dataesr/scientific-paragraphs-categorization huggingface dataset:
11355 Phosphorus, plasma standard solution, Specpure RNeasy plant mini kit Thermo Fisher Qiagen Cat#11301439 Cat#74904 Glutaraldehyde RNase-Free DNase set Sigma-Aldrich Qiagen Cat#G5882 Cat#79254 Formaldehyde Bioanalyzer RNA 6000 pico kit Sigma-Aldrich Agilent Cat#F8775 Cat#5067-1513 Sodium cacodylate GenomeWalker 2. 0 kit Sigma-Aldrich Clontech Cat#C0250 Cat#636405 Quetol 651 Monarch DNA gel extraction kit TAAB Laboratories NEB Cat#Q002 Cat#T1020S NSA Luna Universal qPCR master mix TAAB Laboratories NEB Cat#N010 Cat#M3003S MNA Monarch Plasmid DNA miniprep kit TAAB Laboratories NEB Cat#M012 Cat#T1010S BDMA Spectrum plant total RNA kit TAAB Laboratories Merck Cat#B008 Cat#STRN10 Potassium hexacyanoferrate(II) trihydrate Direct-zol RNA miniprep kit Sigma-Aldrich Zymo Cat#P3289 Cat#R2050 Sodium azide Deposited data Sigma-Aldrich Cat#71290 3,3'-diaminobenzidine tetrahydrochloride Raw mRNA-seq reads Sigma-Aldrich This paper Cat#D5637 NCBI SRA: PRJNA910570 Cobalt(II) chloride hexahydrate Raw small RNA-seq reads SLS Scientific This paper Cat#CHE1618 NCBI SRA: PRJNA910570 Cetrimonium bromide (CTAB) Combined reference transcriptome Sigma-Aldrich This paper Cat#H9151 NCBI TSA: GKEE00000000 Polyvinylpyrrolidone (PVP) GdEXPA7 mRNA sequence Sigma-Aldrich This paper Cat#PVP10 NCBI Genbank: OQ032504 RNAse A GdSPL1 mRNA sequence Thermo Fisher This paper Cat#EN0531 NCBI Genbank: OQ032505 SuperScript III reverse transcriptase GdmiR156 small RNA sequence Thermo Fisher This paper Cat#18080093 Figshare: 21750110 Phusion Hot Start II polymerase GdmiR162 small RNA sequence Thermo Fisher This paper Cat#F549S Figshare: 21750110 Murashige & Skoog medium Silwet L-77 Transcriptome annotations DNAse I Lists of differentially expressed transcripts Hind III ICP-OES results Pst I EXPA alignment file for phylogenetic analysis T4 DNA ligase Lists of differentially expressed small RNA PCRBIO Taq DNA polymerase miRbase v.
The combined reference transcriptome is deposited on NCBI TSA. Sequences of GdEXPA7 and GdSPL1 are deposited on NCBI Genbank. All other datasets generated in this study are deposited on Figshare. All datasets generated in this study are publicly available as of the date of publication. Accession numbers are listed in the key resources table. This paper does not report original code. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. Continued REAGENT or RESOURCE SOURCE IDENTIFIER Verios 460 scanning electron microscope Thermo Fisher N/A PP3010T Cryo-SEM preparation system Quorum N/A DigiTUBE, 15ml, RackLock QMX Laboratories Cat#QX100233 SCIMED DIGIPrep MS block QMX Laboratories Cat#QX100232 PlasmaQuant PQ 9000 Elite ICP-OES spectrometer Jena Analytik N/A Ultracut E ultramicrotome Leica N/A BX41 light microscope Olympus N/A CFX384 Touch real-time PCR detection system BioRad Cat#1855484 M205 FA fluorescence stereomicroscope Leica N/A TCS SP5 confocal microscope Leica N/A ASP300 tissue processor Leica N/A BX41 light microscope Olympus N/A VHX-5000 digital microscope Keyence N/A DNBseq-G400 sequencer MGI Tech N/A 15% Mini-PROTEAN TBE-Urea gel BioRad Cat#4566053 Hybond-NX Nylon hybridization membranes Merck Cat#GERPN203T Illustra Microspin G25 columns GE Healthcare Cat#27-5325-01 EXPERIMENTAL MODEL AND SUBJECT DETAILS Gorteria diffusa cultivation Seed bearing capitula of the G.
