dataesr/scientific-paragraphs-categorization huggingface dataset:
For each subject of our cohort, a manual segmentation has been accurately made using the 3D Slicer software 45 by an experienced user, trained by veterinary specialists. In order to assess the quality of manual segmentations, two veterinary experts were asked to independently perform a manual segmentation of two different canine MR images. The veterinary segmentations were compared with those performed by the experienced user, giving a Dice score of 0. 98 and a Jaccard index of 0. 97. We thus consider that the experienced user's segmentations are highly similar to those performed by veterinary experts. Brain extraction. We tested the VIBE algorithm taking into account multiple variables that can be encountered in veterinary MRI: we applied it to different animals (dogs and cats), different canine cranial conformations (brachycephalic, mesocephalic, dolichocephalic), different MRI sequences (T1 3D BRAVO, T2 FRFSE, T2 FLAIR) and different acquisition planes (sagittal, dorsal, transverse).
Molecular biology techniques We obtained  new samples for this study, including nine muscle samples from V. variegata in the CryoArks Biobank at National Museums Scotland (derived from animals in UK zoos during -) and  hair (with follicle), whole-blood and Whatman FTA card (Merk, Darmstadt, Germany) blood samples from captive ruffed lemurs collected by zoo and/or veterinary professionals in Madagascar and Europe, including samples from V. rubra, V. v. subcincta and V. variegata of undetermined subspecies (but phenotypically not of V. v. subcincta; Supplementary Table   ). In addition, we retrieved  mitochondrial DNA (mtDNA) D-loop sequences (accession numbers KJ-KJ, AF-AF, AF-AF, AF-AF and AY) from GenBank (we later removed AF, AF, AF and AF from the analysis because of their short sequence length).
Results We obtained  new D-loop sequences belonging to the genus Varecia (GenBank accession numbers MZ-MZ; Supplementary Table   ). These included  from captive lemurs in Madagascar, of which three were V. v. subcincta and  were V. variegata (unidentified subspecies), four from the EEP V. rubra (from ZooParc de Beauval),  from the EEP V. variegata, of which three were V. v. subcincta (from Port Lympne Reserve) and  were V. variegata of undetermined subspecies but not V. v. subcincta phenotypically (six from Howletts Wild Animal Park, two from Marwell Zoo and nine from specimens at National Museums Scotland), and four from captive V. variegata of undetermined subspecies held in Europe but not included within the EEP (from FBC).
Genetic diversity The DNA sequence alignment for the whole dataset ( D-loop sequences) was  base pairs long and contained  sites (excluding gaps and missing data) from which  were invariable,  were informative and three were singletons. We found the highest Hd and π values in the sample from captive V. variegata in Madagascar, and the lowest values in the sample from wild populations of V. v. editorum south of the Mangoro River (Table   ). Genetic diversity in the sample from the EEP V. variegata was much lower than in the SSP sample, and the EEP sample had the second lowest diversity value across all groups (Table   ). Consistent with the low genetic diversity values, we found small pairwise differences between sequences in the samples from wild populations of V. v. editorum south of the Mangoro River and from the EEP V.
Discussion We assessed the mitochondrial genetic diversity of captive ruffed lemurs in Madagascar, Europe and North America and compared this to published data from wild lemurs to ascertain the geographical provenance of the founders of the global captive population. The results could inform decision-making regarding the potential conservation value and roles of the captive ruffed lemur populations and the potential integration of ex situ and in situ conservation practices for ruffed lemurs. We report  mtDNA haplotypes that have not yet been recorded from wild or captive Varecia populations. We found one of the novel haplotypes in the four samples from the European captive V. rubra population but there was only one published V. rubra haplotype from wild populations for comparison, with which the captive haplotype clustered.
