dataesr/scientific-paragraphs-categorization huggingface dataset:
Dynamic Light Scattering (DLS) measurement DLS measurements were carried out using a DynaPro MS800 instrument (Wyatt Technologies Corp. ) equipped with a gallium aluminium arsenide 825 nm laser. The total light scattering intensity of HEWL solutions (1 mg/ml) was collected at a 90°angle. All measurements were made in 3 mm quartz cell at 25°C. The acquisition of data (usually 30-40 points) was made with an acquisition time of 30 s and the obtained data were averaged. The autocorrelation curves were deconvoluted using Dynamics V6 software to obtain size distribution and hydrodynamic radii (<R h >). Each experiment was repeated three times to ensure reproducibility.
To address this issue, the HEWL samples (monomeric and fibrillar) were analyzed under reducing conditions (+TCEP). As indicated in the Fig  11 (panels B-E ), the intensity of the HPLC peak observed at 22. 9 min decreases with the incubation time of the protein in the presence of the reducing agent for the benefit of another peak located at 24. 2 min. This latter peak, for which the retention time and the intensity remain unchanged for long incubation times (>12 h), corresponds to the full-length HEWL with reduced disulfide bonds. Accordingly, the data in the panels A (disulfide-intact full-length HEWL) and E (fully reduced full-length HEWL) indicate that the incubation of lysozyme in acidic pH at 55°C with agitation generates amyloid fibrils containing exclusively the full-length protein.
Shorelines fronting deltas evolve in their quest to achieve equilibrium state. Such activities have encountered the increasing human development in deltaic regions and resulted in the destruction of the environment and resources. This paper discusses the shoreline evolution trends of the Volta Delta in Ghana. The shoreline was divided into four sections based on the orientation and human interventions. Data sources for the study included satellite imageries of 1986, 1991, 2001, 2004 and 2013. Trends were statistically analysed using the AMBUR software. It emerged that the 27 years period under study, the shoreline is accreting at an average rate of about 0. 53 m/yr. Section one is accreting at a rate of about 0. 136 m/yr; section two is accreting a rate of about 1. 703 m/yr; section three is accreting at a rate of about 2.
Therefore the issue of the most appropriate method of calculating rates from shoreline position data has been subjected to considerable interest  [6, 18]. A variety of software has also been developed that incorporates the numerous statistical methods for calculating historic rate of shoreline change. These include Digital Shoreline Analysis System (DSAS)  [19] , SCARPS  [20] , Beach Tools  [21]  and Analyzing Moving Boundaries Using R (AMBUR) developed by  [22]. The main components of these tools define a baseline, generate orthogonal transects at a user defined separation along the coast and calculate rates of change. The AMBUR software improves on previous shoreline analysis tools by providing functions for new transects casting techniques, detailed output from statistical analyses, generation of summary reports and graphics, and shoreline position forecasting  [23].
It analyses shoreline change by capturing the positions of each historical shoreline along transects and measure changes that occur along each transect using advanced statistical analysis  [22]. AMBUR provides a collection of scripts to assist with the shoreline change analysis taking advantage of R's statistical, graphical and geospatial capabilities  [23]. It allows users to choose any commercial or open source GIS software for editing, preparing and viewing shape files. Along with the traditional ''perpendicular'' transect method used, AMBER uses two new transect methods, ''near'' and ''filtered, '' which assist with quantifying changes along curved shorelines that are problematic for perpendicular transect methods. Output from the analyses using AMBUR is useful in rapidly assessing trends and potential errors in the dataset.
Methodology Division of the study area into four sections enabled the shoreline evolution trends to be analysed for the sections using the orthogonal transect method. Data for the study included satellite imageries for  1986, 1991, 2001, 2007, 2013  (Table  1 ). The images were acquired from the United States Geological Survey, Earth Resources Observation and Science Centre. The Landsat TM data were resampled to 15 m resolution to ensure compatibility with the ASTER data. The Landsat ETM+ panchromatic data with a resolution of 15 m was used to sharpen the six multispectral bands to obtain a new image at 15m. The Landsat images, which were acquired at level L1T, have absolute geodetic accuracy. The ASTER data was obtained in the raw state. Geometric correction/rectification was therefore carried out on the data.
