dataesr/scientific-paragraphs-categorization huggingface dataset:
Here we chose to not define subgenera to avoid introduction of multiple new names for lineages whose morphological identities are not entirely understood at present. However, we retained in the remarks, brief descriptions of the potential subgroups for reasons of clarity and usability. In a third step, we investigated the species that were difficult to allocate with confidence to the phylogenetically defined genera. We found that in many cases these species formed groups, which, when diagnosed for distinctive morphological features, were found to be attributable to currently accepted genera for which no DNA sequence data is available. As no phylogenetic data is available on these genera, we cannot resolve their status with certainty, and thus such genera are retained as valid. In the systematic section below, for each genus we propose a formal diagnosis or description, depending on our ability to identify the characteristics that differentiate a proposed genus from other groups in Terebridae and provide a list of member species.
We compiled molecular diagnoses to supplement traditional taxonomic diagnoses and descriptions in response to the fact that recognition of primarily phylogenetically defined genera by morphological means was often problematic. As the four-gene (COI, 18S, 12S, and 23S) phylogenetic analysis  (Gorson et. al. submitted)  was not comprehensive for all species and specimens per clade, the credibility of a molecular diagnosis derived from this dataset would have been questioned. To combat this we used only the COI dataset including only sequences with no more than 5 ambiguously called nucleotides (963 records), representing 179 putative species. Initially, species were attributed to pre-defined clades based on the results of the four-gene analyses of  Gorson et. al. (2018)  and that was consistent with allocations to genera and subfamilies in a classification detailed below.
The total length of the COI alignment was 658 base pairs (the "barcode" fragment defined by the  Folmer et al (1994) primers) , which were thus numbered from 1 to 658. First, an attempt was made to find pure diagnostic single-nucleotide positions (i. e. at a given position, all the members of the clade and none of the non-members of the clade share a given nucleotide) using the R package SPIDER 1. 4-1  (Brown et al. 2012). However, only one diagnostic single nucleotide character was identified for each of the clades A1 and E5A, and none in the others. Thus we explored the possibility of providing molecular diagnoses based on diagnostic combinations of nucleotides (i. e. at two or more positions, all the members of the clade and none of the non-members of the clade share a combination of nucleotides).
To our knowledge, only CAOS  (Sarkar et al. 2008 ) is capable of detecting such diagnostic combinations. However, as CAOS implements a tree-based algorithm, it poses certain requirements to the data to run efficiently, such as a pre-defined group is only provided diagnosis (referred to as characteristic attributes or CA's by  Sarkar et al. 2008 and Rach et al. 2008)  if it corresponds to a clade in the provided tree. The tree reconstructed using all our COI sequences  (Gorson et al. submitted)  did not retrieve all the subfamilies and genera monophyletic, thus making CAOS not applicable. Consequently, we developed a custom Python script (available upon request) that is based on an alternative, purely statistical approach, and implements iterated random search to identify diagnostic combinations of nucleotides for each predefined group in an assemblage of aligned sequences.
Levels of confidence in attribution to genera Valid recent species entries (401) from current WoRMS database (as per February 1 2018) were used in our analysis. DNA sequence data were available for 135 currently accepted species  (Gorson et al. submitted ) that constitute 134 species entries (except Oxymeris troendlei  (Bratcher, 1981) , which was found to be a junior synonym of O. maculata (Gmelin, 1791)). These 134 species as well as seven new species revealed by the phylogenetic analysis of  Gorson and coauthors (2018)  and described herein are allocated to genera with a superscript 1 ( 1 ), indicating highest degree of confidence. The use of morphological characters in combination with geographic distribution resulted in a non-ambiguous placement into one of the groups recognized phylogenetically of 166 more species; these are marked with superscript 2 ( 2 ).
