dataesr/scientific-paragraphs-categorization huggingface dataset:
elfii DSM9442 and P. elfii subsp. lettingae were processed, revealing a very high overall quality of samples and an extensive coverage of reference genomes. Two samples from each set showed deviations from their corresponding replicates and were discarded from further analyses. All conditions retained at least 3 replicates for downstream analyses. Genes were considered significantly differentially expressed if their log 2 -fold change was above 1. 3 and the Benjamini-Hochberg corrected p-value was below 0. 01. To provide functional information about differentially expressed genes (DEG), the full KEGG (Kyoto Encyclopedia of Genes and Genomes) database was downloaded  [39] [40] [41]. Sequences for all annotations in reference genomes were aligned to KEGG Orthology (KO) annotation sequences by using "blastx" (E-value < 1 × 10 -10 , BitScore > 50)  [42].
Comparative Genomics of the Deep and Surface P. elfii Strains We conducted a comparative genomics study between the piezophilic strain P. elfii DSM9442 (accession number: AP014507) (optimal growth at 20 MPa) and the piezotolerant strain P. elfii subsp. lettingae DSM14385 (accession number: CP000812) (optimal growth at 0. 1 MPa). The genetic closeness of the two genomes in combination with different sensitivities of the strains to high hydrostatic pressure provided a valuable framework to identify potential genetic determinants associated with the piezophilic lifestyle in the genus Pseudothermoga. As previously reported, P. elfii and P. elfii subsp. lettingae have intra-species relationships with an average nucleotide identity score of 99. 12%  [21]. We found a strong genome collinearity (results not shown), indicating a high degree of gene order conservation.
1 from 10 17. 8 eV to 10 19. 6 eV (the same binning chosen for the energy spectrum) plus one additional larger bin containing events with energies above 10 19. 6 eV. In this last bin, the median energy is 10 19. 70 eV and that of the most energetic event is 10 20. 02 eV, hence we effectively only have composition information up to the suppression energy. The total number of collected events is 31,085; it ranges from 5,476 in the first energy bin to 35 in the last. In each of these energy bins, the X max distribution is binned in intervals of 20 g • cm -2. There is a total of 329 non-empty bins in the whole dataset.
1. 3, normalised so that j G mod i,j = 1 for each i. The best-fit parameter values for each scenario are then those with which the total deviance D = D J + D Xmax attains its minimum value D min , which we locate using the Minuit package  [83] ; the statistical uncertainties on the spectral parameters correspond to the half extent of the 1D profile in the parameter space where D ≤ D min + 1, as computed using the MINOS routine of Minuit; the uncertainties on the emissivity and on the mass fractions are computed with Monte Carlo simulations, as explained in the next section.
Acknowledgments The successful installation, commissioning, and operation of the  Pierre Auger Observatory  would not have been possible without the strong commitment and effort from the technical and administrative staff in Malargüe. We are very grateful to the following agencies and organizations for financial support: Argentina -Comisión Nacional de Energía Atómica ;  Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT) ;  Consejo Nacional de Investigaciones Científi -No. DE-AC02-07CH11359 , No. DE-FR02-04ER41300 , No. DE-FG02-99ER41107  and No. DE-SC0011689 ;  National Science Foundation , Grant No. 0450696 ; The  Grainger Foundation ;  Marie Curie-IRSES/EPLANET ;  European Particle Physics Latin American Network ; and  UNESCO.
Segmentations and 3D Image analysis For electron tomography datasets, a binned version of the reconstructed tomogram was segmented using the Weka 3D segmentation plugin in Fiji/ImageJ to visualize the mitochondria and the meisosomes. The cuticle was visualized by the Amira-embedded Volume Rendering plugin from a manually segmented mask. A cropped area of interest of the full resolution electron tomogram was segmented in iLastik to visualize a representative portion of the organelle. For Serial Blockface datasets, the segmentation of the meisosome and mitochondria was generated using the Weka 3D segmentation plugin in Fiji/ImageJ. Animations and snapshots were generated in Amira.
