Instrumental Bias Research Articles

Optimizing Zr-doped MC-ICP-MS sample-standard bracketing to simultaneously determine 87Sr/86Sr and δ88Sr for high sample-throughput

In recent years, the increasing demand for extensive strontium (Sr) datasets across various scientific fields has prompted the development of fast, precise, and reliable protocols. These protocols aim to achieve high sample-throughput without compromising data quality. A novel method termed the zirconium (Zr) doped sample-standard bracketing (SSB) has been recently introduced for isotope measurements using multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS). This method allows for simultaneous acquisition of 87Sr/86Sr and δ88Sr data without the need for sample spiking. However, the reliability of this method in handling large datasets and ensuring long-term reproducibility requires additional documentation. A comprehensive examination of the reliability of Sr isotope data over two years involves the implementation of systematic tests on reference materials from the National Institute of standards and technology (NIST) with the standard reference material (SRM) numbers: 1400, 1515, 987, 2910b, 1486, as well as a Hawaiian volcano observatory basalt (BHVO-1). The results of this study lead to the definition of a set of quality control parameters. The developed protocol applies various controls to ensure precise matching of Sr and Zr concentrations for both the samples and the bracketing standard (NIST SRM 987), along with the identification of instrumental biases. The outlined quality control ensures the reproducibility of the results (87Sr/86Sr = 0.710247 ± 0.000026, 2SD, n = 557; and δ88Sr = 0.001 ± 0.053 ‰, 2SD, n = 537 for NIST SRM 987), proving invaluable for archaeological, geological, and ecological studies requiring the fast acquisition of extensive datasets (n > 100) to create isotopic baselines.

"Characterization of a New Iron Isotopic Reference Material Using Two Independent Methods for Instrumental Mass Bias Correction "

"Characterization of a New Iron Isotopic Reference Material Using Two Independent Methods for Instrumental Mass Bias Correction "

High-z gamma-ray burst detection by SVOM/ECLAIRs: Impact of instrumental biases on the bursts’ measured properties

Context. Gamma-ray bursts (GRBs) can be detected at cosmological distances, and therefore can be used to study the contents and phases of the early Universe. The 4−150 keV wide-field trigger camera ECLAIRs on board the Space-based multi-band Variable Object Monitor (SVOM) mission, dedicated to studying the high-energy transient sky in synergy with multi-messenger follow-up instruments, has been adapted to detect high-z GRBs. Aims. Investigating the detection capabilities of ECLAIRs for high-redshift GRBs and estimating the impacts of instrumental biases in reconstructing some of the source measured properties, focusing on GRB duration biases as a function of redshift. Methods. We simulated realistic detection scenarios for a sample of 162 already observed GRBs with known redshift values as they would have been seen by ECLAIRs. We simulated them at redshift values equal to and higher than their measured value. Then we assessed whether they would be detected with a trigger algorithm resembling that on board ECLAIRs, and derived quantities, such as T90, for those that would have been detected. Results. We find that ECLAIRs would be capable of detecting GRBs up to very high redshift values (e.g. 20 GRBs in our sample are detectable within more than 0.4 of the ECLAIRs field of view for zsim > 12). The ECLAIRs low-energy threshold of 4 keV, contributes to this great detection capability, as it may enhance it at high redshift (z > 10) by over 10% compared with a 15 keV low-energy threshold. We also show that the detection of GRBs at high-z values may imprint tip-of-the-iceberg biases on the GRB duration measurements, which can affect the reconstruction of other source properties.

Interlaboratory comparison of high-throughput protein biomarker assay quantifications for radiation exposure classification.

