dataesr/scientific-paragraphs-categorization huggingface dataset:
We found that familial ALS is associated with a younger age of onset, consistent with previous studies, and that this is also true for those with C9orf72 expansion mutation, regardless of family history  [34] [35] [36]. Furthermore, our results support previous studies finding that the frequency of the C9orf72 expansion mutation in the general population is about 0. 2%  37. Previous independent research has shown C9orf72 repeat  expansion in healthy individuals in whom the expansion was confirmed by standard laboratory methods  37. To confirm that the expansion can be seen in unaffected individuals, we have calculated the number of controls with C9orf72 repeat expansion in new data in the Project MinE dataset and found 10 more with this expansion. While it might seem strange that a major ALS risk gene should be seen in unaffected control individuals, it is in fact expected, since there is age-dependent penetrance, penetrance is incomplete, and the effects of the expansion mutation are pleiotropic, increasing risk for several conditions other than ALS.
The rate we observe is similar to that seen in other studies and in public databases. VCP inversion is associated with longer survival as well as younger age of onset. Our findings also suggest that VCP structural variation might be a marker for cognitive impairment and ALS-FTD, supporting previous work showing an association of common variation in the VCP gene with FTD and cognitive impairment  26, 38, 39. Although, age of onset can be a good predictor of disease course, age of onset is not determinative of age at which death occurs. We have shown that both age of onset, age at death and disease duration are highly variable between individuals and genetically influenced. The genetic associations we have found in apparently sporadic ALS are in genes previously identified from family-based studies (C9orf72, ERBB4 and VCP) supporting the notion that familial and sporadic ALS are not mutually exclusive categories but rather a spectrum  36, [40] [41] [42].
We have analysed whole genome sequencing data generated using two sequencing platforms, the HiSeqX Illumina platform, and the HiSeq2000 platform, which increases the possibility of a batch effect. However, cases and controls are similarly distributed between the platforms (HiSeq2500 66% cases, 34% controls and HiSeqX 70% cases and 30% controls) (p = 0. 54). To overcome this potential weakness, all the samples used were sequenced at the same Illumina lab using two industry-leading sequencing platform for all samples, as well as designing the study to minimize batch effects by having cases and controls sharing the same sequencing plate, and taking sequencing platform into account as a covariate in our analyses. Furthermore, although we have assessed reported ALS-associated rare missense and loss of function variants in linkage disequilibrium with the structural variants, we cannot exclude the possibility that the differential risk and phenotypes observed could be modulated by small common single-nucleotide variants or indels in linkage disequilibrium with the structural variants.
Using GeneVar SV data browser  43  , the estimated frequency of VCP inversion is 0. 0005 and ERBB4 insertion 0. 82. However, allele frequencies tend to vary across human populations, and different SV callers may give varying results between datasets. To allow full comparison between studies therefore requires the sequencing platform, population tested and SV callers are identical  44. Another limitation is that we restricted the analysis of structural variation to known ALS genes. Extending the analysis to the entire genome would give a comprehensive view of this type of genomic variation in ALS, but with current technology is extremely resource intensive. Finally, ALS is a disease of the central nervous system, but our WGS data are derived from leukocyte DNA, since our DNA source was whole blood, and somatic mutation affecting the nervous system cannot therefore be assayed with our method.
However, our findings have the advantage of a large sample size of more than 4300 cases. Our analysis was restricted to a statistical genomics approach. Although the sample size is large, replicating our results in other ALS datasets and performing wet lab confirmation will be needed to validate the findings. We are reassured by the observation that VCF calls were matched with raw BAM file reads when tested. Analysis of structural variation shows that such genetic variations influencing site of onset also modify risk, as is true for single nucleotide variations. Our finding that 71. 4% of people with respiratory onset ALS have insertion in the ERBB4 gene is an important clue to disease mechanism and factors that determine which group of motor neurons are most vulnerable at disease onset, a key issue in neurodegenerative disease research.
