dataesr/scientific-paragraphs-categorization huggingface dataset:
A general discussion about the misinterpretation of latent and observed variables, and the bias caused by assuming that observables depend on intrinsic properties in the mocks but not in the likelihood modeling in the context of GWs, is also provided 40. Inconsistency 5: simulating dark siren events along the same line of sight. The statistical method used in TH21 is for a highgalaxy-density regime, but this is inconsistent with the generation of data from D 11 and D 21 , where it is possible for multiple GW events to be drawn from the same low-redshift host galaxy. Some of the H 0 biases for D 11 and D 21 might originate from this inconsistency, as we have shown in Section 3. 3. However, in our own analyses, this produced a noticeable bias less frequently than the other potential sources , and 30%.
Conclusions We have presented an introduction to the dark siren statistical method. We have explicitly demonstrated that, contrary to TH21, the dark siren statistical method can robustly recover an unbiased estimate of H 0. We expect the method to produce unbiased estimates of additional cosmological parameters. We have also shown that, in the limit of the absence of galaxy clustering, the dark siren statistical method continues to provide an unbiased estimate of H 0. Notebooks showing how to reproduce this analysis can be found at https://github. com/ simone-mastrogiovanni/hitchhiker_guide_dark_sirens. We emphasize that this work is not a forecast, but instead is meant to provide a pedagogical introduction to the dark siren approach, highlighting common pitfalls. Because of the specific assumptions that we have made, this is not a realistic simulation of GW events and their detections, and we do not provide estimates for the future sensitivity of dark siren probes (see, e.
g. , Chen et al. 2018 and Gray et al. 2020 for forecasts). This is why we do not specifically quote precision measurements of H 0 throughout the manuscript. Moreover, we remind the reader that a data-analysis framework able to analyze real GW data is, in some aspects, more complicated than what is discussed in this paper. On the GW side, as has already been noted in the literature, selection effects based on GW sensitivity studies injecting signals in real data should also be carefully taken into account for standard sirens with counterparts (e. g. ,  Farr 2019; Mortlock et al. 2019). Moreover, assumptions about the underlying population of compact object binaries can have a significant impact on estimating the Hubble constant not only for the galaxy catalog approach (Abbott et al. 2023b) but perhaps more importantly for a GW-only approach to standard siren cosmology  (Mastrogiovanni et al.
Acknowledgments We thank  Dragan Huterer  and  Emery Trott  for clarifying the details of their analysis and for sharing their galaxy sample from the MICEcat simulation so as to enable us to provide a direct comparison to their work. We thank the  Kavli Institute for Cosmological Physics at the University of Chicago  for hosting "The quest for Precision Gravitational Wave Cosmology Workshop," organized by  Jose Maria Ezquiaga  and DEH, where part of this work has been discussed. The research of  A. Ghosh  is supported by the  Ghent University BOF  project  BOF/STA/202009/040  and the  Fonds Wetenschappelijk Onderzoek (FWO)   iBOF  project  BOF20/IBF/124. A. P. acknowledges support for this work was provided by  NASA  through a  NASA   Hubble Fellowship  grant No. HST-HF2-51488. 001-A , awarded by the  Space Telescope Science Institute , which is operated by  Association of Universities for Research in Astronomy, Inc.
In this paper, we tackle these questions using an Observation System Simulation Experiment (OSSE) based on hourly observations of near-surface ocean currents and near-surface winds collected at five surface moorings. These observations are sampled according to ODYSEA sampling scenarios to generate synthetic satellite measurements. The synthetic data are then used within an optimization framework based on a linearized slab model  [24, 25]  to produce a reconstruction. The optimization framework and the methods used for observations and modeling are discussed in Section 2. The results are presented in Section 3. The discussions and conclusion are in Section 4 and 5, respectively.
