dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , PP_5421, Supporting Information Table  S4 ). The RNAmmer  (Lagesen et al. , 2007)  and tRNAscan-SE  (Lowe and Eddy, 1997)  programs were used to predict rRNA and tRNA-encoding genes, respectively, whereas other RNA structures like small RNAs and riboswitches were identified using the RFAM database  (Burge et al. , 2013)  (n 5 65) and from publications (n 5 3)  (Frank et al. , 2011). Finally, intrachromosomal repeats were detected using the method described by  (Achaz et al. , 2000).
Functional automatic annotation The predicted/annotated genes were subjected to sequence similarity searches using the gapped blastP algorithm against the UniProtKB protein sequence knowledgebase (The UniProt Consortium, 2014) and several protein family resources: COG  (Galperin et al. , 2015) , HAMAP  (Pedruzzi et al. , 2015)  and FIGfam  (Meyer et al. , 2009). They were also processed using the InterProScan software to predict potential sequence motifs, patterns and protein family assignments compiled in InterPro  (Mitchell et al. , 2015). In addition, genes encoding enzymes were also classified using the PRIAM profiles  (Claudel-Renard et al. , 2003). In terms of predicted structural features, a-helical transmembrane regions were searched with the TMHMM program  (Krogh et al. , 2001)  and signal peptides with SignalP  (Petersen et al.
Automatic genome-scale metabolic network reconstruction The metabolic network of P. putida KT2440 was reconstructed from the re-annotated genome sequence stored in PkGDB using the MicroScope automatic reconstruction pipeline, which is based on the BioCyc pathway reconstruction software  (Karp et al. , 2011). Pathway Tools uses the set of genome annotations as input data to automatically project the set of reference metabolic pathways stored in MetaCyc database  (Caspi et al. , 2014) , generating a specific Pathway Genome Database (PGDB) in a two-step process: first, the Reactome projection step, where associations between genes and metabolic reactions are inferred from gene annotations, and the Pathway projection step where reference pathways are projected based on these gene-reaction associations (see  Karp et al.
In silico gene essentiality analysis The Cobra Toolbox  (Schellenberger et al. , 2011)  and the Gurobi solver (Gurobi Optimization. , Houston, TX) were used for FBA simulations with MatLab (Mathworks Inc. , Natick, MA) based on the GSMM iJP962 of P. putida KT2440  (Oberhardt et al. , 2011). We expanded the original gene essentiality testset of iJP815  (Puchałka et al. , 2008)  by adding new experimental data including auxotrophies  (Molina-Henares et al. , 2010). Each knockout gene was simulated by blocking its associated reactions using the "deleteModelGenes" function of the COBRA toolbox. The in silico media were adjusted to the minimal media described for the experiments in  Puchałka et al. (2008)  and Molina-Henares et al. (2010) (see Supporting Information Tables  S12  and S14 ). We discriminated between growth and non-growth phenotypes of the mutants based on a threshold value equal to 50% of the wild-type growth rate in the same conditions.
, 2012). The outcome of these methods was further improved by extensive manual literature searches to add additional support to functional assignments. Finally, before the integration into the GSMM iJP962, GPR associations which include more than one gene, were manually curated to distinguish isozymes (OR relationships) from protein complexes (AND relationships). Biochemical reactions and Gene-Reaction associations resulting from the curation process were manually validated in MicroScope using the MetaCyc  (Caspi et al. , 2014)  and the Rhea  (Morgat et al. , 2015)  reaction databases. Rhea was mainly used to manage biochemical reactions that are absents from the current MetaCyc repository. This implies the creation of new reactions directly in the Rhea database, starting with chemical compounds defined in the Chemical Entities of Biological Interest ontology (ChEBI)  (Hastings et al.
• Low-power wide area networks, with a greater than 1000-m range, essentially low-power versions of cellular networks, with each "cell" covering thousands of end-devices. This category includes LoRaWAN, but also protocols, such as Sigfox, DASH7, etc. This section provides a perspective on LoRaWAN by giving a brief overview of these related IoT communication technologies. 1. 1. 1. IEEE802. 15. 4 IEEE 802. 15. 4 [4]  is a standard specifying the physical layer and data link layer for Low-Rate Wireless Personal Area Networks (LR-WPANs). Supporting three un-licensed frequency bands (868 MHz, Europe; 928 MHz, North America; 2. 4 GHz, worldwide), IEEE 802. 15. 4 can offer data rates up to 250 kbit/s at a transmission range largely dependent on the environment; while for a clear line-of-sight, up to 1000 m is possible; alas in most cases, the transmission range is measured in tenths of meters.
