difference (ΔE1) influenced Sal.s. Models validation of actual vs. predicted values showed a R2 of 0.97 and 0.90 for PA, and 0.95 and 0.97 for Sal.s. for lower and higher salt-tolerant, respectively. This outcome confirms the method as a cost-effective tool for managing S. europaea breeding.ConclusionsThe CVS effectively extracted morphological and colour features from S. europaea cultivated at different salinity levels, enabling classification and plant sorting through image and multivariate analysis. Biomass and salinity substrate were accurately predicted by modelling non-destructive parameters. Enhanced by AI, machine learning and smartphone technology, this method shows great potential in ecology, bio-agriculture, and industry.

Mechanical properties variation of samples fabricated by fused deposition additive manufacturing as a function of filler percentage and structure for different plastics

One of the key factors in manufacturing products by fused deposition molding (FDM) or layer-by-layer printing technology is the material intensity of the product. The task of reducing the amount of material required to manufacture a product without significant loss of mechanical properties is one of the most practically important technological tasks. Material saving in FDM printing of products allows to reduce financial costs and increase the speed of manufacturing of the final product without reducing (or not significantly reducing) the quality properties of the product. In our work it is demonstrated that using Combs filling type and materials of poly lactic acid (PLA) and polyethylene terephthalate glycol (PETG) it is possible to achieve material savings of up to 23% at 50% filling for PLA and 17% at 75% filling for PETG without significant reduction of product strength in comparison with other filling types. Exceptions are PLA samples with 100% fill and Lateral fill. Application of Kruskal-Wallis criterion and Dunn’s criterion with Bonferroni multiple comparison correction showed that there were no statistically significant differences within the strength limits of samples made by FDM printing technology from PLA and PETG plastics (p-value = 0.0514), as well as samples with Triangle and Grid filling type (p-value = 1). Based on this result, three groups of samples statistically significantly differing in ultimate strength were identified by methods of hierarchical cluster analysis; in each group (except for group 1, which included samples made of PLA plastic with Lateral filling type and 100% filling), correlation analysis was performed (Spearman correlation was used). The results of the correlation analysis showed a stable average correlation between the percentage of filling, modulus along the secant 0.05–0.2% strain, ultimate strength and strain corresponding to the yield stress. Analysis of the correlation graph showed that the main parameter correlating with all mechanical properties of the specimen is the 0.05–0.2% strain modulus. Based on this conclusion, robust regression equations predicting the 0.05–0.2% strain modulus as a function of the percentage of specimen filling were constructed for the two selected groups. Analysis of the equations showed that in the third group of specimens, the average modulus of 0.05–0.2% strain is more than twice the modulus of 0.05–0.2% strain in the second group. The detected statistical regularities can be explained by the mechanism of strain hardening, the actual value of which depends on the structure of the macrodefect (type of filling), properties and volume of the material (percentage of filling) used in the fabrication of samples using FDM printing technology.

Antagonistic effects of endophytic fungi from Camellia reticulata pedicels on yeasts: implications for antimicrobial mechanism of nectar.

Endophytic fungi are extensive in plant tissues and involved in the defense against stress from harmful microbes. The interaction between pedicel endophytic fungi and nectar yeasts is critical for maintaining nectar homeostasis. This study used Camellia reticulata as the research subject. High-throughput sequencing revealed that the community composition of endophytic fungi in the pedicel is dominated by Ascomycota and Basidiomycota. Their abundance varies at different taxonomic levels, showing sample variability. In total, 27 endophytic fungal isolates were isolated and screened from the pedicel under laboratory conditions. They exhibited antagonistic effects against three nectar yeasts (Metschnikowia reukaufii, Cryptococcus laurentii, and Rhodotorula glutinis) and displayed morphological and physiological diversity. The isolates were classified into the phylum Ascomycota and further categorized into the genera Alternaria, Trichoderma, Fusarium, and Dactylaria. The endophytic fungus D23, which effectively antagonizes nectar yeasts, was identified as Alternaria alternata. This fungus produces various secondary metabolites, including antibiotics such as penicillin G, grandiomycin, and cephalosporin C. The metabolic pathways involved include the biosynthesis of plant secondary metabolites, phenylpropanoids, amino acids, nucleotides, and antibiotics. The endophytic fungal community in C. reticulata pedicel is rich and diverse, making it a valuable material for screening antagonistic strains. This study provides a theoretical basis for the antagonistic effects of endophytic fungal metabolites from the pedicel of C. reticulata against nectar yeasts, highlighting their significance in maintaining nectar stability and reproductive fitness in cross-pollinated plants.