Acknowledgements We thank  Lemurs Park ,  Lemurialand ,  Vakona Lodge ,  Parc Ivoloina ,  Tsimbazaza Zoo, Howletts Wild Animal Park, Port Lympne Reserve ,  ZooParc de Beauval, Marwell Zoo, National Museums of Scotland  and the  Fenn Bell Conservation Project  for the provision of samples;  Jamie Robertson ,  Matt Ford ,  Ellen Holding ,  Antoine Leclerc ,  Baptiste Mulot ,  Justine Shotton ,  Danielle Free  and  Natalie Terry  for facilitating sample collection;  Christiane Schwarz  for laboratory work;  Lisbeth Høgh ,  Gina Ferrie  and  Christie Eddie  for providing unpublished data on the European and international studbooks for Varecia variegata; and two anonymous reviewers and the editor for their suggestions and comments. Tissue samples from  National Museums Scotland  were made available through CryoArks (supported by  BBSRC  grant  BB/R015260/1 ).
Funding was provided by  Parc Zoologique de Paris ,  Muséum National d'Histoire Naturelle  and The  Aspinall Foundation , with in-kind contributions from  Canterbury Christ Church University  and the  German Primate Center. Author contributions Study design: RV, TK; collection of samples: JH, ACK, JC, DR, ER, JDH; laboratory work: RV, JDH, CR; data analysis: RV, TK; writing and editing: all authors.
Neuromechanical studies investigate how the nervous system interacts with the musculoskeletal (MSK) system to generate volitional movements. Such studies have been supported by simulation models that provide insights into variables that cannot be measured experimentally and allow a large number of conditions to be tested before the experimental analysis. However, current simulation models of electromyography (EMG), a core physiological signal in neuromechanical analyses, are mainly limited to static contractions and cannot fully represent the dynamic modulation of EMG signals during volitional movements. Here, we overcome these limitations by presenting NeuroMotion, an open-source simulator that provides a full-spectrum synthesis of EMG signals during voluntary movements. NeuroMotion is comprised of three modules.
The first module is an upper-limb MSK model with OpenSim API to estimate the muscle fibre lengths and muscle activations during movements. The second module is BioMime, a deep neural network-based EMG generator that receives nonstationary physiological parameter inputs, such as muscle fibre lengths, and efficiently outputs motor unit action potentials (MUAPs). The third module is a motor unit pool model that transforms the muscle activations into discharge timings of motor units. The discharge timings are convolved with the output of BioMime to simulate EMG signals during the movement. Here we also provide representative applications of NeuroMotion. We first show how simulated MUAP waveforms change during different levels of physiological parameter variations and different movements. We then show that the synthetic EMG signals during two-degree-of-freedom hand and wrist movements can be used to augment experimental data for regression.
Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. NeuroMotion is the first full-spectrum EMG generative model to simulate human forearm electrophysiology during voluntary hand, wrist, and forearm movements. All intermediate variables are available, which allows the user to study cause-effect relationships in the complex neuromechanical system, fast iterate algorithms before collecting experimental data, and validate algorithms that estimate non-measurable parameters in experiments. We expect this full-spectrum model will complement experimental approaches and facilitate neuromechanical research.
MSK Model with OpenSim OpenSim is an open-source software platform that is used for modelling and analysing neuromusculoskeletal systems  [12, 13]. With this platform, researchers can build MSK models, simulate dynamic movements, and perform motion analysis for biomechanical studies that advance movement science. Here, we use OpenSim as a bridge between movements and physiological system states for three purposes: (1) to define and visualise movements of MSK models, (2) to track the changes in muscle fibre lengths during the movement, and (3) to predict the muscle activations across muscles. Since we mainly focus on simulating EMG signals from the human forearm during hand, wrist, and forearm movements, we chose the ARMs Wrist and Hand Model  [17]  (ARMs in abbreviation) from the large database of opens-source OpenSim models as the October 6, 2023 3/20 The MSK model in NeuroMotion can be customised to the subject's anthropometry in three ways.
