Robust Interpretation Research Articles

The effects of weathering and sediment source mixing on whole-rock geochemical provenance studies, Cook Inlet forearc basin, south-central Alaska, USA

The provenance of most basin systems today is interpreted based on radiogenic ages or the geochemical composition of detrital minerals, which has all but replaced the use of whole-rock geochemical approaches that can effectively complement provenance information inferred from detrital approaches. Here, we further investigate previous provenance models developed using detrital zircon U-Pb geochronology by applying whole-rock major and trace element geochemistry of fine-grained clastic rocks from the late Oligocene−middle Miocene Tyonek Formation, late Miocene Beluga Formation, and Pliocene Sterling Formation in the Cook Inlet Basin, Alaska, USA. When taken alone, our new geochemical data suggest solely intermediate igneous sediment sources to the basin. When paired with existing detrital zircon U-Pb data, however, significant mixing of felsic and mafic sediment sources is evident, which indicates that thorough mixing of geochemically distinct source terranes can mask the input from individual sources in whole-rock geochemical studies. Furthermore, we demonstrate that both weathering and provenance influence the major element chemistry of sediment source terranes as well as the resultant basinal strata. Our conclusions indicate that the combination of whole-rock geochemistry with other detrital approaches provides a robust interpretation of sedimentary basin provenance.

Optimization of cellulose nanocrystal (CNC) concentration in polycaprolactone bio-composites for bio-plotting: a robust interpretation of the reinforcement mechanisms

Bioabsorbable and biodegradable composites have experienced rapid growth, owing to their high demand in the biomedical sector. Polymer-cellulose nanocrystal (CNC) compounds were developed using a medical-grade poly (ε-caprolactone) (PCL) matrix to improve the stiffness and load-bearing capacity of pure PCL. Five PCL/CNCs filament grades were melt-extruded, pelletized, and fed into an industrial bioplotter to fabricate specimens. To assess the effects of CNCs on pure PCL, 14 tests were conducted, including rheological, thermomechanical, and in situ micro-mechanical testing, among others. The porosity and dimensional accuracy of the samples were also documented using micro-computed tomography while scanning electron microscopy was employed for morphological characterization. Overall, the 4.0 wt % CNCs loading accomplished the optimum mechanical response, with an increase in its tensile (19.1%) and flexural strength (12.6%) compared to pure PCL. Concurrently, this grade exhibited the highest MFR, minimum porosity, and highest nominal-to-actual geometric accuracy, thereby convincingly interpreting the reinforcement mechanisms.

Learning place representations from spatial interactions

The development of geospatial artificial intelligence (GeoAI) systems depends on the ability to learn effective representations of places. To learn accurate place representations from spatial interactions, it is important to extract features that capture both the spatial and non-spatial driving factors. However, existing methods lack a robust interpretation and the explanatory power of the learned representations on spatial factors remains unexplored. Here, we propose an approach to learning place representations from spatial interactions. Our method is inspired by flow allocation, which is the main focus of single-constrained gravity models. We first validate the method on synthetic flows with known driving factors and then apply it to multi-scale real-world flows. Results show that the learned representations can effectively capture features that explain place characteristics, along with the impact of spatial impedance. Our study not only contributes an efficient method to learn place representations from spatial interactions but also offers insights into pre-training procedures in GeoAI.

3D convolutional neural networks predict cellular metabolic pathway use from fluorescence lifetime decay data.

