dataesr/scientific-paragraphs-categorization huggingface dataset:
Annotation and cloning of TaBCAT1 for subcellular localization The expression levels of 37 transcripts annotated as involved in BCAA biosynthesis were assessed during fungal infection in publicly available wheat RNA-seq data using the wheat expression browser  (Borrill et al. , 2016 ; Figure  3 ). The predicted subcellular location of the three homoeologous copies of TaBCAT1 (TraesCS4A02G059800. 1, TraesCS4B02G 235400. 3, TraesCS4D02G236800. 1) was assessed using iPSORT  (Nakai and Horton, 1999) , MitoProt  (Claros, 1995) , and TargetP  (Emanuelsson et al. , 2000)  using default parameters (Supplemental Table  3 ). Sequence alignments were performed using Clustal omega  (Sievers and Higgins, 2014)  and visualized in Jalview  (Clamp et al. , 2004). Sequence similarity of TaBCAT1 to the A. thaliana AtBCAT family was assessed using BLASTp  (Altschul et al.
Acknowledgements We thank  Brande Wulff  ( John Innes Centre: JIC ) for helpful suggestions throughout the study,  Jessica Meades ,  Guru Radhakrishnan  and  Nicola Cook  ( JIC ) for technical assistance,  Kostya Kanyuka  and  Kim Hammond-Kosack  ( Rothamsted Research ) for providing the VIGS constructs,  Sarah Holdgate  ( NIAB ) for providing Pst urediniospores,  Paul Fenwick  ( Limagrain UK Ltd. ) for providing Pgt isolate UK-01, all those who submitted Pst-infected wheat samples to the UK cereal pathogen virulence survey (UKCPVS) and the  NBI Computing infrastructure for Science (CiS) group.
Funding This project was funded primarily by a  European Research Council  grant (no. 715638 ) to D. G. O. S. P. C-M. was supported by the  Biotechnology Biological Sciences Research Council (BBSRC)   Norwich Research Park Biosciences Doctoral Training Partnership  ( BB/M011216/1 ), Y. M. L. M. by a  British Society for Plant Pathology  undergraduate vacation bursary and L. A. M-P. by the  Erasmus + program. Additional funding was provided to D. G. O. S. by the  BBSRC   Institute Strategic Program   BB/P012574/1  and  BB/P016855/1  and the  John Innes Foundation.
Study population We used the Multi  We included adult patients admitted directly to the ICU from the emergency department with AP. Patients with AP were identified based on the ICD-9 code (577. 1), and confirmed by elevated serum amylase and/or lipase greater than three times the upper limit of normal (ULN), and/or finding on CT abdomen consistent with AP. Furthermore, patients' cases were manually rechecked by two independent investigators. The data were collected from the observation charts of patients both clinical and demographics. The baseline characteristics of patients including age, sex, alcohol consumption, comorbidities, and the etiology of AP were explored and compared between survivor and non-survivor groups. The laboratory data at admission and severity scores including SOFA, APS III, and OASIS were also evaluated.
Another limitation was the inability to compare inflammatory markers (C-reactive protein or interleukins) or established prognostic factor such as BMI that were because these data were not available in the database or not collected routinely. Also, we focused in this study on patient admitted directly to ICU. Clinical, biological, and discharge summaries of non-ICU patients are not available on MIMIC-III database preventing the analysis of patients admitted initially to the floor. Serum phosphate, BUN, creatinine, total bilirubin, AST, ALT, and alkaline phosphatase can be used as a reliable prognostic marker in predicting the mortality of AP. In the ROC analysis, the AUC of serum phosphate level for mortality has the highest value. Future prospective studies would be the cogent next step in validating its predicting value.
-parameter Intelligent Monitoring in Intensive Care (MIMIC-III (version 1. 4)) database, developed conjointly by researchers from the Laboratory for Computational Physiology at Massachusetts Institute of Technology (MIT), Cambridge, MA, United States, and the Department of Medicine at the Beth Israel Deaconess Medical Center (BIDMC) in Boston in the United States. This large, single-center database contains the information of 46,520 critically ill patients admitted to BIDMC from 2001 to 2012 and has detailed information about ICU patient stays, including high-resolution monitoring data, therapeutic interventions including medications, hydrations, and procedures, discharge summaries, laboratory data, and radiology reports. Given the fact that all patients were de-identified in a Health Insurance Portability and Accountability Act-compliant manner, the institutional review boards (IRBs) of BIDMC and MIT approved the use of the MIMIC-III database.
