dataesr/scientific-paragraphs-categorization huggingface dataset:
Again, the analysis may terminate earlier according to predefined rules. Early termination of the study is to be validated by the investigator, the sponsor and the independent monitoring board of the study. In the present work, because of difficulties to recruit neonates born at more than 35 weeks GA, analysis was conducted by combining data with those of neonates born between 32 and 35 weeks GA. This was performed through a meta-analysis of the dose and outcomes observed in the two groups  [15]. Intra-group comparisons before and after administration of omeprazole were performed using a Wilcoxon signed rank test or conditional logistic regression models. Descriptive statistical analyses were performed with the SAS 9. 2 (SAS Inc, Cary, NC, USA) software package for PC and Bayesian statistical analysis with a program written in C language  [16].
The MED was found to be 2. 5 mg/kg/day with an estimated mean posterior probability of success of 97. 7% (95% credibility interval: 90. 3%-99. 7%) (Fig  2). Individual data and sequential estimations of the mean posterior probabilities of success are presented in Table  3. As a total of 30 neonates were included in this group, no second stage analysis was required. Among neonates with GA >32 weeks (n = 24), 18 (75%) were treated with 1 mg/kg and 6 (25%) with 2 mg/kg daily. All neonates but one (in the 1 mg/kg group) responded to the omeprazole treatment. In this group, a second-stage analysis was required and the pooled MED was  found to be 1mg/kg/day. Sequential estimations of mean posterior probabilities of success after each cohort are detailed in S1.
Acknowledgments We would like to thank  Lila Benadjaoud , the staff of the  Center of Clinical Investigations  ( CIC1426 ) and the  Clinical Epidemiology Unit of Robert DebreHospital  for their contribution in the conduct of this study. This study was presented as an abstract in the 19th Annual Meeting of  French Society of Pharmacology and Therapeutics , 21-23 April 2015 Caen, France with the following reference: ' F Kaguelidou ,  C Alberti ,  V Biran ,  O Bourdon ,  C Farnoux ,  S Zohar ,  E Jacqz-Aigrain : Dosefinding study of omeprazole in infants with gastroesophageal reflux using a Bayesian sequential approach, Fundamental and Clinical Pharmacology, Special Issue: Abstracts of the 19th Annual Meeting of  French Society of Pharmacology and Therapeutics , 36th Pharmacovigilance Meeting, 16th APNET Seminar, 13th CHU CIC Meeting 21-23 April 2015 Caen, France, 2015; 29 (Suppl.
III. DESIGN AND IMPLEMENTATION CHALLENGES This sections presents an overview of existing open source implementations of the 6TiSCH stack, and discusses related design and implementation challenges. Our focus on opensource software enables us to compare various technical aspects and implementation choices on the stack's internals. We select the implementations from the following four operating systems: OpenWSN, Contiki, RIOT, and TinyOS, presented in Table  I. After a brief overview of the OS' specifics, we review a number of design challenges, and discuss how existing implementations address them. A. Open-Source CPS Implementations OpenWSN. The OpenWSN project  [24]  is an open-source implementation of a fully standards-based protocol stack rooted in the IEEE802. 15. 4e TSCH link layer, also featuring the IPv6-enabled IETF upper stack (6LoWPAN, RPL, CoAP).
Contiki. The Contiki OS was created in 2001, with a focus on Internet connectivity for constrained devices, i. e. ∼ kB of RAM, tens of kB or ROM. The project, available on GitHub (https://github. com/contiki-os/contiki) has a large community in both academia and industry: it has contributors from top universities such as ETH Zurich and Oxford University, and from leading IT companies such as Cisco, Atmel, or ST Microelectronics. Its main article  [25]  was cited over 1600 times (Google Scholar, May 2015). Contiki has a lightweight event-driven kernel, and uses protothreads, a programming abstraction that compiles sequential source code into events. It is currently supported by tens of different hardware platforms, typically sensor/actuator and IoT nodes. Since 2008, Contiki features a fully certified IPv6 stack.
It has been regularly updated ever since with early implementation of IETF protocols for low-power IPv6 connectivity in the IoT, including 6LoWPAN, RPL or CoAP. In 2014, a first implementation of TSCH able to run in 6LoWPAN+RPL networks was released (https://github. com/EIT-ICT-RICH/contiki), and is still under development, with partial support for IETF 6TiSCH specifications.
