dataesr/scientific-paragraphs-categorization huggingface dataset:
W. ) following individual muscle delineation, avoiding inclusion of other muscles, subcutaneous and intermuscular fat, tendons, and major blood vessels. To assess water T 2 , ROIs delineated the interior of the muscle, avoiding visible fasciae and blood vessels (by J. L. L. ). 25  A third observer (R. F. T. ) compared the two different segmentations and instructed the two observers to correct where necessary. Fat fraction and CSA values were computed using the Dixon images (co-registered to the five MSE image slices). Dixon data were reconstructed using a six-component lipid model and considering a single T 2 * decay. 27, 28 FF was calculated as SI (fat)/((SI)fat + (SI)water)*100 (with SI = signal intensity). FF maps that included subcutaneous and bone FF values < 95%, or partial fat-water swaps, were excluded for analysis.
01*FF))). 12 We evaluated the global segment for cCSA, which corresponds to the sum of the individual muscle cCSA values, per segment. For the global values for thigh and leg FF and water T 2 , a weighted mean of the values as determined in the individual muscles was calculated. 24 To evaluate disease progression, the annual and 3-year changes in FF, ΔFF% (expressed in %, absolute difference), and cCSA, ΔcCSA rel (expressed in %, relative difference compared with the precedent cCSA value), were evaluated. 21 All MRI outcome measures are reported as mean values of all pixels in the ROI averaged over the five slices. Phosphorus magnetic resonance spectroscopy data were processed as previously described,  13, 21  using AMARES in jMRUI  29  and Topspin (Bruker Medical GMbG, Ettlingen, Germany) to calculate P i,tot /PCr (total inorganic phosphate over phosphocreatine), P i,b /P i,tot (alkaline P i over P i,tot ), P i,tot /γATP (P i,tot over adenosine triphosphate), PCr/γATP, PDE/γATP (phosphodiesters over γATP), and PME/γATP (phosphomonoesters over γATP) ratios, as well as values of pH w (i.
Statistical analysis Statistical analyses were conducted using SPSS software Version 22 (SPSS, Chicago, IL, USA). The Mann-Whitney tests were performed for comparing demographic data between groups and to assess differences in MRI and 31 P MRS outcomes between controls and patients, with significance level set at P < 0. 05. We also analysed the data using a multilevel linear mixed model (LMM). A first analysis was performed for the MRI parameters to investigate left-right asymmetry, with side as a main variable, adding segment/muscle, group (ambulant vs. non-ambulant), and site as additional factors, and years since symptom onset and body mass index (BMI), for FF and cCSA, or FF for water T 2 , as continuous predictors to account for these confounding effects. The full analysis also included time point as a main variable and the interaction between time point and group.
21 The 31 P MRS data were generally obtained in patients with very low muscle fat replacement, revealing that changes in disease activity can be measured before the occurrence of any macroscopic muscle destruction, as demonstrated in DMD. 14  We also found that the shorter the disease duration, the higher the level of P i,tot /PCr was, which is an index that reflects the degree of muscle energy wasting. Interestingly, the patients with the highest P i,tot /PCr also seemed to have a history of a more vigorous physical activity regime,  30  as compared with patients with lower P i,tot /PCr, which might be an indication that there is a potential negative effect of physical exercise in dysferlinopathy patients. 33 As exercise is usually found to be beneficial in neuromuscular disorders,  37  this might also have implications for future treatment of these patients, including physical therapy.
Acknowledgements The Jain COS consortium would like to thank the study participants and their families for their invaluable contribution and would also like to acknowledge the ongoing support the  Jain Foundation  provides in the development, management, and analysis of this study. The  Jain Foundation , based in Seattle,  USA , is entirely focused on LGMD R2/dysferlinopathy/ Miyoshi myopathy. The foundation does not solicit funding from patients, but instead funds research and clinical studies worldwide with the goal of finding treatments for dysferlinopathy. Please visit www. jainfoundation. orgfor more information about the foundation and if you are a patient suffering from dysferlinopathy, please consider enrolling into their interactive dysferlinopathy registry that seeks to build a strong, engaged, and supportive community (patients@jain-foundation.
