dataesr/scientific-paragraphs-categorization huggingface dataset:
Parameters The parameters used in the mathematical model are collected from the literature. In particular, the physical parameters of the electrode and the thermistor can be found in  [3]. The blood physical parameters are found in the virtual population database from the IT IS foundation  [11]. A porcine cardiac tissue is considered with electrical properties as appear in  [2]  and thermal properties as described in  [12]. In particular, a linear decrease of the specific heat with respect to temperature of 0. 42% from the value 3017 J kg -1 K -1 at body temperature is considered. Similarly, for the thermal conductivity a decrease of 0. 05% from the value 0. 518 W m -1 K -1 at body temperature, and for the electrical conductivity an increase of 1. 5% from the value 0. 54 S m -1 at body temperature.
The sample containing indexed amplicons was then loaded onto the MiSeq reagent cartridge v3 (MS-102-3003; San Diego, CA, USA) and onto the instrument along with the flow cell. Automated cluster generation and paired-end sequencing with dual indexes reads was performed (2 × 300 bp run). After demultiplexing, paired end reads were joined together with the fastq-join program (https://expressionanalysis. github. io/ea-utils/). Only reads that had quality value (QV) scores of ≥20 for more than 99% of the sequence were extracted for further analysis. All sequences with ambiguous base calls were discarded. After filtering, sequence reads were assigned to operational taxonomic units (OTUs) based on sequence similarity for each read to 16S rRNA genes from the NCBI nt database by using BLASTN function.
The data obtained by HTS can throw light on the behavior of potentially pathogenic and spoilage bacteria in foods during storage. Nevertheless, the influence of the method used to assess the biodiversity on the results needs to be taken into consideration. For example, differences in copy number of the 16S rRNA gene can cause quantification problems. Furthermore, results will also depend on sample variability. Considering these limitations, the results obtained in the present study by HTS revealed that the main bacterial groups detected in the fresh fillets after being packed in vacuum (Listeria, Acinetobacter, Pseudomonas, Chryseobacterium, and members of Enterobacteriaceae) were rapidly displaced by other bacterial groups during storage (mainly members of Vibrio, Photobacterium, and Shewanella together with other groups such Cloacibacterium, Lactobacillales, and Comamonadaceae appearing during late storage).
PET/MRI Acquisition. e PET/MRI acquisition was performed using a 3. 0T hybrid system (Biograph mMR, Siemens Healthineers, Erlangen, Germany). e system is equipped with a 32-channel spine and flexible body array coil for MRI signal reception and 56 lutetium oxyorthosilicateavalanche photodiode (LSO-APD) PET detector blocks. Subjects were asked to fast for 6 hours prior to the examination to eliminate effects of postprandial glucose levels on portal blood flow  [20]. Approximately one hour before the PET/MRI examination, an intravenous dose of 5. 18 MBq/kg 18 F-FDG was administered to the subjects. PET data were acquired during the MRI acquisition and were corrected for attenuation using a Dixon-based method. PET images were reconstructed to 127 axial images with a field-of-view of 72 cm, matrix size 172 × 172, and slice thickness 2.
DCE-MRI Analysis. Prior to pharmacokinetic modeling, motion correction was performed on the DCE-MRI images using a 3D rigid registration algorithm in an open-source image analysis software package (FireVoxel, CAI2R, New York University, New York, NY, USA). e ROI placement for the DCE-MRI images was done in the same FireVoxel software. For determination of the vascular input function, ROIs were drawn in the portal vein on the registered images and in the abdominal aorta at the level of the celiac trunk on the unregistered images  [21]. In addition, single-slice ROIs were drawn in the liver parenchyma and the HCC lesion(s) of each patient. e dynamic signal intensity (SI) curves averaged for each ROI were exported, and further analysis was done using custom-written scripts in MATLAB (version R2016b, MathWorks, Natick, MA, USA).