LoRaWAN Message Format LoRaWAN uses the physical frame format described in Section 3. 3. The header and CRC are mandatory for uplink messages, which makes it impossible to use a spreading factor of six in LoRaWAN. Downlink messages have the header, but not the CRC. The code rate that should be used is not specified and neither is when the end-devices should use the low data rate optimization. The message format is detailed in Figure  8. DevAddr is the short address of the device. FPort is a multiplexing port field. The value zero means that the payload contains only MAC commands. When this is the case, the FOptsLen field must be zero. FCnt is a frame counter. MIC is a cryptographic message integrity code, computed over the fields MHDR, FHDR, FPort and the encrypted FRMPayload. MType is the message type, indicating among other things whether it is an uplink or a downlink message and whether or not it is a confirmed message.
Acknowledgments are requested for confirmed messages. Major is the LoRaWAN version; currently, only a value of zero is valid. ADR and ADRAckReq control the data rate adaptation mechanism by the network server. ACK acknowledges the last received frame. FPending indicates that the network server has additional data to send and that the end-device should send another frame as soon as possible so that it opens receive windows. FOptsLen is the length of the FOpts field in bytes. FOpts is used to piggyback MAC commands on a data message. CID is the MAC command identifier, and Args are the optional arguments of the command. FRMPayload is the payload, which is encrypted using AES with a key length of 128 bits. The minimal size of the MAC header is 13 bytes; its maximal size is 28 bytes. Knowing this, it is possible to compute the maximum channel capacity available for application data payloads with given modulation parameters thanks to Equations (  1 ) and (  3 ).
LoRaWAN MAC Commands LoRaWAN defines many MAC commands that allow customizing end-device parameters  [23]. One of them, LinkCheckReq, can be sent by an end-device to test its connectivity. All of the others are sent by the network server. These commands can control the data rate and output power used by the device, as well as the number of times each unconfirmed packet should be sent (LinkADRReq), the global duty cycle of the device (DutyCycleReq), changing parameters of the receive windows (RXTimingSetupReq, RXParamSetupReq) and changing the channels used by the device (NewChannelReq). One command is used to query the battery level and reception quality of a device (DevStatusReq).
Single Device Maximal throughput and MTU The goal of this experiment is to evaluate the maximal throughput that a single device can obtain. This depends more on the physical layer than on the MAC protocol, but it gives an idea of what is possible when using LoRaWAN. The experiment was conducted by having a device send data as soon as the channel limitations and the protocol allow it. Tests were conducted with six channels of 125 kHz and using spreading factors from 7-12. No MAC commands were sent, so the size of the MAC header was always 13 bytes. The results, depending on the payload size, are visible in Figure  9 , which are measured over about 100 packets transmitted in each test. Fifty-one bytes are the maximum payload size allowed by the implementation used for the tests. This experiment revealed that at low packet sizes, the limiting factor was not the channel duty cycle limitations, as could have been expected, but the duration of the receive windows.
Indeed, the device has to wait for the two downlink receive windows following the transmission to be over before sending another packet. However, this situation is not the use case LoRaWAN was designed for: the goal of LoRaWAN is rather to manage large quantities of devices that send a few bytes of data from time to time. In the tests above, the MAC header is always 13 bytes. However, in practice, the LoRaWAN header can be a variable size between 13 and 28 bytes. Moreover, the maximum size of the frame depends on the data rate used  [23] , and LoRaWAN does not have a mechanism to split large payloads over multiple frames. As of the current specification, the application above LoRaWAN has no way of knowing what the maximal size of the packet that it will be able to send in the next transmission is, which might be problematic.
Estimation of the Collision Rate As of the current specification, the devices and the can transmit at any time. There is no listen-before-talk or CSMAmechanism. This makes LoRaWAN very similar to ALOHA  [24] , but contrary to ALOHA with a variable packet length. Because of the legal duty-cycle limitations of 1% in the EU region where this analysis took place, 100 devices would have been needed to emulate a load of one, and this number would have grown proportionally to the maximum link load we wanted to test. As there were not this many devices on hand, simulations are used to evaluate LoRaWAN's behavior under load. A simulator was built to simulate the random process of packet emissions. Five-hundred-thousand packets were simulated for each data point. If the transmission time of two packets overlaps, we consider that a collision happens and that none of the two packets reaches the gateway.