Assuming tendons to be rigid at constant slack length, the muscle fibre lengths are deduced and imported into BioMime to generate the dynamic MUAPs. Furthermore, the individual muscle activations responsible for the movement are estimated with the built-in Static Optimization tool in OpenSim. Static Optimisation solves the inverse dynamics by minimising the cost function of muscle activations under the constraints of muscle activation-to-force conditions. The exported muscle activations are further normalised and imported into the Motor Unit Pool model to act as the neural input to the October 6, 2023 4/20 motoneuron pools, according to the linearity properties of the motoneuron pools  [21]. OpenSim's built-in tools have been integrated into NeuroMotion by its python API.
True / False Generator Encoder Decoder The user can keep this well-trained model or train their individual BioMime. Detailed instructions on how to train BioMime can be found in https://github. com/shihan-ma/BioMime. The newest version of BioMime supports changes in seven physiological parameters, including the conductivity of the fat layer and the fibre number, depth and medial-lateral position, innervation zone, conduction velocity, and fibre length of each motor unit (Details in the supplementary of  [16] ). Some of these parameters can be measured experimentally. For example, the location and the fibre length of a motor unit could be estimated by using ultrasound  [22, 23]. The location of the innervation zone and the muscle fibre conduction velocity can be estimated, for example by using high-density EMG recordings  [24, 25].
Define Movement Movements are simulated by using the ARMs model in NeuroMotion. A Python class MSKModel was implemented to handle the related functions. NeuroMotion supports three approaches to defining the movement of the ARMs model. The most basic way is by assigning joint angles to each DoF by using the function 'load mov' (Figure  3a ). The joint angles can be obtained from motion capture data (like in  [34] ) or be measured from sensors (as by data glove 1 and angle sensors). There are 24 DoFs in the ARMs model and each can be changed separately within a predefined range. NeuroMotion automatically checks whether each input joint angle is within the correct range. It is possible that a movement defined by a set of joint angles is not feasible even though each joint angle is reasonably assigned.
For example, a combination of joint angles may result in a sudden and unlikely change in muscle fibre lengths. We encourage the users to visualise the movement in OpenSim GUI and to check if there are such aberrations. The motion file required by OpenSim can be generated from joint angles by using the function 'write mov' in NeuroMotion (Python code example at Line 10 in Code Block 1). The second way to define a movement is by interpolating between predefined poses. NeuroMotion provides eight default poses, including hand open/grasp, wrist flexion/extension, wrist radial/ulnar deviation, and forearm pronation/supination (Figure  3b ). Users can define their customised poses by creating new poses in OpenSim GUI. A movement can be simulated by setting one pose at each stage from the predefined poses and setting the durations of each transition (Python code example at Lines 1-5 in Code Block 1).
If the location of a motor unit consistently changes with the radius of the cross-section, the depth of the motor unit can be estimated as well. Therefore, changes in three physiological parameters can be estimated during a movement. These changes can be obtained by using the function of 'len2params' in NeuroMotion (Python code example at Line 9 in Code Block 1). NeuroMotion provides three ways to set the input to each motoneuron pool. First, the user can set predefined and commonly used activation profiles, which include constant, trapezoidal, triangular, and sinusoidal activations. The amplitude and duration of each type of activation can be easily changed. Second, as described in Section MSK Model with OpenSim, muscle activations during a movement can be estimated by OpenSim with the Static Optimisation Tool.
This allows the investigation of how MUAP changes when the commonly used forearm DoFs are activated, which will further provide insights into designing and improving current adaptive decomposition algorithms. NeuroMotion can also be used to study the "inverse" relations between the outputs and the inputs of the system, for example, to pre-train algorithms that regress joint angles from EMG signals and to augment the experimental dataset. Here we gave an example to show that ridge regressors trained on the synthetic dataset can be directly applied to regress joint angles from experimental signals. Furthermore, we found that the performance of the regressors could be improved when trained on the augmented dataset. These findings are consistent with the perspective of using synthetic datasets to augment the limited actual dataset and facilitate algorithm training  [10, 42].