In the event of a widespread radiological incident, thousands of individuals will require rapid assessment of exposure using validated biodosimetry assays to inform clinical triage. In this scenario, multiple biodosimetry laboratories may be necessary for large-volume sample processing. To meet this need, we have developed a high-throughput assay for the rapid measurement of intracellular protein biomarkers in human peripheral blood samples using an Imaging Flow Cytometry (IFC) platform. The objective of this work was to harmonize and validate the reproducibility of our blood biomarker assay for radiation exposure across three IFC instruments, two located at Columbia University (CU) and the third at Health Canada. The Center for Radiological Research (CRR) at CU served as the central laboratory and reference instrument, where samples were prepared in triplicate, labeled with two radiation responsive leukocyte biomarkers (BAX and phosphor-p53 (Ser37)), and distributed for simultaneous interrogation by each IFC. Initial tests showed that significantly different baseline biomarker measurements were generated on each instrument when using the same acquisition settings, suggesting that harmonization of signal intensities is necessary. Subsequent tests harmonized biomarker measurements after irradiation by modulating laser intensity using two reference materials: unstained samples and standardized rainbow beads. Both methods generated measurements on each instrument without significant differences between the new and references instruments, allowing for the use of one master template to quantify biomarker expression across multiple instruments. Deming regression analyses of 0-5 Gy dose-response curves showed overall good correlation of BAX and p53 values across new and reference instruments. While Bland-Altman analyses indicated low to moderate instrument biases, ROC Curve analyses ultimately show successful discrimination between exposed and unexposed samples on each instrument (AUC values > 0.85).

Mendelian Randomization Analysis of the Causal Effect of Cigarette Smoking on Hospital Costs.

Knowledge of the impact of smoking on health care costs is important for establishing the external effects of smoking and for evaluating policies intended to modify this behavior. Conventional analysis of this association is difficult because of omitted variable bias, reverse causality, and measurement error. We approached these challenges using a Mendelian Randomization study design; genetic variants associated with smoking behaviors were used in instrumental variables models with inpatient hospital costs (calculated from electronic health records) as the outcome. We undertook genome-wide association studies to identify genetic variants associated with smoking initiation and a composite smoking index (reflecting cumulative health impacts of smoking) on up to 300 045 individuals (mean age: 57 years at baseline, range 39-72 years) in the UK Biobank. We followed individuals up for a mean of 6 years. Genetic liability to initiate smoking (ever vs. never smoking) was estimated to increase mean per-patient annual inpatient hospital costs by £477 (95% confidence interval (CI): £187 to £766). A one-unit change in genetic liability to the composite smoking index (range: 0-4.0) increased inpatient hospital costs by £204 (95% CI: £105 to £303) per unit increase in this index. There was some evidence that the composite smoking index causal models violated the instrumental variable assumptions, and all Mendelian Randomization models were estimated with considerable uncertainty. Models conditioning on risk tolerance were not robust to weak instrument bias. Our findings have implications for the potential cost-effectiveness of smoking interventions. We report the first Mendelian Randomization analysis of the causal effect of smoking on health care costs. Using two smoking phenotypes, we identified substantial impacts of smoking on inpatient hospital costs, although the causal models were associated with considerable uncertainty. These results could be used alongside other evidence on the impact of smoking to evaluate the cost-effectiveness of antismoking interventions and to understand the scale of externalities associated with this behavior.

Disequilibrium oxygen isotope distribution among aqueously altered minerals in Ryugu asteroid returned samples

AbstractOxygen 3‐isotope ratios of magnetite and carbonates in aqueously altered carbonaceous chondrites provide important clues to understanding the evolution of the fluid in the asteroidal parent bodies. We conducted oxygen 3‐isotope analyses of magnetite, dolomite, and breunnerite in two sections of asteroid Ryugu returned samples, A0058 and C0002, using a secondary ion mass spectrometer (SIMS). Magnetite was analyzed by using a lower primary ion energy that reduced instrumental biases due to the crystal orientation effect. We found two groups of magnetite data identified from the SIMS pit morphologies: (1) higher δ18O (from 3‰ to 7‰) and ∆17O (~2‰) with porous SIMS pits mostly from spherulitic magnetite, and (2) lower δ18O (~ −3‰) and variable ∆17O (0‰–2‰) mostly from euhedral magnetite. Dolomite and breunnerite analyses were conducted using multi‐collection Faraday cup detectors with precisions ≤0.3‰. The instrumental bias correction was applied based on carbonate compositions in two ways, using Fe and (Fe + Mn) contents, respectively, because Ryugu dolomite contains higher amounts of Mn than the terrestrial standard. Results of dolomite and breunnerite analyses show a narrow range of ∆17O; 0.0‰–0.3‰ for dolomite in A0058 and 0.2‰–0.8‰ for dolomite and breunnerite in C0002. The majority of breunnerite, including large ≥100 μm grains, show systematically lower δ18O (~21‰) than dolomite (25‰–30‰ and 23‰–27‰ depending on the instrumental bias corrections). The equilibrium temperatures between magnetite and dolomite from the coarse‐grained lithology in A0058 are calculated to be 51 ± 11°C and 78 ± 14°C, depending on the instrumental bias correction scheme for dolomite; a reliable temperature estimate would require a Mn‐bearing dolomite standard to evaluate the instrumental bias corrections, which is not currently available. These results indicate that the oxygen isotope ratios of aqueous fluids in the Ryugu parent asteroid were isotopically heterogeneous, either spatially, or temporary. Initial water ice accreted to the Ryugu parent body might have ∆17O > 2‰ that was melted and interacted with anhydrous solids with the initial ∆17O < 0‰. In the early stage of aqueous alteration, spherulitic magnetite and calcite formed from aqueous fluid with ∆17O ~ 2‰ that was produced by isotope exchange between water (∆17O > 2‰) and anhydrous solids (∆17O < 0‰). Dolomite and breunnerite, along with some magnetite, formed at the later stage of aqueous alteration under higher water‐to‐rock ratios where the oxygen isotope ratios were nearly at equilibrium between fluid and solid phases. Including literature data, δ18O of carbonates decreased in the order calcite, dolomite, and breunnerite, suggesting that the temperature of alteration might have increased with the degree of aqueous alteration.