Identification of COI DNA based diagnoses Numerous diagnostic combinations were returned for all but three genera, and were sorted by length with priority given to short combinations. The diagnostic nucleotides identified for the clades A1 and E5A by SPIDER were also recovered by our in-house Python script. However, we failed to identify diagnostic combinations of nucleotides for the genera Terebra and Punctoterebra, and only found diagnostic combinations for Hastula H. Adams & A. Adams, 1853, when four sequences with a substitution at the position 181 of the COI fragment were removed from the dataset. As a result, for Terebra, Punctoterebra, and Hastula taxa we propose separate diagnoses for the inferred phylogenetic clusters within each genus.
ACKNOWLEDGMENTS The material in this paper originates from several shore-based expeditions and deep sea cruises, conducted respectively by  MNHN and Pro-Natura International (PNI)  as part of the  Our Planet Reviewed programme  ( SANTO 2006 , INHACA 2011, ATIMO VATAE and KAVIENG 2014; PI Philippe Bouchet) and/or by  MNHN ,  Institut de Recherche pour le Développement (IRD)  and other partners as part of the  Tropical Deep-Sea Benthos  programme (SALOMON 2, EXBODI, TERRASSES, MAINBAZA, MIRIKY, KARUBENTHOS 2, MADEEP, NANHAI 2014, ZHONGSHA 2015;  PIs Philippe Bouchet ,  Sarah Samadi ,  Wei-Jen Chen ,  Tin-Yam Chan ). Incountry scientific partners included the  University of Papua New Guinea (UPNG) ;  National Fisheries College (Kavieng, Papua New Guinea );  Universidade Eduardo Mondlane (Maputo) ;  Parc National de la Guadeloupe , and  Université des Antilles; University of Taipei and National Taiwan Ocean University (Keelung, Taiwan).
All expeditions operated under the regulations then in force in the countries in question and satisfy the conditions set by the  Nagoya Protocol  for access to genetic resources. The authors would like to thank  Philippe Maestrati ,  Virginie Héros ,  Barbara Buge  and  Julien Brisset  for their help in curating the vouchers. The authors are grateful to  Peter Dance  for his remarks on taxonomy, and to  Yuri Kantor  and  John Taylor  for generously sharing their data on anatomy. The authors are grateful to  Andreia Salvador  for images of the NHMUK type specimens (photos copyrighted by NHMUK). This work was supported in part by the  CONOTAX  project funded by the  French National Research Agency  (grant number  ANR-13-JSV7-0013-01 ), by funding from the  Camille  and  Henry Dreyfus Teacher-Scholar Award  and  NSF  awards  CHE-1247550  and  CHE-1228921  to MH and by the  Russian Science Foundation  grant  RSF 16-14-10118  ( PI Yuri Kantor ) to AF.
We also plan to report determinants of medication adherence from perspectives of patients, carers, healthcare professionals and other third parties. A unique feature of this systematic review in the context of mental health is the use of the Theoretical Domains Framework (TDF) to organise the literature identified determinants of medication adherence. Methods and analysis The protocol adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols and ENhancing Transparency in REporting the synthesis of Qualitative research (ENTREQ) guidelines. This review will include both qualitative and quantitative primary studies exploring determinants of medication adherence in bipolar disorder. We will search the following databases using a preplanned strategy: CINAHL, Cochrane Library (CENTRAL), Embase, LiLACS, Medline, PsychINFO, PubMed without date restrictions.
27 This early identification of relevant behaviour change techniques affords a substantial advantage in terms of informing the design of theory and evidencebased medication adherence interventions for people under prescribed medication for bipolar disorder. Approach to searching, search strategy and data sources We will employ a preplanned search strategy to seek all relevant studies. Our search strategy will consist of three parameters: disease (bipolar disorder), treatment (medication) and outcome (adherence). Following a scoping exercise of search terms (on Pubmed, Medline and Embase) to define our search strategy, we decided to use the MeSH (Medical Subject Heading) terms 'Treatment Adherence and Compliance', 'Bipolar Disorder' and 'Psychotropic Drugs' for our search. We will adapt these search terms for the databases that do not permit MeSH terms or use different MeSH terms.
