Reference Distribution Research Articles

Model diagnostics for censored regression via randomized survival probabilities

Residuals in normal regression are used to assess a model's goodness-of-fit (GOF) and discover directions for improving the model. However, there is a lack of residuals with a characterized reference distribution for censored regression. In this article, we propose to diagnose censored regression with normalized randomized survival probabilities (RSP). The key idea of RSP is to replace the survival probability (SP) of a censored failure time with a uniform random number between 0 and the SP of the censored time. We prove that RSPs always have the uniform distribution on (0, 1) under the true model with the true generating parameters. Therefore, we can transform RSPs into normally distributed residuals with the normal quantile function. We call such residuals by normalized RSP (NRSP residuals). We conduct simulation studies to investigate the sizes and powers of statistical tests based on NRSP residuals in detecting the incorrect choice of distribution family and nonlinear effect in covariates. Our simulation studies show that, although the GOF tests with NRSP residuals are not as powerful as a traditional GOF test method, a nonlinear test based on NRSP residuals has significantly higher power in detecting nonlinearity. We also compared these model diagnostics methods with a breast-cancer recurrent-free time dataset. The results show that the NRSP residual diagnostics successfully captures a subtle nonlinear relationship in the dataset, which is not detected by the graphical diagnostics with CS residuals and existing GOF tests.

Application of a drift compensation low-level radio frequency system based on time-multiplexing pick-up/reference signals.

The high accuracy, low drift low-level radio frequency (LLRF) system is essential for the long-term stability of the accelerator RF and the acquirement of low emittance, high intensity electron beams. A time-multiplexing pick-up/reference signal based LLRF system is proposed to deal with the component temperature related phase drift and has been deployed and applied at the Xi'an Gamma-ray Light Source (XGLS) injector. The long term dual-receiver out-of-loop stability experiments with a continuous wave laser based phase reference distribution system (PRDS) show that the LLRF system can achieve ∼40 fs Root-Mean-Square (rms) phase accuracy and 51 fs/52 fs peak-peak drift (in 7 days/17 h with the high power RF system, respectively) while the reference phase varies both ∼30 ps. An ∼4 h beam-based experiment has also been conducted to evaluate the overall performance of the whole XGLS timing and synchronization system, which shows that the PRDS, LLRF system, high power RF system, and laser oscillator laser-RF synchronization system can keep long-term phase stability.

Imaging Conductivity Changes in Monolayer Graphene Using Electrical Impedance Tomography.

Recently, graphene has gained a lot of attention in the electronic industry due to its unique properties and has paved the way for realizing novel devices in the field of electronics. For the development of new device applications, it is necessary to grow large wafer-sized monolayer graphene samples. Among the methods to synthesize large graphene films, chemical vapor deposition (CVD) is one of the promising and common techniques. However, during the growth and transfer of the CVD graphene monolayer, defects such as wrinkles, cracks, and holes appear on the graphene surface. These defects can influence the electrical properties and it is of interest to know the quality of graphene samples non-destructively. Electrical impedance tomography (EIT) can be applied as an alternate method to determine conductivity distribution non-destructively. The EIT inverse problem of reconstructing conductivity is highly non-linear and is heavily dependent on measurement accuracy and modeling errors related to an accurate knowledge of electrode location, contact resistances, the exact outer boundary of the graphene wafer, etc. In practical situations, it is difficult to eliminate these modeling errors as complete knowledge of the electrode contact impedance and outer domain boundary is not fully available, and this leads to an undesirable solution. In this paper, a difference imaging approach is proposed to estimate the conductivity change of graphene with respect to the reference distribution from the data sets collected before and after the change. The estimated conductivity change can be used to locate the defects on the graphene surface caused due to the CVD transfer process or environment interaction. Numerical and experimental results with graphene sample of size 2.5 × 2.5 cm are performed to determine the change in conductivity distribution and the results show that the proposed difference imaging approach handles the modeling errors and estimates the conductivity distribution with good accuracy.