To assess survival in respiratory onset ALS we also used Kaplan-Meier survival analysis. Statistical tests were performed using IBM SPSS Statistics 24. 0 (SPSS Inc. , Illinois), RStudio, R Foundation for Statistical Computing 3. 4. 1. We tested four structural variation categories: deletion, insertion, inversion, and duplication, in 25 genes. Therefore, we used 0. 0005 [0. 05/(25*4)] as the Bonferroni-corrected threshold for multiple testing correction.
Quality control There were 6,195 samples (4,315 from people with ALS and 1,880 controls) passing quality control from a total of 6580 whole genome sequences. Quality control was preformed separately on genotyped data of each population according to the Project MinE methods published previously  51. Sample mismatch was tested using sex checks, where genetic sex was compared to reported gender. After quality control, the full set of genomic Variant Call Format files (gVCFs) were merged together by first converting the gVCFs to Plink format and then merging all files together. This generated a single dataset containing all variant sites across all individuals. Non-autosomal chromosome and multi-allelic variants were excluded from pilot analyses. Sample and SNP quality control were performed using Plink  51, 52  and VCFtools 53.
Furthermore, the power to detect any break-end falls to almost zero as the break-end repeat length approaches the fragment size. Therefore, we used Expansion Hunter and data obtained from real time PCR to confirm C9orf72 expansion status. Following Expansion Hunter tool instructions, the genome coordinates that were used to confirm C9orf72 expansion status were chr9:27573527-27573544 and the motif GGCCCC  50. Additionally, 29 off-target regions were also included to determine the C9orf72 repeat size. (Please refer to the Expansion Hunter Github page (https://github. com/Illumina/ExpansionHunter) for the exact coordinates of the 29 off-target regions). If 30 or more repeats was reported, an allele was considered expanded  50. Furthermore, Manta is unable to detect inversions less than about 200 bases in size.
ACKNOWLEDGEMENTS Samples used in this research were in part obtained from the  UK National DNA Bank for MND Research , funded by the  MND Association  and the  Wellcome Trust. We thank people with  MND  and their families for their participation in this project. We acknowledge sample management undertaken by  Biobanking Solutions  funded by the  Medical Research Council at the Centre for Integrated Genomic Medical Research, University of Manchester. This research was funded in whole, or in part, by the  Wellcome Trust  [Grant number]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The authors acknowledge use of the research computing facility at King's  College London, Rosalind  (https://rosalind.
A. A. K. is funded by The  Motor Neurone Disease Association (MNDA) ,  NIHR Maudsley Biomedical Research Centre  and and  ALS Association Milton Safenowitz Research Fellowship. This project was funded by the  MND Association  and the  Wellcome Trust. This is an  EU   Joint Programme-Neurodegenerative Disease Research (JPND)  project. The project is supported through the following funding organisations under the aegis of JPND -www. jpnd. eu( United Kingdom ,  Medical Research Council  ( MR/L501529/1  and  MR/R024804/1 ) and  Economic and Social Research Council  ( ES/L008238/1 )). A. A. C. is an  NIHR Senior Investigator. C. E. S. and A. A. C. receive salary support from the  National Institute for Health Research (NIHR) Dementia Biomedical Research Unit at South London and Maudsley NHS Foundation Trust and King's College London.
The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR  or the Department of Health. The work leading up to this publication was funded by the  European Community 's  Health Seventh Framework Program  ( FP7/2007-2013 ; grant agreement number  259867 ) and  Horizon 2020 Program  ( H2020-PHC-2014 -two-stage; grant agreement number  633413 ). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 772376 -EScORIAL. The collaboration project is co-funded by the  PPP Allowance made available by Health~Holland ,  Top Sector Life Sciences & Health , to stimulate public-private partnerships. Project  MinE Belgium  was supported by a grant from  IWT , the  Belgian ALS Liga  and a grant from  Opening the Future Fund (KU Leuven ).
PVD holds a senior clinical investigatorship of  FWO-Vlaanderen  and is supported by  E. von Behring Chair for Neuromuscular and Neurodegenerative Disorders , the  ALS Liga België  and the  KU Leuven funds "Een Hart voor ALS", "Laeversfonds voor ALS Onderzoek " and the " Valéry Perrier Race against ALS Fund ". R. L. McL. is supported by  Science Foundation Ireland  ( 17/CDA/4737 ). MinE USA  is funded by the  US ALS Association. We thank the patients and families from the  Emory ALS  Clinic participating in this research. Funding was provided by  US National Institutes of Health (NIH)/National Institute of Neurological Disorders and Stroke (NINDS)  ( R01NS073873 , J. E. L. ) and the  American ALS Association  (J. E. L. ).