RIOT. RIOT is a free, open-source OS developed for the IoT based on a micro-kernel architecture inherited from FireKernel  [33] , providing real-time capability, multi-threading and IPC support, as well as a tickless scheduler that works without any periodic events. RIOT aims for a developer-friendly programing model and API  [26] , aiming at similarity to what is experienced on Linux. While the OS is written in C (ANSI99), applications and libraries can be implemented either in C or in C++. RIOT is developed as such since 2012, by a growing, world-wide open source community including both academic and industrial contributors. The source code is available on GitHub (https://github. com/RIOT-OS/RIOT) under LGPLv2. 1. To fulfill strong real-time requirements, RIOT enforces constant periods for kernel tasks (e.
g. scheduler run, inter-process communication, timer operations). An important prerequisite for guaranteed runtimes of O(  1 ) is the exclusive use of static memory allocation in the kernel. Constant runtime of the scheduler is achieved by using a fixed-sized circular linked list of threads. RIOT features a variety of network stacks, including (i) gnrc, an implementation of the full IPv6/6LoWPAN stack, and (ii) CCN-lite, an information-centric networking stack  [34] ,  [35] , as well as (iii) OpenWSN implementations of TSCH and 6TiSCH protocols. Similar to ports on BSD, RIOT features a package system that allows for easy integration of libraries from third-party providers (this feature is used e. g. to make OpenWSN implementations available in RIOT) TinyOS. As one of the first custom operating systems for resource-constrained networked embedded systems, TinyOS  [27]  has been a dominant software platform for sensor network research, enabling significant academic research and commercial products for the last fifteen years.
The main design goals of minimal resource usage and flexibility are supported by a component-based architecture codified by the nesC language  [36] , a variant of C. Maximal concurrency under limited resources is supported by an event-driven execution model with two process categories: interrupt service routines and tasks-basic deferred processing primitives that are scheduled by a non-preemptive FIFO scheduler. TinyOS uses a three-layer hardware abstraction model that offers a flexible hardware/software interface with combined access arbitration and energy management  [37]. Facilitating protocol stack research is another major design goal for TinyOS, and a reason for its popularity. Many academic precursors of today's standardized protocols for low power and lossy networks have been prototyped using TinyOS.
Together with Contiki, it was one of the first sensor network operating systems that offer an IPv6 stack  [38] , with support for 6LoWPAN, RPL and CoAP. Since 2009, it also provides a platform-independent implementation of the IEEE802. 15. 4-2006 MAC. The TinyOS source is a mature code-base released under a BSD license and available on GitHub (https://github. com/tinyos/tinyos-main). Support for the TSCH extensions of IEEE802. 15. 4e, and partial support for the rest of the IETF 6TiSCH stack have recently been developed as part of the EIT Digital RICH Activity. The source code for these extensions is also made available on GitHub (https://github. com/EIT-ICT-RICH/tinyos-main).
C. Hardware/Software Interface Due to the relative novelty of the standard, at the moment, there is scarcity of transceiver chips that provide native support for the IEEE802. 15. 4e TSCH extensions. As a result, most current solutions rely on software implementation of the missing functionality on top of the generic services provided by the hardware platform. For portability and abstractions, the OS's define their own hardware/software interface on top of which the 6TiSCH stack is implemented. This interface is critical when it comes to achieving the strict timing requirements of TSCH. Beyond achieving the pure functional requirements, the hardware/software interface has competing goals. It must  (1)  raise the level of abstraction and make the upper layers portable while (2) being able to exploit platform-specific features required for high performance.
D. Upper and Lower Stack Hard real-time support for a general purpose OS is difficult to achieve and for none of the four surveyed OS's real-time behavior has been formally proven. The event-driven systems support execution models with two process categories: interrupt service routines (ISRs) for the asynchronous and latencysensitive code, and tasks or threads as deferred execution constructs for the serial and less latency-sensitive code. On a preemptive, multi-threading system, ISRs are usually kept as short as possible, and latency-sensitive code is executed with a high priority task. Many modern micro-controllers allow to specify priorities for certain interrupt sources. This can be used to prioritize the critical timer and radio interrupts for TSCH. For older architectures, low-priority interrupt sources (such as UART or GPIOs connected to sensors) have to be either disabled completely or after crossing a certain threshold during timecritical periods.
