dataesr/scientific-paragraphs-categorization huggingface dataset:
Immunostaining and confocal imaging Larvae were dissected in ice-cold Ca 2+ -free HL3 saline and fixed in 4% formaldehyde for 10 min and washed in PBS containing 0. 05% Triton X-100 (PBST) for 30 min. After washing, larval fillets were stained with phalloidin-TRITC (1:300 diluted in PBST, Sigma) for 40 min at room temperature and subsequently washed for 3x 20 min with 0,05% PBST. Larvae were mounted in Vectashield containing DAPI (Vector Laboratories). Confocal microscopy was performed with a Leica SP8 confocal microscope (Leica Microsystems, Germany). Confocal imaging of larval fillets was done using a z step of 0. 5 μm. The following objective was used: 63× 1. 4 NA oil immersion for confocal imaging. All confocal images were acquired using the LCS AF software (Leica, Germany). Images from fixed samples were taken from third instar larval fillets (segment A2, muscle 6/7).
3c. As a result, the following eigenstrain tensors were used for the simulation: ε * E,Cr ¼ aε 2 * 11,Cr þ bε * 11,Cr þ c 0 0 0 ε * 22,Cr 0 0 0 ε * 33,Cr 0 B @ 1 C A ð6Þ ε * E,CrN ¼ ε * 11,CrN 0 0 0 ε * 22,CrN 0 0 0 ε * 33,CrN 0 B @ 1 C A ð7Þ For the pre-loaded reference state, a small notch through the HSS substrate was added to the model of the clamped cantilever, as shown in Fig. 4. Finally, linear elastic material properties and symmetry along the vertical centreline of the cantilever were assumed. Subsequently, the indentation loads F ind = 150 and 460 mN were applied at the centre of the clamped cantilever, keeping the found eigenstrain distribution unchanged to ensure that the residual stress was accounted for during the simulation of the indentation. As the experimental SAXS map data (cf.
Ex situ CSnanoXRD analysis Results of the CSnanoXRD analysis performed ex situ on an undeformed region of the sample are shown in Fig. 3. Fig. 3a  shows averaged diffraction data including Cr 110, CrN 111 and 200 reflections and reflections emanating from the substrate. In addition, weak Cr 2 N reflections were detected in the transition zones in the vicinity of the CrN-Cr interfaces (Fig. 3a ). The sharp transition between the individual Cr and CrN sample regions confirms the excellent sample alignment with respect to the incident X-ray beam, also documented by Suppl. Fig. 1. Diffraction data from the non-deformed region were furthermore used for qualitative texture analysis. In the lower Cr sublayer, a 〈102〉 fibre texture was detected across the whole layer thickness, as evident by the azimuthal maxima of the 110 Debye-Scherrer ring at azimuthal angles of ±15, ±45 and ± 75 deg.
Unfortunately, no 2D stress pattern was recorded at loads between ~300 and 400 mN, which could have resolved the influence of the plastic zone formed in compression on the stresses in tension. However, the influence of the plastic zone formed in front of the crack tip may be minor, since cracks in thin films are supposed to propagate along grain boundaries of low cohesive energy, as seen for example also in Ref. [48] , where intergranular fracture of ultra-fine-grained Cr has been observed during microbending tests. This is furthermore supported by the fact that the fracture toughness calculated from the load-displacement curve and the experimental stress data (under the assumption of LEFM) is comparable to literature values  [3, 45, 48]. When the stress intensity overcomes the fracture toughness of the Cr sublayer, unstable crack starts to grow, which is stopped at the adjacent Cr-CrN interface (Fig.
Acknowledgement The work has been financially supported by  Christian Doppler Research Association. The financial support by the  Austrian Federal Ministry of Science, Research and Economy  and the  National Foundation for Research, Technology and Development  is also gratefully acknowledged. Special thanks are devoted to  J. F. Keckes  for help with the artwork and  G. Felber  for the mechanical preparation of the synchrotron lamella. Special thanks are also devoted to  M. Alfreider  for the fruitful discussion about fracture mechanics.