Acknowledgments The authors like to thank  T. P. Armstrong  ( FTECS ) for useful discussions on the LEMMS response,  Andreas Lagg  (MPS) and  Jon Vandegriff  ( JHU/APL ) for extensive software support, and  Martha Kusterer  ( JHU/APL ) for reducing the MIMI data. The work at  JHU/APL  and  NASA/GSFC  was funded through  NASA's Cassini Data Analysis Program (CDAP) , grant  80NSSC20K0478. Earlier the JHU/APL authors were partially supported by the  NASA Office of Space Science  under task order 003 of contract  NAS5-97271  between  NASA/GSFC  and  JHU. The MPS authors as well as the German contribution of the MIMI/LEMMS Instrument were in part financed by the  German BMWi  through the  German Space Agency DLR  under contracts  50 OH 0103 ,  50 OH 0801 ,  50 OH 0802 ,  50 OH 1101 ,  50 OH 1502  and by the  Max Planck Society.
All specimens were weighed before and after respirometry on a microbalance. Airflow was regulated at LiCor software (Li-Cor, Lincoln, NE, United States). Activity was monitored using an infra-red activity detector (AD-2, Stable Systems International, Las Vegas, NV, United States). Animals were given a 10-min equilibration period at 25 C, after which the temperature in the chamber was ramped up at a constant heating rate of 0. 25 C min À1 to 57 C using a programmable circulating and heating water bath (CC410wl, Huber, Berching, Germany). Baseline recordings were taken before and after each run to correct for potential drift, which was typically negligible. Expedata v. 1. 9. 10 (Sable Systems International) was used to transform recorded CO 2 values in ppm to VCO 2 in ml CO 2 /h, to correct for potential baseline drift for all respirometry files, and to visualise activity data.
Statistical analyses Data were analysed using R 4. 0. 4 (R Core Team, 2021) with appropriate packages including MuMIn  (Barton, 2022)  and DoBy  (Højsgaard & Halekoh, 2021). For the HKD experiment, data were expressed as included all species in between these two extremes. We ran a oneway ANOVA followed by a Tukey post hoc test to explore every possible pairwise comparison within the three groups. The same combination of tests was used to compare flying species (n = 4, Table  1 ) and non-flying populations (n = 15, Table  1 ), respectively.
ACKNOWLEDGEMENTS The authors thank the  Western Cape Nature Conservation Board  (Permit No. CN44-30-4229 ) and the  Cape Research Centre ,  South African National Parks  (Permit No. CRC/2019-2020/012-2012/ V1 ) for permission to collect specimens in areas under their control. The authors acknowledge  Paula Strauss ,  Gary Beukman , and  Michael Lutzeyer  for access to the  Grootbos Private Nature Reserve  and their friendly assistance during sampling. The authors thank  Laurène Dubreuil  for introducing  Julien Haran  and  Marion Javal  to the Montagu region. Henrika Bosua  ( Stellenbosch University ) is warmly thanked for her help with the respirometry measurements. This study was supported by recurring funding from  Montpellier University (Marion Javal)  and  Cirad (Julien Haran ).
, 2018; Pitts & Griffin, 2012). The Capability Approach, from economics, has also been applied archaeologically, but deemphasizes income (or wealth) and focuses on well-being, namely that income differences among individuals do not inherently translate into differential abilities of doing and being, as applied by  Arponen et al. (2016). The Capability Approach may be particularly useful in identifying and evaluating heterarchies, where unequal status does not imply power imbalance. Finally, public health tools like the World Health Organization's "Dirty War Index", used to evaluate undesired and prohibited outcomes in a given conflict (e. g. , child mortality), can be applied to bioarchaeological datasets  (Zuckerman & Banks, 2017). Not only do these efforts improve our ability to research social inequality in a bioarchaeological context, but they also connect bioarchaeological studies to contemporary social science research and help forge additional interdisciplinary links and further improve public outreach.
