dataesr/scientific-paragraphs-categorization huggingface dataset:
3. 6  (Pertea et al. , 2015). To extract transcript sequences, gffread v0. 11. 4 was applied to the merged GTF files. To count gene abundances, we made use of the script prepDE. py (retrieved from StringTie webpage,  Pertea et al. 2015). Subsequently, the predicted amino acid sequences of the transcripts were retrieved using TransDecoder v5. 5. 0  (Haas et al. , 2013)  and functionally annotated using InterProScan v5. 46-81. 0  (Jones et al. , 2014). Finally, we obtained the KEGG IDs of the amino acid sequences using the online tool of EGGNOG mapper 2. 1. 8  (Cantalapiedra et al. , 2021). The gene count matrix was analyzed using DESeq2  (Love et al. , 2014) , fitting a negative binomial distribution. We used a likelihood ratio test (LRT) to compare a model that included parasite load and colony identity as factors, to a reduced model including only colony identity, and gene expression was considered to differ between at least two of the parasite load categories when the Benjamini-Hochberg adjusted p-value was below 0.
Furthermore, we performed KEGG enrichment analysis by comparing the KEGG IDs of the transcriptome to the ant DEGs within different clusters using the clusterProfiler package on R  (Wu et al. , 2021). Finally, the transcriptome was BLASTed to a protein database for all invertebrates (downloaded on 10/08/2021) using the blastx algorithm of diamond BLAST  (Buchfink et al. , 2014)  with an e-value cut-off of 1e-5. All BLAST hits per gene with the lowest E-value were extracted to identify which proteins the transcripts are potentially encoding. The individual gene functions of these BLAST hits were studied in the literature and using UniProt. We processed the raw RNAseq data from our cestode samples in the same way as for the ants, except that we filtered out contaminating sequences from T. nylanderi, H.
sapiens, E. coli and adapters, and vectors using FastQScreen. Furthermore, we used a transcriptome assembly of A. brevis  (Stoldt et al. , 2021)  to map reads and based on this, estimated gene abundances. We also analysed the cestode gene count matrix using DESeq2, with a likelihood ratio test (LRT) to compare a model that included parasite load (lowly infected and highly infected) and colony identity as factors, to a reduced model including only colony identity. This produced a list of cestode genes with expression differences associated with the parasite load (cestode DEGs). We divided the cestode DEGs into upregulated and downregulated in the high parasite load category. The cestode DEGs in these groups were diamond BLASTed using the same invertebrate database as used for the ant DEGs and functionally annotated using TopGO.
Gene functions were once again studied in the literature using UniProt. Furthermore, similar to the ant DEGs, we performed enrichment analysis on the extracted KEGG IDs from EGGNOG mapper using clusterProfiler. Finally, to investigate whether some of those genes encode for proteins that are secreted into haemolymph of the host ants  (Hartke et al. , 2022) , we BLASTed (BLAST v. 2. 9. 0;  Altschul et al. 1990 ) the cestode DEGs against the RNA sequences of secreated cestode proteins. We also compared gene expression between uninfected ants from infected colonies and uninfected colonies. When we plotted a PCA for the uninfected samples we found one sample in the uninfected colonies to be an outlier (figure  S2 ). However, considering that we found no further indication that this sample was problematic (e.
WGCNA analysis We performed a weighted gene co-expression network analysis (WGCNA;  Langfelder & Horvath, 2008)  to investigate the effect of parasite load on gene networks. For this, we transformed the gene count data using variance stabilizing transformation from DESeq2. Networks were constructed using all the ant samples including the samples from uninfected colonies. WGCNA was performed with a soft threshold power of 5 and a minimum module size of 250. To test whether parasite load affected gene expression of the constructed networks, we correlated the module expression to parasite load using Pearson's correlation and performed GO enrichment analysis on the significantly correlated modules.
Influence of parasite load on cestode gene expression Analysis of the cestode dataset revealed 377 DEGs between cestodes from highly and lowly infected ants (cestode DEGs), of which 357 were upregulated in cestodes co-infecting a single host with many others. Ten highly expressed genes in this group are involved in the serine/glycine interconversion pathway. Serine proteinases are associated with multiple functions in cestodes, including defence against the host immune system  (Izvekova et al. , 2021; Patel, 2017)  and lipid metabolism  (Webb & Mettrick, 1975). In addition, we found overexpression of mitochondrial uncoupling protein 4, which is involved in the response to starvation in C. elegans (Figure  4a ) and regulates the long-chain fatty acid metabolism, primarily in response to nutrient availability.
