dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and Discussion The results obtained were processed separately: on the one hand, mechanical data (load, displacement, time) led us to identify dynamic moduli, see Equations (  1c ) and (1d), as functions of the testing temperature  0 and of the loading frequency  0 ; on the other hand, IR data gave us the temperature variations of the specimen inside the environmental chamber due to the cyclic loading. 3. 1 Estimates of dynamic moduli  ′ and  ′′ Classically, DMTA is used to identify the evolution of dynamic moduli. All devices directly provide  ′ and  ′′ moduli (or equivalently  ′ and tan ), as they are equipped with a specific processing data software. In the present case, we decided to process the raw stress and strain data recorded as a function of time, the dynamic moduli have thus been estimated using a simple least-squares method of the stress and strain signals.
Series of frequency sweeps gave the evolution of  ′ and  ′′ shown in Figure  4  and presented as functions of the loading frequency  0 and environmental chamber temperature  0. These data were extracted from (Yadav 2019). It was observed that the evolution of  ′ and  ′′ have the same trends for both materials (PS and PA6. 6): for the used frequency range,  ′ is increasing with the increasing frequency whereas  ′′ is decreasing. On the other hand, an increase in the testing temperature will tend to decrease the storage modulus and increase the loss modulus. Finally, it is worth mentioning the difference in the order of magnitude between  ′ and  ′′ , loss moduli being, roughly speaking, dozens of times weaker than storage moduli. Naturally, this type of result is very common in the literature and widely used in the polymer material characterization centers to construct master curves associated with the famous, and sometimes controversial, time-temperature superposition principle  (Sokolov and Hayashi 2007).
Acknowledgements The authors are grateful to  S. Castagnet  ( CNRS, Université de Poitiers, ISAE ENSMA, Poitiers, France ) for fruitful discussions and her friendly support,  P. Yadav  ( LMGC, Université de Montpellier, CNRS, Montpellier, France ) for his help during the testing campaign,  G. Robert  ( Solvay Engineering Plastics, Lyon, France ) for providing the PA6. 6 samples,  J. J. Robin  ( ICGM, Université de Montpellier, CNRS, Montpellier, France ) with whom we conducted our first DMTA tests and DSC measurements and  N. Le Moigne  ( PCH, IMT Mines Ales, Ales, France ),  S. Corn  ( LMGC, IMT Mines Ales, Ales, France ) and  V. Placet  ( FEMTO-ST Institute, Université Bourgogne Franche-Comté, Besançon, France ) for the DMTA comparative test campaign.
Cellular Internalization 3. 5. 1. Flow Cytometry with AMNIS Imaging Cells were seeded in 25 cm 2 culture flasks at the determined density and cultured for 36 h. Cells were then treated with 2H-TPyP-arene-Ru or Zn-TPyP-arene-Ru at the determined IC 50 values. After 1-4 h incubation, the natural fluorescence of 2H-TPyP-arene-Ru (excitation: 405 nm; emission: 650 nm) or Zn-TPyP-arene-Ru (excitation: 561 nm; emission: 655 nm) was analyzed using flow cytometry coupled with AMNIS ® image analysis, and the data were analyzed with IDEAS ® software (version 3. 0; Merck).
Confocal Microscopy Cells were seeded for 36 h in an incubation chamber (ibidi µ-Slide 8 well; Clinisciences, Martinsried, Germany) coated with a gel containing acetic acid (20 mM) and type I collagen (3 mg/mL). Cells were then treated at the determined IC 50 values. Photographs were taken using a Carl Zeiss LSM 510 confocal laser microscope. Meta-×1000 (Marly-le-Roi, France). Co-localization was analyzed using the ImageJ software (version 1. 54f).
Quantitative Analysis of Activated Caspases-3/-7 Cells were seeded in 96-well plates at the determined density and cultured for 36 h. Cells were then treated or not with 2H-TPyP-arene-Ru or Zn-TPyP-arene-Ru at the determined IC 50 values. After 24 h incubation, the culture medium was replaced by the corresponding phenol red-free medium, and the cells were then irradiated or not. Cells were then treated with caspases-3/-7 green reagent (5 µM) and placed in the IncuCyte ® S3 live cell analysis system (Sartorius, Dourdan, France). Every 2 h, cells were imaged at a rate of 4 images per well in phase contrast and green fluorescence using ×20 magnification. The number of cells in apoptosis was quantified with IncuCyte ® software (version 2022A Rev1 ; Sartorius) using the ratio of the number of fluorescent cells.