Fluorescence lifetime imaging of the co-enzyme reduced nicotinamide adenine dinucleotide (NADH) offers a label-free approach for detecting cellular metabolic perturbations. However, the relationships between variations in NADH lifetime and metabolic pathway changes are complex, preventing robust interpretation of NADH lifetime data relative to metabolic phenotypes. Here, a three-dimensional convolutional neural network (3D CNN) trained at the cell level with 3D NAD(P)H lifetime decay images (two spatial dimensions and one time dimension) was developed to identify metabolic pathway usage by cancer cells. NADH fluorescence lifetime images of MCF7 breast cancer cells with three isolated metabolic pathways, glycolysis, oxidative phosphorylation, and glutaminolysis were obtained by a multiphoton fluorescence lifetime microscope and then segmented into individual cells as the input data for the classification models. The 3D CNN models achieved over 90% accuracy in identifying cancer cells reliant on glycolysis, oxidative phosphorylation, or glutaminolysis. Furthermore, the model trained with human breast cancer cell data successfully predicted the differences in metabolic phenotypes of macrophages from control and POLG-mutated mice. These results suggest that the integration of autofluorescence lifetime imaging with 3D CNNs enables intracellular spatial patterns of NADH intensity and temporal dynamics of the lifetime decay to discriminate multiple metabolic phenotypes. Furthermore, the use of 3D CNNs to identify metabolic phenotypes from NADH fluorescence lifetime decay images eliminates the need for time- and expertise-demanding exponential decay fitting procedures. In summary, metabolic-prediction CNNs will enable live-cell and in vivo metabolic measurements with single-cell resolution, filling a current gap in metabolic measurement technologies.

Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). This perspective aims to (1)justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2)recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

THE IMPACT OF E-SERVICE QUALITY ON E-LOYALTY THROUGH THE MEDIATING EFFECTS OF E-SATISFACTION AND E-TRUST IN LEBANON

Purpose: This study seeks to explore the relationships between e-service quality, e-satisfaction, e-trust, and e-loyalty within the context of Lebanon's digital landscape. By investigating the mediating effects of e-satisfaction and e-trust, the research aims to provide nuanced insights into the intricate dynamics shaping e-loyalty in the Lebanese online environment. The findings are anticipated to contribute valuable knowledge for businesses and policymakers seeking to enhance electronic service offerings and foster customer loyalty in the digital realm. Design/methodology/data analysis: In this comprehensive quantitative study, a validated questionnaire was deployed to survey Lebanon's diverse online shopper demographic. The participation of 2378 individuals from varied backgrounds added depth and representativeness to the data. The analytical phase employed T-test, ANOVA, and Structural Equation Modeling (SEM) techniques, leveraging SPSS software for robust statistical analysis and interpretation. This methodological approach ensures a thorough examination of the dynamics influencing online shopping behavior in the Lebanese context. Findings: Highlighting critical insights for E-commerce and e-retail, this study emphasizes the pivotal role of website efficiency and information quality. The findings underscore that a streamlined website contributes significantly to fostering customer trust, satisfaction, and loyalty. These essential factors identified in the study illuminate the pathway for businesses to enhance their online platforms, ultimately strengthening the bonds with customers and cultivating sustained loyalty in the digital realm. Originality/value: This study is important because the pandemic and lockdowns have changed consumer purchasing behaviour. Customers are more critical of e-commerce platforms and more comfortable using multiple devices to access online stores and apps as they shop more online. Practical implications: This study is timely and relevant for businesses trying to stay competitive in the changing e-commerce landscape due to consumer behaviour changes.

Genetic Anti‐Blackness: Exploring the Use of Analogies in Understanding Contemporary Racism

AbstractSince slavery, racism and discrimination have continued to impact the social outcomes of Blacks despite efforts and laws put into place to alleviate the disadvantages. A contemporary project developed by Nikole Hannah‐Jones and the New York Times called for the re‐examination of the legacy of slavery. With re‐examining the consequence of slavery, the lens has been widened, resulting in a more robust interpretation of racism. Social issues have historic roots and thus require a multilayered approach to map out and examine the culminated effects of phenomenon. In this paper, the author uses Beckert and Rockman’s DNA analogy (2016) to merge biology, history, and sociology to provide a critical overview of theories of racism that have emerged since slavery, and to detail the phenomenon of contemporary racism. By connecting past eras of racism using something familiar, racial discourse can potentially move beyond the frustration of racism being a permanent entity in society.

