dataesr/scientific-paragraphs-categorization huggingface dataset:
These interactions are described by structure factors calculated within the Percus-Yevick approximation for hard-sphere or sticky hard-sphere potential. However, the modeling of multiscale aggregation of particles is not obvious and gamma or lognormal particle-size distributions are not available. Another approach is proposed by  Bressler et al. (2015b)  with the McSAS software and gives a distribution of an arbitrary shape from a single form factor. Exploitation of the SAXS intensity can also be performed in a less classical way thanks to Boolean models, either by direct calculation of IðqÞ from a known covariance or by inverting the relation to extract ðrÞ. The direct approach has been applied on a one-scale Boolean model of spheres  (Gille, 2011) , a union and intersection of Boolean models, a dead leaves model, a clipped Gaussian field model, and a Gaussian field intersection model  (Gommes, 2018).
Implementation in plug im! module Equations (  25 ), (  8 ) and (  1 ) have been implemented in Fortran code and provided through a module called SAXSBME for the plug im! platform  (Moreaud, 2018). The integral in equation (  25 ) is numerically evaluated by the QUADPACK routines of the SLATEC library  (Piessens et al. , 1983). Intersections and unions of Boolean models of ellipsoids on up to three scales are provided. Monomodal, exponential, gamma and lognormal size distribution laws are implemented with independent parameters for each scale. The aspect ratio of ellipsoids is kept constant for each scale. Equation (  1 ) is implemented using the FFT routines of the Intel Math Kernel Library. The sampling of the covariance (h) is performed in direct space with 2 n points equally spaced from 0 up to h max ¼ 2 m R 1 G , where R 1 G is the expectation of the gyration radius of the larger scale.
SAXS data acquisition SAXS was carried out on the SWING beamline of synchrotron SOLEIL (Saclay, France) with a 12 keV incident beam. Boehmite and alumina extrudates were first crushed to obtain fine powders, before being pelleted (thickness 0. 2 mm) for analysis. In order to cover a wider range of q values, three sample-detector distances (1, 3 and 6. 7 m) were employed. The scattering images were recorded using an AVIEX PCCD170170 detector. To increase the statistics of the scattering intensity, ten images were acquired at each detectorsample configuration. Through 1D reduction, raw data were corrected with respect to acquisition time, geometrical effects like the projection of the detector plane on the sphere with radius equal to the sample-detector distance and the incoming photon flux, and then averaged to increase the statistics.
Conclusions Numerical computation of scattered intensities of multiscale Boolean models of spheroids allows one to take into account anisotropic primary grains such as alumina platelets or sulfur slabs. We provide a Fortran implementation via the plug im! platform. The code has been validated by considering analytical formulae and results from the SASFit software in the asymptotic case of diluted models (isolated grains). Implementation is efficient enough to compute scattered intensities on timescales of the order of a second. We have shown how such an approach may help to interpret SAXS data acquired on alumina catalyst supports. It has allowed us to interpret the experimental data through geometrical parameters such as the volume fraction, radius and elongation of particles, aggregates, and agglomerates.
The COVID-19 pandemic has pointed out the need for new technical approaches to increase the preparedness of healthcare systems. One important measure is to develop innovative early warning systems. Along those lines, we first compiled a corpus of relevant COVID-19 related symptoms with the help of a disease ontology, text mining and statistical analysis. Subsequently, we applied statistical and machine learning (ML) techniques to time series data of symptom related Google searches and tweets spanning the time period from March 2020 to June 2022. In conclusion, we found that a long-short-term memory (LSTM) jointly trained on COVID-19 symptoms related Google Trends and Twitter data was able to accurately forecast up-trends in classical surveillance data (confirmed cases and hospitalization rates) 14 days ahead.
Google trends Google allows all users to access and process anonymized data on relative search volume behavior with Google Trends. Using the Google Trends API, we obtained a daily time series of the search frequencies for the German symptom keyword through the Pytrends library (version 4. 9. 0). The TrendReq method was used to pass host language ("de-DE") and timezone offset (60) in the initialization step. We queried each symptom keyword term in a measure entitled "interest_over_time". Parameters such as "start_year" (2020), "start_mon" (1), "stop_year" (2022), "stop_mon" (6), and "geo" ("DE") were given in the function. The relative search result for a given keyword is between 0 and 100. A value of 0 indicates the lowest relative search interest for the given keyword, whereas a value of 100 indicates the date with the maximum search interest.
