dataesr/scientific-paragraphs-categorization huggingface dataset:
A statement of Cypher is a sequence of clauses, with next clause taking as its input the result of the previous one. Moreover, static and dynamic features are mixed freely: one can start with a pattern matching clause, then update a database based on the matched patterns, and then proceed with another pattern matching clause on the updated database -all in one single statement. When it comes to dynamic aspects of the language, the facilities it provides are of two kinds. First, there are analogs of the usual insertion, deletion, and modification clauses of SQL, though more complex ones, to reflect the fact that both graph topology and stored data can be updated. Then there are facilities that reflect more recent additions to the SQL Standard, that mix updates and insertions in a single statement, but again they do it in a more complicated fashion reflecting the datamodel.
Specifically, some pattern matching clauses are designed in a way that they never fail: if a match is not found, the database is updated in a way that creates a match. One of many possible uses of such a facility is to populate a database with nodes and relationships: those can be read from a file, and added to the database, but only if they do not already exist. The goal of this paper is to tell the story of dynamic aspects of Cypher and Neo4j. However, we do not only describe graph database updates. Instead, we explain what they are, what the main issues with the current state of affairs are, how to fix problems, and what future update features will be. In more detail, we do the following. • We first explain the current state of affairs. Update features were added to Cypher in a rather ad hoc manner, to address specific users' needs.
We survey them, and explain a number of issues caused by the update facilities of Cypher 9, the current release of the language. • We then describe how these issues were resolved in a process that involved a collaboration between Neo4j, its academic partner (the database group at the University of Edinburgh), and users of the Neo4j product. A number of alternative solutions were proposed by the academic team. They were presented to many users of the Neo4j product (including participants of the open-Cypher Implementers Meeting, as well as Field Engineering and Developer Relations Teams of Neo4j), who were invited to comment on the proposals and the real-life usage of update operators. Their answers were then used to choose possible solutions for solving problems with current update facilities.
• We present the outcome of this consultation, describing new update features of that will be available in the future releases of Cypher. These could be useful for other existing property graph query languages  [23, 7] , and could inform the design of new ones. Organization. In Section 2 we introduce the property graph model and Cypher by example. In Section 3 we describe updates as they currently are in Neo4j's implementation of Cypher. Section 4 explains the main issues with the current state of Cypher's dynamic features. In Section 6 we outline various proposals for fixing these problems. Section 7 presents the decisions about new update features based on interactions with Cypher's user base. In Section 8 we outline the formal semantics of new Cypher updates. Section 9 provides a final summary and outline of the implementation plans in the Neo4j product.
QUERYING GRAPHS WITH CYPHER We give a brief introduction into the property graph model and the Cypher query language, by means of examples. Unlike the relational model, the graph data model stores information in the form of a graph that consists of nodes and relationships between those nodes. Nodes usually model real-world entities, while relationships model connections between those entities. Additional information about the entities and the connections between them is stored in both nodes and relationships in the form of node labels, relationship types and (node or relationship) properties. As a running example, consider the back-end of an online marketplace where users can buy products that are offered either by professional vendors or by other users. In this example, nodes of the graph database represent users, vendors and products, and relationships detail which products were ordered by which users, and which users or vendors offer which products.
• Both nodes and relationships may have an optional property map, given as a set of key-value pairs. In the example, all nodes have id and name properties, while relationships do not have any properties; in general, property keys can also vary between nodes (or relationships, respectively). We next describe the basic features of the Cypher language, the query language of the Neo4j graph database product. The querying fragment of the Cypher language for property graphs uses an "ASCII art" representation of nodes and relationships. For instance, the query MATCH (p:Product)<-[:OFFERS]-(v:Vendor) -[:OFFERS]->(q:Product) WHERE p. name = "laptop" RETURN v can be used to find all vendors offering two products, one of which has the name "laptop". We use this query for a very brief introduction to the read-only fragment of Cypher; for a more comprehensive overview, see e.
This was part of an ongoing effort to provide a formal language specification as part of creating a standard for Cypher. Similar efforts for SQL have a long history  [9, 18]  and are still ongoing  [11, 14]. While the read-only fragment of Cypher, along with its formalization, are relatively straightforward, it turns out that updates to property graphs present additional challenges compared to the way that updates are handled in the relational model. In particular, Cypher's modular nature allows for chaining arbitrary sequences of reading and writing clauses in one single query, which raises questions regarding atomicity of statements and transaction boundaries. In the next section we present a brief overview of Cypher updates, before outlining some of the most significant issues that the current state of the art leads to.