The Network Server Role LoRaWAN specifies the behavior of the devices, but not the behavior of the network server. As shown in Section 5. 3, it is important to keep the load on the network low, and the network server has to enforce this by sending MAC commands to the devices. However, as this is not part of the specification and as there is no open source reference implementation (as of the writing of this article), a correct behavior of the network server is hard to be evaluated. The network server can easily degrade the performance of the network. For instance, it can use the LinkADRReq command to configure the number of times a device will send each data frame. This parameter is advertised as a way to control the quality of service for a device. Setting this parameter to more than one will increase the load on the network, increasing the amount of collisions, and thus, should be done very cautiously.
For baseline seronegative, denominators for the Asian-Pacific region were 129 and 59 for vaccinated and control subjects and for Latin America 258 and 149. [3]  and these include all age groups down to age 2 years. Consequently, we do present country-stratified data for this region. b VPDI for Latin America was calculated from Table  S1  [4] as control group incidence density multipled by vaccine efficacy. Raw data were not presented, so confidence limits were not calculated. we estimated incidence as cases divided by number of subjects enrolled divided by length of the study, in this case 25 months or 2. 1 years, times 100,000 to provide cases prevented per 100,000 persons per year. Unlike VPDI, NNV is not a rate but instead the overall number of cases prevented for a given number of persons vaccinated, and thus incorporates the length of the trial.
Lastly, we found variation by baseline seropositivity (among the subgroup that had seroprevalence determined) but not co-linear with VE. For example, in Asia the VPDI was higher and the NNV lower among those who were seronegative at baseline despite a substantially lower VE among this group. This occurred despite available data being limited to etiologically confirmed disease for dengue while comparisons were made to VDPIs based on syndromic disease. VPDI for dengue was calculated for etiologically confirmed disease while comparison conditions were calculated in part against syndromic disease. VPDI calculated for etiologically confirmed disease likely will be lower than that for syndromic disease because diagnostic tests have imperfect sensitivity, not all persons with suspected dengue are tested, and system errors (e.
(E)VLA The I22134 star-forming region was observed with the VLA at 6. 0, 3. 6, 1. 3, and 0. 7 cm wavelengths in the A, B, and C configurations of the array in 2007 and 2009 with 10-21 EVLA antennas in the array. The data reduction followed the VLA standard guidelines for calibration of high-frequency data, using the NRAO package AIPS. We complemented our continuum observations with 1. 4 GHz continuum archival data from the NRAO VLA Sky Survey (NVSS)  (Condon et al. 1998). We list the main parameters of the observations in Table  1. The EVLA was also used on April 23 and May 07, 2010 to observe the continuum emission at 6. 0 and 3. 6 cm in its D configuration with a bandwidth of 2×256 MHz (up to 5 times better than the standard VLA observations). The data reduction followed the EVLA standard guidelines for continuum emission, using the Common Astronomy Software Applications package (CASA).
72 GHz. The 4IF spectral line mode was used to observe the NH 3 (1, 1) and (2, 2) lines simultaneously with two polarizations. The bandwidth was 3. 1 MHz, with 128 channels that had a channel spacing of 24. 2 kHz (∼ 0. 3 km s -1 ) centered at v LSR = -18. 3 km s -1. The data was reduced with the software package AIPS following the standard guidelines for calibrating high-frequency data. Imaging was performed using natural weighting, and the resulting rms and synthesized beams are listed in Table  3. The same data were also presented in  Sánchez-Monge et al. (2013). 1 The Very Large Array (VLA) is operated by the National Radio Astronomy Observatory (NRAO), a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. 2 Supports for CARMA construction were derived from the Gordon and Betty Moore Foundation, the Kenneth T.
and Eileen L. Norris Foundation, the Associates of the California Institute of Technology, the states of California, Illinois, and Maryland, and the National Science Foundation. Ongoing CARMA development and operations are supported by the National Science Foundation under a cooperative agreement and by the CARMA partner universities. 3 The Submillimeter Array is a joint project between the Smithsonian Astrophysical Observatory and the Academia Sinica Institute of Astronomy and Astrophysics and is funded by the Smithsonian Institution and the Academia Sinica. Notes. ( a) Bootstraped flux is in Jy. ( b) The detailed description for NVSS data can be found in  Condon et al. (1998).