4d ), which is a serine-dependent protease that ensures the maintenance of the integrity of the mitochondrial genome  (Kaunisto & Suhonen, 2013; Nargund et al. , 2012). In the GO enrichment of upregulated genes in cestodes from lowly infected hosts, we found vesicle-mediated transport as a significantly enriched function, as well as carbohydrate transmembrane transport (both 2 genes) and fructose 6-phosphate metabolic process (Fig. 4e ), both involved in the absorption of nutrients. No significantly enriched KEGG IDs were found in the entire dataset. Finally, we detected one BLAST match with a secreted cestode protein in the DEGs, but again the gene had no annotation.
Influence of crowding on cestode morphology and gene expression We can show that crowding (multiple cestodes in a single host) leads to reduced body size in cysticercoid larvae of our parasitic cestode. This has been previously noted in A. brevis  (Scharf et al. , 2012)  and several other cestode species  (Benesh, 2019; Heins et al. , 2002)  and may indicate reduced nutrient availability for cestodes residing in heavily infected workers. This was also evident in the transcriptional shifts observed with crowding. When looking at the functional annotation of the cestode DEGs, many functions related to transport become apparent, similar to the ant dataset. For parasites living under crowded conditions, these include carbohydrate transmembrane transport (3 genes), pH regulation (3 genes) and sodium ion transport (3 genes).
Acknowledgements We would like to thank  Jenny Fuchs  for creating the illustrations of T. nylanderi and A. brevis, as well as designing Figure S1. Furthermore, thanks go to  Marina Choppin  for her help with the dissection setup and  Marion Kever  for doing some of the RNA isolations needed for this study as well as her instructions on how to do them. Finally, we would like to thank  Falk Butter  and  Peter Baumann  for their input and fruitful discussions regarding the data analysis. This project was funded by the  Deutsche Forschungsgemeinschaft (DFG, German ResearchFoundation ) - GRK2526/1 -Projectnr. 407023052.
The data were calibrated using a CeO 2 reference and integrated using pyFAI software. For the evaluation of plane strains, the integration was performed in 72 azimuthal sections. The volumetric strains were evaluated using the average center of mass of the first strong diffraction maximum q COM /q 0 of the glassy material. The fitting procedure was reproduced for a representative diffraction pattern in Fig. S1. For the plane strains ε 11 , ε 12, and ε 22 , the normalized center of mass of the first diffraction maximum q(φ)/q COM was fitted using the following equation for the azimuthal angle φ similar to the established procedure  28  : φ ¼ ε 11 cosðφÞ 2 þ ε 12 cosðφÞ sinðφÞ þ ε 22 sinðφÞ 2 ð1Þ The high flux of EBS and new fast pixel array detectors with single photon counting ability allowed correlation of the mesoscale information occurring in the plastic deformation of the notched MG sample with the formation of individual shear bands and their interactions.
Molecular dynamics simulations A qualitative and elemental atomistic picture of the confined deformation in a notched MG was provided through classical molecular dynamics simulations carried out with (Large-scale atomic/molecular massively parallel simulator) LAMMPS software  30. Because no appropriate interatomic potentials exist for Zr 61 Ti 2 Cu 25 Al 12 , Cu 64 Zr 36 MG was used as a prototype material since a reliable EAM potential was made available by Mendelev et al. 31. The amorphous Cu 64 Zr 36 alloy structures predicted by the potential were in good agreement with X-ray diffraction data, and the potential could be reliably used to simulate the structure and properties of amorphous Cu-Zr alloys  32, 33. The starting liquid structure was created by randomly distributing 968000 atoms in a box of 111.
Acknowledgements The authors acknowledge financial support from the  Deutsche Forschungsgemeinschaft (DFG)  through Grant No. SO 1518/1-1 , the  European Research Council  under the  ERC Advanced Grant INTELHYB  (grant  ERC-2013-ADG-340025 ),  Austrian Science Fund (FWF) :  Y1236-N37 , the  China Scholarship Council  (CSC,  201806220096 ), the  National Natural Science Foundation of China  (No. 52171159 ) and the  Natural Science Foundation of Shanghai  (No. 22ZR1422500 ). We acknowledge the  European Synchrotron Radiation Facility (ESRF)  for providing synchrotron radiation facilities, and we would like to thank  Dr. Carlotta Giacobbe  for assistance and support in using beamline ID11. Calculations for this research were conducted on the Lichtenberg highperformance computer of the  Technische Universität Darmstadt.
