dataesr/scientific-paragraphs-categorization huggingface dataset:
Methodological considerations From a spatial point of view, reconstructing EEG brain activity from deep cortical regions (such as the hippocampus) is still a subject of discussion  (Pizzo et al. , 2019). As such our approach to symmetrically integrate FC fMRI and FC EEG was limited to temporal lobe without the hippocampus as defined by Desikan   et al. and (2006) and  Yeo et al. (2011)  but including neighboring cortical regions such as the parahippocampal gyrus and temporal pole As improving SNR of FC EEG from hippocampal regions is still ongoing research, future work might profit form integrating monomodal FC fMRI asymmetrically in this framework. Further, while separating rTLE patients between the two recording sites, we demonstrated that results exist individually for each site. We included only three lTLE patients for the 64Ch-3T dataset, nevertheless, using the proposed bootstrap approach, we did not observe any systematic effects of dataset-site when pooling all the subjects together.
In this study we selected only clear cases of lateral temporal lobe epilepsy sampled out of a database of ~200 EEG-fMRI recordings for the 256Ch3T dataset and ~60 recordings for the 64Ch3T dataset to assure relative homogeneity of the groups. The final group of 34 patients was the most homogenous group with reasonable sample size. Though, when comparing TLE patients to controls, we demonstrated global changes of the FC fMRI and FC EEG relationship, we were unable to extract a common network of reorganization based on pair-wise connections (both for EEG and fMRI). Better understanding of individual functional networks linked to epilepsy beyond the group-averaged approach taken here  (Marek et al. , 2022; Wirsich et al. , 2021 Wirsich et al. , , 2016) )  will need a larger database with data pooling in an even more multicentric approach  (Marek et al.
understand how the crossmodal correlation is influenced by age, sex, epilepsy duration (as epilepsy onset and duration are correlated, we decided to use only duration), etiology and IEDs we generated -label + dataset-site , Model III: patients: r(EEG-fMRI) ~ age + sex + epilepsy-duration + isHS + recorded IEDs per minute + group-label + dataset-site; isHS=binary dummy variable coding for hippocampal sclerosis or not, for etiology distribution other than HS see patient description in Error! Reference source not found. , epilepsy-duration is coded in full years, dataset-site=dummy variable coding for 256Ch-3T or 64Ch-3T dataset). To test for significance of the contribution to the EEG-fMRI connectivity correlation, the T-value of each coefficient/variable in the linear model was compared to a null-model that bootstrapped (1000 iterations with replacement) the averages of the same model having the target variable permuted across the dataset (e.
Acknowledgments We acknowledge support from  Swiss National Science Foundation  (SNSF, under grants  CRSII5_209470 , and  192749  to SV,  163398  and  180365  to MS and  188769  to FG,  P500PM_206720  to LS). JW is supported by a research position of the  Faculty of Medicine, University of Geneva. This work was supported by  Centre d'Imagerie BioMédicale (CIBM)  of the  UNIL ,  UNIGE ,  HUG ,  CHUV ,  EPFL  and the  Leenaards and Jeantet Foundations , the  CNRS , the  ANR CONNECTEPI  (grant number  ANR-07-NEUR-0010 ).
g. group-labels switched between lTLE and controls, 5000 iterations). several bootstrapped distributions (with replacement, Matlab bootstrap function, 1000 iterations) of the average EEG-fMRI correlation. This bootstrapping method will generate an average value for each EEG and fMRI connection that can be used to generate a bootstrapped FC EEG -FC fMRI correlation alongside with subject specific variables such as age (e. g. , one bootstrap iteration might result in an EEG-fMRI correlation of r=0. 3 an average female/male ratio of 0. 4 and an average age of 33. 1 while the next iteration will end up with r=0. 35, ratio=0. 45 and average age of 34. 2). Each iteration of the bootstrapped averages were then used in three linear models to identify the relationship of each bootstrapped, averaged variable to the bootstrapped, averaged EEG-fMRI correlation (Model I: controls-lTLE patients: r(EEG-fMRI) ~ age + sex + group-label + dataset-site, Model II: controls-rTLE patients: r(EEG-fMRI) ~ age + sex + group.
