dataesr/scientific-paragraphs-categorization huggingface dataset:
A priori assessment of the number of doses most suitable for BMD analysis indicates that three or more doses and a control are a reasonable starting point, although additional doses will typically improve the precision of the estimated PoD. The precision of the BMD can be indicated by the BMDL to BMDU ratio. The importance of being able to define a usable PoD metric for all datasets is underscored by the subsequent comparisons of BMDL values across the endpoints investigated. For example, the results presented in Table  II  suggest that significant increases in Dlb1 mutations in the small intestine occur at lower ENU doses than that required to elicit significant increases in LacZ transgene mutations (Table  II ). For MNU, significant increases in Hprt gene mutations in spleen tissue occur at lower doses in comparison with that required to elicit a significant increase in LacI transgene mutations (Table  III ).
There are no carcinogenicity studies for EMS (Carcinogenic Potency Database); therefore, it is difficult to compare carcinogenicity rankings. Nevertheless, the lowest genotoxicity BMDL 10 values for ENU and MNU, 0. 001 and 0. 85 mg/kg/day, respectively, are within two orders of magnitude as the reported TD 50 values for MNU (i. e. , 0. 0927 and 1. 23 mg/kg/day for rats and mice, respectively). Moreover, the TD 50 values reported in the Carcinogenic Potency Database (Table  V ) yield a ranking from most potent to least potent of MNU > ENU > MMS. This corresponds to the aforementioned ranking based on genetic toxicity BMDs, and provides additional support for the use of genetic toxicology PoDs in human health risk assessment.
g. , from 10 to 3) or even removed, based on the ability of the BMD approach to account for variability in the data as compared to, for example, the NOGEL. For example, a study with lower statistical power and greater variance would produce a lower BMD (and BPD/STD), but a higher NOGEL. If chemical-specific adjustment factors (CSAFs) are available for interspecies differences and human variability, their use also would be considered in the selection of uncertainty/safety factors (WHO/IPCS, 2005). Therefore, when considering methods for incorporation of PoD metrics into human health risk assessments, it is important to remember that some PoD metrics are more conservative than others. Moreover, the same uncertainty/safety factors would not necessarily be applicable when using the BMD compared to the NOGEL, and thus should not necessarily be applied in all instances.
Whether one uses the RfD approach or an MOE approach for genetic toxicity endpoints, the results can be evaluated with all the other available toxicity data to provide an improved and informed human health risk assessment. If determination of the PoD, and the subsequent comparative assessment indicates that genetic toxicity is the driving concern for human health considerations, then genetic toxicity data could become the basis for regulatory decision making. CONCLUSIONS 1. MNU and ENU both elicit sub-linear dose responses that yield PoD metrics for gene mutation and chromosomal damage endpoints in vitro and in vivo. 2. Among the methods/approaches investigated here, the BMD approach yields the most conservative PoDs (i. e. , BMDL 10 ). 3. The BMD 10 is comparable to, and recommended alongside, the BMD 1SD , as the most suitable metrics for defining PoDs for continuous genetic toxicology data.
4. The BMD method is the preferred PoD determination method, followed by the NOGEL method, the smoothing regression spline to determine the STD, and then the segmented methods to determine the BPD (now supersedes the L&L for BPD determination). 5. PoD metrics from genetic toxicology dose-response data, via the derivation of regulatory limits such as the RfD or risk management metrics such as the MOE, can be used for evaluations of human risk and regulatory decision making. Routine determination of PoD metrics for genetic toxicity dose-response data, and routine use of genotoxicity PoD values for regulatory evaluations of new and existing substances will require application of the preferred methodology (i. e. , the BMD approach) to a wider range of compounds with a diverse array of MOAs. We have already begun collecting and analyzing detailed doseresponse data for other recognized genetic toxicants.