Study of solar brightness profiles in the 18–26 GHz frequency range with INAF radio telescopes

Context. One of the most important objectives of solar physics is to gain a physical understanding of the solar atmosphere, whose structure can also be described in terms of the density (N) and temperature (T) distributions of the atmospheric matter. Several multi-frequency analyses have shown that the characteristics of these distributions are still under debate, especially for outer coronal emission. Aims. We aim to constrain the T and N distributions of the solar atmosphere through observations in the centimetric radio domain. We employed single-dish observations from two of the INAF radio telescopes at the K-band frequencies (18–26 GHz). We investigated the origin of the significant brightness temperature (TB) detected up to the upper corona (at an altitude of ∼800 mm with respect to the photospheric solar surface). Methods. To probe the physical origin of the atmospheric emission and to constrain instrumental biases, we reproduced the solar signal by convolving specific 2D antenna beam models. We performed an analysis of the solar atmosphere by adopting a physical model that assumes the thermal bremsstrahlung as the emission mechanism, with specific T and N distributions. We compared the modelled TB profiles with those observed by averaging solar maps obtained at 18.3 and 25.8 GHz during the minimum of solar activity (2018–2020). Results. We probed any possible discrepancies between the T and N distributions assumed from the model and those derived from our measurements. The T and N distributions are compatible (within a 25% of uncertainty) with the model up to ∼60 mm and ∼100 mm in altitude, respectively. Conclusions. Our analysis of the role of the antenna beam pattern on our solar maps proves the physical nature of the atmospheric emission in our images up to the coronal tails seen in our TB profiles. Our results suggest that the modelled T and N distributions are in good agreement (within 25% of uncertainty) with our solar maps up to altitudes of ≲100 mm. A subsequent, more challenging analysis of the coronal radio emission at higher altitudes, together with the data from satellite instruments, will require further multi-frequency measurements.

Ionospheric corrections tailored to Galileo HAS: validation with single-epoch navigation

The Galileo high accuracy service (HAS) is a new capability of the European global navigation satellite system, currently providing satellite orbit and clock corrections and dispersive effects such as satellite instrumental biases for code and phase. In its full capability, Galileo HAS will also correct the ionospheric delay on a continental scale (initially over Europe). We analyze a real-time ionospheric correction system based on the fast precise point positioning (F-PPP), and its potential application to the Galileo HAS. The F-PPP ionospheric model is assessed through a 281-day campaign, confirming previously reported results, where the proof of concept was introduced. We introduce a novel real-time test that directly links the instantaneous position error with the error of the ionospheric corrections, a key point for a HAS. The test involved 15 GNSS receivers in Europe acting as user receivers at various latitudes, with distances to the nearest reference receivers ranging from tens to four hundred kilometers. In the position domain, the test results show that the 95th percentile of the instantaneous position error depends on the user-receiver distance, as expected, ranging in the horizontal and vertical components from 10 to 30 cm and from 20 to 50 cm, respectively. These figures not only meet Galileo HAS requirements but outperform them by achieving instantaneous positioning. Additionally, it is shown that formal errors of the ionospheric corrections, which are also transmitted, are typically at the decimeter level (1 sigma), protecting users against erroneous position by weighting its measurements in the navigation filter.