Airyscan super-resolution microscopy Airyscan imaging were performed using a commercial Zeiss confocal microscope LSM 880 equipped with an Airyscan module (Carl Zeiss AG, Jena, Germany) and images were taken with a 63x/1. 40NA M27 Plan Apochromat oil objective. In this mode, emission light was projected onto an array of 32 sensitive GaAsP detectors, arranged in a compound eye fashion. The Airyscan processing was done with Zen Black 2. 3 software by performing filtering, deconvolution and pixel reassignment to improve SNR. The Airyscan filtering (Wiener filter associated with deconvolution) was set to the default filter setting of 6. 1 in 2D.
Fluorescent Image analysis To extract the morphological properties of organelles we devised an automatic Fiji segmentation procedure (GitHub https://github. com/centuri-engineering/BD_BlobsSeg). We first restricted the analysis to manually drawn ROIs and isolated organelles (foreground image) from the background by using the "remove outliers" function of Fiji (radius = 30 pixels and threshold = 30). We next applied a Gaussian blur (sigma = 1 pixel) on the foreground image and automatically defined a threshold value to binarize the newly blurred image. This threshold was determined automatically by multiplying the background value (retrieved by averaging the fluorescent levels of the background image) by a constant coefficient. This allowed us to normalize the segmentation, since the expression levels of fluorescent proteins may vary from one animal to another.
Atomic Force Microscopy (AFM) Worms were prepared as described before  (Essmann et al. , 2016). Briefly, staged 1-day-old young adult worms were paralysed in 15 mg/ml 2, 3-butanedione monoxime (Sigma) for 2 h at room temperature and transferred to a ~2 mm thick 4 % agarose bed in a petri dish (30 mm). Heads and tails were fixed with tissue glue (Dermabond, Ethicon) and the dish filled with a 2. 5 ml M9 buffer. AFM data of worms were obtained using a NanoWizard3 (JPK) under aqueous conditions. Type qp-CONT-10 (0. 1 N/m; nanosensors) cantilevers were used for imaging in contact-mode at setpoint 0. 3 V and 0. 5 Hz scanning speed, and NSC12 tipless cantilevers (7. 5 N/m; MikroMash) with a 10 μm borosilicate bead attached (produced by sQUBE www. sQUBE. de) were used in force spectroscopy mode to obtain force-indentation measures at 450 nN force setpoint and 0.
5 μm/s indentation speed. Cantilever sensitivity and stiffness (k) were calibrated using the JPK system calibration tool before each experiment. AFM raw data were analysed using the JPK analysis software. All force curves were processed to zero the baseline, to determine the tip-sample contact point and to subtract cantilever bending. The Young's Modulus was calculated within the software by fitting the Hertz/Sneddon model respecting the indenter shape (10 μm bead) to each curve. All topographical images are flattened using the plane fitting option of the JPK software at 1-2 degree to correct for sample tilt and natural curvature of the worm. 6,6S1, 5S3 (Taffoni et al. , 2020)   IG1930 frEx624[pML670(VHA-5::GFP, unc-122p::GFP)] 3,4,6,4S3,5 S1,6S1 This study XW18042 qxSi722[dpy-7p::DPY-7::sfGFP; ttTi5605] II 7,4S5  (Miao et al.
Acknowledgements We thank  Benoit Delapiot  for writing the  Fiji Macro ,  M. Labouesse ,  M. Barkoulas  and  Ari Pani  for sharing strains and reagents,  Chris Crocker at Wormatlas  for diagrams, and  Roxane Fabre  for the Airyscan images. Worm sorting was performed by  Jerome Belougne  using the facilities of the  French National Functional Genomics platform , supported by the  GIS IBiSA and Labex INFORM. We thank the  imaging core facility (ImagImm) of the Centre d'Immunologie de Marseille-Luminy (CIML)  and the  PiCSL-FBI core facility (IBDM, AMU-Marseille ) supported by the  French National Research Agency program  ( France-BioImaging ;  ANR-10-INBS-04-01 ). Electron tomography in figure 2S1 were performed at the  New York Structural Biology Center , with help from  KD Derr and William Rice.