ACKNOWLEDGMENTS The authors would like to thank  Dr. Wout Slob  ( RIVM ) and  Dr. Kenny Crump  for their expert input during the preparation of this manuscript. They would also like to thank the  HESI Genetic Toxicology Technical Committee  for its financial and intellectual support, and the International Workshop on Genetic Toxicology Working Group on Quantitative Approaches to Genetic Toxicology Risk Assessment (IWGT, 2013), who are developing two manuscripts that build upon this manuscript. Health Canada funding under the government of  Canada's Chemicals Management Plan; National Centre for the Replacement, Refinement and Reduction of Animals in Research  ( NC3Rs , contract number  NC/K500033/ 1 ) provided funding for  Dr. Johnson. Bristol-Myers Squibb  for providing us with previously unpublished data.
g. , dose standard error forming selection and dose spacing. Not com- the confidence monly defined. In Tables II and III it (90% CI) was calculated for only 8/34 datasets using L&L and 15/34 using segmented, furthermore when the prerequisites were considered, this was reduced to 2/34 and 3/34 respectively. STD Slope The lowest dose for mgcv STDL, STDU, P Plotting of the non-linear model is possibly Lower power tends to provide smaller Transition which the lower bound value too flexible compared to pre-defined PoDs; May be appropriate when Dose of the 95% confidence models; Model is still being developed mechanistic data are available; interval of the slope and validated; Ability to define an STD Unlike the BPD, can readily be com- exceeds zero is highly dependent on the study design, pared to other models e.
g. quadratic; e. g. , dose selection and dose spacing. In Less affected by distribution and Tables II and III it was calculated for variance only 15/34 datasets BMD 10 Benchmark A dose that produces a PROAST BMDL 10 , Requires consensus on appropriate biologi-Lower power tends to provide smaller Dose 10 10% increase over the BMDU 10 , cally relevant benchmark response PoDs a ; Fits function to entire dose- fitted background BMDU/BMDL (BMR); Continuous and quantal data are response, not just the tested doses; ratio modeled differently; Often produces very Currently used by many regulatory low BMDL metrics agencies; Suitable for use in compar- ing PoD metrics between different endpoints; Requires fewer data points than BPD and STD; Commonly defined. In Tables II and III it was calculated for 31/34 datasets BMD 1SD Benchmark 10% excess risk for BMDS BMDL 1SD , Requires consensus on appropriate biologi-Lower power tends to provide smaller Dose 1 individuals below and BMDU 1SD , cally relevant benchmark response PoDs; Fits function to entire dose- Standard above the 2nd and 98th BMDU/BMDL (BMR); Continuous and quantal data are response, not just the tested doses; Deviation percentiles, respectively ratio modeled differently; Comparisons Currently used by many regulatory between endpoints and historical datasets agencies; Requires fewer data points more influenced by background level than BPD and STD; Commonly and variance than the BMD 10 approach defined.
In Tables II and III it was calculated for 34/34 datasets NOGEL, no observed genotoxic effect level; BPD, breakpoint dose; BPDL/U, breakpoint dose lower/upper confidence interval; STD, slope transition dose; STDL/U, slope transition dose lower/upper 10 , benchmark dose 10, BMDL/U 10 , confidence interval; BMD 1SD , benchmark dose 1 standard deviation lower/upper confidence interval; BMD 1SD , benchmark dose 1 standard deviation; BMDL/U benchmark dose 10 lower/upper confidence interval. a.
Micronucleus: bone marrow cells, 7 days gavage. MNU ENU MMS EMS Gene mutation in vitro (mM) 0. 006 a 0. 68 a 4. 72 b 8. 70 a in vivo (mg/kg) 0. 0007 c 0. 09 d 1. 34 e 9. 29 f Micronucleus in vitro (mM) 0. 03 a 0. 17 a 1. 00 g 4. 35 a in vivo (mg/kg) 0. 02 h 1. 36 d 1. 74 i 56. 68 f Cancer bioassay in vivo (mg/kg/day) 0. 093 j 0. 95 j 31. 8 j Not available Bryce et al. (2010)10), TK6 cells, 24-30 hr treatment. h LeBaron (2009), Rat, Blood, 4 days gavage. i LeBaron et al. (2008), Rat, Blood, 4 days gavage. j Lowest TD 50 from the Carcinogenic Potency Database (http://toxnet. nlm. nih. gov/cpdb/). Values adjusted for differences in treatment duration.
