dataesr/scientific-paragraphs-categorization huggingface dataset:
The imago does not grow or allocate to reproduction. It mobilizes reserve to match constant (somatic plus maturity) maintenance. Hemi-metabolic insects skip the pupal stage, do not convert larval structure to reserve. Imago structure equals larval structure when reproduction buffer density hits a threshold. Overview of typified models in AmP. The pie in Fig  6  presents their relative frequency in the collection. Certain typified models are better resolved that others, because of number of entries and data with the target groups.
Comparisons of species on the basis of parameter values The AmP database not only allows us to test model against data and evaluate implications, but also to identify evolutionary and ecological patterns in parameter values. The number of papers on patterns in parameter values is now increasing rapidly  [10, 15, 30, 31, 33, [36] [37] [38] [39] [40] [41]. Apart from being of direct scientific relevance, future improvements of DEB parameter estimation methods might exploit these patterns, since lack of data is the rule rather than the exception and having some testable prediction is better than no prediction at all. Where, for instance, (measured) maximum body weight is treated as an independent variable in the eco-physiological literature, DEB theory sees this as a property resulting from underlying processes that are quantified via parameter values.
The h-models differ mainly from the a-models by the fact that acceleration extends into the adult stage. Fig  7  presents evolutionary relationships among animal taxa with a color code to indicate the amount of acceleration in their species. Since the oldest animal group, the Radiata, and the oldest deuterostomes, the echinoderms, accelerate, it might well be that acceleration became suppressed in several other groups and this suppression evolved several times in evolution  [36]. The Ecdysozoa (n = 99 at 2018/01/01) beautifully illustrate the link between model type and taxonomic relationship. Fig  7  shows that Chaetognatha, Tardigrada, Nematoda and Entognatha hardly accelerate metabolism, just a factor 2 or less. The basic insects, the ephemeropterans and odonata accelerate a bit more, while the crown groups, the holometabolic insects, accelerate very much.
The latter might be due to a variety of reasons. Most mammalian embryos, for instance, have a period to prepare for growth during which the fetus does not increase in size. The onset of growth is typically quite clear, since consistent with DEB expectations, structural length starts to increase linearly  [44, 45]. Data on embryo development is relatively scarce. The colors in the taxa names of Figs 7, 8 and 9 reflect the range of values of metabolic acceleration that were found in the various taxa. Although over 1000 entries is very large for a database of this type, compared to the 10 million existing animal species, it is close to nothing. Thus we cannot assume that the species in the collection are fully representative for the species in nature. Moreover, the number of species in each taxon, both in nature and in the database, varies enormously; some have just a single species.
Moreover, by being mechanistic (= based on first principles), DEB models interpret data, rather than just describe it. They can therfore reveal inconsistencies in data and predict un-measured properties of species as functions of parameters. We present and discuss how DEB parameters can be extracted from eco-physiological data: the AmP approach. The two associated software packages, DEBtool and AmPtool, are freely available via GitHub and have online user manuals. We demonstrate the applicability of DEB theory, by showing that it is possible to extract DEB parameters for animal species even when there is little data. We evaluate goodness of fit with respect to data completeness per species and overall the models fit data well. We found that a family of related DEB models, which share the same 14 DEB parameters, are needed to capture the diversity of life-cycles in the animal kingdom.
A main metabolic feature which distinguishes the life-cycles is that some groups have 'metabolic acceleration', which has links with larval stages. We present the latest evolutionary overview on which groups were found to have metabolic acceleration. Knowledge gaps are highlighted. The AmP project was initiated at 2009/02/12 and meanwhile 125 authors contributed with entries. It has 1035 entries at 2018/03/12, including all larger phyla and all chordate orders, and is both the smallest as well as the largest database of this kind, since it is unique. We expect that it will remain unique for a long time to come, in view of the huge amount of effort to arrive at the state we are presently in and because we think that DEB models will not have alternatives with matching generality, simplicity and realism.