PROBLEMS WITH UPDATES While the update operations described in the previous section may seem clear and simple on the surface, they do in fact have some problems that appear when one takes a closer look. In this section, we discuss how the more complex structure of the graphs and tables that Cypher operates on (compared to the relational tables for SQL) leads to violations of some very basic properties one would expect from update operations in databases. In particular, we will see that updates violate the basic principle of atomicity. Intuitively, the reason behind this is that updates appear in a statement where they are preceded by other clauses, and thus they operate over the driving table, going over it tuple by tuple (in a way similar to for each row triggers). While doing so, such updates can see intermediate results, i.
Since tables in Cypher are assumed to be unordered and may be reordered at will by the query-processing engine, this means that the behavior of a MERGE clause may be nondeterministic. For an illustration, consider the following example. : O R D E R E D : O F F E R S : O F F E R S : O R D E R E D : O R D E R E D : O F F E R S (b) u1 p v1 u2 v2 : O R D E R E D : O F F E R S : O F F E R S : O R D E R E D Example 3. In this example, we demonstrate how the behavior of MERGE may be nondeterministic in the current Neo4j implementation. For this example, we assume that the input graph contains only nodes and no relationships, and that the input table is already populated. This assumption is reasonable, as the feedback from the user base tells us that MERGE is often used to populate a graph based on a table that has been produced by importing from a relational database or a CSV file, and that it is a common practice to input nodes first and relationships later.
INPUT FROM NEO4J USERS In order to come up with the redesign of update features that would not only eliminate problems outlined in the previous section but would also satisfy the needs of Neo4j users, it was decided to consult the user base to understand how update features are used, and what is expected of them in practice. Although there was a recent survey of users of graph databases  [22]  that revealed much useful information, it was not specific enough to address the usage of a particular language features, as we need here. We have consulted three groups of users. • The first group consisted of participants of the open-Cypher Implementers Meeting (oCIM). This is the meeting of the openCypher project  [5]  that aims to deliver a full and open specification of the language; it is attended by implementers of the language from several companies and research projects.
Strong Collapse MERGE. It lifts the restriction from Col- lapse MERGE in that it also collapses relationships with identical types, properties, source and target nodes (after collapsing) that match different positions in the input pattern. This behavior lifts the restriction that patterns are matched by traversing each edge at most once. Indeed, after collapsing relationships, it is possible that the pattern in MERGE is satisfied by traversing a relationship more than once. We now give several examples illustrating the different possible semantics proposed for MERGE. Throughout these examples, we often assume that the input graph is empty, while the input table is already populated. As explained in the previous section, this assumption is not artificial: it reflects the way in which a graph database may be initially populated by importing data from a relational database or a CSV file.
SPECIFICATION OF UPDATES Unlike many other languages that are defined by means of a specification in natural language, the read-only core of Cypher has been fully formalized  [13, 12] , in the same spirit of some programming languages  [17, 6, 15, 16] , as well as partial specifications of SQL's semantics  [10, 24, 14]. In this section we explain how to extend the formal semantics of the read-only core of Cypher to the full language, which includes querying and update features. Specifically, we provide the formal semantics of the update constructs specified by the SET, DELETE, CREATE, REMOVE and MERGE clauses. Giving full details of the semantics of all constructs entails significant space requirements; thus, we only  A complete version of the semantics is available in  [12].
as a function [[C]] ro G that takes a driving table and returns a modified driving table (and similarly for queries consisting of read-only clauses). In order to take into account the dynamical aspects considered in this article, we extend this definition as follows. • [C]](G, T ) = (G, [[C]] ro G (T ) ). Semantics of read-only queries is done similarly. In other words, the semantics of read-only clauses and queries does not modify the graph database, as expected. Note that the semantics of a query Q should not be confused with the output of Q. Indeed, the evaluation of a query starts with the graph in the database and the table containing one empty tuple. This graph-table pair is then progressively changed by applying functions that provide the semantics of Q's clauses. The composition of such functions, i.
CONCLUSIONS We analyzed update features of Cypher, a widely used graph query language implemented in the Neo4j database product, and several others. We identified a number of issues and shortcomings in the current implementation and proposed a number of fixes, with which we then consulted the user base, to decide on the final modifications to Cypher updates. These restored atomicity and determinism of update clauses in the language. The issues of the existing semantics of Cypher 9 update clauses revealed in this study as well as the proposed refinements and corrections are very valuable input to the Neo4j engineering teams. Neo4j plans to integrate the refinements and corrections into its Cypher implementation in future version of the Neo4j database system under the existing deprecation regime to avoid or minimize query breakage for customers.