,18  It is possible to access this database by passing an examination and obtaining the certification. One author (AH) obtained access and was responsible for the data extraction. The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki as reflected in a prior approval by the institution's human research committee. A written informed consent was obtained from each patient included in the study. Since the study was an analysis of an anonymized publicly available database with preexisting IRB approval, IRB approval from our institution was exempted.
Data The training corpus for the ASR acoustic model (AM) comprises of several datasets: • 5k instances for Congolese Swahili speech provided by the IWSLT-2021 organizers 1  ; • a training subset of the ALFFA corpus  (Gelas et al. , 2012)  (read speech and broadcast news); • a subset of the IARPA Babel Swahili Language Pack 2 (conversational and scripted telephone speech that spoken in the Nairobi dialect region of Kenya). The total size of the training corpus is about 74 hours. In our preliminary experiments, we also tried to include a swa dataset (5k instances of Coastal Swahili), provided by the IWSLT-2021 organizers, into the training corpus, but this does not improve the ASR performance. Hence, for the submitted system and for the results reported in the paper, this corpus was not used.
Data For the swa-eng sentence pairs, training dataset for machine translation system includes: • OPUS 4  english-swahili parallel data : CCAligned and MultiCCAligned (El-Kishky et al. , 2020), WikiMatrix, Wikimedia, XLEnt and ParaCrawl. • 5k parallel swa-eng dataset provided by IWSLT-2021. The total size of the training dataset for swa-eng is about 3. 2M sentence pairs. We applied language identification filtering LangID  (Lui and Baldwin, 2012)  keeping only swa-eng sentence pairs with correct English. Sentence pairs where the English side is detected as noisy are removed from the swa-eng training dataset. In total, we filter out about 30% of the original training set and obtains a dataset of 2. 2M sentence pairs. As for swc-fra NMT system, training data includes parallel corpora made available by the organizers in addition to the available corpora for this language pair on OPUS website.
Results The ASR results in terms of word error rate (WER) are reported in Table  1  on the development datasets for different types of acoustic features. We can see that using wav2vec features significantly decreases the WER and provides about 8% of relative WER reduction for both datasets. The MT results in terms of BLEU  (Papineni et al. , 2002)  score are reported in Table  3. Notice that while the WER of the outputs of the ASR fed by wav2vec features is lower than the one fed by MFCC features, for the swc-fra language pair, the BLEU score of the translation from the MFCCbased ASR system is higher than the one got on the wav2vec-based ASR. By lack of time, we did not yet investigate the reason of this, but we will do as soon as possible.
Model architecture Our system is based on the Dual-decoder Transformer  (Le et al. , 2020)  which consists of an encoder and two decoders. This architecture jointly transcribes and translates an input speech. Each of the decoders is responsible for one task (ASR or ST) while interacting with each other. We refer the reader to the paper for further details. We initially followed  Le et al. (2020)  and used 12 encoder layers, 6 decoder layers, and a hidden dimension of d = 256. However, this model produced poor results. We hypothesize that with this configuration, the model capacity is too large for the dataset described in the previous section. In the end, we ended up using only 6 encoder layers and 3 decoder layers (with the same d = 256). In addition, we also trained a Transformer model having the same encoder of 6 layers but with only one decoder as the baseline (hereafter called singledecoder model).
Implementation details For text pre-processing, we normalize the punctuation and build the vocabulary on the concatenation of the transcript and translation text using Senten-cePiece  (Kudo and Richardson, 2018)  without pretokenization. We used 10k unigram vocabulary as it performed slightly better than a vocabulary of 8k tokens in our preliminary experiments. The speech features are 80-dimensional log Mel filterbank. Utterances having more than 3000 frames are removed for GPU efficiency. We used SpecAugment  (Park et al. , 2019)  with Librispeech double (LD) policy for data augmentation. es-en es-fr es-pt es-it fr-en fr-es fr-pt pt-en pt-es it-en it-es For the target-forcing mechanism, we prepended a language-specific token to the target sequence  (Inaguma et al. , 2019; Le et al. , 2020).
Five independent replicates of the phytocomplex were extracted and measured. The chromatography system used for the quantification of rosmarinic acid consists of an Acquity UPLC BEH C18 1. 7 µm column, size 2. 1 mm × 100 mm, coupled to an Acquity UPLC BEH C18 1. 7 µm VanGuard Pre-Column 3/Pk, size 2. 1 mm × 5 mm. The platform used for the UPLC-DAD (Ultra Performance Liquid Chromatography-Diode Array Detection) analysis comprises a UPLC system (Waters Corporation, Milford, MA 01757, USA) consisting of an eluent management module, Binary Solvent Manager model I Class, and an autosampler, Sample Manager-FTN model I Class, coupled to a PDA (Photo Diode Array) eλ diode array detector. Empower 3 (Waters) software was used to acquire and analyze the data. The chromatography method used was the following: solvent A: water, 0.
