dataesr/scientific-paragraphs-categorization huggingface dataset:
Automated Protein Extraction AutoZooMS was developed on Hamilton Method Editor for the Hamilton Microlab STAR Liquid Handling System using the multiprogramming software Venus. The method uses 50 µL, 300 µL and 1000 µL CO-RE Hamilton Tips and is split into 5 runnable methods (Fig. 1 ): 1 -HCl addition, 2 -HCl removal, 3 -lter plate wash and elute, 4 -trypsin addition and 5 -matrix addition and spotting. Reagent volumes differ slightly to HT. The machine used 8 individual pipetting channels to add 0. 95 mL 0. 6 M HCl to each 48well plate and 1. 9 mL to each 24-well plate. Then of this acid-soluble protein, 0. 3 mL was removed and ultra ltered; 10 kDa molecular weight cut-off (MWCO) ultra ltration plates were used during method optimisation and 30 kDa MWCO plates thereafter. A 96-channel Multi-Probe-Head (MPH) was used for ABC addition, sample resuspension and transfer to 96-well plates.
Effect of Sample Size on Species Abundance and Richness To the ZooMS sample size has on our understanding of the composition of archaeofaunal assemblages, the species abundance and richness of 96 LT samples, 469 HT samples and 1707 AutoZooMS samples were compared (Figure  3 ; Table  2 ). Results showed that by facilitating the processing of larger sample sizes AutoZooMS provides higher resolving power to identify less abundant species such as mammoth; LT exhibited the lowest species richness and AutoZooMS the highest. Assemblage composition does differ between the three data sets, notable differences include the percentage of pig (Sus), which in HT analyses comprised only 4. 7% of the identi able assemblage whereas in AutoZooMS and LT it was 13. 5% and 17. 2% respectively. Equine was also notable, composing 33.
Automated ZooMS The overriding focus of this study was to develop an automated method of extracting taxonomic data from archaeofaunal assemblages with the aims to increase zooarchaeological data yields and reduce current archaeofaunal waste. Our AutoZooMS approach was shown to be more successful than traditional HT collagen ngerprinting for species identi cation, yielding the potential to increase individual, creating bias in our understanding of these sites. This would otherwise have been mitigated by the more ideal zooarchaeological measure of minimum number of individuals (MNI) relying on using unique skeletal (including sided) elements. As Grotte Mandrin has recorded hominin presence, fragmentary bone could be representative of hominin butchery for subsistence or tool making, i. e. , bones being purposefully broken into smaller pieces.
acquisition of large-scale ZooMS datasets (e. g. , Buckley et al. 2016; Brown et al. 2021 ). Across a ve-day working week, AutoZooMS could process ~ 2000 samples with under 1-hour human intervention each day; all 1612 samples used in this analysis were spotted in < 3 hours with 30 minutes of human intervention. If transferred to a fully automated system (that includes centrifugation and incubation) using parameters laid out in the supplementary material (Supplementary Figs S1 & S2), > 1000 samples could be fully processed in 24 hours. One other factor to consider would relate to costing and laboratory consumable e ciency; as protein preservation varies between sites, when working with a sample set from a new zooarchaeological site it would be bene cial to evaluate protein abundance and establish optimum protease to protein ratios before any ZooMS analysis takes place, in this case 20 µg trypsin/0.
4 mL buffer. Recently, Rüther et al. (2022) presented a shortened LC-MS/MS process, proposing that up to 200 samples could be analysed per day. By comparison, ZooMS can process ~ 2x this, but more taxonomically limited relative to LC-based methods. LC-MS/MS approaches have been used for decades to identify protein (and species) composition within a sample, to greater accuracy than ZooMS, but the taxonomic resolution is more closely linked to amino acid sequences present in the search databases. For example, Mackie et al. (2018)  were able to distinguish between chicken and duck egg white from a 14th century Italian wall painting whereas due to the highly conserved nature of bird collagen and the coincidental matching of peptides with different amino acid sequences, such taxonomic separation is di cult to con dently achieve with ZooMS peptide mass ngerprinting (Buckley 2018 ).