Exploring the Aggregation Propensity of PHF6 Peptide Segments of the Tau Protein Using Ion Mobility Mass Spectrometry Techniques.

Peptide and protein aggregation involves the formation of oligomeric species, but the complex interplay between oligomers of different conformations and sizes complicates their structural elucidation. Using ion mobility mass spectrometry (IM-MS), we aim to reveal these early steps of aggregation for the Ac-PHF6-NH2 peptide segment from tau protein, thereby distinguishing between different oligomeric species and gaining an understanding of the aggregation pathway. An important factor that is often neglected, but which can alter the aggregation propensity of peptides, is the terminal capping groups. Here, we demonstrate the use of IM-MS to probe the early stages of aggregate formation of Ac-PHF6-NH2, Ac-PHF6, PHF6-NH2, and uncapped PHF6 peptide segments. The aggregation propensity of the four PHF6 segments is confirmed using thioflavin T fluorescence assays and transmission electron microscopy. A novel approach based on post-IM fragmentation and quadrupole selection on the TIMS-Qq-ToF (trapped ion mobility) spectrometer was developed to enhance oligomer assignment, especially for the higher-order aggregates. This approach pushes the limits of IM identification of isobaric species, whose signatures appear closer to each other with increasing oligomer size, and provides new insights into the interpretation of IM-MS data. In addition, TIMS collision cross section values are compared with traveling wave ion mobility (TWIMS) data to evaluate potential instrumental bias in the trapped ion mobility results. The two IM-MS instrumental platforms are based on different ion mobility principles and have different configurations, thereby providing us with valuable insight into the preservation of weakly bound biomolecular complexes such as peptide aggregates.

Iron depletion in mineral dust grains from Saturn’s main rings

ABSTRACT During the Grand Finale orbits, Cassini’s Cosmic Dust Analyzer (CDA) recorded in situ mass spectra of ice and mineral nanodust grains ejected from Saturn’s main rings falling into the planet’s atmosphere. We present a compositional analysis of the mineral dust fraction employing a spectral deconvolution method to determine the elemental composition of these grains. The results indicate a relatively homogenous composition of exclusively Mg-rich silicates, with Mg, Si, and Ca close to CI chondritic abundances but a significant depletion in Fe and only traces of organic material at best. The Fe depletion becomes even more pronounced when compared to Fe-rich interplanetary dust particles encountered by CDA in the Saturnian system, which are assumed to contaminate and darken the main rings over time. We discuss potential explanations for the depletion, from which we favour compositional alteration of the infalling dust grains by impact-triggered chemistry in combination with dynamical selection effects and instrumental bias as the most plausible ones. This might cause an accumulation of Fe in the main rings over time, most likely in the form of oxides.

Causal linkage between type 2 diabetes mellitus and inflammatory bowel disease: an integrated Mendelian randomization study and bioinformatics analysis.