Ackowledgements This work was performed as part of the RING project at Université de Lorraine. We would like to thank the industrial and academic sponsors of the RING-GOCAD Consortium managed by ASGA for their support, and Chevron for funding the Ph. D. of Benjamin Chauvin. We also acknowledge Paradigm for the SKUA-GOCAD software and API, and Inria for the Geogram library used in RINGMesh. We thank IFPEN and C&C Reservoirs, DAKS TM -Digital Analog Knowledge System, for the analog model data set. We also acknowledge Justin Herbert, Donald Medwede and Richard Groshong for discussions, feedback and involvement in this work. Frantz Maerten, Robert Worthington and Frank Zwaan provided helpful comments that improved the quality of this manuscript.
). In the e-health application domain, different research works use the ZigBee communication technology to interconnect different IoT objects. In  [18] , a wireless healthcare monitoring system based on ZigBee is specified in order to provide real-time information concerning the health condition of patients. Furthermore, the authors in  [19]  developed a continuous patients monitoring system based on ZigBee protocol. In fact, ZigBee, 6LoWPAN and other communication technologies use IEEE 802. 15. 4 as a basis for their lower layers (physical and medium access control layer). The expanding number of connected devices in the IoT makes the IPv6 usage a necessity. Consequently, 6LowPAN  [17] , based on IEEE 802. 15. 4 for the physical (PHY) and medium access control (MAC) layers, will be greatly used in the IoT environment for services requiring low data rate and low range coverage.
QoS Motivation and Challenges in IoT The exponential increasing number of IoT connected objects leads to a greater amount of data created in the IoT environment. In addition, the generated data has different and even contradictory characteristics that we have to cope with by providing QoS guarantee within the IoT environment, which is a challenging task. QoS models such as IntServ and DiffServ are difficult to be used within the IoT environement due to the constraints in terms of memory and computing capacity of IoT objects but also due to the large scale of the IoT and the random deployment of objects. Therefore, new and well-adapted QoS models must be proposed to provide IoT services performance guarantees  [24]. Various international organizations consider that QoS integration in IoT environments is very important and necessary.
• The IoT-SP offers IoT services for different IoT-Cs. It has its own cloud infrastructure or subscribes specific SLAs with different cloud service providers (CSPs). The cloud infrastructure stores the huge amount of data created, provides computation capabilities and hosts different IoT applications. In addition, the IoT-SP subscribes SLAs with different network service providers (NSPs) to interconnect the IoT objects infrastructure with the corresponding cloud infrastructure. The IoT service is divided into two parts: an application part and an objects infrastructure part. The objects infrastructure is managed by two categories of gateways in order to retrieve information or execute orders. The first category of gateways is called high level gateways (HL-Gws). They ensure minimal bandwidth consumption while transmitting data to the cloud using the data aggregation techniques and offer fog computing capacities for non-delay tolerant data processing in order to minimize the corresponding end-to-end delay.
Thus, the IoT sensing characteristics part of the iSLA includes all the characteristics of the gateways (HL-Gw, LL-Gw) and the IoT smart objects group. Then, the IoT application characteristics part of the iSLA includes parameters related to the cSLA such as the application ID, the maximum number of users, the mean number of requests per second per user, the time of support and the storage parameters. Finally, the IoT QoS parameters part of the iSLA is classified into two different categories; the qualitative parameters and the quantitative parameters. The qualitative characteristics define the application type by specifying the QoS class of the data generated (i. e. , RTMC: real-time mission-critical, RTNMC: real-time non-mission-critical, Streaming, and NRT: non-real-time). The quantitative QoS parameters include the end-to-end delay, availability, lifetime, and data quality (standard deviation, sensing frequency, and data error ratio).
999%) and a high data quality. The end-to-end delay declines into IoT application response time (cloud), the IoT sensing response time (objects) and the network delay. As for the IoT service availability, QBAIoT does not take into consideration radio noise issues. Thus, QBAIoT could provide high availability only if no noise issues exist in the sensing environment. As shown in the MSC (see Figure  6 ), the IoT-C initiates the iSLA establishment by sending a request concerning the desired IoT service with its requirements. Then, the IoT-SP will classify the requirements into three types; cloud requirements, network requirements and gateways/sensing requirements. After this classification step, the IoT-SP chooses among the best offers available from the different CSPs and NSPs in order to serve the IoT-C.
