dataesr/scientific-paragraphs-categorization huggingface dataset:
01) (Fig. 3A ). Subsequently, we investigated the APO composition in lipoproteins isolated from serum, paying particular attention to APOB. As shown in Fig. 3B  for LDL and HDL (and in Supporting Fig. S5  for VLDL), we isolated the major APOs specific for lipoprotein fractions. We could identify both APOB100 and, to a lesser extent, APOB48 in all LDL samples from LHM (band quantification in Supporting Fig. S6 ). However, in LHM the APOB100 levels were more similar to those of the human reference sample than to the mouse  Note: Sera were pooled based on the group, and phospholipid transfer was analyzed with a fluorometric assay in kinetic measurement. Data are presented as mean ± SEM from three different triplicates. Human and mouse sera are reference samples. LMM served as negative control.
S3 ) in the liver of LHM-M1 treated with GW3965, eliminating the possibility of evaluating any effect on this gene. As shown in Fig. 5B , we assessed the haPG binding of lipoproteins, which was higher FIg. 4. LHM lipoproteins bind haPG similarly to human lipoproteins. One microliter of serum was added to the immobilized proteoglycans, and after incubation the amount of bound cholesterol was determined. To correct for differences in serum cholesterol in each individual sample, the bound cholesterol was divided for the concentration of serum cholesterol. Data are presented as the median (depicted by the line) of scattered dots. Kruskal-Wallis test was followed by multiple comparisons test; *P < 0. 05, **P < 0. 01, and ***P < 0. 001. Human sera are reference samples; LMM served as negative control.
Acknowledgment: We thank  M. Olin ,  A. Lövgren-Sandblom ,  L. Larsson ,  K. Littman ,  C. Hammarstedt ,  G. Bial ,  L. Foquet ,  G. Camejo ,  C. Pramfalk , and  O. Ahmed  for their expert technical support and helpful discussion throughout the work. For the help with the histology analysis, we thank  Raoul V. Kuiper  and the  Unit for Morphological Phenotype Analysis  ( FENO, Clinical Research Center, Department of Laboratory Medicine, Karolinska Institutet ). We also thank all of the consortium members of the European Unionfunded research project HUMAN (Health and the  Understanding of Metabolism, Aging and Nutrition) , who have directly and indirectly contributed to the discussion of the results.
A step-by-step description of the research design using a simulation script written in the R2019b version of MATLAB and its LTE toolbox, is presented in Section 5. 2 before the obtained results are presented in Section 5. 3 and critically analyzed and discussed. In this section various simulation results using MATLAB are presented to demonstrate the effectiveness of the proposed scheme. The existing approaches are compared under the same setting to show the strength of the proposed EEACC scheme compared to the traditional repetition-based as well as the NBLA schemes. Finally, conclusions are drawn in Section 6 based on the research objectives and a recommendation for future work is formulated.
Efficient Selection of Modulation Coding Scheme (MCS) The design of an energy-efficient channel coding scheme for NB-IoT is directly linked to the selection of an appropriate modulation coding scheme (MCS). In order to achieve long-range communication, some work on efficient NB-IoT designs found in literature  [3, 21, 45, 46]  have proposed efficient techniques for modulation scheme selection. The common idea behind most proposed approaches consists of trading off high data rates for higher energy in each transmitted bit (or symbol) at the physical layer (PHY). This design technique allows for a signal that is more immune and that can travel longer transmission distances. Therefore, in general, the identified aim of most designs is to achieve a link budget of 150 ± 10 dB which can translate into a few kilometers and tens of kilometers in urban and rural areas respectively  [3].
• On the other hand, when a certain number of NACKs are successively decoded at the NB-IoT receiver, then the MCS level is decreased. In general, the number of ACKs is greater than that of NACKs to ensure a slow increase of the MCS level with ACK feedback and a quick decrease of the MCS level with NACK feedback. Due to the narrowband and low data rate for NB-IoT systems, the settings for LTE systems may not be applicable. Therefore, two aperiodic and event-based actions are defined for the EEACC approach namely the fast upgrade (FUG) and the emergency downgrade (EDG). In the event of FUG, the MCS is increased by one while in the event of an EDG it is decreased by one. Thus, the EEACC approach introduces a compensation factor ∆C(t) which can be modelled as ∆C(t) =                        min {∆C(t -1) + C stepup , ∆C max }, i f H ARQ f eedback = ACK; max {∆C(t -1) -C stepdown , ∆C min } i f H ARQ f eedback = N ACK; ∆C(t -1), i f H ARQ f eedback = N/A; (26) where • ∆C max and ∆C min are the upper and lower limits of the compensation factor ∆C(t), • C stepup and C stepdown are the incremental compensation step sizes which is modelled as per the formula, C stepdown = C stepup × 1 -BLER target BLER target (27) • N/A implies discontinuous transmission (DTX) which happens when the eNB does not detect any NPUSCH signal.