Observational studies have indicated associations between type 2 diabetes mellitus (T2DM) and both colorectal cancer (CRC) and inflammatory bowel disease (IBD). However, the underlying causality and biological mechanisms between these associations remains unclear. We conducted a bidirectional Mendelian randomization (MR) analysis employing summary statistics from genome-wide association studies involving European individuals. The inverse variance weighting (IVW) method was the primary method used to assess causality. Additionally, we applied MR Egger, Weighted median, Simple mode, and Weighted mode to evaluate the robustness of the results. Outliers were identified and eliminated using the MR-PRESSO, while the MR-Egger intercept was used to assess the horizontal pleiotropic effects of single nucleotide polymorphisms (SNPs). The heterogeneity was evaluated using the Cochrane Q test, and sensitivity analysis was performed using leave-one-out method. The F statistic was calculated to evaluate weak instrumental variable bias. Finally, a pilot bioinformatics analysis was conducted to explore the underlying biological mechanisms between T2DM and IBD/UC. The IVW results demonstrated that T2DM significantly reduced risks of IBD (OR=0.885, 95% CI: 0.818-0.958, P=0.002) and ulcerative colitis (UC) (OR=0.887, 95% CI: 0.812-0.968, P=0.007). Although the 95% CIs of MR Egger, Weighted median, Simple mode, and Weighted mode were broad, the majority of their estimates were consistent with the direction of IVW. Despite significant heterogeneity among SNPs, no horizontal pleiotropy was observed. The leave-one-out analysis showed that the causality remained consistent after each SNP was removed, underscoring the reliability of the results. Reverse MR analysis indicated that genetic susceptibility to both CRC and IBD had no significant effect on the relative risk of T2DM. Ten hub genes were identified, which mainly enriched in pathways including maturity onset diabetes of the young, thyroid cancer, gastric acid secretion, longevity regulating pathway, melanogenesis, and pancreatic secretion. The presence of T2DM does not increase the risk of CRC or IBD. Moreover, T2DM might reduce risk of IBD, including UC. Conversely, the occurrence of CRC or IBD does not influence the risk of T2DM. The association between T2DM and IBD/UC may be related to the changes in multiple metabolic pathways and CTLA-4-mediated immune response.

A Systematic Investigation of the Effects of Standard‐Sample Concentration Mismatch during Fe Isotope Measurement by MC‐ICP‐MS

Advances in multi‐collector inductively coupled plasma‐mass spectrometry (MC‐ICP‐MS) have led to the widespread use of iron (Fe) isotopes to elucidate the (bio)geochemical history of a range of environments. To generate Fe isotope ratio measurements, standard‐sample bracketing (SSB) is commonly used to correct for instrumental mass bias inherent to MC‐ICP‐MS. However, SSB is only accurate when sample and isotope standard Fe concentrations match, in addition to the bulk solution matrix. When the Fe concentrations differ, Fe isotope ratio measurement results may be inaccurate, a phenomenon known as the "self‐induced matrix effect." This study systematically characterised the self‐induced matrix effect for dry plasma Fe isotope ratio measurements on three MC‐ICP‐MS instruments and three introduction systems. Our extensive dataset indicates that: (1) the degree of mass bias is consistent regardless of MC‐ICP‐MS front‐end design, (2) the degree of mass bias becomes less reproducible as the concentration difference between the sample and bracketing standard increases, and (3) this applies to both pure Fe solutions and solutions from geological materials. This study reinforces the requirement to match bracketing standard and sample concentrations within 10% and provides a correction method for that fall beyond the recommended concentration range to subsequently allow for proper concentration matching during SSB.

Rasch analysis of the dermatology life quality index (DLQI) in patients with mild to moderate-severe psoriasis.

Background The Dermatology Life Quality Index (DLQI) is a valuable tool for assessing the quality of life in adult patients with psoriasis. Aims To analyse the reliability and validity of the DLQI to measure the quality of life in patients with mild to moderate-severe psoriasis. Methods This was a secondary validation study nested in a follow-up study. The Rasch-Andrich model was utilised to perform response function, item and person fit, differential item functioning, dimensionality, and reliability analyses. Results A total of 1439 patients were analysed, 52.1% male, mean age of 48.7 years (SD 16.1). Psoriasis vulgaris was the phenotype in 43.1% of patients, and 86% had a mild Psoriasis Area Severity Index (PASI). Adequate adjustment of the response function and the items was observed in the best-fit sample, except for item 7 (work and study). The measure explained 60.9% of the variance and presented a reliability of 0.86. Differential item functioning was identified by age, with a relevant bias in the estimation for older adults. Item-person maps are provided. Limitations This study was performed at a single centre, with most patients presenting a mild PASI score, limiting generalisation of the findings. Conclusion The validity evidence favours the use of the DLQI in moderate-severe psoriasis. However, the instrument biases the estimate of older adults. This population group should consider a specific version of the instrument.

Navigating Mendelian Randomization in Sleep Medicine: Challenges, Opportunities, and Best Practices.