PdBI Observations in the 2 mm continuum and the N 2 D + (2-1) line at 154. 217 GHz were obtained on June 2 and November 28, 2010, with the PdBI in the D and C configurations, respectively. The phase center was R. A. 22 h 15 m 10 s. 00 Dec. +58 • 49 02. 6 (J2000). Baselines range from ∼19-178 m. Atmospheric phase corrections were applied to the data, and the averaged atmospheric precipitable water vapor was ∼5 mm. The bandpass calibration was carried out using 1749+096. Phase and amplitude of the complex gains were calibrated by observing the nearby quasars 2146+608 and 2037+511. The phase rms is 10 • to 40 • , and the amplitude rms is below 10%. The adopted flux density for the flux calibrator, MWC349, was 1. 45 Jy. Calibration and imaging (natural weighting for N 2 D + (2-1) and robust = -2 for the continuum) were performed using the CLIC and MAPPING software of the GILDAS 4  package with the standard procedures.
1995). Notes. ( * ) The beam size and and rms listed here for CARMA and PdBI observations are derived by combining the data from both configurations. Notes. ( a) The frequencies for the lines were obtained from the Cologne Database for Molecular Spectroscopy (CDMS,  Müller et al. 2001 Müller et al. , 2005)) , except for NH 3 lines whose frequencies were obtained from the JPL Molecular Spectroscopy database  (Pickett et al. 1998 ). ( b) The rms listed here is per channel.
Acknowledgements. The work is supported by the  STARFORM Sinergia  Project  CRSII2_141880  funded by the  Swiss National Science Foundation. Y. W. also acknowledges support by the  NSFC   11303097  and  11203081 , China. Á. S. -M. acknowledges support by the collaborative research center project  SFB 956 , funded by the  Deutsche Forschungsgemeinschaft (DFG). G. B. is supported by the  Spanish MICINN  grant  AYA2011-30228-C03-01  (cofunded with FEDER funds). A. P. acknowledges financial support from a  UNAM-DGAPA-PAPIIT IA102815  grant, México. We thank  Dr. Nanda Kumar  for providing the  NIR UKIRT images. We thank the productive discussion with  Prof. Michael Meyer  and  Dr. Anastasios Fragkos. We acknowledge the referee  Dr. K. Dobashi  for improving the manuscript.
FINDCLUMPS is a part of the open source software package Starlink, which was a long-running UK Project supporting astronomical data processing. It was shut down in 2005, but the software continued to be developed at the Joint Astronomy Centr until March 2015, and is now maintained by the East Asian Observatory. with the algorithm ClumpFind (Williams et al. 1994 ) and 3σ boundaries (1σ=0. 26 mJy beam -1 for 2 mm and = 0. 50 mJy beam -1 for 1. 3 mm continuum map) to identify the cores. The identified cores that are located outside the FWHM of the primary beam (32 for the PdBI, and 55 for the SMA) or have a peak intensity less than 5σ are rejected manually. Eventually, we identified 6 cores at 2 mm and 5 at 1. 3 mm. The properties of the cores we identified are listed in Table6. The sizes and fluxes are derived from ClumpFind, and the peak intensities and positions are measured from the continuum maps directly.
Results: To determine the transcriptional and chromatin state of the sex chromosomes after meiosis, we re-analyzed ten ChIP-Seq datasets performed on mouse round spermatids and four RNA-seq datasets from male germ cells purified at different stages of spermatogenesis. For this, we used the last version of the genome (mm10/GRCm38) and included reads that map to several genomic locations in order to properly interpret the high proportion of sex chromosome-encoded multicopy genes. Our study shows that coverage of active epigenetic marks H3K4me3 and Kcr is similar on the sex chromosomes and on autosomes. The post-meiotic sex chromatin nevertheless differs from autosomal chromatin in its enrichment in H3K9me3 and its depletion in H3K27me3 and H4 acetylation. We also identified a posttranslational modification, H3K27ac, which specifically accumulates on the Y chromosome.
