Research with structured Electronic Health Records (EHRs) is expanding as data becomes more accessible; analytic methods advance; and the scientific validity of such studies is increasingly accepted. However, data science methodology to enable the rapid searching/extraction, cleaning and analysis of these large, often complex, datasets is less well developed. In addition, commonly used software is inadequate, resulting in bottlenecks in research workflows and in obstacles to increased transparency and reproducibility of the research. Preparing a research-ready dataset from EHRs is a complex and time consuming task requiring substantial data science skills, even for simple designs. In addition, certain aspects of the workflow are computationally intensive, for example extraction of longitudinal data and matching controls to a large cohort, which may take days or even weeks to run using standard software. The rEHR package simplifies and accelerates the process of extracting ready-for-analysis datasets from EHR databases. It has a simple import function to a database backend that greatly accelerates data access times. A set of generic query functions allow users to extract data efficiently without needing detailed knowledge of SQL queries. Longitudinal data extractions can also be made in a single command, making use of parallel processing. The package also contains functions for cutting data by time-varying covariates, matching controls to cases, unit conversion and construction of clinical code lists. There are also functions to synthesise dummy EHR. The package has been tested with one for the largest primary care EHRs, the Clinical Practice Research Datalink (CPRD), but allows for a common interface to other EHRs. This simplified and accelerated work flow for EHR data extraction results in simpler, cleaner scripts that are more easily debugged, shared and reproduced. R R  "SELECT * FROM clinical WHERE practid == 255"        ## [1] "SELECT * FROM clinical WHERE practid == 255 AND medcodes in"rEHR provides convenience functions for these common situations. The functions run a select_events query and then group by patient id and selects only the earliest/latest event for each patient:Frequently, users need to find the first clinical event for a given patient (e.g. to identify dates of diagnosis of chronic diseases) or the most recent clinical event (e.g. to identify if a drug therapy has been prescribed within a certain time period). first_DM <-                     last_DM <-                    ##  patid  eventdate medcode## 1  1004 2007-12-25    351## 2  1005 2004-08-31    351## 3  1008 2002-03-02    351## 4  1010 2014-04-11    351## 5  1012 2012-05-28    351## 6  1015 2008-08-16    351##  patid  eventdate medcode## 1  1004 2007-12-25    351## 2  1005 2009-03-09    351## 3  1008 2002-03-02    351## 4  1010 2014-04-11    351## 5  1012 2013-02-14    351## 6  1015 2013-08-17    273select_by_year function provides a simple interface to extract longitudinal data. On posix-compliant computers , this function can make use of parallel processes to select data for different years concurrently, greatly accelerating the extraction process on multicore machines. The function runs a series of selects over a year range and collects in a list of dataframes.Researchers will often want to extract data over a range of different time-points, for example they may want to calculate the prevalence of a condition and how this changes through time. When working with flat text files, this must be done with a complex nested loop that is both slow and error-prone. The cores must be set to 1 and the open database connection must be set with db. This is because the use of parallel::mclapply means that new database connections need to be started for each fork and temporary files are only available inside the same connection.The function applies a database select over a range of years and outputs as a list or a dataframe. Either a database object or a path to a database file can be supplied. If multiple cores are being used (i.e. cores > 1), a path to a database file must be used because the same database connection cannot be used across threads. In this case, a new database connection is made with every fork. Note that when working with temporary tables, columns argument is a character vector of column names to be selected. The individual elements can be of arbitrary length. This means it is possible to insert SQL clauses e.g. “DISTINCT patid”.Queries can be made against multiple tables, assuming that the columns being extracted are present in all tables. The year_range argument to specify the years to select data for. Selection is done according to the function passed to the selector_fn argument. select_events is the default but first_events and last_events can also be used, as well as custom selection functions. The where argument works in the same way as in select_events except that year-start and year-end criteria can be added as ‘STARTDATE’ and ‘ENDDATE’. These are translated to the correct year- start and end dates. Different start and end dates can be specified by supplying a function to the year_fn argument. This function must accept a single year argument and return a list with two elements—“startdate” and “enddate”, each of which must be date characters in posix format (i.e. “%Y-%m-%d”). Three functions are provided to define years  and a convenience function, build_date_fn is provided to which users can supply lists of year offsets, months and days for year- start and end to return a function that can be supplied as the year_fn argument. Finally the user can set the as_list argument to determine whether data from each year is returned as a separate list element or as a single data frame.A numeric vector of years is passed to the ramework , and qofTo show the utility of the package we demonstrate how one might extract an incident and prevalent cohort of diabetes patients from the simulated example data. Prevalent events for a chronic condition are selected by the earliest diagnostic event prior to the end of the time period in question. The denominator for the calculation of the prevalence is the total number of patients registered at that time point.registered_patients <-                                                                            ## Using open database connection## Classes 'tbl_df', 'tbl' and 'data.frame': 1005 obs. of 8 variables:## $ patid    : int  1001 1002 2002 3002 4002 1003 2003 1004 2004 …## $ practid   : int  1 2 2 2 2 3 3 4 4 4 …## $ gender   : int  1 1 1 1 0 0 1 0 1 1 …## $ yob   : num 1989 1942 1965 1959 1932 …## $ crd   : chr   "1998-03-22" "2003-07-10" "1997-10-15" …## $ tod   : chr  NA NA NA NA …## $ deathdate : chr  NA NA NA NA …## $ year    : int  2008 2008 2008 2008 2008 2008 2008 2008 2008 …#### 2008 2009 2010 2011 2012##  189  195  201  206   214select_by_year returns a dataframe in long form, with a year column for the longitudinal component. Next we collect the incident cases, which are those patients with first diagnoses at any point before the end of the year in question, plus the dates for the first diagnoses. In this case we include events matching our list of diabetes clinical codes in either clinical or referral files. Because we only want the first diagnosis dates we set the selector_fn argument to first_events:Notice that incident_cases <-                                                                                ## Using open database connection## Classes 'tbl_df', 'tbl' and 'data.frame': 262 obs. of 5 variables:## $ patid   : int 1004 1005 1008 1015 1025 1035 1037 1038 1043 …## $ eventdate : chr "2007-12-25" "2004-08-31" "2002-03-02" …## $ medcode  : int 351 351 351 351 351 293 277 273 351 257 …## $ table  : chr "Clinical" "Clinical" "Clinical" "Clinical" …## $ year   : int 2008 2008 2008 2008 2008 2008 2008 2008 2008 …dplyr package is imported to the namespace when the rEHR package is loaded. This simplifies and accelerates merging operations, using left_join from the dplyr package in the example below, and is an important part of the rEHR workflow:Note that in this case extra columns have been added for both year and table, to identify the table the event was found in. Because events were taken from more than one table , the incident_cases dataframe should be sorted and duplicates removed to ensure that only the first events are kept. The two datasets are then merged to give the dataset from which the denominators and numerators can be calculated. The ## All patients are kept )prevalence_dat <- ## Remove duplicates across clinical and referral tables:incident_cases %>%        ungroup -> incident_casesprev_terms and prev_totals. prev_terms adds logical columns for membership of incidence and prevalence denominators as well as a column for the contribution of the individual to that year’s followup time. prev_totals summarises this information to calculate the denominators and numerators for prevalence and incidence, according to the users’ grouping factors. The criteria for membership of the incidence and prevalence numerators and denominators as well as for followup time are shown in Prevalence and incidence can be calculated by the built-in functions An example in the use of these functions is provided below:prevalence_dat <- totals <- totals$prevalence$year_counts## Source: local data frame [5 x 4]####   year numerator denominator prevalence## 1 2008     31   174.6721  17.74754## 2 2009     35   179.3785  19.51181## 3 2010     41   183.1403  22.38721## 4 2011     50   185.4182  26.96607## 5 2012     55   191.5838  28.70806totals$incidence$year_counts## Source: local data frame [5 x 4]####   year numerator denominator incidence## 1 2008      4   143.9014  2.779680## 2 2009      3   144.4983  2.076149## 3 2010      4   142.2806  2.811345## 4 2011      7   135.5893  5.162648## 5 2012      5   137.4675  3.637224Here we see that, in our simulated dataset, we have a diabetes prevalence of 17.7% in 2008 raising to 28.7% in 2012 and an incidence of 2.8% in 2008 increasing to 3.6% in 2012.In this section we demonstrate how to convert the longitudinal data from the previous section to a cohort dataset suitable for survival analysis and also illustrate algorithms to match controls to cases and to cut cohort data by time-varying covariates.build_cohort function. This returns a dataset with a single row for each patient and includes only patients in the numerator or denominator for whichever cohort type is chosen . Columns are added for start and end dates and for start and end times as integer differences from the cohort start date. A binary column is added to indicate membership of the case group. All patients with start dates greater than their end dates are removed from the dataset. The diagnosis_start argument is used to include the diagnosis date in the definition of the start dates for the patients. If it is not required for the diagnosis date to be included in the start date definition, this argument can be set to NULL. Here, we will first merge in practice data (i.e. dates for when practices are deemed to be up to standard) and then construct the cohort:One of the most common uses of EHR data in research is to build cohorts for survival analyses. The longitudinal data in the previous section is easily converted to survival cohort format using the practices <- prevalence_dat <- cohort <-                         , The cohort is now ready for analysis, e.g. with a relatively simple proportional hazards regression model that only includes gender and exposure as predictors:## Add a logical column for death during cohortcohort$death <-                        ######      coef exp(coef) se(coef)   z   p## gender 0.506   1.659  0.837   0.605 0.55## case  -0.645   0.524  1.081 -0.597 0.55#### Likelihood ratio test = 0.81 on 2 df, p = 0.667 n = 199,## number of events = 7rEHR package provides three methods for matching cases to controls:Matching cases to controls is an important pre-analysis step. The Incidence density matching (IDM)Exact matchingMatching on a dummy index date sourced from consultation filesget_matches function. With IDM, controls are selected for a particular case at the time of diagnosis (or other event such as death) from other members of the cohort who, at that time, do not have the diagnosis. The IDM sampling procedure allows the same patient to be selected as a control for more than one case, thus providing a full