format of spike trains, the dynamically changing MUAPs, and the interference surface EMG signals. Given the above information, NeuroMotion can be used to analyse the impact of a specific parameter on the EMG signals and to provide synthetic datasets for data augmentation and validations of EMG-related algorithms. Here we displayed the variations of MUAPs during dynamic contractions and compared the similarities between MUAPs within and across muscles. A case study was provided in which the synthetic dataset generated by NeuroMotion was used to augment an experimental dataset for improving the accuracy in estimating joint angles from EMG signals. Synthetic MUAPs during Dynamic ContractionsMUAPs When One, Two, and Three Parameters Change NeuroMotion allows researchers to estimate the changes in muscle fibre length, conduction velocity, and motor unit depth during the movement of the ARMs model (Sections MSK Model with OpenSim and Estimate Parameter Changes).
RFig 8. 8 Fig 8. Representative results of using NeuroMotion's output to train regression algorithms and to augment the experimental dataset. a), The regressors trained on the synthetic dataset could predict wrist and MCP joint angles from the experimental data, with the Pearson correlation coefficient (R) 0. 62 and 0. 72, respectively. b), When trained on the dataset augmented by the synthetic signals, the ridge regressors achieved better performance compared with the regressors trained only on the experimental data.
With NeuroMotion, users can synthesise large EMG datasets under a vast repertoire of hand and wrist movements with all intermediate variables available. One potential application is to fast iterate regression and classification algorithms on a synthetic dataset before collecting experimental data (this data augmentation example will be used later in the Results). Another application is the validation of information extraction algorithms (e. g. , EMG decomposition algorithms) when experimental data lacks the ground truth. Data, codes, and instructions are available at https://github. com/shihan-ma/NeuroMotion. Methods Overview NeuroMotion is a surface EMG generative model that is designed to simulate EMG signals during voluntary human upper limb movements. NeuroMotion takes the kinematics of human hand, wrist, and forearm as inputs and outputs the synthetic surface EMG signals that correspond to the myoelectric activities responsible for the movement.
Dangles Recall that sparsification was discussed before (e. g. in SparseMFEFold) only for the simplest and least accurate variant of the Turner model, namely the one without dangling ends contributions. Before we improve this situation, let's look in more detail at dangling ends and different common ways to handle them. Specifically, we discuss different dangle models "no dangle" (model 0), "exclusive dangle" (model 1), and "always dangle" (model 2) as implemented by RNAfold of the Vienna RNA package (and available via respective command line options -d0, -d1, and -d2). Dangling end contributions occur only at the ends of stems (either in multiloops or externally) due to stacking interaction between the closing base pair of the stem and one or both immediately adjacent unpaired bases. In contrast, dangling end terms are not considered within (interior loops of) stems by the energy model.
RNAFold RNAFold is part of the Vienna RNA package  [9]. As discussed in Section 2. 1, RNAfold is an O(n 3 ) time and O(n 2 ) space algorithm. It takes an RNA sequence as input and provides the MFE structure as output. RNAFold is well-maintained and highly optimized and is used here as a benchmark for a fast implementation of Zuker and Steigler type MFE algorithm.
Results Experiments were performed on an Azure Virtual machine. The virtual machine contained 8 vCPUs with 128 GiB of memory. We measured runtime using user time and memory using maximum resident set size. The solid line shows an asymptotic fit (c1 + c2n x ) for sequence length n, constants c1,c2, and exponent x for the fit. We ignored all values < 1000. (b) Run-time (s) versus length (log-log plot) over all benchmark instances. For each tool in both plots, we report (in parenthesis) the exponent x that we estimated from the benchmark results; it describes the observed complexity as Θ(n x ). 1  We tabulate the results of the comparison between RNAFold and SparseRNAFolD when given only sequences with length > 2500 from our dataset as input. We looked at time (s) and memory (maximum resident set size in KB) for the minimum, median and maximum length sequence within the constrained dataset.