Our transect within Europe spans 15 degrees of latitude. Exact collecting locations with GPS coordinates and sample sizes can be found in Table  1. Table  1. Collecting sites included in this study. The "Use" column contains a sub-column for the two aspects of our study: the common garden experiment (CG) and the transect sampling. The transect sub-column contains either "P" (data for adult phenotypes) and/or "G" (genetic data), or "O" (outgroup genetic data). In the sample size column ("N"), the number in [square brackets] indicates additional females collected to achieve reasonable sample sizes for the common garden experiment. Populations in bold text have data for both aspects (transect and common garden).
Mass and body size (spiderlings) After counting the spiderlings, we immediately took the mass of 15 individual spiderlings per clutch using a Sartorius ME5 scale (Sartorius AG, Goettingen, Germany). After weighing, the spiderlings were stored individually in 80% ethanol at room temperature. Later, the preserved spiderlings were removed from ethanol, and the first leg on the right side was removed and photographed using a Zeiss Discovery V20 Microscope in combination with a Zeiss AxioCam MRc camera with a 0. 63X objective (Zeiss AG, Oberkochen, Germany). From the photos, we measured the length from the proximal end of the patella to the distal end of the tibia ("Tibia-Patella length"), using the Carl Zeiss AxioVision SE64 Software (v. 4. 9. 1. 2, Zeiss AG, Oberkochen, Germany). Tibia-patella length is a commonly-used proxy for body size in spiders  (Uhl & Vollrath, 2000).
Temperature data from the thermocouples was gathered by Pico USB TC-08 thermocouple data loggers (Pico Technology, Cambridgeshire, United Kingdom) once every second for the duration of the trial. Trials started at 5°C and ramped down at a rate of approximately -0. 4°C/minute for a total of 150 minutes. Data was copied from the data logger output program into Excel, and then analyzed with a custom R script (Supplemental File 2). The supercooling point was calculated as the temperature immediately before a temperature increase above the threshold of 0. 3°C/second. The threshold was determined by running the script on several sets of data with different thresholds; 0. 3°C/second was sensitive enough to detect the more gradual peaks, but not so sensitive that background changes in temperature were recorded as supercooling points.
Modeling approach We analyzed all common garden data using (generalized) linear mixed models in R, using the package 'glmmTMB'  (Brooks et al. , 2017)  for non-Gaussian distributions and the package 'lme4' for Gaussian distributions  (Bates et al. , 2015). Details of the models for each response variable follow below. For the data on clutch size, we used a negative binomial distribution with default link function and linear parameterization ("nbinom1" in R). Hatching success was modeled using a betabinomial distribution with default link. The hatching success data showed zero inflation due to oviposition latency, so we included a zero inflation formula to account for the excess of zero hatching success in late-laid egg sacs. We used a betabinomial distribution with default link for the survival model.
We did not remove any fixed effects, only the interactions, according to AIC. The model with the lowest AIC was then assessed further. For models with a ΔAIC of less than four, we always proceeded with the most parsimonious (i. e. least complex) model within those criteria. Model assumptions were checked using the R package 'DHARMa', which uses simulated residuals to provide easily interpretable checks of assumptions for many model and distribution types  (Hartig, 2021). Where possible, we used the Nakagawa & Schielzeth method  (Nakagawa & Schielzeth, 2013)  to calculate a pseudo-R 2 , implemented in the 'performance' R package  (Lüdecke et al. , 2021) , to assess how much of the variation in the data was explained by the full model (conditional pseudo-R 2 ), and by the model without random effects (marginal pseudo-R 2 ).
The Nakagawa & Schielzeth method is not reliable for betabinomial models, thus we used a crude calculation suggested in the 'glmmTMB' FAQs (https://bbolker. github. io/mixedmodels-misc/glmmFAQ. html) to understand the goodness of fit for our betabinomial models (hatching success, overwintering survival, and lower lethal temperature); precisely, we calculated the squared correlation of the predicted values with the observed values of the response variable (calculated in R: cor(model. response(model. frame(model)), predict(model, type = "response"))^2). This does not provide a comparison of conditional and marginal R 2 , thus for betabinomial models we only report the conditional R 2. We refer to "significant" effects as those where the 95% confidence interval (CI) does not cross zero. To visualize the results, we used the 'ggpredict' function from the 'ggeffects' R package  (Lüdecke, 2018)  to compute estimated marginal means and 95% CI of the response variable for specific model terms.