set controls for each case while still producing unbiased estimates of risk ####  id start  end  event  drug_1 drug_2  drug_3_start drug_3_stop stage_1## 1 1   0 1000   0   NA   NA      110     400   300## 2 2   0  233   1   NA   234      110     400   NA## 3 2  234  689   1   NA   234      110     400   NA## 4 3   0  553   0   NA   554      111     400   NA## 5 3  554 1000   0   NA   554      111     400   NA## 6 4   0  122   1   340   123      109     400   NA## Variables not shown: stage_2 (dbl), drug_1_state (dbl),## drug_2_state (dbl)## Binary covariates:tv_out3 <-     id, drug_3_state)tv_out3 <-     id, drug_3_state)## Source: local data frame [6 x 11]####  id start  end event drug_1 drug_2 drug_3_start drug_3_stop stage_1## 1 1   0  109   0   NA   NA     110     400  300## 2 1  110  399   0   NA   NA     110     400  300## 3 1  400  1000   0   NA   NA     110     400  300## 4 2   0  109   1   NA   234     110     400  NA## 5 2  110  399   1   NA   234     110     400  NA## 6 2  400  689   1   NA   234     110     400  NA## Variables not shown: stage_2 (dbl), drug_3_state (dbl)## incremental covariates:inc_1 <-          inc_1 <-          ## Source: local data frame [6 x 11]####  id start  end event drug_1 drug_2 drug_3_start drug_3_stop stage_1## 1 1   0  299   0   NA   NA     110     400   300## 2 1 300  449   0   NA    NA     110     400   300## 3 1 450 1000   0   NA   NA     110     400   300## 4 2   0  689   1   NA   234     110     400   NA## 5 3   0 1000   0   NA   554     111     400   NA## 6 4   0  874   1  340   123     109     400   NA## Variables not shown: stage_2 (dbl), disease_stage (dbl)## Chaining combinations of the above using %>%tv_test %>%                        head## Source: local data frame [6 x 14]####  id start  end event drug_1 drug_2 drug_3_start  drug_3_stop  stage_1## 1 1   0  109   0   NA   NA     110     400   300## 2 1  110  299   0   NA   NA     110     400   300## 3 1  300  399   0   NA   NA     110     400   300## 4 1  400  449   0   NA   NA     110     400   300## 5 1  450 1000   0   NA   NA     110     400   300## 6 2   0  109   1   NA   234     110     400   NA## Variables not shown: stage_2 (dbl), drug_1_state (dbl), drug_2_state## (dbl), drug_3_state (dbl), disease_stage (dbl)In this section we briefly discuss some miscellaneous functions provided in the package.R implementation of this methodology is now part of the rEHR package.An important part of EHR analyses is the construction of lists of clinical codes to define conditions, comorbidities and other clinical entities of interest to the study . We haveMedicalDefinition, containing the terms to be searched for in the lookup tables. MedicalDefinition objects can be instantiated from terms defined within R or imported from a csv file. The constructor function can be provided with lists of: terms, codes , tests (test search terms), drugs (drug search terms), drugcodes (drug product codes). Within the individual argument lists, vectors of length > 1 are searched for together , in any order. Different vectors in the same list are searched for separately . Placing a “-” character at the start of a character vector element excludes that terms from the search. Providing NULL to any of the arguments means that this element will not be searched for. Underscores are treated as spaces. When searching for codes, a range of clinical codes can be searched for by providing two codes separated by a hyphen. e.g “E114-E117z”.Building draft lists of clinical codes is a two-stage process: First, the search is defined by instantiating an object of class ## Example construction of a clinical code listdef <-                                       MedicalDefinition objects using the import_definitions(input_file = "path/to/file.csv") function.Code lists can be defined in a csv file with format as shown in MedicalDefinition objects are then used to run searches against lookup tables provided with EHRs via the build_definition_lists function:The ## Use fileEncoding = "latin1" to avoid issues with non-ascii charactersmedical_table <- drug_table <- draft_lists <- cprd_uniform_hba1c_values function accepts a single argument of a dataframe in the CPRD “Additional” table form containing only entity types for HbA1C and Fructosamine and converts any HbA1C and fructosamine values to a common mmol/mol scale. Once this conversion has taken place, the function also removes obvious mis-coding errors that are far outside the possible range. A dataframe is returned with an extra column hba1c_score.HbA1C tests for glycated haemoglobin are one of the best recorded clinical tests in UK primary care databases, to a large extent because of testing being incentivised under the UK Quality and Outcomes Framework pay-for-performance scheme ; 25. HowR expertise. We have provided the to_stata function to export dataframes to stata dta format. This function compresses a dataframe to reduce file size in the following ways:Sometimes researchers may need to share data with others in the same group who may not have date_fields argument) are converted to integer days from 1960-01-01 to avoid compatibility issues between R and Stata. An alternative origin can be set with the origin argument.Date variables (as specified by the integer_fields are converted from numeric to integer.Fields specified in the stata13 boolean argument indicates whether files should be stored in Stata13 format (Using readstata13::savedta13) or in Stata 12 compatible format (using foreign::write.dta). The former includes a further compression step, similar to the compress command in Stata.the R dataframes. For example, extractions of clinical events for a common condition such as diabetes or asthma will require the extraction of millions of rows of data. These may be easily stored as temporary database tables. This is also useful if you are working with a protected database that you only have read-only access to. The rEHR package has a suite of functions to deal with temporary database tables:The size of EHR databases may require keeping intermediate data extractions as database tables, rather than as in-memory temp_table is used to construct temporary tables and is illustrated in section 3append_to_temp_table appends rows to a temporary table based on a specified select statementto_temp_table exports a dataframe to a temporary database tabledrop_temp_table checks if a temporary table exists and then deletes if it doesdrop_all_temp_tables drops all temporary tables from the databaseselect_by_year) can only be used in the single core mode (i.e. set cores = 1) since multicore processes open up multiple parallel connections.Note that temporary tables are only associated with the currently open database connection. This means that functions capable of parallel processing  obtained under licence from the UK Medicines and Healthcare products Regulatory Agency. However, the interpretation and conclusions contained in this paper are those of the authors alone. The study was approved by the independent scientific advisory committee (ISAC) for CPRD research (reference number: 16_115R). No further ethics approval was required for the analysis of the data.


Integrins are adhesion receptors on the cell surface that enable cells to respond to their environment. Most integrins are heterodimers, comprising α and β type I transmembrane glycoprotein chains with large extracellular domains and short cytoplasmic tails. Integrins deliver signals through multiprotein complexes at the cell surface, which interact with cytoskeletal and signaling proteins to influence gene expression, cell proliferation, morphology, and migration. Integrin expression on γδ T cells (γδTc) has not been systematically investigated; however, reports in the literature dating back to the early 1990s reveal an understated role for integrins in γδTc function. Over the years, integrins have been investigated on resting and/or activated peripheral blood-derived polyclonal γδTc, γδTc clones, as well as γδ T intraepithelial lymphocytes. Differences in integrin expression have been found between αβ T cells (αβTc) and γδTc, as well as between Vδ1 and Vδ2 γδTc. While most studies have focused on human γδTc, research has also been carried out in mouse and bovine models. Roles attributed to γδTc integrins include adhesion, signaling, activation, migration, tissue localization, tissue retention, cell spreading, cytokine secretion, tumor infiltration, and involvement in tumor cell killing. This review attempts to encompass all reports of integrins expressed on γδTc published prior to December 2017, highlights areas warranting further investigation, and discusses the relevance of integrin expression for γδTc function. Although much was known about integrins on lymphocytes as early as 1990 -induced adhesion via integrin activation; while α2β1 was required for collagen binding, FN binding relied on both α4β1 and α5β1. Most polyclonal γδTc only expressed α4β1, whereas individual clones showed variation attributed to extended culturing and selection during cloning . While no α3, αv, or β3 expression was observed, less than 30% expressed α1, α2, or α5 chains. CD8 cloning , corrobo cloning . Admitte cloning .+CD4−CD8− γδTc, and lack of α3 or α6 was confirmed. Activated CD25hi γδTc bound FN better than resting CD25low γδTc, mediated mostly by α4 and partly by α5. Culturing cells on immobilized anti-γδ TCR antibodies together with FN enhanced proliferation and increased CD25 expression, suggesting both signaling and adhesion roles for α4 and α5 integrins. While γδTc adhesion required activation through the TCR, surface levels of α4 and α5 remained unaltered  to endothelial cells, fibroblasts, and epithelial cells independent of activation. Both αβTc and γδTc required CD11a/CD18 and α4β1 to bind endothelial cells, whereas CD11a/CD18-ICAM-1 interaction facilitated adherence to fibroblasts and epithelial cells. Phorbol dibutyrate treatment of PBMCs and cytokine stimulation of monolayers greatly enhanced T cell adhesion, correlated with their expression of CD11a/CD18 and α4β1 . CD11a, −CD4− populations co-expressing γδTCR and CD8; cervical γδTc (>20%) also expressed CD11c. α1β1 and α4β7 were co-expressed on CD11c+CCR7−CD4− T cells, of which γδTc were a part, but unfortunately not specifically analyzed. CD11c expression was associated with T cell homing and activation, and interferon γ (IFNγ) secretion in a fraction of (E)-4-hydroxy-3-methyl-but-2-enyl pyrophosphate-stimulated γδTc , was found on human γδTc intraepithelial lymphocytes (IELs). While peripheral blood T cells did not express much αEβ7 the authors posited its upregulation after T cells extravasate in the lamina propria, since αEβ7 expression positively correlated with nearer proximity to epithelium . IL-2 an avidity . On αβTc avidity . Peters oduction .in vivo, and the importance of this interaction for Vδ1 activation and localization  and fibroblast cells more tightly , confirmand α5β1 .Vδ1 predominance has been reported in tumor infiltrating lymphocytes from lung , colon , renal cCD11a/CD18 facilitates effector-target cell conjugation . This in− Vδ2 cells’ demise ; this inferred HIV envelope glycoprotein susceptibility resulting in CD4’ demise . While V’ demise .+ T cells  expression on peripheral blood T cells increased with age, leveling out later in life. γδTc expressed more CD11b than αβTc across all ages; and while not shown, CD11b was thought important for migration to spleen and liver, and to indicate antigen-specific memory T cells . Later s T cells , 65, but T cells . A longi+ and CD8+ αβTc; however, their role in γδTc development remains unknown , murine γδTc differentially expressed β2 integrins and produced more IFNγ and tumor necrosis factor α in lymph nodes, spleen, and spinal cord compared to αβTc . At basemulation . While ne to PMA . Indeed,e to PMA . In a muoduction . Adoptivoduction . Itgax, he mouse . Murine 1c+ γδTc .−/− hybridoma line transfected with CD3ζ fusion proteins, VNR- but not TCR-engagement by ligand was required in conjunction with CD3ζ chain signaling for IL-2 production ; its expression on splenic T cells was only observed after a minimum of 1 week of stimulation . VNR expoduction . VNR recoduction . While hoduction , VNR hasoduction .+ γδTc expressed α4β7 that enabled their mobilization by CCL25 in inflamed tissue, which in turn modulated IL-17 levels  versus cows (αβ)  on the lamina propria . In a mo7 levels . Blockin induced . Bovine Prevalence of “inflammatory” γδTc (iγδTc) co-expressing high levels of gut-homing α4β7 and αEβ7 correlated with disease severity in both spontaneous and induced murine colitis models. Cytotoxicity, cytokine production, and NK cell receptor genes were upregulated on iγδTc compared to other γδTc subsets (expressing α4β7 or αEβ7) isolated from mesenteric lymph nodes in induced colitis, suggesting profound functional relevance of integrin co-expression on these cells .Salmonella typhimurium or Toxoplasma gondii, drastically reducing pathogen translocation and emphasizing the ability of αEβ7 to limit γδTc IEL migration and impact host defense against infection (−CD8− T cells expressing αEβ7 in mesenteric lymph nodes; in IEL, significantly increased CD62L− αEβ7-expressing CD4−CD8− cells were observed, hypothesized to result from enhanced homing and retention, respectively (In αEβ7-knockout mice, γδTc IEL migration within the intraepithelial compartment was enhanced  and remanfection . In a stectively .T cells use classical cell biological pathways in new ways . Thus, uGS reviewed the literature, wrote the manuscript, and designed both the table and the figure.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


