dataesr/scientific-paragraphs-categorization huggingface dataset:
Descriptive statistics of the transcriptomes In the present study, we analysed leaf and grain transcriptomes in eight parent-synthetic combinations that are based on two different genotypes of T. turgidum subsp. durum (Langdon and Joyau) and two different genotypes of Ae. tauschii (Ae. tauschii-87 and Ae. tauschii-109). The synthetics (Table  1 ) are named according to their parental combination (e. g. , Lx109, indicating a cross Langdon × Ae. tauschii-109), with the polyploid generation appended at the end (C1-C4 or S5; see Methods). We used Illumina technology to obtain between 66 and 124 million read pairs (2 × 150 bp) per RNA-Seq library, totalling 1. 37 Tb of data (Supplementary Tables 1, Additional file 2). The read mapping and summarization, together with the downstream analyses, were performed with two different pipelines in parallel (see Methods and Supplementary Tables 2, Additional file 2).
[29]. These data had been sampled from leaves of a T. turgidum subsp. turgidum genotype AS2255, Ae. tauschii genotype AS60, their allotriploid hybrid and first polyploid generation. A collection of DEGs we identified in these hybrid and polyploid samples forms a pattern that is consistent with the data produced by us. In particular, most of the DEGs are located on the D subgenome, without clear dominance of down-or up-regulation (Fig. 2 ), and with significant heritability across generations (Fig. 3c ). Our results confirm the previous observation that transcriptomic changes occur already in the inter-specific hybrid, i. e. before the whole-genome duplication  [29].
Subsequently, the synthetic and midparent RPM datasets were analysed with a GLM in edgeR, using a > 3 fold change and < 0. 01 FDR thresholds. Results show that the transcription level of most TE families in the synthetics is statistically similar to the MPVs (Supplementary Fig. 7 , Additional file 3). Nonetheless, several TE families (two families of Copia LTR retrotransposons, two families of CACTA DNA transposons, and one unclassified LTR retrotransposon family) appear significantly upregulated in some synthetic samples. In particular, RLC_famc4 and RLX_famc22 are upregulated in both Lx109-C2 and Jx109-S5 (grain); RLC_famc24 is upregulated in Jx87-S5 (grain); DTC_ famc15 is upregulated in Lx109-C2 (grain); and DTC_ famc41 is upregulated in both 109xL-C1 and Lx109-C3 (leaves). These TE families are not among those with significant parental differences (Supplementary Table  7 ), suggesting that a relaxation of TE silencing is indeed a likely explanation of the upregulation signals in the synthetics.
Similar numbers of DEGs were identified when lowconfidence genes were also considered (104-1,374 DEGs per synthetic; Bowtie2-pipeline; Fig. 2b ). Since these results are in a stark contrast with the previous studies, we have included the data produced by Hao et al. [29]. In our pipelines, these data yielded DEGs within the same range as our original samples (Fig. 2a, b ). Even after relaxing the DE thresholds (FDR < 0. 05, no fold change threshold, as in  [29] ), we detected only 203 and 261 DEGs (BOWTIE2-and STAR-pipeline, respectively) in the first allohexaploid generation, compared to 4,293 in the original study. This ~ 20x difference in the number of DEGs detected in the same dataset must stem from differences in the analytical procedures. We have identified several critical points that may lead to systematic biases (Supplementary Note) and we concluded that differences in library normalizations have probably the highest impact on these two analyses.
Read processing and transcript quantification Raw RNA-seq reads produced in this study (62 libraries) or previously  ([29] ; Sequence Read Archive IDs SRR3474187, SRR3406932, SRR3474179, SRR3474185, SRR2474176, SRR3474199, SRR3474194, SRR3474201) were processed with Trimmomatic  [43] , removing adapters, trimming low-quality regions and retaining only paired reads with a minimum length of 36 nucleotides each. Subsequently, two different read mapping and summarization pipelines were used for all samples. The 'STAR-pipeline' employed the intron-aware STAR aligner  [44]  to map the trimmed reads onto a merged reference consisting of the assemblies of the durum cultivar Svevo v2  [45]  and Ae. tauschii subsp. strangulata AL8/78 v4. 0  [46] , removing reads with non-canonical intron motifs and reads with > 3% mismatches against the reference.