For instance, see  Laborde and Martin (2018)  for the link between economic growth and rural poverty;  Laborde (2011)  and  Laborde and Valin (2012)  for an assessment of biofuel policies; and  Laborde et al. (2020a)  for GHG accounting of farm policies. The MIRAGRODEP model has also contributed to policy and political processes in various countries. The modeling initiative proposed in this chapter brings the modeling innovations of the last decade into an integrated framework and builds on existing partnerships to extend its scope. It builds and extends the existing development for the Ceres2030 project,  3  looking at several aspects of SDG2 (hunger, environmental sustainability, poverty, and smallholders' income). In particular, a strong comparative advantage of the MIRAGRODEP CGE is its integration with household data and the use of detailed household information.
The Python Anesthesia Simulator (PAS) models the effect of drugs on physiological variables during total intravenous anesthesia. It is particularly dedicated to the control community, to be used as a benchmark for the design of multidrug controllers. The available drugs are propofol, remifentanil, and norepinephrine, the outputs are the bispectral index (BIS), mean arterial pressure (MAP), cardiac output (CO), and tolerance of laryngoscopy (TOL). PAS includes different well-known models along with their uncertainties to simulate inter-patient variability. Blood loss can also be simulated to assess the controller's performance in a shock scenario. Finally, PAS includes standard disturbance profiles and metric computation to facilitate the evaluation of the controller's performance. The statement of need of this package is first discussed; then some pieces of information about the programmed models are provided to the reader.
(2021). If this is the first step to a FAIR (Findable, Accessible, Interoperable, Reusable) science, it can be improved. In fact, the measurement noise and disturbance scenarios are not yet specified. Moreover, while the simulator could incorporate them, the level of uncertainty to take into account in the patient model is also not fixed. Finally, the metrics used to compare the performance must be explicit and used by all users to compare the proposed control methods. With the Python Anesthesia Simulator, a full pipeline is proposed to test control methods for drug dosage in anesthesia. By using Python, an open-source language, we hope that everyone will be able to use our simulator. If the control community has historically used Matlab, the use of the control package  (Fuller et al. , 2021)  in PAS facilitates the transition, with Matlab compatibility functions.
Moreover, from an interdisciplinary point of view, Python is already widely used by the data science and machine learning community. On the anesthesia topic, the database VitalDB  (Lee & Jung, 2018)  has a Python API and could be used together with PAS to develop data-based approaches, as already done in Aubouin  Pairault et al. (2023). In addition to all the models available to link drugs to their effects, some useful functionalities are available in PAS: • The uncertainties associated with Pharmacokinetic (PK) and Pharmacodynamic  (PD)  models are available to model inter-patient variability; • Initialization of the patient simulator in the maintenance phase (around an equilibrium point) is possible. • As this simulator also includes hemodynamics, the cardiac output can be used to actualize the PK models as proposed in  Bienert et al.
Over the years, several computational models have been proposed that can be used to describe sound representations at the different stages of this acoustic-to-semantic transformation chain. On the acoustic (input) side, these include biophysically inspired models approximating sound representations at the peripheral, subcortical and early cortical levels  [11] [12] [13]  , and psychophysically informed models of the dimensions of auditory sensation  [14] [15] [16]. On the semantic (output) side, recent models developed in the context of natural language processing (NLP) derive numerical representations of abstract semantic entities and concepts-the output of sound recognition  [17] [18] [19]. Finally, end-to-end deep neural networks (DNNs) have been trained on large datasets of human-labeled sounds to map the acoustic input (waveform, spectrogram) into predefined sets of semantic categories  9, 20.
1a ). Behavioral data consisted of perceived dissimilarities estimated with a hierarchical sorting task  35  , whereby experiment participants (N = 40) merged similar stimuli or groups of stimuli until they were all grouped together (N stimuli (sounds or sound-identification labels = 80, 40 for each of two categories) living object and nonliving object; median sound duration = 5. 1 s) (Fig. 1b ). fMRI dissimilarity matrices were obtained from response patterns in six auditory cortical regions of interest (ROIs). Participants (N = 5) performed a one-back sound-repetition detection task and listened to 288 sounds (72 for each of six categories: human nonspeech vocal sounds, speech sounds, animal cries, musical instruments, scenes from nature and tool sounds, with sound duration = 1 s) (Fig. 1c ).