R0 is given by linear next infection operator, which determined the dynamic behaviors of system. We obtain that when R0 < 1, the disease-free periodic solution is globally asymptotically stable and when R0 > 1 by Poincaré map we obtain that disease is uniformly persistent. Numerical simulations support the results and sensitivity analysis shows effects of parameters on R0, which provided references to seek optimal measures to control the transmission of lymphatic filariasis.In this paper a mosquito-borne parasitic infection model in periodic environment is considered. Threshold parameter  Lymphatic filariasis is a parasitic disease caused by filarial nematode worms and is a mosquito-borne disease that is a leading cause of morbidity worldwide. Lymphatic filariasis affects 120 million humans in tropical and subtropical areas of Asia, Africa, the Western Pacific, and some parts of the Americas .W. bancrofti parasites, which account for 90% of the global disease burden, dwell in the lymphatic system, where the adult female worms release microfilariae (mf) into the blood. Mf are ingested by biting mosquitoes as a blood meal of a mosquito, through several developmental stages, that is, first into immature larvae and then L3 larvae. Infective stage larvae L3 actively escape from the mosquito mouthparts entering another human host at the next blood meal through skin Sm∗(t). So, for any small ϵ existing a t0, for all t ≥ t0 we havet→∞supNm(t) ≤ (Mm∗ + ϵ)Δ, where Δ = supt>0 (r(t) + μ2(t))/inft>0μ2(t). For ϵ small enough, Nm(t) ≤ Mm∗Δ.by the comparison principle and R0 < 1, the disease-free periodic solution (Sh∗(t), 0, Sm∗(t), 0) is globally asymptotically stable. And if R0 > 1, it is unstable.If R0 < 1, (Sh∗(t), 0, Sm∗(t), 0) is locally stable. Next we prove that when R0 < 1 the disease-free solution (Sh∗(t), 0, Sm∗(t), 0) has global attractivity.By R0 < 1 and by (iii) of ρ(ΦF−V(ω)) < 1. So there exists a small enough constant ε1 > 0 such that ρ(ΦF−V+ε1N(ω)) < 1, where ε1 > 0 there exists t1 > 0 such that Sh(t) ≤ Sh∗(t) + ε1 and Sm(t) ≤ Sm∗(t) + ε1, so for all t > t1 we haveω-periodic solution v1(t) such that J(t) ≤ eptv1(t), where J(t) = (Ih(t), Im(t))T and p = (1/ω)lnρ(ΦF−V+ε1N(ω)) < 0. Then limt→∞J(t) = 0; that is, limt→∞Ih(t) = 0 and limt→∞Im(t) = 0.When Sh(t), Sm(t), we getMoreover, from the equations of f system  is globaX and X0 are positively invariant, and ∂X0 is also a relatively closed set in X.DefineWe have 3∂X0=X∖X0=P : X → X be the Poincaré map associated with system  is the unique solution of system  = x0. P is compact for the continuity of solutions of system  for all m > 1 and P1 = P. Now, prove Sh, 0, Sm, 0) : Sh > 0, Sm ≥ 0}⊆M∂.We further define M∂∖{ : Sh > 0, Sm ≥ 0} ≠ ∅, then there exists at least a point  ∈ M∂ satisfying Ih0 > 0 or Im0 > 0. We consider two possible cases.If Ih0 = 0 and Im0 > 0, then it is clear that from system (Im(t) ≥ 0 for any t > 0. From the second equation of system  ≥ f system  and Sh >Im0 = 0 and Ih0 > 0, then Ih(t) = Ih0e0t[μ1(τ)+υ(τ)]dτ−∫ > 0. From the third equation of system , Ih(t), Sm(t), Im(t))∉∂X0, so  ∉ M∂. This leads to a contradiction; there exists one fixed point E0 = (Sh∗(t), 0, Sm∗(t), 0) of P in M∂.If R0 serves as a threshold parameter for the extinction and the uniform persistence of the disease.In the following, we will discuss the uniform persistence of the disease, and R0 > 1, then system ) > 1. For an arbitrary small constant η > 0, that ρ(ΦF−V−ηN(ω)) > 1, N(t) is the same as in H2), we obtain any small enough ε > 0, ∫0ω[r(t) − α(t)ε]dt > 0. Consider perturbed equationsω-periodic solutions Sεh∗(t) and Sεm∗(t). For the continuity of solutions with respect to ε, and for η > 0 there exists ε1 > 0 for all t ∈ ; thus we haveFrom mma 2 in  and Lemmmma 2 in , we obtaequationsSεh′t=Λt−−μ1tSεht,Sεm′t=rtSx0 =  ∈ X0, according to the continuity of the solution with respect to the initial condition; there exists δ for given ε1, for all x0 ∈ X0 with ‖x0 − E0‖ < δ; it follows ‖u − u‖ < ε1 for all t ∈ .Denote x0 ∈ X0. Without loss of generality, we can assume thatt ≥ 0,  t = mω + t′, where t′ ∈  and m = [t/ω] is the greatest integer less than or equal to t/ω, so we haveIh(t) ≤ ε1 and 0 ≤ Im(t) ≤ ε1 for all t ≥ 0. Then from the first and third equations of  such that (J(t) = v2(t)ep1t, where p1 = (1/ω)ln(ρ(ΦF−V−ηN(ω))). For ρ(ΦF−V−ηN(ω)) > 1, Following, we prove4limm→∞supConsider ; there eh∗t−η.By  and 48)(41)limm→y system:Iht¯=β1tSM∂∖{ : Sh > 0, Sm ≥ 0} = ∅ and {M1} is globally attractive in M∂, and all orbit in M∂ converges to {M1}. By R0. It wag system :53)dwdtdwdtR0. IR0. We choose parameters Λ(t) = 0.6 + 0.4sin(2πt/12), μ1(t) = 0.5 + 0.1sin(2πt/12), μ2(t) = 0.8 + 0.1sin(2πt/12), β1(t) = 0.6 + 0.1sin(2πt/12), β2(t) = 0.7 + 0.1sin(2πt/12), α1(t) = 0.2 + 0.1sin(2πt/12), υ(t) = 0.02 + 0.03sin(2πt/12), r(t) = 0.5 + 0.4sin(2πt/12), K(t) = 0.9 + 0.3sin(2πt/12). By numerical calculations, we obtain R0 = 0.9243 < 1; then the disease will be extinct; see β1(t) = 0.9 + 0.1sin(2πt/12), β2(t) = 1.2 + 0.1sin(2πt/12), then R0 = 1.4662 > 1; the disease is permanent; see Sh, Ih) and  are in Figures Firstly, by the means of the software Matlab we compute β1(t), β2(t), K(t), and α1(t), we fix all parameters as in β01 = (1/12)∫012β1(t)dt,  β02 = (1/12)∫012β2(t)dt,  k0 = (1/12)∫012K(t)dt, and α0 = (1/12)∫012α1(t)dt.In order to perform sensitivity analysis of parameters k0 and α0 on R0. From α0, R0 decreases, and the gradient also decreases, so this strengthens the psychological hint of susceptible human individuals to be benefit for the extinction of the disease. In k0 the sensitivity of R0 increases. That is to say, the carrying capacity of environment for mosquito is bigger and the disease is widespread more easily, so decreasing the circumstance fit survival for mosquitoes, such as contaminated pool or puddle and household garbage, is a necessary method for the extinction of disease.We first fix other parameters and detect the effect of parameters of β10 and β20 on R0; in R0 may be less than 1 when β10 and β20 are small; the smaller β20 the more sensitive the effect on R0.Next, we consider the combined influence of parameters R0 is affected by β10 and k0; with the increasing of k0 the sensitivity of R0 increases; if we fix β10 as a constant the case will be similar to β10 on the sensitivity of R0, so in the season in which temperature and humidity are more beneficial for mosquito population to give birth and propagate taking measures to avoid more bites is necessary.In R0; when R0 < 1 disease-free periodic solution is globally asymptotically stable and when R0 > 1 disease is uniformly persistent. We also give some numerical simulations which support the results we prove, confirming that R0 serves as a threshold parameter. Sensitivity analysis show effects of parameters on R0, which contribute to providing a decision support framework for determining the optimal coverage for the successful prevention programme.In this paper, we have studied the transmission of lymphatic filariasis; lymphatic filariasis is a mosquito-borne parasitic infection that occurs in many parts of the developing world. In order to systematically investigate the impact that vector genus-specific dependent processes may have on overall lymphatic filariasis transmission, we, according to the nature characteristic of lymphatic filariasis and considering the logistic growth in periodic environments of mosquito, model the transmission of lymphatic filariasis. The dynamic behavior of system  is deter


Flabellinidae underwent a major revision and 17 new genera were proposed. The Abstract should read “17 new genera”.In our recently published study the traditional Borealia, Occidentella, and Orientella with Borealia Maksimova, 1977, an extinct trilobite, Occidentella Hoffmann, 1929, a junior synonym of Onchidella, a pulmonate slug, and Orientella Repina & Okuneva, 1969, another extinct trilobite. In a stroke of irony the two trilobite names were published in little known Russian journals, with Russian descriptions. To avoid homonymy, throughout the text, figures, and all supplementary materials replacement names for these three genera are as follows: Borealea Korshunova et al., nom. n., Occidenthella Korshunova et al., nom. n., and Orienthella Korshunova et al., nom. n. We are grateful to Jimmy Gaudin for alerting us to this unfortunate circumstance. Other corrections are as follows:Immediately after publication our attention was drawn to homonymy of three generic names: On p. 14 “…the same region our…” should be “…the same region as our…”On p. 16 “…additional rows of of small…” should be “…additional rows of small…”Paracoryphellaignicrystalla should be “…rachidian tooth with up to 15 denticles...”On p. 17 In the diagnosis of On p. 18 “…cusp of nealy 1/3 of the tooth…” should be “…cusp of nearly 1/3 of the tooth…”Coryphellapolaris Voldochenko, 1946…” should be “…Coryphellapolaris Volodchenko, 1946…”; “……” should be “……”; “……” should be “……”On p. 19 “…Coryphellaverrucosa and Itaxiafalklandica should be 17.2%.On p. 26 Table 1. In fifth column: minimum uncorrected p-distances between On p. 27 “…original description in Verril, 1880…” should be “…original description in Verrill, 1880…”; “…detailed redescripton in Kuzirian…” should be “…detailed redescription in Kuzirian…”Fjordiachriskaugei should be “...orange-brown to reddish brown, sometimes blackish…”On p. 31 In the diagnosis of Fjordialineata should be: “…orange-brown to salmon, bright red, and dark brown, sometimes almost black...”On p. 33 In the diagnosis of Guleniamonicae should be “...delineated from central cusp...”On p. 35 In the diagnosis of Guleniaorjani should be “…delineated from central cusp...”On p. 37 In the diagnosis of F.funeka and F.ishitana is not…” should be “…inclusion of F.funeka and F.ischitana is not…”; “…and “Piseinotecus” gabienerei must be…” should be “…and “Piseinotecus” gabienerei must be…”On p. 52 “…inclusion of Paraflabellinaishitana…” should be “…related to Paraflabellinaischitana…”On p. 53 “…related to On p. 54 “… (pleuroproctic in higher acleiproctic position)...” should be “…(pleuroproctic in higher acleioproctic position)…”On p. 57 “…on distinct elonagate elevations…” should be “…on distinct elongate elevations...”; “… (pleuroproctic in higher cleiproctic position)…” should be “… (pleuroproctic in higher acleioproctic position)…”U.nihonrossija should be “….apical parts of cerata without white pigment... rachidian tooth with up to seven denticles, central cusp without small denticles, double proximal receptaculum seminis…”.On p. 58 In the diagnosis of On p. 66 “…due to the lack of the morphlogical description…” should be “…due to the lack of a morphological description…”On p. 68 “…always posses oral glands…” should be “…always possess oral glands…”On p. 74 “…reduction of the triserial radula into a unserial one…” should be “reduction of the triserial radula into a uniserial one…”Chlamyllaborealisborealis Bergh, 1886On p. 79 Figure 3. Should be On p. 91 “” should be “”Chlamyllaatypica should read Chlamyllaborealisborealis Bergh, 1886. Localities for Himatinatrophina  GQ292023 and Microchlamyllaamabilis  GQ292022 should be “WA, USA”.Supplementary material 2.