Integrating expert opinion and traditional ecological knowledge in invasive alien species management: Corbicula in Eastern Europe as a model

The rate of alien species introduction is on the rise and seems unstoppable at a global scale. Once an alien species has been introduced to a new system, early detection and rapid eradication is the only cost-effective alternative to containment and long-term control. Here, we provide an integrative approach combining three sources of information to assist the management of invasive alien species. We used the Asian clam Corbicula sp. invasion in the Lower Danube as a model. First, the reference distribution data for the species was obtained through a crowdsourcing campaign, including a bibliographic review, public repositories and unpublished data. Next, we weighted the importance of different introduction pathways and dispersal vectors, focusing on the opinion and experience of an expert panel. Finally, information retrieved with semi-structured interviews was contrasted against the present distribution of the species. The expert panel highlighted the opening of the Rhine–Main–Danube Canal in 1992 as the most probable pathway of Corbicula introduction in the Lower Danube. However, the bibliographic review and interviews pointed to an earlier introduction event throughout the Danube Delta. In the subsequent spread phase, all three sources of information refer to zoochory, but also human mediated activities, as the vectors responsible for Corbicula dispersal to Danube tributaries. Our study presents a methodology on how expert opinion and traditional ecological knowledge can be effectively integrated to identify introduction pathways and dispersal vectors that facilitate the spread of invasive alien species. This approach can be used for enhancing collaboration between academic and research institutions and for raising stakeholders’ awareness to be used in management.

Kullback-Leibler Divergence of a Freely Cooling Granular Gas.

Finding the proper entropy-like Lyapunov functional associated with the inelastic Boltzmann equation for an isolated freely cooling granular gas is a still unsolved challenge. The original H-theorem hypotheses do not fit here and the H-functional presents some additional measure problems that are solved by the Kullback–Leibler divergence (KLD) of a reference velocity distribution function from the actual distribution. The right choice of the reference distribution in the KLD is crucial for the latter to qualify or not as a Lyapunov functional, the asymptotic “homogeneous cooling state” (HCS) distribution being a potential candidate. Due to the lack of a formal proof far from the quasielastic limit, the aim of this work is to support this conjecture aided by molecular dynamics simulations of inelastic hard disks and spheres in a wide range of values for the coefficient of restitution () and for different initial conditions. Our results reject the Maxwellian distribution as a possible reference, whereas they reinforce the HCS one. Moreover, the KLD is used to measure the amount of information lost on using the former rather than the latter, revealing a non-monotonic dependence with .

A consensus approach for estimating health risk: Application to inhalation cancer risks

Conducting a risk assessment is challenging because various and contrasting risk indicators are available, which can lead to discrepancies and, sometimes, conflicting conclusions. Constructing and using a consensus risk indicator (CRI) could provide a reliable alternative that is consistent and supports direct comparisons. The goal of this study is to propose a structured and pragmatic approach for constructing a CRI distribution and demonstrate its feasibility and easy implementation when conducting risk assessments. A CRI distribution is constructed as a weighted combination of existing indicators where the weights are obtained by using the overlapping areas of an individual indicator's distribution and an aggregated reference distribution. The approach is illustrated through an assessment of human cancer risk following inhalation exposure. The CRI is constructed using eight risk indicators. The CRI distribution parameters for 199 human carcinogenic chemicals associated with inhalation exposure were determined and are presented in an interactive table. To aid the wider implementation of the CRI approach, a user-friendly and interactive web application, named InCaRisk, was created to facilitate the cancer risk estimation following inhalation exposure. Our approach could be useful for enhancing the quality of regulatory decisions and protecting human health from environmental pollutants; our approach can be applied for a given health outcome, route of exposure and exposure setting.

Assessing the Response of Satellite Solar-Induced Chlorophyll Fluorescence and NDVI to Impacts of Heat Waves on Winter Wheat in the North China Plain

Global warming has increased the chance of concurrent extreme climate events (weather or climate events that are rare within their statistical reference distributions in a particular place, such as heat waves, floods, and droughts). Crops grow best within specific temperature intervals, and excessive heat is detrimental to the physiological processes of crops and eventually affects yield levels. Analysing historical changes in concurrent extreme high temperatures is critical to preparing for and mitigating the negative effects of climatic change. The North China Plain (NCP) is the most important wheat production area in China. In this study, the spatiotemporal variations in temperature and heat wave trends in the NCP were analysed. Furthermore, we examined the potential of solar-induced chlorophyll fluorescence (SIF) to capture the influence of heat wave impacts on wheat crops in the NCP by comparing satellite remote sensing data of SIF and normalized difference vegetation index (NDVI) and validated ground-based yield data. The results indicate that temperatures and the number of heat wave days in the study region all show increasing trends, especially daily minimum temperature, which has increased by 0.38°C per decade for the past 30 years. Spatially, the southern NCP has suffered greater increasing-temperature trends and more heat wave days than the northern region. Regarding the response of SIF and NDVI to heat waves, SIF can better capture wheat yield decline due to heat waves compared to NDVI; thus, the SIF result indicated more sensitivity to heat waves compared to NDVI.