Therefore, a problem specific approach may be a good way of avoiding pitfalls. The problem we will be interested in is intimately related to the problem of fair clustering. Classical (or standard) cluster analysis or clustering is the task of dividing a set of objects in such a way that elements in the same group or cluster are more similar, according to some dissimilarity measure, than elements in different groups. Such methods have been extensively investigated in the literature. We refer to  Hennig et al. (2015)  and references therein for an in-depth overview. However, classical clustering is insensitive to notions of mathematical fairness. Suppose we observe data that includes information about attributes that we know, or suspect are biased with respect to the protected class. If the biased variables are dominant enough, a standard clustering on the unprotected data will result in some biased clusters, that is, clusters with possibly large differences in distribution of the protected attributes.
Hence, a fair clustering would be the situation in which, in the partition of the data, the proportions of the protected attributes are the same in each cluster (hence, the same as the proportions in the whole dataset). We might argue that balance (demographic parity) is not necessarily equivalent to fairness, and proper consideration must be given to the problem of when it is appropriate and useful to identify fairness with balance. Nonetheless, diversity in itself may be a desirable property. Even more, diversity may be easier to formalize and impose, since it is less charged with ethical connotations. Hence, we will focus on clustering procedures that favour diversity in the protected attributes, in what we call diversity enhancing clustering. In our setting, diversity enhancing clustering is a relaxation of diversity preserving clustering (which is usually considered as a fair clustering method).
The latter can be described as follows. Let us have a dataset DS = {(X 1 , S 1 ),. , (X n , S n )}, with S i ∈ S, and for simplicity assume that the protected attributes S are discrete. Notice that the unprotected attributes X i and the protected ones S i should be correlated for diversity preserving clustering to be relevant. A diversity preserving clustering or a diversity preserving partition, C f = {C i } k i=1 , where C i ⊂ DS, fulfils that |{(x, s) ∈ C i : s = j}| |C i | = |{(x, s) ∈ DS : s = j}| |DS| for every j ∈ S and i = 1,. , k. (1) In this case the proportion of individuals in any cluster for any protected class is the same as the respective proportion in the total data, hence we could say that proportions are independent of the cluster. This means that any decision taken with respect to a particular cluster will affect individuals in the same proportion as if it were taken for the entire population.
-A new methodology based on a heuristic that promotes diversity and is applicable to almost any clustering algorithm with no or minimal modifications to the original objective functions. -A call for diversity and, more broadly, fairness in clustering to be considered jointly with other relevant aspects that make a partition sensible. -A methodology that is easily adaptable to non-Euclidean data and clusters with non-convex boundaries. -An R package to perform Attraction-repulsion clustering freely available at https://github. com/HristoInouzhe/AttractionRepulsionClustering The structure of this work is the following. Section 2 presents a brief overview of related works. In Sect. 3 we introduce attraction-repulsion dissimilarities. Clustering methods are developed in Sect. 4 while Sect. 5 is devoted to their extension to non-Euclidean spaces through a kernel transformation.
We study the tuning of our methods in Sect. 6. Experiments for our technique are given in Sect. 7. We provide a discussion on synthetic datasets in 7. 1. In Sect. 7. 2, we provide comparison between our methods and the ones proposed in  Chierichetti et al. (2017) , on synthetic and real data, showing the different behaviour of both procedures. In Sect. 7. 3 we give a full example of attraction-repulsion clustering on a real dataset. Section 8 is devoted to some final remarks and some future work proposals.
-If minΔ i, j ≤ 0, transform the original dissimilarity to have positive entries: Δ i, j = Δ i, j + |minΔ| + , where is small. -For δ 1 , δ 2 , δ 3 : Δ i, j = Δ i, j. -Use MDS to transform (X 1 , S 1 ),. , (X n , S n ) into X 1 ,. , X n ∈ R d , where D i, j = X i -X j is similar to Δ i, j. -Apply a clustering procedure on the transformed data X 1 ,. , X n. This procedure will be studied in Sect. 7 for some synthetic and real datasets.