In vivo quantitative ultrasound image analysis of femoral subchondral bone in knee osteoarthritis” [P value was corrected from “P = 0.000” to more appropriate “P < 0.001.” In the article titled “thritis” , there w


Nature Communications 10.1038/s41467-018-03958-7; published online 26 April 2018Correction to: 2-rich Martian atmosphere (μ♂ = 1.3 × 10−5 N s m−2) at 6.9 mbar and 210 K, which gives ρ♂ = 1.6 × 10−12 kg m−3.’ The correct version states ‘ρ♂ = 1.6 × 10−2 kg m−3’ in place of ‘ρ♂ = 1.6 × 10−12 kg m−3’. This has been corrected in both the PDF and HTML versions of the Article.The original version of this Article contained an error in the last sentence of the second paragraph of the ‘Atmospheric rarefaction effects’ section of the Results, which incorrectly read ‘The other one emulates the rarefied, CO


Scientific Reports6: Article number: srep3636410.1038/srep36364; published online: 11032016; updated: 03302017P. arionides’, “Chu-bu” should read “Chubu”.This Article contains typographical errors in Table 1. For Location E, the ‘L1’ value “2 (0%)” should read “2 (100%)”. Additionally, the Region for ‘


Additionally, the 4th line from the bottom incorrectly reads “9月8日.” It should read “9/8” in the 1st line from the bottom. The correct version of


This paper considers a high-dimensional stochastic SEIQR (susceptible-exposed-infected-quarantined-recovered) epidemic model with quarantine-adjusted incidence and the imperfect vaccination. The main aim of this study is to investigate stochastic effects on the SEIQR epidemic model and obtain its thresholds. We first obtain the sufficient condition for extinction of the disease of the stochastic system. Then, by using the theory of Hasminskii and the Lyapunov analysis methods, we show there is a unique stationary distribution of the stochastic system and it has an ergodic property, which means the infectious disease is prevalent. This implies that the stochastic disturbance is conducive to epidemic diseases control. At last, computer numerical simulations are carried out to illustrate our theoretical results. Mathematical models for differential equations have been widely applied in various fields –7. Speciμ is the outflow rate corresponding to natural death and emigration. Since the quarantine process, using the standard incidence β is the transmission coefficient between susceptible individuals and infected individuals. λ is the quarantine rate of infected individuals, φ is the recovery rate of quarantined individuals, and ε1 and ε2 stand for the rate of disease-related death of infected and quarantined individuals, respectively. γ is the recovery rate of infected individuals. Furthermore, all the parameters are positive and the region R0, which determines disease extinction or permanence, whereIn fact, the main meaning of the research of infectious disease dynamics is to make people more comprehensively and deeply understand the epidemic regularity of infectious disease; then more effective control strategies are adopted to provide better theoretical support for the prevention and control of epidemics. To this end, many mathematical biology workers considered more realistic factors in the course of the study, such as population size change, migration, cross infection, and other practical factors. In the course of epidemics and outbreaks of infectious diseases, people always take various measures to control the epidemic in order to minimize the harm of epidemic diseases. Quarantine is one of the important means to prevent and control epidemic diseases; it has been used to control contagious diseases with some success. Specifically, during the severe acute respiratory syndrome (SARS) outbreak in 2002, remarkable results were also achieved. Among them, mathematical models have been used to investigate their impact on the dynamics of infectious diseases under quarantine –22, whicS˙=Λ−βSINS˙=Λ−βSINN(t) = S(t) + E(t) + I(t) + Q(t) + R(t), δ  (0 ≤ δ < 1) is the vaccine coverage rate, p  (0 ≤ p ≤ 1) is the vaccine efficacy, and α is the rate at which the exposed individuals become infected individuals. Other parameters are the same as in system ∣S ≥ 0, E ≥ 0, I ≥ 0, Q ≥ 0, R ≥ 0, S + E + I + Q + R ≤ Λ/μ} is a positively invariant set of system  = (Λ/(δp + μ), 0,0, 0, δpΛ/μ(δp + μ)) which is globally asymptotically stable in the region D. That means the epidemic diseases will die out and the total individuals will become the susceptible and recovered individuals.When , system  has a unR1 > 1 holds, system  in the region D, which means the epidemic diseases will persist.When , system  has a unIn the real world, with the development of modern medicine, vaccination has become an important strategy for disease prevention and control in addition to quarantine, and numerous scholars have investigated the effect of vaccination on disease –30. For S(t), E(t), I(t), Q(t), and R(t). Then corresponding to system (Bi(t)   is the mutually independent standard Wiener process with Bi(0) = 0 a.s. σi(t)   is a continuous and bounded function for any t ≥ 0 and σi2(t)   are the intensities of Wiener processes.In the natural world, deterministic model is not enough to describe the species activities. Sometimes, the species activities may be disturbed by uncertain environmental noises. Consequently, some parameters should be stochastic –40. Thero system , a stoch(P1) Under what parameter conditions, will the disease die out?(P2) Under what conditions, will system  have a uIn this paper, we are mainly concerned with two interesting problems as follows:Ω, ℱ, {ℱ}t≥0, ℙ) be a complete probability space with a filtration {ℱt}t≥0 satisfying the usual conditions . Further Bi(t)   is defined on the complete probability space.Throughout this paper, let , ℝ+5 = {x =  ∈ ℝ5 : xi > 0  }.x(t)∈. For each integer k ≥ k0, let us define the stopping time as follows:∅ = ∞ (∅ represents the empty set). Evidently, τk is strictly increasing when k → ∞. Let τ∞ = limk→∞τk; thus τ∞ ≤ τe a.s. So we just need to show that τ∞ = ∞ a.s. If τ∞ = ∞ is untrue, then there exist two constants T > 0 and ς ∈  such that ℙ{τ∞ ≤ T} > ς. Thus there exists k1 ≥ k0  (k1 ∈ N+) such thatC2-function M0 is a positive constant. Henceτk∧T and then taking the expectation, we haveΩk = {τk ≤ T}, k ≥ k1 and by (P(Ωk) ≥ ς. Notice that, for every ω ∈ Ωk, there exists S, E, I, Q, or R which equals either 1/k or k. ThusΩk(ω) is the indicator function of Ωk(ω). Let k → ∞, which implies τ∞ = ∞. This completes the proof of nt. HencedV^≤M0dt+such thatPτk≤T≥ς,kr k. ThusV^Sτk,ω,EIn this section, we mainly explore the parameter conditions for extinction of the disease in system . Before S(0), E(0), I(0), Q(0), R(0)) ∈ ℝ+5, the solution (S(t), E(t), I(t), Q(t), R(t)) of the system (μ > (1/2)(σ12∨σ22∨σ32∨σ42∨σ52) holds, thenFor any given initial value (The proof of μ > (1/2)(σ12∨σ22∨σ32∨σ42∨σ52). For any given initial value (S(0), E(0), I(0), Q(0), R(0)) ∈ ℝ+5, ifLet V0 byt and dividing by t on both sides of  as a time-homogeneous Markov process in 𝔼n ⊂ ℝn, which is described by the stochastic differential equation𝔼n stands for a n-dimensional Euclidean space. The diffusion matrix takes the following form:Assume U ⊂ 𝔼n with regular boundary Γ such that U and some neighborhood thereof, the smallest eigenvalue of the diffusion matrix In the domain x ∈ 𝔼n∖U, the mean time τ at which a path issuing from x reaches the set U is finite, and supx∈Θ𝔼xτ < ∞ for every compact subset Θ ⊂ 𝔼n.If Assume that there is a bounded domain X(t) has a stationary distribution π(·). Furthermore, when f(·) is a function integrable with respect to the measure π, then x ∈ 𝔼n.When F is uniformly elliptical in any bounded domain H; hereZ such thatU and a nonnegative C2-function V such that ∀x ∈ 𝔼n∖U, LV(x) < 0.To demonstrate Using S(0), E(0), I(0), Q(0), R(0)) ∈ ℝ+5. Ifπ(·) and it has ergodic property.For any given initial value  are positive constants satisfying the following conditions: Υ > 0 large enough such that Uw =  ×  ×  ×  × . Since ℝ+5 which is the minimum point of C2-function V: ℝ+5 → ℝ+ by M = Λ + Γ + δp + α + φ + ε2 + 4μ + σ12/2 + σ22/2 + σ42/2 + σ52/2.Define a D: ϵ is a sufficiently small constant satisfying the following conditions:ℒV for any  ∈ ℝ+5∖D.Next let us consider the following compact subset Case I. If  ∈ D1, (R) ∈ D1,  and 48)Case I. ICase II. If  ∈ D2, (R) ∈ D2,  and 49)Case II. Case III. If  ∈ D3, it follows from (ows from  and 50)Case III.Case IV. If  ∈ D4, (R) ∈ D4,  and 51)Case IV. Case V. If  ∈ D5, it follows from (ows from  and 52)Case V. ICase VI. If  ∈ D6, (R) ∈ D6,  and 53)Case VI. Case VII. If  ∈ D7, (R) ∈ D7,  and 54)Case VII.Case VIII. If  ∈ D8, it follows from (ows from  and 55)Case VIIICase IX. If  ∈ D9, (R) ∈ D9,  and 56)Case IX. Case X. If  ∈ D10, it follows from  and it has ergodic property. The proof of ows from  and 54)Case X. IR∗ > 1 holds, then system , the expression of R∗ coincides with the basic reproduction number R1 of system (σ12∨σ22∨σ32∨σ42∨σ52) and When R∗ = (μ(1 − δp)βα)/((δp + μ + σ12/2)(α + μ + σ22/2)(λ + ε1 + γ + μ + σ32/2)) > 1 holds, then system (π(·) and it has ergodic property.When n system  has a unThis paper studies the stochastic SEIQR epidemic model with quarantine-adjusted incidence and imperfect vaccination and obtains two thresholds which govern the extinction and the spread of the epidemic disease. Firstly, the existence of a unique positive solution of system  with anyζi,k   stands for N distributed independent random variables and time increment Δt > 0.To illustrate the results of the above theorems, we next carry out some numerical simulations by the Matlab software. Let us consider the following discretization equations of system :(73)Sk+1S(0) = E(0) = I(0) = Q(0) = R(0) = 0.3, Λ = 2, μ = 0.55, β = 0.25, γ = 0.2, α = 2.5, δ = 0.5, p = 0.5, ε1 = 0.1, ε2 = 0.1, λ = 0.18, φ = 0.15, σ1 = 0.15, σ2 = 1, σ3 = 1, σ4 = 0.5, σ5 = 0.25, and Δt = 0.01. ThenIn S(0) = E(0) = I(0) = Q(0) = R(0) = 0.5, Λ = 0.3, μ = 0.1, β = 1.5, γ = 0.2, α = 0.3, δ = 0.1, p = 0.2, ε1 = 0.05, ε2 = 0.05, λ = 0.18, φ = 0.15, σ1 = σ2 = σ3 = σ4 = σ5 = 0.03, and Δt = 0.01. Then π(·) and it has ergodic property. In S(0) = E(0) = I(0) = Q(0) = R(0) = 0.1, Λ = 0.2, μ = 0.55, β = 2.65, γ = 0.2, α = 2.5, δ = 0.1, p = 0.1, ε1 = 0.1, ε2 = 0.1, λ = 0.18, φ = 0.15, and Δt = 0.01.In σi = 0  ; then In σ1 = 0.15, σ2 = 1, σ3 = 1, σ4 = 0.5, σ5 = 0.25. Obviously, Synchronously, in