Background Elevational gradients and the associated biotic and abiotic changes provide useful frameworks to elucidate the drivers behind species distribution and their response to climate change  (Cruz-Maldonado et al. 2021; Fukami and Wardle 2005; McCain and Grytnes 2010). Environmental factors such as temperature, radiation, and precipitation shift with altitude and strongly influence soil biophysical properties, including soil structure and moisture. Plant communities also change along altitudinal gradients, further modifying soil properties through litter and root carbon (C) inputs, thereby affecting soil biodiversity. Here, we collected belowground and aboveground data to elucidate the mechanisms through which altitude and the associated shifts in climate and vegetation affect soil properties and ecosystem processes, at different elevations.
The ecosystem services provided by (sub)alpine soils, e. g. , C sequestration, nutrient cycling, and water retention, are threatened by a changing climate and land use conversion, as high mountains are particularly fragile ecosystems  (Mountain Research Initiative 2015). These ecosystem services depend on soil physicochemical properties, its biodiversity, as well as on plant species composition and community structure. Therefore, our dataset includes data on soil porosity and aggregate stability at different soil depths and altitudinal levels and measurements of soil water infiltration and retention, which will allow a better understanding of the water flow dynamics along this Neotropical altitudinal gradient. We also collected data on soil macro-and meso-fauna diversity, as well as on microbial communities at the bulk soil and rhizosphere level, thus allowing researchers to explore the biotic and abiotic drivers behind soil biodiversity, which is key in mediating soil biophysical processes  (Collins et al.
2020; Hernández-Cáceres et al. 2022). This dataset complements the already published data from our twin study in France  (Stokes et al. 2021 ) and represents the largest freely available collection of data on plant traits, soil physicochemical variables and soil biodiversity along a 1200 m elevational gradient in a Neotropical climate. Methods for data collection and analysis are provided in the data files and supplementary materials (Table  1 ).
Methods Data file 1 shows an aerial map of the whole protected area named "Área Natural Protegida Pico de Orizaba (ANPPO)", within which our study sites are located. Data file 1 also provides the names and numbers of each plot, with their respective coordinates and elevations. The studied elevational gradient lies on the North-East flank of Pico de Orizaba, the highest peak of Mexico. We hereby present data collected in 2017 and 2018, at six altitudes (elevational bands of 200 m each) along an elevational gradient ranging from 3400 to 4600 m. a. s. l. and from 3400 to 4400 m. a. s. l. for soil profile and soil moisture (Data files 2 and 4, respectively), at Pico de Orizaba volcano, Mexico. Climatic data (Data file 3) are also provided and were measured over 2 years  (2018) (2019) (2020). The underlying bedrock is composed of intermediate to acid lavas and pyroclastic (volcaniclastic) material.
Soil profiles were described for each altitudinal level (two soil profiles made per altitudinal band). Soil physico-chemical properties were analyzed from each identified soil horizon. The main soil units correspond to Endosilandic Andosols at Sleketic Regosol. The volumetric and tension soil moisture, as well as the soil surface temperature were monitored for one year, in six soil profiles distributed along the altitudinal gradient (Data file 4). To obtain the floristic list of our study site, five 20 × 20 m plots were established at each altitude (every 200 m along our 1000 m long elevational gradient) up to 4000 m of elevation, to include keystone and communitystructuring species with different growth forms: Pinus hartwegii (tree), Oxylobus arbutifolius and Chionolaena lavandulifolia (shrubs), and Lupinus montanus and Senecio roseus (herbs).
From 4200 m. a. s. l. and above, the distance between our plots had to be reduced to 5 m for space availability reasons (mountain tapering). A botanical survey was performed inside each plot (Data file 5) and one adult individual of each structuring species was selected per plot. At the limit of the individual's canopy on the downslope side, infiltration tests were performed to estimate water flow through soil and hydraulic conductivity of the quasi-steady phase was calculated (Data file 6). Aggregate stability measurements were made from soil blocks (10 × 10 × 15 cm depth) taken beside the monoliths (Data file 7). An earthworm survey was made on soil monoliths of 0. 25 × 0. 25 × 0. 15 m in the field to link the abundance of these ecosystem engineers with soil processes (Data file 8). To investigate the relationships between soil biophysical properties and vegetation, plant community composition was described in a 1 m 2 subplot within each plot, which was established near each structuring species (Data file 10).