Fundings Work is funded by the  French National Research Agency   ANR-16-CE15-0001-01  and  ANR-10-INBS-04-01  ( France Bio Imaging ), by the " Investissements d'Avenir" French Government program  ( ANR-16-CONV-0001 ) and from  Excellence Initiative of Aix-Marseille University -A*MIDEX  and institutional grants from  CNRS ,  Aix Marseille University, National institute of Health and Medical Research (Inserm)  to the CIML; and by a  NIH   OD 010943  to DHH.
However, in the literature, there is no method to systematically generate, evaluate, and select the most relevant FAPs. In this paper, we propose an original semi-automated method to generate FAPs and to evaluate them. The proposed procedure is based on a branch-and-bound algorithm that allows the user to nd the "best" FAP, according to prede ned evaluation criteria, without evaluating each potential FAP. The proposed method combines automated steps and user's intervention to limit the phenomenon of combinatorial explosion and to make the solution explainable. This method and its associated procedure have been used to improve an existing software, CapLog, e cient for the optimization of manufacturing systems, like end-of-life systems or assembly systems. A case study is used to illustrate the steps of the proposed method.
Most FAPs are built manually. In this paper, we propose a new semi-automatic method to generate and evaluate a net of FAPs and to select the best FAP. This method covers both the step of assembly plan generation and the step of the best FAP search. It allows to represent the expert's expertise in the models and guide him/her step by step to make the solution explainable and then acceptable. The presented work was led by the AS2M department of the FEMTO-ST laboratory, in order to improve the Cap'Eco method  [Movilla 13 ] [  Movilla 16 ], that is supported by CETIM and its related software CapLog  [CapLog 19 ]. The remainder of this paper is structured as follows. Section 2 is a literature review that rst describes the global approach to design an assembly system, and then presents each step of assembly planning in detail (product modelling, generation of assembly plans, generation and selection of functional assembly plans).
The CapLog software CapLog is a software, developed by CETIM for more than 10 years, to support the Cap'Eco method. It is composed of two main modules: Module 1. Studies on the optimization of reuse and recycling of end-of-life products. Module 2. Optimization of the assembly system of the products to be assembled by comparison between solutions (execution times, costs). CapLog uses data of functional assembly plans. The designer must provide the software with a handmade functional assembly plan as data input. The previous section has proposed the assisted generation of a FAP net to provide data inputs for the second module (optimization of the assembly system).
A) User's needs At the beginning of our project, the assembly expert used a manual method to create a single assembly plan and then manually de ne a functional assembly plan that was an input to the CapLog user. Each error or modi cation requires starting the study from the beginning. Calculating a set of relevant assembly plans and generating the best FAP would overcome this shortcoming. B) Choice of the method to de ne assembly plans: a systematic "bottom-up" method The above limitations lead us to make improvements to the software by creating a wizard to design relevant assembly plans and the corresponding FAP. Because of its effectiveness in dealing with the phenomenon of combinatorial explosion (through the use of the assembly strategy and other similar methodological tools), we choose to implement the bottom-up assembly plan generation method.
C) Product model: choice of data to handle In order to improve the e ciency of the APN generation (and to restrain the combinatory explosion phenomenon), it was necessary to add more information to the product modelling proposed by  [Henrioud 89b ]. Two kinds of information were added: Product modelling and structure constraints representation Using the liaison graph  [Bourjault 84 ] improved by non-geometric added-values  [Henrioud 89b ], we joined to this modelling the added-values anteriority graph  [Perrard 12 ] in order to pre-constrain the future computation sub-steps. This modelling conveniently ts with systematic generation methods. Integration of the assembly directions of the geometric liaisons and non-geometric added-values of the end product (geometrical module) Assembly direction sets are used into CapLog software.
By the "classical" way The considered sub assembly is studied as an end product to assemble; then, its APN is the result of the proposed study, starting from the product modelling, the strategy de nition and the APN generation. The resulting APN of this study will be added to the main study, By the "manual" way, added to the "classical" way However, the resulting APN, issued from the previous method, may induce too many assembly plans. According to the assembly strategy, each of them is equivalent to the others. Then, in order to reduce the combinatorial explosion phenomenon, the user can select only one of them. For this, a particular procedure implemented into the CapLog software allows the user to choose one assembly plan among the APN by browsing the APN and choosing one operation after the other at each sub-assembly meeting.