How Hydrothermal Cooling and Magmatic Sill Intrusions Control Flip‐Flop Faulting at Ultraslow‐Spreading Mid‐Ocean Ridges

Abstract“Flip‐flop” detachment mode represents an endmember type of lithosphere‐scale faulting observed at almost amagmatic sections of ultraslow‐spreading mid‐ocean ridges. Recent numerical experiments using an imposed steady temperature structure show that an axial temperature maximum is essential to trigger flip‐flop faults by focusing flexural strain in the footwall of the active fault. However, ridge segments without significant melt budget are more likely to be in a transient thermal state controlled, at least partly, by the faulting dynamics themselves. Therefore, we investigate which processes control the thermal structure of the lithosphere and how feedbacks with the deformation mechanisms can explain observed faulting patterns. We present results of 2‐D thermo‐mechanical numerical modeling including serpentinization reactions and dynamic grain size evolution. The model features a novel form of parametrized hydrothermal cooling along fault zones as well as the thermal and rheological effects of periodic sill intrusions. We find that the interplay of hydrothermal fault zone cooling and periodic sill intrusions in the footwall facilitates the flip‐flop detachment mode. Hydrothermal cooling of the fault zone pushes the temperature maximum into the footwall, while intrusions near the temperature maximum further weaken the rock and promote the formation of new faults with opposite polarity. Our model allows us to put constraints on the magnitude of two processes, and we obtain most reasonable melt budgets and hydrothermal heat fluxes if both are considered. Furthermore, we frequently observe two other faulting modes in our experiments complementing flip‐flop faulting to yield a potentially more robust alternative interpretation for existing observations.

Methodological differences between studies confound one-size-fits-all approaches to managing surface waterways for food and water safety.

Even though differences in methodology (e.g., sample volume and detection method) have been shown to affect observed microbial water quality, multiple sampling and laboratory protocols continue to be used for water quality monitoring. Research is needed to determine how these differences impact the comparability of findings to generate best management practices and the ability to perform meta-analyses. This study addresses this knowledge gap by compiling and analyzing a data set representing 2,429,990 unique data points on at least one microbial water quality target (e.g., Salmonella presence and Escherichia coli concentration). Variance partitioning analysis was used to quantify the variance in likelihood of detecting each pathogenic target that was uniquely and jointly attributable to non-methodological versus methodological factors. The strength of the association between microbial water quality and select methodological and non-methodological factors was quantified using conditional forest and regression analysis. Fecal indicator bacteria concentrations were more strongly associated with non-methodological factors than methodological factors based on conditional forest analysis. Variance partitioning analysis could not disentangle non-methodological and methodological signals for pathogenic Escherichia coli, Salmonella, and Listeria. This suggests our current perceptions of foodborne pathogen ecology in water systems are confounded by methodological differences between studies. For example, 31% of total variance in likelihood of Salmonella detection was explained by methodological and/or non-methodological factors, 18% was jointly attributable to both methodological and non-methodological factors. Only 13% of total variance was uniquely attributable to non-methodological factors for Salmonella, highlighting the need for standardization of methods for microbiological water quality testing for comparison across studies.IMPORTANCEThe microbial ecology of water is already complex, without the added complications of methodological differences between studies. This study highlights the difficulty in comparing water quality data from projects that used different sampling or laboratory methods. These findings have direct implications for end users as there is no clear way to generalize findings in order to characterize broad-scale ecological phenomenon and develop science-based guidance. To best support development of risk assessments and guidance for monitoring and managing waters, data collection and methods need to be standardized across studies. A minimum set of data attributes that all studies should collect and report in a standardized way is needed. Given the diversity of methods used within applied and environmental microbiology, similar studies are needed for other microbiology subfields to ensure that guidance and policy are based on a robust interpretation of the literature.

An Interpretation between Small Punch Creep and Uniaxial Tensile Creep Test: Computational and Empirical Approach

The relationship between Small Punch (SP) creep testing and uniaxial tensile creep testing is crucial for translating findings from the innovative SP methodology to more conventional testing paradigms. This study involves both computational and empirical approaches to correlate the two testing methods effectively. The computational approach primarily utilizes finite element analysis (FEA) to model and simulate the stress states and deformation behaviors in SP creep tests. The empirical approach involves developing empirical relationships based on experimental data from both SP and uniaxial tensile creep tests. By integrating computational and empirical approaches, researchers can achieve a robust and reliable interpretation of SP creep test data in terms of conventional tensile creep test results.