87054 0. 73682 0. 77674 0. 75082 0. 82411 0. 49047 256Ch3T Split half 0. 87817 0. 80862 0. 7131 0. 56457 0. 57558 0. 35359 64Ch3T Cross dataset 0. 77132 0. 73061 0. 66702 0. 64033 0. 68208 0. 4403 lTLE Split half 0. 78767 0. 71863 0. 7437 0. 75605 0. 82403 0. 58585 256Ch3T rTLE Split half 0. 62129 0. 679 0. 7551 0. 75985 0. 80257 0. 56043 256Ch3T Split half 0. 78417 0. 58941 0. 46924 0. 37836 0. 48847 0. 28072 64Ch3T Cross dataset 0. 66777 0. 65887 0. 58191 0. 45151 0. 59768 0. 25093 SI delta theta Alpha beta gamma All 0. 37 0. 40 0. 42 0. 42 0. 31 Controls 0. 33 0. 35 0. 36 0. 40 0. 25 lTLE 0. 29 0. 33 0. 35 0. 32 0. 26 rTLE 0. 42 0. 46 0. 48 0. 45 0. 32 P (5000 delta theta alpha beta gamma iterations) Controls > 0. 0824 0. 2612 0. 4858 0. 0034 0. 6396 lTLE.
Structural modeling (Peptide-protein docking) and cleavage site prediction The 3D conformation of the human PrP peptide 217-YERESQAYYQRGS-230 was initially predicted using PEP-FOLD3 69. To generate a starting template for subsequent flexible docking using the Rosetta FlexPepDock protocol 70 , the peptide was aligned onto the crystal structure of the ADAM10 catalytic domain in complex with the C-terminal region of an adjacent ADAM10 molecule as substrate (PDB: 6BE6). Structure visualization and analysis were carried out using PyMOL (Schrödinger LLC). IceLogo plots for cleavage preferences of ADAM10 and ADAM17 were published earlier as indicated.
Acknowledgements We thank  Kristin Hartmann ,  Edda Thies , and  Celina Soltwedel  as well as the  Mouse Pathology Core  and the  Microscopy Imaging Facility 'UMIF'  (all at  UKE Hamburg, Germany ), and  Bradley Groveman  ( NIH Hamilton, USA ) for excellent technical assistance. N2a cells depleted for PrP (PrP-KO) were kindly provided by  Dr. Michael Willem  ( LMU, Munich, Germany ) and LN235 cells by  Prof. Dr. Monika E. Hegi  ( University of Lausanne, Centre hospitalier universitaire vaudois, Epalinges/Lausanne, Switzerland ). Some human brain tissue samples were kindly provided to BTCS (upon completion of diagnostic procedures and full anonymization) by  Prof. Dr. Mara Popović  ( Institute of Pathology, University of Ljubljana, Slovenia ),  Prof. Dr. Herbert Budka  ( Medical University Vienna, Austria ) and  Prof.
Funding This research was kindly supported by the  CJD Foundation Inc. , USA , the  Alzheimer Forschung Initiative (AFI) e. V. ,  Germany , and the  Werner-Otto-Stiftung, Germany  (all to HCA),  Deutsche Forschungsgemeinschaft (DFG)   CRC877  " Proteolysis as a regulatory event in pathophysiology " (project A12 to MG),  DFG  under  Germany 's  Excellence Strategy (EXC ) within the framework of the  Munich Cluster for Systems Neurology  ( EXC 2145 SyNergy -ID 390857198  to SFL),  DFG  ( TA 167/6-3  and  EXC 2033 -390677874 -RESOLV  to JT),  DFG  ( SCHA 2248/2-1  to FSch),  China Scholarship Council  (grant # 202108080249  to FSo),  Slovene Research Agency  (grant numbers:  L3-3435 ,  L3-6006 ,  L3-0206 ,  P4-0176 ,  P3-0171  and PhD student funding),  CLH  and  STF  are funded by the intramural research program of  NIAID (NIH, USA ), AM-A's contributions were supported by the  European Union 's  Horizon 2020 research and innovation program  (through the  Marie Skłodowska Curie  grant agreement # 101030402 ).
cellosaurus. org); Prof. Dr. M. E. Hegi, Neurosurgery & Neuroscience Research Center, Epalinges; Centre hospitalier Treatment with LN235 Human, astrocytoma universitaire Vaudois, Lausanne antibodies or protease Switzerland inhibitor (accession # CVCL_3957; www. cellosaurus. org) Merck/Sigma-Aldrich, #8061901 Treatment with U373-MG Human, glioblastoma (accession # CVCL_2818; antibodies or protease www. cellosaurus. org) inhibitor.