Soil texture, bulk and real density, cationic exchange capacity, pH, electric conductivity, organic C, nitrogen (N) content, nitrate and ammonium concentrations, total and available phosphorus (P), and macronutrients (Ca, Mg, K, Na) concentrations were determined on pooled soil samples harvested within each monolith (Data file 9). A soil monolith (0. 25 m × 0. 25 m × 0. 15 m depth) was excavated within each subplot (n = 83). Above the monolith, litter layer thickness and aboveground biomass per species were measured (Data file 10). Soil samples were collected from the monolith and from rhizospheric soil attached to fine roots of the three structuring plant species. DNA was extracted from these soil samples and 16S rDNA amplicon sequences were obtained to determine the changes in bacterial communities at the bulk soil and at the rhizosphere level along the elevational gradient (Data file 11).
Soil mesofauna was collected beside each monolith from litter and soil with a cylinder (5 cm diameter and 10 cm high) and then extracted with a Berlese funnel in the laboratory. All the organisms were kept in 70% alcohol and identified at order taxonomical level, except for Collembola for which identification was made at the family level (Data file 12). Soil macroinvertebrates were hand-sorted from each monolith and fixed in 70% ethanol, except earthworms, which were fixed in 96% ethanol for the DNA barcoding. Macroinvertebrates were identified at the order level, except for earthworms for which morphological diagnoses were combined with DNA barcoding to obtain species level assignations. Invertebrates within each taxon were counted and weighed (Data file 13).
Limitations Although this dataset comprises a large number of field and laboratory data collected from 83 monoliths in 30 plots, it was not always possible to include all structuring species within the same plot. At higher altitudinal levels (4000-4400 m. a. s. l. ), additional shrub and herbaceous species were sometimes included (S. roseus and C. lavandulifolia) to complement the data when L. montanus and O. arbutifolius were not present. Moreover, our focus on some keystone/structuring plant species does not allow generalization of rhizosphere processes under other species. Finally, our datasets do not provide information on plant functional traits nor on soil microbial functional diversity (i. e. , enzymatic activities or microbial gene transcripts), the latter being a factor to understand soil functioning as microorganisms are main contributors to soil biophysical processes.
Before the formation of embolism/occlusion of vessels or the cell death of cambium, there must be responses from living cells. The contribution to the current Special Issue from Belair et al. [1]  reviews the cell wall thickening in paratracheal parenchyma that impedes lateral hyphae penetration in xylem parenchyma cells and looks at cell wall modifications in living fibers and ray parenchyma that are associated with suberin rather than lignin deposits. The contribution by Sun et al. [6]  to the current Special Issue identifies 15 genes encoding stress-associated proteins (SAPs) from grapevines (Vitis vinifera) via sequence comparisons. The tissue-and development-specific expressions were analyzed with database help. In recent decades, the ban of numerous phytosanitary products has limited the number of solutions to treat fungal diseases.
We hypothesized that this failure may be due to particular barriers inhibiting the cloning of the M. gallisepticum genome in yeast. Among the known cloning barriers, restriction modification (RM) systems are already known to lower the cell-to-cell genome transfer efficacy  22. Using an analysis based on the Rebase Database  42  , two RM systems were identified (Table  S5 ). However, similar RM systems are also found in the Mmc, Mcap, Mmm and Mccp genomes and they do not seem to inhibit cloning of the genome of these mycoplasmas. Therefore, we suspected that other putative barriers could be involved in this failure, including cytoplasmic, secreted or membrane nucleases with lower specificity than those associated to the RM systems. Indeed, we hypothesized that the specific recombination template used in our approach, provided as a linear double-stranded PCR product and carrying yeast elements, may be degraded by such nucleases.
The results are summarized in demonstrated that the presence of these systems could strongly decrease the efficiency of cell fusion 22. In Table  S5 , we have listed all type I, II, and III RM systems predicted for all the species used during this work. We identified them using the online tool Rebase DataBase  42  (http://rebase. neb. com/rebase/rebase. html), as well as the Molligen 3. 0 and Molligen 4. 0 databases (https://services. cbib. u-bordeaux. fr/molligen/ and http://www. molligen. org)  50  , and Padloc 51 (https://padloc. otago. ac. nz/padloc/). In this table, we have specified whether the restrictases were described in the literature or identified as putative (on Molligen). For M. bovis PG45 T , no less than 10 RM systems were predicted by Rebase and 9 restrictases (some of them referenced as truncated, e.