, 2020) , and improvements to aDNA methods and accessibility to researchers will ensure this successful recovery rate increases. These ever-growing time-series datasets present an invaluable opportunity for bioarchaeologists to engage fully with the origins and impact of zoonotic infections. For example, the earliest Yersinia pestis genome, the causative agent of plague, was recently recovered from a 5000 year-old skeleton from Rin ¸n¸ukalns Latvia  (Susat et al. , 2021). This genome represents a lineage that diverged recently after Y. pestis shared a last common ancestor with Yersinia pseudotuberculosis. Y. pestis was only identified in a single individual from a burial of four that, combined with the observation that most Bronze Age Y. pestis genomes have been recovered from single burials, led the authors to suggest that these early Neolithic and Bronze Age strains caused terminal or low transmissibility zoonotic infections in humans  (Susat et al.
| Conclusions Well-dated, contextualized bioarchaeological data can contribute substantially to reconstructions of pathogen phylogeographies, including clarifying the role of human economic, demographic, and social behavior in the spread and maintenance of diseases at regional and global scales  (Bos et al. , 2014; Bravo Lopez et al. , 2020; Mühlemann et al. , 2020; Spyrou et al. , 2019). Contextually rich bioarchaeological datasets with temporal control also have much to offer evolutionary biology and population genetics. Time series datasets spanning hundreds to thousands of years present an unparalleled way for tracking evolution over the long-term. Securely dated pathogen genomes can be used to calibrate molecular clocks, and datasets of ancient human genomes enable evolution to be tracked directly though time via allele frequency changes.
| Small datasets Bioarchaeologists are often faced with relatively small sample sizes that preclude (or, in some cases, are mistakenly perceived to preclude) the use of analytical approaches more commonly used in other fields, such as those in epidemiology and demography. There are examples in which sample sizes truly are too small to be compatible with rigorous analytical approaches, and it is not the goal here to undermine the potential value of these data. Further, there are cases in which it is One strategy that is commonly used in bioarchaeology for dealing with the limitations of small samples sizes is to pool data within a site (by age-at-death, sex, or other variables) or pool samples across sites spatially or temporally to produce a working dataset sufficiently large to generate acceptable levels of statistical power, fulfill the criteria of specific statistical approaches, and yield interpretable results.
| Missing data A further issue that is not limited to small sample sizes, but that might disproportionately skew findings, is missing data. Following  Rubin (1976, p. 19)  and  Little and Rubin's (2019)  formalization,  Stojanowski and Johnson (2015)  provide an overview of the ways in which data (in their case, of dental traits) can be missing and offer a case study of the implications of "missingness. " Data can be categorized as missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR), and all mechanisms of missingness can potentially affect a single dataset. Data that are MCAR are missing for reasons that are unrelated to the value of the variable of interest or to any other variables included in analysis, and there is no pattern to the values of the missing data; data that are MCAR produce no systematic differences between individuals in the samples with and without missing data  (Mack et al.
, 2018) , and observed data can be viewed as a random subsample of the hypothetically complete data  (Baraldi & Enders, 2010). Data that are MAR are missing for reasons that are related to some other variable in the dataset but not related to the value of the variable of interest itself; as with data that are MCAR, there is no pattern to the values of the missing data. Data are MNAR if they are missing for reasons that are related to the variable of interest's values, and the values of missing data are not random; for example, incisor shoveling data are MNAR if the probability that they are missing is related to the severity of shoveling  (Stojanowski & Johnson, 2015). Unfortunately, it is possible to test empirically only for the MCAR mechanism, as the MAR and MNAR mechanisms depend on the unobserved data  (Baraldi & Enders, 2010).
| R programming language Broader adoption of rigorous, informative, and suitable quantitative methods may also be enhanced by the increased use of R by bioarchaeologists. R is a programming language and environment that is valued for its flexibility, transparency (e. g. , details of R algorithms are publicly available), availability of a large number of methods, and the level of control it gives to the user  (Marwick, 2018; Mascar o et al. , 2014). In contrast to commercial software such as IBM-SPSS and SAS, R users are not limited to built-in functions or reliant on sluggish rates of additions of new methods (which can be particularly problematic for relatively narrow markets), but can use code produced by others or create their own  (Carlson, 2017; Marwick, 2018). R code can be shared easily, including via searchable online repositories such as Comprehensive R Archive Network (CRAN), Bioconductor, and GitHub, and the code can be published with (or as supplementary material to) articles that report findings produced using the code.