The modern technological innovations provide small radios with ability to broadcast over vast areas with minimum energy consumption that will significantly influence the future of the Internet of Things (IoT) communications. The majority of IoT implementations demand sensor nodes run reliably for an extended time. Furthermore, the radio settings can endure a high data rate transmission while optimizing the energy-efficiency. The LoRa/LoRaWAN is one of the primary Low-Power Wide Area Network (LPWAN) technology that has highly enticed much concentration recently from the community. The energy limits is a significant issue in wireless sensor networks since battery lifetime that supplies sensor nodes have a restricted amount of energy and neither expendable nor rechargeable in most cases. A common hypothesis in previous work is that the energy consumed by sensors in sleep mode is negligible.
Recently numerous investigation illustrated the power usage and current level of wireless sensor nodes in LoRaWAN networks without proposing an energy model to determine and enhancing energy consumption and battery lifetime  [28, 29, 30]. The authors in  [31]  proposed an energy consumption model for LoRaWAN devices. They determine the energy consumption for different devices, regardless of the network behaviour. Determination data are obtained employing the existing common LoRa hardware platform, MultiConnect mDot, based on the SX1272 transceiver. In contrast with  [31] , our proposed work estimates the energy cost and also evaluates the energy efficiency of LoRaWAN networks, considering the network with massive number of end nodes. The main goal of this research is to gain insight into competing LPWAN technologies, especially with regards to power consumption, which can assist IoT developers make well-informed decisions when choosing internet enabling technologies.
CPU -Processing unit: is composed of central processing unit (CPU) and the memory. The processing unit is responsible for the data processing and operation of the whole sensor node, storing the collected data of this node and the data sent by other nodes. Our study in this paper uses embedded system that is based on the STM32L073 microcontroller from ST Microelectronics  [1]  because these microcontrollers can be optimized for very low power consumption. -Communication unit: is responsible for wireless communication with other sensor nodes, exchanging control messages and receiving and receiving data. Our model based on LoRa/LoRaWAN Semtech Sx1272 transceiver. The current usage in each state and supply voltage is taken from the datasheet of the SX1272 in [43].
-As considered in  [48, 49]  , the processing unit is in on-state along the working sequence. The presumption can enhance optimizing the MCU unit by constructing it in low-power modes through most of the activity cycle. -A constant time duration characterizes each step of the sensor working sequence. -The radio module sends a packet of data at a specified transmission power level. The energy consumed in sleep mode is calculated as: E Sleep = P Sleep × t Sleep (14) Where P Sleep and t Sleep are the power consumption and duration in sleep mode, respectively. The energy consumed in active mode can be determined as: E Active = E W U + E m + E proc + E W U T + E T x + E Rx + E SP (15) Where the energies from the states in Figure  5  are described. The energies are determined the same way the energy in sleep mode by multiplying their power consumption with their duration as follows: E proc = I proc × V × t proc (16) E T Rx = E T x + E Rx = I T Rx × V × t T Rx (17).
Analysis the Proposed Scenarios In this section, we will estimate and simulate the performance of our energy consumption model using Class-A dense LoRaWAN network consisting of a single gateway and various end nodes. The presented range is sufficient for our application, and this enables for saving the use of the battery. The uplink transmission of the end nodes is based on the ALOHA protocol. Furthermore, scenarios are proposed for the sensor node battery usage acceleration and transmit therefore the modules send data are every 30 s. This leaves the device with three possible message transaction scenarios illustrated in Figure  6  as follows: -Scenario 1: An unacknowledged transmission, where both receive windows are ignored. -Scenario 2: An acknowledged transmission, where only one receive window is decoded Rx2.
-Scenario 3: An acknowledged transmission, where only one receive window is decoded Rx1. The sensor node implements acceleration measurement and sends the acceleration value every 30 s. The operating frequency considered for the microcontroller is equal to 4 MHz. Table  3  illustrates the power and time parameters of the model. These parameters are given in the datasheets of BMA220, STM32L073 and SX1272 [43,  46, 47]. In this scenario, we suppose that the sensor node has not received RX1 and RX2. The main energy consumers are the micro-controller unit, the sensor unit and the transceiver unit. Suppose the down-link message is lost for any reason. The LoRa specification recommends sending packets up to 8 times. In this scenario, we consider that the sensor node transfers data to the gateway then receives an acknowledgement RX2 without receiving the RX1 response to verify that the transmission was successful.