Observations In-situ measurements of (near) surface currents are derived from various historical oceanography campaigns. We utilize hourly surface velocity data from five mooring stations equipped with meteorological packages that include hourly winds. Data from three moorings are supplied by the Upper Ocean Process Group at WHOI; namely, the Northwest Tropical Atlantic (NTAS) mooring, the Stratus mooring in the Southeast Pacific, and the WHOI Hawaii Ocean Time-series Site (WHOTS) situated 100km north of Oahu. Surface currents are nominally measured at a 10m depth and winds at a 3m height. We also examine observations from two stations deployed and maintained by PMEL. One is the Kuroshio Extension Observatory (KEO) surface mooring, situated south of the Kuroshio Extension current (https://www.
pmel. noaa. gov/ocs/KEO). The other is Ocean Station Papa, an important site for continued monitoring of ocean climate (Papa). For these two stations, the surface current is nominally measured at a 5m depth and winds at a 4m height. The coordinates of the mooring stations, the inertial periods of the regions, and the duration of collected data for the five stations are listed in Table  1  and illustrated in Figure  1. The upper and lower panels correspond to the Clockwise (CW) and Counter-Clockwise (CCW) components, respectively. The blue lines are the spectra of the original hourly measurements from observations. The orange (green) lines represent the spectra from the ODYSEA sampling with a 1800km (1000km) swath linearly interpolated on to an hourly temporal grid (denoted as CW-1. 8k and CW-1.
Data Availability Statement: The mooring data are availabled at the WHOI website http://uop. whoi. edu/ ReferenceDataSets/index. html. Data from the Stratus Ocean Reference Station were made available by Dr. Robert Weller of the Woods Hole Oceanographic Institution; these data were collected with support from the Pan American Climate Study and Climate Observation Programs of the Office of Global Programs, NOAA Office of Oceanic and Atmospheric Research, Grants NA17RJ1223, NA17RJ1224, and NA17RJ1225. Data from the NTAS Ocean Reference Station were made available by Dr. Albert Plueddemann of the Woods Hole Oceanographic Institution; these data were collected with support from the NOAA Climate Program Office, Ocean Observing and Monitoring Division. Observations from the WHOI-Hawaii Ocean Timeseries Site (WHOTS) mooring is supported by the National Oceanic and Atmospheric Administration (NOAA) through the Cooperative Institute for Climate and Ocean Research (CICOR) under Grant No.
In this paper, we present a test of an interactive modelling scheme in real conditions. The aim is to use this scheme to identify the physiological responses of microorganisms at different scales in a real industrial application context. The originality of the proposed tool, Biosys-LiDeOGraM, is to generate through a human-machine cooperation a consistent and concise model from molecules to microbial population scales: If multi-omics measurements can be connected relatively easily to the response of the biological system at the molecular scale, connecting them to the macroscopic level of the biosystem remains a difficult task, where human knowledge plays a crucial role. The use-case considered here pertains to an engineering process of freeze-drying and storage of Lactic Acid Bacteria. Producing a satisfying model of this process is a challenge due to (i) the scarcity and variability of the experimental dataset, (ii) the complexity and multi-scale nature of biological phenomena, and (iii) the wide knowledge about the biological mechanisms involved in this process.
The Biosys-LiDeOGraM tool has two main components that can have to be utilized in an iterative manner: the Genomic Interactive Clustering (GIC) module and the Interactive Multi-Scale modellIng Exploration (IMSIE) module, both involve users in their learning loops. Applying our approach to a dataset of 2,741 genes, an initial model, as a graph involving 33 variables and 165 equations, was first built. Then the system was able to interactively improve a synthetic version of this model using only 27 variables and 16 equations. The final graph providing a consistent and explainable biological model. This graphical representation allows various user interpretations at local and global scales, an easy confrontation with data, and an exploration of various assumptions. Finally Biosys-LiDeOGraM is easily transferable to other use-cases of multi-scale modelling using 'functional' graphs.