This study addresses the question of whether native Mandarin Chinese speakers process and comprehend subject-extracted relative clauses (SRC) more readily than object-extracted relative clauses (ORC) in Mandarin Chinese. Presently, this has been a hotly debated issue, with various studies producing contrasting results. Using two eye-tracking experiments with ambiguous and unambiguous RCs, this study shows that both ORCs and SRCs have different processing requirements depending on the locus and time course during reading. The results reveal that ORC reading was possibly facilitated by linear/temporal integration and canonicity. On the other hand, similarity-based interference made ORCs more difficult, and expectation-based processing was more prominent for unambiguous ORCs. Overall, RC processing in Mandarin should not be broken down to a single ORC (dis)advantage, but understood as multiple interdependent factors influencing whether ORCs are either more difficult or easier to parse depending on the task and context at hand. When comparing sentence processing strategies between languages, several cross-linguistic differences have been observed, making it unclear whether strategies differ across languages. Occasionally, competing models make dichotomous predictions for a certain language, for example, when processing relative clauses in Mandarin Chinese (henceforth “Mandarin”). In Mandarin, past studies are divided on their support for different contending models. In the present study, we employ eye-tracking to empirically investigate several relative clause processing models within different contexts to explore their interrelationships. First, we briefly introduce the topic of relative clauses, followed by several available processing accounts, and then discuss how these models might function in Mandarin.Example of a Mandarin Chinese relative clause: SRC / ORCtiZhǐzé Shìzhǎng / Shìzhǎng Zhǐzé ti      De] Jìzhěi      Cǎifǎng-Le      LiNà[ti criticize mayor / mayor criticize    ti REL] reporteri interview-ASP Li Na[i [who ti criticized the mayor/the mayor criticized ti] interviewed Li Na.’‘The reporterwh-movement and filler-gap dependencies . Furthermore, the gender of the nouns was controlled such that male and female names were distributed equally. Animacy has been a well-known issue for RC processing in Mandarin with ORCs having the preference of having an inanimate head and animate RC noun whereas SRCs are preferred to have an inanimate RC noun and animate head. Within animate-animate contexts, however, subject-modified SRCs appeared to be more frequent in comparison to subject-modified ORCs  LiángYuániGāngcái      Chídào Le[ti  invited Li Fang / Li Fang invited ti REL] Liang Yuan  just.now      late ASP[‘Liang Yuan [who invited Li Fang / Li Fang invited] was late just now.’Example of Implausible RC DistractorZhāng Wěi Chī Le De] Li Qiáng Yǐjīng Huí Jiā Le[[Zhang Wei ate REL] Li Qiang already returned‘Li Qiang who Zhang Wei ate already went home.’Experiment 1 involved exposing participants to two different tasks. Each task was done in a separate session and half of the participants first took one task before the other. In both tasks, items were counterbalanced such that no participant would see the same item twice within a single task, nor would they see an identical item between tasks. Stimulus sentences were displayed horizontally on the centre left of a 17-inch Mitsubishi LCD monitor at a distance of 70 cm from the head and chin rest mount. All characters were displayed in Chinese MingLiU 30pt. At this distance, each character subtended a visual angle of 2.5°. Eye-movements were recorded using an EyeLink 1000 Core System. Prior to the experiment, participants were instructed in Mandarin that they would be reading Mandarin sentences displayed one at a time on a computer monitor, and were given the opportunity to ask questions about the procedure. Prior to each session, the camera was calibrated by a 9-point calibration method and subsequent validation. Calibration was periodically repeated throughout each session after block sessions (eight items).For the plausibility task, participants were instructed in Mandarin to read each sentence naturally and judge if the sentence meaning was plausible, that is, if the actions or ideas depicted would be able to exist in a real world, everyday setting. If the sentence meaning was plausible, they were instructed to press a button on a gamepad labelled “True”; conversely, if the sentence meaning was not plausible, they were instructed to press a button labelled “False”. Participants were instructed to read and judge each sentence within eight seconds. After pressing either button, the stimulus was immediately removed from the screen. Reading times were measured from the onset of the stimulus to the button press event. Eight practice trials were given to ensure participants understood the task.Li invite Liang?). Reading times were measured from the onset of the stimuli to this button press event. For the question, participants had up to eight seconds to answer. When answering, participants were instructed to press the “True” button for correct or true probes or the “False” button for incorrect or false probes. Eight practice trials were given to ensure participants understood the task. For both tasks, since reading times were measured from the onset of stimuli until the button response events  reading times and eye-movements are comparable between the two tasks.For the verification task, only minor changes were made to the procedure. Participants were instructed to read each sentence naturally and that after reading the sentence a comprehension question would appear. Again, participants were asked to read each sentence within eight seconds. When they were finished reading the sentence, participants were instructed to press a button that would replace the sentence with a comprehension/verification question , and go-past time, the combined RT for an interest region  before it is exited to the right  for the first time including any regressive readings out of the region to the left . Go-past times are thus greater than or equal to first-pass times for a region. Regression-in and regression–out  proportion measures, the total reading time of the sentence and accuracy are also reported. While accuracy for the plausibility task denoted whether the participant accurately judged the experimental RCs as plausible, accuracy for the verification task indicated whether the participant accurately judged the probe to be true/correct or false/incorrect. The interest regions for analyses were the sentence, the RC structure , the relativizer (DE), the head noun (N2), the adverb (ADV), and the matrix verb (V2). Prior to the analyses, eye-fixations were first treated. Fixations below 80 ms were merged into a neighbouring fixation, and the remaining fixations under 80 ms and those exceeding 1000 ms were removed (523 fixations or 2.91%). Refer to The earliest reading time measure reported here is lme4 package (ti beat    waiter    /    waiter beat    ti  more.than.once REL]that.one [last.night N2        V2        N3          Gùkèi    Tīngshuōguò    Lǎobǎn    Bìngqiě    Jìdé    Tā        customeri  heard.of    boss        and    remember him              ‘(The) customer [who beat up the waiter / the waiter beat up last night] has heard of the boss and remembers him.’The procedure was similar to Experiment 1. All characters were displayed in simplified Chinese SimSun 22pt font, a visual angle of 1.8°. The font and size were changed to better fit the longer stimuli used in Experiment 2. Here, participants now had a maximum of 12 seconds to read the sentence and press any button when they were finished reading to replace the sentence with the question. Participants still had a maximum time of eight seconds to answer the verification/comprehension probe. The increase in allotted time also accommodated for the increased length of the items.Eye-fixations were treated following the same procedure as Experiment 1 which resulted in the removal of 1,963 fixations or 7.34%. The same LME methods were used as in Experiment 1. RC condition (ORC vs. SRC) and determiner type (Empty vs. DCL) were considered as fixed effects, and subject and item composed the random effects. If interaction of condition:type was significant, a pairwise analysis was conducted. Data trimming for each model resulted in the removal of 1.68% of the data. Refer to Tables D-H for means and standard errors and LME results within Accuracy. The analysis on the accuracy for the verification probes revealed no significant differences for RC condition (p = .516), determiner type (p = .920) or condition:type interaction (p = .531). The mean scores were rather close between items.Total reading time of the sentence. For the reading of the sentence, while both RC condition (p < .001) and determiner type (p < .01) were significant, interaction was not (p = .800). The general pattern of results revealed that the ORC condition had longer RTs than the SRC condition and that the DCL type had longer RTs compared to the Empty type.First-pass RT. For the RC condition (p = .068), even though ORCs were read quicker than SRCs, the result was not significant. For determiner type (p < .001), the Empty type had significantly longer RTs than the DCL type. Interaction was not significant (p = .428).Re-reading Time. In contrast to first-pass RT, ORC re-reading time was significantly longer than SRC re-reading time at this later stage of processing (p < .001). For determiner type, while the Empty type had longer RTs in comparison to the DCL type, the difference did not reach significance (p = .063). There was still no effect of interaction (p = .504).Go-past Time. While there was no significant difference between RC conditions (p = .129), there was a significant difference in determiner type (p < .05) showing unsurprisingly that the DCL type had longer RTs than the Empty type since the DCL type items had one additional region compared with the Empty type items. During this stage, there was a significant effect of interaction (p < .001). The pairwise comparison revealed that ORC:DCL had significantly longer RTs than SRC:DCL (p < .001). While ORC:Empty had the lowest RTs, it was not significantly faster than SRC:Empty in the pairwise analysis.Regression-out. The RC condition (p < .01) and determiner type (p < .01) revealed that ORCs were more likely to have a regression out than SRCs, and the DCL type was more likely than the Empty type. Again, there was a significant effect interaction showing that ORC:DCL was more likely to regress out than SRC:DCL (p < .001). Consequently, it appears that ORC:DCL was driving the effects for this measure.Regression-in. While RC condition (p = .299) was not significant, determiner type (p < .001) demonstrated that the Empty type was more likely to have a regression made back into the RC in comparison to the DCL type. Interaction was not significant (p = .141).First-pass RT. At the first-pass reading of the frequency phrase, there were no differences between RC conditions (p = .179), but determiner type (p < .05) demonstrated that the Empty type had longer RTs compared to the DCL type. Interaction was not significant (p = .075).Re-reading Time. RC condition (p = .146) and interaction (p = .368) did not show significant differences during re-reading. Again, determiner type (p < .05) revealed that the Empty type had significantly longer RTs compared to the DCL type sentences.Go-past Time. RC condition (p = .844) was still not significant during go-past time, while determiner type (p < .05) still demonstrated that the Empty type had longer RTs compared to the DCL type. Interaction of condition:type was significant (p < .05). However, this only demonstrated that ORC:Empty had significantly longer go-past RTs than ORC:DCL (p < .01).Regression-out. RC condition (p = .514), determiner type (p = .680) and interaction (p = .540) revealed no significant differences.Regression-in. RC condition (p = .131) was not significant, but determiner type (p < .05) revealed that the Empty type was more likely to have a regression back into the frequency phrase than the DCL type. There was a significant effect for interaction (p < .01), demonstrating that ORC:DCL was less likely to have a regression back into the phrase than SRC:DCL (p < .05).First-pass RT. For the RC condition (p < .05), it was shown that ORCs had significantly longer RTs than SRCs. Neither determiner type (p = .554) nor interaction (p = .415) at the relativizer were significant.Re-reading Time. In later re-reading times, the RC condition (p = .543) was no longer significant. However, determiner type (p < .01) indicated that the Empty type had longer RTs than the DCL type. Interaction was not significant (p = .311).Go-past Time. Only the RC condition (p < .05) revealed a significant difference in RTs, showing that ORCs as a whole had longer RTs in comparison to SRCs. There was no significance for determiner type (p = .103) and interaction (p = .640).Regression-out. The RC condition (p = .230), determiner type (p = .791) and interaction (p = .617) did not reveal any significant differences.Regression-in. For the RC condition (p < .01), ORCs were significantly more likely to have a regression back into the relativizer than SRCs (p < .01). However, determiner type (p = .670) was not significant. While there was a significant interaction effect found (p < .05), it only indicated that ORC:Empty was more likely to have a regression back into the relativizer than SRC:Empty (p < .01), despite both ORCs having higher regression-in means than their SRC counterparts.First-pass RT. RC condition (p = .497), determiner type (p = .578) and interaction (p = .778) revealed no significant differences during first-pass reading.Re-reading Time. For fixations made after first-pass, RC condition (p < .05) demonstrated that ORCs had longer RTs than SRCs, and