For consistency, the daily data values are scaled by multiplying the daily value by the number of searches per month divided by 100 (https:// github. com/ Gener alMil ls/ pytre nds/ tree/ master/ pytre nds, accessed on June 15, 2022). The Google Trends dataset includes data from January 5, 2020 to June 28, 2022. We eliminated symptoms which consistently showed no Google queries throughout the entire time span.
Twitter microblogs The study leveraged Twitter's streaming API and Tweepy library (version 4. 8. 0) to collect Twitter data. The streaming API allows for a stream of public tweets from the platform in real time that can be filtered using multiple parameters. Credentials of Academic Developer Portal are applied for getting the full-archive daily tweets counts using "get_all_tweets_count" in the case of a client application with the support of Pagination. Pagination programmatically retrieves the entire result set by setting "flatten" with a larger limit. In our study, each German symptom keyword and language ("DE") were specified at the query part. The Twitter dataset contains the daily search amount for certain German symptoms between January 1, 2020 and June 28, 2022. Similar to the situation with Google Trends, we removed symptoms for which no tweets could be found.
Surveillance data In this study, we obtained daily COVID-19 case reports from Robert Koch-Institute (RKI) website as the groundtruth, including RKI confirmed cases, RKI deaths (https:// github. com/ robert-koch-insti tut, accessed on March 24, 2023), and RKI hospitalization (https:// github. com/ robert-koch-insti tut/ COVID-19-Hospi talis ierun gen_ in_ Deuts chland, accessed on February 22, 2023). The time series of surveillance data started on March 1, 2020, and we sliced it till June 28, 2022 for further analysis. Overall, it provided insight into the changing trends and patterns of the monitored area over a two and a half year period. Data has been normalized based on the number of corresponding cases per 100,000 people.
Surveillance and digital traces data is fitted within a successive 14-days time interval (sliding window). For each sliding window, the statistically significant coefficient and adjusted P values are calculated, e. g. An "up-trend" (1) is given for the sliding window as the coefficient β is larger than 0 and the adjusted P value is less than 0. 05 threshold. (1) A true-positive (TP) event was declared, if an up-trend in the digital data source (an alert) fell within a 30 days window ahead of the onset of up-trends detected in surveillance data (confirmed cases, hospitalization, and deaths). The 30 days were counted from the beginning of each sliding window. (2) A false-negative (FN) event was declared, if an up-trend onset was detected in the surveillance data, but no alert was found in the digital data source within the 30 days time window ahead of this event.
(3) A false-positive (FP) was declared, if an alert in the digital data source was found, but no up-trend was detected within a 30 days time window in the surveillance data afterwards. Given these definitions, sensitivity, precision, and F1 score could be calculated. More specifically, the sensitivity is the ratio between the TP events ("successful alerts") and the sum of TP and FN events (the number of up-trend onsets in the surveillance gold standard), and the precision is the ratio between TP events and the sum of TP and FP events (the number of alerts observed in digital traces). Evaluation metrics for down-trends can be calculated in a likewise manner.
General setup The general trend forecasting procedure performed is presented in Fig. 3. Trend forecasting was carried out using the same symptom corpus as during the trend analysis. We first processed the datasets using the sliding window approach, followed by separating into training and testing subsets. Next, we generated Random Forest and LSTM models and evaluated their performance. In order to tune the hyperparameters, time series cross-validation was utilized within the training set. The optimized forecasting model was evaluated on the independent test set by sensitivity, precision, and F1 score.
Learning from the time series As outlined above, a multivariable longitudinal dataset was generated combining the time-stamped slope coefficients of all log-linear regression models, hence reflecting the short-term trends in the original time series data. This data was used to train machine learning models. The time period of the overall dataset ranged from March 2020 to June 2022. We employed a sliding window approach to collect information on temporal dependence, where the window size was set to 28 days. The sliding window stride was 1 day. Note that we had previously assigned a time-stamp to each log-linear regression slope coefficient based on the trend observed in the subsequent 14 days. To avoid overoptimism of model predictions we thus assigned a class label to each sliding window according to the type of trend (up-, down-, no-trend) observed in the surveillance data 14 days after the end of the sliding window in digital traces.