ACKNOWLEDGMENT The authors thank  Christophe Boury  and  Erwan Guichoux  members of the  Genome Transcriptome Facility of Bordeaux  (https://pgtb. fr/, Grants from  Investissements d'avenir, Convention attributive d 'aide EquipEx Xyloforest,  ANR-10-EQPX-16-01 ), for their help in genome sequencing performance. We also thank the  UMR CIRAD-INRAE ASTRE from Montpellier  for providing the Mccp Niger and Tanzania strains; and the  UMR IHAP INRAE-ENVT from Toulouse , for providing the M. bovis PG45 T strain and the M. agalactiae PG2 T.
Data availability: We declare that the data supporting the findings of this study are available within the paper and its supplemental material. The sequences from this study are available from the NCBI under SRA accession no. PRJNA910329. The updated version of the Mccp strain 14020 Tanzania genome was deposited in GenBank under the BioProject PRJNA940743, the BioSample SAMN33578400 and the accession number CP121686. 1.
Hardware components The Pilowtech sensor is composed of a soil moisture probe, a data acquisition module and a power supply. The capacitance soil moisture sensormodel EK1940 v1. 2 -was chosen for its resistance to corrosion, its low cost and availability. In addition, it operates over a voltage range of 3. 3 V to 5 V, making it compatible with low-power microcontrollers. The data acquisition module includes a microcontroller, a communication microchip and small electronics (wires, resistors, breadboard). The microcontroller chosen was the Arduino Pro-Mini, with a 3. 3 V voltage and a frequency of 8 MHz. In addition to the advantages of Arduino systems (widely used, easy to use and open-source), thanks to its small size, the Pro-Mini is easy to embed and is one of the cheapest controllers on the market.
It is also compatible with the LoRaWAN (Long-Range Wide-Area Network) communication protocol. The LoRa SX1276 communication module makes it possible to send and receive data via the LoRA radio network. The whole device is powered by a rechargeable Li-ion 18,650 battery (3. 7 V and 3500 mAh). To allow onsite and instantaneous data reading, a reader was further developed based on an Arduino Uno board and a liquid crystal display (LCD) shield. The system is powered by a rechargeable Li-ion power bank with a 3. 7 V output. To avoid damage caused by humidity, the probes were assembled at the end of PVC pipes (Ø40). Silicone was used to guarantee a tight sealing at the junction of the probe/pipe and the heat-shrink tubing at the wire junctions. The case of the acquisition module was printed using a 3D printer.
Software and data workflow The microcontroller was programmed in C/C++ language using the open source Arduino software (IDE) 1. 8. 19. The program was uploaded from the computer to the Arduino Pro-mini using a FTDI wire. To minimize energy consumption, the program code was written to maintain the sensor in sleeping mode (low µA consumption). The system woke up at 5-minute intervals to collect the analog signal produced by the probe, to convert it into digital data and send it through the LoRa network. Theoretically, this optimization enables the sensor to be energy autonomous throughout the irrigation season. The Arduino program is also available in the research data section. The data produced transited locally through the gateway via the LoRa network and was then routed online to the free server "The Things Network Cloud (Europe 1)"  [24].
Within this server, the gateway was declared on the cloud and an application gathering sensor (device) IDs was generated. This server allowed real time data reading online but not storage or data processing. The free and open-source flow-based programming tool Node-RED  [25] , was thus used to gather the data from this online server and store it on a local server via an InfluxDB database  [26]. The Node-RED flow is also available in the research data section.
User access to data Three different types of user access to data were provided through multiple channels to serve end-users with different technology literacy levels: (i) an online platform with access to data time series; (ii) a mobile phone messaging alert system; and (iii) an onsite data reader (Fig. 1 ). The online data reading is done using Grafana software  [27] , a free open-source data visualization and processing platform. A dashboard was produced to visualize data from each pilot farm. The dashboard comprised time series graphics for each soil moisture sensor (specified by its location and depth) and a map showing the location of the sensors. Data could be combined for visualization at different levels (plot, farm, irrigation schemethe highest level being defined by the network).