1% formic acid; solvent B: 100% acetonitrile. The initial condition is 99% solvent A; moreover, the flow remains constant at 0. 350 mL/min throughout the duration of the analysis. The chromatography column was temperature controlled at 30 • C. Elution of the molecules was conducted by alternating gradient and isocratic phases, as indicated in Table  1. For quantification of the rosmarinic acid, the chromatogram obtained at the wavelength of 330 nm was used. The rosmarinic acid was quantified thanks to the calibration curve of the authentic commercial standard of rosmarinic acid (CAS 20283-92-5; purity≥95%; Sigma Aldrich). The data analysis was carried out with Empower 3 software.
N-acetyl-L-cysteine 5 mM (Sigma Aldrich) and Rutin at 0. 1% w/v were used as positive controls and the Melissa officinalis phytocomplex was used at 0. 1% w/v. Probe fluorescence intensity was measured at excitation 485 nm-emission 535 nm, in the absence or presence of 1. 1 µM H 2 O 2 used as oxidative stimulus, using a Multilabel Plate Reader VICTOR X3 (PerkinElmer). Fold increases in ROS production were calculated using the equation: (F treatment -F blank )/(F control -F blank ) ( 1 ) where F is the fluorescence reading. The statistical analysis was performed using GraphPad Prism version 6 for Windows (GraphPad Software, San Diego, CA, USA). Results are presented as mean ± SD of 3 experiments. An unpaired Student's t-test was used to compare ROS values, and p values <0. 05 were considered statistically significant.
Elastin immunostaining was revealed by immunofluorescence using an Alexa Fluor®488-conjugated secondary antibody (Life technologies, Thermo Fisher Scientific, Carlsbad, CA, USA, A11008). Nuclei were counterstained with propidium iodide (Sigma-Merck KGaA, Darmstadt, Germany). The microscopical observations were realized using an Olympus BX63 microscope. Pictures were digitized with a numeric DP74 Olympus camera with cellSens Dimension storing software (Olympus, Tokyo, Japan). The intensity level of each immunostaining was scored from 1 to 6.
ab76026, clone EP1809Y), anti-elastin antibody (Novotec, Bron, France, ref. 25011), anti-MMP-1 antibody (Sigma, Merck KGaA, Darmstadt, Germany, ref. M4696). For immunohistochemistry, a ready-touse biotinylated universal secondary antibody has been used (Vector Laboratories, Burlingame, CA, USA), combined with a streptavidin-peroxidase amplifying system and revealed by the VIP chromogen (Vector Laboratories, Burlingame, CA, USA). Elastin immunostaining was revealed by immunofluorescence using an Alexa Fluor® 488-conjugated secondary antibody (Life technologies, Thermo Fisher Scientific, Carlsbad, CA, USA, A11008). Nuclei were counterstained with propidium iodide (Sigma-Merck KGaA, Darmstadt, Germany). The microscopical observations were realized using an Olympus BX63 microscope. Pictures were digitized with a numeric DP74 Olympus camera with cellSens Dimension storing software (Olympus, Tokyo, Japan).
The UPLC-DAD analysis was used to estimate the content of rosmarinic acid and total polyphenols content into the Melissa officinalis phytocomplex. The chromatographic profile of the phytocomplex at 330 nm is shown in Figure  2. The data obtained show that the content of rosmarinic acid in the phytocomplex is 7. 6 ± 0. 1% w/w and the content of total polyphenols expressed as equivalent in rosmarinic acid is 9. 2 ± 0. 1% w/w. Thus, the main component of total polyphenols in the phytocomplex is represented by rosmarinic acid (83% of the total polyphenols). the Melissa officinalis phytocomplex. The UPLC-DAD analysis was used to estimate the content of rosmarinic acid and total polyphenols content into the Melissa officinalis phytocomplex. The chromatographic profile of the phytocomplex at 330 nm is shown in Figure  2.