E) Integration of a 3D module for the candidate assembly operation In order to facilitate the user's decision during the assembly plan generation phase, a 3D visualization module of the assembly operations has been added to the APN generation module (Fig. 11 ). F) Assembly constraints management the APN generation, each answer (given by the user or by the geometrical module), is stored into an assembly constraint database. The management of this database is described in  [Henrioud 89b  and  [Perrard 07 ] in order to: automatically deduce some new assembly constraints from the database, resume an interrupted study avoiding to re-question the user about the previously known assembly situations.
CapLog software results Regarding the electric plug study, the procedure supported by CapLog was used by about 150 designers (students) who used it from product modelling to FAP generation. In general, the problem was solved in four hours. The most complicated part was the product modelling followed by the de nition of the assembly strategy which took about two hours. Solving the APN took less than a minute. However, getting the APN right required some corrections to the initial assembly strategy (due to the lack of knowledge of these novice designers). With the assembly constraint database module, APN solving was more e cient and no longer asks any questions. The bottom-up algorithm, designed to discover the geometric assembly constraints as early as possible, is very e cient. The addition of the assembly direction solver avoids many questions to the user (in the case of products with simple assembly directions, no questions are asked during this solving substep).
Conclusion And Further Works This paper proposes an original semi-automatic method to build and evaluate FAPs, starting from a "correctly designed product" (result of a DFA phase) in order to obtain the best ones, according to the user's preferences and constraints. This work has explored different ways to overcome the huge combinatorial explosion phenomenon, and has proven to be able to handle "medium size" products ( § 6. 5). The proposed approach has then been implemented to enrich a software called CapLog. Today, CapLog is an operational software that allows to create relevant FAPs in an e cient and pragmatic way. User knowledge is still required during this process, but solving medium-sized problems takes a very acceptable amount of time for the users.
We outline the "dark siren" galaxy catalog method for cosmological inference using gravitational wave (GW) standard sirens, clarifying some common misconceptions in the implementation of this method. When a confident transient electromagnetic counterpart to a GW event is unavailable, the identification of a unique host galaxy is in general challenging. Instead, as originally proposed by Schutz, one can consult a galaxy catalog and implement a dark siren statistical approach incorporating all potential host galaxies within the localization volume. Trott & Huterer recently claimed that this approach results in a biased estimate of the Hubble constant, H 0 , when implemented on mock data, even if optimistic assumptions are made. We demonstrate explicitly that, as previously shown by multiple independent groups, the dark siren statistical method leads to an unbiased posterior when the method is applied to the data correctly.
2018). Indeed, this approach has already been implemented using dozens of available GW detections  (Fishbach et al. 2019; Abbott et al. 2021a; Soares-Santos et al. 2019; Palmese et al. 2020 Palmese et al. , 2023;; Abbott et al. 2023b; Finke et al. 2021). This paper aims to act as a point of introduction for those new to the field of GW cosmology, and the dark siren + galaxy catalog method in particular, using mock data to build up a basic analysis following a Bayesian approach. This is motivated by the recent claim that GW dark sirens generally provide biased estimates of H 0 even under simplified assumptions  (Trott & Huterer 2021, hereafter TH21). We show here that this is not the case, and how it is incorrect modeling assumptions that lead to biased measurements. Given this, it is important to stress that a lot of the inconsistencies and biases explored in this work are not relevant for the case of realistic data, and instead primarily arise due to simplified assumptions and toy models for the GW sources and galaxy catalogs.
Indeed, this is the correct procedure for identifying potential sources of systematic error. However, this is only useful if a consistent analysis has first been shown to give unbiased results, and if the types of modifications to the datagenerating process are controlled and physically well motivated. Any sources of bias identified in this way can then be mitigated by increasing the complexity of the analysis model. This paper is organized as follows. Section 2 introduces the basic Bayesian framework, then describes the mathematics of how the mock GW and EM galaxy catalog data should be constructed. Section 3 gives details of the simulated data, then shows results for the scenario in which 200 GW detections are used to constrain H 0 , and how the constraint changes in the limit of a large number of detections.