determiner type (p < .05) revealed that the Empty type had longer RTs compared to DCL type items. Interaction did not show significant differences (p = .806).Go-past Time. While the RC condition (p = .692) and interaction (p = .340) were not significant, the determiner type (p < .05) showed that the Empty type items required longer RTs before moving on to the matrix clause verb.Regression-out. The RC condition (p < .05) revealed that the ORC condition was significantly more likely to make a regression out of the head noun back into previous parts of the sentence in comparison to SRCs. Determiner type (p = .624) was not significant. However, interaction (p < .05) was significant and demonstrated that ORC:Empty was significantly more likely to make a regression out of the head than SRC:Empty (p < .05).Regression-in. Only the RC condition (p < .05) was significant showing that ORCs were more likely to have a regression back into the head from later parts of the matrix clause. Determiner type (p = .911) and interaction (p = .938) were not significant.First-pass RT. RC condition (p = .955), determiner type (p = .302) and interaction (p = .728) revealed no significant differences during first-pass reading.Re-reading Time. For the RC condition (p < .01), ORCs had significantly longer RTs than SRCs, whereas determiner type (p = .440) was not significant. While interaction (p < .05) was significant, the pairwise analysis revealed that ORC:Empty only had significantly longer RTs than SRC:Empty (p < .01).Go-past Time. While RC condition (p = .209) and interaction (p = .967) were not significant, determiner type (p < .01) indicated that the Empty type sentences had significantly longer RTs in comparison to DCL sentences.Regression-out. RC condition (p = .414) and interaction (p = .921) were not significant; determiner type (p = .061) also revealed no significance even though the Empty sentences had a higher likelihood to regress out than DCL sentences.Regression-in. RC condition (p = .567), determiner type (p = .377) and interaction (p = .748) revealed no significant effects.First-pass RT. For the RC condition (p = .058), no significant differences were found. Also, neither determiner type (p = .136) nor interaction (p = .417) indicated significant differences during first-pass reading.Re-reading Time. RC condition (p = .643), determiner type (p = .997) and interaction (p = .863) revealed no significant effects.Go-past Time. RC condition (p = .562), determiner type (p = .921) and interaction (p = .377) revealed no significant effects.Regression-out. RC condition (p = .405), determiner type (p = .264) and interaction (p = .295) revealed no significant effects.Regression-in. RC condition (p = .912), determiner type (p = .537) and interaction (p = .886) revealed no significant effects.Next, we present the additional analyses as described above. Refer to Tables G and H for means, standard errors and LME results in First-pass RT. There was a significant effect for the RC condition (p < .01) showing that ORCs were read faster than SRCs. Determiner type (p < .001) was also significant and revealed that the Empty type sentences had longer RTs during first-pass reading compared to DCL sentences. Interaction (p = .069), however, did not reach the significance threshold.Re-reading Time. The RC condition (p < .001) and determiner type (p < .05) were both significant which demonstrated that ORC conditions had significantly longer RTs than SRCs and the Empty type items had significantly longer RTs compared to DCL items. Interaction (p = .993) was not significant.Go-past Time. The RC condition (p = .069) did not reveal a significant difference between ORCs and SRCs. Determiner type (p = .151) was also not significant. Interaction (p < .001) of condition:type was significant and demonstrated contrasting effects for the ORC types. This interaction showed that the ORC:DCL condition had significantly longer RTs than SRC:DCL (p < .001). On the other hand, it was revealed that the ORC:Empty condition had significantly faster RTs than the SRC:Empty (p < .05) condition.Regression-out. The RC condition (p < .001), determiner type (p < .001) and interaction (p < .001) were all significant. It was shown that the ORCs conditions and DCL types were significantly more likely to regress out than their counterparts. However, the pairwise analysis indicated that it was only the ORC:DCL condition which was significantly more likely to regress out of the RC structure than SRC:DCL (p < .001).Regression-in. While the RC condition (p = .097) was unable to reveal significant differences between conditions, determiner type (p < .001) and interaction (p < .01) were both significant. While the Empty type was significantly more likely to have a regression back into the RC structure, the pairwise analysis revealed that, opposite to regression-out, it was only ORC:Empty which was more likely to have a regression made back into the RC structure in comparison to SRC:Empty (p < .01).First-pass RT. While the RC condition (p = .640) and determiner type (p = .216) were not significant, interaction (p < .01) was significant. However, from the pairwise analysis, it was only revealed that ORC:Empty was significantly faster than SRC:Empty (p < .05).Re-reading Time. The RC condition (p < .001) revealed that ORCs had significantly longer re-reading times than SRCs, and determiner type (p < .01) showed that the Empty sentences had significantly longer RTs compared to DCL sentences. Interaction (p = .271) was not significant.Go-past Time. The RC condition (p < .01), determiner type (p < .001) and interaction (p < .01) were all significant. As with re-reading time, ORCs had significantly longer RTs compared to SRCs, and the Empty sentences had significantly longer RTs in comparison to their DCL counterparts. In contrast to first-pass time, the pairwise analysis showed that ORC:Empty now had significantly longer go-past times in comparison to SRC:Empty (p < .001).Regression-out. For the RC condition (p = .087), ORCs only had a trending likelihood of regressing out of the matrix clause in comparison with SRCs. However, determiner type (p < .01) revealed that the Empty type items were significantly more likely to regress out than DCL type items. Interaction (p < .01) was significant, and similar to go-past time, the pairwise analysis indicated that ORC:Empty was significantly more likely to have a regression out of the matrix clause in comparison to SRC:Empty (p < .001).Regression-in. RC condition (p = .661), determiner type (p = .088) and interaction (p = .942) revealed no significant differences between conditions and types.In contrast to Experiment 1, Experiment 2 clearly showed that ORCs were more difficult to process than SRCs. Nonetheless, the results also indicated that multiple processing factors were involved in the processing of Mandarin RCs revealing both ORC advantages and disadvantages: Canonicity (ORC facilitation), expectation (ORC disadvantage), and perhaps similarity interference (ORC disadvantage) as well.While integration resources were not directly supported in Experiment 2, evidence of canonicity was nevertheless present for both unambiguous and ambiguous ORC items during early RTs within both RC regions. Additionally, Jäger et al. (refer to Table 13 in ) also apIn addition to canonicity effects, the initial benefit for ORCs may also loosely provide indirect support for linear/temporal metrics of integration. However, integration was not directly supported at the relativizer or head noun which we attribute to antilocality effects. In other words, with the introduction of syntactic cues , there would be greater expectation or anticipation  for the For expectation-based effects, the general pattern of results observed in Jäger et al.  was replSimilarity-based interference was again hinted at by the indication of ORC difficulty at the matrix clause. Since Jäger et al.  also fouIn summary, while canonicity facilitated ORCs early on with indirect support for linear/temporal integration, the influences of expectation-based processing later reversed this within the RC. At the matrix clause, similarity-based interference was also observed to be a potential factor responsible for increasing ORC difficulty. In all, the reading of these sentences was seen to be influenced by multiple factors of processing.In this study, we sought out to determine which Mandarin relative clause structures are more demanding to process. We investigated the reading of ambiguous RCs as well as unambiguous RCs using eye-tracking. More specifically, we aimed to determine how the initial clause type ambiguity and processing factors such as canonicity, expectation, integration and similarity-based interference influence the reading of Mandarin sentences containing RCs. The results of Experiment 1 revealed that ambiguous ORCs were generally easier to process than SRCs, regardless of task design supporting canonicity, expectation, storage and integration-based effects. Yet, in the long run, ORCs became more difficult to process at the matrix clause, a result which may provide support for similarity-based interference as well as accounts on animacy preferences in Mandarin RC processing. The results of Experiment 2 revealed that canonicity and possibly locality facilitated the early readings of the ORC within the relative clause. Also, ambiguous ORCs remained easier to process compared to SRCs longer than unambiguous ORCs. ORCs were still more difficult during later RTs within the RC and matrix clause as explained by expectation-based processing and similarity interference. Experiment 2, however, did not provide direct evidence supporting linear/temporal integration-based models at the relativizer or head noun. This was possibly due to antilocality effects or due to the inclusion of the frequency phrase in items used in Experiment 2, given the irregular position of the phrase for ORCs.One particular framework of processing and cognitive behaviour can support the findings of this study, that is, Lewis and Vasishth’s ,38 activWe view canonicity as a top-down mechanism based upon a coarsely-tuned account of a language’s structural or thematic regularities. While expectation and anticipatory effects may be more dependent on fine-tuned structural and collocational frequencies, canonicity can influence processing even for less frequent structures based solely on regularities of the language. This interpretation would therefore differ from and supplement previous notions of canonicity which have been based upon both statistical frequency and regularity . We findIn contrast with canonicity, as syntactic cues which helped give an RC interpretation were introduced into the sentence , anticipatory processes greatly influenced the processing for the more frequent SRC structure. This caused SRCs to be processed more easily than ORCs at the relativizer and during later reading times for the RC  and RC structure  in Experiment 2. However, we understand this greater expectation or anticipation for the SRC structure to be an antilocality effect. We believe this effect could have possibly prevented the observation of a linear or temporally defined integration metric at the relativizer and head noun. As mentioned above, locality is a constraint on the reactivation of an item from memory. In general, after the initial activation of an item, the activation level will begin to decay, and the more distant a gap is to its filler, the greater the decay will be. Since ORCs would have less activation decay due to the gap and filler being more local defined by either a linear or temporal metric, ORCs should be easier to process when integrating filler-gap dependencies. This was clearly supported by the results of Experiment 1. Experiment 2, on the other hand, only was able to support effects of locality beyond the scope of the specific loci of integration in Mandarin Chinese. If we consider that locality does influence processing, then the fact that the results of Experiment 2 conflict with ORC locality is best explained by antilocality effects, rather than a structural-phrase integration metric. Lastly, there was partial