For both TM and SSM modeling, v e was constrained to a value between 0 and 1 and K trans to a value between 0 and 3 min -1. Other parameters needed as input for the modeling included the contrast agent's relaxivity at 3. 0T (6. 3 mM -1 •s -1  [23] ) and the blood hematocrit value for which a fixed value of 0. 45 was used. e   [11]  was fit to the mean signal curve in the ROI at different b values to estimate the pseudodiffusion coefficient D * , diffusion coefficient D, and the perfusion fraction PF. e fitting procedure was performed in MATLAB using a Bayesian algorithm  [24]. In addition, the apparent diffusion coefficient (ADC) was determined by calculation of the slope of a linear fit through the logarithmic signal data at the different b-values. For ADC estimation, only b-values of 0 and >150 s/mm 2 were included to avoid a disproportionate effect of perfusioninfluenced measurements on the ADC  [14].
BOLD MRI Analysis. Similar to the IVIM analysis, ROIs of the BOLD MR images were drawn in OsiriX. Using a custom-written script in MATLAB, a monoexponential model was fit to the mean signal intensity curves at the different echo times in the liver and HCC ROIs to estimate R * 2. is analysis was done for the acquisitions before and after O 2 challenge, and ΔR * 2 was determined (R * 2 post-O 2 - R * 2 pre-O 2 ). 2. 8. Statistical Analysis. Statistical analysis was performed in MATLAB. Nonparametric tests were used, given the small sample size. Wilcoxon signed-rank tests were used to evaluate the differences in PET/MRI parameters between the liver parenchyma and HCC lesions. In patients with more than one HCC lesion, the average of the parameters from multiple HCC lesions was taken for statistical analysis.
The systems can be controlled by using Matlab through open user interface, allowing access to RF data of all channels and in particular, to display reconstructed data for ultrasound guidance in real time and offering extreme flexibility and precision. Standard or customized US transducers arrays can be connected to the systems, after declaration of the specifications of the US probe. Synchronization box. A special Multi-System Synchronization Module capable of distributing the Master system clock to up to 8 Vantage systems via common HDMI cables designed and commercialized by Verasonics was used. One of the 4 systems is the "Master", providing the 250 MHz clock, trigger and other signals for accurate temporal synchronization. Therefore, the module triggers in transmission and reception all the connected systems (considered as "Slaves") to co-start the acquisitions, with a less than 10 ns delay.
Data Processing The RF data were acquired individually for each of the 4 US systems from the active elements to which it was connected to. After the acquisition, the data were collected and merged together, carefully taking into account the inactive line distribution of the US probe in each system. One large matrix containing the information of all the active elements (up to 1024 elements) was obtained and the beamformed RF data were post-processed offline  [33]. A dataset for an entire volume was further provided. For Power Doppler visualisation of the images, 3D slicer software  [34]  was used, allowing 3-D view rendering, and the animations are shown in supplementary material.
Such a method provides access to a statistical model that uses a low-dimensional subspace to represent the shape of the corresponding object, and is also able to estimate the plausibility of such a shape. This approach requires a labeled database that shows the object under consideration in many different shapes that are related to the motion to be observed. Polygon meshes already have been successfully used for statistical shape analysis of, e. g. , human bodies  (Allen et al. , 2003) , faces  (Blanz and Vetter, 1999) , or tongues  (Badin and Serrurier, 2006). As we want to separate the anatomy related variations from the ones related to the speech related tongue pose, we use a method that leads to a so-called multilinear model. This statistical model type is able capture those different types of variation separately.
In this section, we outline how the acquired mesh collection can be aligned to only contain speech and anatomy related tongue shape variations, and how the model is derived. We then turn to the evaluation of our approach in Section 6, where we apply it to MRI scans of two datasets. Afterwards, we investigate the validity of our obtained mesh collection by means of a preference test in Section 7. Furthermore, we conduct experiments to evaluate the compactness, generalization, and specificity properties of the acquired model in Section 8. In Section 9, we use the model for tracking speech related motion capture data of a new speaker. Finally, we conclude in Section 10 and outline possible future work.