6  and Supplementary Table  4 ). Unlike in HG, semantic and acoustic models predicted a similar portion of variance. In all STG regions, the AudDims model outperformed all others (P = 0. 0001, for all relevant contrasts in all STG ROIs; Supplementary Fig. 6  and Supplementary Table  4 ), which is in agreement with the notion that AudDims entails a higher level of acoustic representations (pitch, loudness, brightness and so on) compared with spectrotemporal modulations (MTF). GNewsW2V was the best semantic model in pSTG and mSTG, which is consistent with previous analysis of the behavioral data (P ≤ 0. 0034). Overall, these results indicate that intermediate sound representations, such as those emerging in hierarchical DNNs, describe neural sound representations in STG (as measured with fMRI) better than other proposed acoustic (low-level, MTF; high-level, AudDims) or semantic representations (both continuous (word2vec) and discrete categories; see also the detailed analysis of DNNs below).
The original set of stimuli used in the fMRI study included speech as one of the sound categories. Speech sounds differ from the other sounds in terms of low-level acoustic features; furthermore, they evoke significantly higher responses in STG than other sounds  22. To control that our results were determined not solely by differences in responses to speech stimuli compared with the other sounds, we performed identical analyses of model comparison and variance partitioning after removing the speech stimuli and corresponding responses from the data (Supplementary Figs. 7  and 8  and Supplementary Tables  5  and 6 ). We observed a reduction of overall response levels and consistency, especially in STG regions (as indicated by the lower noise level; Supplementary Figs. 7  and 8 ). However, the results of the model comparison and variance partitioning reproduced those obtained with the full set of stimuli, thus confirming the general predominance of DNN models.
10  and Supplementary Table  9 ). Interestingly, for fMRI responses, we observed distinct prediction profiles for HG and STG regions, with incremental contribution of the late layers in STG regions but not in HG (Fig. 5 , Supplementary Fig. 10  and Supplementary Table  10 ). Despite the large differences between behavioral and fMRI data in terms of sound sets, experiment participants and experimental paradigm (dissimilarity judgment versus one-back task), the separate analyses of both datasets gave highly consistent indications of the ability of the different computational models and, in particular, of sound-to-event DNNs, to predict responses to natural sounds. We thus tested the degree of generality and behavioral relevance of the modeled neural representations by carrying out an analysis predicting behavioral perceived sound dissimilarities from the fMRI responses.
Mapping the stimuli for both datasets onto a common space of DNN models made this possible (Fig. 6 , Supplementary Fig. 11  and Supplementary Tables  14 15 ). We found that DNN-weighted fMRI from all ROIs together predicted a substantial amount of the variance of the perceived sound dissimilarity (35% of the noise ceiling; P = 0. 0001; Fig. 6 , left panel) and that HG and pSTG contributed unique aspects to the prediction of behavior (P = 0. 0001). We obtained the same results in a control analysis that excluded the speech fMRI stimuli (Supplementary Fig. 11 ). A further control analysis revealed a comparatively limited ability of DNN-weighted fMRI data to predict perceived word dissimilarity (Fig. 6 , right panel, and Supplementary Fig. 11 , right panel).
Discussion We investigated the representation of natural sounds through model-based analyses of two largely different datasets obtained with different methodology (behavioral measure versus fMRI), stimuli and paradigms (perceived dissimilarity versus one-back repetition detection) and participants. Both datasets gave highly consistent indications of the ability of existing computational models to predict responses CV across CV folds, each with a corresponding box-plot (center, median; lower/upper box limits, first/third quartile; bottom/ top whisker, data within 1. 5 interquartile ranges from first and third quartiles, respectively); gray, permutation distribution of the across CV-folds median R 2 CV ; orange, distribution of noise ceiling across CV folds (dashed line, median noise ceiling R 2 CV ).