Introduction Over the past two decades, knowledge management in biological research has experienced a profound transformation as a result of the availability of high-throughput experimental technologies and advances in mathematical and computer modelling. Consequently, large omics datasets are now commonly found in studies focused on integrated, system-level approaches in biology  [1, 2]. The interpretation of omics data is also largely enabled by the availability of multiple reference databases and several open-source bioinformatics software packages. These packages allow the combination of different types of high-dimensional data at the genome level, and thus help experts reach new insights on physiological responses, from genotype to phenotype  [3, 4]. Computer modelling and machine learning have also become increasingly important for the integration of multi-omics datasets to address fundamental biological questions  [5].
These approaches have enabled the establishment of biological networks and have facilitated the extension of analyses to a more complete, multi-scale reconstruction of biological processes, thereby providing comprehensive overviews of entire biological systems  [1, 6]. The integration of these multi-omics datasets has thus led to major advances in the holistic understanding of the molecular mechanisms that govern the functional dynamics of living individual cells and their communities in response to environmental change  [2, 7, 8]. Typically, sophisticated computational algorithms are required to integrate large omics datasets in order to facilitate their reuse and connection with other data resources. Some modelling approaches are widely used for the analysis of biological systems  [1, 2, 7, 8].
They are now widely used to provide a clear and accurate overview of molecular mechanisms in spatially heterogeneous dynamical systems  [11, 12]. Although more powerful and accurate, these methods have limitations. Statistical approaches generally need large sets of physiological parameters that are often not easily accessible through experiments. Moreover, these statistical approaches rely primarily on a priori knowledge to reduce the scope of the investigation, thus limiting the ability to identify unknown behavior  [13]. In addition, these methods frequently struggle to incorporate high dimensional datasets that are both large and complex in terms of size, heterogeneity, and content  [14]. Usually, Bayesian statistical or frequentist inference methods are the most widely used approaches for modelling complex multiscale, multi-level and multi-dimensional aspects of biological systems  [15]  These mathematical approaches have the advantage of offering a better prediction of the behavior of individuals facing environmental change, based on various characteristics that are known or assumed to be associated with some scientific evidence  [1].
Although nowadays, several studies have determined various ways to improve their resistance  [25, [29] [30] [31] , bacterial survival is highly dependent on the cell physiology of LAB confronted with stresses at each step of the bioprocess. In order to validate this framework, we rely on a combination of multi-scale datasets including the transcriptome, cell membrane fatty acid composition, biological activity (acidifying activity and viability) following different fermentation conditions (culture temperature and cell harvesting time) that have been generated in a spatio-temporal biological context along a bioprocess chain including the fermentation, stabilisation and storage steps. The objective is to be able to provide the biological expert with new perspectives for the optimization of LAB bioprocess engineering.
lactis TOMSC161, hereafter called L. lactis TOMSC161, a strain of industrial interest that is robust to the stressful conditions that occur during fermentation and stabilization processes, the results of which were previously published by  [25, 51]. In summary, the described datasets consist of: (i) the colony-forming units (UFC) counts;(ii) acidifiying activity measurements; (iii) fatty acid data obtained in GC-HPLC; (iv) anisotropies, and (v) transcriptomic data from the study of cells harvested at an early stationary growth-phase (ES) and a late stationary growth-phase (LS) (6h after ES) during two independent fermentations leading to 22 °C and 30 °C, and repeated in triplicate  [25]. The objective of these works was to get a deep understanding of the effect of each step of the fermentation, stabilization and storage processes that could influence the biological system, affect their viability and influence the behavior of LAB.
Data inputs of Biosys-LiDeOGraM: a multi-scale description of the biosystem The cell concentrates were characterized at each step of the stabilization process: concentration, lyoprotectants addition, freezing, freeze-drying, and after three months of storage. In addition, transcriptomic data in early and late stationnary growth phases of fermentation at 22 and 30 °C have been included in the dataset. In total, 16 variables at different mesoscopic scales and 2,741 differentially expressed (DE) genes were considered. The various measured variables are related to four different scales as follows (Table  1 ).