There is a large community of R users, who are continuously producing new packages for R  (Li, 2018) , and the online R community is notably friendly to novices  (Marwick, 2018). According to  Lynch and Stephan (2018, p. 236) , "more than half of currently available computer analytic tools in forensic anthropology use R," and, according to  Marwick (2018, p. 1) , R is the "most widely used scientific programming language in archaeology. " Many R users inside and outside the field generously share their code online, providing a potential source of guidance for bioarchaeologists adopting R for their own purposes. R users can also share the scripts they are produced during data analysis, which report each step in the analysis, further increasing transparency and furthering the reproducibility of findings in the field.
After admittance, requirements, or informal expectations regarding fieldwork, especially field excavation, are often designed around the presumption of the participation of ablebodied individuals and are therefore not inclusive. Further, consideration should be given to harassment and assault based on perceived gender or sexual orientation and against archaeologists of color and those with disabilities in field settings and how these limit fieldwork opportunities  (Clancy et al. , 2014; Nelson et al. , 2017; Voss, 2021). Considering the numbers of previously excavated skeletal collections in museums, universities, and other institutions, local, nonresidential options for experiential, on-the-ground training are increasingly available to many graduate students. Thus, while in some cases important contextual information may be missing from existing datasets and that a lack of standardization of data collection methods may limit metanalyses, expectations regarding students' training in field and lab methods should ultimately be tailored to their program of study and appropriate to their career goals.
Our field's most publicly recognized achievement, the impactful conclusions developed from the initiative to examine health at the origins of agriculture  (Cohen, 1989; Cohen & Armelagos, 1984; Cohen & Crane-Kramer, 2007) , involved comparisons across regionally contextualized datasets. The visibility and significance of these findings urges us to consider whether this is the best way to proceed further with other major questions, such as the impact of climate change. Bioarchaeologists recognize that "natural disasters" are frequently episodes of crisis and chaos ultimately of anthropogenic origin. Such calamities are also recurring tests of human resilience, requiring socio-cultural change and biological adaptation. When framed in this manner, we remove the tendency to think of change as bad and to inadvertently reify pre-existing structures as somehow "good" or "natural" for that time and place.
of standardized data collection can facilitate DOHaD research. Specifically, variables that might best capture critical periods of development and reflect stressors of both bioarchaeological interest and that have parallels with those studied in human biology to bolster arguments about mechanisms within bioarchaeological contexts and to engage in cross-disciplinary research should be investigated. Standardization would ideally facilitate metanalyses, which might overcome some of the limitations of small and incomplete datasets. As mentioned in the context of the osteological paradox, participants suggested targeted training and published guidelines/models for those interested in addressing DOHaD would be beneficial, such as dissemination of what types of data are needed and what types of analyses are ideally suited to address DOHaD.
As previously noted, approximately one quarter of the graduate programs evaluated explicitly require at least one quantitative methods course. That said, fluency in quantitative methods comes from exposure to different statistical methods and practice using them. Bioarchaeology graduate courses should routinely discuss quantitative methods and encourage students to use datasets for course-based research projects when relevant. Additionally, ecology, biology, epidemiology, and biostatistics departments often offer courses in bioarchaeologically-relevant statistical methods like Bayesian statistics (as opposed to psychology, computer science, or statistics departments, which tend to rely on frequentist statistics). While Bayesian statistics are beneficial in contexts with large amounts of missing data and small datasets, taking machine-learning courses will also strengthen foundational skills and broaden the quantitative methods toolkit.
ACKNOWLEDGMENTS We thank the following organizations for providing generous funding to support the Workshop:  National Science Foundation (NSF  grant  BCS-1916946 , " Workshop in 21st Century Bioarchaeology "), the  School of Human Evolution and Social Change (SHESC, ASU) , and the  Center for Bioarchaeological Research (CBR, ASU). We thank for the invitation to contribute this manuscript. We thank  Lyle Konigsberg , Editor-in-Chief, and anonymous reviewers who provided thoughtful, constructive feedback on this article. Dr. Kent Johnson  also offered useful suggestions to the sections on kinship and identity. We also thank  SHESC  and  CBR  for providing the facilities for hosting the Workshop. We are also grateful to  Dr. Connie Mulligan  ( University of Florida ) for sharing her experience and expertise in workshop organization.