As a result, for both types of OS's, the implementation of the full stack is typically split in two parts: (i) the time-sensitive TSCH functions are implemented in interrupt mode and (ii) the upper protocol layers are implemented in task mode. A significant source of complexity in the lowest levels of the stack is the implementation of the state machine of the TSCH protocol. As mentioned above, this state machine is typically realized as low-level event-driven code that directly reacts to timer or transceiver interrupts. In contrast to the synchronous upper layers, that can leverage process categories like tasks or threads, this code is typically implemented rather monolithically, without structural and scheduling support from the operating system. Recently, dedicated software frameworks for this low-level and latency-sensitive MAC code have been proposed, that can improve the developer productivity and the robustness  [43].
A. Internet Integration A gateway node, known as IPv6 Low Power Border Router (6LBR)-see Section II-is in charge of adapting and routing IPv6 packets to and from the 6TiSCH network. The 6LBR is a fundamental network component which manages the routing control plane, including source routes. It acts as sub-network routing root and time source neighbor of first hop nodes. There are two general approaches: the 6LBR can be running either on a computer or a wireless node. 6LBR on a Computer. In OpenWSN and Contiki, the 6LBR runs on a computer. The mote connected to the 6LBR acts as a link-layer bridge, relaying packets from the 6LBR over a serial interface to/from the 6TiSCH network. OpenWSN implements the 6LBR in a Python application called OpenVisualizer. The application is connected to the link-layer bridge via a serial port, and to the computer's IP stack and in turn the Internet through a tun virtual interface.
In RIOT, the 6LBR is implemented on the mote by providing full IPv6 and multiple interface support. Packets are transmitted over the serial port using SLIP encapsulation and are received by a tun virtual interface on the host side. The TinyOS implementation is similar. The PppRouterC application is installed on the gateway node and advertises itself as a RPL DODAG root, tunneling packets with the host over a PPP bridge. Contiki can also run as a 6LBR on a node, tunneling packets via SLIP. A native program on the host, tunslip6, is in charge of routing IPv6 traffic to/from the PAN.
VI. ACKNOWLEDGMENT This work was partly supported by the distributed environment  Ecare@Home  funded by the  Swedish Knowledge Foundation   2015-2019 , by the  EIT Digital RICH Activity , by  ANR  within  SAFEST  project ( ANR-11-SECU-004 ), and by  VINNOVA (Sweden's Innovation Agency). The authors thank  Jasper Büsch  and  Moksha Birk  for their work on the TinyOS implementation and their valuable comments.
Samples with light partons only are generated separately for the parton multiplicities of 0, 1, 2 and 3, samples with the heavy flavor partons are generated including additional 0, 1 and 2 light partons. The MC cross sections for all Drell-Yan samples are scaled up with a next-to-LO (NLO) K-factor of 1. 3, and cross sections for heavy-flavor samples are scaled up with additional Kfactors of 1. 52 for (Z → ℓℓ)+b b and 1. 67 for (Z → ℓℓ)+cc, as estimated with the MCFM program  [35]. In the simulation of diboson events, the pythia generator is used for both hard scattering and parton showering. To simulate effects from additional overlapping pp interactions, "zero bias" events are selected randomly in collider data and overlaid on the simulated events. Generated MC events are processed using a geant3-based  [36]  simulation of the D0 detector.
Western blot analysis Expression levels were performed by western-blot analysis using mouse anti-p53 and anti-β-actin (Sigma-Aldrich), anti-bax (Oncogene), anti-p21 (Beckton Dickenson), anti-Mdm2 (BD Pharmingen), anti-BCL-XL, and rat anti-HSC70 (Stressgen). Proteins were quantified by Image J software.