MRI acquisition and head-cast construction Prior to MEG data acquisition, each participant underwent an MRI session using a 3T Siemens Sonata system (Erlangen, Germany). A T1-weighted scan was acquired using a magnetization-prepared rapid gradient-echo (MPRAGE) pulse sequence with 1 mm isotropic voxel size (256 256 256 voxels), a repetition time (TR) of 2100 ms, an echo time (TE) of ⨉ ⨉ 3. 33 ms, inversion time (TI) of 900 ms, and a grappa factor of 3. For the co-registration of the MRI and the MEG data, vitamin E tablets were placed at the nasion and the left and right ear canals. We used the 1 mm T1 MRI volumes of the subjects in order to construct an individualized foam head-cast for each participant in order to reduce between-session co-registration error and within-session head movement  (Bonaiuto et al.
MEG acquisition and preprocessing MEG data were acquired using a 275-channel Canadian Thin Films (CTF) MEG system with superconducting quantum interference device (SQUID)-based axial gradiometers (CTF MEG Neuro Innovations, Inc. Coquitlam, Canada) in a magnetically shielded room. Participants were in the supine position during the recording. A video projector (Propixx VPixx, VPixx Technologies Inc. , Canada) was used to display visual stimuli on a screen with a refresh rate of 120Hz ( 80 cm from the participant), and a joystick (NATA Technologies, Canada) was ∼ used for participant responses. The data collected were digitized continuously at a sampling rate of 1200 Hz. Eye movement data was collected using Eyelink 2000 eye tracker (SR Research, Ontario, Canada) which tracked monocular eye movements at 1000 Hz and was calibrated before the start of recording.
Data preprocessing was performed using the MNE-Python toolbox  (Gramfort et al. , 2014)  unless stated otherwise. MEG data was downsampled to 600 Hz and filtered (low pass 120 Hz zero-phase FIR filter with a Hamming window). Line noise (50 Hz) was removed using an iterative version of the Zapline algorithm (de Cheveigné, 2020) implemented in the MEEGKit package (https://nbara. github. io/python-meegkit/), using a window size of 20 Hz for polynomial fitting and 5 Hz for noise peak removal and interpolation. Ocular movement and cardiac related artifacts were isolated by running Independent Component Analysis (ICA, InfoMax, 25 components were extracted) on a copy of the MEG data (band pass filtered from 1 to 60 Hz), implemented in the scikit-learn library  (Pedregosa et al. , 2011). The eye tracking data was first cropped and resampled to match the MEG signal, and then components containing ocular artifacts were identified by correlating each of the 25 components with the horizontal and vertical eye movement signals.
Blink detection was done by thresholding the vertical eye movement beyond the vertical resolution of the screen. Each component time course was then correlated (Pearson's r) with the horizontal and vertical gaze position signals before and after removing blinks. Each component thus had 4 correlation coefficients, and was classified as an ocular movement artifact if all correlation coefficients were above r = 0. 15, and the average correlation was above r = 0. 25. Cardiac artifacts were identified by applying the ECG R peak detector (https://github. com/berndporr/py-ecg-detectors; Porr & Howell, 2019)  to each component, and choosing the one with the lowest inter-peak temporal variance. The component choice was manually verified prior to removal from the original (prior to band pass filtering) dataset.
R. Jones et al. , 2009). Open source code for the burst detection algorithm is available at https://github. com/danclab/ burst_detection. All measures of burst rate and mean beta amplitude over time were baseline corrected as the percent change from the mean value from 500 to 250 ms before the onset of the visual stimulus. The number of bursts detected per trial was compared between the visual and motor epochs using R (v4. 2. 0, R REF) with a generalized linear model with a Poisson distribution and log link function, with epoch type as a fixed effect and subject-specific offsets as random effects (lme4 v1. 1. 29; REF). The effect of epoch type was then assessed using a type II Wald Χ 2 test (car v3. 1. 0; REF). Burst features in TF space (duration, peak amplitude, peak frequency, and frequency span) were compared between the visual and motor epochs using two-sample Kolmogorov -Smirnov tests  (Pratt & Gibbons, 1981).
