dataesr/scientific-paragraphs-categorization huggingface dataset:
Sample and Procedures Data came from a UK-based prospective birth cohort, called the Avon Longitudinal Study of Parents and Children (ALSPAC)  (Boyd et al. , 2013; Fraser et al. , 2013). The ALSPAC is a longitudinal population-based cohort based in Avon, UK. Pregnant women residing in the area with estimated delivery dates between  April 1, 1991, and December 31, 1992, were invited  to participate in the study. The initial sample included 14,514 women who returned at least one questionnaire or attended one focus clinic by July 19, 1999. Out of the initial sample, a total of 14,676 fetuses and 13,988 children who were alive at 1 year of age were included. A second wave of participants were recruited when the oldest children in the cohort reached approximately J o u r n a l P r e -p r o o f 7 years of age, which consisted of eligible individuals who did not join the study initially, resulting in the enrollment of an additional 913 children.
With the two waves combined, the ALSPAC sample consisted of 14,901 children alive at age 1 year  (Boyd et al. , 2013; Fraser et al. , 2013). Our analytic sample came from a subsample of ALSPAC, called the Accessible Resource for Integrated Epigenomics Studies (ARIES). The ARIES subsample includes 1,018 motherchild pairs from whom blood-based DNAm data were collected. Participants in the ARIES subsample were randomly selected from ALSPAC participants with complete data across at least five timepoints of data collection  (Relton et al. , 2015). There were 785 singleton children with DNAm at age 7 in our analytic sample, who had complete data on all covariates and at least one type of childhood adversity across all time points. Ethical approval for ALSPAC and ARIES was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committee.
Consent for biological samples was collected in accordance with the Human Tissue Act (Human Tissue  Act, 2004). Please note the study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool (http://www. bristol. ac. uk/alspac/researchers/our-data/). Secondary analyses of ALSPAC data were approved with oversight by the Mass General Brigham Institutional Review Board (IRB) (Protocol 2017P001110). and (g) neighborhood disadvantage. These adversities were selected because they were frequently examined in prior studies of early life stress on epigenetic profiles  (Barker et al. , 2018; Dunn et al. , 2019; Krause et al. , 2020). They also had at least four repeated assessments before age 7 in ALSPAC, enabling analysis of adversity timing.
J o u r n a l P r e -p r o o f DNAm profiles were obtained from blood samples collected at age 7 and assayed using the Illumina Infinium Human Methylation 450k BeadChip microarray, which measured DNAm at 485,577 CpG sites (Illumina, San Diego, CA). The 450K microarray captures DNAm variation at 99% of RefSeq genes. To minimize batch effects, samples were semi-randomly assigned across time points to different plates  (Relton et al. , 2015). Beta values for DNAm were analyzed, which represent the proportion of cells methylated at each CpG. Data quality control was performed using the protocol described by  Min et al. (Min et al. , 2018). The pipeline applied functional normalization and background correction using the meffil R package. Additionally, cross-hybridizing CpGs, polymorphic CpGs, and CpGs located in sex chromosomes were removed before analyses.
Secondary analysis To further interpret our results, we pursued a secondary analysis using data available from the EWAS Catalog (http://www. ewascatalog. org). The EWAS catalog is a well-curated database containing published results from EWAS of DNAm and phenotypes spanning multiple domains, including biological functioning, disease risk, and social characteristics  (Battram et al. , 2021). We annotated the 530 probes analyzed here to any trait associations reported in the catalog. All associations from published analyses with a p-value<1x10 -4 were reported.
To provide a contrast for our results, we investigated whether aggregating exposure status across the seven types of adversity, instead of examining each type separately as in the primary analysis, changed the results. During each developmental period (very early childhood, early childhood, and middle childhood), children exposed to any of the seven types of childhood adversity were coded as exposed, resulting in three models for each gene set (i. e. , one for each sensitive period). Fourth, as the gene set-based approach was meant to complement EWAS, meaning the agnostic search for signals across the genome, we were interested in determining if combining CpGs into genes and pathways yielded more information than CpG-level analyses. Therefore, we compared our findings to those obtained from a previous EWAS of the same dataset  (Lussier et al.