Acknowledgments We thank the  Winterbourne garden (Birmingham) ,  Jane  and  Lucie Hotier  ( Toulouse ) for providing bees,  Dietmar Schmucker  for a rabbit anti-Dscam antibodies,  Anna Lassota  for help with stainings and imaging,  Reinhard Stöger  for discussions and comments on the manuscript. For this work we acknowledge funding from the  Sukran Sinan Fund , the  Genetics Society , the  Biochemical Society ,  MRC DTP  and  BBSRC. JMD acknowledges funding from the  CNRS  and  Université Paul Sabatier.
Non-native species Scale-invariance second-and leading double-digits of the monetary costs arising from biological invasions, as reported in the InvaCost database, were compared with the null expectations under Benford's Law. There was strong evidence that the reported monetary costs of biological invasions departed considerably from Benford's Law and the departures were of a scale equal to that found in global macroeconomic data. The rounding upwards of costs appears to be widespread. Furthermore, numerical heaping, where values cluster around specific numbers was evident with only 901 unique cost values accounting for half of the 13,553 cost estimates within the InvaCost database. Irrespective of the currency, the value of 1,000,000 was the most common cost estimate. An investigation of anomalous data entries concluded that non-peer reviewed official government reports need to provide greater detail regarding how costs are estimated.
Introduction It has been well established that the frequency distribution of the first or leading digit in a set of numbers, rather than being uniform, generally follows a logarithmically decaying distribution  (Berger and Hill, 2021; Miller, 2015). This first-digit law, often referred to as Benford's Law, has been shown to hold across a diverse range of environmental datasets including the travel distances of tropical cyclones  (Joannes-Boyau et al. , 2015) , daily pollen counts in cities  (Docampo et al. , 2009) , annual streamflow of rivers  (Nigrini and Miller, 2007) , the duration of volcanic eruptions  (Geyer and Marti, 2012) , the emissions of greenhouse gases per country  (Sambridge et al. , 2010) , as well as phytoplankton abundance in lakes  (Ozkundakci and Pingram, 2019). Despite Benford's Law being widely applicable, it is not generally known, and thus it is assumed that individuals manipulating data would be unlikely to replicate a realistic digit distribution  (Moul and Nye, 2015; Nigrini, 2012; Tam Cho and Gaines, 2007).
For this reason Benford's Law has been applied to detect voting fraud  (Breunig and Goerres, 2011) , the manipulation of international trade statistics  (Cerioli et al. , 2019) , and the trustworthiness of COVID-19 case reports  (Silva and Figueiredo, 2021). More recently, Benford's Law has been applied to detect data manipulation and anomalies in environmental datasets including air quality information  (Stoerk, 2016) , the integrity of emission reduction data  (Cole et al. , 2020) , and the reliability of fish-catch reporting  (Noleto et al. , 2022). While the majority of environmental studies have focused on the frequency distribution of the first-digit, Benford's Law involves a more general framework based on the joint distribution of the whole set of significant digits of each number  (Barabesi et al.
, 2021). Integrating the analysis of the first-, second-and the leading double-digit in a set of numbers (e. g. , in the number $6543. 21, they are 6, 5 and 65, respectively) has been shown to be an effective means to reveal potential data irregularities  (Nigrini, 2012). As a result, Benford's Law can identify varying levels of imprecision in cost data (Fig. 1 ). Nevertheless, not all datasets conform to Benford's Law and it is generally applicable only where there is a large sample of individual estimates (N > 1000) that range across more than three orders of magnitude and exhibit positive skew such that the mean is greater than the median  (Melita and Miraglia, 2021; Tam Cho and Gaines, 2007). Furthermore, Benford's Law does not apply to sets of sequential numbers, assigned numbers (such as postcodes, car license plates or telephone numbers) or numerical series that are restricted to certain maximum or minimum bounds  (Clippe and Ausloos, 2012; Pinilla et al.
Thus, would data that have been scaled-up from observations at individual sites to provide countrylevel cost estimates or that are projected from a subset of data be expected to show fewer deviations from null expectations? To find answers to these questions, the distributions of first-, second-and leading doubledigits of costs reported as arising from the impact and management of biological invasions and collated in the extensive global Invacost database  (Diagne et al. , 2020)  were examined with reference to the null expectations under Benford's Law.