Solution of nonlinear Burger's equation arising in longitudinal dispersion phenomena

The present article showcases a novel approach for obtaining the approximate solution of the nonlinear Burgers' equation, a frequently occurring equation in the study of wave propagation and dispersion phenomena. The Double Power Series Method, a methodology not conventionally applied to this equation, introduces a unique analytical perspective. The method's versatility in handling two independent variables makes it particularly well-suited for addressing the complexities of the Burgers' equation, providing a comprehensive representation of fluid concentration for arbitrary distances and times. Through this novel approach, we study the intricate dynamics governing the relationship between convection and diffusion forces in longitudinal dispersion. This research not only enriches the theoretical foundation of nonlinear partial differential equations but also holds promise for practical applications across fluid dynamics, environmental engineering, and beyond. Both numerical and graphical solutions are meticulously computed through MATLAB coding, facilitating a robust physical interpretation of the results. This study contributes valuable insights into the behavior of fluids in dispersion scenarios, offering a practical approach to understanding and predicting such phenomena.

Binary classification based on a combination of rough set theory and decision trees

The subject of the study is to improve the accuracy and efficiency of classification algorithms using decision trees by integrating the principles of Rough Set theory, a mathematical approach to approximating sets. The aim of the study is to develop a hybrid model that integrates rough set theory with decision tree algorithms, thereby solving the inherent limitations of these algorithms in dealing with uncertainty in data. This integration should significantly improve the accuracy and efficiency of binary classification based on decision trees, making them more robust to different inputs. Research objectives include a deep study of possible synergies between approximate set theory and decision tree algorithms. For this purpose, we are conducting a comprehensive study of the integration of approximate set theory within decision tree algorithms. This includes the development of a model that utilizes the principles and algebraic tools of approximate set theory to more efficiently select features in decision tree-based systems. The model uses the theory of approximate sets to efficiently handle uncertainty and weighting, which allows for improved and extended feature selection processes in decision tree systems. A series of experiments are conducted on different datasets to demonstrate the effectiveness and practicality of this approach. These datasets are chosen to represent a range of complexities and uncertainties, providing a thorough and rigorous evaluation of the model's capabilities. The methodology uses advanced algebraic tools of approximate set theory, including the formulation of algebraic expressions and the development of new rules and techniques, to simplify and improve the accuracy of data classification processes using decision tree systems. The findings of the study are important because they show that integrating approximate set theory into decision tree algorithms can indeed provide more accurate and efficient classification results. Such a hybrid model demonstrates significant advantages in dealing with data with embedded uncertainty, which is a common challenge in many complementary scenarios. The versatility and effectiveness of the integrated approach is demonstrated by its successful application in the areas of credit scoring and cybersecurity, which emphasizes its potential as a versatile tool in data mining and machine learning. The conclusions show that integrating approximate set theory can lead to more accurate and efficient classification results. By improving the ability of decision trees to account for uncertainty and imprecision in data, the research opens up new possibilities for robust and sophisticated data analysis and interpretation in a variety of industries, from healthcare to finance and beyond. The integration of approximate set theory and decision trees is an important step in the development of more advanced, efficient, and accurate classification tools in the era of big data.

Nanoscale Operando Characterization of Electrolyte-Gated Organic Field-Effect Transistors Reveals Charge Transport Bottlenecks.

Charge transport in electrolyte-gated organic field-effect transistors (EGOFETs) is governed by the microstructural property of the semiconducting thin film that is in direct contact with the electrolyte. Therefore, a comprehensive nanoscale operando characterization of the active channel is crucial to pinpoint various charge transport bottlenecks for rational and targeted optimization of the devices. Here, the local electrical properties of EGOFETs are systematically probed by in-liquid scanning dielectric microscopy (in-liquid SDM) and a direct picture of their functional mechanism at the nanoscale is provided across all operational regimes, starting from subthreshold, linear to saturation, until the onset of pinch-off. To this end, a robust interpretation framework of in-liquid SDM is introduced that enables quantitative local electric potential mapping directly from raw experimental data without requiring calibration or numerical simulations. Based on this development, a straightforward nanoscale assessment of various charge transport bottlenecks is performed, like contact access resistances, inter- and intradomain charge transport, microstructural inhomogeneities, and conduction anisotropy, which have been inaccessible earlier. Present results contribute to the fundamental understanding of charge transport in electrolyte-gated transistors and promote the development of direct structure-property-function relationships to guide future design rules.