Indeed, by ChIP-Seq, Kcr does not appear to cover more the X chromosome than autosomes  [39] , and H4K8_hib is even less present on the X chromosome than on autosomes  [41] ; this is despite the fact that both marks are enriched at the transcriptional start site (TSS) of many post-meiotic activated X-encoded genes (i. e. , genes which are upregulated in spermatids compared to spermatocytes)  [39, 41]. No conclusion could be drawn for the Y chromosome since, in the mm9 version of the mouse genome available at that time (mm9), only 15% of the Y chromosome was assembled (cf. Fig. 1c ). Therefore, in the present study, we sought to compare the composition of the sex chromatin with that of autosomal using ChIP-Seq and the last version of the mouse genome (mm10/GRCm38). We re-analyzed ten ChIP-Seq datasets performed on mouse round spermatids and showed that active epigenetic marks are similarly present on the sex chromosomes and on autosomes, but that the post-meiotic sex chromatin nevertheless differs from autosomal chromatin in its content in repressive chromatin marks and in histone acetylation.
We also identify a histone PTM, H3K27ac, which specifically accumulates on the Y chromosome, suggesting that regulation of the X and Y chromosomes may differ during sperm differentiation. In parallel, we analyzed RNA-Seq datasets from purified male germ cells to investigate the dynamics of XY gene expression during spermatogenesis, and in particular, after meiosis. We found that the X and Y chromosomes are enriched in genes expressed post-meiotically and display a higher proportion of spermatid-specific genes compared to autosomes though with a range of gene expression values starting lower than autosomal genes. Finally, we found that chromosome 14 and the sex chromosomes share some specific features, such as enrichment in H3K9me3 and the presence of clusters of spermatid-specific genes amplified only in rodents, suggesting that part of chromosome 14 is under the same evolutionary constraints than the sex chromosomes.
Conserved and specific features of the post-meiotic sex chromatin There have been several reports of enrichment of histone variants or histone posttranslational modifications on the PMSC based on immunofluorescence observations. This is the case of H2A variants H2A. Z and H2A. B3 (aka H2A. Lap1)  [24, 38] , H3. 3 variant  [22] , and of histone PTM H3K4me2  [19] , H3K4me3  [22] , H3K9me2  [19, 22, 25, 26] , H3K9me3  [22] , H4K16ac  [25] , histone lysine crotonylation Kcr  [39]  and histone 4 lysine 8 2-hydroxybutyrate H4K8_hib  [41]. ChIP-Seq analyses also highlighted the specific enrichment of Kcr and H2A. B3 at the TSS of a subset of activated X-linked genes after meiosis, as well as the depletion of H4K8_hib on the X chromosome  [38, 39, 41]. We sought to determine to what extent sex chromatin differs from autosomal chromatin by systematically re-analyzing 10 ChIP-Seq datasets of histone PTM performed in mouse purified round spermatids using the last version of the mouse genome (mm10/GRCm38).
This last version (mm10) is more complete than the previous version (mm9, NCBI built 37) especially regarding Y chromosome sequence (see Fig. 1c ) but is still scarcely used in the literature. Importantly, to include multicopy genes in our analysis, reads which mapped perfectly to several genomic regions were not ignored and were distributed to one location picked randomly. For each ChIP-Seq dataset, we compared the coverage, i. e. , total number of base pairs covered by the chromatin mark, on the X chromosome, the Y chromosome and all autosomes (Fig. 2 ; Additional file 1). We particularly compared coverage between the sex chromosomes and autosomes of equivalent length and number of genes (i. e. , chromosomes 3 and 6 for the X chromosome, and chromosomes 16 and 18 for the Y chromosome, see Table  1  and Additional file 1).
To confirm that, we studied a mouse model with a large deletion (>96%) of its Y chromosome (deletion of the male-specific region of the Y chromosome long arm, MSYq-males)  [8, 46]. In this model, the vast majority of round spermatids (59/65) displays a bright diffuse H3K27ac enrichment, without enrichment over the PMSC (Fig. 3 ; Additional file 4) confirming that H3K27ac enrichment is specific of the Y chromosome. Looking at other available round spermatid ChIP-Seq datasets for chromatin marks, we found that PMSC is particularly depleted in histone 4 lysine 8 2-hydroxybutyrate (H4K8_hib) in acetylated histone H4 (H4ac) and globally in histone acetylation (K_acetylation) (Fig. 2 ; Additional files 1, 2 and 3). This confirms previous observations that the X chromosome is depleted in H4K8_hib and H4K8_ac (histone 4 lysine 8 acetylation) in spermatocytes and round spermatids  [41].