dataesr/scientific-paragraphs-categorization huggingface dataset:
Central macular thickness (CMT) was assessed in the central 1-mm-diameter circle of ETDRS thickness map using ophthalmic software (Heidelberg Eye Explorer 1. 9. 11. 0; Heidelberg, Germany). Choroidal thickness (ChT) was manually measured as the distance between the Bruch's membrane interface and the sclerochoroidal interface under the fovea. The different AMD phenotypes before NV development (RPE drusen, RPD, and GA) were also assessed and recorded, and presence of NV was classified based on anatomic localization. Dry AMD is a clinical designation including any RPE drusen, RPD, and/or the advanced stage GA. Wet AMD encompasses any AMD form associated with NV, independently of their type. According to the Beckman classification,  11  sub-RPE drusen were defined as intermediate if 63-125 lm in diameter and large if ‡125 lm in diameter.
Statistical analyses were performed using statistical software (SPSS Statistics Version 20; IBM Corp. , Armonk, New York, USA). All values of descriptive analyses were reported as counts (percentages) for categorical variables, and as average 6 standard deviation for quantitative variables. Comparisons of VA, CMT, and ChT between the baseline and the follow-up examination were performed using Student's paired t-test or ANOVA t-test for repeated measures. In all analyses, values of P < 0. 05 were considered as statistically significant.
Potential limitations of our study should be noted. We acknowledge the small sample size, due to the retrospective design and the strict inclusion and exclusion criteria. Our investigation was not a true population-based study. Therefore, larger and longer prospective studies are needed to provide more robust analysis and validation of the results of our report, especially the frequency (11. 9%) of AMD in an older population with high myopia. Furthermore, our data was obtained from tertiary referral centers. This selection bias may have skewed our findings toward a more severe variant of the disease, and, moreover, may have led us to an overestimation of AMD frequency in HM eyes. Eventually, the triggering cause (AMD versus HM) in charge for the NV development was not demonstrable based on clinical or multimodal imaging features only.
Acknowledgments Disclosure:  E. Corbelli , None;  M. Parravano ,  Allergan (F) ,  Bayer (F) ,  Novartis (F) ;  R. Sacconi , None;  D. Sarraf ,  Amgen Inc. (C) ,  Bayer Shering Pharma (C) ,  Nuvelution (C) ,  Novartis (C) ,  Genentech (C) ,  Optovue (C) ,  Allergan Inc. (C) ,  Heidelberg (C) ,  Regeneron (C) ;  S. -Y. Yu , None;  K. Kim , None;  V. Capuano , None;  A. Miere , None;  E. Souied ,  Allergan Inc. (C) ,  Bausch & Lomb (C) ,  Bayer Shering-Pharma (C) ,  Novartis (C) ;  M. Varano ,  Allergan (F) ,  Bayer (F) ,  Novartis (F) ,  SIFI (F) ;  A. Boninfante , None;  B. Chae , None;  A. Carnevali , None;  L. Querques , None;  F. Bandello ,  Alcon (C) ,  Alimera Sciences (C) ,  Allergan Inc. (C) ,  Farmila-Thea (C) ,  Bayer (C) ,  Shering-Pharma  (C),  Bausch & Lomb (C) ,  Genentech (C) ,  Hoffmann-La-Roche  (C),  Novagali Pharma (C) ,  Novartis (C) ,  Sanofi-Aventis (C) ,  Thrombogenics (C) ,  Zeiss (C) ;  G.
Flow behavior of milk dessert The flow curves of milk desserts were measured by recording shear stress values when shearing the samples with a linear increasing shear rate from 1 to 200 s -1 for a period of 60 s. Data were fitted to the Ostwald de Waele (Eq. 1) and Herschel-Bulkley (Eq. 2) models, where r (Pa) is shear stress, K (Pa s n ) is consistency coefficient, c (s -1 ) is the shear rate, r o (Pa) is yield stress, and n which is flow index is dimensionless, using Haake RheoWin software, version 3. 61 (ThermoHaake, Germany). r ¼ Kc n ð1Þ r ¼ r o þ Kc n ð2Þ Time depending behavior of milk dessert was evaluated by recording up and down shear stress values between shear rates of 1-200 s -1 for a period of 60 s, and in a reverse sequence for the same time. The thixotropic area (A T ) showing the difference between areas under the upstream data point curve and under the downstream data point curve were obtained using Haake RheoWin software, version 3.