Proplatelet formation assay CD41 + GFP + MKs were sorted at day 8 of culture and plated in 96-well plates at a concentration of 2,000 cells/well in serum-free medium containing TPO, and quantification of PPT bearing MKs was performed as previously described  [33]. The percentage of PPT bearing MKs was estimated by counting MKs exhibiting one or more cytoplasmic processes with areas of constriction. A total of 200 cells per well were counted during 4 days. Images were obtained using an inverted microscope (Carl Zeiss, Göttingen, Germany) at a magnification of 40× using the Axio Vision 4. 6 software.
Little is known on the longevity of the sea daffodil, although potted plants at Naples and Catania Botanical Gardens have a documented age of > 80 years. For further details about the species, see Materials and Methods (Study Species). In this study, an integrated approach was used, combining genetic and environmental data with a niche modelling approach, aimed to investigate:  (1)  the effect of climate change on the geographic range of this species at different times {past: the last interglacial (LIG, ~120-140 kya BP), the last glacial maximum (LGM, ~21 kya BP); the present conditions (CURR,  ~1950-2000) ; and near-future (FUT, ~2070)} and (2) the possible influence of environmental variables on the genetic structure of this species in the current period. As a side issue, a simulation analysis was performed correlating current genetic structure with past environmental data  (LIG and  LGM periods), to evaluate as past climatic changes may have influenced present-day genetic structure of this plant.
To this aim, we employed nuclear microsatellite markers (nrSSR, Short Sequence Repeat) to investigate patterns of the diversity and genetic structure in P. maritimum on a large and representative sample of populations and individuals. This choice is motived by the nature of SSRs which are codominant, neutral, highly polymorphic and reproducible markers. To evaluate a distribution model for this plant, the World Climate Database was consulted, using the available climatic information for three historical periods (LIG, LGM, CURR, and FUT).
maritimum populations, we calculated the Human Footprint Value (HFP) through the Global Human Footprint Database, v2 (1995 -2004)  [36]  using a circular buffer of a 1-km ray around each of the 48 sampled P. maritimum populations (Table  1 ). The Euclidean geographic distance matrix among the populations was calculated posing mainland as an insurmountable barrier (nautical distance) using the package "gdistance" in the R environment software  [37]  (S1 File). This choice was made because of the presence of the hydrochory as an important dispersal phenomenon in the sea daffodil biology.
Genetic analysis By using the microsatellite library obtained from P. maritimum  [37] , after a preliminary amplification test, six nuclear microsatellites (SSRs) were chosen (SSR15, SSR25, SSR27, SSR30, SSR31, and SSR38) and genotyped for the 48 populations in study (867 individuals). The genomic DNA extraction, PCR conditions, and genotyping screening were as previously described in De Castro et al. [23]  and Di Maio and De Castro  [38]. Standard procedures were used to minimize scoring errors as suggested in DeWoody et al. [39]. To achieve genotyping accuracy, we analysed SSR data with the Micro-Checker version 2. 2. 3 software  [40]  to determine the existence of stuttering (slight changes that occur in the allele size during PCR), dropout alleles (large alleles do not amplify as efficiently as small alleles), and null alleles  [39].
For each microsatellite locus, we assessed genetic polymorphism by calculating the total number of alleles (A T ), observed and expected heterozygosity (H O and H E ), and inbreeding indexes (F IS ) using GenAlEx version 6. 5 software  [41]. The fixation index (F ST ) was computed with FreeNA software, which implements the ENA correction method to amend for the positive bias that is induced by the presence of possible null alleles in F ST estimation and provides an accurate estimation of F ST in the presence of null alleles  [42]. Deviation from Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD) between microsatellites were tested using GENEPOP, version 4. 1 software  [43] , within each population and for each locus. Sequential Holm-Bonferroni correction was used to determine the significance levels for all tests with an initial level of 0.
05  [44]  using the macro Holm-Bonferroni Sequential Correction: An EXCEL Calculator-version 1. 2  [45]. For each population, we estimated genetic diversity across all loci using the percentage of polymorphic loci (P), observed number of alleles (A T ), number of private alleles (S), average observed (H O ), expected heterozygosity (H E ), and unbiased heterozygosity (uH E ), the latter representing the expected heterozygosity under HWE within populations, adjusted for sample size using GenAlEx version 6. 5 software. The allelic richness (A R ) was also calculated for each population using the FSTAT version 2. 9. 3. 2 software  [46]. Briefly, A R is the number of alleles independent of sample-size as determined using the rarefaction method of El Mousadik and Petit  [47]. The inbreeding coefficient F IS was calculated using the same previous software  [48] , and deviation from zero (Hardy-Weinberg genotypic proportions) was also evaluated by permuting alleles within populations (5000 permutations) using sequential Holm-Bonferroni correction  [45].