Charcoal morphologies and morphometrics of a Eurasian grass-dominated system for robust interpretation of past fuel and fire type

Abstract. Recent developments in morphological and morphometric analyses of charcoal particles have improved our ability to discern characteristics of burnt plant fuel and interpret fire-type changes. However, burning experiments linking known plants to these metrics are limited, particularly in open ecosystems. This study presents novel analyses of laboratory-produced charcoal of 22 plant species from the steppe regions of Eurasia (Romania and Russia), along with selected samples from three Holocene charcoal and pollen records from the same areas. We characterise charcoal production, morphologies and morphometrics in these grass-dominated environments, thereby enabling more robust interpretations of fuel sources and fire types for palaeofire research. Our experiments demonstrate that fire temperature can introduce biases in charcoal produced among species. Grass charcoal production was significantly lower and decreased more strongly with fire temperature compared to forbs. This suggests an underrepresentation of terrestrial graminoids in sedimentary charcoal assemblages. Morphometric analyses revealed that graminoid charcoal particles were more elongated (length-to-width ratio L/W=4) and narrower (width-to-length ratio W/L=0.38) than forbs (L/W=3.1 and W/L=0.42, respectively), in agreement with a global compilation for graminoids (L/W=4.3 for grass 5.4 grass and wetland graminoids) and forbs (L/W=2.9). However, overlapping L/W values present a challenge for establishing cut-off values for fuel type identification in charcoal assemblages with mixed fuel sources. Based on our analyses and compiled datasets from experimental burns, L/W values above 3.0 may indicate predominantly herbaceous morphologies in temperate grassland-dominated ecosystems, though values are likely to be higher for grass than forb-dominated grasslands. Notably, terrestrial grasses exhibit shorter aspect ratios (L/W=4.3) than wetland graminoids (L/W=6.4), highlighting that the aspect ratio needs tailoring to the specific environment of its application, i.e. wetland vs. terrestrial ecosystems. The long forms of graminoid charcoal particles also suggest their potential for atmospheric longer-distance transport compared to more spherical particles, meaning they likely provide insights into regional fire history. An important finding is that charcoal of herbaceous plants closely corresponded to the pollen record, highlighting a solid link between the dominant vegetation and fuel burnt in grassland-dominated environments. However, the relationship between woody charcoal and tree pollen may be more complex, as tree pollen can travel atmospherically longer distances compared to woody charcoal. Our results also highlight the complex interplay between local vegetation and charcoal composition with human fire use that needs to be considered when interpreting charcoal morphological records. A critical takeaway from this study is the importance of not assuming the universality of previous research findings and instead employing experimental approaches to characterise charcoal particles in new ecosystems prior to the application of these techniques. Furthermore, this study also highlights recommendations for further research in new geographical areas and proposes methodological adjustments to enhance the usefulness of charcoal analysis in fire research.

Cluster-Based Multinomial Logistic Regression Model for Health-Related Quality of Life Among People Living with HIV in Brazil.