• Going from the principle that for a multi-cycle Microprocessor without Interlocked Pipelined Stages (MIPS) which considers the following five types of instructions with their respective number of machine cycles, 1. Load (5 cycles) 2. Store (4 cycles) 3. R-type (4 cycles) 4. Branch (3 cycles) 5. Jump (3 cycles) • By using a MATLAB R2019b Instruction Classification Learner application (Table  1 ), it was found that the program has the algorithm implementing the EEACC algorithm has an average total instruction count of n = 15 instructions and the following percentages of instructions per type. This leads to an average of Cycles per instructions (CPI) of CPI = 5×52+23×4+14×4+9×3+2×3 100 = 4. 41 The execution time of the algorithm can be computed as follows: T Ex = CPI × n × T mc = 4. 41 × 15 × m f clk (28) This means that for the implementation on a typical NB-IoT node with a basic cheap microcontroller such as the PIC16F627A running a on a external crystal oscillator with a clock frequency f clk = 16 MHz with m = 4 clock cycles per machine cycle, the execution time of the EEACC algorithm can be computed as follows, This processing duration significantly reduces with faster modern microcontrollers.
• A meshnet topology is formed as NB-IoT nodes are progressively deployed. Upon deployed, every node broadcasts a small interest message to identify its neighbours and path to the BS as guided by the Friis transmission equation. It is also important to mention that the Meshnet is the most preferred deployment approach for the NB-IoT networks  [57, 58]. This is due to the self-forming and self-healing nature of Mesh Networks which presents the following advantages when used for NB-IoT systems: 1. Better efficiency: With its ability to quickly create robust ad-hoc networks, mesh technology is proven to be a high-performance data transfer solution which happens to be the core of most NB-IoT applications (example of Wireless Sensor smart metering NB-IoT networks and many more).
• A MATLAB timer class with an instantiated object for each NB-IoT node, based on the simulation computer's clock is used to keep track of the timing function at each node. • Based on a random uniform process, each NB-IoT node is due to send a single data packet at a predefined rate (could be hourly, daily, etc depending on the application at hand). For every single transmission, every single NB-IoT node described as an object of a defined class (mote), implements the EEACC algorithm as presented in Algorithm 1 for transmission of data packets.
However, the APEI no. 3, which is located in an oligotrophic province and separated from the CCFZ by the Clarion Fracture Zone, showed the lowest densities, lowest diversity, and a very low and distant independent similarity in community composition compared to the contract areas, thus questioning the representativeness and the appropriateness of APEI no. 3 to meet its purpose of diversity preservation. Among the four exploration contracts, which belong to a mesotrophic province, the distance decay of similarity provided a species turnover of 0. 04 species km -1 , an average species range of 25 km and an extrapolated richness of up to 240 000 polychaete species in the CCFZ. By contrast, nonparametric estimators of diversity predict a regional richness of up to 498 species. Both estimates are biased by the high frequency of singletons in the dataset, which likely result from undersampling and merely reflect our level of uncertainty.
5 mM), 0. 5 µL of PCR nucleotide mix (final concentration of 0. 2 mM of each dNTP), 9. 875 µL of nuclease-free water, 2. 5 µL of each primer (final concentration of 1 µM), 2 µL Overlapping sequence (forward and reverse) fragments were aligned into consensus sequences using Geneious Pro 8. 1. 7 (2005 -2015; Biomatters Ltd). For COI, the sequences were translated into amino-acid alignments and checked for stop codons to avoid pseudogenes. The minimum length coverage was 207 bp for 16S, 327 bp for COI and 1615 bp for 18S. The sequences were blasted in GenBank to check for the presence of contamination. Each set of genes was aligned separately using the following plugins: MAAFT  (Katoh et al. , 2002)  for 16S and 18S and MUSCLE  (Edgar, 2004)  for COI. All sequences obtained in this study have been deposited in BOLD (http://www.