Historical climate data across transect We extracted historical climate data for all collecting sites along the transect, excluding the outgroups in Russia and Japan, using an R script and data for the 19 standard bioclimatic variables downloaded from WorldClim at 30 second (~1km 2 ) resolution  (Fick & Hijmans, 2017). To summarize and reduce the number of environmental variables in downstream analyses, we summarized the 19 variables into fewer dimensions using a principal components analysis (PCA) using the R package 'FactoExtra'  (Kassambara & Mundt, 2020)  (Supplemental File 3), following the recommendation of  Hoban et al. (2016). The first five principle components (PCs) accounted for 97. 6% of the variation in the climate data, and were used as explanatory variables for phenotype-environment and genotype-environment associations.
Statistical analysis of transect phenotype data To test if the female traits (body size, pigmentation, clutch size and hatching success) varied with the environment, we used generalized linear mixed models in R, using the package 'glmmTMB'  (Brooks et al. , 2017). The female traits were used as response variables, and the five environmental principle components were used as explanatory variables. Body size was modeled with a Gaussian distribution, clutch size with a negative binomial distribution with quadratic parameterization ("nbinom2" in R), pigmentation with a binomial (light vs dark pixels) distribution, and hatching success with a betabinomial distribution. Sampling site was included as a random effect. All models fit linear assumptions.
Sequence processing and SNP filtering We demultiplexed the raw reads based on the individual barcode sequences, then used Trimmomatic (v. 0. 39)  (Bolger, Lohse & Usadel, 2014)  to remove adapter sequences. We mapped the reads of each sample onto the genome assembly using BWA-MEM (v. 0. 7. 12-r1039) with default settings  (Li, 2013) , then sorted and indexed the mapped reads using SAMtools sort and SAMtools index (SAMtools v. 1. 3. 1)  (Li et al. , 2009). We generated a VCF file for all samples using SAMtools mpileup. We then filtered the SNP data using SNPcleaner (v. 2. 24)  (Fumagalli et al. , 2014). SNPs were retained for downstream analyses if they had a minimum coverage of 3x per individual, no more than 40% of missing data for individual sites, and a maximum coverage of 240x per individual.
Population genetic structure and demography Because our sampling covered a broad geographic area, it was important to quantify the underlying demographic patterns and population genetic structure before searching for genomic signatures of adaptation. To this end, we used several methods: using neutral SNPs, we performed a PCA on the genotype likelihoods, as implemented in ngsTools "PCAngsd"  (Fumagalli et al. , 2013). Also using the neutral SNPs, we calculated the per-individual inbreeding coefficients, as well as the pairwise relatedness of all individuals included in the study, using ngsF  (Vieira et al. , 2013)  and ngsRelate  (Korneliussen & Moltke, 2015). Using all SNPs, we calculated the pairwise FST for each sampling site in ANGSD  (Fumagalli et al. , 2013). We interpreted the FST results using a heatmap, calculated in R (R Core Team, 2017), as well as a neighbor-joining tree calculated in the R package 'ape'  (Paradis & Schliep, 2019) , visualized with FigTree  (Rambaut & Drummond, 2018).
Further, using all SNPs from a random subset of five individuals per population (to reduce run time), we calculated a phylogenetic tree with a combination of ngsDist  (Vieira et al. , 2016)  and FastME (v. 2. 1. 6. 1)  (Lefort, Desper & Gascuel, 2015). We then looked for signatures of admixture at the individual level using ngsAdmix  (Skotte, Korneliussen & Albrechtsen, 2013). Due to the stochastic nature of simulation-based clustering programs like ngsAdmix and the like (i. e. STRUCTURE and ADMIXTURE  (Pritchard, Stephens & Donnelly, 2000; Alexander, Novembre & Lange, 2009) ), for the ngsAdmix analysis, we ran 50 replicate analyses with values of K ranging from K=2 to K=20. The results of all replicates were summarized using the program CLUMPAK, which identifies the most common results across replicates for each value of K  (Kopelman et al.