Improvement in treatment options has increased the survival of people living with HIV (PLHIV). Thus, we evaluated the factors associated with better health-related quality of life (HRQoL) among PLHIV in Brazil. This was a cross-sectional study carried out among 349 PLHIV. Data were collected using an interview-based questionnaire, and HRQoL was assessed by the Brazilian version of the WHOQOL HIV BREF instrument. We used non-hierarchical cluster analysis (K-means) to compile the WHOQOL HIV BREF's overall and domain scores into a unique more multidimensional measure for HRQoL consisting of three clusters: poor, fair and good; associations with clusters of better HRQoL were assessed using multinomial logistic regression models. The mean and median overall HRQoL scores were 15.13 (SD = 3.39) and 16, respectively. The reliability and validity of the Brazilian version of the WHOQOL HIV BREF instrument was confirmed among PLHIV in a non-metropolitan, medium-sized municipality of Brazil, which reaffirmed the cross-cultural validity of this instrument. The factors male sex; heterosexual and asexual orientations; higher individual income; undetectable viral load; absence of any comorbidity and presence of an infectious or a chronic comorbidity, with mental illness as the reference; and never having consumed illegal substances were independently associated with good HRQoL. Thus, the compilation of the WHOQOL HIV BREF's overall and domain scores into a unique multidimensional measure for HRQoL, which this study proposed for the first time, may facilitate more robust interpretations and models of predictors. These differentials could simplify HRQoL as an indicator of health and wellbeing to be routinely used as a key outcome in the clinical management of patients and in the global monitoring of health system responses to HIV.

Stochastic inversion for equivalent hydraulic fracture characterization using low-frequency distributed acoustic sensing data

Distributed fiber-optic sensing, a game-changing technology, has been successfully applied in monitoring hydraulic fracturing treatments. The cross-well fiber-optic strain/strain-rate responses are directly related to the fracture-propagation process and dynamic fracture geometry. Extracting the insightful information regarding hydraulic fractures requires robust interpretation workflows. In this paper, we aim to develop a stochastic inversion approach to characterize the dynamic equivalent fracture geometry and quantify the associated uncertainties using cross-well low-frequency distributed acoustic sensing (LF-DAS) measurements. A fast analytical forward solver based on the 3D displacement discontinuity method is developed to model the strain. The objective function is the sum-of-squares error between the modeled strain and the measured strain. We focus on an equivalent fracture geometry with a predefined length centered around the monitoring well, which is a reasonable assumption considering the spatial sensitivity of LF-DAS. Therefore, the model parameters reduce to fracture width and height of the equivalent fracture. We adopted the delayed rejection adaptive metropolis (DRAM) algorithm, an efficient Markov-chain Monte Carlo (MCMC)-based method, to perform the optimization. The efficiency and accuracy of the algorithm are tested using a synthetic case that has three monitoring wells at different locations. The mean values of fracture width and height are both close to the true values with negligible standard deviations for all the three monitoring wells. The algorithm performance is not sensitive to the initial guess and the predefined length needs to be large enough. Then, a field case with a single cluster from the HFTS-2 project is presented. The fracture width increases from about 0.5 mm to about 0.9 mm during the pumping stage and gradually decreases during the shut-in period. The fracture height changes during the pumping stage and stabilizes at about 150 m during the shut-in period, the trend of which is consistent with the LF-DAS signals from the offset vertical monitoring well. To the best of our knowledge, this is the first algorithm that can efficiently quantify the fracture width and height simultaneously. The results could provide novel insights on the characteristics of fracture geometry and significantly improve the understanding of complex fracture propagation processes in subsurface reservoirs.

Degenerate Interpretations of O3 Spectral Features in Exoplanet Atmosphere Observations Due to Stellar UV Uncertainties: A 3D Case Study with TRAPPIST-1 e

TRAPPIST-1 e is a potentially habitable terrestrial exoplanet orbiting an ultracool M dwarf star and is a key target for observations with the James Webb Space Telescope. One-dimensional photochemical modeling of terrestrial planetary atmospheres has shown the importance of the incoming stellar UV flux in modulating the concentration of chemical species, such as O3 and H2O. In addition, three-dimensional (3D) modeling has demonstrated anisotropy in chemical abundances due to transport in tidally locked exoplanet simulations. We use the Whole Atmosphere Community Climate Model Version 6 (WACCM6), a 3D Earth system model, to investigate how uncertainties in the incident UV flux, combined with transport, affect observational predictions for TRAPPIST-1 e (assuming an initial Earth-like atmospheric composition). We use two semiempirical stellar spectra for TRAPPIST-1 from the literature. The UV flux ratio between them can be as large as a factor of 5000 in some wavelength bins. Consequently, the photochemically produced total O3 columns differ by a factor of 26. Spectral features of O3 in both transmission and emission spectra vary between these simulations (e.g., differences of 20 km in the transmission spectrum effective altitude for O3 at 0.6 μm). This leads to potential ambiguities when interpreting observations, including overlap with scenarios that assume alternative O2 concentrations. Hence, to achieve robust interpretations of terrestrial exoplanetary spectra, characterization of the UV spectra of their host stars is critical. In the absence of such stellar measurements, atmospheric context can still be gained from other spectral features (e.g., H2O), or by comparing direct imaging and transmission spectra in conjunction.