IoT Gateway QoS Based Access Method The IoT LL-Gw of our architecture adopts the proposed QBAIoT mechanism as a QoS based access method enabling QoS guarantee for IoT objects according to an achieved iSLA. We specify in Figure  8  the finite state machine that illustrates the QBAIoT process within the LL-Gw. In state S0, the LL-Gw tests if there is a new configuration to be sent to the objects of its cluster. This new configuration is specified by the HL-Gw thanks to a QBAIoT self-configuring capability (cf. Section 3. 5). Thus, if a new configuration exists, the LL-Gw updates the former beacon and sends it to the IoT nodes while reaching State S1. If there is no new configuration the beacon is sent directly to the IoT nodes at the beacon interval time and the LL-Gw switches to state S1. In State S1, the LL-Gw receives data from the different nodes according to their QoS CAPs.
3. First, the average delay refers to the average time experienced by a generated packet from the IoT object to be received by the LL-Gw. It is computed by dividing the sum of received packets delays by the number of received packets. Then, the packet delivery ratio (PDR) evaluates the reliability degree achieved by the QBAIoT based sensing layer in terms of successful transmissions. It is computed by dividing the number of received packet by the number of generated packets. In the first set of simulations, we consider seven different scenarios described in Table  3  that shows the number of objects per QoS class in each scenario with the corresponding slots configuration. For all scenarios, the objects data generation interval time is fixed to 0. 25 s resulting in 400 generated packets for each object during the simulation.
Then, the packet delivery ratio (PDR) evaluates the reliability degree achieved by the QBAIoT based sensing layer in terms of successful transmissions. It is computed by dividing the number of received packet by the number of generated packets. In the first set of simulations, we consider seven different scenarios described in Table  3  that shows the number of objects per QoS class in each scenario with the corresponding slots configuration. For all scenarios, the objects data generation interval time is fixed to 0. 25 s resulting in 400 generated packets for each object during the simulation. Scenarios 1 to 4 take into consideration objects that generate the data at the same time, whereas Scenarios 5 to 7 take into consideration objects that do not generate the data at the same time. The first scenario corresponds to an IoT e-health service with three RTMC nodes resulting in allocating the 16 slots to the three nodes.
Thus, this work aims to improve urban surface water mapping by suppressing noise in heterogeneous urban scenes, which consist of water bodies, vegetation, soil, roads, building roofs, and shadows. The proposed approach is expected to generate accurate urban surface water maps in a semi-automated way and regardless of the complexity of the urban environment. The overall workflow of the proposed method is shown in Fig. 2. Taking six separate cities in China and France as study areas, freely available Sentinel-2 top-of-atmosphere (TOA) reflectance images are preprocessed into the bottom-of-atmosphere (BOA) reflectance data with uniform spatial resolution in the official SNAP software (Section 2). The proposed method is conducted in two remote sensing image processing platforms, namely, ENVI for the pixel-oriented process and eCognition for the object-oriented application.
(i) The noise region of interest (ROI) is initially obtained by the differentiation of the MNDWI and AWEI sh water maps and classified into two subclasses by an unsupervised classification, namely, low-albedo objects, which are typically shown as shadows and bituminous streets, and extremely high-albedo objects (Section 3. 1). (ii) The CEM target detection approach is then employed to build the possible noise map, which presents the potential to be included in a water map and should be eliminated (Section 3. 2). (iv) The final water map is created by eliminating the noise from the AWEI sh water maps. In addition, two object-oriented water extraction methods, namely, eCognition commercial software and SVM classification, are adopted to compare and evaluate the proposed approach by the confusion matrix and Kappa coefficient (Section 3.
