dataesr/scientific-paragraphs-categorization huggingface dataset:
t. ); 3 washes in 2% BSA in PBS (MW 170 W, 1 min). Slides were finally mounted in a 1:1 solution of AF1 antifadent (Citifluor) with PBS, containing calcofluor as a cell wall counterstain and imaged by confocal laser scanning microscopy (Zeiss LSM700 with Zen Black edition software v. 14. 0. 27. 201). For immunolocalizations longitudinal to wood fibres, fixed wood samples (greenhouse or in vitro grown) were hand dissected with razor blades longitudinally to the stems and callose immunolocalizations were performed in tubes with gentle agitation as follows: 3 washes in PBS, blocking step: 2% BSA in PBS, 1 ml per slide, 1 h at r. t. ; primary antibody (anti (1→3)-β-glucan (Biosupplies, 400-2)), 1/400 in 2% BSA in PBS, 500 µl per tube, ON 4 °C; 3 washes in 2% BSA in PBS; secondary antibody (Alexa Fluor 488 anti-mouse IgG, Thermo Fisher, A-11017) 1/800 in 2% BSA in PBS, 500 µl per slide, ON 4 °C; 3 washes in 2% BSA in PBS.
Samples were then left in tubes containing a 1:1 solution of AF1 antifadent (Citifluor) with PBS, containing 0. 1% (w/v) Direct Red 23 (Sigma 212490) and left ON at 4 °C under gentle agitation before being imaged by confocal laser scanning microscopy (Zeiss LSM700 with Zen Black edition software v. 14. 0. 27. 201). For TEM immunolocalizations, 80-100-nm thin sections were obtained with a Leica EM UC7 ultramicrotome using an Ultra 45° diamond knife (DiATOME) and laid on 200-mesh formvar-coated Nickel grids (EM Resolutions, F200Ni100). Sections were blocked with filtered 2% BSA in PBS at r. t. , labelled with mouse anti-callose IgG1 (anti-(1→3)-β-glucan; 1:10; Biosupplies, 400-2) ON at 4 °C, washed 6 times with 2% BSA in PBS (2 min each), incubated for 2 h at r. t. with donkey anti-mouse IgG coupled to 18 nm colloidal gold (1:30, Abcam), washed again 6 times with 2% BSA in PBS (2 min each), washed 3 times with water and post-stained for 5 min with uranyl acetate in 50% methanol before observation.
The detailed macro code can be found in Supplementary Document 1. To display the fine callose deposition patterns in longitudinal pictures in Fig. 4 , immunolocalizations, acquired as z-stacks, were deconvoluted with Huygens Essential v. 22. 04 software using the recommended parameters of the deconvolution wizard. The deconvoluted z-stacks were then projected as maximal projections for display. For vessel size and fibre size measurement, we used either the magic wand tool of ImageJ (v. 1. 53t) to define vessel regions of interest before measurement, or the software LithoGraphX v. 1. 2. 2 with Builder v. 1. 2. 2. 7 (https://sourceforge. net/projects/lithographx/) to analyse maximal projections of calcofluor-stained sections following a previously described protocol  71  with modification. Images were pre-processed in Fiji (v.
Monosaccharide analysis The monosaccharide composition was determined using two-step sulfuric hydrolysis followed by high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD) analysis, according to a previously reported procedure  76. In brief, 1-2 mg CWE (triplicate) was hydrolysed using 72% H 2 SO 4 for 3 h at room temperature, followed by a second hydrolysis step with 1 M H 2 SO 4 for 3 h at 100 °C. The hydrolysed samples were then filtered through a Chromacol 0. 2 µm filter (Thermo Fisher), diluted with Milli-Q water and analysed on an ICS3000 system (Dionex) with a Dionex CarboPac PA1 column at 30 °C, using the same elution programme as previously described  77  and Chromeleon software v. 7 (Dionex, Thermo Fisher). Quantification for HPAEC-PAD analysis was performed by external calibration using standard solutions containing different neutral monosaccharides (fucose, rhamnose, xylose, arabinose, glucose, mannose and galactose).