The complete ABD reference was used for all samples (including the tetraploid and diploid parents; see Supplementary Note). Reads mapping to high-confidence genes were summarized with featureCounts  [47] , counting individual reads except multi-mapping reads (reads having multiple equally-good mapping choices in the reference genome) and multi-overlapping reads (reads overlapping multiple annotated features). We considered this pipeline to be optimal for the analysis of high-confidence genes, since the mapping procedure accounts for introns and the reference genome used is genetically closest to our plant material. However, this pipeline cannot evaluate the transcription of TEs, since TEs are not annotated on the Svevo assembly. To compensate for this shortcoming, we also mapped all datasets onto the Chinese Spring assembly.
In silico karyotype check and data consistency checks Euploidy of karyotypes was determined in silico for each polyploid sample on the basis of median transcription over large chromosomal fragments. For this purpose, raw read counts produced by the STAR-pipeline were converted to TPMs (transcript per million). Lowly expressed genes with TPM > 0. 01 in less than two samples were removed, analysing seed and leaf transcriptomes separately. Parental genotypes (Langdon, Joyau, Ae. tauschii-87, Ae. tauschii-109) were presumed to be euploid, and their TPMs were averaged across replicates and used as a reference. Median TPM values were calculated for windows of 200 position-ordered genes for each polyploid sample and the reference. Corresponding median values were then compared in a ratio (sample/reference), and the resulting ratios along each chromosome were plotted on graphs.
Under the expectation of euploidy, the obtained ratios should be close to one. Aneuploidy or smaller scale changes (e. g. , loss of larger chromosomal fragments) should be indicated by values deviating from one (~ 0. 5 for monosomy; ~1. 5 for trisomy). Additionally, data consistency was checked via a clustering heatmap and a principal component analysis (PCA). The PCA was computed with RPMs in edgeR. For the heatmap, a matrix of Pearson's correlation was calculated from log-transformed RPMs (bowtie2-pipeline; increased by + 0. 001 to enable the transformation) for all pairwise combinations of the samples, and the data was clustered with the UPGMA algorithm.
Acknowledgements We thank  Joseph Jahier  ( INRAE-UMR IGEPP ) for producing and sharing some of the wheat synthetics. We thank the  VégéPôle Céréales platform (INRAE, Clermont Ferrand)  for their assistance with plant cultivation and sampling. We also thank  Centre de Ressources Biologiques Céréales à paille  ( INRAE, Clermont Ferrand ) for providing the seeds for the experiments. We are grateful to the  Mesocentre Clermont Auvergne bioinformatics platform  (https://doi. org/10. 18145/aubi)for providing computing and storage resources.
Funding This project was funded by  INRAE BAP division (projects SeedEX, SeedENCODE and MethylWheat)  and  Fonds de Soutien à l'Obtention Végétale  (FSOV 2018 H, project  PolyBléD ). This article was supported by the  Auvergne-Rhône Alpes Region  (Project Pack Ambition Research 2018 -TransBlé N°  18 012004 01 ).
The sample was located between the RF coil and marble plate, on top of which the Peltier element was located. The temperature was monitored using PT100 sensor. The authors also introduced the second design for the temperature range of 35 C to 60 C. In this design, the temperature was controlled by a heater mat, contrary to the Peltier element. Apart from that, the design is quite similar to the first one. In both devices, the heating/cooling was controlled using PID controller, and the temperature was stabilized up to AE 0. 5 C. The second approach was proposed by Williamson et al  [38] [39] [40]  and was designed to measure data from ex vivo of mouse spinal cord samples. The chamber was designed to keep the spinal cord alive for many hours without a flow of oxygenated artificial cerebrospinal fluid.
| Design of the variable-temperature device We have built our device to be compatible with NMR-MOUSE PM 25 device (Magritek, Aachen, Germany) working with Kea2 console (Magritek, Aachen, Germany), which is operating using Prospa (Magritek, Aachen, Germany) software, that allows modification and writing of custom acquisition macros. The variabletemperature device consists of the main modules: temperature controller (PID UR32742A, Wachendorff Prozesstechnik GmbH, Geisenheim, Germany), inline air heater (AHP-5052 OMEGA Engineering, Manchester UK), and 3D printed sample holder (CAT box). The PID is controlled from the same computer that controls the NMR spectrometer using RS485 protocol (USB to RS485 Serial Converter Cable USB-RS485-WE-1800-BT, Future Technology Devices International Limited, Glasgow, UK).