The results were plotted on the imageries to check their positional accuracy, which further increased confidence in the reliability of the data. The high waterline (HWL), which is used as shoreline proxy in Ghana  [3]  was adopted as the shoreline position. The imageries were taken during the high water time to ensure compatibility and facilitate change detection. The HWL were extracted using both semiautomatic and manual methods. The water-land boundary was identified using band ratio between the mid infrared (band 5 [b5]) and the green (band 2 [b2]) for the Landsat data. This reduced the level of subjectivity in delineating the shoreline. Band ratio was implemented using the band ratio model in the ENVI software (thus b5/b2). The resultant image with ratio values between 0 and 3 was sliced and segmented to form a binary image.
Analyzing historic shoreline rates of change Historic rates of change were analysed using the AMBUR software. The AMBUR software was adopted because of the advantages it has over other methods. The digitized five shoreline proxies were appended in ArcGIS environment into a single shapefile. A baseline was also digitized by mimicking the outer shoreline and saved as a shapefile. Both the shoreline and the baseline shapefiles were uploaded in AMBUR and their attribute fields populated to satisfy conditions for the software. The positional accuracy of the shoreline was set as 1m based on  [3]. Perpendicular transects were automatically cast at 50 m intervals from the baseline across the appended shoreline. Although studies by  [39] , identified that transect spacing below 100 m does not result in improved estimates of shoreline change rate, however shorter intervals enable more details to be included in the rates of change estimation which reduces uncertainties.
Thirdly, is there evidence for a local increase in recombination rate in the vicinity of genes with worker-biased expression? It has been suggested that this could be selectively advantageous due to the importance of worker phenotypes in the evolution of eusociality. Finally, what effects do the extremely high levels of recombination in honeybee have on the strength of gBGC? How does gBGC impact genome variation and the frequency of CpG sites in the genome? We can address these questions by analysing the shape of the site frequency spectrum for different SNP categories and estimating the value of B. Here we construct a fine-scale map of recombination rate variation honeybee using population-scale resequencing dataset  [37]  with the aim of addressing these questions. Our estimates show good correspondence with a previous genetic map  [3].
563). After observing gBGC among transitions in particular (see below), we next removed all transitions and also observed a positive correlation between diversity and recombination (R 2 = 0. 586). These patterns favour linked selection as a major force in shaping variation in the genome. These correlations are only slightly weaker than the correlations observed when the dataset is randomly subsampled to the same size (R 2 = 0. 573 and R 2 = 0. 594 respectively), which suggests that gBGC has at most a small effect on determining the magnitude of genetic variation in a genomic region. Average Tajima's D is negative (-1. 178) reflecting of skew towards rare variants, as already observed in this African honeybee population  [37] , which is indicative of population expansion. Tajima's D (measured in 100 kb windows) shows a weak negative correlation with both GC content (R 2 = 0.
114) and recombination rate (R 2 = 0. 015), which indicates a slightly higher skew towards rare variants in regions of high recombination. Pervasive linked selection is expected to generate a skew towards rare variants in regions of low recombination  [32] , which we do not observe. This could indicate the action of additional factors. In order to assess whether the association between genetic diversity and inferred recombination rates could be an artefact of having more power to detect recombination in regions of high genetic variation, or due to other biases, we estimated LD-based maps of recombination using datasets where SNPs were removed or using different parameter as follows: i) we produced a dataset where genetic variation (θ w ) in each 100 kb window was capped at 0. 002, effectively subsampling data in 98% of 100 kb windows; ii) we produced a dataset where rare variants (minor allele frequency<0.
1) were removed; iii) we evaluated the effect of increase the block penalty to 10, which affects the probability of changes in recombination rate between genomic regions. In each case, the resulting LD maps were strongly correlated with the original map (S3 Fig). The strong correlation between levels of genetic variation (in the original dataset) and inferred rates of crossing over remained in the LD maps produced using different parameters (S3 Fig) and are similar to that observed in the original dataset. We therefore conclude that variation in genetic variation across the honeybee genome does not generate biases in inference of recombination and that the correlation between recombination and genetic variation is real. Levels of genetic variation are reduced close to genes, indicative of an effect of linked selection  [37].