After hydrolysis, the released sugars were reduced with sodium borodeuteride in 1 M ammonia solution at room temperature for 1. 5 h, quenched and dried (3×) with 10% acetic acid in methanol and further washed and dried (3×) with pure methanol. For derivatization, the samples were acetylated with pyridine and acetic anhydride (1:1 v/v, 200 µl) at 100 °C for 60 min. The per-O-methylated alditol acetates (PMAAs) were then extracted with ethyl acetate and quantified using an HP-6890 gas chromatograph and an HP-5973 electron-impact mass spectrometer fitted with an SP 2380 capillary column, using ChemStation D03 software (Agilent). The programme used was initially set to 163 °C, followed by ramping at 1 °C min -1 until 213 °C, ramping at 3 °C min -1 until 230 °C, ramping at 10 °C min -1 until 260 °C and holding for 10 min.
Lignin acetyl bromide analysis For lignin analysis, dried debarked wood samples from trees grown on soil or in hydroponics conditions were used. The lignin content was quantified by spectrophotometry using bvda software v. VA1. 176 (VWR), following the acetyl bromide method  79  using an exact mass of 20 mg of sample per assay. Samples were ground in liquid nitrogen for homogeneity before analysis. The chemicals were laboratory grade from Sigma Aldrich and the analyses were performed as three independent replicates, with the lignin content expressed as a percentage of dry matter.
Water vapour sorption measurement Water vapour sorption and desorption measurements were performed at 25 °C using a regulated atmosphere Cahn D200 microbalance equipped with DVS-Advantage control software v. 2. 1. 5. 1 (Dynamic Vapour Sorption from Surface Measurement Systems) with a mass resolution of 0. 1 µg. The DVS apparatus allows collection of kinetic data by recording mass evolution vs time as a function of a given water activity. The initial mass of dried samples was ~20 mg. After exposure to dry nitrogen flux until a constant sample mass was reached (m d ), water vapour was flushed at controlled pressure and the mass uptake was measured as a function of time. For this, successive water activity steps ranging from 0. 1 to 0. 9 by steps of 0. 1 were performed. The sorption kinetics were followed step by step until equilibrium.
Biomass crystallinity At 3 months after transfer to soil, greenhouse-grown wood samples were harvested from saplings, debarked and dried overnight in an oven at 55 °C. Crystallinity was determined on eight individuals per line using a Bruker D8 Discover wide angle X-ray diffraction instrument equipped with an area detector (GADDS). The measurements were collected in transmission mode using CuKα radiation source emitting at 0. 154 nm wavelength, with X-ray optics θ1 = 17° (θ1 = source, θ2 = detector). Diffraction intensities were collected at Bragg angles 2θ = 4°-40. The X-ray diffraction profile was integrated at Chi between -180° and 0° using GADDS software (Bruker AXS). The data were normalized and resolved using the crystallinity calculation method of Vonk  89. First, the background diffraction signal was subtracted, then the amorphous curve was fitted to the diffraction pattern and finally, linear regression analysis was conducted to obtain cell wall crystallinity.
The clamping distance was set to ~1 cm, the test speed to 10 µm s -1 and the specimens continuously moistened using a pipette during the test to avoid drying. The loading conditions and data collection were performed via a Labview (v. 10. 0) interface. In total, ten technical replicates for each of the ten biological replicates of WT, Const. L11 and Const. L17 were tested.
22 Å -1. All data handling was performed using Xenocs XSACT software v. 2. 6. Cell wall orientation was analysed for the cuts used in the wide-angle X-ray scattering experiment for three biological replicates of WT, Const. L11 and Const. L17. The cell wall orientation was then averaged for the three different positions (periphery of the stem, centre of the stem and in-between) of the cuts for each line and this average was used for the MFA determination. To obtain the cell wall orientation of the cuts, cross sections of the analysed cuts were prepared using a rotary microtome (Leica RM2255). Images were taken using an optical microscope (Olympus BX51 equipped with an Olympus SC50 digital camera) and the cell wall orientation was analysed using ImageJ (v. 1. 53k). Doctoral School of Exact and Natural Sciences, Jagiellonian University, Krakow, Poland.