The results are afterwards used as a reference to align the tongue meshes. To this end, we use a speaker's palate mesh that was earlier aligned to the corresponding scan. We then estimate the rigid transformation that maps this aligned palate mesh to its Procrustes variant and apply the same motion to the corresponding tongue mesh. By doing so, we remove any translational and rotational differences related to head motion or position differences without eliminating any speech or anatomy-specific information. Finally, we have to ensure that for each speaker the meshes for all selected poses are available. This might be needed because some MRI scans may be corrupt or even missing in the used dataset. In these cases, we reconstruct a missing pose shape of a speaker by averaging available data: first, we compute the average shape of all meshes that are present for the speaker.
Deriving a tongue model Our next goal is to apply the described framework to MRI data and obtain a tongue model. We do not undertake a quantitative evaluation of the tongue meshes extracted from the MRI scans, a decision that is necessitated by the fact that we are working with real-world data, and thus have no ground truth reference available. There exists the possibility of manually annotating the MRI scans to create a reference solution, but this procedure is very time consuming and expensive if the number of scans in the dataset is very large. Moreover, such hand-labeling is error prone and the anatomical expert(s) involved may introduce a subjective bias. Instead, we chose to perform a qualitative analysis: we inspected the results manually as a post-hoc analysis in order to decide whether they are acceptable.
Background information about data used We use two datasets to derive our model: the dataset of  Baker (2011)  and the full dataset of the Ultrax project (Ult, 2014), which provides us with data of 12 speakers in total. The Ultrax dataset consists of static MRI scans of 11 adult speakers of British English where 7 are female and 4 are male. All speakers are phonetically trained and were recorded while sustaining the vocal tract configuration for different phones for around 20 s. For each speaker, 13 speech related scans are available that correspond to the phone set [i, e, E, a, A, 2, O, o, u, 0, @, s, S]. The Baker dataset was recorded as part of the Ultrax project, but released separately. It contains 25 scans of one male speaker that are speech related and represent different articulatory configurations.
The data was recorded at the Clinical Research Imaging Centre in Edinburgh using a Siemens Verio 3T scanner; the scans were acquired with an echo time of 0. 93 ms and a repetition time of 2. 36 ms. The individual scans consist of 44 sagittal slices with a thickness of 1. 2 mm and a slice size of 320 × 320 pixels. The grid spacings are h x = h y = 1. 1875 mm and h z = 1. 2 mm. For our analysis, we decided to exclude one speaker of the Ultrax dataset that showed a high activity of the soft palate, which caused problems in our framework. Furthermore, we use the whole phone set that was recorded for the Ultrax data. However, the Baker dataset is lacking scans for the phones [a, O, 0, @, s, S] where the shape information has to be reconstructed. In total, we are using the shape information of 11 speakers with 13 different tongue shape configurations.
In Figure  8 , some of these shapes can be seen. This means that we arrive at a tensor A ∈ R 11×13×9300 where the dimension of the vertex mode is determined by the vertex count of the tongue template we are using. It is important to state that a tongue model derived from this data might lack some typical tongue pose related variations because the phonetic coverage of the underlying dataset can be considered incomplete. Due to these data related constraints, we believe that this model's applicability to some of the application areas we described earlier, may be limited. However, assembling and using MRI recordings is a demanding task: First, it requires access to an MRI scanner, along with specialized equipment and technical staff experienced in performing such recordings. Second, appropriate speakers have to be found who are phonetically trained and whose articulation is not impacted by the MRI scanner.
Experiment setup We prepared the following data for the experiment: for each of the 137 scans in our dataset, we created three versions of the same sagittal slice. One version showed the unannotated slice. The second version showed the slice with the tongue mesh contour after the initial template matching. The last version visualized the tongue mesh contour after the final bootstrapping. Afterwards, we randomly partitioned our scan set into 4 subsets of roughly equal size. These partitions were then randomly assigned to the participants such that overall, each scan was seen by 3 to 4 participants. 15 speech experts took part in the experiment. On average, they had 11 years of research experience with speech production data. Each participant was asked to view all scans of the assigned partition and to select the preferred annotated version of the shown sagittal slices.