Análisis estadístico Los datos se analizaron utilizando SPSS v. 20. 0 (IBM Corp. , Armonk, NY, EE. UU. ). Las variables continuas se representaron como media con desviación estándar y las variables categóricas como mediana y rango intercuartil. La prueba t de Student (dos colas, independiente) se usó para los parámetros métricos en escala continua entre dos grupos y la prueba U de Mann Whitney se utilizó para variables discretas. Chi-cuadrado y la prueba exacta de Fisher se utilizaron para comparar los parámetros en escala categórica entre dos o más grupos. El análisis de regresión logística binomial se utilizó para identificar factores predictivos independientes de recidiva y sobreestadificación. Se realizó un análisis de supervivencia de Kaplan Meier para comparar la mediana de supervivencia entre la sobreestadificación de cT1 a pT3a con aquellos que no sobreestadificaron y el tiempo de recurrencia.
Given this diversity and flexibility, empirical research on kinship in human societies is needed to inform our understanding of the potential for reproductive conflict and cooperation among women. In this study, our aim is to investigate the extent to which movement influences kinship patterns across human communities and whether this is associated with predictable variation in individual levels of kinship across female lifespans. Given the expected flexibility in human settlement patterns, and the extent to which individuals adhere to these rules, we consider variation in group kinship across the lifespan for immigrants (of both sexes) in each community, drawing contrasts to natal residents to contextualize these changes. To assess these patterns, we compile genealogical and demographic data from 19 communities to examine age-related variation in relatedness to group members.
Arguably, because sharing and competition over resources may be particularly acute within residential clusters of close kin (i. e. sub-units of the larger community), the measure of community relatedness in this study could be too broad  [16]. However, the present compilation of datasets do not permit the identification of relevant subclusters within the communities, a methodological consideration that we address in the discussion. Nevertheless, the numerator in the calculations of average relatedness for a given individual largely reflects the number of close, co-resident kin in the population. Therefore, in larger communities, although most individuals may be distantly related to the individual, variability in average relatedness frequently reflects the presence of close kin and the concomitant opportunities for reproductive conflict and cooperation.
These calculations employ standard methods for estimating the coefficient of relatedness from genealogies  [17]. For any given dyad, the coefficient is therefore constrained to lie between 0 and 1, which we anticipate to provide a useful approximation of genetic similarity. The genealogies from our study sites include at least three generations of depth for residents, typically permitting us to distinguish cousins and closer genealogical relationships. As shown by Pemberton  [18] , pedigrees of three generations capture much of the variation in genetic relatedness among individuals. Some datasets provide even greater depth, and this varying thoroughness introduces between-site variation into the sample. In some communities, most notably the matrilineal Mosuo community, women were often unable to reliably indicate the father of their children.
The data are Population pyramids for the study communities. In the plot, sites are ordered in row-wise fashion according to the ratio of adult female natality to adult male natality (the quotient of the respective quantities in table  1 ). The numbers on the horizontal axes represent the percentage of the total represented by the respective age-sex categories. royalsocietypublishing. org/journal/rstb Phil. Trans. R. Soc. B 374: 20180069 cross-sectional and represent the composition of the community at a single point in time. In our primary analysis, we examine the average relatedness of adults to other adult residents in the community. A focus on adults has parallels to the ethological literature and its attention to inbreeding avoidance among reproductively mature individuals  [19]. Accordingly, we consider females to be adults when they are 18 years or older and males to be adults when they are 21 years or older.
These ages align with evidence that Ache ẃomen typically give birth the first time at 19 years old and that men in natural fertility populations are commonly 3 years older than women when their first child is born  [20, 21]. The analysis is oriented primarily toward illuminating demographic patterns within the respective study sites. We therefore model average relatedness at each site separately using regression models with the main effects and interaction terms of the three predictor variables: age, sex and natality. In some datasets, there were missing values for individuals' age and natality. Typically, these individuals were not permanent residents of the respective communities, so although their genealogical connections were included in the calculations of dyadic relatedness, the average relatedness of these individuals were not included in the statistical models.