Overview of ADCF ADCF is based on the IEEE 802. 15. 4 2. 4 GHz DSSS physical layer and classical superframe structure. On the one hand, ADCF proposes a distributed beacon scheduling mechanism in a beacon only period which spatially reuses the timeslots over 2-hop. On the other hand, the contribution of ADCF lies in a data slot allocation mechanism, which makes GTS possible in a mesh topology. Generally, the nodes can switch between two stages: initialization stage and working stage. In initialization stage, which is not using the superframe structure, the nodes access medium by unslotted CSMA/CA mechanism. In working stage, the time is divided into superframe and each superframe includes three parts: {BOP, Active Period, Inactive Period}. As shown in Figure  1 , BOP is organized by CFBS (Collision Free Beacon Slot).
In other words, we estimate the wireless link quality by beacon loss. Four states are defined for a link or a node connected by the link. 1) Preliminary. 2) Unconfirmed. 3) Confirmed. 4) Deleted. Therefore, node join or node failure here means the presence or disappearance of one node in its neighbours' NT. Both unconfirmed nodes and confirmed nodes are stored as 1-hop neighbours in NT. But only confirmed nodes are chosen as 1-hop neighbours when constructing beacon frame. Asymmetric links are possible and acceptable. In ADCF, both CFBS and CFDS mechanisms are over a 2-hop NT. If two neighbours connected with an asymmetric link choose the different slot to transmit the beacon or data frame, the collision could be avoided and that's what we expect. If two neighbours connected with an asymmetric link choose the same slot, they will transmit the beacon or data frame at approximate the same time.
Hence IEEE 802. 15. 4 spends more time for idle listening. In theory, no matter what topology, if BOPL is less than the active period, ADCF always consumes less energy than the IEEE 802. 15. 4 standard. As shown in Figure  3 , there are 7 sources with 1-hop traffic or 3 sources with multi-hop traffic. When Packet Interarrival Time decreases from 1. 0 s to 0. 1 s, the traffic load will increase. Therefore, End-to-end delay becomes larger. When Packet Interarrival Time is about 0. 4 s, there are radical changes caused by buffer overflow. With the current superframe (2 s) and active period configurations, the end-to-end delay for 1-hop traffic is about 1 s on average, including the time from packet generation to the scheduled data slot. ADCF has 0. 06 s advantage than IEEE 802. 15. 4. If a packet arrives just before its scheduled data slot, the node can send this packet immediately.
Both of them have the common features: 1) IEEE 802. 15. 4 2. 4 GHz transceiver. 2) 8-bit MCU. 3) There are 4KB of RAM and 60KB of on-chip Flash. Two tools are mainly used for debugging, measuring and evaluating the protocol performance in real conditions: the date-logger console of nodes and SNA (Sensor Network Analyzer) [29]. Daintree's SNA is known as an expert tool providing comprehensive solution for IEEE 802. 15. 4 and ZigBee. It includes a protocol analyzer software fitted to sensor networks and a sensor network adapter hardware. The sensor network adapter hardware is a powerful node, which can capture all the packets of a network but do not participate in this network. By SNA software that includes a protocol decoder allowing user to drill down to packet, field and byte level, we can view all network nodes and interactions simultaneously, without disturbing the original network.
It switches off the lighting when the user leaves the carpet. When the lighting is switched on/off, node @10A sends a message to node @105 containing a return state of lighting. Node @105 receives messages from node @106, @107, @108, @10A and logs actions in a journal. A voice synthesis software eSpeak [34] is also available and announces the reception of information from sensors (motion, carpet, etc. ). Several scenarios using the sensor information have been implemented; for example, when the sink detects the opening of the fridge for more than 10 seconds, the user can hear a voice alert. In the initialization stage or rebuilding stage, frames are transmitted immediately without any medium access control precaution. In working stage, frames are transmitted using CFDS. Figure  7  shows a result of the above network deployment.