evidence supporting a similarity-based interference when the matrix verb needed to retrieve its subject  from memory. This was indicated by the ORC difficulty found at the matrix clause for both experiments and all ORC types. We believe that similarity-based interference provides the most suitable explanation for the ORC difficulty here. The difficulty for ORCs at the matrix clause verb can be explained by the proactive interference of the ORC relative clause subject on the activation level of the matrix clause subject. On the other hand, the SRC relative clause object should not lower the activation level of the matrix clause subject. Thus, during the retrieval of the subject at the matrix verb, ORCs should have greater processing difficulty compared to SRCs.In summary, the results seem to be compatible with activation-based constraints on processing showing multiple influences on sentence processing. In the current study, we limited these to more global interpretations on sentence processing; as such, see Vasishth and Lewis ,38 and cThe current study is not without limitation and there are several issues left to be addressed. Both experiments potentially involved issues since animacy, passivation and object-modification were not addressed as independent factors. Consequently, the current study is somewhat limited in its overall interpretability. One issue, for example, is that the current study cannot dissociate semantics and syntax for canonical order effects. Yet, considering that ORCs are preferred to include the passive marker, the thematic canonicity of agent-to-patient may admittedly have a greater influence on processing compared to grammatical SVO word order.In the current study, while subject/object asymmetry was only investigated for RC processing in Mandarin, subject biases have also been observed within other structures as well. For instance, Simpson, Wu, and Li  using a Concerning canonical order facilitation, while the current study found clear benefits of canonicity at the RC structure for both experiments, it is still unclear what role statistical frequency can be attributed to for the items with attenuated clause type ambiguity. Hsiao and MacDonald  found thA notable issue of this study was that the frequency phrase in Experiment 2 still acted as a syntactic cue to help attenuate ambiguity. Thus, the items lacking the Det+Cl were still less ambiguous than the items of Experiment 1. Furthermore, the position of the frequency phrase is unnatural for the ORC condition. Consequently, the difficulty found at the relative clause or head noun for ORCs during later RTs may be attributed in some part to the unnaturalness of the frequency phrase for ORCs. Since the phrase is not in a canonical position for ORCs, it may also be the case that semantics rather than word order may have been facilitating ORCs during early RTs at the full RC region. Future studies using eye-tracking should further address the issue of semantics and also address the frequency phrase as experimental factors to determine its influence inside the RC and at head noun.In a similar vein, since the Det+Cl can either appear prior or after the RC, it may be best to compare such a design to determine the influence of modification position on the processing of the head noun using eye-tracking. In fact, previous research  has alreAn additional issue was that object-modified RCs were not addressed in this study. Considering that in situ object-modified RCs are not preferred, we believe future studies should follow Lin and Garnsey  and invet = 5.14, p < .001], neither item modification  nor the interaction of the two  were significantly different. It was found for both the modified and unmodified items, SRCs  were rated significantly higher than ORCs . In Jäger et al.  argued for an overall SRC advantage, they allowed that random variability may possibly contribute to this appropriate inconsistency to some extent. If there is random variability, then the possible contributing factors should be determined. It is possible that differences in experimental items, the method or the number of cues to attenuate temporary ambiguity, the experimental methodology , and participant-pools  may all contribute to the random variability. For instance, there have been many studies using Gibson and Wu’s (27) items and unambiguous design ,20,28, bAlthough the previous studies and the current study used native Mandarin speakers (with the majority recruiting participants originating from either Mainland China or Taiwan), there is still variability among regional dialects of Mandarin. For instance, even among similar Pǔtōnghuà and Guóyǔ standard dialects of Mandarin , there are differences in grammar, phonology, and vocabulary. As such, it may be of empirical interest for future studies to assess the influence of dialect.In an effort to further previous eye-tracking studies that used either ambiguous relative clauses in Mandarin or syntactic cues to attenuate ambiguity, the current study shows that canonicity and linear/ temporal-based integrations metrics support an ORC advantage. However, these effects are more prominent when the structure of the RC is initially ambiguous. As such, we also show that as additional syntactic cues are given, the more likely, quickly and severely the expectations generated from the structural frequency will impact the processing of object-relative clauses. We view this as an antilocality effect. In addition, we also show evidence for a similarity-based interference within the matrix clause regardless of ambiguity. We argue along the lines of Vasishth and Lewis ,38 that At Nagoya University, Japan ethic committees are operated separately from the main institution within each graduate school; however, not all graduate schools have a committee. Since the Graduate School of Languages and Cultures at Nagoya University, Japan did not have an ethics committee at the time of the study, approval from such a committee could not be obtained. Instead, this research was approved by the faculty of the Graduate School of Languages and Cultures at Nagoya University, Japan which adheres to the Declaration of Helsinki for research using human subjects. In the current study, all participants first signed the informed written consent form prior to participating in the study and received monetary compensation at the end of their session. All personal information collected from participants was stored in a secured location, and participants were given pseudonyms for data analysis purposes. Participants were not subject to harm and could only experience mild discomfort from prolong seating and reading. Lastly, we declare that each of the participating authors did not have any conflict of interest during the completion of the study.The following sentences are all the ORC experimental items from Experiment 1. The SRC sentence condition is given only for the first item. The interest regions are designated between asterisk marks .1ORC: *李芳邀请*的*梁媛*刚才*迟到了*Li Fāng yāoqǐng de Liáng Yuán gāngcái chídàoleLi Fang invited REL Liang Yuan just late‘LiangYuan who LiFang invited was late just now.’1SRC: *邀请李芳*的*梁媛*刚才*迟到了*yāoqǐng Li Fāng de Liáng Yuán gāngcái chídàoleinvited Li Fang REL Liang Yuan just late‘LiangYuan who invited LiFang was late just now.’2*王磊联系*的*张艳*刚才*进门了*WángLěi liánxì de Zhāng Yàn gāngcái jìnménleWang Lei contact REL Zhang Yan just entered‘Zhang Yan who Wang Lei contacted just entered the door.’3*王艳走访*的*王伟*前天*参赛了*Wáng Yàn zǒufǎng de Wáng Wěi qiántiān cānsàileWang Yan visited REL Wang Wei day.before.yesterday entered.competition‘Wang Wei who Wang Yan visited entered the competition the day before yesterday.’4*张伟勾结*的*李强*去年*入狱了*Zhāng Wěi gōujié de Lǐ Qiáng qùnián rùyùleZhang Wei conspired REL Li Qiang last.year jailed‘LiQiang who ZhangWei conspired with went to jail last year.’5*杨敏辅导*的*杨昊*今天*就任了*Yáng Mǐn fǔdǎo de Yáng Hào jīntiān jiùrènleYang Min mentor REL Yang Hao today inducted‘Yang Hao who Yang Min mentored was inducted today.’6*王强关注*的*王洁*上周*获胜了*Wáng Qiáng guānzhù de Wáng Jié shàngzhōu huòshèngleWang Qiang follow.with.interest REL Wang Jie last.week won‘Wang Jie who Wang Qiang follows with interest won last week.’7*王霞拥护*的*马超*今天*下台了*Wáng Xiá yōnghù de Mǎ Chāo jīntiān xiàtáileWang Xia supports REL Ma Chao today resigned‘Ma Chao who Wang Xia supports resigned today.’8*李丽抢救*的*罗英*前天*牺牲了*Lǐ Lì qiǎngjiù de Luó Yīng qiántiān xīshēngleLi Li saved REL Luo Ying day.before.yesterday sacrificed‘Luo Ying who Li Li saved sacrificed his life the day before yesterday.’9*李昊寻找*的*王刚*已经*逝世了*Lǐ Hào xúnzhǎo de Wáng Gāng yǐjīng shìshìleLi Hao search REL Wang Gang already passed.away‘Wang Gang who Li Hao is searching for has passed away already.’10*王军关心*的*李婧*去年*结婚了*Wáng Jūn guānxīn de Lǐ Jìng qùnián jiéhūnleWang Jun cares.about REL Li Jing last year married‘Li Jing who Wang Jun cares about married last year.’11*王芳推荐*的*张强*刚才*上台了*Wáng Fāng tuījiàn de Zhāng Qiáng gāngcái shàngtáileWang Fang recommended REL Zhang Qiang just.now appear.on.stage‘Zhang Qiang who Wang Fang recommended appeared on the stage just now.’12*李明聘请*的*刘梦*去年*转行了*Lǐ Míng pìnqǐng de Liú Mèng qùnián zhuǎnhángleLi Ming hired REL Liu Meng last.year switched.profession‘Liu Meng who Li Ming hired switched to another profession last year.’13*王茜提及*的*李明*去年*搬家了*Wáng Qiàn tíjí de Lǐ Míng qùnián bānjiāleWang Qian mentioned REL Li Ming last.year moved‘Li Ming who Wang Qian mentioned moved last year.’14*李艳采访*的*王丽*昨天*自杀了*Lǐ Yàn cǎifǎng de Wáng Lì zuótiān zìshāleLi Yan interviewed REL Wang Li yesterday committed.suicide‘Wang Li who Li Yan interviewed committed suicide yesterday.’15*李明资助*的*刘洋*去年*破产了*Lǐ Míng zīzhù de Liú Yáng qùnián pòchǎnleLi Ming funded REL Liu Yang last.year bankrupted‘Liu Yang who Li Ming funded went bankrupt last year.’16*周婕提拔*的*张梦*上周*违纪了*Zhōu Jié tíbá de Zhāng Mèng shàngzhōu wéijìleZhou Jie promoted REL Zhang Meng last.week broke.rules‘Zhang Meng who Zhou Jie promoted broke the rules last week.’17*张杰负责*的*刘鹏*去年*辞职了*Zhāng Jié fùzé de Liú Péng qùnián cízhíleZhang Jie in.charge.of REL Liu Peng last.year resigned‘Liu Peng who Zhang Jie was in charge of resigned last year.’18*王静录用*的*李莲*今天*加班了*Wáng Jìng lùyòng de Lǐ Lián jīntiān jiābānleWang Jing employed REL Li Lian today worked.overtime‘Li Lian who Wang Jing employed worked overtime today.’19*王静信任*的*李涛*今天*缺席了*Wáng Jìng xìnrèn de Lǐ Tāo jīntiān quēxíleWang Jing trust REL Li Tao today absent‘Li Tao who Wang Jing trusts is absent today.’20*王阳培养*的*周洁*去年*掌权了*Wáng Yáng péiyǎng de Zhōu Jié qùnián zhǎngquánleWang Yang mentored REL Zhou Jie last.year in.power‘Zhou Jie who Wang Yang mentored came into power last year.’21*刘刚思念*的*高军*昨天*生病了*Liú Gāng sīniàn de Gāo Jūn zuótiān shēngbìngleLiu Gang missed REL Gao Jun yesterday sick‘Gao Jun who Liu Gang missed was sick yesterday.’22*张涛招待*的*李艳*去年*退休了*Zhāng Tāo zhāodài de Li Yàn qùnián tuìxiūleZhang Tao entertained REL Li Yan last.year retired‘Li Yan who Zhang Tao entertained retired last year.’23*周超指导*的*李勇*刚才*出事了*Zhōu Chāo zhǐdǎo de Lǐ Yǒng gāngcái chūshìleZhou Chao tutored REL Li Yong just.now accident‘Li Yong who Zhou Chao tutored had an accident just now.’‘Something bad happened to Li Yong who Zhou Chao tutored just now.’24*王芳栽培*的*张静*上周*去世了*Wáng Fāng zāipéi de Zhāng Jìng shàngzhōu qùshìleWang Fang mentored REL Zhang Jing last.week died.‘Zhang Jing who Wang Fang mentored died last week.’25*王娟支持*的*李勇*昨天*获奖了*Wáng Juān zhīchí de Lǐ Yǒng zuótiān huòjiǎngleWang Juan support REL Li Yong yesterday won.award‘Li Yong who Wang Juan supports won an award yesterday.’26*张超宴请*的*王婕*刚才*道歉了*Zhāng Chāo yànqǐng de Wáng Jié gāngcái dàoqiànleZhang Chao entertained REL Wang Jie just.now apologized‘Wang Jie who Zhang Chao entertained apologized just now.’27*张静逮捕*的*李鹏*那天*受伤了*Zhāng Jìng dàibǔ de Lǐ Péng nàtiān shòushāngleZhang Jing arrested REL Li Peng that.day injured‘Li Peng who Zhang Jing arrested was injured that day.’28*刘杰赏识*的*高燕*最终*受骗了*Liú Jié shǎngshí de Gāo Yàn zuìzhōng shòupiànleLiu Jie admire REL Gao Yan eventually deceived.