Texture analysis of milk dessert The texture profile analyses test (TPA) were performed, in six repeats, by a penetration test using stainless steel cylindrical probe (15 mm diameter) at the crosshead speed of 1 mm/s and 70% deformation. The TA-XT2i Texture Analyser (Stable Microsystems, Goaldming, UK) with software Texture Export ver. 1. 22 was used in the study. Five parameters were measured during TPA: hardness (N), springiness (m), cohesiveness (no unit), gumminess (N) and chewiness (J). SMP-Skim milk powder, samples description; Con-control (0%-oat gum, 0%-j-carrageenan); OG0(K), OG01(K), OG03 (K), OG05(K)samples with different oat gum concentration, respectively 0, 0. 1, 0. 3 and 0. 5%, K-0. 1% j-carrageenan addition.
64 ϫ 10 Ϫ5 mg/ml [1ϫ] or 1. 28 ϫ 10 Ϫ5 mg/ml [2ϫ]) for up to 90 min. Complete protease inhibitor cocktail (Roche) was added to stop proteinase K digestion after 90 min. The protein-bead complexes were supplemented with Laemmli sample buffer, heated to 99°C for 5 min, and then subjected to SDS-PAGE and Western blot analysis with polyclonal antibodies to the HRC region of F. Relative protein levels were quantified using a Kodak ImageStation and Kodak Molecular Imaging software  (7, 20, 21). Protein expression and purification. The HPIV3 heptad repeat (HR) domains at the N and C termini (HRN and HRC) and the 6 helix bundle complex (6HB) were produced as previously reported  (49). Briefly, the N42-SGG (AKQARSDIEKLKEAIRDTNKAVQSVQSSIGNLIVAIKSVQDYSGG) and C37-SGG (SGGVANDPIDISI ELNKAKSDLEESKEWIRRSNQKLDSIG) fragments were subcloned into the pTMHa expression vector encoding a N-terminal His 9 -TrpLE chimeric protein  (50).
Different nucleotide misincorporation patterns are expected depending on the molecular tools used during library preparation  (37) (38) (39) , and library amplification  (37, 40) , and software such as mapDamage  (34) (35)  has been developed to automate detection of these signals. While observing these patterns in aDNA datasets is compatible with DNA that has originated from an ancient source, it does not rule out the possibility of contamination, as mixtures of aDNA templates and fresh modern contaminants can coexist and generate bona fide patterns. As contamination can originate from many sources (e. g. cross-contamination, microbial colonization, modern human contamination), it is crucial to assess the authenticity of aDNA sequence data sets prior to using these data for downstream analyses.
Here we describe several tools that are useful in the assessment of data authenticity in ancient DNA data sets. We begin by discussing the basic prerequisites for performing these analyses. We then describe the automated PALEOMIX pipeline (section 2). Next, we describe how the signatures of post mortem damage may be examined using mapDamage2. 0 (section 3). Finally, we discuss how the amount of contamination may be estimated for the mitochondrial genome using schmutzi (section 4) and for the nuclear genome using ANGSD and DICE (section 5).
% pip install paleomix In addition to PALEOMIX itself, the following software also be installed: AdapterRemoval v2  (46) , SAMTools  (59) , and BWA  (54). In addition, the Picard Tools 'picard. jar' file must be downloaded and placed in ~/install/jar_root/. This software is used by the BAM pipeline, an automated pipeline for processing and mapping sequencing reads that is included as part of PALEOMIX. mapDamage2. 0  (35)  and the Genome Analysis ToolKit (GATK)  (61)  are optional components for the BAM pipeline, but for the purpose of these instructions we will not make use of these when running this pipeline. To start a new mapping project, first create a default configuration file using output of the built-in 'makefile' command in the PALEOMIX 'BAM pipeline':.