Because the outcome variable, average relatedness, is bounded to lie between 0 and 1, we use beta regression models  [22]. Since average relatedness for some individuals is zero, we add a constant (0. 0001) to all values for identifiability of model parameters. 2 We interpret the models graphically, plotting their predictions as a function of age interacted with the categorical predictors, sex and natality. As a supplemental analysis, we model average relatedness among all individuals in the study communities, including children. We also consider a multilevel analysis of the aggregated cross-cultural dataset, and we evaluate simpler site-specific models that omit sex as a predictor. Models are fit with Markov chain Monte Carlo estimation using functions from the Rstan package  [23]  and auxiliary functions from the rethinking package  [24].
Shaded intervals depict 89% confidence intervals around model predictions. Note that the scale of the vertical axis changes for each community. Sites are ordered in row-wise fashion according to the ratio of adult female natality to adult male natality (the quotient of the respective quantities in table  1 ). royalsocietypublishing. org/journal/rstb Phil. Trans. R. Soc. B 374: 20180069 these communities have more kin ties ( potentially including siblings) than women who arrive as adults. There is also clearer evidence for age-related increases in relatedness among natal males and females in the Miskito community (site 2). As an additional supplementary analysis, we aggregated and analysed the cross-cultural dataset using a multilevel beta regression model. For this analysis, we calculated the proportion of adult males and females in each community who are natal residents to reflect patrilocal or matrilocal biases (these quantities are reported in table  1 ).
However, the low number of male immigrants in these communities precludes strong conclusions about this demographic subgroup. 4 These coefficients of relatedness assume monogamous mating. 5 Bottom-heavy population pyramids are evident at other study sites (e. g. Maasai) without accompanying age-related increases in average relatedness, which suggests that other factors can potentially counteract the age-related accumulation of close kinship ties in expanding populations. 6 The dataset for that paper is the same used for the Gambian sites in this paper. Unlike the 2012 paper, however, the present study depicts results separately for each community rather than modelling individuals in an aggregated dataset. 7 In addition to the Social Relations Model that has been used by evolutionary anthropologists (e.
Consultation by the editorial board or interested researchers may nevertheless be considered, subject to prior determination of the terms and conditions of such consultation and in respect for compliance with the applicable regulations. Data access may be requested to Lauren Demerville (lauren. demerville@aphp. fr) and the promoter of the study (secretariat-direction. drc@aphp. fr). oocyte quality, and impaired ovulation and implantation  [34]. In the aforementioned studies, obesity estimation was based on BMI values. However, anthropometrics are not the most sensitive parameters for estimating body composition alterations  [43]. To our knowledge, very few studies have explored body composition or adiposity in association with fertility, especially fat mass and fat-free mass parameters. Recent studies have used waist circumference and BMI as proxy measures of body composition  [44, 45] ; one used dual-energy X-ray absorptiometry for fat, fat-free mass, and bone mass in 41 young infertile women  [46].
Materials and methods Participants were recruited in the ALIFERT case-control multicentric observational study ("ALImentation et FERtilite", ClinicalTrials. gov identifier: NCT01093378), which evaluated the associations between nutritional parameters and fertility among infertile and fertile couples. The institutional review board approved the study (ALIFERT study-national biomedical research P071224/AOM 08180: NEudra CT 2009-A00256-51). Data were recorded from 302 French participants, with included 159 infertile (79 men and 80 women) and 143 fertile (72 men and 71 women). Men under 45 years of age and women under 38 years of age were included. Infertile participants had a history of primary idiopathic infertility for at least 12 months of unprotected sexual intercourse, with no diagnosed etiology for their infertility.
Data collection Participants completed self-administered questionnaires on sociodemographic and lifestyle characteristics (sex, age, educational level, and smoking status), dietary intake (semi-quantitative validated food frequency questionnaire), physical activity and sedentary behavior. Anthropometrics, body composition, and blood pressure were measured using standardized procedures (tensiometer; Omron M5-I). Blood samples were used to evaluate metabolic syndrome with plasma high-density lipoprotein (HDL), triglycerides and fasting glycaemia in mmol/L. Assessments were performed after an 8-hour fasting period.
Physical activity was estimated through a self-assessment questionnaire. The risk of declarative data on PA and sedentary behavior on the last-7-day recall must be taken in consideration because it can increase the risk of over-or under-estimation  [79]. PA level corresponds to usual PA at the time of inclusion, though data on lifetime PA and variations in PA over time should be studied further. Notably, PA history during adulthood has been positively associated with PA level  [57]. Thus, we can expect that, in our population, current PA behaviors are reflective of PA behavior in the recent past. Moreover, the IPAQ questionnaire does not allow for the investigation of FITT parameters, which would have provided more specific data on associations between the specific dimensions of PA and infertility.