study screening methods We will use computer software Covidence,  35  an online systematic review program, for screening retrieved studies. Screening of studies for inclusion in this review will involve three distinct stages: I. Title Screening: After removal of duplicates using the reference manager software Mendeley, the remaining studies will be screened for their relevance to the review. Definite non-relevant studies will be excluded while relevant, or unclear studies, will be retained for abstract screening. II. Abstract Screening: Abstracts of the remaining studies will be screened by the primary reviewer (AP) and second reviewers (CG, DB, FS, GM, JW and SS) independently to identify studies that potentially meet the inclusion criteria outlined earlier. Any Open access disagreement between the two reviewers will be resolved through further discussion and referral to a third reviewer (DB) if there is a failure to achieve agreement.
III. Full Article Screening: Full articles will be reviewed independently by two reviewers (AP, CG, DB, FS, GM, JW and SS) using predefined inclusion/exclusion criteria. Any disagreement between two reviewers will be resolved through discussion or the involvement of the third reviewer. We will use Cohen's kappa to report the level of agreement between first and second reviewers. Within published syntheses of qualitative research, there is often a lack of transparency about the search processes employed, with neither the search strategy nor databases detailed. 28 For a comprehensive approach, we will use the PRISMA flowchart for reporting different phases of searching, screening and identifying studies for inclusion in the qualitative synthesis as recommended by ENTREQ. 28 ta extraction and mapping We will use the computer software program Nvivo V.
12  36  to extract data and to map the modifiable determinants of medication adherence to the domains of the TDF. While medication adherence is generally described as taking ≥80% doses of prescribed medications, some studies report adherence in gradient terms (eg, good, moderate, low adherence and non-adherence). 4  Yet, in some cases (eg, in HIV), adherence means taking ≥95% doses of prescribed medications. 36 37  Acknowledging this wide variation on definition of medication adherence, we will report the definition used for adherence in included studies for transparency and comparison among studies. Extracted information will include study characteristics (eg, title, year of publication, country, study design, population, number of participants, definition and rate of adherence) and modifiable determinants of medication adherence in patients with bipolar disorder.
Others Perspectives. Within each theme we will create two subthemes (Barriers and Facilitators), and within each of these subthemes we will create 15 domains (14 TDF domains plus 'Others'). Two reviewers will pilot data extraction and coding of determinants of adherence to the domains of TDF from four studies. For example, if the following text was extracted from a study 'Forgetting to take medication or being careless at times about taking medication was reported to be experienced by x participants', this would be coded to the TDF domain 'Memory, attention and decision process'. The reviewers will then compare and discuss their coding to generate consensus in interpretation of literature identified determinants. After piloting, all data will be extracted by one reviewer and independently checked by second reviewer for completeness.
SimMATE  [16]  is a trace-driven simulator that operates on top of gem5 and is devoted to the exploration of in-order many-core architectures. Traces collected on a reference architecture in Full-System mode are made of outgoing memory transactions collected at Level-1 caches, i. e. cache misses. In trace replay phase CPU cores are replaced with Trace Injectors (TIs) that are connected to the interconnect subsystem cache and initiate the transactions recorded in the trace database. The interconnect and memory subsystems remain fully simulated so as to account for the latencies incurred by the traffic in the given simulated architecture configuration. SimMATE takes into account inter-core synchronizations: additional information such as barriers are recorded in the traces through a redefinition of the used shared-memory API functions (e.
Trace-Driven simulation of OmpSs programs OmpSs is a programming language based on StarSs and OpenMP directives. It makes use of task-based programming to enhance the parallelization of the applications. One of its main advantages is that it does not use synchronization points between threads, but allows the application to be executed in an asynchronous way accordingly to the data flow  [17]. OmpSs is implemented by using Mercurium and Nanos++ runtime  [18]. Task constructs are interpreted by Mercurium compiler and calls are generated by the Nanos++ runtime. The runtime checks if the dependencies of a newly created task have been resolved to properly schedule free-dependency tasks. A trace-driven approach for OmpSs applications was proposed in  [19]. The application is annotated with Nanos++ calls.