f and g, vessel and fibre size measurement on greenhouse grown constitutive lines (6 weeks after transfer to soil, n = 5) with LithographX software. A minimum of 248 vessels or 689 fibers were measured for each genotype. Statistical analyses were done using one-way analysis of variance (ANOVA; Extended Data Fig. 4a , p-value = 2e-16; Extended Data Fig. 4b , p-value <2e-16; Extended Data Fig. 4c , p-value = <2e-16) followed by Tukey's multiple comparison test in b, c and e and with Kruskal Wallis one-way analysis of variance (ANOVA) followed by non-parametric multiple comparison test from nparcomp R package in f and g. Significance values for P <0. 05 were grouped and are displayed as letter groups above bar plots. Extended Data Fig. 4  | Monosaccharide and linkage analysis of callose enriched cell walls do not show major compositional shift after callose integration.
Acknowledgements We thank  G. Evans  for technical support with microscopy experiments;  L. Wang  for statistical analysis assistance;  G. Hindle ,  J. Salmon  and  S. Ward  for media preparation;  K. Blajecka  for technical assistance;  J. Daff ,  L. Tully ,  B. Fidget ,  A. Jootoo  and  G. Porteous  for Horticulture.
Identification of Relevant Studies To address the research questions, a methodical search of English-language articles published between 2015 and 2022 was performed in the PubMed, IEEE Xplore, ScienceDirect, Scopus, and Web of Science library databases. Initially, key terms such as bladder monitoring, urine incontinence, and wearable devices were used to identify relevant articles. The results of this initial search were used to optimize the search strategy and to refine the key terms used during the searches into more specific terms, including ultrasound, optical technology, electrical bioimpedance, urine leakage collection, and technologies that have undergone testing on human subjects. The articles considered aimed to present information on (1) non-invasive technologies applied for BUV monitoring and UI management, (2) the basic characteristics and recent advancements of these technologies, and (3) information on other technologies widely utilized for BUV monitoring and UI management.
Ultrasound Technology Ultrasound technology is an imaging technique that provides a wealth of information related to voiding dysfunction. The working principle of ultrasound is based on sending out and receiving a sound wave at a frequency that is beyond the range of human hearing. An ultrasound wave transmitter and receiver are attached to the skin above the bladder and used for scanning the bladder and for determining its shape. The ultrasound technology method of operation can be seen in Figure  2. This section is divided into two main parts: Section 3. 1 presents technologies related to non-invasive BUV monitoring, and explains their modes of operation and the latest advances made in each of them, while Section 3. 2 deals with solutions for UI management. The database searches resulted in the identification of 207 articles, out of which 34 met the inclusion criteria for further analysis.
The device comprises 32 transmitters and receivers, and a 32-element phased array having a 3 MHz transducer. It is based on data digitization for signal reconstruction and subsequent image processing, where all reconstruction algorithms are executed on the GPU, enabling real-time reconstruction and imaging. To evaluate the accuracy of the trained algorithm and the system, MoUsE was used to acquire different bladder filling levels from four volunteers. The results indicate that the proposed approach demonstrates sufficient sensitivity for applications where a significant increase in BUV can occur. However, for applications requiring a precise quantitative assessment of BUV, further enhancement of the performance is necessary. Additionally, further investigations are required to enhance the performance of the system, with a particular focus on increasing the neural network size to enhance the system's func-tionality on mobile devices with limited computing resources.
It was powered by either a rechargeable battery or AC power and communicated with a controlling computer via Bluetooth. The pilot testing was performed on 15 male volunteers, aged 21-87, who were recruited from a rural medical clinic. The results demonstrated the reproducibility of chromophore concentration data and showed that changes in chromophore concentration in the detrusor muscle during voiding could be monitored in pigmented subjects using NIRS. The successful monitoring was attributed to the device's technical specifications, optimized application methodology, and a new protocol that improved device positioning and reduced signal contamination  [52]. Fong et al. [53]  investigated the use of NIRS in developing a wearable, BUV sensing device that provides alerts to patients with spinal cord injuries.