Mendelian randomization (MR) has become an influential method for elucidating causal links between sleep traits and disorders, and health outcomes. This article provides sleep medicine specialists with an overview of MR, emphasizing its applications and limitations in health research, particularly in the context of sleep research. The article addresses key challenges in conducting and interpreting MR studies on sleep, focusing on the core assumptions of relevance, exchangeability, and exclusion restriction. The importance of proper genetic instrument selection, bias mitigation, and cautious result interpretation is emphasized. Strategies are recommended to enhance the quality of MR studies in sleep medicine, including collaborations between MR experts and sleep specialists. The paper also explores sleep medicine-specific issues like analyzing binary traits and addressing heterogeneity in pooled analyses. Guidance is provided on transparent reporting of MR findings, stressing the need for comprehensive effect estimates, confidence intervals, and p-values. We conclude by advocating for rigorous MR implementation in sleep research to deepen our understanding of sleep-health relationships. By following best practices in study design, analysis, and reporting, researchers can reinforce the credibility and impact of MR findings in sleep medicine, ultimately improving patient care and public health strategies.

The causal relationship between sleep characteristics and multi-site pain perception: a two-sample Mendelian randomization study.

This Mendelian Randomization (MR) study aims to explore the potential causal relationships between four sleep traits and pain in 10 different body sites. The study utilizes exposure and outcome data from the GWAS database, employing the Inverse Variance Weighting Method (IVW) for primary causal estimates. Cochran Q and Rücker Q heterogeneity tests are conducted using IVW and MR-Egger methods, with the Egger-intercept method for pleiotropy testing, leave-one-out sensitivity analysis, and calculation of F-statistics to assess the presence of weak instrument bias. The study reveals that genetically predicted insomnia significantly increases the risk of unspecified pain, chest pain, gum pain, upper abdominal pain, and lower abdominal pain occurrence. Daytime napping is associated with a moderate reduction in the likelihood of joint pain but may concomitantly elevate the risk of chest pain, upper abdominal pain, and generalized abdominal pain. Neither sleep chronotype nor sleep duration demonstrated a definitive causal relationship with pain perception. This study elucidates the causal relationships between four sleep characteristics and pain across 10 different body regions. Overall, the contribution of insomnia and sleep deficiency to pain in multiple body regions is more pronounced. Conversely, the association between adequate sleep and the likelihood of somatic pain is relatively lower and less significant.

Multiplexed Antibody Glycosylation Profiling Using Dual Enzyme Digestion and Liquid Chromatography-Triple Quadrupole Mass Spectrometry Method

Antibody glycosylation plays a crucial role in the humoral immune response by regulating effector functions and influencing the binding affinity to immune cell receptors. Previous studies have focused mainly on the immunoglobulin G (IgG) isotype owing to the analytical challenges associated with other isotypes. Thus, the development of a sensitive and accurate analytical platform is necessary to characterize antibody glycosylation across multiple isotypes. In this study, we have developed an analytical workflow using antibody-light-chain affinity beads to purify IgG, IgA, and IgM from 16 μL of human plasma. Dual enzymes, trypsin and Glu-C, were used during on-bead digestion to obtain enzymatic glycopeptides and protein-specific surrogate peptides. Ultra-high-performance liquid chromatography coupled with triple quadrupole mass spectrometry was used in order to determine the sensitivity and specificity. Our platform targets 95 glycopeptides across the IgG, IgA, and IgM isotypes, as well as eight surrogate peptides representing total IgG, four IgG classes, two IgA classes, and IgM. Four stable isotope-labeled internal standards were added after antibody purification to calibrate the preparation and instrumental bias during analysis. Calibration curves constructed using serially diluted plasma samples showed good curve fitting (R2 > 0.959). The intrabatch and interbatch precision for all the targets had relative standard deviation of less than 29.6%. This method was applied to 19 human plasma samples, and the glycosylation percentages were calculated, which were comparable to those reported in the literature. The developed method is sensitive and accurate for Ig glycosylation profiling. It can be used in clinical investigations, particularly for detailed humoral immune profiling.