Article https://doi. org/10. 1038/s41593-023-01285-9 to natural sounds. Among the models we considered, sound-to-event DNNs provided the best overall predictions in both the behavioral and neural datasets (Figs. 3  and 4 ). Furthermore, projecting fMRI data onto the DNN model space, we could predict a sizeable portion of the behavioral data variance (Fig. 6 ), indicating that the DNNs capture a representational level common to behavioral and neural responses. The DNNs considered here are convolutional hierarchical models trained to categorize sound-producing objects and events. As such, they can be considered candidate computational implementations of the acoustic-to-semantic transformations underlying the recognition of everyday sounds. Although a mechanistic account of this transformation remains difficult, the comparative analysis with acoustic and semantic models for different tasks (behavior) and for different regions (fMRI) provides important insights into the interpretation of the unique contribution of DNNs to the predictions and on the nature of neural sound representations in STG.
The DNNs outperformed the other models for the sound dissimilarity task in the behavioral dataset and in nonprimary STG regions in the fMRI dataset. On the contrary, the semantic models outperformed the DNNs in the word dissimilarity task (behavior) (Fig. 3 ) and a spectrotemporal acoustic model matched the DNNs performance in HG (fMRI) (Fig. 4 ). This dissociation of results suggests that the additional contribution of the DNNs reflects a sound representation level that is neither acoustic (as reflected in HG responses) nor semantic (as reflected in the word task). We refer to this level as 'intermediate. ' The variance-partitioning analysis corroborates this observation statistically, as it shows a significant unique contribution of DNNs in the sound dissimilarity task (behavior) and in STG regions (Figs.
3  and 4 ). Our findings have relevant implications for current models of natural sound representation in nonprimary auditory cortex. A dominant view interprets fMRI response patterns to natural sounds in STG as evidence for a localist code implementing a one-to-one correspondence between highly selective neuronal populations and semantic categories  10, 22. The superiority of DNN models compared with categorical and other (nonauditory) continuous semantic representations suggests instead that auditory semantic information in STG is distributed spatially and is 'componential,' with neuronal populations encoding primitive components (dimensions) of multidimensional representations. Within this framework, sound categories may then be resolved in higher-level cortical areas (for example, ventro-lateral prefrontal cortex) through task/context dependent read-out of STG responses  36  (Fig.
This is consistent with previous observations in the visual cortex  37  , with the notable difference that STG responses are best explained by intermediate DNN layers whereas the responses to natural images in higher-level visual cortex and in perceived image dissimilarity are best explained by late DNN layers  38. This may reflect an important difference between visual and auditory cortex, although further investigations will be required to eliminate effects of architectural differences between sound and image classification networks. Interestingly, the direct prediction of behavioral data from fMRI data revealed a significant unique contribution of HG and early auditory regions, together with STG. Judging the similarity of complex sounds requires actively attending to and comparing sounds' acoustics  5  , which may be reflected in the responses of HG and early areas.
The set of models we considered included continuous models of semantics, obtained by neural network analyses of large natural language corpora. Importantly, these semantic models outperformed all other models in the word dissimilarity task and predicted a unique part of the perceived sound dissimilarity variance. This latter behavioral finding is, in our analyses, without a clear fMRI counterpart. Our fMRI dataset did not include (pre-)frontal cortex, potentially explaining why we did not find higher-order semantic representations in fMRI data. Future extensions should consider a larger brain coverage, including frontal as well as other regions of the semantic network  27. Finally, our selection of DNNs was limited to a convolutional architecture previously employed to predict fMRI responses  9  , and two closely related convolutional DNNs (VGGish and Yamnet) developed by Google  20.
The superior performance of these latter models is most probably due to the much larger and heterogeneous set of sounds and event categories used for training  34. It will be interesting, in future work, to examine in detail how the set of training sounds, their semantic description and organization, and the task modulates the correspondence between DNNs and human behavioral and neural responses. Furthermore, it will be important to compare other neural network algorithms for automated sound recognition (including recurrent neural networks, long short-term memory networks and transformers) that exhibit ever improving performances. By releasing the data and code, this study contributes to an open-ended comparison framework that can set a benchmarking baseline for new comparisons and analyses of natural sound representation in behavior and in the brain.