Establishment of NSE/Hgb Ratio to Address Hemolysis Interference in NSE Tumor Marker Assays

Abstract Neuron specific enolase (NSE) is a serum soluble tumor marker for certain neuroendocrine tumors and a marker of some types of brain trauma. Red blood cells (RBC) also contain substantial NSE concentrations, making even slight hemolysis a source of potential false positives and reason for sample rejection. Unfortunately, hemolysis can be common in specimens from patients with in vivo hemolysis due to trauma or medical intervention, or in vitro hemolysis due to difficult draws (e.g., pediatrics). While laboratories are appropriately reluctant to report false elevations of NSE, specimen rejection due to any evidence of hemolysis underserves these patients. Correction of NSE results in hemolyzed serum was previously reported as a possible solution; however, broad inter-individual differences in RBC NSE concentrations require measuring NSE in RBCs from a whole blood specimen submitted concurrently with a hemolyzed serum sample. Unfortunately, logistical challenges impede the effective implementation of this strategy. Our objective was to establish a reference distribution of NSE concentrations in RBCs (as a ratio of NSE/Hgb (ng/mg)) that would facilitate the derivation of a cutoff to distinguish true NSE elevations from those concentrations arising from in vivo or in vitro hemolysis. Serum NSE was measured by the Fujirebio NSE enzyme immunoassay kit, and H-index was measured using the Roche cobas c501 (previously shown to demonstrate good agreement with spectrophotometrically measured hemoglobin, Hgb). Purposely hemolyzed samples (n=80) were diluted to an H-index of ~100, and NSE was measured to establish the distribution of NSE/Hgb ratios. De-identified NSE results (n=5,441) previously analyzed in our laboratory were extracted from our data warehouse to determine the distribution of NSE values in non-hemolyzed clinical samples. R statistical software was used for linear regressions, power transform analysis, and subsequent inverse square root transformation to enable appropriate normalization of each distribution for curve fitting purposes. Median NSE/Hgb ratios in the hemolyzed and non-hemolyzed distributions were 21 and 125, respectively. Curve fitting of the transformed hemolyzed distribution provides an upper 99% value of 46 NSE/Hgb. To determine whether that cut-off is appropriate at different levels of hemolysis, NSE was measured in samples (n=60) from subjects (n=15) prepared at four levels of hemolysis (H-index ~25 to 400). The mean NSE/Hgb ratio did not change significantly across the range of H-index values: regression analysis documented a mean of 23 NSE/Hgb and an upper 99% prediction value of 41 NSE/Hgb. Additionally, the increase in NSE was related to the H-index as follows, NSE=-715.3 + 21.42*H-index. The slope (representing NSE/Hgb) was again in agreement with the mean ratio of 21 NSE/Hgb observed in the initial 80 hemolyzed samples. Overall, the derived reference distribution of NSE/Hgb facilitates the interpretation of clinically relevant NSE increases from elevated NSE concentrations due to in vivo or in vitro hemolysis.

Reference values of trace elements in blood and/or plasma in adults living in Belgium.

Trace elements (TEs) from natural and anthropogenic sources are ubiquitous. Essential or not, their relevance for human health and disease is constantly expanding. Biological monitoring is a widely integrated tool in risk assessment both in occupational and environmental settings. However, the determination of appropriate and accurate reference values in the (specific) population is a prerequisite for a correct interpretation of biomonitoring data. This study aimed at determining the reference distribution for TEs (Al, As, Sb, Be, Bi, Cd, Co, Cu, Mn, Hg, Mo, Ni, Pb, Se, Tl, Sn, V, Zn) in the blood and/or plasma of the adult population in Belgium. Blood and plasma samples were analyzed for 178 males and 202 females, recruited according to an a priori selection procedure, by inductively coupled plasma mass spectrometry (ICP-MS). Reference values were established with high confidence for AsT, Cd, Cu, HgT, Mn, Mo, Pb, Sn, Se, Tl and Zn. Compared to previously published data in the Belgian population, a decreasing time trend is observed for Zn, Cd and Pb. Globally, the results also indicate that the current exposure levels to TEs in the Belgian population are similar to those from other recent national surveys. These reference values and limits obtained through validated analytical and statistical methods will be useful for future occupational and/or environmental surveys. They will contribute to decision-making concerning both public health policies but also exposure assessments on an individual scale.