A simple Python script called from Prospa software sets and checks the temperature on PID. The controller utilizes information from the PT100 temperature sensor (Honeywell, Charlotte, North Carolina, USA) located inside the CAT box and controls the current going through the inline air heater. The latter can be fed either with cold air from SmartCooler™ BCU-I (Bruker, Germany), room temperature air from the compressor, or cold nitrogen gas from an evaporator. The choice of the gas source depends on the desired temperature range, for example, using BCU we can reach temperatures as low as À20 C. The temperature range can be lowered by using a nitrogen evaporator and also by adding thermal insulation to the setup. The highest temperature is limited by the melting point of the 3D printed container (150 C for polylactic acid and 200 C for acrylonitrile-butadiene-styrene).
ACKNOWLEDGMENTS Financial support from the  H2020 European Research Council  (project number  772110 ),  H2020 Marie Skłodowska-Curie Actions  (Grant  896824 ),  Academy of Finland  (Grants  321701  and  340099 ),  National Science Centre, Poland  (Grant  2021/41/B/ST4/01286 ), the  University of Oulu  ( Kvantum Institute ), and the  KAUTE Foundation  is gratefully acknowledged. Part of the work was carried out with the support of the  Centre for Material Analysis, University of Oulu, Finland. The authors would like to thank Kitku the cat for posing for the TOC graphic.
Conduct of the Study The study protocol will be proposed to all patients' legally appointed representatives when we receive a PET-CT request to assess global brain metabolism during coma. Following signatures of informed consent, subjects meeting all inclusion criteria and no exclusion criteria will undergo a PET-CT session under a bolus plus infusion protocol. This protocol allows for a final standard interpretation (summed images) and produces temporal data we will use to functionally interpret the exam. Each legal representative will be asked to participate in the emotional auditory stimulation during the exam. Clinical assessment of CRS-R will be retrieved from medical records at 6, 12 and 18 months.
Remote Syringe Pump Prototype To control the injection sequence, we developed a prototype based on a commercial electronic syringe pump (Agilia Injectomat, Fresenius Kabi (Bengaluru, India)). We therefore developed a remotely controlled system to safely operate it. The system was built around a Raspberry Pi 3 Model B+ (Raspberry Pi Foundation (Cambridge, UK)). This small and affordable single-board computer was originally designed to promote the teaching of basic computer science. However, its low cost, modularity, and open design have prompted its wide use in robotics, for example. Whole inputs (filling sensor, camera) and outputs (servomotors, relay board) are connected to its dedicated interface: a general-purpose input-output (GPIO) connector. Its operating system is based on Linux, and it can be controlled with a mouse and a keyboard via a remote display.
PET Data Acquisition All PET scans will be performed with a Siemens Biograph mCT20 flow (Siemens AG, Munich, Germany). The patient's medical team will be instructed to stop enteral or parenteral nutrition at least 6 h before scanning. Patients will receive an i. v. line for tracer application. Blood glucose will be measured before scanning with a glucometer before patient installation in the PET room. PET data will be acquired in list mode. Acquisition begins at t = 0 s. To improve the signal-to-noise ratio for early time frames, we used the administration of a 20% bolus prior to constant infusion  [22]. The syringe pump prototype is programmed to perform a 20% bolus of the 18 FDG total activity at t = 10 s, which is followed by continuous infusion until the end of the acquisition. List-mode data will be reconstructed with an ordinary Poisson-ordered subset expectation maximization algorithm (OP-OSEM, 3 iterations, 21 subsets) with a frame duration of 30 s (note that this setup can be modified later to optimize the sensitivity of the PET analysis).