Data selection and setup for experiments We selected the following data of the female subject VP05 in the dataset: one recording that contains repeated consonant-vowel combinations of the consonants [f, s, S, ç, x, K, m, n, N, l] and the vowel  [u]. Furthermore, we used an EMA recording of the German translation of the "Northwind and the Sun" passage, a standard specimen in phonetic research  (International Phonetic Association, 1999). To combine this EMA data with our model, we had to register dynamic speech data of an unknown speaker containing new phonemes, which is a significant challenge. However, we want to point out that the phonetic inventories of the English and German languages are very similar. Thus, we think that the pose subspace of our tongue model should be compatible with motion capture data originating from producing German speech.
Experiments In our experiments, we used the following energy to fit our model to the current EMA data frame: E Track (s, p) = α E data (s, p) + β E bias (s, p) + γ E coherence (s, p) (18) The data term E data measures the distances between the vertex locations and their corresponding EMA coil positions. The bias term E bias penalizes deviations from the mean weights of the model. We added this term to the energy to provide the approach with information about the average tongue shape in order to cope with the sparsity of the data. Finally, the consistency term E coherence favors a temporal coherence between consecutive frames. For all experiments, we used α = 1 and β = γ = 5 to provide a good compromise between these model ideas. Furthermore, we set the scale factor h to 5 to give the approach sufficient freedom during the optimization.
Conclusion In this work, we have presented a multilinear tongue model that was derived from volumetric MRI scans in a minimally supervised way. We also verified the validity of the extracted meshes by conducting a preference test. Afterwards, we observed in the experiments that a model with a low dimensionality can reliably register unknown data with an acceptable precision. Moreover, we explored in two experiments whether the model that was acquired from static data was suitable for tracking sparse motion capture data of the tongue. We found that fixing the anatomical features of the model to a speaker specific shape provided the most acceptable results. However, we also discovered indications that the current multilinear model might be missing some shape variations. A previous version of our multilinear model was used in a text-to-speech framework that is able to synthesize audio with synchronized tongue motion  (Le Maguer et al.
, 2017). For this purpose, the audio and EMA recordings of the mngu0 articulatory database  (Richmond et al. , 2011)  were used, where we utilized the output tongue pose weights from a tracking approach similar to the one described in Section 9 as feature vectors for training the synthesis framework. In the future, we plan to investigate whether more shape variations can be obtained using more data. To this end, we want to use additional datasets in our framework. This implies that we also have to extract the shapes of phones like  [g, k]  that are characterized by contact with the soft palate. In this regard, we have to address the issue of recovering in the corresponding scans the surface of the soft palate, which can deform in a non-rigid way. Additionally, the datasets we use might differ with respect to the recorded phones, which leads to missing data in our training set.
Data Acquisition Recordings (ECoG along with EMG activity) were performed in awake, freely moving animals for 150 min for six consecutive days using a Multi Channel Systems W2100 wireless interface board with an acquisition frequency of 1 KHz and a 4-channel W2100-HS4-opto Headstage (weight of 1. 9 g + 3. 8 g for the battery). In addition, video tracking of the mice was carried out for all experiments. After surgery, all mice were kept in the same room where the recordings followed. We also habituated the animals to the recording arena 2 days before the recordings. In the first day of experiment (baseline day), no injection was performed. On the next five consecutive days, recordings were performed before (10 min) and after 150 (min) kainic acid (Sigma) injections. Mice were briefly anesthetized with isoflurane (SC.
Data Analysis We only included data acquired from confirmed recording sites. All data were visually inspected, and intervals contaminated by artifacts were excluded from further analysis. Consequently, an average of 80% of recordings was retained. Power spectral density estimates were obtained with the Welch method with 2 s windows overlapping 1 s from the 1 KHz signal (MATLAB function pwelch). Changes in the ECoG power spectral density bands (Delta: 0. 5-3. 5 Hz, Theta: 4-12 Hz; Beta: 13-30 Hz; Low-Gamma: 30. 5-48 Hz; High-Gamma: 52-100 Hz) were analyzed before and during all days of kainic acid administration for all the areas examined: motor, somatosensory and parietal cortices. Moreover, the real and imaginary coherence variations per band were investigated, as well as the evolution in time throughout the 5 days of chronic application of kainic acid for all the investigated brain regions.