From the ADCF journal and application journal displayed on the screen, we can see that each node can join or leave the network freely and the rest of the network works properly. Generally, the network rebuilding time is less than 10 s. At this moment, 6 nodes occupy 6 CFBS as they are all 1-hop neighbours. 5 CFDS are negotiated to transmit the corresponding application data. Thanks to a buzzer available on the nodes, which is activated for each data frame reception, we can verify that the bounded time is verified as expected. For example, end-to-end delay is always less than 2 s when the superframe is fixed as 1. 5 s. It satisfies our application requirements. In addition, the superframe with its slot allocation is totally consistent with what we proposed in Section 3.
Conclusions and Perspectives This paper presented an original MAC protocol ADCF based on the IEEE 802. 15. 4 standard. This protocol was designed to build and to maintain a wireless mesh sensor network providing QoS-guaranteed medium access and energy saving solution for home monitoring application. The work was organized in three phases: protocol  presentation, implementation and results from simulation and from prototype. In conclusion, the contributions of this paper are: 1) The 2-hop CFBS and CFDS mechanisms were proposed and described. CFBS enables beacon collision to be avoided and therefore efficient multi-hop mesh network to be formed. Thanks to CFBS, CFDS enables the application data to be transmitted in a bounded time without loss. A smart repair protocol was also proposed to improve the flexibility and robustness of a mesh network.
This network can always converge in about 10 s with the suitable parameters configuration and, thanks to the buzzers we can hear the reception of an application data on the sink in 2 s at most, which confirms that ADCF is verified in a real environment as expected. In the future, we will optimize the prototype such as enabling the evaluation of energy consumption of hardware and compare the results with the simulation. Another perspective is in the framework of cross-layer design, such as reuse the 2-hop NT, to reduce routing overhead and further optimize network performances. Finally, we will test various application scenarios and invite the elderly and the disabled to use the system in the smart home.
Steps 4 and 5: Assessment of reverse logistics scenarios and result analysis After one or more scenarios are chosen, the different performance indicators are calculated. A comparison between the scenarios is provided. The evaluation of the reverse logistics network is achieved via life cycle assessment software (SimaPro)  [33]. This is possible within the SuPLight project which aims at providing a collaborative platform  [34]  allowing the communication and exchange of information between different softwares such as the LCA software and the reverse logistics demonstrator. After the life cycle assessment of the different reverse logistics scenarios is received from the LCA software, a full analysis combing this assessment and the one achieved via the reverse logistics prototype (total cost of the network, quantity of recycled material, etc.
Reverse logistics prototype (demonstrator) The proposed method was the basis of the development of the software prototype. It has been designed with a flexible architecture in order to allow portability and interoperability with any external application. The software prototype implementation is based on the MVC software architecture pattern, using HTML5 and CSS to create the view and for the application logic the JAX-RS Java API for RESTful web services which are controlled by JavaScript (JQuery). The reverse logistics prototype is focused on reverse logistics scenario definition allowing the user to choose the partners of the reverse logistics network and their roles and visualize them on a map; define reverse logistics routes -a single route is characterized by flow and transportation data (the flow type and the quantity exchanged between two partners, transportation mean and distance); visualize the summary of the created scenario and the final route on the map; display LCA indicators for the current scenario; compare several scenarios on a radar graph based on the indicator value and the total RL cost; use the decision aid system to choose partners based on input criteria; determine recommended quantities for each route; and compute RL indicators for each scenario (total cost of the network, quantity of recycled material, etc.
Identifying potential partners Potential partners were identified for the three roles: collector, sorter, and recoverer. These and the related collected data are not shown in this article due to confidentiality issues. In total, 20 potential partners were identified. Step 3: Defining different reverse logistics scenarios Using the reverse logistics plug-in, two main RL scenarios were identified. These are presented in Figures  7, 8 , 9, and 10. In the figures, a coefficient is indicated for every selected partner. This is equal to the coefficient of output/input. It represents the percentage of the covered input the end of the activity achieved at this partner. The collector coefficient corresponds to the average percentage of Al in a car. The coefficient of sorting and recycling activities is recovered from AL recycling literature.