The coincidence of high environmental association with high differentiation between native and invasive populations and relatively low differentiation between invasive and Japanese populations would be taken as support for the hypothesis that introgression provided the substrate for selection in A. bruennichi. For the genetic differentiation outlier approach, using all SNPs, we analyzed genome-wide patterns in FST as implemented in ANGSD  (Fumagalli et al. , 2013). We calculated FST in sliding windows, with 50kb windows and 10kb steps, with our sampling sites grouped into three populations: native, invasive and Japanese. We do not perform an explicit significance test with FST, but look at positions with values above the genome-wide average FST. We performed association tests in ANGSD using the -doAsso method, which uses a generalized linear framework to associate genotype likelihoods with binary, count, or continuous variables  (Skotte, Korneliussen & Albrechtsen, 2012) ; in this case, we tested the association of genotypes with continuous environmental variables, namely the first five PCs from the historical climate data (see section "Historical climate data" above).
Within the European (native and invasive) populations, FST was quite low, ranging from 0. 025-0. 130. FST was lower among "native" populations (max: 0. 074) than among "invasive" populations (max: 0. 130) (Figure  8B ), and FST was very low among Russian populations (max: 0. 036) (Supplemental File 1: Table  S1 ). As an additional visualization, we used the pairwise FST matrix to calculate a neighbor-joining tree in the R package 'ape'  (Paradis & Schliep, 2019) , which showed the native populations in one cluster, while the invasive populations did not cluster as clearly, and were more similar to the Russian and Japanese populations (Figure  8C ). We constructed another phylogenetic tree using ngsDist  (Vieira et al. , 2016)  and FastME (v. 2. 1. 6. 1)  (Lefort et al. , 2015) , which revealed the same large-scale groupings of native, invasive, Russian and Japanese populations as the PCA, admixture, and FST analyses (Supplemental File 1: Figure  S8 ).
Acknowledgements We would like to thank  Lydia Smith  and  Natalie Graham  at  UC Berkeley  for their invaluable advice on ddRAD library preparation and sequencing. We are grateful to  Cynthia Wang  for her work on adapting the library preparation protocol. Many thanks to  Heidi Land  for her incredible organizational skills helping with the coordination of animal caretaking, and more. We also thank  Alexandra Machnis ,  Timon Möller ,  Birte Schadlowski ,  Anja Junghanns , and  Julia Balk  for their help with spider husbandry, counting, and measuring offspring. We also thank  Marina Wolz  for her very helpful advice on the overwintering procedure and how to count and measure offspring.
Data availability The scripts for filtering SNPs in and outside of exons were written by KJH and can be shared upon reasonable request. The genome-wide results for all chromosomes for each environmental PC (65 graphs, thus excluded from the supplement for brevity) can be shared by MMS upon request. Observed means and variances of spider traits will be uploaded to the World Spider Trait Database (https://spidertraits. sci. muni. cz/) for utility in meta-analyses.
When they reach the detector system, they are counted in the focal-plane detector (FPD)  [41] , a monolithic silicon p-i-n diode segmented into 148 equalarea pixels. The FPD and its readout electronics are elevated to the post-acceleration potential, and preamplified signals are transmitted to the data-acquisition (DAQ) system via optical fiber. Each FPD pulse is digitized in a 12-bit analog-to-digital converter (ADC), and its amplitude and timing are reconstructed online by the sequential application of two trapezoidal fil-ters  [41, 42]. These values are then recorded using the Object-oriented Real-time Control and Acquisition (ORCA) framework  [43] , which can also communicate directly with the main-spectrometer high-voltage system using a web-based database tool  [44]. Pulse amplitudes are translated into energies in near-time processing (Sec.
III. THE KNM1 MEASUREMENT CAMPAIGN In this section we describe the operating conditions of the KATRIN experiment during its first high-purity tritium campaign (KNM1), which took place from 10 th April to 13 th May 2019. In particular, we characterize the system performance in terms of the source-gas isotopic purity (Sec. III A) and column density (Sec. III B), as well as the reproducibility, homogeneity, and stability of the electron starting potential in the source (Sec. III C) and the retarding potential in the analyzing plane (Sec. III D). We also discuss the detection of β electrons and the definition of a region of interest (Sec. III E) as well as the processing and analysis pipeline for the data (Sec. III F). The requirements for system stability arise from the method adopted to measure the tritium β spectrum by repeatedly scanning the retarding potential in alternating up and down sweeps (Sec.