The Sentinel-2 satellite images (Fig. 3 ) used in this study were collected on May 3, 2016 (Beijing); October 10, 2016 (Yantai); December 9, 2016 (Changsha); February 14, 2017 (Yangjiang); April 13, 2017 (Lyon); and April 4, 2017 (Strasbourg), under clear weather conditions. The Sentinel-2 level 1C dataset was the standard product of the TOA reflectance freely downloaded from the Sentinels Scientific Data Hub (https://scihub. copernicus. eu/dhus/#/ home) and then was preprocessed by the official software Sentinel Application Platform (SNAP). The official atmosphere correction model, Sen2Cor, 1  was utilized to transfer the TOA reflectance to the BOA data. In this study, only VIS, NIR, and SWIR bands were utilized. The two SWIR bands in 20 m were resized to 10 m by the resampling tool in SNAP, which divided the pixel on 4 pixels with the same values of gray to maintain the same spatial resolution as the VIS and NIR bands.
Water indices and noise ROI generation Two urban water indices, namely, MNDWI and AWEI sh , were initially calculated to enhance the difference between water and nonwater bodies and to generate two initial water body maps. Water bodies tend to positive values in the MNDWI and AWEI sh , whereas soil, vegetation, and built-up classes are expected to hold negative values  (Xu, 2006; Feyisa et al. , 2014). Thus, the normally empirical threshold is zero, which is adopted in this study. On the basis of the binary threshold (zero), the initial water map was obtained via object-oriented multiscale segmentation and classification to replace the direct pixellevel surface water mapping. Homogeneous blocks were built using multiresolution segmentation in eCognition software. Some blocks were categorized as water when their spectral mean value is positive.
Through a comparative assessment with varying thresholds (i. e. , -0. 2 to 0. 3) and an automated threshold, the empirical threshold (zero) presents theoretically and experimentally stable results. The high threshold (possible larger than 0. 2) cannot generate enough noise samples for the CEM detection, while the loose threshold (possible lower than -0. 1) may bring too much noise that cannot be effectively eliminated. This paper took Sentinel-2 image as dataset to explore its potential on urban surface water bodies mapping. Sentinel-2 multispectral imagery has advantages on spatial resolution and repeated coverage compared to conventional freely available Landsat series imagery. Although the spatial resolution has been improved to 10 m, the resolution is still not fine enough to extract the small pools and brooks.
The proposed power amplifier for microwave ISM applications is designed, simulated and optimized by employing Advanced Design System (ADS) software by Agilent. The PA shows good performances in terms of return loss, output power, power gain and stability; the circuit has an input return loss of -38dB and an output return loss of -33. 5dB. The 1-dB compression point is 8. 69dBm and power gain of the PA is 19. 4dBm. The Rollet's Stability measure B1 and the stability factor K of the amplifier is greater than 0 and 1 respectively, which shows that the circuit is unconditionally stable. The total chip size of the PA is 73. 5× 36 mm 2.
Necessary primers within a constant region of the TCRβ were designed based on de novo assembled transcriptomes-an approach previously tested using highly complex system of duplicated MHC genes  30. We used RNA isolated form spleens of bank voles from a genetically diverse laboratory colony (see Materials and Methods) as a starting material for sequencing library preparation. We adopted molecular protocols available for human and mouse  15  (Supplementary File S1) to amplify TCRβ chains transcripts containing full CDR3 region, as well as the entire V and J segments. These sequences were subjected to detailed qualitative analysis (description of V and J genes, V-J segment usage, CDR3 length distribution) using our newly developed software: AmpliTCR and AmpliCDR3. We further used a modified molecular protocol incorporating UMIs  18, 19  , to allow stringent sequencing error correction (also implemented in AmpliCDR3), necessary for quantitative estimation of the TCR repertoire size.
Our tools are freely available on-line as an easy to navigate web server. The tools are a part of AmpliSAT suite (see Materials and Methods), which already comprises AmpliSAS  31  , an increasingly popular tool for multi-locus MHC genotyping in non-model species. Our focal species, the bank vole, is a small rodent of the Cricetidae family, widespread in western Europe and northern Asia. It has been extensively studied in various subjects, ranging from adaptive radiation  32, 33  , genetic basis of adaptation  34  , and response to selection  35  to processes shaping sexual  36  and behavioural traits  37. Primary interest in its immunity was driven by its role as a reservoir of a pathogenic Puumala hantavirus (PUUV)  38, 39  , which causes a mild form of haemorrhagic fever with renal syndrome in humans.