‘Gao Yan who Liu Jie admires was deceived eventually.’29*王强培育*的*张勇*今年*创业了*Wáng Qiáng péiyù de Zhāng Yǒng jīnnián chuàngyèleWang Qiang mentored REL Zhang Yong this.year started.business‘Zhang Yong who Wang Qiang mentored started a business this year.’30*王静照顾*的*李娜*上周*捐款了*Wáng Jìng zhàogù de Lǐ Nà shàngzhōu juānkuǎnleWang Jing take.care REL Li Na last.week made.donation‘Li Na who Wang Jing takes care of made a donation last week.’31*王猛服务*的*李刚*今年*入选了*Wáng Měng fúwù de Lǐ Gāng jīnnián rùxuǎnleWang Meng served REL LI Gang this.year selected‘Li Gang who Wang Meng served was selected this year.’32*李茜担心*的*陈倩*今天*住院了* Lǐ Qiàn dānxīn de Chén Qiàn jīntiān zhùyuànleLi Qian worried.about REL Chen Qian today hospitalized‘Chen Qian who Li Qian worried about is in the hospital today.’The following sentences are all the ORC experimental items from Experiment 2. The SRC sentence condition is given only for the first item. For the unambiguous condition, the determiner + classifier region which is the first region for all of the sentences was removed. The interest regions are designated between asterisk marks .1. ORC: *那个*昨晚*服务生揍了*一顿*的*顾客*听说过*老板*并且记得他。*nàgè zuówǎn fúwùshēng zòule yī dùn de gùkè tīngshuōguò lǎobǎn bìngqiě jìde tāthat.one last.night waiter beat more.than.once REL customer have heard of boss and remember him‘The customer who the waiter beat up last night has heard of the boss and remembers him.’1. SRC: *那个*昨晚*揍了服务生*一顿*的*顾客*听说过*老板*并且记得他。*nàgè zuówǎn zòule fúwùshēng yī dùn de gùkè tīngshuōguò lǎobǎn bìngqiě jìde tāthat.one last.night beat waiter more.than.once REL customer have.heard.of boss and remember him‘The customer who beat up the waiter last night has heard of the boss and remembers him.’2. *那辆*下午*摩托车追了*很久*的*轿车*发现了*记者*所以停了下来。*nàliàng xiàwǔ mótuōchē zhuīle hěnjiǔ de jiàochē fāxiànle jìzhě suǒyǐ tíngle xiàláithat.car afternoon motorcycle chased long.time REL car found reporter so stopped‘The car that the motorcycle chased for a long time in the afternoon found the reporter so it stopped.’3. *那个*今天*男孩打了*几次*的*女孩*看到了*校长*所以假装读书。*nàgè jīntiān nánhái dǎle jǐcì de nǚhái kàndàole xiàozhǎng suǒyǐ jiǎzhuāng dúshūthat.one today boy hit several.times REL girl saw principal so pretended to read‘The girl who the boy hit several times today saw the principal and thus pretended to read.’4. *那辆*当时*自行车撞了*两次*的*吉普车*拦住了*警察*并且要求调查清楚。*nàliàng dāngshí zìxíngchē zhuàngle liǎngcì de jípǔchē lánzhùle jǐngchá bìngqiě yāoqiú diàochá qīngchuthat.car then bike hit twice REL jeep stopped police and asked investigation clear‘The jeep that the bike hit twice then stopped the police and asked for a clear investigation.’5. *那个*刚才*男孩推了*一下*的*妇女*偷了*店员*并且打伤了她。*nàgè gāngcái nánhái tuīle yīxià de fùnǚ tōule diànyuán bìngqiě dǎshāngle tāthat.one just.now boy pushed a.bit REL woman stole clerk and wounded her‘The woman who the boy pushed just now stole from the clerk and wounded her.’6. *那个*上个月*男孩邀请了*几次*的*女孩*认识*王老师*因为上过她的课。*nàgè shànggèyuè nánhái yāoqǐngle jǐcì de nǚhái rènshi wánglǎoshī yīnwèi shàngguò tā de kèthat.one last.month boy invited several.times REL girl knows teacher.WANG because went to her class‘That girl who the boy invited several times last month knows teacher Wong because she went to her class.’7. *那条*去年*主人救了*好几次*的*狗*喜欢*小男孩*所以很兴奋。*nàtiáo qùnián zhǔrén jiùle hǎojǐcì de gǒu xǐhuan xiǎonánhái suǒyǐ hěn xīngfènthat.animal last.year master saved several.times REL dog like little.boy so it be very excited‘The dog that the master saved several times last year likes the little boy so it was very excited.’8. *那个*刚才*职业选手推了*一下*的*业余选手*骂了*裁判*而且威胁了他。*nàgè gāngcái zhíyè xuǎnshǒu tuīle yīxià de yèyú xuǎnshǒu màle cáipàn érqiě wēixiéle tāthat.one just.now professional.player pushed a.bit REL amateur scolded referee and threatened him‘The amateur who the professional player pushed a bit just now scolded the referee and threatened him.’9. *这个*上个月*杀手监视了*一段时间*的*侦探*讨厌*当地人*所以没有寻求帮助。*zhège shànggèyuè shāshǒu jiānshìle yīduàn shíjiān de zhēntàn tǎoyàn dāngdìrén suǒyǐ méiyǒu xúnqiú bāngzhùthis.one last.month killer watched a.while REL detective hated local so did.not ask for help‘The detective who the killer watched for a while last month hated the locals so he did not ask for help.’10. *那位*最近*房东抱怨了*好多次*的*住户*找了*律师*而且打算*起诉。*nàwèi zuìjìn fángdōng bàoyuànle hǎoduōcì de zhùhù zhǎole lǜshī érqiě dǎsuàn qǐsùthat.person recently landlord complained many.times REL tenant found lawyer and intended sue‘The tenant who the landlord complained about many times recently found a lawyer and intends to sue.’11. *那个*上个月*教练骂了*一顿*的*球员*爱上了*女歌星*还送她礼物。*nàgè shànggèyuè jiàoliàn màle yīdùn de qiúyuán àishàngle nǚ gēxīng hái sòng tā lǐwùthat.one last.month coach scolded more.than.once REL player fell.love.with female singer and sent her gift‘The player who the coach scolded last month fell in love with a female singer and sent her a gift.’12. *那位*以前*指挥家崇拜了*很久*的*作曲家*结识了*小提琴手*并且两人常见面。*nàwèi yǐqián zhǐhuījiā chóngbàile hěnjiǔ de zuòqǔjiā jiéshìle xiǎotíqínshǒu bìngqiě liǎng rén cháng jiànmiànthat.person before conductor respected long.time REL composer met violinist and both meet often‘The composer who the conductor respected for a long time in the past met a violinist and they meet often.’13. *这个*去年*电视台批评了*几次*的*女演员*很欣赏*金城武*因为他个性坦率。*zhège qùnián diànshìtái pīpíngle jǐcì de nǚ yǎnyuán hěn xīnshǎng jīnchéngwǔ yīnwèi tā gèxìng tǎnshuàithis.one last.year TV.station criticized several.times REL actress very.appreciate Jincheng Wu because his frank personality‘This actress who the TV station criticized several times last year appreciates Jincheng Wu very much because of his frank personality.’14. *那位*上个月*飞行员约了*两次*的*空姐*惹怒了*经理*因为她常迟到。*nàwèi shànggèyuè fēixíngyuán yuēle liǎngcì de kōngjiě rěnùle jīnglǐ yīnwèi tā cháng chídàothat.person last.month pilot ask out twice REL stewardess angered manager because she often late‘The stewardess who the pilot asked out twice last month angered the manger because she was often late.’15. *这位*今天*导演称赞了*多次*的*男明星*批评了*影评家*并且表示很难过。*zhèwèi jīntiān dǎoyǎn chēngzànle duōcì de nán míngxīng pīpíngle yǐngpíngjiā bìngqiě biǎoshì hěn nánguòthis.one today director praised many.times REL male star criticized critics and said he was very sad‘The male star who the director praised many times today criticized the critics and said he was very sad.’16. *那位*昨天*作家采访了*两个小时*的*记者*质疑了*县长候选人*而且扬言报复。*nàwèi zuótiān zuòjiā cǎifǎngle liǎng gè xiǎoshí de jìzhě zhíyíle xiàn zhǎng hòuxuǎnrén érqiě yángyán bàofùthat.person yesterday writer interviewed two.hours REL reporter questioned county.magistrate candidate and threatened revenge‘The reporter who the writer interviewed for two hours yesterday questioned the county magistrate candidate and threatened revenge’17. *那个*今早*犯人追了*一阵*的*小狗*嗅出*主人*并且停了下来。*nàgè jīnzǎo fànrén zhuīle yīzhèn de xiǎo gǒu xiùchū zhǔrén bìngqiě tíngle xiàláithat.one this.morning criminal chased a.while REL puppy sniffed.recognize the master and stopped‘The puppy who the prisoner chased a while this morning sniffed and recognized the mater and stopped.’18. *那位*昨天*邻居教训了*一番*的*大妈*通知*管理员*然后诉了苦。*nàwèi zuótiān línjū jiàoxunle yī fān de dàmā tōngzhī guǎnlǐ yuán ránhòu sule kǔthat.person yesterday neighbor taught for.a.while REL aunt noticed administrator and complained‘The aunt who the neighbor taught a lesson yesterday noticed the administrator and complained.’19. *这位*去年*外交部访问了*一次*的*政治家*支持*外交官*并且相信他。*zhèwèi qùnián wàijiāobù fǎngwènle yīcì de zhèngzhì jiā zhīchí wàijiāoguān bìngqiě xiāngxìn tāthis.person last.year ministry.foreign.affairs visited once REL politician support diplomat and believed him.‘This politician who the Ministry of Foreign Affairs visited once last year supports the diplomat and believes him.’20. *那个*今年*作家批评了*一番*的*评论家*洽询了*出版商*而且建议了出版内容。*nàgè jīnnián zuòjiā pīpíngle yīfān de pínglùnjiā qiàxúnle chūbǎnshāng érqiě jiànyìle chūbǎn nèiróngthat.one this.year writer criticized for.a.while REL critic consulted publisher and suggested publication.content‘The critic who the writer criticized for a while this year consulted the publisher and suggested the content for publication.’21. *那位*下午*学生称赞了*多次*的*老师*认出*家长*然后打招呼。*nàwèi xiàwǔ xuéshēng chēngzànle duōcì de lǎoshī rènchū jiāzhǎng ránhòu dǎzhāohūthat.person afternoon student praised several.times REL teacher recognized parents and.then greeted‘The teacher who the student praised several times this afternoon recognized the parents and greeted them.’22. *那位*上周*护士请教了*一次*的*营养师*看了*医生*而且确定病征。*nàwèi shàngzhōu hùshì qǐngjiàole yīcì de yíngyǎngshī kànle yīshēng érqiě quèdìng bìngzhēngthat.person last.week nurse consulted once REL nutritionist went.to.see doctor and identified symptoms‘That nutritionist who the nurse consulted once last week went to see a doctor and identified symptoms.’23.*这位*上个月*新同事介绍了*几次*的*员工*说服过*上司*然后转了部门。*zhèwèi shànggèyuè xīn tóngshì jièshàole jǐcì de yuángōng shuōfúguò shàngsi ránhòu zhuǎnle bùménthis.person last.month new.colleague introduced several.times REL employee persuaded boss and.then transferred department‘This employee who the new colleague introduced several times last month persuaded the boss and then transferred to another department.’24. *这位*上午*妻子陪了*许久*的*丈夫*问了*旅行社*然后决定规划旅行。*zhèwèi shàngwǔ qīzi péile xǔjiǔ de zhàngfū wènle lǚxíngshè ránhòu juédìng guīhuà lǚxíngthis.person morning wife accompany long.time REL husband inquire travel.agency and.then decide plan trip‘The husband who his wife accompanied a long time this morning inquired the travel agency and then decided to plan a trip.’25. *那个*上午*店员询问了*一番*的*女孩*找到*妈妈*然后回了家。*nàgè shàngwǔ diànyuán xúnwènle yī fān de nǚhái zhǎodào māmā ránhòu huíle jiāthat.one morning clerk asked for.a.while REL girl found mom and.then returned home‘The girl who the clerk asked about in the morning found the mom and then returned home.’26. *这个*去年*新娘恭喜了*几次*的*商人*吻了*女儿*并且举办喜宴。*zhège qùnián xīnniáng gōngxǐle jǐcì de shāngrén wěnle nǚ'ér bìngqiě jǔbàn xǐyànthis.one last.year bride congratulated several.times REL businessman kissed daughter and held wedding banquet‘This businessman who the bride congratulated several times last year kissed the daughter and held a wedding banquet.’27. *这位*昨晚*科学家相信了*一时*的*播报员*联系*物理专家*并且调查了真相。*zhèwèi zuówǎn kēxuéjiā xiāngxìnle yīshí de bōbàoyuán liánxì wùlǐ zhuānjiā bìngqiě diàochále zhēnxiàngthis.person last.night scientist believed momentarily REL announcer contacted physician and investigated truth‘This announcer who the scientist believed momentarily last night contacted a physician and investigated the truth.’28. *那个*前天*小说家取悦了*许久*的*歌剧家*想到*大学教授*所以联络了。*nàgè qiántiān xiǎoshuōjiā qǔyuèle xǔjiǔ de gējùjiā xiǎngdào dàxué jiàoshòu suǒyǐ liánluòlethat.one day.before.yesterday novelist flatter long.time REL opera.singer thought.of university.professor so contacted‘The opera singer who the novelist flattered for a long time the day before yesterday thought of the university professor so she contacted him.’29. *这个*刚才*姑姑接了*一次*的*叔叔*送了*伯伯*并且一起坐车去。*zhège gāngcái gūgū jiēle yīcì de shūshu sòngle bóbo bìngqiě yīqǐ zuòchē qùthis.one just.now aunt picked up once REL the.younger.uncle sent the.older.uncle and went by car‘The younger uncle who the aunt picked up once just now sent off the older uncle and went by car together.’30. *这个*今晚*客人迎接了*多次*的*男生*遇见*一位女孩*然后要了电话号码。*zhège jīnwǎn kèrén yíngjiēle duōcì de nánshēng yùjiàn yīwèi nǚhái ránhòu yàole diànhuà hàomǎthis.one tonight guest greeted many.times REL boy met one.girl and.then asked for phone.number‘This boy who the guest greeted many times tonight met a girl and then asked for her phone number.’31. *那个*刚刚*女同学邀请了*一次*的*男生*拒绝*好朋友*并且离开了教室。*nàgè gānggāng nǚ tóngxué yāoqǐngle yīcì de nánshēng jùjué hǎopéngyǒu bìngqiě líkāile jiàoshìthat.one just.now female.classmate invited once REL boy refused good.friend and left classroom‘The boy who the female classmate invited once just now refused a good friend and left the classroom.’32. *那位*昨天*亲戚拜访了*两次*的*生意人*撞到*青梅竹马*并且感到讶异。*nàwèi zuótiān qīnqi bàifǎngle liǎngcì de shēngyìrén zhuàngdào qīngméizhúmǎ bìngqiě gǎndào yà yìthat.person yesterday relatives visited twice REL businessman hit childhood friend and felt surprised‘The businessman who the relatives visited twice yesterday hit the childhood friend and felt surprised.’S1 TablesWithin S1 Tables, Tables A-to-H are provided. Tables A to C detail the means and LME models for Experiment 1. The remainder of the tables detail the means and LME models for Experiment 2.(PDF)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.