Contributions and Paper Organization The definition of the optimal trajectory of an UAV to collect data from buried sensor nodes is an emerging application that, to the best of our knowledge, has not yet been addressed in the literature. The contribution of this paper is to propose an efficient algorithm, fast and easy programmable, to solve the CETSP applied to this application. The algorithm is decomposed of three steps, namely the definition of new target locations in case of intersections, the use of an evolutionary algorithm to find a sub-optimal order, and finally an iterative geometrical reduction to significantly reduce the trajectory length. This algorithm is programmed in a software developed in C++. In comparison to the previously presented work, this algorithm combines the resolution of a combinatorial problem (TSP) with geometrical heuristics, applied to a problem including the possibility of intersections.
Preliminary Test The algorithm is first tested on a situation with a limited node numbers to easily highlight the different steps of the algorithm. To that end, 15 nodes are considered. That leads to 14! 8. 10 10 route possibles. The coordinates of the nodes are given in Appendix A, Table  A1 , and presented in Figure  10  (screenshot of our software developed in C++). A1. We first applied the evolutionary algorithm on the data without taking account the intersections and communication ranges. The parameters are a population of 100 combinations, 25 groups, and stop criteria defined as 1000 iterations. The result is the node order "15 6 4 8 12 3 2 1 5 11 9 10 13 14 7" leading to a total route length of 4635 m, see Figure  11. The proposed algorithm in three steps is then applied. In step 1, the fact to take into account the intersections leads to reduce the node number to 13 as depicted on Figure  12a.
In particular, if the evolutionary algorithm is applied directly without geometrical heuristics on the 50 nodes, the obtained route length was 15,249 m. That distance was reduced by 28. 4% (10,910 m) if the complete algorithm is applied. This algorithm has been implemented in a software developed in C++. The advantage of this algorithm is to have a low implementation cost. It is easily programmable without the need of external libraries. The inputs, displayed in a geo-referenced map, are the GPS coordinates of the target locations with their respective communication ranges. These coordinates are first converted into Cartesian ones, to apply next the algorithm. The evolutionary algorithm proposed in this paper (genetic algorithm), as well as the geometrical heuristics, are easy to program and deliver a sub-optimal solution in a few seconds.
Our framework provides a generic DRAM model capturing a set of controller properties as proof obligations, which all implementations must comply with. We focus on properties related to the assertiveness that timing constraints are respected, every incoming request is handled in bounded time, and the DRAM command protocol is respected. We refine our specification with two implementations based on widelyknown arbitration policies-First-in First-Out (FIFO) and Time-Division Multiplexing (TDM). We extract proved code from our model and use it as a "trusted core" on a cycle-accurate DRAM simulator.
Different than model checking, deductive verification allows modelling of more complex systems, since computation does not dependent on the number of possible states. In other words, more often than not, model checking approaches do not scale well for models with large/unbounded number of elements and have to apply abstractions to keep the state space manageable. More specifically, we use Coq, 1  an interactive theorem prover. It allows its user to write specifications, implement them, and discharge proof obligations through the aid of proof scripts. We choose Coq over other prove assistants for a variety of reasons-largely based on the points made by  Chlipala (2022)  in the book Certified Programming with Dependent Types: 1. Coq's specification language supports Higher-Order logic-which allows us to enjoy the benefits of conventional functional programming languages.
Novel Contributions -We propose a framework written in Coq that offers a higher degree of trust w. r. t DRAM controllers design. It contains a generic and reusable model of memory controllers in the form of a formal specification that can be refined through actual implementations. The specification carries correctness criteria as proof obligations. -We refine the specification with two instances: one based on First-In First-Out (FIFO) arbitration and the other on Time-division multiplexing (TDM). For both, proof obligations are met and certified by Coq's kernel. The proofs written for these two instances serve as a model for future implementations and uses of the framework. -The framework cleanly separates the specifications and proofs from the implementation, which allows us to extract executable code from Coq that can be used as a "root of trust" in a cycle-accurate DRAM simulator.
