dataesr/scientific-paragraphs-categorization huggingface dataset:
Our study is the first to address this critical gap in the epigenetic mediation literature, both by providing clarity on the methods available and by assessing their strengths and weaknesses under different settings. Moreover, although the analysis is centered around DNAm, the methods we deploy are not specific to epigenetics, and our results and guidelines should be similarly useful for researchers studying high-dimensional mediation problems in other fields. We include, supplementary to our study, an R package for implementing the methods, called "hdmed," so that researchers have access to a centralized resource they can draw from in their own high-dimensional mediation analyses.
Notations and General Framework Before proceeding, it will be useful to provide an overview of the relevant mediation model and to summarize the types of methods which have become available. To begin, suppose we have a dataset of n individuals: an exposure A i , a continuous outcome Y i , and continuous mediators M i measured for the i th person, i varying from 1 to n. We write M i in bold to indicate its status as a vector-in this case, a set of p mediators M i (j) , j varying from 1 to p. Let C i be a vector of q covariates. When p is greater than (2) to estimate the mediating role of M i in the causal pathway between the exposure and outcome  33. Model (1) is the outcome model and model (  2 ) is the mediator model. In model (1), β m is a p-vector in which the j th component, (β m ) j , is the linear association of j th mediator with Y i adjusting for the other variables; while β a is the association between A i and Y i adjusting for mediators and covariates.
True positive rate Fig. 3  compares the TPR detecting active mediators of the Group 1 methods and the one-at-a-time method. The value shown is the mean TPR over 100 simulated datasets and a 95% empirical confidence interval (CI). On each dataset and for each method, thresholding was used to keep the false discovery rate (FDR) below 10%. For the non-sparse setting, we show the TPR for detecting mediators whose (α a ) j and (β m ) j were both sampled from Normal(0,1) rather than Normal(0,0. 2 2 ). We include the Group 1 methods HIMA, HDMA, MedFix, pathway LASSO, and BSLMM. We focus on TPR but not false positive rate (FPR) because the FDR correction was highly conservative, the mean FPR ranging from 0 to 5. 1x10 -4 across all settings and methods. For a sample size of 2,500 and a PVE IE of 0.
Scalability We evaluated the scalability of the methods by running them 30 times on a common computing platform, and recording their run time (Table  1 ). This was done in both a small data setting (n = 100, p = 200) and a big data setting (n = 1,000, p = 1,000). On the larger dataset, the methods MedFix, HDMA, and PCMA posed insignificant computational burden; whereas BSLMM took an average of 40. 1 minutes per run (assuming 30,000 posterior samples), HILMA an average of 40. 9 minutes per run, pathway LASSO an average of 192. 6 minutes per run, and SPCMA an average of 842. 5 minutes per run (assuming 100 principal components). Run times were substantially lower in the smaller dataset, the slowest method, pathway LASSO, only taking an average of 18. 71 minutes. The memory consumption of the methods is included in Supplementary Table.
DNAm data analysis results from MESA For our real data analysis, we applied the methods on a dataset with high-dimensional epigenetic mediators. Our exposure of interest was low SES-measured by educational attainment below a four-year degree-while our outcome variable was HbA1c level and our potential mediators were DNAm measurements at 402,339 CpG sites. Since the methods are incapable of handling so many CpG sites at once, we reduced our scope to only include the 2,000 sites with the strongest association with low SES. This was based on a linear mixed-model adjusting for age, sex, race, and the estimated proportions of residual non-monocytes as fixed effects and methylation chip and position as random effects. Our final dataset contained these 2,000 CpG sites and 963 samples. HbA1c, DNAm, and all other continuous variables were standardized prior to analysis.
1% in HIMA to 100% in HILMA. age, sex, race, and the estimated proportions of residual non-monocytes as fixed effects, along with methylation chip and position as random effects to address potential batch effects. We provide only point estimates, not interval estimates, because some of the methods are either not capable of producing interval estimates or do not provide the code for producing them in their software. For HIMA, HDMA, and MedFix, which as coded do not directly provide estimates of the direct effect, we first estimate the total effect by fitting the outcome model with the CpG sites omitted, then estimate the direct effect by subtracting the indirect effect from the total effect. Note also that, for HIMA, HDMA, MedFix, and pathway LASSO, we used additional screening to reduce the number of mediators in advance for the sake of statistical and computational efficiency, so only n/log(n) ≈ 141 CpG sites were seen by the multivariate model rather than 2,000 (this approach is recommended by the HIMA and HDMA authors).