, 2022). Specifically, Lussier et al. performed a SLCMA to compare multiple encoded exposures of childhood adversity and identify the life course hypothesis most strongly associated with DNAm at each locus across the epigenome using the ALSPAC cohort. All analyses were performed using software package R. We made the scripts publicly available on GitHub (https://github. com/thedunnlab/geneset). J o u r n a l P r e -p r o o f.
Beyond hypotheses about developmental plasticity, our gene set-based approach can also be extended to test hypotheses about alternate neurobiological or physiological pathways underlying the effect of adversity, such inflammation or functionalities of the hypothalamic-pituitary-adrenal axis  (Cecil et al. , 2020). Our analytic process was straightforward to implement, and the scripts we provide can be adapted to assess other pathways of interest. Preliminary data on the statistical power of the method are also provided. These data may offer guidance for future applications, especially with more refined gene sets or modest sample sizes (Figure  S4 ). We also encourage simulation-based power calculations to be performed for other pathways of interest in specific study contexts. This study has several limitations.
In future studies, potential interactions between time points and adversity types should be more carefully assessed. Third, our analyses may have been underpowered, due to the low prevalence of adversity during certain developmental periods, modest expected effect sizes, and the fact that statistical power of LDA decreases with the number of features included  (Helmer et al. , 2021). Although our sample size per feature was in line with prior recommendations for sufficient power and estimation stability  (Barcikowski and Stevens, 1975; Helmer et al. , 2021) , we were inherently less powered to detect associations with larger gene sets, such as the closing set, due to the higher number of PCs analyzed. Thus, using our approach with larger datasets or conducting meta-analyses are critical next steps.
Finally, several factors could impact the generalizability of our results. For instance, we analyzed DNAm data from blood samples, despite our gene sets capturing developmental plasticity in the brain. While brain and blood DNAm levels are generally only modestly correlated  (Braun et al. , 2019) , DNAm variations in peripheral tissues may still serve as J o u r n a l P r e -p r o o f important biomarkers for brain-based disorders. In our analyses, almost half (28 out of the 58) of the genes included at least one CpG with strong blood-brain correlations (ρ≥0. 6; Figure  S5 ). Thus, analyses of blood DNAm can allow identification of potential targets for follow-up experiments, where brain tissues might be accessible. Furthermore, ALSPAC participants are predominantly of European ancestry and the subsample with epigenetic data had lower levels of adversity compared to the larger cohort.
Visual-Based SLAM Concepts This section presents concepts related to visual-based SLAM and odometry algorithms, and the main characteristics of the visual-based approaches covered in this paper. The visual-based SLAM techniques use one or more cameras in the sensor system, receiving 2D images as the source of information. In general, the visual-based SLAM algorithms are divided into three main threads: initialization, tracking, and mapping  [10]. Figure  1  shows a general view of the three main parts generally present in visual-based SLAM approaches. The depth and inertial data may be added to the 2D visual input to generate a sparse map (generated with the ORB-SLAM3 algorithm  [22]  in the MH_01 sequence  [23] ), semi-dense map (obtained with the LSD-SLAM  [24]  in the dataset provided by the authors), and a dense reconstruction (Reprinted from  [25] ).
The depth data computation differs according to the employed algorithm (Section 3 addresses individually each algorithm providing detailed explanations). Finally, the mapping processes shall result in a sparse, semi-dense, or dense 3D reconstruction, according to the implemented technique. Although we mainly refer to the concepts as belonging to the SLAM methodology, we consider, in this paper, both visual-SLAM and visual-odometry (VO) techniques, since they are closely related. The VO algorithms also seek to estimate a robot's position through cameras as a source of information. The main difference between visual-SLAM and VO lies in considering, or not, the global consistency of the estimated trajectory and map  [14]. While VO performs only local optimizations, visual-SLAM algorithms also employ loop closure detection (see Section 3), being capable of correcting drifts accumulated at the end of the robot's trajectory.