A 128Gb 1-bit/Cell 96-Word-Line-Layer 3D Flash Memory to Improve the Random Read Latency With tProg = 75 μs and tR = 4 μs

A 128-Gb 1-bit/cell 3-D flash memory chip has been developed with 96-word-line-layer technology. A novel chip floorplan architecture with less time constants of wordline and bitline realizes fast read access time. A newly introduced program sequence achieves for higher reliability with less read-retry even after write/erase cycles. External VPP supply (12 V), current mode reference distribution, and auto temperature code refresh are also adopted to boost the performance of the chip. A new duty cycle corrector succeeds in obtaining wider unit interval of the DQS. Consequently, the proposed chip has a read access latency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4~\mu \text{s}$ </tex-math></inline-formula> and a program time of 75 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> , which is 12–13 and 4–5 times faster than conventional 3-D flash memory with the same technology [Maejima <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , (2018)]. The random read latency (tRRL) is estimated to be less than 50 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> , which enables in the reduction of total read access time of solid state drive (SSD) system.

Nonparametric cluster significance testing with reference to a unimodal null distribution.

Cluster analysis is an unsupervised learning strategy that is exceptionally useful for identifying homogeneous subgroups of observations in data sets of unknown structure. However, it is challenging to determine if the identified clusters represent truly distinct subgroups rather than noise. Existing approaches for addressing this problem tend to define clusters based on distributional assumptions, ignore the inherent correlation structure in the data, or are not suited for high-dimension low-sample size (HDLSS) settings. In this paper, we propose a novel method to evaluate the significance of identified clusters by comparing the explained variation due to the clustering from the original data to that produced by clustering a unimodal reference distribution that preserves the covariance structure in the data. The reference distribution is generated using kernel density estimation, and thus, does not require that the data follow a particular distribution. By utilizing sparse covariance estimation, the method is adapted for the HDLSS setting. The approach can be used to test the null hypothesis that the data cannot be partitioned into clusters and to determine the optimal number of clusters. Simulation examples, theoretical evaluations, and applications to temporomandibular disorder research and cancer microarray data illustrate the utility of the proposedmethod.

Clustering-Based Component Fraction Estimation in Solid-Liquid Two-Phase Flow in Dredging Engineering.

Component fraction (CF) is one of the most important parameters in multiple-phase flow. Due to the complexity of the solid–liquid two-phase flow, the CF estimation remains unsolved both in scientific research and industrial application for a long time. Electrical resistance tomography (ERT) is an advanced type of conductivity detection technique due to its low-cost, fast-response, non-invasive, and non-radiation characteristics. However, when the existing ERT method is used to measure the CF value in solid–liquid two-phase flow in dredging engineering, there are at least three problems: (1) the dependence of reference distribution whose CF value is zero; (2) the size of the detected objects may be too small to be found by ERT; and (3) there is no efficient way to estimate the effect of artifacts in ERT. In this paper, we proposed a method based on the clustering technique, where a fast-fuzzy clustering algorithm is used to partition the ERT image to three clusters that respond to liquid, solid phases, and their mixtures and artifacts, respectively. The clustering algorithm does not need any reference distribution in the CF estimation. In the case of small solid objects or artifacts, the CF value remains effectively computed by prior information. To validate the new method, a group of typical CF estimations in dredging engineering were implemented. Results show that the new method can effectively overcome the limitations of the existing method, and can provide a practical and more accurate way for CF estimation.