III G), and from the fact that KNM1 data from all pixels and all scans are combined into a single spectrum for fitting. In the final analysis, then, experimental parameters are essentially averaged over both space (across the detector) and time (across like scan steps throughout the KNM1 data-taking period). Later on, Sec. VII explores the justification for this analysis method in the statistics-dominated KNM1 data set. For the KNM1 campaign, the sequence of scan steps, each consisting of a retarding-potential set point distributed in the interval [E 0 -91 eV, E 0 +49 eV], resulted in a typical scan duration of 2. 5 h. Therein, each scan step corresponds to a measurement time varying from 17 s for high-rate points deeper in the spectrum to 576 s near the endpoint region, as will be shown later in Fig.
A custom-built post-regulation system  [35]  ensures stability at higher frequencies, up to 1 MHz. In order to stack multiple scans for the KNM1 analysis (Sec. VII B), it is not only necessary to have 3 ppm monitoring, but also to achieve comparable precision in both the stability at each scan step and the reproducibility of the retarding potential from scan to scan. Figure  4  shows the achieved high-voltage stability while acquiring data at individual scan steps over the full measurement interval. This stability is on average below 15 mV, significantly exceeding requirements. The observed increase in standard deviation as a function of scan-step duration is described well by a simple statistical model that combines a random-walk diffusion process with a feedback loop. The reproducibility of retarding potentials from scan to scan follows a Gaussian distribution with a width of σ =34(1) mV.
F. Data pipeline Following each pixel trigger (Sec. III E), the DAQ records the trigger timestamp from a 20 MHz clock and the energy information as raw ADC counts integrated over the shaping time of the trapezoidal filter. A scan is divided into scan steps. Each scan step is defined by its HV set point, and its duration is determined according to the measurement-time distribution of the scan (Sec. III G). Prior to acquisition start at each scan step, handshakes between the DAQ and the HV control system ensure that the HV read-back value has reached the setpoint value within a defined accuracy of 50 mV, as measured by a four-point moving average over the last 8 s. The inter-spectrometer electron catcher is inserted and removed during this change of scan steps, so that it does not obstruct the beamline during data-taking.
A series of pulse-per-second (PPS) pulses from a precision clock synchronized to the Global Positioning System (GPS) defines both the start and stop times of scan steps, providing boundary time accuracy better than 1 ns. The 50 ns digitization timestamps are also phase-locked to 10 MHz pulses from the same precision clock. The readout system is capable of handling a pixel rate of 100 kcps and a total rate of 3 Mcps. Therefore, no deadtime is expected for the actual tritium scan, which has a maximum count rate of 7 kcps. A typical two-hour scan produces roughly 120 MB of data. Immediately after completion of a scan, data files are processed automatically. This processing includes the transfer to storage computers, time-wise event sorting, conversion to offline data formats, and indexing into a run database, followed by automated user-side analysis including the reduction of data in user-specified data files.
Except for the handshakes between the DAQ and HV systems, slow-control channels are independent from the tritium scans. Each slow-control sensor has a defined recording interval, typically between 2 and 10 s. This is a heterogeneous system for which timestamps are taken from computer timestamps synchronized to the Network Time Protocol (NTP). In the offline analysis, special care is taken for synchronization among different slow-control channels, as well as between the DAQ and slow controls. An intermediate data layer, consisting of user-side shared data storage with version management, splits the data analysis chain. The first half of the chain covers analysis at the event and time-series levels, and the second half provides higher-level analysis including model fitting. For each scan, results of the first-level analysis are summarized in digest files that contain analyzed FPD counts with efficiency corrections, individual scan steps, calibrated slow-control values (including LARA isotopolog concentration and column density, and analyzed rates extracted from β-induced x-ray spectroscopy and the forward beam monitor), and data-quality flags.
Some experimental parameters, such as beamline alignment information and magnetic-and electric-field values determined by measurements and simulations, are shared across all scans in a given measurement period; each such period is summarized in a digest file containing the values of these parameters. During data-taking, acquisition occasionally began before the HV readback values achieved stability due to minor synchronization errors. The first two seconds of every scan step were removed from the data to address these issues. Count-rate, livetime, efficiency, and stability calculations are performed after these data-quality cuts. Cumulative measurement-time distribution for KNM1. The 27 scan steps of the [E0-37 eV, E0+49 eV] analysis interval are shown in blue below E0, and in black above E0.