Time series cross-validation The sktime library (version 0. 14. 1) provides "SingleWindowSplitter" class to split the training set into temporal folds with constant size. The corresponding training subset of each fold consisted only of observations that occurred before the observation that forms the validation subset. During the time series cross-validation, we set the length of the training period to 90 days/fold. Note that over each of those 90 days we subsequently ran a sliding window to generate training samples, as described earlier. Moreover, we specified the test subsets of 30 days and the stride of 70 days to generate temporal dataset frames. We avoided data leakage caused between training and test set by setting a "gap" between them. In this case, the gap was equal to the 14-days sliding window size in log-linear regression model.
Evaluation of trend decomposition We initially evaluated the applicability of the STL method to forecast the extracted trends from the raw data. Specifically, we utilized the "STL forecast" function from Python's statsmodels library (version 0. 13. 5) to extend time series data from an interval [1, t] to a future time point t + k , represented as T ν (t + k) = T ν (1 : t). The "STL forecast" model was applied to the training data, covering the period from February 2020 to May 2022, and we extrapolated the digital trace time series extending from June 01, 2022, to June 28, 2022. Subsequently, we conducted the STL decomposition on the extrapolated data to derive the corresponding trend component. Through this process, we assessed the correlation between the extrapolated trend and the trend component for the same time period, which was extracted from a STL decomposition of the entire dataset, i.
Harmonizing multiple symptoms and digital traces into a combined indicator We next sought to further investigate whether the combination of several symptoms from each digital trace (Google Trends and Twitter) could lead to more accurate detection of up-trends in surveillance data. For this purpose, we focused on the 20 most significant terms according to the hypergeometric test. More specifically, for each symptom ontology, we selected the synonym with the highest search volume in Google Trends and Twitter during the period from February 2020 to February 2022. We then combined these synonyms using the harmonic mean P-value (HMP) method  33. Subsequently, we evaluated the alert detection performance of each digital trace relative to outbreaks in the surveillance data in the same way as described before.
The median time lag between hospitalization vs. combined trace was 6 days. For hospitalization vs. Google Trends alone the median time lag was only 1 day (Fig. 7 ). Noteworthy, these time lags were counted as days from the end of each sliding window. As depicted in Fig. 7 , we conducted a similar analysis for down-trends, emphasizing the alert capabilities of both Google Trends and the combined data trace. For instance, there was a median time lag of 5 days between confirmed cases and Google Trends, while for hospitalization vs. the combined trace, the median time lag was 4 days. Altogether, our analysis shows that trends observed in digital data traces have the potential to serve as an early warning indicator, which could help to save critical time for preparation of governmental counter-measures.
Conclusion To our knowledge this is the first study to analyse and model COVID-19 related digital traces derived from a systematic literature text mining approach in Germany. Our work specifically demonstrates the potential of combining Google Trends and Twitter data to derive an early warning indicator and to accurately forecast trends in standard surveillance data two weeks in advance. Systematic tracking of digital traces could in the future complement other approaches, including more established surveillance data assessment, mobility data mining and text mining of news articles in order to react earlier to future pandemic situations in Germany. Our work in this regard can be seen as a first step towards establishing an accurate and comprehensive early warning system.
Acknowledgements This work has been supported by the  AIOLOS (Artificial Intelligence Tools for Outbreak Detection and Response)  project. The project was supported by the  French State  and the  German Federal Ministry for Economic Affairs and Climate Action  (grant number  01MJ22005A ) and the  French Ministry of Economy and Finance  in the context of the  France 2030 initiative  and the  Franco-German call on Artificial Intelligence technologies for risk prevention, crisis management , and resilience. We thank  Nicolas Lambert  for constructive discussions and  Tristan Fauvel  for comments on the manuscript.