Analisa Course Scoring dari Kurikulum Prodi Teknik Listrik UNP

The Associate Degree Study Program of Electrical Engineering (PSTL) is a department at FT UNP whose aim is to produce graduates as professional workers in the electrical engineering field in accordance with the needs of the industrial world. In this study, researchers can find that the objectives of the PSTL have not been fulfilled because there are still many graduates who find it difficult to get a job because they do not get special competencies while in college. This study uses a descriptive qualitative method with an ex post facto approach focusing on comparative causal with closed questionnaire dissemination conducted to PSTL graduates who have worked in the industry. The research results obtained by researchers are that all courses in the Associate Expert program currently meet industry needs based on the distribution of questionnaires and references at the IQF level. However, the existing competencies in the PSTL curriculum have not been implemented well so that graduates find it difficult to get a job.

ERRATUM TO M. Heydarian et al., (2020)

ArchaeometryVolume 62, Issue 5 p. 1078-1078 ErratumFree Access ERRATUM TO M. Heydarian et al., (2020) This article corrects the following: The provenance and distribution of Early Bronze ceramics in the Kolyaei Plain, central Zagros, Iran M. Heydarian, F. Abdorrahimian, S. M. A. Emami, S. I. Beheshti, Volume 62Issue 4Archaeometry pages: 694-711 First Published online: March 29, 2020 First published: 11 September 2020 https://doi.org/10.1111/arcm.12602AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat In Heydarian et al. 2020, author M. Emami's name has been corrected. The online version of the article has been updated. We apologize for the error. We apologize for these errors. REFERENCE Heydarian, M., Abdorrahimian, F., Emami, M., and Beheshti, S. I., 2020, The provenance and distribution of early bronze ceramics in the Kolyaei plain, central Zagros, Iran, Archaeometry, 62(4), 694– 711. Volume62, Issue5October 2020Pages 1078-1078 ReferencesRelatedInformation

Bandwidth selection for kernel density estimation: a Hermite series-based direct plug-in approach

In this paper we propose a new class of Hermite series-based direct plug-in bandwidth selectors for kernel density estimation and we describe their asymptotic and finite sample behaviours. Unlike the direct plug-in bandwidth selectors considered in the literature, the proposed methodology does not involve multistage strategies and reference distributions are no longer needed. The new bandwidth selectors show a good finite sample performance when the underlying probability density function presents not only ‘easy-to-estimate’ but also ‘hard-to-estimate’ distribution features. This quality, that is not shared by other widely used bandwidth selectors as the classical plug-in or the least-square cross-validation methods, is the most significant aspect of the Hermite series-based direct plug-in approach to bandwidth selection.

Fiber orientation measurement of fiber injection molded nonwovens by image analysis

The fiber injection molding process is an innovative approach for the manufacturing of long fiber nonwoven preforms with little to no waste. An important property for the mechanical characteristics of the composite parts is the fiber orientation of the fiber injection molded nonwovens. In this paper a newly developed assemble method based on Fast Fourier Transform and improved Structure Tensor methods for the computation of the fiber orientation distribution in the local orientation by image analysis of transmitted light images is presented. For the computation of the fiber orientation, the Fast Fourier Transform and Structure Tensor methods are used. The new method is evaluated using simulated images and transmitted light images of real nonwovens to evaluate their accuracy. The computed fiber orientation distributions are compared to reference distributions by means of the Kullback–Leibler divergence. It is shown that the assemble method can perform accurate and reliable measurement of fiber orientation measurement and the modified Structure Tensor method improves results significantly compared to the current state of the art.

Validation of a Genome-Wide Polygenic Score for Coronary Artery Disease in South Asians

BackgroundGenome-wide polygenic scores (GPS) integrate information from many common DNA variants into a single number. Because rates of coronary artery disease (CAD) are substantially higher among South Asians, a GPS to identify high-risk individuals may be particularly useful in this population. ObjectivesThis analysis used summary statistics from a prior genome-wide association study to derive a new GPSCAD for South Asians. MethodsThis GPSCAD was validated in 7,244 South Asian UK Biobank participants and tested in 491 individuals from a case-control study in Bangladesh. Next, a static ancestry and GPSCAD reference distribution was built using whole-genome sequencing from 1,522 Indian individuals, and a framework was tested for projecting individuals onto this static ancestry and GPSCAD reference distribution using 1,800 CAD cases and 1,163 control subjects newly recruited in India. ResultsThe GPSCAD, containing 6,630,150 common DNA variants, had an odds ratio (OR) per SD of 1.58 in South Asian UK Biobank participants and 1.60 in the Bangladeshi study (p < 0.001 for each). Next, individuals of the Indian case-control study were projected onto static reference distributions, observing an OR/SD of 1.66 (p < 0.001). Compared with the middle quintile, risk for CAD was most pronounced for those in the top 5% of the GPSCAD distribution—ORs of 4.16, 2.46, and 3.22 in the South Asian UK Biobank, Bangladeshi, and Indian studies, respectively (p < 0.05 for each). ConclusionsThe new GPSCAD has been developed and tested using 3 distinct South Asian studies, and provides a generalizable framework for ancestry-specific GPS assessment.