Acidifying activities at the bioprocess scale Starters LAB strains are usually marketed on the basis of their acidifying efficiency, which is essential in the industrial manufacturing processes of fermented products  [52]. Therefore, the acidifying activity dataset was also included in our multi-scale application. Measurements of the acidifying activity of this strain were carried out on cells harvested at 3 h from the stationary growth phase of fermentation, and were obtained continuously throughout the bacteria growth by the Cinac system, as described in the previous work  [25]. The indicator tpH07 (corresponding to the time required to obtain a decrease of 0. 7 pH units, in min) was used to characterize the acidifying activity of cells, in order to determine the effect of each step of the stabilization process (concentration, lyoprotectants addition, freezing, freeze-drying, and after one or three months of storage).
Cell membrane at the cellular scale The cell envelope of microorganisms plays a crucial role in cellular integrity. Specifically, the survival of bacteria depends on their ability to maintain phospholipid membrane properties to meet environmental requirements. However, the cell membrane has been reported as a major site of damage during freezing, freeze-drying or rehydration where transitions of membrane phases occur  [30]. This results in the modification of membrane structure, viscosity and permeability, leading to viability and acidifying activity looses  [53]. Therefore, the cell membrane properties of L. lactis TOMSC161 for each tested condition were integrated in this analysis. The biological datasets used to characterize cell membrane modifications have already been published in  [51].
Transcriptome responses at the genomic scale Many studies on LAB were also referenced to understand the molecular mechanisms that govern an adaptive response in these microorganisms, leading to a physiological state of tolerance, and thus survival under normally lethal stress conditions  [24, 50]. In previous work, the physiological responses of L. lactis TOMSC161 have been investigated by RNA-Seq transcriptome analysis to understand its tolerance to freeze-drying and storage (data not published). RNA-Seq data used in this study were deposited in the Gene expression Omnibus of the NCBI under the accession number GSE74554. The detailed experimental approaches for RNA extraction, RNA-Seq librairies preparation, RNA-seq sequencing and bioinformatics data analysis were performed as described in  [54, 55].
Briefly, only uniquely mapped reads of L. lactis TOMSC161 (NCBI Bioproject accession number: PRJEB4823,  [56] ) were used for data analysis. Differential gene expression was determined by comparing the number of uniquely mapped reads for each gene in the three replicates, from the early and the late stationary growth phases. A count table was generated to select only data corresponding to CDS (CoDing Sequence) features that exhibited an average of more than 50 mapped reads. A differential gene expression analysis between the two conditions was performed using DESeq2 (version 1. 8. 1) package in the statistical environment R  [57]. The p-values were adjusted using the Benjamini and Hochberg method, which assesses the false discovery rate  [58]. Therefore, overall responses defined by DE genes were also included.
Methods Step 1: Genomic Interactive Clustering (GIC) One objective of the Biosys-LiDeOGraM approach is to reduce the number of variables at the genomic scale while improving prediction performance. Additionally, selecting the most relevant variables  [59] , i. e. performing a dimensionality reduction, facilitates the modelling process and possibly reduces the associated computational time. The GIC step is based on a user-guided hierarchical clustering. Among the variety of algorithms dedicated to the analysis of gene expression in the field of bioinformatics  [60] , hierarchical clustering provides clusters organized in hierarchies, where each cluster is a subset of the next higher cluster in the hierarchy. It is often convenient for visualizing the structure of a dataset and identifying relationships or outliers in the data.
However, a challenge lies in fine-tuning these algorithms. For instance, the ideal number of levels and clusters is rather difficult to estimate, and even when routines to automatically estimate it are available, manual settings are often preferred. Various metrics can also be used to evaluate the distances between gene expression profiles. They need to be appropriately chosen, but efficient parameter tuning is strongly dependant on the application and data  [61]. A way to overcome this difficulty is to use a semi-automatic approach, where domain experts can review gene clusters and interactively adjust the parameters. Another difficulty is due to the small size of the datasets, and the presence of error and noise: proposed clusters could be due to random correlations. To deal with such aberrant clusters, we use a hierarchical density-based spatial clustering of applications with noise (DB-SCAN) method  [62] [63] [64] [65]  (with an α parameter set to 0.