-The fact that there is no formal link between system implementation and the mathematical abstractions used to perform timing analysis may also introduce untrustworthiness. et al. (2019)  model the DRAM timing protocols with timed Petri Nets and generate executable code that can be used for simulation-based validation. However, as their approach only takes the device's timing constraints into consideration, it cannot handle system properties, such as the worst-case latency or the protocol correctness. Li et al. (2016)  use timed automata (TA) models of a memory controller to derive the Worst-Case Bandwidth (WCBW) and Worst-Case Response Time (WCRT) through the Uppaal model checker  Larsen et al. (1997). However, when performing worst-case analysis, in order to keep the state-space limited, they assume that each requestor has at most one outstanding request, i.
Several research groups and companies are promoting the use of Coq for the development of trustworthy hardware. Researchers at Google have developed a plug-in replacement of the cryptographic core of the OpenTitan 4  silicon root of trust. The hardware is implemented in Cava 5  -a Domain-Specific Language (DSL) based on Lava  Bjesse et al. (1998)  and embedded within Coq. Cava allows to leverage Coq's proof-engine and to derive SystemVerilog code. Kami, a similar system, was first developed by  Choi et al. (2017)  and later adopted by SiFive. It has its roots in Bluespec, 6  which also serves as the target language derived from Kami. The verification procedure in Kami is based on proving that modules refine a given specification based on trace inclusion, i. e. , the specification defines traces of observable events which have to be respected by its implementation-an approach similar to the one we adopt in this paper.
A brief introduction to Coq In the rest of the work we will heavily rely on Type Classes. While presenting details about Coq's proof engine is out of the scope of this work, we will try to give an intuitive explanation of these type classes and their use. A type class in Coq is, in some regards, similar to abstract classes or interfaces from object-oriented programming languages-it allows us to specify a set of members. Listing 1 shows an example of a container-similar to the Container 9  and Collection 10  concepts in C++ or Java, respectively. Listing 1: Interface specification for a container/collection. Class container_t {T C : Type} := mkContainer { append : T → C → C; (* append element to container *) contains : T → C → bool; (* is element in container? *) size : C → nat; (* get number of element in container *) app_inc : forall (x : T) (c : C), size (append x c) = (size c).
Coq, however, allows to make the expected behaviour explicit-as illustrated by the members app_inc and app_in. These members are proof obligations (POs), i. e. , properties that every instantiation of the container class has to respect. They indicate that the size of a container should increment by one when an element is appended and that a newly appended element always has to be present in the container. The usage of type classes is similar to classes in 1 3 Real-Time Systems (2023) 59:664-704 conventional programming languages-they can be used as parameters in functions and other type classes, where its members can be used. Furthermore, the Coq command Context allows us to introduce variables in the local context-which behave like abstract instances, i. e. , their members (functions and logical propositions/POs) can be used in the local context, even if concrete instances do not yet exist.
As an example, as shown in Listing 1, we introduce the types Q and R in the local context, followed by X, which is an abstract instance of container_t using the previously defined types Q and R. We can now use the member functions of X-the abstract instance of container_t-to write other functions, such as concatenate_list (Listing 2). It takes a list of type seq Q 11  and recursively appends its elements into a variable of type R (previously defined in Listing 1). Note how the append function is used within the function. Moreover, we can prove properties about concatenate_list, as shown by the Theorem size_concatenate in Listing 3-a theorem stating that the size of a container resulting from concatenating a list to a container is the sum of the sizes of each. The code between commands Proof and Qed is a proof script.
e. , exactly i ⋅ SN ⋅ SL cycles after t c. (Induction over i) Step 4: Request_processing_starts We use steps 1, 2, and 3 to prove that if ra arrived at t a , then it will get to the head of P(t x , A) at t x (Listing 17). Note that the proof script consists of using the Coq tactic apply, which simply makes use of existing lemmas. (Follows directly from steps 1,2, and 3) Step 5. 1: Request_slot_start_aligned We prove that the internal counter (represented by TDM_counter of type Counter_t) is equal to 0 at t x , i. e. , t x marks the beginning of a slot. (Follows from modulo arithmetic on TDM_ counter from Coq's arithmetic libraries) 1 3 Step 5. 2: Request_starts We prove that, for any given t, if ra is the head of P(t, A) and TDM_counter equals 0, then the request starts to be processed the very next cycle.