% bam_pipeline makefile > project. yaml As indicated by the extension, the makefile is based on the YAML markup language (http://www. yaml. org/), a text format for organizing data that is both easy to read and easy to manipulate using any standard text editor. The following instructions will focus on mapping to the mitochondrial genome, and assume that the mitochondrial genome is located in a read/writable directory at /genomes/mitochondria. fasta, but this may be substituted by any other path. To specify that the aDNA fragments should be mapped to this genome, open the 'project. yaml' file in a text editor (vim, emacs, nano, sublime, etc), and locate the section of the file starting with 'Prefixes:'. There specify the name and location of the genome that we wish to map to (named 'Mitochondria' in the following example): Prefixes: Mitochondria: Path: /genomes/mitochondria.
3. Remove any reads not mapping to the reference genome. 4. Process mapped reads using the SAMtools 'calmd' and 'fixmate' commands, to ensure that mate information is correct, and to ensure that the 'MD' is present. 5. Remove reads identified as PCR duplicates (here using the Picard 'MarkDuplicates' command). 6. Index the BAM for fast retrieval of arbitrary regions. Based on the settings listed above, the resulting BAM file, containing alignments against the mitochondrial genome, will be located in the current directory with the filename 'SRR123456. mitochondria. bam'. This file is suitable for analysis following the instructions laid out in section 4. 2 aiming at quantifying present-day human contamination from mitochondrial data and for obtaining a consensus call for the endogenous mitochondrial genome.
Post mortem damage and fragmentation plots Before proceeding, first install mapDamage2. 0 and its dependencies as described at http://ginolhac. github. io/mapDamage/. To carry out basic plotting using mapDamage2. 0, two files are required: Firstly, a reference sequence in FASTA format, and secondly, a BAM file containing alignments to that reference sequence. The BAM file must fulfill the requirements described in section 2, with the exception of the 'MD' tag information, which is not required. Once these requirements are met, basic plotting may be carried out using the following command (shown here for the example BAM produced in section 2): % mapDamage -r /genomes/mitochondria. fasta -i SRR123456. mitochondria. bam --nostats The --no-stats option is required to disable the modeling of post mortem DNA damage parameters, which is otherwise carried out by default.
Firstly, the presence of post mortem damage can create false signals of mutation, especially at low coverage (see  (72)  for further discussion). Secondly, at high levels of present-day human contamination, a simple consensus call might predict the contaminant base due to its higher relative abundance (see Figure  2 ). Based on the hypothesis that a higher rate of post-mortem damage is expected in endogenous fragments, compared to present-day human contamination, algorithms have been designed to isolate endogenous DNA fragments showing signs of post mortem damage. These have been found to be able to reliably predict the endogenous mitochondrial genome for even highly contaminated data sets  (73). The computational strategy to predict the endogenous mitochondrial genome depends on the level of present-day human contamination.
The underlying assumption is that the reads matching the base observed for humans must represent contamination, whereas reads matching the Neanderthal base are endogenous in origin. Although this approach is suited for Neanderthal samples with high mitochondrial coverage, it suffers from a few shortcomings. First, it is impossible to include the probability of seeing an endogenous fragment matching the contaminating base either due to sequencing errors or post mortem DNA damage, in the computation of the contamination estimate. Second, it is impossible to know, a priori, which bases on the mitochondrial genome are fixed in both Neanderthals and humans, which might bias any analyses of low-coverage data from an individual belonging to either an individual from a previously unsampled Neanderthal population or to a novel archaic group.
This method used as input the predicted endogenous mitochondrial genome, the aligned aDNA fragments and a database of potential contaminants. This method has the advantage of allowing for multiple, different mitochondrial genomes as putative contaminants, each of which may contribute different amounts to the present-day human contamination. A caveat is that this method has a single error parameter, representing both deamination and sequencing errors. This error parameter is estimated on regions of the mitochondrial genomes that the program calls as monomorphic across all individuals including the endogenous one. This method also requires as input the sequence of the endogenous mitochondrial genome, which cannot be derived from simple consensus calls for highly contaminated samples.