In detail mode tasks results are gathered into a list that keeps track of the history of executed tasks. This history is then used in the fast-forward mode to fasten the simulation. This approach has been proven in the X86 architecture, however, it cannot yet be applied to ARM architectures. ElasticSimMATE leverages the benefits of both Elastic Traces and Sim-MATE trace-driven approaches in gem5 for multicore architectures: Elastic Traces provides an accurate modeling of CPU core instruction pipeline for Outof-Order cores whereas SimMATE brings a solution that makes it possible to account for the inter-core execution dependencies. It offers a single simulation solution of great interest for a fast and accurate exploration of next-generation multicore systems. In addition, it makes use of OmpSs programming languages to provide strong-scaling analysis in a fast and accurate manner.
It can be used in two modes, synchronization-mode, and task-mode. ElasticSimMATE is composed of four phases, code annotation, checkpoint creation, trace collection, and trace replay. In this section, we explain in detail these phases for the synchronization-mode, and then the main changes to use it in Task-Mode. The red-colored #pragma omp statements listed in the source are read by the pre-processor in the usual case and result in the insertion of calls to the OpenMP runtime. In ElasticSimMATE, these calls further require calling a tracing function that will make it possible to record the start and end of a parallel region in the trace. The resulting binaries are then executed in a Full-System (FS) simulation (Trace Collection phase) so as to generate the execution traces. Three traces are created: instruction and data dependencies trace files (as per the Elastic Traces approach) and an additional trace file that embeds synchronization information.
Code Annotation In order to collect the traces, gem5 has to be capable of detecting the beginning and end of the recorded events. To do so we annotate the code with a pseudo-instruction that is recognized by gem5 and starts the collection process. We called to this pseudo-instruction m5 trace() and it is added before compilation either manually or automatically. This pseudo-instruction is only valid int he gem5 framework, and it generates the collection of traces once is detected during the execution of the application. The aim of using this pseudoinstruction is to detect the beginning and the end of a given parallel event. We need to insert it on the source code every time an OpenMP event is called during run-time. This insertion can be done manually or automatically. However, • Source to source approach.
This approach relies on parsing application input files and automatically inserting the m5 trace calls wherever needed. We have verified that the proposed approach works for C/C++ but would require being ported to other languages. • Automatic tracing call insertion. This solution relies on modifying the API runtime so that whenever a parallel code region is detected a tracing call gets automatically inserted right at the precise instant where parallel execution starts. It is regarded as the most suitable solution as it is accurate and only requires to work with a specific version of the runtime system. We selected the automatic tracing call insertion, which is available for OpenMP using the Nanos++ runtime system and the Mercurium compiler. The advantage of this approach is that we can annotate any code with the m5 trace().
It means that every time a selected OpenMP event is detected, the m5 trace() pseudo-instruction will be detected by gem5 and activate/stop the recording of the synchronization event. To do so, we modified the source code of Nanos++ that add a macro function that encapsulates the pseudo-instruction, i. e. we included it on the specific functions of Nanos++ that creates the selected OpenMP events. A summary of the currently supported set of constructs is in table 1.
Traces collection In this phase, ElasticSimMATE restores the system state from the checkpoint and begins trace collection. Three types of events are considered in parallel sections: instruction executed, dependencies (load/store), and synchronization events. Instruction and data dependency traces are captured thanks to an enhanced TraceCPU model. This enhancement is directly related to the incorporation of the synchronization mechanism and arbiter during the replay phase. The following information is captured into the synchronization trace for each CPU and each event: • Tick: the tick count of a CPU at the entrance in the parallel region. • Program Counter: the program counter at the beginning of a parallel region; it will be used during the replay phase for identifying parallel sections. • Thread ID: the thread ID assigned by the scheduler.