Cochleagram. We used the NSLtools implementation of the cochleagram representation  13  , modeling the dynamic spectral analysis at the periphery of the auditory system (outer and inner ear, and cochlear filtering  11  ). The cochleagram representation consisted in a time-varying signal (temporal resolution = 8 ms, N time samples = 1,999 and 999 for the behavioral and fMRI experiment stimuli, respectively) output from each of 128 cochlear filters with log 2 spaced frequencies (179-7,246 Hz). The cochleagram model included two components, the time-varying cochleagram (32,000 parameters for the behavioral dataset and 16,000 parameters for the fMRI dataset) and the time-averaged cochleagram (128 parameters for both datasets).
Stabilized auditory image. We considered the AIM-MAT v. 1. 5 implementation of the stabilized auditory image model  12. SAI (one model component) implements a periodicity analysis (roughly akin to an autocorrelation) of the neural activity pattern simulated at the level of the subcortical auditory system  12. More specifically, the SAI is a time-varying representation (temporal resolution = 5 ms, N time samples = 401 and 201 for the behavioral and fMRI experiment stimuli, respectively) of the short-term signal periodicity (lags from 0 to 35 ms at sound sampling rate resolution) in different cochlear channels (linearly spaced on an equivalent rectangular bandwidth (ERB)-rate scale between 100 and 6,000 Hz). The SAI model included two components, the time-varying SAI (33,768,000 parameters for the behavioral dataset and 16,968,000 parameters for the fMRI dataset) and the time-averaged SAI (84,000 parameters for both datasets).
Modulation transfer function. We used the NSLtools implementation of the multiresolution spectrotemporal model of Chi et al. 13. The MTF quantifies the spectrotemporal modulations (scale from 0. 25 to 8 cycles/octave; unsigned rate from 4 to 156 Hz) in each channel of the https://doi. org/10. 1038/s41593-023-01285-9 input cochleagram representation (128 channels on; 179-7,246 Hz) as a function of time (temporal resolution = 8 ms). The MTF representation produced complex numbers in output. The MTF model comprised six components, three for the time-varying MTF (MTF magnitude, MTF phase and MTF magnitude and phase together, used to generate a between-stimulus distance in the complex plane, see below) and three for the time-averaged MTF (magnitude, phase and phase plus magnitude; total parameters for MTF model across these six components = 19,790,848 and 9,934,848 for behavioral and fMRI dataset, respectively; minimum number of parameters = 19,712 for both datasets; maximum number of parameters = 9,856,000 and 4,928,000 for the behavioral and fMRI dataset, respectively).
For each temporal frame, loudness (measured on a sone scale) was then defined as the sum of the instantaneous specific loudness across frequency bands, whereas timbre brightness was defined as the spectral centroid, that is, as the specific loudness weighted average ERB-rate frequency  33. Time-varying pitch (measured on an ERB-rate scale) and periodicity (ratio of periodic to aperiodic power, in dB) were estimated using the Yin model by Cheveigné and Kawahara 14. Time-varying roughness  16  was finally estimated using the model implemented in the MIRtoolbox v. 1. 7. 2. The auditory dimensions model included three components for each of the five auditory dimensions (time-varying dimension and first two moments: mean and s. d. of the time-varying dimension), for a total of 15 components. The auditory dimensions model included 50,005 and 10,005 parameters for the behavioral and fMRI dataset, respectively (minimum = 1 for both datasets; maximum = 1,999 and 999 for the behavioral and fMRI dataset, respectively).
Semantic models. We considered two classes of models quantifying the semantics of natural sounds in terms of attributes of the sound source. 3] [24]  for category sensitivities in the auditory brain). For both datasets, we also considered three NLP models (one model component each) measuring the embedding of semantic data for the sound stimuli (verb plus noun sentences identifying the sound source). For the behavioral datasets, name plus verb sound descriptors were derived from the results of a preliminary verbal identification experiment in Giordano et al. 30  , Experiment 1) during which 20 individuals, who did not take part in Experiment 2, were asked to identify the sound-generating events using one verb and one or two nouns. In particular, for each of the sound stimuli the name plus verb sound descriptors considered for the analyses in this study, and evaluated by participants in the word condition, were the modal verbs and nouns (that is the most frequent verbs and nouns) across the 20 participants to the verbal identification experiment in Giordano et al.