The pairwise genetic differentiation F ST was estimated with FreeNA software corrected for null alleles  [42]  using 1000 bootstraps to compute 95% confidence intervals. The presence of isolation-by-distance (IBD) was tested by correlating Rousset's  [49]  genetic distance (F ST ⁄1-F ST ) and geographical distance (logarithmic scale) between populations using a Mantel test in Arlequin version 3. 1 software  [50]  with 1000 permutations. An analysis of molecular variance (AMOVA) was used to partition the total genetic variation within and among population components using Arlequin version 3. 5. 1. 3 software. The significance of the AMOVA was assessed with 1000 permutations of the data. To estimate the distribution of individuals among natural genetic groups (K), the dataset was also analysed by Bayesian-model-based clustering methods, as implemented in the software STRUCTURE version 2.
3. 4 software  [51, 52, 53, 54]. The analysis was performed without prior information concerning the geographic origin of the accessions. The STRUCTURE algorithm was run using the ''admixture model", assuming a ''correlation among allele frequencies", with 30 independent replicate runs per K value (number of clusters) ranging from 1 to 10. Each run involved a burning period of 50,000 iterations and a post burning simulation length of 100,000 iterations. To obtain the appropriate K from the data according to Evanno et al. [55] , we used the STRUCTURE HARVESTER version 0. 6. 93 software  [56]. Finally, the estimated cluster membership coefficient (Q) matrices of multiple runs that were generated by STRUCTURE were analysed using CLUMPP version. 1. 1. 2 software  [57]  to calculate the average pairwise similarity (H').
To graphically represent the results obtained, the average results of the assignments of STRUCTURE were plotted on maps generated with ArcMap as implemented in ArcGIS version 10. 3 software (ESRI, Redlands, CA, USA). Finally, a demographic analysis was performed using the BOTTLENECK version 1. 2. 02 software  [58]. Of the several available tests that are implemented in the software (i. e. sign test  [59] , including standardized differences test  [60] , Wilcoxon sign-rank test  [61] , and modelshift tests  [62] ), only the Wilcoxon sign-rank was chosen because it is the most accurate in cases of a low number of polymorphic loci (SSRs loci < 20) and individuals per population (n > 10)  [58, 61]. Briefly, a population that has been subjected to a drastic reduction in the number of individuals shows a reduction in the number of alleles (k) and heterozygosity.
Species Distribution Models To evaluate current, past and future presence suitability for P. maritimum, we trained Species Distribution Models (SDMs) using environmental predictors referring to the current time, and then projecting the models over past and future versions of these predictors. Occurrence data used for the training of SDMs were gathered from our database, literature, public databases and personal communication with experts (S2 File and  Fig 1). We filtered the data by removing duplicated records and those with unrealistic coordinates. In addition, the spatial accuracy of the records was assessed by including only occurrence data points given to at least 2 decimal places  [64] , obtaining a final dataset of 537 occurrences (S2 File and  Fig 1). A set of 10,000 pseudoabsences were randomly placed over a region identified by all the WWF Terrestrial Ecoregions  [65]  that included species records  [66, 67].
As an initial set of environmental predictors, we considered the 19 bioclimatic variables derived from the WORLDCLIM database at a spatial resolution of 30 arc-seconds (ca. 1 km)  [68]  and the Euclidean distance from the coastline. To take into account the pairwise correlation between the predictors, the variables were subselected considering a variance inflation factor (VIF) less or equal to 3  [69]. The final environmental predictors used for the model training were: Euclidean distance from the coastline, Temperature Seasonality (BIO4), Mean Temperature of Wettest Quarter (BIO8), Mean Temperature of Driest Quarter (BIO9), Precipitation Seasonality (BIO15), and Precipitation of Coldest Quarter (BIO19). All the procedures were carried out with the packages "spatstat", "maptools", "rgeos" and "raster", in the R environment software  [37].