Error Analysis for Transport Properties of Lithium-Ion Battery Electrolytes

Accurate determination of electrolyte transport properties in lithium-ion batteries (LIBs) is pivotal for understanding battery performance and optimizing their design. Electrolyte transport properties, encompassing ionic conductivity, diffusion coefficients, and transference numbers, are critical parameters that influence charge and discharge rates, ion mobility, and energy efficiency.[1,2] However, experimental measurements are inevitably accompanied by uncertainties stemming from experimental conditions, instrument calibration, and data analysis methods. Error propagation in electrolyte transport properties has substantial ramifications for battery research and development. Inaccurate measurements can lead to flawed conclusions about battery behavior and hinder the design of optimal electrolyte formulations.[3] Based on the literature, measurements of concentration cells for the determination of electrolyte properties were conducted at different temperatures and concentrations to study its impact on transport properties, and the Gaussian error propagation method was used to analyze the data. The concentration cell experiment is essential to determine the transport factor a. [4] The equation to determine the transport factor is shown in Figure 1a and the exemplary results are represented in Figure 1b. The results show that the error bars are significantly higher at certain concentration pairs.The main aim of this research is to identify the sources that contribute to the prominent difference in the error bars across different concentrations and temperatures. It also discusses the significance of systematic errors, which arise due to calibration issues or instrumental biases, and random errors stemming from variations in experimental conditions.[5] Recognizing the nature of these errors is critical for accurate interpretation of experimental data. To determine the electrolyte transport properties and the corresponding error or uncertainty, experimental datasets from the literatureare studied and repeated in our laboratories.

Cannabis use and the risk of primary open-angle glaucoma: a Mendelian randomization study

Several observational studies have investigated the association between cannabis use and intraocular pressure, but its association with primary open-angle glaucoma (POAG) remains unclear. In this study, we leveraged human genetic data to assess through Mendelian randomization (MR) whether cannabis use affects POAG. We used five single-nucleotide polymorphisms (SNPs) associated with lifetime cannabis use (P-value < 5 × 10–8) from a genome-wide association study (GWAS) (N = 184,765) by the International Cannabis Consortium, 23andMe, and UK Biobank and eleven SNPs associated with cannabis use disorder (P-value < 5 × 10–7) from a GWAS meta-analysis of (17,068 cases and 357,219 controls of European descent) from Psychiatric Genomics Consortium Substance Use Disorders working group, Lundbeck Foundation Initiative for Integrative Psychiatric Research, and deCode. We associated the selected five SNPs from the GWAS of lifetime cannabis use and the eleven SNPs from the GWAS of cannabis use disorder, with the largest to date GWAS meta-analysis of POAG (16,677 cases and 199,580 controls). MR analysis suggested no evidence for a causal association of lifetime cannabis use and cannabis use disorder with POAG (odds ratio (OR) of outcome per doubling of the odds of exposure (95% confidence interval): 1.04 (0.88; 1.23) for lifetime cannabis use and 0.97 (0.92; 1.03) for cannabis use disorder). Sensitivity analyses to address pleiotropy and weak instrument bias yielded similar estimates to the primary analysis. In conclusion, our results do not support a causal association between cannabis use and POAG.

Instrumental Receiver Bias Estimation for Ionospheric Total Electron Content by Neural Network Model

Total Electron Content (TEC) is one of the most important parameters in the study of the ionosphere, especially for determining ionospheric disturbances. The TEC levels are typically estimated from dual-frequency GPS observation data. Since the measured TEC contains discrepancies such as satellite and receiver biases, they need to be removed to obtain more accurate TEC values. In this work, we estimate the receiver bias using a neural network technique. Based on the exhaustive evaluation, we design a neural network (NN) model with two-hidden layers, and it is trained with datasets from three GNSS observation stations in Thailand. The prediction from the proposed neural network deviates from the baseline reference using the minimum standard deviation method with significantly faster computational time. The trained NN model is also tested for estimating the receiver bias values at other untrained stations in Thailand.

GOES GLM, biased bolides, and debiased distributions

The large combined field of view of the Geostationary Lightning Mapper (GLM) instruments onboard the GOES weather satellites makes them useful for studying the population of other atmospheric phenomena, such as bolides. Being a lightning mapper, GLM has many detection biases when applied to non-lightning and these systematics must be studied and properly accounted for before precise measurements of bolide flux can be ascertained. We developed a Bayesian Poisson regression model which simultaneously estimates instrumental biases and our statistic of principal interest: the latitudinal variation of bolide flux. We find that the estimated bias due to the angle of incident light upon the instrument corresponds roughly with the known sensitivity of the GLM instruments. We compare our latitudinal flux variation estimates to existing theoretical models and find our estimates consistent with GLM being strongly biased towards high-velocity bolides.