Bayesian maximum a posteriori estimate The task of estimating present-day human contamination is intertwined with the problem of inferring the endogenous mitochondrial genome. Conversely, the strategy used to infer the endogenous genome is highly dependent on the level of present-day human contamination. The software package schmutzi, freely available at http://grenaud. github. io/schmutzi/, contains two main modules to perform both tasks based on Bayesian maximum a posteriori algorithms  (69). The first module, endoCaller, predicts the endogenous mitochondrial genome given a set of deamination parameters, a contamination prior and the fragment length distribution for both the endogenous and present-day human contamination. The second module, mtCont uses the endogenous mitochondrial genome as well as a representative database of known human mitochondrial genomes to predict the rate of present-day human contamination.
schmutzi requires a Unix environment along with a C++ compiler, a Perl interpreter and a recent version of R with certain packages. This section also assumes that the user has a mapped, sorted and indexed BAM file prepared as described in section 2. A wrapper-script allows the user to iteratively run endoCaller and mtCont. As endoCaller is the first program called as part of this iterative procedure, it requires a prior for the amount of present-day human contamination as well as rates of post mortem damage for the endogenous fragments. This prior information is supplied by another submodule called contDeam. pl, which is detailed in the next section.
Prior based on deamination patterns Estimating the rate of present-day human contamination using the deamination patterns has first been suggested by  (15). The schmutzi package contains an implementation under a probabilistic framework. The principle undergirding an estimation of present-day contamination using the frequency of C to T misincorporations at the ends of the fragments is illustrated in Figure  3. Assuming that the misincorporation rates of the endogenous fragments are known, and that the fragments stemming from the present-day human contamination are free of post-mortem damage, it follows that the final observed frequency of C to T misincorporations could provide an estimate of the proportion of both sources. The misincorporation rate for the endogenous fragments can be obtained using a double conditioning procedure whereby fragments with deamination on their 5' end are retained, and the deamination on the other end is measured and vice versa.
Iterative consensus call and contamination estimates The output of contDeam. pl, which was detailed in the previous subsection, is used as input for endoCaller. This sub-module aims at producing the sequence of the endogenous mitochondrial genomes given a prior on present-day human contamination, rates of postmortem damage as well as fragment length distribution for the endogenous and contaminant independently. It is also worth noting that this module is also useful for calling non-hominin haploid genomes where the impact of post-mortem deamination on consensus called needs to be mitigated (e. g. animal mitochondrial genomes, chloroplasts, viruses). If endoCaller is used to mitigate the impact of post-mortem damage on the consensus call, the following command can be used:.
% endoCaller -cont [contamination prior] -single -seq [output prefix]. fa -log [output prefix]. log -deam5p [output prefix]. endo. 5p. prof -deam3p [output prefix]. endo. 3p. prof [mt reference] [input bam file] The -single flag represents the prior assumption that contamination stems predominantly from a single mitochondrial genome (see description of the various options for endoCaller below). The endoCaller program accepts the following options to modify the calculation of the endogenous base and (potentially) the contaminant base: • -deam5p [file]: The [file] contains the frequency of misincorporations due to deamination starting from the 5' end for the endogenous fragments. Equivalent options are found for the 3' end of endogenous fragments. Again, equivalent options are found for contaminant fragments to account for the possibility of having deamination for contaminant fragments.
• -cont [c]: this option specifies the prior on the rate of present-day human contamination. • -single: this flag tells the algorithm to assume that there is a single mitochondrial genome for the present-day human contaminant. This is especially useful at high levels of present-day human contamination. When this flag is not used, endoCaller can only mitigate the presence of low amounts of present-day human contamination (below ~30%). Once the endogenous mitochondrial genome has been obtained, present-day human contamination can be estimated using mtCont, as follows: • -deam3p [file]: Same as above except for the 3' end of endogenous fragments. To obtain accurate results, users should use endoCaller and mtCont iteratively until results are stable. To facilitate this process, a wrapper script calls both programs iteratively.
% schmutzi. pl --notusepredC --uselength --ref [mt reference] --out [out prefix]_npred [output prefix] [path to schmutzi]/eurasian/freqs/ [input bam file] where options --notusepredC and --uselength instruct the program not to use the predicted contaminant as a contaminant profile (see comments about mtCont above) and used the length of the aDNA fragments to help the identification of the endogenous base. The term [output prefix] is the same used by contDeam. pl. The [path to schmutzi]/eurasian/freqs/ is a path to a set of potential mitochondrial contaminants that is provided with the software package. As the name indicates, it focuses on Eurasian mitochondrial sequences. However, users can build their custom library of putative contaminants and the path to this new database can be modified.