Feature-Based Methods SLAM algorithms based on features consider a certain number of points of interest, called keypoints. They can be detected in several images and matched by comparing their descriptors; this process provides the camera pose estimation information. The descriptor data and keypoint location compose the feature, i. e. , the data used by the algorithm to process the tracking and mapping. As the feature-based methods do not use all the frame information, they are suitable to figure in embedded implementations. However, the feature extraction may fail in a textureless environment  [28] , as well as it generates a sparse map, providing less information than a dense one.
VI-SLAM algorithms can be divided according to the type of fusion between the camera and IMU data, which can be loosely or tightly coupled. The loosely coupled methods do not merge the IMU states to estimate the full pose: instead, the IMU data are used to estimate the orientation and changes in the sensor's position  [18]. On the other side, the tightly coupled methods are based on the fusion of camera and IMU data into a motion equation, resulting in a state estimation that considers both data. In addition, VI-SLAM algorithms present different implementations according to their back-end approach, which can be filtering-based or optimization-based. The front-end of filtering-based approaches for VI-SLAM relies on feature extraction, while optimizationbased methods (also known as keyframe-based approaches) rely on global optimizations, which increase the system's accuracy, as well as the algorithm's computational cost.
Delmerico and Scaramuzza  [65]  used different hardware platforms based on CPU architectures to implement visual-inertial SLAM algorithms. The authors implemented the algorithm in three different embedded boards-Intel NUC, Up Board, and ODROID. However, the Jacobian calculations performed by the algorithm may cause inconsistency and loss of accuracy  [66]. Open Keyframe-based Visual-Inertial SLAM (OKVIS)  [67]  is an optimization-based method. It combines the IMU data and reprojection terms into an objective function, allowing the algorithm to jointly optimize both the weighted reprojection error and temporal error from IMU. The algorithm builds a local map, and then the subsequent keyframes are selected according to the keypoints match area. The algorithm can be depicted as shown in Figure  14.
SLAM++ (2013) The SLAM++ algorithm  [94]  is an object-oriented SLAM algorithm that takes advantage of previously known scenes containing repeated objects and structures, such as a classroom. After the system initialization, SLAM++ operates in four steps: camera pose estimation, object insertion, and pose update, pose-graph optimization, and surface rendering. The first step estimates the current camera pose by applying the ICP algorithm, considering dense multi-object prediction in the current SLAM graph. Next, the algorithm searches to identify objects in the current frame using the database information. The third step inserts the considered objects in the SLAM graph by performing a pose-graph optimization operation. Finally, the algorithm renders the objects in the graph, as shown in Figure  21.
Semantic-Based Algorithms Incorporating semantic information on the visual-SLAM problem is a growing field, and has been attracting more attention in recent years. One important and recent study in this area is presented in  [122]. The authors propose a new methodology for data association that incorporates information from an object detector, proposing a solution that can represent both data association and landmark class in a factor graph solution. This method presents reduced errors compared to other solutions incorporating semantic data association techniques. More methods containing semantic data are discussed in Section 4. 3 as a solution to dynamic SLAM algorithms. As this field grows, it is also necessary to establish methods to validate the semantic-based algorithms. Authors in  [123]  introduce a new synthetically generated benchmark dataset that, besides the traditional ground truth of the trajectory, contains semantic labels, information about the scene composition, ground truth 3D models, and the pose of the objects.
Datasets and Benchmarking Among all the SLAM algorithms in the literature, it is essential to achieve a fair comparison between them to determine which one presents a better performance in certain situations. Several benchmarking datasets with different characteristics are proposed in the literature to explore the SLAM capabilities and robustness. Here, we present the publicly available benchmark dataset used to evaluate the presented SLAM algorithms in their original articles. The TUM RGB-D dataset  [128]  consists of several image sequences containing color and depth images recorded in indoor environments with a Microsoft Kinetic in two different platforms: robot and handheld. The system was synchronized with a motion-capture system to provide the ground truth. In addition, the authors propose two metrics to evaluate the local accuracy and the global consistency of the trajectory; they are relative pose error and absolute trajectory error, respectively.