Our scalability comparison revealed that HIMA, HDMA, MedFix, and PCMA were easily scalable to large datasets (e. g. , n = 1,000 and p = 2,000), whereas SPCMA and pathway LASSO were extremely computationally costly. On DNAm data from MESA, 11 CpG sites were selected by at least two of the methods as mediators between low SES and HbA1c level. Of the many genes related to these sites, SOCS3, RIN3, and HADH have the strongest potential biological connections to HbA1c  45, 47, 48, [50] [51] [52]  , which contributes to the already rich literature on DNAm as a mediator between the exposome and health outcomes. Moreover, the methods generally produced similar estimates of the mediation contributions, with the exception of BSLMM. It is possible that since BSLMM is non-sparse, the estimated mediation contributions end up severely shrunken compared to the methods which directly select features.
Mediation Model with Multiple Mediators Let M be a set of p variables, M  (1)  , M  (2)  , to M  (p)  , each a potential mediator in the causal pathway between A and Y. We assume that the ordering of the potential mediators is arbitrary and that Y is continuous. Given a dataset of n individuals, with A i , Y i , M i , and q covariates C i measured for each subject i, we can evaluate the mediating role of M with the models (2) We refer to these as the outcome and mediator models. Bolded terms distinguish vectors from scalars. Under certain assumptions, the parameters of this model can be used to derive causal effects of interest: Namely, in addition to the baseline assumption of temporality, we assume (1) that there is no unmeasured confounding in the exposure-outcome association after conditioning on C, (2) that there is no unmeasured confounding in the mediator-outcome associations after adjusting for the exposure and C, (3)   that there is no unmeasured confounding of the exposure-mediator associations after conditioning on C, and (4) that the measured confounders of the mediator-outcome associations are not caused by the exposure (which would make those confounders mediators themselves).
Acknowledgements MESA and the  MESA SHARe  project are conducted and supported by the  National Heart, Lung, and Blood Institute (NHLBI)  in collaboration with MESA investigators. Support for MESA is provided by contracts  75N92020D00001 ,  HHSN268201500003I ,  N01-HC-95159 ,  75N92020D00005 ,  N01-HC-.
Image analysis Quantitative image analysis was done using Fiji software with MicrobeJ plugin  (Ducret et al. , 2016). The Manual-editing interface of MicrobeJ was used to optimize cell detection and the mean intensity fluorescence and cell length parameters were automatically extracted and plotted. We defined the timing of TAP acquisition (time t = 0) by analyzing the increase of the sfGFP fluorescence signal conferred by the TAPs. Plasmid acquisition was validated when a 12% sfGFP fluorescence increase was observed in the transconjugant cells. Fluorescence profiles of each cells were then aligned according the defined t = 0 to generate the graphs presented in Fig. 3D  and E.
, 2021). We asked CSTB algorithm to generate spacer sequences targeting S. Thyphimurium strain LT2 (Sthy spacer targeting seven loci), K. pneumoniae strain LM21 (Kp spacer targeting two loci), V. cholerae O1 biovar El Tor str. N16961 (Vc spacer targeting three loci on each chromosome I and II), P. aeruginosa strain PAO1 (PAO1 spacer targeting one locus) without targeting any other bacterial genome present in the database. We quantified that acquisition of TAP RP4 -Cas9-Sthy induces a ~ 4 log viability loss of S. Thyphimurium carrying or not the pSLT plasmid, TAP RP4 -Cas9-Kp a ~ 2 log viability loss of K. pneumoniae and that Vc and PAO1 spacers induce at least a ~ 3 and 4-log viability loss respectively, as no TAP RP4 -Cas9-Vc V. cholerae and TAP RP4 -Cas9-PAO1 P. aeruginosa transconjugants could be detected (Fig.