Worst-Case Expected Shortfall with Univariate and Bivariate Marginals

Computing and minimizing the worst-case bound on the expected shortfall risk of a portfolio given partial information on the distribution of the asset returns is an important problem in risk management. One such bound that been proposed is for the worst-case distribution that is “close” to a reference distribution where closeness in distance among distributions is measured using [Formula: see text]-divergence. In this paper, we advocate the use of such ambiguity sets with a tree structure on the univariate and bivariate marginal distributions. Such an approach has attractive modeling and computational properties. From a modeling perspective, this provides flexibility for risk management applications where there are many more choices for bivariate copulas in comparison with multivariate copulas. Bivariate copulas form the basis of the nested tree structure that is found in vine copulas. Because estimating a vine copula is fairly challenging, our approach provides robust bounds that are valid for the tree structure that is obtained by truncating the vine copula at the top level. The model also provides flexibility in tackling instances when the lower dimensional marginal information is inconsistent that might arise when multiple experts provide information. From a computational perspective, under the assumption of a tree structure on the bivariate marginals, we show that the worst-case expected shortfall is computable in polynomial time in the input size when the distributions are discrete. The corresponding distributionally robust portfolio optimization problem is also solvable in polynomial time. In contrast, under the assumption of independence, the expected shortfall is shown to be #P-hard to compute for discrete distributions. We provide numerical examples with simulated and real data to illustrate the quality of the worst-case bounds in risk management and portfolio optimization and compare it with alternate probabilistic models such as vine copulas and Markov tree distributions.

Investigating object compositionality in Generative Adversarial Networks

Deep generative models seek to recover the process with which the observed data was generated. They may be used to synthesize new samples or to subsequently extract representations. Successful approaches in the domain of images are driven by several core inductive biases. However, a bias to account for the compositional way in which humans structure a visual scene in terms of objects has frequently been overlooked. In this work, we investigate object compositionality as an inductive bias for Generative Adversarial Networks (GANs). We present a minimal modification of a standard generator to incorporate this inductive bias and find that it reliably learns to generate images as compositions of objects. Using this general design as a backbone, we then propose two useful extensions to incorporate dependencies among objects and background. We extensively evaluate our approach on several multi-object image datasets and highlight the merits of incorporating structure for representation learning purposes. In particular, we find that our structured GANs are better at generating multi-object images that are more faithful to the reference distribution. More so, we demonstrate how, by leveraging the structure of the learned generative process, one can ‘invert’ the learned generative model to perform unsupervised instance segmentation. On the challenging CLEVR dataset, it is shown how our approach is able to improve over other recent purely unsupervised object-centric approaches to image generation.

Low [formula omitted] values in microbialgenic methane result from combinatorial isotope effects

Methane generated by microorganisms is most often depleted in the doubly substituted isotopologue 12CH2D2 relative to the stochastic reference distribution. To constrain the controls on depleted Δ12CH2D2 values, we experimentally isolated the root cause with microorganisms that produce methane from methylphosphonate via the C-P lyase pathway. This mechanism of methane production preserves the three hydrogens from methylphosphonate and adds one hydrogen from water. When maintaining the same methylphosphonate source, but varying the D/H composition of growth medium water, we observed significant shifts in methane Δ12CH2D2 values, but little to no change in Δ13CH3D values. We reproduced these observations with a model that considers only the combinatorial isotope effect. The variation in Δ12CH2D2 values of product methane resulted from the differences in D/H between reactants water and methylphosphonate. This work validates the hypothesis that combinatorial effects can strongly influence methane Δ12CH2D2 values, and must be considered for low temperature, abiotic or biotic systems where methane hydrogen is derived from multiple reservoirs.