The energy consumption by the communicating sensor illustrates in Figure  9. As shown, the distinction from Scenario 1 is the dissipated energy by the LoRa receiver Rx2 (ER = 0. 42 mJ) and the consumed energy by the MCU unit. Figure  10  presents the sensor node lifetime using the battery characteristics (capacity equals 50 mAh and supply voltage of 3 V). The sensor node autonomy is about at 139 days when the measurement period is equal to 30 seconds shorter than Scenario 1. For this scenario, we assume that the sensor node transmits data to the gateway and then receives RX1 acknowledgment excluding the RX2 acknowledgment to verify the transmission success, which means that it will consume more energy than scenario 2. The dissipated energy by the communicating sensor is given in Figure  11.
We note that the consumed energy is half that consumed by the LoRa receiver Rx1 (ER = 0. 21 mJ). The sensor node lifetime is illustrated in Figure  12  using the battery characteristics (capacity equals 50 mAh and a supply voltage of 3 V). The sensor node autonomy is about at 164 days when the measurement period is equal to 30 s. According to the theoretical lifetime of an end-device is computed, employing average energy consumption results acquired for unacknowledged transmission and acknowledged transmission by using Equations (  18 ), (  19 ) and  (20). Figure  13  and Figure  14  shows the results of energy consumption and battery life in different scenarios, respectively. The sensor node lifetime in the ideal case (data transmission with acknowledgement reception and without transmission error), so it can be concluded that the more energy is consumed from the sensors and gateways, the shorter the battery life is.
Abbreviation Internet of Things (IoT), Low-Power Wide Area Network (LPWAN), radio-frequency identification (RFID), Quick Response (QR), machine-type communications (MTC), Chirp Spreading Spectrum (CSS), coding rate (CR), bandwidth (BW), the network server (NS), spreading factor(SF), the time-on-air (ToA), Carrier Frequency (CF), packet error rate (PER), the number of payload symbols (N P load ), number of payload bytes (P L), cyclic redundancy check (CRC), the number of preamble symbols (n preamble ), the packet duration (t packet ), the transmission power consumption (P T X ), the energy consumption per transmission (E packet ), the number of data transfers per day (N msg ), the current sending cycle (T C ), analog to digital converter (ADC), central processing unit(CPU), The total consumed energy (E T OT AL ), the energy consumed when the system is active (E Active ), the energy consumed when the system is in sleep mode (E Sleep ), the power consumption in sleep mode (P Sleep ), the duration in sleep mode (t Sleep ), the lifetime of the devices (LT ), the battery capacity (E Bat ), the energy consumption per day (E day ), the number of transactions (n msg ), the battery capacity (C Battery ), the battery life (D BL ).
Introduction The era of fungal genomics began in 1996 with sequencing of the baker's yeast (Saccharomyces cerevisiae) genome  [1]. In 2003, the genome of the first filamentous fungus, Neurospora crassa, was sequenced  [2]. These two milestones in genome research provided the impetus for subsequent fungal work and by 2010, the genome sequences for more than 100 fungi were available in public databases  [3]. Towards the end of 2023, one of the main repositories for fungal genomes, MycoCosm Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions, or products referred to in the content.
One such species is the pine pitch canker pathogen, Fusarium circinatum, for which the whole genome sequences of 17 strains are currently available in the database of the National Center for Biotechnology Information (https://www. ncbi. nlm. nih. gov/; accessed November 2023). Together with many other socioeconomically important species, this pathogen forms part of the so-called Fusarium fujikuroi species complex (FFSC)  [15]. It can infect more than 60 Pinus species at all growth stages and is globally regarded as one of the most important risks to pine-based forestry enterprises  [16]. The pathogen typically causes chlorosis and the development of large resinous cankers at infection points on the trunks and branches of trees, often leading to dieback  [17]. In the case of younger plants, especially in seedlings in commercial nurseries, infection by F.