Diversity and quality of a clustering There are different measures for diversity, which, in a context where diversity is a good proxy for fairness, can also measure fairness. We will be mainly interested in two of them. As introduced in  (Chierichetti et al. 2017) , the balance of a set of points X with protected attributes S = {red, black} is defined as balance (X ) = min # Black # Red , # Red # Black and the balance of C, a clustering of the data in X , is given by balance (C) = min C∈C balance(C). ( 13 ) Let C be a clustering of a dataset into K clusters. For each cluster k, 1 ≤ k ≤ K , there is an associated proportions vector p k , where p k is formed by the proportion of each value of the protected attributes in the cluster k. A simple measure for the diversity of a partition is imbalance(C) = 1 K K k=1 p k -p t , ( 14 ) where • denotes the usual Euclidean norm and p t the vector of proportions of each value of the protected attributes in the whole dataset.
Comparison with fair clustering through fairlets In this section we present a comparison of the results of our diversity enhancing clustering methods with results obtained by implementing, in Python and R, the fair clustering procedure introduced in  Chierichetti et al. (2017)  based on fairlets decomposition. We   16 ), for fixed V = ((1, -1) |(-1, 0) ), v = 20 and w = 0. 05. First two columns contain the proportion of points with S = (0, 1) in the clusters found with tclust in the transformed space. Last two columns show the silhouette of the original classes in the MDS u Prop. in cluster 1 Prop. in cluster 2 Silhouette for (0, 1)   2017 ) for comparison since it is well suited for this situation. Our implementation of the case when the size of both protected classes is the same, which reduces to an assignment problem, is implemented using the function max_bipartite_matching of the package igraph in R.
Our method gives a lower balance value; hence groups are less diverse, but as we see from the silhouette and k-median objective function values, the groups are more identifiable and more compact. Even more, comparing the middle-right of Fig. 4  and left of Fig. 6 , we see that our procedure is even able to capture the underlying geometry of the data. Our last comparison is on a real data set known as the Ricci dataset, consisting of scores in an oral and a written exam of 118 firefighters, where the sensitive attribute is the race of the individual. This dataset was a part of the case Ricci v. DeStefano presented before the Supreme Court of the United States. For applying our attraction-repulsion clustering we codify white individuals as S = 1 and black and hispanic individuals as S = -1. The appropriate parameters for the dissimilarities are chosen to give a good performance (after a grid search as suggested in Sect.
Civil rights data collection In this section we are going to apply our procedure to the Schools Civil Rights Data Collection (CRDC) for the year 2015-2016 available for download in the link https:// ocrdata. ed. gov/DownloadDataFile. We are going to work with the data for entry, middle level, and high schools in the state of New Jersey. From the CRDC data we can collect the number of students in each school that belong to six distinct categories: Hispanic, American Indian/Alaska Native, Asian, Native Hawaiian/Pacific Islander, Black, White. An entry of our dataset looks like this. Additionally, using geolocate of the package ggmap in R we can extract latitude and longitude coordinates of the schools in New Jersey. Hence we have a precise location for the schools and can calculate distances in a straight line between them, i.
At this point, we should be aware that diversity and vicinity requirements may be conflicting, and therefore Fig. 8  Left: location of schools in the state of New Jersey. Right: mds-embedding of the straight-line distances between the schools some kind of trade-off between them is advisable. This setting is the one that we adopt and present attraction-repulsion clustering results below. We will measure the degree of diversity of the partition by comparing the ethnic composition of each of its clusters to the ethnic composition of the overall dataset which is p t = (0. 2423, 0. 0018, 0. 0988, 0. 0028, 0. 1604, 0. 4741). This is done by using the imbalance index defined in (  14 ). Recall that the average silhouette index is a measure of the compactness of the clusters in a partition. Hence our methodology is to use attraction-repulsion clustering where tuning is done over a mesh of distinct parameters, and the best parameters are those that give the lowest imbalance while keeping the average silhouette index over some threshold τ.
itividad, grant  MTM2017-86061-C2-1-P  and  Junta de Castilla y León , grants  VA005P17  and  VA002G18. Research partially supported by  ANITI program  and DEEL IRT. Research partially funded by the  Basque Government  through the  BERC 2018-2021 program  and by the  Spanish Ministry of Science, Innovation, and Universities (BCAM Severo Ochoa accreditation   SEV-2017-0718 ). Funding Open Access funding provided thanks to the  CRUE-CSIC agreement with Springer Nature.