Taxonomic identification and feeding guilds classification Preserved specimens were examined under a Leica M125 stereomicroscope and a Nikon Eclipse E400 microscope, counted (anterior ends only) and morphologically identified using the deep-sea polychaete fauna bibliography  (Fauchald, 1972 (Fauchald, , 1977;; Böggemann, 2009)  at the lowest taxonomic level possible (morphospecies). We separated closely related species (specimens that could not be discriminated morphologically) using the principle of phylogenetic species, whereby the genetic divergence among specimens belonging to the same species (intraspecific) is smaller than the divergence among specimens from different species (interspecific)  (Hebert et al. , 2003a). In the distribution of pairwise divergences among all sequences of a typical bar code dataset, a gap can be observed between intraspecific and interspecific variations.
Univariate analyses Abundance and number of species per box core (Table  1 ) and averaged by area (Table  2 ) were used as descriptors of alpha diversity. A few cryptic or damaged specimens that could not be classified to a lower taxonomical level were included in total abundance calculations but excluded from subsequent diversity analyses. To compare diversity among the studied areas and for all samples (eastern CCFZ), rarefaction curves were computed based on the total number of individuals and the total number of box core samples  (Hurlbert, 1971; Gotelli and Colwell, 2001). Based on these data the expected number of species was calculated for 12 individuals (ES12) and 163 individuals (ES163), as  well as for three samples (S3). Nonparametric estimators of species richness were used to estimate the total number of species at local and regional scales.
Abundance and alpha diversity During the SO239 cruise, 1233 polychaete specimens were sampled in the five study areas. Interestingly, only a large specimen identified as Bathyasychis sp. 150 was found deeper than 50 cm (bottom of box core) and thus not included in the analyses. The dataset has been archived in the information system PANGAEA and is available in open access  (Bonifácio et al. , 2019). The mean abundance in each study area tended to decrease from southeast to northwest, with high variability between neighboring areas (Fig. 2a ). Mean densities ranged from 58 ± 18 ind. 0. 25 m -2 in the BGR area to 5 ± 2 ind. 0. 25 m -2 in APEI no. 3. The abundance per box core (Table  1 ) differed significantly between areas (Kruskal-Wallis test, p < 0. 001). The pairwise comparison test  (Conover-Holm)  showed that (i) APEI no.
The 21 sequences of the 18S gene that have been obtained are mentioned here because they were archived concomitantly with COI and 16S sequences in GenBank and BOLD public datasets, but they are not further considered in this study. Based on both morphological and molecular identification, a total of 275 morphospecies were recognized. The mean number of species per area tended to decrease from southeast to northwest with high variability between neighboring areas (Fig. 2b ). Mean richness varied from 37 ± 10 taxa 0. 25 m -2 in BGR to 3 ± 2 taxa 0. 25 m -2 in APEI no. 3. The number of species per box core (Table  1 ) differed significantly among areas (Kruskal-Wallis test, p < 0. 001). The pairwise comparison test  (Conover-Holm)  showed that the number of species per box core was (i) significantly lower at APEI no.
Integrative taxonomy thus not only provides more accurate estimates of species diversity but also facilitates comparisons across datasets. Over 90 % of the species in the abyssal Pacific are new to science  (Glover et al. , 2002)  and there are few attempts to try to name them  (Paterson et al. , 2016; Bonifácio and Menot, 2019) , although DNA sequences can easily be matched. Therefore, 26 MOTUs are shared between  Janssen et al. (2015)  and our study. The overlap is low, but it should be noted that we had only 71 COI sequences belonging to 45 MOTUs to compare with the 556 COI sequences belonging to 233 MOTUs from  Janssen et al. (2015). This highlights a shortcoming of COIbased bar-coding because success rates for COI sequencing are generally low. A combination of several genetic markers associated to formal morphological descriptions are thus essential to accurately assess species diversity.