The optical components used in their measurement system consisted of multiple high-power LEDs with a peak wavelength of 970 nm and a monolithic silicon photodiode. They performed an experiment that consist of a 1-min measurement on a healthy volunteer immediately before urination (i. e. , when the bladder was full, to measure the light intensity through the diffuse reflectance data), and a 1-min measurement after urination to determine if any change in light intensity appeared. They observed that there was a noticeable drop in the light intensity detected by the photodiode between the full and empty bladder datasets obtained. The experimentation performed helped to demonstrate the feasibility of obtaining a reasonable signal using the setup with LEDs. This is an important step towards using the system in clinical trials on additional human volunteers  [53].
Macnab et al. [54]  also developed a system for wireless NIRS measurements of the bladder and brain simultaneously. The system consisted of two parallel dual-wavelength NIRS devices and proprietary software. The study monitored natural bladder filling and spontaneous voiding in two volunteer subjects using a 23-channel array over the frontal cortex and a 4-channel grid over the bladder. The results showed that simultaneous brain and bladder data were captured, indicating localized brain activity during bladder sensation and function. This study provides new physiological dimensions for evaluating bladder control and function  [54].
Other Technologies Another interesting work providing information on recent progress regarding UI management is a study conducted by Rodas et al. [67] , which estimated the emptying of the bladder in paraplegic and elderly people. To correctly determine when it is time for people suffering from spinal injuries to empty their bladder, they suggested a non-invasive method that consists of combining electrical bioimpedance, the hypogastric region temperature, and an artificial feedforward neural network. For electrical bioimpedance measurements, four Ag/AgCl electrodes were used, and a sine wave current with a frequency of 50 kHz and 1 mA p-p was injected. The bladder region temperature was measured in real time using the chip MAX30205. Data obtained from more than 18 patients when emptying their bladder were then fed into a three-layer feedforward neural network with the BFGS Quasi-Newton algorithm.
B. Creating a Model Layers form the core data structure of our framework. They are expressed as Python classes whose constructors create and initialize the connections between the circuit components, and whose call methods build the netlist. The process of creating a model is heavily inspired by Keras's functional API due to its flexibility at composing layers in a non-linear fashion. In this manner, the user is able to construct models with multiple inputs/outputs, share layers, combine layers, disable layers, and much more. An example of this is given in Fig. 4 , which follows the structure shown in Fig. 3. In the following subsections, we provide details on only those layers that have a unique interpretation in the analog domain. Generally, the input layer is defined according to the dataset.
However, the input layer can be defined slightly differently in the analog domain. • First, since the weights are implemented by resistances, and resistances cannot be negative, a second set of inputs with the opposite polarity of the voltages defined in the dataset is added to the input layer. This accounts for negative weights and effectively doubles the number of inputs. This idea is depicted with two green rectangles in Fig. 4. • Second, in typical software-based models, the bias, when used, is implicitly set to 1. However, since circuits can work with a wide range of voltages, setting the bias voltage to values other than 1 is necessary. Hence, we provide the option to independently set the bias voltage in each layer. Note that it is also possible to learn the bias voltages. 2) Weight layers: Two kinds of weight layers are defined in the framework.
A. Illustrative Example: Learning the Iris Dataset As a first step in the evaluation, we built a model that could learn the Iris dataset. This example is a well-known problem of moderate complexity, containing 150 samples, with 4 input variables and 3 output variables. Two preprocessing steps are needed before the data is ready for training. First, the input variables have to be normalized. Second, since the output variable is categorical and ordering is unimportant, we associate each unique output value with a 3 bit one-hot encoded value. Hence, after the preprocessing step, the dataset has 4 inputs and 3 outputs. We constructed a model with 1 input layer, 1 hidden layer, and 1 output layer, as shown in Fig. 4. The input layer has 9 nodes; 4 for the regular inputs, 4 for the inverted set, and 1 for the bias.
In the preprocessing step, the data was scaled to have a mean of 0 and standard deviation of 4. Therefore, the voltages at the input take real values in the range [-4V, 4V]. The hidden layer was implemented with 10 nodes and the output layer with 6 nodes. The weights were initialized from samples drawn randomly from the range 10 -7 S, 0. 08 √ nin+nout+1 S , where n in is the size of the inputs and n out is the number of nodes. The learning rate of both layers was set to 0. 0001. The dataset was split into two parts: 105 samples for training, and 45 samples to evaluate the model on new data while training. The optimizer was set to ADAM and the model was trained for 400 iterations. It achieved an accuracy of 100% on the test dataset. A plot of the loss and accuracy as a function of the number of the training epochs is shown in Fig.