We performed a second clustering using a classical OTU clustering at 97% similarity with VSEARCH. We assigned a taxonomy to each OTU using the SILVA database  (53) , filtered out chimeras, and built an OTU table indicating for each OTU its abundances in the different spiders' microbiotas. Finally, nonbacterial OTUs and contaminant OTUs present in high abundances in the negative controls were filtered out of the OTU table. We obtained a total of 413 Swarm OTUs and 414 OTUs at 97%, which correspond to totals of 1,297,307 and 1,178,325 reads, respectively. Rarefaction analyses confirmed that the sequencing coverage was sufficient to get most of the bacterial diversity in the majority of the samples  (17). Assessing phylosymbiosis. We assessed phylosymbiosis using two different tests  (13, 14) : (i) a Mantel test between Ariamnes phylogenetic distances and microbiota beta diversities and (ii) a matching cluster analysis  (54) , which tested the topological congruence between the Ariamnes phylogeny and microbiota dendrograms constructed from microbiota beta diversities (see supplemental methods 2 at https://doi.
We first reconstructed the ancestral biogeography of the host phylogeny using stochastic mapping (make. simmap function, phytools R package  [60] ), considering that migrations between areas are punctuated events occurring with different estimated probabilities, represented by a symmetrical matrix of transition (Q). We considered g, the probability for a host switch to occur between hosts from different areas divided by the probability for a host switch to occur between hosts of the same area: if g is equal to 1, host switches between hosts from different areas are as likely as host switches within the same area, whereas a g value of 0 corresponds to a scenario where host switches occur only between hosts sharing the same area. We considered that the probability that a host switch occurs at time t depends on the total number of hosts sharing the same area at time t and the number of hosts alone on their own area at time t multiplied by g, such that: P host switch at time t ð Þ ; X A2 areas; NA t ð Þ.
ACKNOWLEDGMENTS We acknowledge  E. Armstrong ,  J. Lim ,  A. Rueda ,  T. Schol ,  S. Kennedy , and  S. Prost  for helpful discussions. B. P. -L. and H. M. also thank  D. de Vienne ,  M. Elias ,  M. -A. Selosse ,  F. Martos ,  F. Delsuc ,  M. Roy ,  L. Aristide ,  C. Fruciano ,  I. Quintero ,  S. Lambert ,  I. Overcast ,  O. Maliet ,  A. Silva , and  G. Sommeria  for comments on an early version of the manuscript. We also thank the anonymous reviewers for their constructive comments. This work was supported by a doctoral fellowship from the  Ecole Normale Supérieure de Paris  to B. P. L. and the  Ecole Doctorale FIRE -Program Bettencourt. R. G. G. acknowledges support from the  National Science Foundation ,  DEB 1241253. H. M. acknowledges support from the  European Research Council  (grant CoG-PANDA).
7) makes WASP-193 b an exquisite target for a detailed atmospheric characterization by transmission spectroscopy. The WASP (Wide Angle Search for Planets 1 ) ground-based transit survey has discovered almost 200 planets, transiting bright nearby stars mostly, hot Jupiters plus some short-period Saturn-and Neptune-mass objects. Many WASP planets have proven to be favorable targets for detailed atmospheric characterization with large ground-based telescopes (e. g. VLT  2  , E-ELT  3  , Magellan 4 ), HST  5  and JWST  6  , e. g. WASP-17 b  7. The project consists of data obtained at telescopes located at two sites: Sutherland Station of the South African Astronomical Observatory (SAAO) and the Observatorio del Roque de Los Muchachos on La Palma in the Canary Islands. The field-of-view (FOV) of each instrument was 482 deg 2 with a pixel scale of 13.