, 2012; van Zundert et al. , 2016; Mirabello and Wallner, 2017). The outcome of such modelling is often considered to represent metastable conformational substates that exhibit robust stability when subjected to hundreds of nanoseconds of molecular dynamics simulations. However, these criteria are not always sufficient to approach functional questions. Here, we focus on two representative examples of assemblies where structural information alone is not sufficient to obtain mechanistic insights. The first is the ryanodine receptor (RyR). To date, RyRs are the largest known ion channels, with a molecular mass of over 2. 3 MDa. Each subunit of the homotetrameric channel contains more than 5,000 residues. The number of RyR cryo-EM datasets is steadily increasing  (Baldwin et al. , 2018; Spurgeon et al.
Multiple Alignments Three mammalian isoforms of RyR exist in cells: RyR1 predominates in skeletal muscle; RyR2 is the most abundantly expressed isoform in cardiac muscle; and RyR3 is expressed differentially in brain  (Takeshima et al. , 1989; Nakai et al. , 1990; Fill and Copello, 2002; Lanner et al. , 2010) , endocrine cells and other tissues. At least two of these isoforms, RyR1 and RyR2, share the same structural organization with an overall mushroom-like shape as observed by electron microscopy  (Van Petegem, 2015; Zalk et al. , 2015; Georges et al. , 2016; Peng et al. , 2016; Dhindwal et al. , 2017). These isoforms have a high percentage (ca. 70%) of sequence identity. We selected reviewed RyR sequences from the UniProtKB database  (Boutet et al. , 2016). The entries used for this study were: P11716, P21817, E9PZQ0, P30957, Q92736, E9Q401, Q9TS33, Q15413, and A2AGL3.
Secondary Structure and Transmembrane Domain Predictions RyR1 homologues have a high percentage of sequence identity (on the order of 70%), and their secondary structures are well conserved. We were particularly interested in the interspersed disordered region between residues 4254 and 4539, which separates the cytoplasmic from the transmembrane domains. To obtain information on this region, which we call "Big Loop" (BL), we first examined its sequence conservation and residue composition. Since we focus on the RyR1 isoform, we used five homologous protein sequences and performed a multiple sequence alignment using the Clustal Omega software. Secondary structures were predicted using several web servers, such as PSIPRED and PSSPRED  (Jones, 1999; Yan et al. , 2013). Transmembrane regions were predicted using the TOPCONS and CCTOP servers  (Dobson et al.
, 2015; Tsirigos et al. , 2015). The ion binding sites were predicted via the IonCom program  (Hu et al. , 2016). For the BL, we used PEP-Fold3  (Lamiable et al. , 2016) , particularly for residues 4320 to 4345. Finally, we assessed the presence of an auxiliary transmembrane helix (or helices) and other secondary structure elements by visual inspection of the cryo-EM density maps.
Before refining the entire model, several loops or side chains were optimized by a combination of side-chain reorientation and loop placement using in-house routines for flexible and interactive molecular dynamics implemented in the BioSpring tool  (Molza et al. , 2014)  followed by energy-minimization steps using the YASARA Structure software (version 17. 12. 24) (Krieger et al. , 2002). This procedure was particularly useful for refining interfacial loops between adjacent subunits (residues  4290-4299, 3086-3120, 3067-3075, and 4346-4426).
Model Refinement For refining the coordinates of the modelled residues and verifying the side-chain placements, Molecular Dynamics Flexible Fitting (MDFF) simulations were performed using the NAMD software  (Phillips et al. , 2005; Trabuco et al. , 2008). Our initial models were refined using the real-space refinement procedure from the PHENIX package  (Afonine et al. , 2013; Urzhumtsev et al. , 2016)  in order to avoid the propagation of geometry errors from the input structure (PDB and de novo modelling output files) during the MDFF procedure. The secondary structures of residues folded into α-helices or β-sheets were preserved during simulations by imposing harmonic restraints with force constants of 20 kcal mol -1 rad -2 for dihedral angles and hydrogen bonds, involving backbone atoms of the same subset of residues using the ssrestraints command.