The KITTI dataset  [129]  contains outdoor sequences recorded by color and grayscale stereo cameras. The KITTI also present data from a 3D laser scanner and the ground truth provided by an INS/GPS. The sensor system is synchronized and mounted on a car. In addition, the authors provide tracklets for a dynamic objects classification and benchmarks to evaluate robotics tasks, such as visual odometry and SLAM. Another main benchmark dataset is the ICL-NUIM  [130]. The dataset focuses on RGB-D algorithms and provides data for the evaluation of the 3D reconstruction through eight synthetically generated indoor scenes. A handheld RGB-D camera generates the sequences, and the ground truth consists of a 3D surface model and the estimated trajectory by a SLAM algorithm  [131]. The EuRoC benchmark dataset  [23]  is widely used to evaluate visual-only and visual-inertial SLAM and odometry algorithms.
The data were collected in two indoor environments by a micro aerial vehicle (MAV), and it provides eleven sequences of stereo images and IMU data. The ground truth is obtained by a total station and a motion capture system. A dataset commonly used to evaluate monocular systems is the TUM MonoVO  [30]. It contains several photometrically calibrated indoor and outdoor sequences provided by two handheld non-stereo monocular cameras. Due to the variety of the scenes, the authors do not provide a ground-truth from the poses, but they perform large sequences that start and end at the same position, allowing the evaluation of the loop drifts. Lastly, a dataset provided for visual-inertial systems evaluation is the TUM VI dataset  [132]. It provides several indoor and outdoor sequences captured by a stereo camera synchronized with an IMU.
The molecular line transitions we covered are summarized in Table  3. In addition, we retrieved archival SMA data towards the luminous ultra compact (UC) Hii region G10. 624-0. 38, which remains deeply embedded in a M gas =10 3 -10 4 M molecular clump and harbors a cluster of OB stars. These observations cov-ered the CH 3 OH J=5-4 and J=7-6 and the CH 3 CN J=19-18 line multiplets. We refer to Baobab  Liu et al. (2010) ,  Liu et al. (2011), and Liu et al. (2012)  for details of these observations. We followed the standard SMA data calibration strategy. The application of system temperature (T sys ) information and the absolute flux, passband, and gain calibrations were carried out using the MIR IDL software package  (Qi 2003). The absolute flux scalings were derived by comparing the visibility amplitudes of the gain calibrators with those of the absolute flux standard sources of the SMA, which were Callisto and Neptune for the subcompact and compact array observations, respectively.
After calibration, we performed zeroth-order fitting of continuum levels from line-free channels and the joint weighted imaging adopting robust weighting of all continuum data were performed using the Miriad software package  (Sault et al. 1995). The resultant synthesised beam is typically 4. 5 at 241 GHz. The sensitivity (3σ) of continuum observation is ∼0. 04 Jy beam -1 and of lines ∼0. 5 K. For clump G18, we do not obtain robust detection of the thermometer lines of CH 3 CCH, H 2 CS and CH 3 CN (Table 3) with this achieved line sensitivity. We used the previously published result from IRAM 30m telescope observations of 3 mm CH 3 CCH and CH 3 CN lines  (Giannetti et al. 2017 ) instead.
5 to 2. 5 mm. The pointing was determined by continuum observations on Saturn when available, or CO J=2-1 observations on bright nearby evolved stars (e. g. RAFGL2135, R-Dor). The pointing accuracy was found to be within 3. Focus was checked on Saturn every 2-4 hours. The main beam efficiency (η mb ) for the PI230 instrument varies over the observing period, with a range of ∼63%-72% 1. The calibration uncertainty is typically within 20%, estimated based on the flux measurement of the pointing sources. Basic data reductions were done with the GILDAS software package 2 , including flagging of bad spectra, baseline subtraction, unit conversion (T * A to T mb ), and building spectral cubes. Final spectral cubes are re-sampled to 0. 5 km/s spectral resolution for all lines.