Germline methylated genes have low rates of crossing over Average rates of crossing over are reduced in coding sequence and UTRs compared to noncoding regions  (Fig 4A). This suggests the presence of specific factors that reduce recombination specifically within genes. We next examined how variation in patterns of gene expression and inferred levels of germline methylation are associated with crossover rate in genes (Fig  4B ). Average recombination rate in coding sequence of all genes (left; ~13,000 genes) and from genes divided according to gene expression patterns and CpG content. Two expression datasets are used. One identifies elevated expression in queens compared to workers (Q>W), workers compared to queens (W>Q) or not significantly different between these two castes (NS). The other that identifies elevated expression in drones compared to workers (D>W), workers compared to drones (W>D) or not significantly different between these two castes (NS).
Genes that are biased in workers compared to drones are enriched in the HCpG class, so it is not clear which of these two factors is driving the association with high recombination rates. We therefore subdivided both datasets of caste-biased genes according to HCpG and LCpG classes. We found that the large differences in crossover rates in HCpG and LCpG remain irrespective of patterns of gene expression (S8 Fig) : for the same gene expression class, crossover rates are 2. 3-3. 4x higher in HCpG compared to LCpG genes. However, within each CpG class, the difference in crossover rates between drone and worker biased genes is smaller (1. 3x higher in HCpG genes and 1. 8x higher in LCpG genes). Hence, variation in CpG content is the strongest predictor of recombination rate in our dataset. One interpretation for this finding is that variation in levels of germline methylation is the strongest factor determining variation in recombination rates within genes in the honeybee genome.
GC-biased gene conversion dominates patterns of genetic variation The site frequency spectrum in our dataset is dominated by low frequency AT alleles, which make up 80% of the rare variants (allele frequency<10%) across all SNPs, but only 51% of common variants (allele frequency 40-50%), a highly significant difference (p<10 -5 , Fisher's exact test; S9 Fig). By comparing homologous genomic regions between A. mellifera and A. cerana, we were able to infer the probabilities that either allele represented the ancestral or derived state at 2,983,700 SNPs using a weighted parsimony method (see Methods). We categorised each allele at a SNP as weak (A or T) or strong (G or C). At strong-to-weak (SW) SNPs, the S allele is ancestral and the W allele is derived, whereas weak-to-strong (WS) SNPs are defined as the reverse.
However, here we find a stronger correlation with GC content. One reason for this could be that GC content is a more accurate indicator of recombination rates than our LD-based map because it is the result of the action of gBGC over evolutionary time. Another possibility is that our LD-based map predominantly measures crossover rates, which may not be strongly correlated with non-crossover rates. It is also possible that gBGC has a stronger correlation with non-crossover rates than crossover rates, as observed in humans  [47]. The method also allows us to estimate the AT mutational bias, λ. We estimate the average bias over the whole dataset as 11. 69. The strong AT mutational bias appeared specific to transitions (λ in transitions 13. 09; λ in transversion 3. 03). Estimates of λ vary slightly across the genome.
5; [80] ). These statistics were averaged across the full window and for each type of functional element (coding, intron, UTRs and intergenic sequence; coordinates according to the recent gene annotations in OGSv3. 2; [80] ) in the window. The African population includes 6. 2 million single nucleotide polymorphisms (SNPs), corresponding to an average level of genetic diversity of θ w = 0. 008. The reversible-jump MCMC algorithm interval of the LDHAT program  [81]  was used to estimate the mean population-scaled recombination rate coefficient ρ (rho) across regions (or intervals), which in honeybees is taken as ρ = 3N E r (3N E is due to honeybee haplodiploidy) and where r is the genetic map distance over a region. The interval method fits a uniform recombination rate over a region from patterns of linkage disequilibrium (LD) among genotypes.