Finally, the file with the _final_endo. fa suffix will contain the unfiltered fasta prediction for the endogenous genome and _final_endo. log contains the per-base likelihood of being the endogenous base. More generally, the wrapper script schmutzi. pl can accept the following options: • --contprior [c]: This is the value in the first iteration for the rate of present-day human contamination to be used as prior for endoCaller. When unspecified, the value predicted by contDeam. pl is used as prior. This option is only recommended if the iterative procedure has not converged. • --uselength: This flag instructs the algorithm to also use the length of the aDNA fragments in the computation to distinguish the endogenous base from the contaminant one. The use of this flag can lead to more accurate results for highly contaminated samples as present-day contaminants are expected to show longer size distributions than short aDNA fragments • --lengthDeam [bp]: this option instructs the algorithm to consider the [bp] bases away from the 5'/3' ends to be the most deaminated.
Limitations The schmutzi software package is able to quantify present-day human contamination as well as infer the endogenous mitochondrial genome even in highly contaminated datasets. However, this package faces certain limitations. First, there is no guarantee that the algorithm will converge. This is especially the case for highly contaminated samples with medium coverage where the algorithm can oscillate between two equally likely states. Furthermore, the algorithm can converge on an incorrect value especially for very low coverage samples (below ~5-fold). Second, this algorithm does not take demography into account when predicting bases. This would be highly useful for low coverage samples as the likelihood of a certain base changes depending on which haplogroup the endogenous mitochondrial belongs to.
Estimating contamination in human nuclear genomics data sets Estimation of present-day human contamination in nuclear genomic data faces several unique challenges compared to working in mitochondrial data sets. First, the relative per-site coverage of the nuclear genome is likely to be low compared to that of the mitochondrial genome. Second, heterozygous sites may exist in both the endogenous and contaminant individual(s), and this can complicate calculations. An initial idea to quantify present-day human contamination for ancient nuclear genome data relied on sites where derived alleles were practically fixed in present-day humans  (38). The method computed the likelihood of observing the data given a certain contamination rate and three different models of endogenous genotypes: homozygous derived, homozygous ancestral and heterozygous for both alleles.
Contamination estimates using the X chromosome The software ANGSD allows for the analysis of next-generation data by allowing users to compute various statistics for population genetics directly from BAM files. This software is freely available from https://github. com/ANGSD/angsd and is written predominantly in C++. Installation instructions are provided with the software. ANGSD requires a Unix environment along with a C++ compiler and a recent version of R with certain packages. The following instructions assume that the user has a mapped, sorted and indexed BAM file prepared as described in section 2. Apart from statistical tests for population genetics, ANGSD can be used to obtain estimates of present-day human contamination using the X chromosome for males. We thus first need to ascertain if the ancient sample is likely a male, for instance using X-to-Autosome coverage ratios and the methodology described in  (80).
Viruses, for example, tend to have few genome reference sequences or custom assembly algorithsm for ancient DNA, and have a rapid rate of molecular evolution that is likely to complicate assembly. Bacteria are present in nearly all aDNA extracts, but it is challenging to determine whether the becteria were present when the organism was alive or at what point they infiltrated the samples. In addition, the representation of the diversity of microbial life in existing genomic reference databases is poor compared to the actual diversity of microbes across the planet, which complicates identification and assembly. Plants can have large polyploid genomes that make the presence of contaminants, particularly from closely related species, challenging to identify. These and other challenges need to be taken into consideration when interpreting results from aDNA experiments, even when care is taken to authenticate data using these or other pipelines.
Mapping and mapDamage • Due to the circularity of the mitochondrial genome, fragments that span the junction in the reference in fasta format might not be aligned properly. This can lead to a spurious drop in coverage in that region and a lack of resolution of those regions. A possible way to mitigate this is to extend the mitochondrial reference by copying the initial 1000bp bases at the end of the fasta reference and mapping the aDNA fragments against this new reference. A tool like bam-rewrap (https://bitbucket. org/ustenzel/biohazard) can allow users to specify the initial length of the mitochondria and copy the alignments exceeding the original length back to their original location at the beginning of the reference. • It is important that the correct adapter sequences be specified when running the PALEOMIX pipeline or when trimming sequences using another program.