, 2020). For example, the IncI1 TP114 plasmid is currently the most promising for antibacterial transfer in vivo, even though testing of TP114's transfer ability has been limited to E. coli and C. rodentium  (Neil et al. , 2021). The TAPs design benefits from being versatile and easily engineered. Future research will be required to develop custom TAPs composed of different origin of transfer modules associated with a set of helper plasmids. The creation of such a toolbox would enable the selection of the best-suited TAP system based on the strain(s) to be targeted. Supplementary data to this article can be found online at https://doi. org/10. 1016/j. plasmid. 2023. 102680. [grant number ANR-19-ARMB-0006-01] and the JPI AMR STARS-TAP [grant number ANR-21-AAMR-0007-01].
Atmospheric Features This spectrum is one of the highest-S/N and broadest spectral coverage dataset of a brown dwarf or planetary-mass companion to date. With a spectral resolution of up to ∼3000, a wealth of atmospheric molecular features are revealed. In this section, we visually identify absorption features in the spectra and in some cases compare them to molecular cross sections. In each subsection, we discuss the molecules found and how they compare to similar brown dwarfs. The full VHS 1256 b spectrum displays several absorption features from atmospheric gases that have been previously observed in brown dwarf and extended mid-infrared coverage that can constrain methane, water, and carbon monoxide. There is evidence for carbon dioxide as well. Across the spectrum, there are at least six detection regions of water, one visible methane feature, and two detections of carbon monoxide, which probe different pressure levels along the object's pressure-temperature profile.
Conclusions We reduced JWST NIRSpec IFU and MIRI MRS earlyrelease science observations of VHS 1256 b to produce the best spectrum of a planetary-mass object to date at medium resolution covering 0. 97-19. 8 μm. The sensitivity (NIRSpec: SNR ∼50-400, MIRI: SNR ∼7-20) and broad wavelength coverage of the data enabled the identification of several medium-resolution features such as water, methane, carbon monoxide, carbon dioxide, and silicate clouds within the atmosphere of VHS 1256 b. The data have sufficient S/N for forward-modeling analysis to estimate the cloudiness, chemical abundance profiles, and strength of atmospheric mixing of VHS 1256 b. Our best attempt at matching the 0. 97 μm-19. 8 μm spectra required combining two relatively thick cloud decks (90% f sed =. 6 and 10% f sed = 1), low surface gravity (log (g) = 4.
This project was supported by a grant from  STScI  ( JWST-ERS-01386 ) under  NASA  contract  NAS5-03127. This work benefited from the  2022 Exoplanet Summer Program  in the  Other Worlds Laboratory (OWL) at the University of California, Santa Cruz , a program funded by the  Heising-Simons Foundation. M. B. acknowledges support in France from the  French National Research Agency (ANR)  through project grant  ANR-20-CE31-0012. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (COBREX; grant agreement  n°885593 ;  EPIC , grant agreement  n°819155 ). This work has benefited from The UltracoolSheet at http://bit. ly/UltracoolSheet, maintained by  Will Best ,  Trent Dupuy ,  Michael Liu ,  Rob Siverd , and  Zhoujian Zhang , and developed from compilations by  Dupuy & Liu  ( 2012 ),  Dupuy & Kraus  (2013), Liu et al.
(2016), and Best et al. (2018, 2021). S. -. P acknowledges the support of  ANID-Millennium Science Initiative Program-NCN19_171. S. M. is supported by a  Royal Society University Research Fellowship. C. D. acknowledges financial support from the  State Agency for Research of the Spanish MCIU  through the  "Center of Excellence Severo Ochoa" award  to the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ) and the  Group  project  Ref. PID2019-110689RB-I00/AEI/10. 13039/50110 0011033. B. B acknowledges funding by the  UK Science and Technology Facilities Council (STFC)  grant no. ST/ M001229/1.
is best practice that models are calibrated and validated to local conditions  (Necpálová et al. , 2015)  whenever they are applied outside the range of usual conditions, for example, in a new climate zone with different soils. Although DayCent has been used to estimate the development of SOM stock in Kenya on a national scale  (Kamoni et al. , 2007)  and recently the efficiency of conservation agriculture in Ethiopia  (Lemma et al. , 2021 ), DayCent's modules of SOM and maize crop growth have never been rigorously calibrated and validated for tropical agroecosystems in SSA. A recent test of DayCent in Kenyan maize growing systems showed that SOM turnover is underpredicted by DayCent  (Nyawira et al. , 2021). Because SOM in biogeochemical models is coupled to nitrogen (N) mineralization, there is the potential that this translates into biased crop responses to N and biased crop growth rates in any upscaling exercise.