Acknowledgements. The research leading to these results has received funding from the  Ifremer program Ressources Minérales Marines (REMIMA) , the  JPI Oceans pilot action "Ecological aspects of deep-sea mining " and the  European Union   Seventh Framework Program  ( FP7/2007-2013 ) under the  MIDAS  project, grant agreement no. 603418. We are grateful to the crew of the RV Sonne and to all of the people involved in the field sampling and sample processing during the SO239 cruise. We would like to thank  Stefanie Kaiser ,  Sarah Schnurr , and  Ana Hilário  for their expertise in washing and sieving samples and  Lenka Neal  for live-sorting the worms. Additionally, we thank  Baptiste François  for sample sorting in the laboratory. We are also grateful to all of the people that were involved in that molecular analysis:  Aliou Dia ,  Guillaume Lannuzel ,  Emmanuelle Omnes ,  Alana Jute ,  Mohamed Dosoky  and  Gavin Campbell.
Special thanks to  Thomas Dahlgren  for looking after and sharing the data of some families of polychaetes and to  Ann Vanreusel ,  Freija Hauquier , and  Felix Janssen  for providing the abiotic data of CCFZ. Finally, we would like to thank  Tina Treude  and the referees ( Paul Dando  and three anonymous referees) for their critical reviews and useful comments, which significantly improved the quality of the final paper. Financial support. This research has been supported by the  Seventh Framework Programme  (MIDAS, grant no. 603418 ). The study also received funding from  REMIMA  and  JPI Oceans. Review statement. This paper was edited by  Tina Treude  and reviewed by  Paul Dando  and three anonymous referees.
Data availability. DNA sequences are available in BOLD (https://doi. org/10. 5883/DS-GKG001; Bonifácio, 2019)  or Gen-Bank databases. The abundance data analyzed in the present study together with BOLD IDs (sample ID and process ID) and GenBank accession numbers are available in the PANGAEA database (https://doi. org/10. 1594/PANGAEA. 902860; Bonifácio et al. , 2019).
Rodent Models As small mammals, the mouse and the rat present several advantages for biomedical research, that include the fact that their physiological and genetic structures are closer to those of humans than the aforementioned models, together with a relatively short life cycle and affordable housing costs  [144]. In addition, in particular thanks to the generalization of the CRISPR/Cas9 genetic editing system, the generation of specific transgenic mouse strains has been significantly facilitated and accelerated. Two useful resources to gather information about specific murine models are the interconnected databases MouseMine (http://www. mousemine. org/mousemine/begin. do) and Mouse Genome Informatics (MGI (Mouse Genome Informatics, a resource for laboratory mouse hosted by the Jackson Laboratory, http://www.
Today, numerous Cre strains carrying cre alleles with tissue-specific and/or inducible recombinase expression have been created in various genetic backgrounds and a large number are commercially available (Cre strains and corresponding expression data and references can be found on the MGI website, an international database resource for laboratory mouse hosted by the Jackson Laboratory, http://www. informatics. jax. org/home/recombinase). In combination with strains carrying floxed alleles of ADPKD relevant genes, these various Cre strains allow the creation of a large number of models to study specific features of ADPKD pathology and to elucidate the underlying molecular pathways and their timing. As it would exceed the scope of this broad review to describe the specific transgenic Pkd1 and Pkd2 murine models, here again we refer our reader to the recently published chapter describing those models in detail, as well as the divers techniques used to assess the PKD phenotype in those animals, in the book Polycystic Kidney Disease edited by Jinghua Hu and Yong Yu  [181].
Finally, the InterMOD project (MOD: Model Organism Database), which "aims to make it easier to carry out cross-MOD comparative analysis and to relate MOD data to medical research"  [210]  should also be mentioned in this place. Different MODs, among those FlyMine (https://www. flymine. org/flymine/begin. do), ZebrafishMine (http://www. zebrafishmine. org/begin. do), MouseMine (http://www. mousemine. org/mousemine/begin. do), RatMine (http://ratmine. mcw. edu/ ratmine/begin. do) and HumanMine (https://www. humanmine. org/humanmine/begin. do), participate in the project and thus permit cross-species analysis of biological data. Therefore, this project constitutes a helpful and interesting tool for the integration and translation of model organism research into medical applications, as well as for the comparison of model organisms in view of a specific research project.