Monetary cost estimates of invasive alien species The most recently updated version of the InvaCost database (v 4. 1) includes 13,553 entries that describe the estimated or reported monetary costs from invasive alien species retrieved from both peer-reviewed as well as other literature sources worldwide  (Balzani et al. , 2022; Jiang et al. , 2022; Kourantidou et al. , 2022; Turbelin et al. , 2023; Wang et al. , 2023). All studies to date using the InvaCost 4. 1 database have standardised these original cost records using appropriate currency conversion and inflation correction factors to enable global scale comparisons  (Diagne et al. , 2023; Jiang et al. , 2022; Kourantidou et al. , 2022; Renault et al. , 2022; Wang et al. , 2023). However, standardisation of economic data to a common currency benchmark often involves sufficient compression or transformation of the data to account for currency conversions or inflation that the base assumptions for conformity with Benford's Law will likely not hold  (Nye and Moul, 2007).
For this reason, only the original raw cost estimates in the local currency, even if accumulated over several years, were used in this analysis since it is these values that have been drawn from reports and publications and most likely to exhibit any underlying departure from Benford's Law. In addition to reporting the estimated monetary costs in local currency at the approximate time of expenditure, the InvaCost 4. 1 database includes multiple additional fields that facilitate the refining of analyses and in particular four fields are the subject of the present analysis  (Leroy et al. , 2022) : a) source reliability (high or low); b) cost realisation (observed or potential), c) type of cost (management or damage), and d) spatial scale (continent, country, region, site etc. ). The perceived reliability of a data entry was classed as either high or low with the former class being assigned only if the entry was associated with well documented, reproducible, and traceable methods.
g. , values expressed to the nearest $1000) yet will alter expectations arising from Benford's Law. Under such circumstances, comparison with the rounded Benford distribution for both the first and second leading digit is often more appropriate  (Günnel and Tödter, 2009; Slepkov et al. , 2015). Benford's Law can be extended to two digits with the expected frequency of digit d 1 d 2 occurring as the leading first two-digits is given by  (Miller, 2015) : First two-digit probability P(d 1 d 2 ) = log 10 ( 1 + 1 d 1 d 2 ) for d 1 d 2 ∈ {10, …99} (3) In contrast to the first-and second-digit tests, the double-digit test is primarily used to provide greater granularity in the analysis by pinpointing abnormal duplications of double digits and possible biases in the data. It is based on the observation that pairs of digits in many datasets are not uniformly distributed and follow a predictable pattern.
Statistical analysis of goodness of fit to Benford's Law Both the first and second non-zero digit left of the decimal point were extracted from each raw cost estimate in the local currency presented in the InvaCost 4. 1 database (accessed on 11th November 2022). The observed frequency of first-, second-and leading double-digits in the raw economic cost data were compared with the expected null (H 0 ) frequency under Benford's Law. Statistical comparisons between the observed and expected leading digit distributions have most often used Pearson χ 2 goodness of fit tests, however, the interpretation of χ 2 is markedly influenced by sample size such that for large samples size (N > 500) any deviation from the expected, no matter how small, can lead to Type I errors  (Cleary and Thibodeau, 2005; Nye and Moul, 2007; Tam Cho and Gaines, 2007).
First-digit distribution There was strong evidence that the first-digit distribution of the overall costs in the InvaCost 4. 1 database did not conform to Benford's Law, with an overrepresentation of the digits 1 and 5 and an under representation of the digits 8 and 9 (Fig. 2a , Table  1 ). This trend was also evident among the different subsets of the dataset but there was stronger evidence for a departure from Benford's Law for data that were deemed more reliable (Fig. 3a ), estimated management rather than damage costs (Fig. 3b ), were observed instead of projected (Fig. 3c ), and that were assessed at a site as opposed to a country spatial scale (Fig. 3d ). Firstdigit distributions of estimates from sources deemed of low reliability, that estimated damage, or were assessed at a country scale appeared to.
Evidence for rounding There was no evidence that either the first-or second-digit distribution was shaped by simple rounding to the nearest significant figure and in all cases the fit to the rounded Benford was worse than to the standard Benford distribution (Table  2 ). Evidence of an overrepresentation of ones (1 s) in the first-digit test combined with an overrepresentation of zeroes (0 s) in the second-digit test, with both digit tests revealing an underrepresentation of nines (9 s) was used to test for evidence that values may have been rounded upwards. Significant departures from either the standard or rounded Benford expected distribution were tested using the confidence intervals derived from an independent binomial distribution of each digit (0, 1 or 9). Results were dependent on whether the rounded or standard Benford distribution was chosen but the most parsimonious interpretation was that there was support for rounding up of values for the entire dataset, but the evidence was stronger for data from sources that were classed as of high reliability, quantified management rather than damage costs, and were observed instead of projected at site rather than country spatial scale (Table  2 ).