Because the bank vole neither has a high-quality annotated reference genome nor are TCR germline gene segments available in the Genbank or International ImMunoGeneTics information system ® (IMGT), we assigned J and V variants extracted from TCRβ transcripts to provisional loci based on their assignment to distinct clades in a phylogenetic tree. Additionally, orthology and similarity (Supplementary Figs  S2  and S3 ) to the mouse references from the IMGT database  3  (Supplementary Table  S1 ) further supported recognition of these clades as loci. Bank vole genes were named BV_TRBV or BV_TRBJ, with the numbering of each locus corresponding to the  The full list of mouse reference sequences with accession numbers is available in Supplementary Table  S1. Sequences of bank vole genes identified in this study are available in Supplementary File S3; only one allele for each locus is present herein for clarity.
We compared TCRβ V segments retrieved in the present work (from directly sequenced transcripts of the bank vole TCRβ) against V segments extracted from a recently assembled draft bank vole genome. The latter had been downloaded from the online database Vgenerepertoire. org  49 , containing automatically extracted V genes from whole-genome assemblies (WGA) for over 200 vertebrate species. Only 17 TCRβ V genes were extracted from the bank vole draft genome, compared to 37 putative loci found herein. Comparison of amino acid sequences (Supplementary Fig. S6 ) showed that the following subgroups of BV_TRBV genes are missing from the WGA: 1, 2, 5, 12, 13, 20, 26, 40, and 42. All of these, except for 40 and 42, have clear murine orthologues, with subgroups 12 and 13 containing multiple duplicated loci.
The reason for this discrepancy most likely is the rather poor quality of the bank vole draft genome (scaffolds: 367 242; contigs: 2 834 384; contig N50: 1 591; L50: 365 195), which did not allow successful recovery of all functional genes. All extracted genes from WGA are, however, present in our dataset, with the exception of V49. This locus is likely non-functional and not transcribed, similarly as its murine orthologue TRBV21 (Supplementary Fig. S6 ), known for alterations in the splicing sites  3. V-J segment usage and CDR3 length distribution. To avoid possible biases introduced by differences in receptor expression between cells, both features were measured by the number of identified, unique nucleotide variants, rather than abundance of the recovered transcripts. Based on the assignment of the V and J segments of the bank vole to the provisional loci, we analysed preferential usage of V and J segments in the rearranged TCRβ transcripts.
One reason for the unclear orthology could be deletion of some genes in one of the species. For example, the bank vole BV_ TRBV41 gene misses the mouse orthologue, but it closely groups with the TRBV10 gene, which is pseudogenised in mice. This suggests that genes belonging to this clade might have become non-functional in mice, and some may have been eventually deleted. Another reason could be expansion after the diversification of species. For example, TRBV12 and TRBV13 groups have multiple genes both in voles and mice. These duplicated genes group by species, which suggests that they have expanded independently, after the split. A database collecting numerous V genes extracted from WGAs (Vgenerepertoire. org  49 ) shows an expansion of TCRβ V segments in many species from the Cricetidae family, with more than 30 genes found in prairie vole (Microtus ochrogaster) and North American deer mouse (Peromyscus maniculatus).
A lower bound of the TCRβ repertoire size in the bank vole was conservatively estimated at 2. 1 × 10 5 unique CDR3 types, which is comparable to the estimate for the mouse, 5-8 × 10 5 (based on Sanger sequencing of a single CDR3 length band and interpolation over the sizes of all bands  7  ). Repertoire size reflects overall diversity, without taking into account abundance. While in some cases information about the number of cells bearing particular TCR might be more interesting, for example if clonal expansions are of interest, such abundance data are harder to confidently obtain when RNA is used as staring genetic material. Although presence of UMIs allows for differentiation between single cDNA templates (alleviating issues of e. g. unequal PCR amplification), still two identical cDNA variants with different UMIs may either represent multiple transcripts from one T cell or transcripts from different T cells with identical TCRs (e.