SMA-APEX combination and imaging For our primary target lines covered by both SMA and APEX, which have extended emission, namely CH 3 CCH J=12-11, CH 3 OH J=5-4, C 2 H J=3-2, H 13 CO + J=3-2, CS J=5-4, C 34 S J=5-4 and SO J = 4 5 -3 4 , we combined the two dataset in the 1 http://www. apex-telescope. org/telescope/efficiency/ 2 http://www. iram. fr/IRAMFR/GILDAS Fourier domain (uv-domain) with Miriad. This combination is essentially imaging together the pseudo-visibilities generated from single-dish observations and interferometer measurements, so that the short-spacing information including zero baseline which is obtained with single dish can be complemented to interferometry data; the method is commonly referred to as joint deconvolution or joint reconstruction method  (Kurono et al. 2009 , Koda et al.
4. Temperature measurement of the outer regions are obtained using the extended CH 3 CCH and H 2 CS emission, and combined with temperatures derived from CH 3 CN which is confined to the central region of the clumps. This combination allows us to establish the full radial temperature profile of the clumps. We also use multi-wavelength single-dish dust emission (SD continuum, as in Figure  4 ) to derive dust temperature maps by building spectral energy distributions (SEDs) assuming single-component modified black-body emission  (Lin et al. 2016 (Lin et al. , 2019)). The dust temperatures at the outer layer of clumps, are used to complete the temperature profile at larger radii for the Table  3. Molecular lines of interest covered by the SMA observations. Information is taken from CDMS database  (Müller et al.
Modeling procedure Using the XCLASS package  (Möller et al. 2017) , we have established a pixel-by-pixel Local Thermodynamic Equilib-rium (LTE) model-fitting procedure for the observed CH 3 CCH, H 2 CS, CH 3 CN, and CH 3 OH ν t =1 (for G10) lines which returns the best fit of source size, rotational temperature (T rot ), molecular column density (N mol ), line width (∆ V) and the source velocity (V source ). In this specific implementation, we fixed the source size to the synthesized beam size (i. e. , assuming beam filling factor of 1) and optimized the rest of free parameters. The optimization procedure employed an initial global parameter search using the bees algorithm  (Pham et al. 2006 ) which was followed by Levenberg-Marquardt iterations. Such a procedure helps to avoid trapping in local minima.
The kinematic state of clumps: radial profiles of molecular line-width and virial parameter Based on the calculations in Section 3. 8, Figure  22  shows the radial profiles of the molecular linewidths (∆V = 2. 355 σ v ) for individual species. We fitted a power-law to the radial linewidth profiles traced by thermometer lines (i. e. excluding the data points from C 34 S and H 13 CO + ). In G08a, G31 and G13, the linewidth traced by these lines decrease radially with power-law indices of -0. 40, -0. 31, -0. 56, respectively. Except for the sources G18 and G19, the linewidths on the extended regions traced by C 34 S, H 13 CO + are systematically larger than that traced by the thermometer lines. Again, for clump G18, we had to rely on pointed observations from IRAM 30m telescope to tentatively mark the radii of the linewidths obtained from thermometer lines, based on a fixed temperature profile (Sect.
The collisional rates of CH 3 OH were quoted from Rabli & Flower (2010) (c. f. , Section 3. 5). To create the distributions of physical properties we adopted the sf3dmodels package  (Izquierdo et al. 2018)  to generate homogeneous grids in Cartesian coordinates, which are then interpolated onto the LIME input format of randomly generated set of points. We used the linear resolution of ∼0. 015-0. 03 pc (depending on the source radius R clump , Table  6 ) as grid size which corresponds to better than 1/5 beam size for each source. On each grid, we specified a mean gas velocity using a random process to mimic the turbulent velocity field: the direction was uniformly sampled from the 4π solid angle while the magnitude of the velocity was drawn from a Gaussian distribution with σ =3. 5 km s -1.
Acknowledgements. This work was partly funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Collaborative Research Council 956 , sub-project  A6. Y. L. is a member of the  International Max-Planck Research School (IMPRS) for Astronomy and Astrophysics at the Universities of Bonn and Cologne. H. B. L. is supported by the  Ministry of Science and Technology (MoST) of Taiwan  (Grant Nos. 108-2112-M-001-002-MY3  and  110-2112-M-001-069 -). The  Submillimeter Array  is a joint project between the  Smithsonian Astrophysical Observatory  and the  Academia Sinica Institute of Astronomy and Astrophysics  and is funded by the  Smithsonian Institution  and the  Academia Sinica.