Trends in overrepresented values While there were 13,553 entries in the InvaCost 4. 1 database, these amounted to only 7,442 distinct values and this points to clustering of costs around certain numbers. A total of 901 different values accounted for 50 % of the 13,553 entries in the InvaCost 4. 1 database (Fig. 6a ). The 20 most frequent values accounted for over 11 % of all records with the number 1,000,000 being the most common, accounting for 1. 57 % of all values (Fig. 6b ). Other multiples of one million were also frequent, including 2,000,000, 3,000,000, and 5,000,000, and there was also an overrepresentation of much smaller numbers, all of which were simple multiples of 25. 6 (e. g. , 512, 256, 128 etc. , Fig. 6b ). These values account for the spikes seen at values 12, and 51 in the double-digit distributions (Fig.
Discussion Estimating the costs of invasive alien species is challenging and a wide variety of methods, each with its own limitations, has been used to derive such estimates  (Emerton and Howard, 2008; Vaissiere et al. , 2022). The researchers developing InvaCost have undertaken a major task to bring together these heterogeneous data sources and have done their best to screen all records in terms of objective measures of reliability  (Diagne et al. , 2020). Nevertheless, despite these quality assurance steps, there was strong evidence that the costs of biological invasions, as reported in the current InvaCost 4. 1 database, do not conform to Benford's Law indicating a problem with reporting and estimating costs. The magnitude of deviations from null expectations was dependent on the particular digit distribution examined    emphasising the need for a broad approach simultaneously examining first-, second-, and leading double-digits.
However, the marked departures from null expectation observed for the second-digit distribution across the entire dataset and all its subsets is of most concern since it is recognised that the second-digit test is the most robust indicator of systematic irregularities in reported values. The scale of these irregularities was greater for sources classed as having high reliability, that quantified management expenditure, were observed rather than projected, and were estimated at a site scale. Apart from the site-scale results, these findings are all contrary to the initial expectations. This suggests that the current screening criteria for data reliability currently employed in InvaCost may require revision and that the reports of cost values by the various authors (government agencies, scientists, or other actors) requires more detailed examination.
1 database stem from hundreds of different published sources and authors drawn across multiple years, regions, and taxa. A strong conspiracy mentality would therefore be required to interpret the departures from Benford's law found in the InvaCost 4. 1 database as a deliberate global collusion to manipulate the estimated monetary costs of biological invasions. Nevertheless, the marked departures, particularly of the second-digit distribution, point to irregularities that are worthy of further investigation. It was evident that in many cases the reported monetary costs of biological invasions are presented as round numbers, defined as an integer which ends with one or more zeroes (0 s). The human tendency to use round numbers is commonly found in financial estimates and forecasts, often termed the round number bias  (Lin and Pursiainen, 2021).
1 database shows considerable support for numerical heaping, where values (often round numbers) cluster around certain values  (Roberts and Brewer, 2001). For example, the value of 1,000,000 appeared repeatedly irrespective of whether the currency was United States, Singapore, New Zealand or Australian dollars, Japanese yen, European euros, or British pounds. Heaping appears to be marked for economic cost values that belong to the set of "Prominent Numbers" which includes the powers of ten (10; 100; 1,000 etc. ), their doubles  (20; 200; 2000 etc. )  and their halves (5; 50; 500 etc. ) that can be formally represented as n10 i where i is any integer and n is ½, 1, or 2  (Converse and Dennis, 2018). Preferential use of prominent numbers would further contribute to the observed marked overrepresentation of 0 as a second digit.
The frequency distributions of double digits highlighted multiple spikes associated with numerical heaping and while evident in all subsets of the InvaCost 4. 1 data, appeared more marked in sources classed as being of high reliability and associated with observed or management costs. Heaping is common in self-reported financial data, and where information retrieval and integration are particularly challenging, which is often the case for calculations of environmental costs  (Gideon et al. , 2017). Heaping indicates imprecision in data and is of concern because it distorts the distribution of the underlying data and may have substantial effects on model fit, leading to biased estimates and inaccurate predictions  (Wang and Heitjan, 2008). Nevertheless, heaping around prominent numbers is a common cross-cultural behaviour related to the way humans express numerical values that are uncertain  (Jansen and Pollmann, 2001)  and while this undoubtedly limits the precision of the cost estimates of biological invasions it does not on its own indicate misconduct.