Genome Sequencing and Assembly DNA was extracted from F. circinatum strains FSP34 and KS17 as described previously  [38]. These DNAs were then subjected to ONT MinION sequencing, as well as Illumina HiSeq 2500 sequencing of a 550bp paired-end library (Macrogen, Seoul, Korea). The data produced by the two systems were then used in a two-step process to compile the respective genomes into assemblies. First, the ONT MinION reads were trimmed and assembled using Canu v1. 7. 1  [39] , with the 'correctedErrorRate' setting adjusted, until assemblies were obtained that displayed the expected macrosynteny known in the FFSC  [12, 25]. The latter was determined using the LASTZ v1. 02. 00 plugin  [40]  of Geneious v 7. 1. 9  [41]. The second step utilized the quality-filtered Illumina reads (>18 bases) returned by CLC Genomics Workbench v 8.
Evaluation of Genome Quality and Completeness Assembly completeness was estimated using the Benchmarking Universal Single-Copy Orthologs (BUSCO) v3 tool, with the "Sordariomyceta" dataset  [47]. We also investigated the level to which improved sequencing technologies allowed for the enhancement of the FSP34 and KS17 assemblies. The analyses included the previous  [19]  and current assemblies for FSP34 (denoted as FSP34previous and FSP34current), and the previous and current assemblies for KS17 (denoted as KS17previous and KS17current).
Compilation of Ecological and Biological Metadata for FSP34 and KS17 To complement the high-quality genomes generated in this study, all relevant source and biological data for the two strains were collected from literature. For this purpose, we focused on their origins in terms of geography, host species, tissue type, as well as the population genetics of their source populations. We also collected information generated using previous laboratory-based experimentation regarding their reproductive biology. In the current study, we additionally investigated these two strain's pathogenicity and growth rate. For this purpose, pathogenicity tests using six-month-old Pinus patula were conducted as described before  [60]. Mycelial growth of the two strains were evaluated at a range of temperatures (10°C to 35°C, at 5°C intervals) on potato dextrose agar medium (20% w/v PDA [Biolab] and 5% w/v agar [BD Difco]).
Chromosome-Level Assemblies for FSP34 and KS17 This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession AYJV00000000 and LQBB00000000 for F. circinatum FSP34 and KS17, respectively. The respective assemblies were 45 020 843 and 44 380 849 nucleotides in length (Table  1 ). For FSP34, this represented an increase in genome size, with the FSP34previous being 1. 02% smaller than the one reported here (i. e. , FSP34current). The opposite was observed for KS17, with the highly fragmented KS17previous assembly being 1. 04% larger than the KS17current assembly. In terms of G+C content, a substantial difference was also observed between the old and new KS17 assemblies and not those for FSP34. G+C content values of 47. 41%, 47% and 47. 26% were obtained for the FSP34previous, FSP34current and the KS17current assemblies, respectively, while the KS17previous had a G+C content of 44.
69%. This could be explained by the G+C bias of the SOLiD technology used in the KS17previous assembly  [61]. 6  2. 81% 8. 75% -8. 60% Genome completeness 7  94. 8% 97. 3% 76. 2% 98. 1%  1  Wingfield et al. [19]. 2 Van Wyk et al. [20]. 3 Annotated using MAKER  [18]. 4 Annotated using WebAUGUSTUS  [20]. 5 Scaffolds were never assigned to chromosomes  [20]. 6 Determined using REPET v2. 5  [58, 59]. Assembly of KS17PREVIOUS was too fragmented to determine the repeat content. 7 Based on BUSCO v3. 0. 2 using the "Sordariomycete" database  [47]. Both new assemblies displayed N50 values exceeding 4. 3Mb (i. e. , contigs ≥4. 3Mb in length cover at least 50% of the assembly). This high contiguity in the two assemblies gave rise to FSP34current and KS17current assemblies containing substantially fewer scaffolds (49 and 96 scaffolds, respectively) than the previous assemblies (Table  1 ).
Direct comparisons between the new and the older versions of the KS17 assembly could not be made as the annotations for the older assembly utilized software lacking the same capabilities as MAKER  [20]. Nevertheless, similar gene densities were estimated for the FSP34current and KS17current assemblies, and this similarity was also generally extended to individual sets of pseudomolecules (results not shown). Overall, pseudomolecule 10 had the highest gene density (i. e. , 377. 20 genes/Mb for FSP34current and 382. 52 genes/Mb for KS17current) while it was lowest on pseudomolecule 12 (i. e. , 325. 67 genes/Mb for FSP34current and 294. 02 genes/Mb KS17current). Of the genes predicted for the FSP34current assembly, only 642 (4. 14%) lacked relevant BLAST hits to any known protein (Table  S4 ).