Coherence estimates were obtained using the MATLAB function mscohere, from power spectral density estimates of pairs of ECoG recordings, using the same parameters investigating the coherence between motor and sensorimotor and between motor and parietal cortices. The imaginary part of the coherence was also computed to estimate the coherence avoiding contamination by volume conduction  (Nolte et al. , 2004). Data were normalized by expressing the results as percentages of the baseline values (the values from the first day of recording). The data obtained were processed in the Excel 2016 software, and then in GraphPrism 6. 0 using repeated measures ANOVA, Dunnett's multiple comparisons test, Friedman test, Dunn's multiple comparisons test, Multiple t-test, Mann Whitney test and Kruskal-Wallis test after testing the normality distribution of the data, as appropriate.
We further analyzed the EMG amplitude estimators (Figures  3E, F ). RMS revealed high amplitudes in the first and last days of kainate administration and minimum ones in day 3. The ARV estimator showed minimum changes in the first 3 days, recorded a maximum in day 4 and dropped immediately in the 5th day of experiment. The values recorded in day 5 were still higher than those recorded in the first 3 days. However, these changes were not statistically significant. This data suggests that kainic acid still has a muscular effect (increased mean frequency) during the last days of recording, even though dystonic score decreases.
FUNDING This work was supported by  TE European Young Team: ''CEROSCO'', PN-II-RU-TE-2014-4 project/UEFISCDI  ( Executive Unit for Higher Education, Research, Development and Innovation ), public institution with legal personality subordinated to the  Ministry of Education, Romania  and by  Agence Nationale de Recherche  to  DP  ( ANR-16-CE37-0003-02  Amedyst).
At present, no formal studies have been conducted to confirm the minimum effective dose in the 1L or later settings, although an additional study to confirm efficacy is balanced with AE profiles at lower doses is underway in patients receiving ≥ 2L treatment (NCT02906696). However, when a lower starting dose is selected, the aim is generally to bring each patient to the standard dose for their indication  [25]. The recommendations for dosing strategies discussed below are based on management of AEs, but it is important to consider the broader clinical setting; in particular, the ability to monitor and manage a patient closely following initiation of treatment is an important consideration regarding starting at the approved dose versus a lower dose  [25].
Acknowledgements Medical writing support was provided by  Rina Vekaria Passmore, PhD , and  Geraldine Thompson  of  Engage Scientific Solutions , and was funded by  Pfizer. JFA is a  NIHR  ( National Institute for Health Research ) Senior Investigator and acknowledges the support of the  Imperial NIHR Biomedical Research Centre. J. E. C. has received consultancy fees from  Pfizer ,  Novartis ,  Forma Therapeutics ,  Daiichi , and  Astellas , and research funding from  Pfizer ,  Novartis ,  Forma Therapeutics ,  Daiichi , and  Astellas. J. F. A. has received honoraria from  ARIAD, Bristol-Myers Squibb, Incyte ,  MSD ,  Novartis , and  Pfizer , and has received research funding from  ARIAD ,  Novartis  and  Pfizer. D. J. D. has served in a consulting or advisory role for  Amgen, ARIAD, Bristol-Myers Squibb ,  Incyte ,  Novartis , and  Pfizer , and has received research funding from  ARIAD.
5 ng/l of random hexamer primer (Invitrogen) using SuperScript III reverse transcriptase (Invitrogen). All RNA samples were analyzed with the Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA), and only those with RNA integrity number (RIN) values (31) ranging from 7. 5 to 8. 5 were selected. Each reaction was performed in a 20-l mixture containing SYBR Premix Ex Taq (TaKaRa), 200 to 300 nM each primer, and the template cDNA diluted 1:2 and run on a StepOnePlus real-time PCR system (Applied Biosystems). The reactions were carried out in two biological replicates with three technical replicates each, and appropriate controls were included to verify the absence of gDNA contamination in the RNA samples and primer-dimer formation. Primers 5=-TTGGAGAATTGACGCACCAG-3= and 5=-ATCCAGCAGGCGAACCAG-3= for pimM and 5=-GACGCAACGCG AAGAACC-3= and 5=-TGCGGGACTTAACCCAACATC-3= for rrnA1 were designed to generate PCR products of 125 and 137 bp, respectively, near the 5= ends of the mRNA.