Challenging issues with the dataset Running conventional machine learning and automated modelling approaches on our dataset is a challenge for several reasons. 1. Its structure and size is strongly unbalanced: 2,741 variables at the genomic scale and 16 at the other scales (i. e. a total of 4 conditions, corresponding to two kinetic points at two different temperatures), with only three replicates for each experimental condition. Finding reliable models is very difficult, as there exist an infinite number of multi-linear models that approximately fit the data. 2. The repeatability of the various measurements is poor (this is rather a common feature for living biological systems). 3. The inter-scale relationships are non-linear  [72] , a large amount of data is needed to appropriately infer reliable models  [73]  and avoid over-fitting.
In our case, the objective is not to prove that the results from the different experimental conditions come from similar or different distributions, but rather to provide a qualitative view of the variance existing in the dataset. We thus propose an estimate of the predictive quality of models, as a measure of separability, called P V , to illustrate the variance existing for each variable V , as shown in Eq. 5: P V = M ean(Std(V C1 ), Std(V C2 ), Std(V C3 ), Std(V C4 )) Std(M ean(V C1 ), M ean(V C2 ), M ean(V C3 ), M ean(V C4 )) (5) where V Ci are the 4 replicate measurements of variable V for the 4 conditions C i , and where Std and M ean are the standard deviation and the mean values respectively. P V compares the mean of standard deviations of the values for each condition with the standard deviation of the mean value for each condition.
lactis TOMSC161 strain under the experimental conditions tested in this study. An overview of the genomic clustering results obtained from 2,741 variables by the GIC method is shown in Fig 2. The interactive interface proposed to the user has four parts: (i) A dendrogram representing the hierarchy of nodes with the number of variables that have been clustered (Fig  2A ). This step is controlled by the user who defines a minimum size of clusters; (ii) A schematic representation of the expression profiles for each gene of the cluster (Fig  2B ). When the expert selects a node of the dendrogram, he has access to the content of the corresponding cluster and can quickly evalute is similarity; (iii) A 2D scatterplot of the projection of all genes of the dataset using the t-distributed stochastic neighbor embedding (t-SNE) dimension reduction method  [77]  (this method position each point in a 2D space in such a way that the distances between points correspond to the true distances in the original multidimensional space) (Fig  2C ).
Discussion The strategy used in the analysis presented here is based on a structured combination of automatic machine learning (or optimization) and user interactions steps. The idea was to support an intuitive human process for designing complex models, which consists in grouping partial knowledge relatively well mastered at some scales (local levels, in our scheme) and comparing them in order to progressively build a coherent global multi-scale interpretation. In this way, a dataset that is too small for efficient machine learning can still be exploited. Artifical Intelligence (AI) is invoked (machine learning and optimization) each time a repetitive task is identified: segmentation steps in the GIC part, multilinear regression and global optimization in the IMSIE part. But the tuning of the respective roles of the machine and the human in such a context is a fairly delicate task.
Computations and visualizations have been designed in a way that leave as much freedom of choice as possible to the expert user without drowning them in an ocean of information (user fatigue issues). We have proposed a balance based on initial bottom-up suggestions computed from the dataset (hierarchical segmentation for GIC, multi-linear regression for the local level and evolutionary optimization for the global level of IMSIE), yielding concrete hypotheses users can then revisit, constrain according to their own expertise, and recompute in order to build a multi-scale global interpretation. The tool was created to simplify the user's task by organizing variables into clusters (GIC component) and giving the user the ability to specify constraints on groups of variables, making it convenient for a multi-scale context.