Comparative volatiles profiling of two marjoram products via GC-MS analysis in relation to the antioxidant and antibacterial effects

Marjoram (Origanum majorana L.), also known as “sweet marjoram” or “sweet oregano” is a Mediterranean herbaceous perennial herb cultivated in Egypt and widely consumed as an herbal supplement for treatment of several ailments. The main goal of this study was to assess volatiles’ variation in marjoram samples collected from two different widely consumed commercial products using two different extraction techniques viz. head space solid phase microextraction (HS-SPME) and petroleum ether using gas chromatography mass spectrometry (GC-MS) analysis and multivariate data analysis. A total of 20 major aroma compounds were identified in samples extracted with HS-SPME found enriched in monoterpene hydrocarbons and oxygenated compounds. The major volatiles included β-phellandrene (20.1 and 14.2%), γ-terpinene (13.4 and 11.7%), 2-bornene (12.3 and 11.5%), p-cymene (9.8 and 4.6%) terpenen-4-ol (16.4 and 7.5%), sabinene hydrate (16.02 and 8.8%) and terpineol (4.2 and 3.2%) in MR and MI, respectively. Compared with HS-SPME, 51 aroma compounds were identified in marjoram samples extracted with petroleum ether, found more enriched in aliphatic hydrocarbons (42.8 and 73.8%) in MR and MI, respectively. While a higher identification score was observed in the case of solvent extraction, SPME appeared to be more selective in the recovery of oxygenated terpenes to account more for marjoram aroma. Multivariate data analysis using principal component analysis (PCA) revealed distinct discrimination between volatile composition of both marjoram samples. The total phenolic and flavonoid contents in marjoram samples were at (111.9, 109.1 µg GA/mg) and (18.3, 19.5 µg rutin eq/mg) in MR and MI, respectively. Stronger antioxidant effects were observed in MR and MI samples with IC50 at 45.5 and 56.8 µg/mL respectively compared to IC50 6.57 µg/mL for Trolox as assayed using DPPH assay. Moderate anti-bacterial effect was observed in MR and MI samples and expressed as a zone of inhibition mostly against Bacillus subtilis (16.03 and 15.9 mm), B. cereus (12.9 and 13.7 mm), Enterococcus faecalis (14.03 and 13.97 mm), and Enterobacter cloacae (11.6 and 11.6 mm) respectively.

Iron isotope fractionation during transcrustal magmatic differentiation: Implications for continental crustal formation in subduction zones

Earth’s continental crust is hypothesized to originate from mantle melting in subduction zones. However, mantle melts are typically Fe-rich, with light Fe isotopes (δ56Fe = 0.05‰), unlike the Fe-depleted and heavy Fe isotope (δ56Fe can be up to 0.2‰) compositions of continental crust. The mechanisms responsible for this paradox remain elusive. Here, we report Fe isotope data from a suite of co-magmatic Middle Triassic continental arc rocks in the Ailaoshan tectonic zone (Yunnan, SW China). Gabbroic diorites have light Fe isotopes (δ56Fe = 0.04‰−0.09‰), while granodiorites and normal-arc granites have slightly heavier values (δ56Fe = 0.07‰−0.14‰). Adakitic granites, with high Sr/Y (61−183) and La/Yb (47−90) ratios, exhibit the heaviest δ56Fe (0.16‰−0.19‰). Rayleigh fractionation modeling results suggest that fractionation of isotopically light Fe minerals (e.g., olivine, pyroxene, and amphibole) can reproduce the Fe isotopic variations in most samples, but fails to explain the high Sr/Y, La/Yb, and δ56Fe values of adakitic granites, which would require Fe-rich garnet fractionation. Notably, the different Fe isotopes of the adakitic granites and non-adakitic rocks reveal two distinct transcrustal magmatic differentiation pathways: lower crustal, high-pressure, garnet-dominated fractionation; and middle−upper crustal, amphibole-dominated fractionation. The heavy Fe isotopic and Fe-depleted characteristics of the Ailaoshan magmatic arc suites are more comparable to the calc-alkaline arc magmas from normal to thick crust arcs (e.g., the Andean arc), but remarkably different from the tholeiitic arc magmas from thin crust arcs (e.g., the Mariana arc). This study highlights the role of amphibole and garnet fractionation in magmatic calc-alkaline differentiation. We propose that a thickened crust would permit a longer magmatic differentiation column, thus facilitating felsic and Fe-depleted continental crustal formation. The fractionated Fe-rich, garnet-bearing arc cumulates with light Fe isotopes are density-unstable and may be recycled into the mantle by lower crustal foundering.