The DayCent model The DayCent model (version 2020 of DD_EVI) is a terrestrial ecosystem model of intermediate complexity  (Del Grosso et al. , 2001). It simulates C and N fluxes within the soil-plant-atmosphere continuum on a daily basis and has been parameterized for many crops and ecosystems  (Necpalova et al. , 2018). It has submodules to simulate plant growth, organic resource and soil organic matter (SOM) decomposition, mineralization of N, soil water and temperature, N gas fluxes, and CH oxidation 4. The net primary productivity of plants is a function of their genetic potential, a simplified phenology, solar radiation, temperature, and stresses, such as reduced water or N availability. Here, we used the non-growing degree day version of the DayCent crop module, which does not simulate phenology but has a seedling stage with reduced growth until a certain biomass (full canopy) is reached.
Measuring frames were permanently installed in the plots for a whole rainy season. The chambers (0. 27 × 0. 375 × 0. 11 m) were made of polyvinylchloride and equipped with a vent tube and a fan to homogenize gas inside them before gas sampling. The measured N 2 O emissions were evaluated at two levels of aggregation. First, as site means per measurement day (from the three replicates, similar to all other data) and second as cumulative emissions over the whole season, for which we first used the trapezoid method at the plot scale  (Levy et al. , 2017) , specifically, the trapz function of R  (Tuszynski, 2021). Site-scale means and variances were then computed for these cumulative N 2 O emissions, similar to all other measurements. Furthermore, data on soil mineral N (N min ), measured as NH 4 + and NO 3 -, and measured moisture content at soil depths of 0-15 cm, were available from several measurement campaigns in the years  2012 , 2013 , 2018 , 2019 and 2020.
Model driving variables and model assumptions The site-specific crop management data used to run the DayCent model was obtained from field management operation records for each season at each site. This included specific dates for the yearly application of organic resources (in all but the control plots), manual plowing before planting, maize planting, split application of mineral N at rates of 40 kg and 80 kg N ha -1 per season, weeding and harvest dates. Pesticide application events and gap filling plus maize thinning are not part of standard DayCent management and were therefore not included in the DayCent schedule file. Our model runs therefore, assumed no occurrence of pests or diseases and an optimal plant density from the start, which in practice was ensured by manual thinning and gap filling.
The climate data used to run DayCent consisted of recorded data at each of the sites. However, filling the data gaps was necessary due to unavailability and loss of recorded data. In Embu and Machanga, manual recordings of daily min- data was performed, using a linear regression with measured data as dependent variable (y) and NASA POWER data as independent variable (x). Specifically, the slope and intercept of the regression equation y = mx + b, were used to produce a corrected estimate of these data. In our specific case, the slopes were not significantly different from 1, but intercepts (b) were significantly different from 0. The specific intercepts for maximum temperature were -0. 3°C, -0. 4°C, +3°C and +6°C for Embu, Machanga, Sidada and Aludeka, respectively. The intercepts for the minimum temperature were -0.
25°C, -0. 5°C, -3°C and +1°C for Embu, Machanga, Sidada, and Aludeka, respectively. For precipitation, no bias correction was done. The data on the hydraulic properties of the soil needed in DayCent (volumetric field capacity, wilting point, and saturated hydraulic conductivity K s ) were calculated based on the soil texture measured at each site. We tested two pedotransfer functions to see which one provided a better fit: (1) the widely applied function of  Saxton and Rawls (2006)  and (  2 ) the function of  Hodnett and Tomasella (2002) , which was specifically designed for tropical soils. Within the  Hodnett and Tomasella (2002)  equation, K s was calculated using the  Saxton and Rawls (2006)  equation, with values of the water retention curve, α and n  van Genuchten, 1982) , taken from  Hodnett and Tomasella (2002) , because their equation does not provide a way to estimate K s.