The literature covered from 1995 to 2019 and considered the first studies on scaling frameworks in development  (Uvin, 1995). The types of documents reviewed were as follows: (1) scientific academic research on scaling frameworks for innovations and (2) grey literature of reports and manuals on scaling frameworks from local, national and international organizations working on development, for example, US Agency for International Development (USAID), World Health Organization (WHO) and World Bank (WB). We excluded scaling up in nondevelopment contexts, businesses and scaling business-like models. The data were collected, first, by conducting a literature search in Academic Search Complete, Web of Science and Google Scholar that included the key words 'social innovation' 'development' 'scaling up approaches' OR 'scaling up frameworks' OR 'scaling up methodologies', which resulted in 13 entries in Google Scholar, two in Academia Search Complete and four in Web of Science; when changing the term to 'innovation', the results were 189, 36 and 30, respectively.
We present a spectroscopic survey of 230 white dwarf candidates within 40 pc of the Sun from the William Herschel Telescope and Gran Telescopio Canarias. All candidates were selected from Gaia Data Release 2 (DR2) and in almost all cases had no prior spectroscopic classifications. We find a total of 191 confirmed white dwarfs and 39 main-sequence star contaminants. The majority of stellar remnants in the sample are relatively cool ( T eff = 6200 K), showing either hydrogen Balmer lines or a featureless spectrum, corresponding to 89 DA and 76 DC white dwarfs, respectively. We also recover two DBA white dwarfs and 9-10 magnetic remnants. We find two carbonbearing DQ stars and 14 new metal-rich white dwarfs. This includes the possible detection of the first ultra-cool white dwarf with metal lines.
For objects with the reddest colours corresponding to Gaia T eff 4500 K, we favoured low-resolution identification spectra using the R500B grating (R ≈ 540, wavelength range 3600-7200 Å) which minimised overheads. For a few warmer objects we favoured the R1000R grating (R ≈ 1100, wavelength range 5100-10 000 Å) in order to possibly detect Hα. One DZ white dwarf was serendipitously discovered using this second setup (WD J1922+0233). It implies other metal lines are likely to be present in the unobserved blue portion of the spectrum, which would allow a detailed chemical abundance analysis. The spectra were de-biased and flat-fielded using the standard starklink 1 packages kappa, figaro and convert. We carried out optimal extraction of spectra using the package pamela 2  (Marsh 1989 ). The extracted 1D spectra were wavelength calibrated and flux calibrated using molly  (Marsh 2019 ).
ATMOSPHERE AND EVOLUTION MODELS For all observed targets we have used spectroscopic and photometric data to determine spectral types by human inspection. We have also derived atmospheric parameters and chemical abundances using the different methods described in this section. Effective temperatures and log g values can be derived for most white dwarfs using photometric fits  (Koester et al. 1979; Bergeron et al. 2001). For DA and DB stars, an independent method is to determine these parameters from spectroscopic line fits  (Bergeron et al. 1992; Beauchamp et al. 1999). For these spectral types we refrain from performing simultaneous fits of the photometric and spectroscopic data, which would not improve accuracy because of the known systematic offset between both techniques  (Tremblay et al.
Photometric parameters Photometric temperatures, log g and masses are determined from Gaia DR2 photometry and astrometry using the same method as that outlined in Gentile  Fusillo et al. (2019). In brief we rely on our own grids of pure-H, pure-He and mixed H/He 1D model atmospheres  (Tremblay et al. 2011; Cukanovaite et al. 2019 ) and the mass-radius relation of  Fontaine et al. (2001)  for thick (H-atmospheres) and 1 The starklink Software Group homepage website is http:// starlink. jach. hawaii. edu/starlink 2 pamela was written by T. R. Marsh and can be found in the starklink distribution Hawaiki and later releases. thin (He-atmospheres) hydrogen layers. The only differences are that we have neglected reddening and adopted grid of model fluxes that correspond to the newly identified atmospheric composition.
RESULTS From the 230 objects observed, all 191 spectroscopically confirmed white dwarfs have spectral types in Table  2. These objects also have at least one set of either Gaia or Pan-STARRS photometric atmospheric parameters (using either pure-H, pure-He or mixed H/He model atmospheres), and in the case of warmer DA and DBA white dwarfs, an independent set of atmospheric parameters from the analysis of the optical spectrum. The resulting Pan-STARRS photometric distribution of log g as a function of T eff is shown in Fig. 1. We flag with an asterisk in the WD J name those objects where the parallax value is below 25 mas but for which 40 pc membership is still possible within 1σ. Many of these objects may therefore be excluded from the sample by the upcoming third data release of Gaia (DR3).