Code Annotation In the task-mode, the application is annotated with calls with a m5 task() gem5 pseudo-instruction. We modified the API runtime to automatically detect the beginning and the end of a task. In order to properly annotate the application, we need to use Mercurium and the modified version of Nanos++. We determine which functions of the runtime control the creation/exit of a task and added in those function the pseudo-instruction. These functions are the nanosSMPThread inlineW orkDependent() for the task creation and the workDescriptor f inish() for the end of the task.
e. all cores issue requests to the same addresses (encoded in the trace) which results in unrealistic data sharing during replay. This is confirmed after analyzing gem5 execution statistics which report well above 80% data sharing in most experiments. Figures  10(a ) and 10(b) show the simulation time versus core count for the matrix multiplication for 2 sets of input sizes, small (16x16 to 64x64) and large (128x128 to 512x512). Simulation times for large input sizes have experimented for systems comprising up to 64 cores. In the worst case (512x512 matrix sizes, 64 cores) simulation time was about 65 hours which remains tractable for scalability evaluation. Experimental results on selected applications:. Similar experiments have been carried out on sample applications extracted from Rodinia and Parsec benchmark suites.
Summary ElasticSimMATE in task-mode uses the task construct capabilities of OmpSs programming languages. By collecting traces in one defined reference system we can perform fast strong-scaling analysis with a high relative accuracy. This means that ESM-TM is able to track correctly how an application scales with the increase in the number of cores. Experimental results show that our approach is in average 8× faster than Full-System gem5 simulation. The average absolute error is close to 18%. In this approach, we replay individual tasks based on a task2core file that works as a static scheduler, which is not the case in FS simulation. In comparison with real-hardware ESM-TM shows great accuracy for different academical benchmarks.
Indeed, the maternal inheritance of the mtDNA genome can be misleading for species delimitation because females and males have generally different dispersal behaviours (female philopatry versus male dispersal)  [5, 6] , and because interspecific hybrid females are generally fertile, whereas hybrid males are often sterile (Haldane's rule), facilitating mitochondrial introgression between closely related species  [7] [8] [9]. To overcome these limitations, most recent taxonomic studies dealing with the delimitation between cryptic mammal species have focused on multi-locus datasets  [10] [11] [12] , as the use of multiple independent DNA markers has been shown to provide a strong and reliable signal for deciphering relationships among closely related taxa  [13] [14]. However, interpreting the results from multi-locus datasets can be difficult, especially when the DNA markers show low genetic variation or conflicting relationships between them.
These difficulties have led to the development of a plethora of new methodological approaches for multi-locus species delimitation  [15, 16] , which may be subdivided into three categories: (1) phylogenetic methods, (2) multispecies coalescent (MSC) approaches, and (3) population genetic methods (Table  1 ). Phylogenetic methods were not originally developed for studying species delimitation, but the species monophyly criterion has been widely used since the origin of molecular taxonomy  [17]. For multi-locus datasets, several phylogenetic approaches can be considered: the concatenation of all markers into a supermatrix (although this approach has been widely criticized  [18] ), the separate analyses of the markers, or more sophisticated methods, such as *BEAST  [19]  or SuperTRI  [20]. Based on the coalescent theory, some authors have suggested that species can be delimited without monophyletic gene trees  [21].
The incorporation of the coalescent model  [22]  in certain software (e. g. *BEAST  [19] , BPP  [23]  and STACEY  [24] ) enabled the inference of species limits from multi-locus data by accounting for incongruences among gene trees in the presence of incomplete lineage sorting  [19]. MSC approaches often require prior assignments of samples to populations or taxa and are hence restricted to the validation of proposed delimitations  [25]. Population genetic approaches are generally applied to detect "cryptic substructure" between groups showing very similar phenotypes. The program STRUCTURE  [26]  is probably the most popular approach for Bayesian clustering using multi-locus data. It has recently gained new interest as the clusters identified with STRUCTURE can be used as preliminary hypothesis for assigning individuals to populations or taxa, which represents the first step of most MSC analyses  [27].