6. This validates the correctness of our framework. To gain more insight into the training process, a sample of 8 conductances were chosen at random and their evolution in the course of the training is plotted in Fig. 7. We observe a complex but smooth evolution of the conductances. Also, despite achieving an accuracy of 100%, the circuit has yet to arrive at the most optimal equilibrium state for the given dataset. For a machine learning task with higher difficulty, we trained a larger model on the Fashion-MNIST dataset. To reduce the simulation time, we encoded the sample images into vectors of length 128 (originally 784) before feeding them into the analog model. We achieved an accuracy of 86% on the test dataset after just 1 epoch with the cross-entropy loss. A similar sized model in Keras achieved an accuracy of 86.
C. Performance To evaluate the performance of the simulator, two experiments were conducted. The first experiment was conducted on the Iris model with the goal of measuring the speed-up gained through parallelism. We fixed the number of samples in the mini-batch and ran the simulation for the same number of epochs on a single thread, followed by 2, and then 4. The result is shown in Fig. 8. While the speed-up factor was indeed almost doubled when the thread count was increased from 1 to 2, doubling the thread count further resulted in just 1. 5x increase in speed. Due to the resulting circuit being relatively simple, and the small batch size, the overhead of starting new processes for every batch is a nontrivial percentage of the overall simulation time. To investigate this further, we artificially increased the dataset size 8 folds to keeo a thread busy for a longer period.
The orange line plot of Fig. 8  confirms this intuition. For the second experiment, we wanted to measure the simulation performance as a function of problem complexity. To this end, we considered 3 datasets; xor, iris, and wine. To obtain an estimate for the complexity of the circuit, we counted the number of nodes only in those models that achieved greater than 95% accuracy on the test dataset. This is due to the fact that the bias-variance trade-off is a property of the model size. The circuits were simulated and the average simulation time in seconds is recorded in table I. For a measure of the intrinsic speed of the simulator, a column with a calculated property K is added. The property is calculated according to equation (  5 ) and takes into account the simulation time T , the number of allocated threads P , the number of nodes in the generated circuit N , the number of epochs E , and the size of the training dataset D.
, 2016). This code leverages the contour detection offered by the Matplotlib library  (Hunter, 2007) , and the image analysis tools from the OpenCV library  (Bradski, 2000)  which provides an efficient way of masking 2dimensional data. The corresponding detection catalogs for AR and AeR, based on MERRA2 vertically integrated fluxes at 1 • x 1 • spatial resolution (see Section 2. 2) and covering the period 1980-2022, along with the processing and detection codes, are available at https://doi. org/10. 5281/zenodo. 8082768  (Lapere, 2023).
MERRA2 data The data used for AR detection is the MERRA2 hourly Vertically Integrated Diagnostics, northward flux of atmospheric water vapor. Similarly, the detection of AeRs is performed using the MERRA2 hourly Vertically Integrated Diagnostics, dust/black carbon/sea salt/organic carbon column v-wind mass flux. The MERRA2 aerosol product was also used in  Chakraborty et al. (2021 Chakraborty et al. ( , 2022) )  for AAR detection. These products are integrated fluxes over the whole atmospheric column (1000 hPa to 0. 1 hPa). Additionally, large scale meteorological conditions (500 hPa geopotential height, 500 hPa winds and surface precipitation) associated with the case studies presented are extracted from the single-level MERRA2 hourly reanalysis. Geopotential height anomalies are computed as the difference between the hourly values averaged over the considered event and the monthly mean climatology for the corresponding month.
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  101003826  via project  CRiceS (Climate Relevant interactions and feedbacks: the key role of sea ice and Snow in the polar and global climate system). VF and JW acknowledge support from the  Agence Nationale de la Recherche  project  ANR-20CE01-0013  (ARCA). AE acknowledges the  EU   H2020 FORCeS  project, contract No  821205. We acknowledge  J. Schmale  at  École Polytechnique Fédérale de Lausanne  and her team/collaborators who conducted the aerosol measurements during the international Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC). These data sets, already published in Dada et al. (2022), were used in this work for the validation of our methodology.