The LDHAT recombination map (hereafter referred to as the LD map) was estimated along the chromosomes in segments of up to 2,000 variable sites. The segments were arranged to never span across scaffolds and had an average physical length of 63 kb. For each segment, the interval program was run for 1. 1 million iterations and the chain was sampled every 10,000 iterations, following a burn-in of 100,000 iterations. We evaluated the performance of different block penalties (see below). A map inferred with a block penalty of 1 was taken as the canonical LD map for the study. Levels of genetic diversity are highly variable along the honeybee chromosomes and correlates with functional elements caste biased expression and nucleotide composition  [37]. We therefore performed an analysis to determine whether our method could be biased towards detecting high recombination in regions of high SNP density.
Three measures were put in place in order to study the effect of local diversity and LD on the inference of broad-scale recombination from our data: i. Reduction of variability in diversity. Within each block of 100 kb, the diversity of every functional element (according to the recent gene annotations in OGSv3. 2; [80] ) was independently capped at θ w %0. 002 by randomly subsampling the SNPs, resulting in a thinned dataset spanning 1. 5 million SNPs. ii. Pruning of rare variants. A substantial fraction of the dataset consists of variants that occur at low frequency in the population. We specifically removed all 4. 5 million SNPs with a minor allele frequency (MAF) <10%, reducing the dataset to 1. 7 million common variants for an average θ w %0. 0022. LDHAT was then rerun with the manipulated datasets using the parameters specified above.
Germline methylation data We estimated levels of germline methylation in genes using data from Drewell et al. [62]. Significantly methylated CpGs (mCpGs) were originally detected using short read bisulfite sequencing of honeybee egg and sperm cells and mapped against v2. 0 of the honeybee genome. In order to estimate methylation levels in different genes, we merged the two methylation tracks into a single germ line track and associated the coordinates of the mCpGs with overlapping coding sequences using the matching gene model annotation (OGSv1. 1). We next measured methylation levels in two ways for each accession: i) the number of mCpGs per kb of coding sequence (controlling for the length of the gene); and ii) the proportion of CpGs in the coding sequence of a gene that were methylated (controlling for the actual CpGs available to methylate).
Given the average GC-content, the AT mutational bias can also be estimated. To get the DAF spectra, the numbers of SNPs detected in each mutational class were summed over the frequency spectrum across the whole dataset. Site frequency spectra were also generated according to bins of local GC (100bp window to either side of the SNP) and regional recombination (1000bp window). We estimated B for all mutations and for transitions and transversion separately. From left to right, the four bars show a) the proportion of WS variants that generate CpG sites compared with those that generate GpC sites at low derived allele frequencies (<0. 1); b) the same ratio at high derived allele frequencies (>0. 9); c) the proportion of SW variants at ancestral CpG sites compared with ancestral GpC sites at low derived allele frequencies (<0.
This study was funded by grants from the  Swedish Research Council Formas  ( 2013-722 ),  Vetenskapsrådet  ( 2014-5096 ) and the  Carl Tryggers Stiftelse  ( CTS14-508 ) to MTW. SG is supported by the  French CNRS  and the  Marie Curie IEF  Grant "SELFADAPT"  623486. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
2008). To reduce collection biases, the installation order of the three colours was rotated between sampling rounds. Bowls were emptied at the end of each sampling day and bees were stored in 70% ethanol. Non-bee insects and other arthropods sampled were not examined further. These included many Coleoptera [Astylus atromaculatus Blanchard (Melyridae) and Cyclocephala signaticollis Burmeister (Scarabaeidae) were the two most abundant species] and a few Diptera (including some Syrphidae), Lepidoptera and arachnids, among others. Fourteen individuals of honeybee were also caught, which were excluded from data analyses because our study focused on wild species. Bees were identified in the laboratory to the finest taxonomic level possible. Taxonomy followed the nomenclature of  Michener (2007).
Acknowledgements We greatly thank  Arturo Roig Alsina  ( Museo Argentino de Ciencias Naturales, 'Bernardino Rivadavia', Buenos Aires, Argentina ) for his help with bee identification,  Luciana D'Acunto  and  Alain Butet  for assistance in field work,  Gonzalo García  and  Clémence Vannier  for Geographic information system support, and the  Ortíz Basualdo family, owners of Estancia 'Las Polvaredas' , for their consent to carry out this research on their farm and for logistic assistance. We also warmly thank  Bernard Vaissière  for providing useful bibliographical references, and  Benoît Geslin  and  Arnaud Le Nevé  for fruitful discussions. We are grateful to two anonymous referees for their constructive comments. VLF was supported by a post-doctoral fellowship of the  French-Argentine 'Bernardo Houssay'  programme.