Statistical Framework Let us assume that we have observed a set of N obs GW events, {x}, from which we are able to measure the luminosity distance of each source. In this basic example, H 0 can be determined by the fact that we measure the source luminosity distance from the GWs and can identify potential host galaxies from the catalog. According to Bayes' theorem, the posterior on H 0 , given a set of detected GW events {x}, can be written as µ ( |{ }) ({ }| ) ( ) ( )  p H x x H p H , 1 0 0 0 where p(H 0 ) is the prior on H 0. The likelihood ({ }| )  x H 0 can be described by an inhomogeneous Poisson process  (Mandel et al. 2019; Vitale et al. 2022) : ò ò  = ´- ({ }| ) [ ( )] ! ( | ( )) ( ) ( ) ( ) ( ) ( )   x H e N H N dz x d z H p z dz P z H p z , ,. 2 N H N i N i L 0 exp 0 obs GW 0 CBC det GW 0 CBC exp 0 obs obs Here, ( | ( ))  x d z H , i L GW 0 is the GW likelihood (the probability of measuring the data x i given some luminosity distance d L ), p CBC (z) is the probability that the source, a compact binary coalescence (CBC), is at redshift z for an observer on Earth, while ( ) p z H , det GW 0 is the GW detection probability used to account for selection biases.
The term ( ) N H exp 0 is the number of expected GW detections for a given value of H 0. This term can be marginalized analytically by assuming a N 1 exp prior on the expected number of events  (Fishbach et al. 2018) , or equivalently the compact object binary merger rate. With this assumption, the pre-factor loses its dependence on H 0 and the likelihood for a single event reduces to ò ò = ( | ) ( | ( )) ( ) ( ) ( ) ( )   x H dz x d z H p z dz P z H p z , , , 3 i i L 0 GW 0 CBC det GW 0 CBC which is the one we use here. The quantity p CBC (z) is being used here to represent the distribution of redshifts from which the GW source is drawn. If this is known, the likelihood, which is the probability distribution of observed data sets, can be found by marginalizing over the distribution of possible redshifts, which is what is done in Equation (3).
In practice, we do not know p CBC (z), but we construct it from observations of galaxies, as described in Section 2. 1 below. The interpretation is then that we are using the posterior from EM observations as the prior for the GW data. This is perfectly legitimate but, to be rigorously correct, what the EM data provides is a distribution of possible GW source distributions, since the EM data is not perfect. In practice, this means that we should treat the unknown true redshifts of the galaxies as population-level parameters that we infer jointly from the EM and GW data, and marginalize over these after combining the individual GW measurements. In simpler terms, we should change the order of the product over events and the integrals over redshift in Equation (2) and the integral over redshift should be over the true redshifts of the galaxies that are possible hosts of the events.
This quantity can be expressed as where t d and t s are the times in the detector and source frame, and the number of CBCs per comoving volume per sourceframe time is by definition the CBC merger rate. For this basic mock data, when calculating Equation (4) we will neglect the rate term in Equation (5) in order to align more closely with the approach taken in TH21. As long as this rate assumption is treated self-consistently when generating the mock data and analyzing it, this will not introduce any bias to the results. Let us note that this is a simplified description where the CBC rates only depend on the universe epoch (redshift); in a more general case, we might even model that more luminous galaxies are more likely to host CBCs. Therefore, in the remaining of the paper, we will approximate » ( ) ( ) ( ) p z p z.
Gravitational-wave Data Modeling The problems of detection and source parameters estimation of GW signals are complex and an active field of current research; we refer the reader to Abbott et al. (2020) for a more in-depth discussion. Here, we just discuss the fundamental aspects of detection and parameter estimation for GW signals. GW signals are detected using low-latency algorithms, able to calculate signal-to-noise ratios and false-alarm rates using either a template bank  (Messick et al. 2017; Nitz et al. 2017; Aubin et al. 2021; Cannon et al. 2021; Dal Canton et al. 2014)  or a superposition of wavelets  (Klimenko & Mitselmakher 2004). Typically, GW signals with false-alarm rates lower than 1-10 per year are selected for population analyses  (Abbott et al. 2021 (Abbott et al. , 2023a)).