The soil parameter k was determined by searching for an optimal match between the gravimetric and simulated soil moisture and minimisation of the error. Table  3  summarizes the parameters obtained. The results showed that the model based on Eq. (  2 ) satisfactorily fitted the data obtained using the gravimetric method for all three soils (0. 875 ≤ R 2 ≤ 0. 983). The parameters related to the capacitance probe ranged between 182 and 198 for ϵ w and between 430 and 479 for ϵ a. The soil parameters obtained with the same soil sample varied little with respect to their mean (3% ≤ c v ≤ 4%). However, parameter k varied more between the different soil samples (6% ≤ c v ≤ 10%), parameter k is thus more sensitive to soil characteristics.
Software and data reading Each farmer connected to the WSN has access to a personal space on the online platform. The data produced by the sensors are gathered for each farm and can be displayed according to the desired location, date and depth. The calibration results allow the raw data to be displayed in the form of volumetric soil moisture (Fig. 5 ). Time series are available for consultation (historical and real time) and can be downloaded in different formats. In the field, the data can be accessed manually using the onsite reader. When visiting the plots, by unscrewing the top of the protective PVC tube at the desired location and depth, the farmer can simply plug in (Fig. 6a ). After several trials, the farmers indicated the most practical visualization is the one that displays the filling percentage of the total available soil moisture (FP = θs-θwp θfc-θwp ).
Ability for monitoring relative soil water status The data obtained for plot P2 were compared with the tension measurement produced by a commercial tensiometer probe (Watermark) at the same location and depths (30 and 60 cm) as the Pilowtech (Fig. 7 ). Monitoring continued from March 8 to June 13, 2022, i. e. shortly after planting until the potatoes were harvested. The tensiometers were installed on March 25, before the start of the irrigation season. Irrigation was regular (frequency of around 3 days) during the development stage (April). Irrigation events were less frequent from the midseason until harvest (from May to June). The time step for data generation is 4 h for the tensiometer and around 5 min for the Pilowtech. Irrigation events are clearly identifiable from the peak moisture contents measured by the Pilowtech, and from the drops in tension measured by the tensiometer.
Ability for absolute soil moisture monitoring In plot P1, the measurements made with a commercial capacitance sensor and the Pilowtech (30 and 60 cm) from April 4 to May 3 were compared (Fig. 8 ). The data generation time step of the commercial sensor is 1 h, and around 5 min for the Pilowtech. Comparison of the soil moisture content characteristics measured in the plot "P1" (θ wp = 7. 00% at pF = 4. 2 and θ fc = 10. 71% at pF = 2. 5) showed that the range of moisture content measured by the commercial probe and by Pilowtech was globally bounded by the permanent wilting point and field capacity. We also compared the potential of detection of irrigation peaks, corresponding to local maximum soil water moisture content. Results revealed an excellent temporal match, confirming that irrigation events were accurately detected by both sensors.
Discussion Has irrigation monitoring using soil moisture probes become accessible to any water user? We developed a soil moisture sensor based on the specifications and feedback provided by the water users of an irrigation scheme. The sensor we developed is (i) low tech, meaning easy to make, maintain and reproduce; (ii) low cost, so small farming systems can afford it; (iii) low power, and consequently relatively self-sufficient and sustainable over time; (iv) meeting farmers' expectations for monitoring water status in the root zone. The data can be accessed in two ways: remotely via an online interface, or in the field through direct reading. Calibration is feasible both in the laboratory and in the field. The sensor rating curve (Eq. (  2 )) is given by only one parameter, k, characteristic of the soil.
Acknowledgments This paper is part of the first author's  PhD  project, funded by the  CIRAD  and the  Occitanie Region (France). The work was carried out as part of the  HubIS  project ( EU's PRIMA program 2019 , grant  ANR-19-P026-0006-02 ). We are grateful to the  CTV Haouaria and GDA Echraf  for participating in the project. The cooperation of the farmers in the Echraf irrigation scheme was greatly appreciated.
It was further possible to study the relationship between numbers of berries on plants and larvae numbers. This was an essential parameter that widely increased the precision of larvae number estimates (Fig. 1 ). To our surprise, our dataset also suggests that the overall reproduction of D. suzukii depends on the density of available fruit locally (Fig  3 ). Greater berry numbers in an experimental unit appeared to reduce larvae numbers, a pattern that was most visible in the control treatment. Our study also bears weaknesses that need to be accounted for. Because we only followed one generation, it is unknown whether the short term effects we report would translate into population scale impacts over longer time scales. When changing scale, the manner strawberries are produced will have a great influence on insect multiplication.