Parent and metabolite and data analysis The concentration of parabens was measured by HPLC-HRMS (Q-Orbitrap), using a standard curve of the corresponding paraben. The analytes of all 16 paraben incubations were separated and identified using Method 1. This method involved the separation of analytes on a Kinetex Phenyl-Hexyl (2. 6u, 50 × 2. 1 mm) column using a gradient of mobile phase A (100% acetonitrile) and mobile phase B (10 mM ammonium acetate in water) at a flow rate of 0. 6 mL/min. The gradient was: 0 min = 5% A; 0. 1 min = 5% A; 0. 8 min = 97% A; 2. 2 min = 97% A; 3. 0 min = 5% A. A complete metabolite FullScan (in the negative electrospray ionization mode, 100-380 m/z) analysis of the samples was performed to identify metabolic species by accurate mass. Blank samples were the respective buffer or medium without test chemical or PHH/S9.
A complete metabolite FullScan (in the positive electrospray ionization mode, 100-600 m/z) analysis of the samples was performed to identify metabolic species by accurate mass. Parent and 4-HBA were quantified using a standard curve of the corresponding paraben and 4-HBA. The half-life (t 1/2 ) was calculated by first plotting in GraphPad software the Ln of the % initial parent concentration remaining versus time (min) for the test sample (paraben with S9 in the absence or presence of cofactors) and the control sample (paraben without S9 or with heat-inactivated S9). The negative slope of the control sample (Sc) was then subtracted from the negative slope of the test sample (St) to give the control-adjusted slope. For all calculations, the slope of the control incubation was subtracted from the test sample slope to account for loss of parent chemical due to non-enzymatic depletion resulting from chemical instability or non-specific binding to the incubation tube.
Our results suggested a dependency toward ARAF kinase, as well as a possible role of ARAF in resistance mechanisms in cutaneous melanoma. The potential role of ARAF in NRAS-induced melanoma was further strengthened by an in silico search in public databases that allowed to identify patients with metastatic melanomas harbouring an ARAF mutation associated with activating NRAS mutations  7. Nevertheless, ARAF remains the less studied member of the RAF family because: i) ARAF displays the lowest kinase activity towards MEK compared to other RAF proteins  8  , ii) in most cellular models, the role of ARAF is hidden by the predominant roles of BRAF and CRAF. Microphtalmia-associated transcription factor (MITF) is a master regulator of the melanocytic lineage since it is essential for the differentiation, survival and proliferation of melanocytes  9, 10.
1a , Table  1 ). Well-known ARAF partners were found enriched in ARAF-only cells, such as its upstream activator, the GTPase NRAS, or its substrate MEK1, indicating the reliability of the experimental approach (Table  1 ). In order to identify ARAF relevant partners, which functionally impact melanoma cell proliferation, we developed a siRNA-based functional screen on 99 selected targets (Fig. 1a ). These 99 interactors were selected as followed: 69 were chosen among 359 proteins enriched in ARAF-only cells with the following parameters: number of peptides>9, ratio>2 and adjusted p-value<0. 001 (Fig. 1a ). 15 were from the 72 proteins exclusively identified in ARAF-only cells. We also included 15 proteins that were found both in our current dataset and in the ARAF interactome published by Zhang et al.
26. ARAF-only cells growth was followed upon knock-down of the selected partners by siRNA pools transfection (Supplementary Fig. 1 ). Among the 99 partners tested, 16 impacted the growth of ARAF only melanoma cells. It appeared that 11 ARAF partners had an anti-proliferative effect while 5 proteins were pro-proliferative (labelled in green and red, respectively in Fig. 1b ). Among the 132 interactors identified in the ARAF proteome by Zhang et al. 26, 27  , 107 were commonly found in our dataset, showing the robustness of the approach. Twenty-four of the common identified partners were included in our screen: 9 were selected by the previously described parameters and 15 additional were chosen after bibliographic analysis. It should be noticed that Zhang et al. have validated their interactome by coimmunoprecipitation experiments on 12 out of 13 randomly selected ARAF interacting proteins with different functions.
We selected ions with charge state from 2+ to 6+ for screening. Normalized collision energy (NCE) was set at 27 and the dynamic exclusion of 40s. For identification, the data were searched against the Mus Musculus UniProt canonical database (22082017 containing 16888 sequences) using Sequest-HT through proteome discoverer (version 2. 0). Enzyme specificity was set to trypsin and a maximum of two-missed cleavage sites were allowed. Oxidized methionine carbamidomethyl cysteines and N-terminal acetylation were set as variable modifications. Maximum allowed mass deviation was set to 10 ppm for monoisotopic precursor ions and 0. 02 Da for MS/MS peaks. The resulting files were further processed using myProMS v3. 5  40. FDR calculation used Percolator  41  and was set to 1 % at the peptide level for the whole study.