Data availability Symptom Ontology data can be retrieved from EBI Symptom Ontology (https:// www. ebi. ac. uk/ ols/ ontol ogies/ symp), Surveillance data can be retrieved from Robert Koch-Institut (RKI) GitHub repository (https:// github. com/ orgs/ robert-koch-insti tut/ repos itori es), and digital trace data can be retrieved via the corresponding API tools. The data needed to evaluate the results are presented in the paper or supplementary documents. The code of the model is publicly available at https:// github. com/ danqi 123/ pande mic_ alert_ model_ social_ media.
Statistical Analysis Descriptive statistics are summarized as the mean ± standard deviation (mean ± SD) or median (interquartile range, IQR). Independent sample T-test or Mann-Whitney test was used for comparison between two groups, when appropriate. All statistical tests were two-sided, and p values less than 0. 05 indicated statistical significance. The statistical analyses were performed using SPSS software for Windows, V. 23. 0 (IBM Corp. , Armonk, NY, USA). In the current study, bar graphs and scatter diagrams were drawn by GraphPad PRISM (version 8. 00, GraphPad Software, San Diego, CA, USA). Meanwhile, R packages were used to draw a heatmap (R studio, version 1. 4. 1717, Boston, MA, USA).
proteinatlas. org/ENSG00000165644). The crystal structure of the fragment of human COMTD1 lacking its N-terminal transmembrane domain (Protein Database accession number: 2AVD) suggests that it forms a dimer and binds the co-factor S-adenosylmethionine (SAM). COMTD1 is predicted to contain an O-methyltransferase domain and shows strong sequence similarity with the well-characterized catechol-O-methyltransferase (COMT). COMT is an O-methyltransferase that catalyzes the transfer of a methyl group from SAM  [26]  to a wide number of catechol-substrates including neurotransmitters  [27] , catechol estrogens  [28] , ascorbic acid  [29] , catecholic xenobiotics  [30] , and dihydroxyindole intermediates of melanin  [31]. Comt-deficient mice show dramatic shifts in catecholamine metabolites  [32] [33] [34].
Like CLC7, SLC45A2 regulates melanosome pH  [79] , but how these SLC45A2 mutations in chicken cause this specific effect on pheomelanin production is unclear. COMTD1 is an O-methyltransferase based on the presence of the conserved O-methyltransferase domain, the binding of S-adenosylmethionine in the crystal-structure (pdb: 2AVD, https://www. rcsb. org/structure/2AVD), and its structural and sequence similarity to COMT. COMT has been implicated the protection of melanocytes from the cytotoxic dihydroxyindole derivatives that are formed during eumelanogenesis by O-methylation  [31] ; indeed, O-methylated forms of the eumelanin intermediates 5,6-dihydroxyindole and 5,6-dihydroxyindole-2-carboxylic acid have been detected in the urine and serum from human melanoma patients and melanoma-bearing mice  [44, [80] [81] [82] [83].
Animals DNA samples from a pedigree developed at the PEAT Poultry Experimental Facility (INRAE, Nouzilly; https://doi. org/10. 15454/1. 5572326250887292E12) were used for the linkage analysis. The pedigree comprised a three-generation intercross between homozygous carriers of the IG allele and RIR birds, non-carriers of this allele, which included 159 informative meiosis for the IG locus. This intercross was part of a larger programme to maintain a resource flock for the analysis of morphological variation. ; The homozygous IG/IG birds, representing five different populations, used in the Identical-By-Descent (IBD)-mapping were collected by Bertrand Bed'hom and Michèle Tixier-Boichard except for the SASSO IG-line that was provided by SASSO (a French breeding company). The genotype data of the wild-type chickens at the IG locus used in the IBD-mapping, were all downloaded from published data  [91].
Linkage analysis Linkage analysis was performed using the CRIMAP software version 2. 4  [96]. Pyrosequencing was used to assay four SNPs and three microsatellite markers were analysed using the Mega-BACE capillary electrophoresis instrument (GE Healthcare, Uppsala). PCR and sequencing primer sequences together with positions of the assayed genetic makers are listed in S6 Table. Genotypes at the 2-bp insertion locus were determined using a custom TaqMan SNP Genotyping assay (Applied Biosystems, CA, U. S. A).