Biophysical model We used the open-source Human Neocortical Neurosolver (HNN) software to simulate a biophysical model of a cortical microcircuit driven by layer-specific synaptic inputs (HNNcore v0. 2; https://hnn. brown. edu; Neymotin et al. , 2020). This model has been fully described in prior publications  (Law et al. , 2022; Sherman et al. , 2016; Shin et al. , 2017). Here we used a slightly modified version of it, which was previously used to simulate beta bursts in the motor cortex  (Bonaiuto et al. , 2021 ). HNN's underlying canonical neural circuit model simulates the generation of electrical currents in layered cortical columns that give rise to measurable EEG/MEG signals. These electrical currents (i. e. , current dipoles), are assumed to be generated by post-synaptic, intracellular current flow in the spatially aligned dendrites of a large population of neocortical pyramidal neurons, and are simulated by HNN via the net intracellular electrical current flow in the pyramidal neuron dendrites, multiplied by their length (in nano-Ampere-meters).
Beta bursts are diverse We analyzed data recorded from a cluster of left central MEG sensors while subjects performed a cued visuomotor task involving presentation of a visual stimulus indicating the target location, a variable delay, followed by a right-handed reaching movement made with a joystick to the target. We detected beta bursts in the 13 -30 Hz range using a novel burst detection algorithm (see Methods), and extracted burst waveforms from two trial epochs: one aligned to the onset of the visual stimulus (visual epochs), and one aligned to the offset of the reaching movement (motor epochs). In line with previous work  (Little et al. , 2019) , the overall burst rate decreased following the onset of the visual cue, further decreased during the reaching movement, and increased again following the end of the reach, closely matching changes in mean beta power (Figure  2A ).
Finally, we have shown that the mean burst waveform changes systematically pre-and post-movement, and that this is caused by changes in the rate of bursts from specific motifs. Sensorimotor beta bursts are therefore not homogeneous events, and as a consequence, burst variability may provide the key to understanding how neural signals with TF-based signatures within the same frequency range can underlie the plethora of functional roles ascribed to "beta activity"  (Engel & Fries, 2010; Kilavik et al. , 2013; Little & Brown, 2014; Pfurtscheller et al. , 1997; Reuter et al. , 2022; Salenius & Hari, 2003). The strategy employed by our burst detection method deviates from those commonly used in the field. A global threshold on beta amplitude or power is most commonly used, often based on a centrality measure (mean or median beta amplitude), calculated from the whole dataset  (Bonaiuto et al.
We analyzed human MEG data at the sensor, rather than source level. We therefore inverted the polarity of bursts with positive negative deflections, since they could have originated from unknown source locations and dipole orientations. However source level analyses also suffer from sign ambiguity, requiring laminar LFP data to resolve the true directionality of intracellular currents. Efforts have been made to resolve this inherent source level polarity ambiguity  (Rossi & Van Schependom, 2022; Vidaurre et al. , 2016) , and cortical column estimation in MEG source reconstruction  (Bonaiuto et al. , 2020)  coupled with biophysical modeling can distinguish between competing models  (Bonaiuto et al. , 2021). However, this is also a strength of this study, as we have shown that beta burst waveform motifs can be identified and classified without source reconstruction, which is especially promising for EEG studies.
, 2022). We have here demonstrated that averaged beta power includes many burst motifs that do not change in rate pre-movement. By filtering out these events and focusing on task-modulated burst motifs, the SNR of the features fed into machine learning algorithms for BCI could be greatly increased, improving classification accuracy. Rather than PCA, supervised or semi-supervised dimensionality reduction techniques such as demixed PCA  (Kobak et al. , 2016)  or a common spatial pattern approach in the time domain (CSP;  Congedo et al. , 2016)  could be used to determine burst waveform motifs whose rate modulations maximally distinguish between movement types. Finally, given a training dataset to define burst motifs and their modulations by the task, an online algorithm could be developed using template matching to detect bursts with particular waveforms.
Acknowledgements This research was supported by grants from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( ERC consolidator  grant  864550 ;  ERC starting grant   716862 ), and the  French National Research Agency (ANR)  within the program " Investissements d'Avenir " ( 2019-ANR-LABX-02 ). The funders had no role in the preparation of the manuscript. We thank  F Lamberton  for assistance with MRI sequence development.