For endogenous immunoprecipitation, cell lysates were incubated overnight at 4°C with anti-ARAF antibody (#4432, Cell Signaling) and Pierce TM protein-A magnetic beads. For immunoprecipitation of recombinant proteins, 15 ng of human recombinant MITF protein (Origene) were incubated overnight at 4 °C in NP40 Buffer with 75 ng of human recombinant flag-ARAF (Origene) and anti-flag magnetic beads (M2, Sigma). Western blotting and antibodies. For SDS-PAGE analysis, the membranes were blocked with 5 % milk in PBS Tween 20 (10 %) for 30 min at room temperature. Membranes were then probed overnight at 4°C with the appropriated primary antibodies: anti-MITF (HPA003259, Sigma), anti-HA (3F10, Roche), anti-myc (9E10, Santa Cruz), anti-flag (M2, Sigma), anti-ARAF (#4432, Cell Signaling) and anti-β-actin (A1978, Sigma) antibodies.
Antigen-antibody complexes were detected by horseradish peroxidase-coupled secondary antibodies followed by enhanced chemiluminescence. Signals were acquired using a cooled-CDD camera (Fusion FX Spectra, Vilber). Proximity Ligation Assay (PLA). Cells were grown on glass coverslips, fixed and permeabilized. PLA (Duolink) was performed according to the manufacturer's instructions (Sigma) using antibodies against ARAF (cs4432, Cell Signaling) and MITF (ab12039, abcam), BRAF (F7, Santa Cruz) and MITF (HPA003259, Sigma), or CRAF (#610151, BD Biosciences) and MITF (HPA003259, Sigma). Images were captured using a 3D Leica fluorescent microscope. The average number of dots per cell (identified by its nucleus stained with DAPI) was determined by analysing at least 200 different cells with Image J software.
a, b, c MITF interaction with ARAF, BRAF or CRAF. HEK293T cells were cotransfected with the myc-MITF construct and each of the three HA-ARAF, HA-BRAF or HA-CRAF constructs (panels A, B, C respectively). RAF proteins were immunoprecipitated with anti-HA antibody. Immune complexes and total extracts were immunoblotted with anti-myc or anti-HA. Quanti cations of the protein ratio in the immunoprecipitation over the total extract IP/TE con rmed the speci city of these interactions. d HEK293T cells are cotransfected with the HA-MITF construct and each of the three myc-BRAFWT, myc-BRAFV600E or myc-BRAFKD constructs. Cell lysates were immunoprecipitated with anti-myc antibody. Immune complexes and total extracts were revealed with anti-HA and anti-myc antibodies. Coimmunoprecipitations were quanti ed using Image J software by measuring signal intensity in immunoprecipitation over total extract and normalized on control lane.
ACKNOWLEDGMENTS This work was funded by grants from the  Ligue Nationale Contre le Cancer (Equipe labellisée) ,  Fondation ARC, Gefluc  and the  Société Française de Dermatologie. C. E. and L. M. were supported by a fellowship from the  Ministère Français de l'Enseignement Supérieur, de la Recherche et de lʼInnovation , and  Fondation ARC. This work was also supported by " Région Ile-de-France " and  Fondation pour la Recherche Médicale  grants (to D. L. ).
This Letter addresses the topic of measurement and mapping of the magnetic modal volume in a photonic crystal cavity on slab. We provide experimental evidence that the magnetic perturbation theory should rely on non-Hermitian physics. In particular, we show that a coated metallic tip in a scanning near-field experiment acts as an anisotropic magnetic perturbation able to give access to the spatial distribution of the complex mode volume. ] [12]  We analyze eq 5 in the case of a photonic crystal in a hexagonal array of holes on a dielectric slab. The cavity consists of four missing holes in a diamond geometry and was named as D2. 14, 15 We numerically solve Maxwell's equations to find the QNMs, which are normalized using the QNM solver QNMEig  16  of the freely available package MAN (Modal Analysis of Nanoresonators).
| Stereomicroscope acquisition of high-resolution images and videos of A ocellaris embryonic development To follow the developmental changes occurring during embryonic development, we performed time-lapse using brightfield transmitted illumination of at least three embryos. Larvae were placed in a drop of filtered sea water on a Petri dish under the stereomicroscope. Live images were acquired on stereomicroscope (OLYMPUS SZX16, 1XPF) using a camera (DP80 using software CellSens DP80) with a picture resolution of 1360 x 1024 pixels and a time exposure of 142. 9 ms (time-frame of 1 image/min). Measurements were performed using ImageJ software (V1. 51k). For time-lapse of hatching, because embryos will not hatch under bright light, we placed a red plastic bag under the petri dish to decrease brightfield transmitted illumination.