Combined Bayesian calibration of plant and soil model parameters Joint Bayesian calibration of the sensitive DayCent parameters was performed using all available data on maize grain yield, aboveground biomass, and SOC stocks. The main reason for only using these data was that the yields, SOC stocks, and their trade-offs were the focus of this study. A second, technical reason, was that the creation and readout of daily simulations outputs, needed to match simulated and measured soil moisture content, mineral N content and /N 2 O fluxes, slowed down the Table  1. DayCent model parameters and possible range of values. Displayed are parameters considered for calibration due to total sensitivity index > 2. 5% (top) and with a total sensitivity index > 1% (bottom). The remainder of parameters (<1%) can be found in the supplementary (Table  A3 ).
It uses the proportionality form of The sampling importance resampling method, which was used in this study, is a direct form of Bayesian calibration, which has recently been used by  Gurung et al. (2020) , to calibrate the parameters of the SOM module of DayCent using a large collection of temperate long-term experiments. It samples the prior by running the model for a large sample of parameter sets of size I from the prior, computing the likelihood for each sample, and filtering the prior based on importance weights w(θ z ) w(θ z ) = p(D, M |θ z ) I i=1 p(D, M |θ i ) (3) where p(D, M |θ z ) is the likelihood function of the zth parameter set and w(θ z ) is the corresponding importance weight. It is consistent with the proportionality form of Bayes' theorem in that it uses the importance weights w(θ z ) as probabilities for sampling from the prior, without replacement, to derive the posterior.
Net global warming potential To compare different ISFM treatments in terms of their greenhouse gas emissions, their net global warming potential (GWP) on a yearly basis (CO 2 eq ha -1 yr -1 ) was derived from the outputs over the whole simulation period. It was calculated from changes in the SOC content and cumulative emissions of N 2 O using 100-year time horizon of global warming potentials  (Necpalova et al. , 2018) : Here, ∆SOC is the change in SOC content (kg C ha -1 yr -1 ), N 2 O the cumulative N 2 O flux (kg N 2 O ha -1 yr -1 ). The CH 4 oxidation capacity was not considered, because it usually makes a very limited contribution to GWP in rainfed maize cropping systems and we did not have data to evaluate the reliability of this simulated flux. In addition to GWP we calculated yieldscaled GWP (in CO 2 eq kg -1 maize grain yield) by dividing the cumulative GWP over the entire simulation period by cumulative simulated yields (dry matter base).
Conclusions In this study, we demonstrated the effectiveness of jointly calibrating the SOM and plant modules of DayCent to simulate maize productivity under integrated soil fertility management (ISFM) in Kenya, using a Bayesian calibration. The calibration successfully constrained the most sensitive model parameters, which were identified by a global sensitivity analysis. Although the default DayCent maize plant parameterization represented the tropical conditions in Kenya well (i. e. , the highest probability posterior parameter values were close to the default parameterization), the highest probability posterior SOM turnover rates were two and eight times faster than the default for the passive and slow SOM pool, respectively. This indicates that there is the potential for large losses of SOC in highly weathered tropical soils.
However, SOM turnover was subject to high uncertainty, showing that the current module structure suboptimally captures SOM dynamics in highly weathered tropical soils. Nevertheless, our leave-one-site out cross validation showed that the calibration-derived parameter set is robust for upscaling of the model to larger areas in Kenya. At the same time, mean maize grain yields were much better represented than the year-to-year variability of yields, posing the question whether the effects of climate change can be adequately captured by DayCent. Finally, while no ISFM treatment was predicted to act as an absolute sink of greenhouse gases, yield-scaled emissions were lowest in treatments with high and intermediate yields. -N Sidada +N Sidada 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 Maize grain yield (t ha -1 -N Sidada +N Sidada 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 2 0 0 5 2 0 1 0 2 0 1 5 2 0 2 0 Maize grain yield (t ha -1.