Surface-Enhanced Raman Scattering-Based Multimodal Techniques: Advances and Perspectives.

Surface-enhanced Raman scattering (SERS) spectroscopy is a versatile molecular fingerprinting technique with rapid signal readout, high aqueous compatibility, and portability. To translate SERS for real-world applications, it is pertinent to overcome inherent challenges, including high sample variability and heterogeneity, matrix effects, and nonlinear SERS signal responses of different analytes in complex (bio)chemical matrices with numerous interfering species. In this perspective, we highlight emerging SERS-based multimodal techniques to address the key roadblocks to improving the sensitivity, specificity, and reliability of (bio)chemical detection, bioimaging, theragnosis, and theragnostic. SERS-based multimodal techniques can be broadly categorized into two categories: (1) complementary methods or systems that work together to achieve a common goal where each method compensates for the weaknesses of the other to culminate in a single enhanced outcome or (2) orthogonal techniques that are independent and provide separate but corroborating results simultaneously without interfering with each other. These multimodal techniques maximize information gained from a single experiment to achieve enhanced qualitative or quantitative analysis and broaden the range of detectable analytes from small molecules to tissues. Finally, we discuss emerging directions in multimodal platform design, instrument integration, and data analytics that aim to push the analytical limits of holistic detection.

A Hierarchical Bayesian Model for Estimating Age-Specific COVID-19 Infection Fatality Rates in Developing Countries.

The COVID-19 infection fatality rate (IFR) is the proportion of individuals infected with SARS-CoV-2 who subsequently die. As COVID-19 disproportionately affects older individuals, age-specific IFR estimates are imperative to facilitate comparisons of the impact of COVID-19 between locations and prioritize distribution of scarce resources. However, there lacks a coherent method to synthesize available data to create estimates of IFR and seroprevalence that vary continuously with age and adequately reflect uncertainties inherent in the underlying data. In this article, we introduce a novel Bayesian hierarchical model to estimate IFR as a continuous function of age that acknowledges heterogeneity in population age structure across locations and accounts for uncertainty in the estimates due to seroprevalence sampling variability and the imperfect serology test assays. Our approach simultaneously models test assay characteristics, serology, and death data, where the serology and death data are often available only for binned age groups. Information is shared across locations through hierarchical modeling to improve estimation of the parameters with limited data. Modeling data from 26 developing country locations during the first year of the COVID-19 pandemic, we found seroprevalence did not change dramatically with age, and the IFR at age 60 was above the high-income country estimate for most locations.

A novel framework for debris flow susceptibility assessment considering the uncertainty of sample selection

The uncertainty arising from random sampling of non-debris flow samples significantly impacts the accuracy of debris flow susceptibility assessments (DFSA). This study introduces a novel uncertainty elimination method, Kernel Density Estimation (KDE), and compares it with Mean and Maximum Probability Analysis (MPA) methods. Furthermore, we investigate the responses of four commonly used machine learning models to sampling uncertainty, comparing two structurally similar models (Random Forest (RF) and Extremely Randomized Trees (ERT)) with two structurally different models (Support Vector Machine (SVM) and Multilayer Perceptron (MLP)). The results indicate that the application of these uncertainty elimination methods can significantly enhance AUC values and zoning accuracy, with the KDE method outperforming the others. Specifically, the AUC values based on KDE for RF, ERT, SVM, and MLP are 0.995, 0.999, 0.999, and 0.853, respectively. The corresponding zoning accuracy for these models is 1.00, 1.00, 1.00, and 0.78, respectively. The study further reveals that the responses to sampling uncertainty vary by model architecture: RF, ERT, and SVM typically exhibit bimodal normal distributions, while the MLP model shows a unimodal distribution. Additionally, MLP is more sensitive to variations in negative samples, whereas RF and ERT are less affected due to the ensemble structure.