The sortable silt mean grain size ( SS; i. e. , the mean of the 10-63 μm size fraction;  McCave et al. , 1995)  was calculated for all cores and a paired Student t-test was carried out to compare SS distributions of cores MD13-3455G and MD13-3459G. End-member modelling of grainsize data was performed using the EMMAgeo package for R  (Dietze and Dietze, 2019). The number of end-members was identified thanks to the test. robustness() function provided by the EMMAgeo package.
For each sample, foraminifera were picked to obtain 4 to 10 mg of pure carbonate. Prior to measurements, epibenthic foraminifera and the B. dichotoma fragment were cleaned in an ultrasonic bath and then dissolved in phosphoric acid. The extracted carbon dioxide was converted to graphite. A more detailed description of the preparation technique is given in  Hajdas (2008). The 13 C-corrected radiocarbon ages were calibrated using the software CALIB REV7. 1. 0  (Stuiver and Reimer, 1993; Stuiver et al. , 2019) , the MA-RINE13 calibration curve  (Reimer et al. , 2013 ) and a reservoir age correction of 390 ± 80 years  (Siani et al. , 2000; McCulloch et al. , 2010). Calibrated ages are presented in calendar years before present (cal. Yrs. BP; P = 1950 CE) with a upper and lower 2σ confidence level and have been rounded to the next decennial.
Acknowledgements The authors would like to dedicate this manuscript to  Prof. dr. Jean-Pierre Henriet , a key figure in mound research along the continental margins and initiator of the  EuroFLEETS GATEWAYS  project. We acknowledge the ship time provided by  IPEV  on  R/V Marion Dufresne  II within the framework of the  EuroFLEETS GATEWAYS  project (grant agreement  228344 ). We also would like to thank the lab technicians that provided critical support during sample preparation and measurements, namely  Alexandre Salzmann  and  Christoph Neururer  ( University of Fribourg ),  Rene Eichstädter  and  Anne-Marie Wefing  ( University of Heidelberg ) and  Nicole Schwendener  ( Institute of Forensic Sciences at the University of Bern ). We further thank the two anonymous reviewers who provided insightful and constructive comments and corrections to this work.
Review strategy The systematic search for this review was carried out using the PubMed database. The search was conducted using key terms associated with biomechanical experimentation, such as "biomechanical", "mechanical", "properties", "behaviour", "response", "stress", "strain", that could be found in the title or abstract of an article in combination with terms for each of the organs studied: oesophagus, stomach, small intestine, large intestine and rectum. The terms used for each organ can be found in Table  1. Even though the rectum is part of the large intestine, it has been treated as a separate organ here due to its unique function in comparison to the remaining large intestine; the rectum is responsible for the storage and excretion of faeces, whereas the other regions of the large intestine absorb water and electrolytes from the consumed material.
The results of the search for each organ were then screened according to certain criteria; these included articles published in peer-reviewed journals, i. e. no pre-prints or conference proceedings, that provided novel (original) experimental data on the macrostructural mechanical properties of the organs in question, in particular experimental data that presented/allowed for the establishment of the stress-strain relations of the tissue or provided the pressure-volume relationship of the organ structure. Experimental studies on the sphincters of the GI tract were not included. There was no lower date limit for the articles, however studies available online after 15 th October 2022 were not included. Any articles not retrieved from the search but known by the authors were added to the pool of articles included in this review.
Moreover, only one database was used, and although particular care was taken to add any articles known by the authors not found in the PubMed search, some experimental studies may have been missed and therefore may not be included in this review. In the interest of brevity, not all the articles presented in the tables were mentioned in the text, with only those that studied something more than the passive behaviour of normal tissue being highlighted. Furthermore, a comparison of the numerical values of mechanical properties presented in each article has not been carried out. This was due to the large number of articles collected in the review, and the complex nature of comparing numerical values obtained from experiments carried out in different loading modes and with different protocols (such as strain rate, sample dimensions, etc.