Genome based definition of transcripts In addition to the Emihu1 current assembly of the genome, the JGI website database includes Sanger sequencing reads that were not included in the current assembly. They are called unplaced genomic reads and consist of 95120247 bases (106669 N's and 95013578 A, C, G or T) in 161432 sequences. The unplaced reads were assembled using Newbler, resulting in 13227 contigs. To evaluate the possible contribution of these contigs to the transcriptome definition, the reads of Sample 10BE which had the lowest number of E. huxleyi reads (Table  1 ), were aligned to these newly assembled contigs. 742435 reads were mapped to the new contigs, as compared to 3. 51 million reads previously aligned to Emihu1. In view of the high number of reads aligned to the new contigs, they were assembled to the available genome using Minimus2  [13]  to create an improved genome version, Emihu1plus (Additional file 3).
Alignment of the two sequences is shown, with the segment from scaffold_11 as the query sequence. Duplication of almost the entire segment can be seen. 3. 56 million aligned reads due to advanced cell lysis (Table  1 ). After alignment, Cufflinks and Cuffcompare  [15]  were applied to all the TopHat outputs to define transcripts. In this process, 90921 potential transcripts were defined.
Transcripts were then constructed and extended as far as possible by running Blast. The sequence was then translated, and a BlastP search was run at NCBI to determine if the putative protein was closest to the target gene in other species, and if it had the proper domains. We found that in many proteins repetitive sequences interrupted the canonical domain composition, and in some cases the domains themselves. After the protein sequence was finalized, multiple alignments and phylogenetic trees were constructed with protein sequences from representative species to see that the sequence indeed belonged, who its closest relatives were, and in the case of multiple family members, to attempt to assign orthology. We compiled a set of 63 "gold standard" genes (Sequences in Additional file 4) that were used afterwards for quality control of the automated transcript assembly.
Validation of 18 of the sequences was performed by real-time PCR with primers designed to the manually constructed sequences or Western blot (Method of construction and validation status: Additional file 5). All of the sequences but two had reads in the RNAseq data of the current dataset, and the remaining two have reads in an additional dataset (not shown). The "gold standard" genes all have known functions, come from at least five different biological pathways, and are distributed in various cellular compartments. They include both globular and transmembrane proteins. They are a mix of short and long transcripts ranging from 569 to 4661 base pairs, with varying numbers of exons, ranging from single exon to 17 exon genes, and are expressed in varying levels according to the RNAseq data (Additional file 5).
First round of quality control using manually defined genes To assess the quality of the transcriptome reconstruction, presence of the 63 "gold standard" genes in the three transcript definition approaches was examined (Figure  2B ). The transcripts were compared to the standard genes using Blat  [17] , with a minimum hit score of 200, in order to ensure significant hits. Four of the genes had less than 10 reads in the RNAseq, and therefore could not be found in the readbased arms of the assembly. In the genome based transcript collection, of the 59 possible genes, eleven were missed. In the de novo assembly, twelve genes were missed. In the EST branch, 44 genes were found out of 48 with ESTs. The four that were missed either had only one EST, or nonoverlapping ESTs that were therefore not clustered.
Two of the four transcripts which did not have enough reads in the Figure  3  Manual gene definition procedure. The procedure of manual gene definition is presented as a decision tree, with the start and end in purple. The procedure starts from the choice of the target gene (purple circle, top middle), which was taken from three species, human, Arabidopsis thaliana, and yeast. The chart flows from top to bottom, with decision points in pale blue diamonds, and analyses in blue rectangles (database searches in bright blue, and other analyses in gray-blue). RNAseq were found in the EST branch, and two were not found at all.
Improving the transcript repertoire In order to define the genes missed by the Tuxedo suite  [14, 15]  (TopHat and Cufflinks), the Partek® Genomics Suite™ software was applied. Partek was used to find regions where reads aligned, excluding regions with Cufflinks transcripts (Figure  4A ). These regions were required to have a minimum length of 300 base pairs and a minimum coverage of 50 reads, as the default Partek settings are permissive. Of the 767778 "unexplained" regions defined by Partek, 10940 met the requirements (Figure  4A ). In comparison to our "gold standard" collection, Partek found only twenty-one genes, but that was due to the exclusion of previously defined transcripts. Of the twentyone, it found four that were missed by Cufflinks, two of which had also been missed by CLC Assembly Cell.