(b) Time vs Length Figure  3  We plot the results of SparseRNAFolD against two state of the art algorithms: RNAFold and LinearFold when given RNA sequence only as input and an "always dangle" model against each other on our dataset and the dinucleotide shuffled version of our dataset. (a) Memory Usage (maximum resident set size in KB) versus length (log-log plot) over all benchmark instances. The solid line shows an asymptotic fit (c1 + c2n x ) for sequence length n, constants c1,c2, and exponent x for the fit. We ignored all values < 1000. (b) Run-time (s) versus length (log-log plot) over all benchmark instances. For each tool in both plots, we report (in parenthesis) the exponent x that we estimated from the benchmark results; it describes the observed complexity as Θ(n x ).
Comparison with Linearfold When comparing SparseRNAFolD with LinearFold, we look at the "always dangle" model as LinearFold does not implement the "exclusive dangle" model. We first compared the two algorithms by their predictive accuracy (F-measure). comparison, we selected all sequences from our dataset whose structure was available on RNAstrand. We further constrained it to sequences which contained hairpins greater than 3 and no pseudoknots. This resulted in 986 sequences. We found that SparseRNAFolD had a marginally better, but not significant, average F-measure of 0. 6394 compared to 0. 6391 of LinearFold. We then assessed their time and space usage. To increase the size of our dataset for this testing, we included a dinucleotide shifted version of our dataset in our test data. We then constrained the size of sequences to those > 400.
The max time and memory used by Linearfold on this dataset was 3. 34 seconds and 118, 848 KB. In contrast, the maximum time and memory spent by SparseMFEFold was 10. 86 seconds and 13, 000 KB. This is illustrated in Figure  3b  and 3a. The results show that SparseRNAFolD uses far less memory on even the largest pseudoknot-free sequences in our dataset. Note that the maximum resident set size is nine-times lower than LinearFold. LinearFold, whose time complexity is O(N b log(b)), did perform faster than SparseRNAFolD as the length of the sequence increased. However, we did find that SparseRNAFolD outperformed LinearFold in practice for sequences of up to about 1000 nucleotides.
Folding with Hard Constraints As partial information on structure has become more available and is extensively used for better prediction of possibly pseudoknotted structures  [13, 10] , we further extend our evaluation of the SparseRNAFolD versions to cases where we are folding with a hard constraint  [18]  in addition to the RNA sequence. We plot the results of the three versions of SparseRNAFolD when given an RNA sequence, an "exclusive dangle" model, and a random pseudoknot-free structure as input against each other based on our dataset. (a) Memory usage (maximum resident set size in KB) versus length (log-log plot) over all benchmark instances. The solid line shows an asymptotic fit (c1 + c2n x ) for sequence length n, constants c1,c2, and exponent x for the fit. We ignored all values < 1000.
Strong/well-integrated youth organizations The first party-level condition to be discussed is the presence of strong youth wings in political parties that are well-integrated into the party structure. Youth wings and their influence on party performance, positioning, or behaviour are notoriously understudied. Existing research consists overwhelmingly of single-case studies focusing on the socialization and experiences of individual youth wing members  (Rainsford 2018; Malafaia et al. 2018)  and single-country studies focusing on the functions or organization of youth wings  (Hooghe et al. 2004; Pickard 2019; Roon 2020). Thus, even though basic comparable data on youth wings has been made available through the Political Party Database  (Poguntke et al. 2016) , our literary research has not shown larger-scale comparative studies including them.
Methods and data After having presented the conditions under which we expect to see a high share of young MPs, we will now discuss our methods, describe the operationalization of the conditions as well as the outcome, and present our case selection. Our article applies two methods. In a first step, we analyse our data using a linear regression model to identify first tendencies and in a second step, we use a fuzzy set Qualitative Comparative Analysis (fsQCA) to further narrow our results. As the two methods are based on different logics, one being statistical and one set-theoretic, they have different strengths and can be fruitfully combined  (Vis 2012; Fiss et al. 2013). Regression analysis aims to identify the impact of individual factors on a dependent variable. But as discussed in the previous sections, high shares of young MPs can be observed under different conditions and seem to be context dependent.