Behavioral Correlate of the Tectum's Spontaneous Neuronal Assemblies Since we found that spontaneous assemblies are related to the optic tectum's role in spatial vision, we thus further studied whether the assemblies' population spatial tuning reflected parameters pertinent to visually guided tectum-dependent behaviors. For this purpose, we compared the functional tuning (i. e. , spatial and size tuning) of spontaneous assemblies with the larva's detection performance during prey-capture behavior. We first studied how the larva's visual field is represented by the tectal spontaneous assemblies. For this, we estimated the receptive fields of the assemblies (the normalized sum of the spatial tuning curves of all the neurons within an assembly). To correct for uneven tectal representations of the visual field, we further divided the spontaneous assemblies' receptive fields by the average receptive field of shuffled null models, resulting in normalized assembly receptive fields independent of any potential sampling bias in the data (i.
Supplemental Information Spontaneous Neuronal Network Dynamics Reveal Circuit's Functional Adaptations for Behavior Sebastián A. Romano, Thomas Pietri, Verónica Pérez-Schuster, Adrien Jouary, Mathieu Haudrechy, and Germán Sumbre  representation of all the significant PCs). Each black dot depicts a neuron, its coordinates are given by the entire neurons' spontaneous activity during 1hr recordings. A typical experiment is shown in the 4 upper panels. Notice the dots extending along straight lines projecting away from the origin. This is not observed for IAI shuffled data (4 bottom panels). The axes that describe these projections are obtained by promax rotation of the PCs (red, green and blue lines). The data is represented over these rotated PCs (rPCs, 4 middle panels), and a threshold (red lines, see c) delineates the assemblies.
The light spots lasted for 1 second and were presented from -45 o to 45 o in steps of 5 o (19 spots). The height (vertical plane) of the stimulation azimuthal plane was chosen according to the maximal neuronal response in the imaged plane. The inter-trial time interval for both bars and light spots was 7 sec. Stimulation order was randomized. All visual stimuli protocols were programmed using the Psychtoolbox  (Brainard, 1997; Pelli, 1997)  for Matlab and synchronized with the two-photon calcium imaging acquisition software  (ScanImage, Pologruto et al. , 2003)  using an I/O TTL board (ActiveWire, inc).
Inference of fluorescence events associated to neuronal activity The method described here infers the statistical significance of single-neuron calcium dynamics in an adaptive and unsupervised manner. Conceptually, it considers that any event in the fluorescence time series data belongs to either neuronal activity process, A, or to an underlying noisy baseline, B. In order to discriminate, with a desired degree of confidence, between these two sources, we built a datadriven model of B, and imposed biophysical constraints of the fluorescent calcium indicator. We then applied a Bayesian odds ratio estimation framework, as explained below, to obtain the significant ∆F/F transients. Non-significant portions of the ∆F/F traces was set equal to 0 for all subsequent analysis. Raw fluorescence datasets consisted of the fluorescence time series of N neurons sampled at T discrete time points, represented as N x T matrices.
The most frequent value of the time series, µ, represented a constant residual value of ∆F/F (µ = 0. 23 ± 0. 06 for a representative dataset) product of an underestimation of b slow. The ∆F/F time series of each neuron were therefore recalculated after correcting b slow by the factor (1+ µ). Samples represented by the left tail were negative ∆F/F deflections from the baseline produced by B, hence not related to neuronal calcium events. A robust estimate of the standard deviation (s. d. ) of B, σ noise , can be obtained if we consider that B is a Gaussian stochastic process, allowing us to fit the left ∆F/F tail with a normal distribution of mean µ and s. d. σ noise. This assumption seemed valid due to the accuracy of the calculated fits (r 2 = 0. 995 ± 0. 002 for a representative dataset) (Figure  S1A ).