Identical-by-descent mapping Partial re-sequencing was performed on homozygous IG birds (IG/IG) and wild-type birds (N/ N), and included chromosomal regions spread across the interval showing no recombination within the IG locus. Additional sequencing of the same panel of birds in regions located in close proximity to COMTD1 was later performed to reduce the IBD-region. All primers were designed using the PRIMER3 plus software  [97]  and DNA sequences were analyzed and edited with CODONCODE ALIGNER 3.
Whole genome resequencing DNA from an IG bird was prepared for sequencing. Illumina paired-end libraries were generated from these DNA samples (mean insert sizes of approximately 220 bases). The library was sequenced on two lanes using an Illumina HiSeq instrument (Illumina, San Diego, U. S. A. ) according to the manufacturer's instructions. The reads were mapped to the chicken genome (galGal6 genome assembly) using the software BWA (version: 0. 7. 12)  [98]  resulting in average read depths of approximately 30X over the chicken genome. Following removal of duplicates using the Picard toolkit  [99]  SNPs and small insertions/deletions were identified from the alignment files using SAMtools (version 1. 6)  [100]  in combination with custom python scripts. The mapping data were used to determine read depths in 1 kb windows over the region of interest.
Analysis of publicly available whole genome sequence (WGS) data Publicly available WGS data from 172 individuals or pooled samples were analyzed (S4 Table ). It included 45 samples from chickens expressing red plumage color, and 127 samples from chickens with known plumage color. All these Illumina paired-end FASTQ data were aligned to the galGal6 genome assembly using BWA (version: 0. 7. 12)  [98]  sorted with SAMtools (version: 1. 6)  [100]  and candidate variants were called with GATK HaplotypeCaller 3. 8  [101].
Alignment of protein sequences The sequences of COMTD1 orthologs were downloaded from the NCBI protein database (Chicken, XP_040530512; Zebra Finch, XP_002192673; Anolis lizard (Anolis carolinensis), XP_003223173; pufferfish, CAG04823; dog, XP_546175; human, NP_653190; salmon, ACI67838; mouse, NP_081241). The first 87 amino acids in the chicken predicted COMTD1 protein sequence (XP_040530512) were not included in the alignment because this sequence did not show sequence identity to COMTD1 sequences from other species. The alignment was performed using Clustal Omega  [103].
8 μm, 100 × 2. 1 mm). Water with 0. 1% formic acid was used as mobile phase A and methanol with 0. 1% formic acid was used as mobile phase B. The column temperature was kept at 40˚C, and the autosampler at 6˚C. The flow rate was set to 0. 2 mL/min. The gradient used was as follows: 0-8. 5 min, 0-100% B; 8. 5-10 min, 100% B; 10-11 min, 100-0% B; 10-15 min, 0% B. The system was controlled using the MassLynx software package v 4. 1 from Waters. High-resolution mass spectra were acquired in positive and negative ionization mode, at a mass range of m/z (massto-charge ratio) 50-1,500. Data acquisition was performed in MSE mode. The samples were injected to the UPLC-MS system in a randomized order with QC samples. Significant features and molecules of interest were primarily annotated by databases (www.
hmdb. ca, https://metlin. scripps. edu/) based on their m/z value and given the high mass accuracy provided by the mass analyzer. Subsequently, in-house built standard library or purchased standards, measured in the same UPLC-MS/MS system, were used for the assignment of the retention time (rt). The chromatograms and mass spectra were processed using the XCMS R package for peak alignment and retention time correction in both positive and negative ionization mode. An overview of the data was provided by principal component analysis (PCA), prior to which the data was auto-scaled using the metabolomics platform (www. metaboanalyst. ca). Partial Least-Squares Discriminant Analysis (PLS-DA) is a supervised multivariate analysis with the full awareness of the class labels. This method can be used for feature selection after cross-validation (CV).
Dots (. ) indicate identity to the full-length chicken master sequence. The arrows indicate amino acids that are in direct contact with the cofactor, SAM, based on the crystal structure model of COMTD1 (Protein Database accession number: 2AVD). The black square indicates the putative O-methyltransferase domain. https://doi. org/10. 1371/journal. pgen. 1010724. g004.