River deltas are diverse, economically and ecologically important ecosystems that are increasingly vulnerable to environmental change. A recent study reported that global-scale human impact on delta morphology has led to net land area gain (Nienhuis et al. 1  ). However, we argue that an unconventional definition of deltas, miscellaneous coastal features unduly characterized as deltas and misidentified delta area changes led to spurious statistics in the study by Nienhuis et al. 1  and that a recently published correction 2 does not address our concerns. We call for the rigorous verification and analysis of large environmental datasets.
up areas, rocky coasts (Fig. 1 ), beach-ridge plains, and multiple distributary mouths of individual deltas. Unfortunately, considering such a collection of coastal morphologies as a simple continuum culminating in 10,848 'river deltas' could undermine the gravity of contemporary concerns on delta vulnerability and the hundreds of millions of people deltas host  [8] [9] [10]. A random check reveals that only ~50% of the inventoried features may be defined as deltas (Supplementary Methods, Extended Data Table  1 ). Nienhuis et al. 1  checked the veracity of delta existence based on 212 deltas in Madagascar, which is a biased sample. Instead, such a check should be based on a random selection of their large global dataset.
We tested Q d river-Q p river against land change using their original datasets and methodology and obtained an R 2 of 0. 16 (hence their 16%, p. 516). However, this value is upheld by the Yellow River delta (Fig. 2 ), a single delta out of 10,848 'river mouths', and simply considered in their published correction  2  as an example for which their methodology may produce errors. Our check shows that this delta's land area change of -8. 3 km 2 /yr in Nienhuis et al. 1  is flawed (Fig. 2a, d ). Our buffer yields +2. 15 km 2 /yr, similar to that published for the same period  11  , which identifies this delta among the fastest growing on Earth. Whether Yellow River data are corrected for true land change or removed, the R 2 is 0 with Q d river-Q p river, 0 with Q d river/Q p river and 0. 09 (p-value: 0.
2) for Q d river/Q p river using binned data, which is not statistically significant (Fig. 2a , Extended data Fig. 1a ). This illustrates the need for caution with particularly influential outliers like this one. Our concern is not only that individual delta change rates are inaccurate, but this inaccuracy permeates into the strong but unsupported conclusion regarding a global-scale link to human-induced sediment flux. This single misidentification led to the incorrect attribution of 16% of land change to human modifications. An R 2 = 0 excludes the possibility of a quantifiable link. Errors are recurrent in the automatically-generated buffers of Nienhuis et al. 1  , as shown by our analysis of: (1) a random sample of 108 (1%) river mouths and (2) the 100 largest deltas in their dataset (Extended Data Fig.
2b, c ). The correlation is very low to nil (R 2 of 0 to 0. 13) with our operator-verified buffers. The flawed land change values are not limited to the Yellow River delta. The automatically-generated buffers of Nienhuis et al. 1  are commonly misplaced (hence the plethora of features identified as deltas and deltas with misidentified land change) and/or yield changes neither coastal nor driven by processes claimed by these authors (Extended Data Fig. 2 , Extended Data Table  1 ). The buffer they delimit for the Yangtze, a major world delta, is misplaced by 100 km, yielding land loss instead of strong land gain. Replacing their land change values with those identified from our buffers for the largest 100 deltas in the original (V1) dataset yields bins that are largest for decreased sediment flux (Q d river/Q p river < 1), revealing the impact of misidentified negative land change in large deltas as shown by the magenta bins in Extended Data Fig.
1aii. The land-cover dataset  12  employed by Nienhuis et al. 1  to mask anthropogenic delta transformations is inadequate, leading these authors to grossly overestimate, in populous Asian deltas, land loss (e. g. , Mekong, Yangtze, Red River), or gain corresponding to human artefacts (Pearl, Krishna) (Extended Data Fig. 2d-k ), such as land conversions into fishponds in the Yellow delta (Fig. 2b ), which they recorded as natural land loss. They base area-change check on 40 deltas in Madagascar and conclude on a standard error of 1%. Madagascar deltas are a biased choice and not a random selection representative of a global-scale dataset. Massive deforestation of this island (44% loss 1953-2014  13  ) generates high sediment discharge favourable to delta land gain. There are further inconsistencies in the successive datasets of Nienhuis et al.