The contemporary stable isotope hydrology of Lake Suigetsu and surrounding catchment (Japan) and its implications for sediment-derived palaeoclimate records

The Lake Suigetsu sediment cores exemplify a high-quality archive of palaeoclimatic change in East Asia during the past 150 ka. Robust interpretation of stable isotope-based proxy reconstructions from the Suigetsu cores can be aided by a greater understanding of the factors affecting the isotope composition of the lake and how it relates to that of precipitation. Here we use extended contemporary monitoring to establish the factors affecting the stable isotope composition (δ18O, δ2H and d-excess) of precipitation, river water and lake water in the catchment surrounding Lake Suigetsu, central Japan. We show that the composition of precipitation is influenced by the dual East Asian Monsoon system, producing minima in δ18O and δ2H and semi-annually varying d-excess values across the year. These signals are then transferred to the lake system, where they are combined with secondary local influences on lake water composition: homogenisation with existing catchment waters, a catchment transit lag, the interaction with saline water from the nearby Sea of Japan, and evaporative enrichment during summer. Our observations suggest that the palaeo-isotope composition of Lake Suigetsu was closely related to the behaviour of the East Asian Monsoon. We highlight lake stratification and proxy seasonality as critical components of signal interpretation.

Toward robust clinical genome interpretation: Developing a consistent terminology to characterize Mendelian disease-gene relationships—allelic requirement, inheritance modes, and disease mechanisms

PurposeThe terminology used for gene-disease curation and variant annotation to describe inheritance, allelic requirement, and both sequence and functional consequences of a variant is currently not standardized. There is considerable discrepancy in the literature and across clinical variant reporting in the derivation and application of terms. Here, we standardize the terminology for the characterization of disease-gene relationships to facilitate harmonized global curation and to support variant classification within the ACMG/AMP framework. MethodsTerminology for inheritance, allelic requirement, and both structural and functional consequences of a variant used by Gene Curation Coalition members and partner organizations was collated and reviewed. Harmonized terminology with definitions and use examples was created, reviewed, and validated. ResultsWe present a standardized terminology to describe gene-disease relationships, and to support variant annotation. We demonstrate application of the terminology for classification of variation in the ACMG SF 2.0 genes recommended for reporting of secondary findings. Consensus terms were agreed and formalized in both Sequence Ontology (SO) and Human Phenotype Ontology (HPO) ontologies. Gene Curation Coalition member groups intend to use or map to these terms in their respective resources. ConclusionThe terminology standardization presented here will improve harmonization, facilitate the pooling of curation datasets across international curation efforts and, in turn, improve consistency in variant classification and genetic test interpretation.

The Origin of Delayed Polymorphism in Molecular Crystals Under Mechanochemical Conditions.

We show that mechanochemically driven polymorphic transformations can require extremely long induction periods, which can be tuned from hours to days by changing ball milling energy. The robust design and interpretation of ball milling experiments must account for this unexpected kinetics that arises from energetic phenomena unique to the solid state. Detailed thermal analysis, combined with DFT simulations, indicates that these marked induction periods are associated with processes of mechanical activation. Correspondingly, we show that the pre-activation of reagents can also lead to marked changes in the length of induction periods. Our findings demonstrate a new dimension for exerting control over polymorphic transformations in organic crystals. We expect mechanical activation to have a much broader implication across organic solid-state mechanochemistry.