dataesr/scientific-paragraphs-categorization huggingface dataset:
It would take twice the time to carry out the second configuration with respect to the first if we were to limit ourselves to the HMM-VIS mode. While that is a significant difference, considering the higher resolution would be beneficial not just for the science case of IGM tomography, but also for the synergy programs (see Section 4. 3). The estimates are realistic, but there are several factors that could affect the real observation times. In the case of multiplex (HMM-VIS) observations, our results are affected by the uncertainty in the atmospheric conditions. The simulations in Section 3 were carried out under the assumption that the seeing FWHM equals 0. 7 arcsec. According to the data of the atmospheric monitoring at the Paranal site in Chile 3 , a seeing of 0. 7 arcsec is about the median value in the period of 2017-2019.
9´2. 9 is obtained automatically. C iv is known to be associated with the photoionized gas of " 10 5  K that is seen to have a high covering fraction in the CGM at low redshifts (e. g. ,  Bordoloi et al. 2014; Burchett et al. 2016). This makes it a useful tracer for CGM studies. With its rest-frame wavelength of " 1550 Å, it lies outside the region that is polluted by the Ly-α forest absorption over a large redshift path. The survey will also result in an observation of the host galaxies for a large fraction of C iv absorbers. This survey will therefore provide a unique dataset for CGM studies at high redshifts. Here we estimate the expected number of C iv systems for the IGM tomography survey redwards of the Ly-α forest. To do so, we used the dN/dz obtained from XQ-100 survey  (Perrotta et al.
Acknowledgements. We wish to thank  C. J. Evans  and  C. Peroux  for fruitful discussions. JJ and LK acknowledge support from  NOVA  and  NWO-FAPESP  grant for advanced instrumentation in astronomy. CL acknowledges funding support from  Adrian Beecroft  and would like to thank  Stephane Arnouts  for discussions and advice. This work is partially supported by the  Spin(e)  grants  ANR-13-BS05-0005  (http://cosmicorigin. org) of the  French Agence Nationale de la Recherche , and  ERC  grant  670193. We thank  S. Rouberol  for smoothly running the Horizon cluster, hosted by the  Institut d'Astrophysique de Paris , where some of the postprocessing was carried out, and  T. Sousbie  for distributing Dis-PerSE. This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration, 2013).
Two-dimensional mass spectrometry (2D MS) is a data-independent tandem mass spectrometry technique in which precursor and fragment ion species can be correlated without the need for prior ion isolation. The behavior of phase in 2D Fourier transform mass spectrometry is investigated with respect to the calculation of phase-corrected absorption-mode 2D mass spectra. 2D MS datasets have a phase that is defined differently in each dimension. In both dimensions, the phase behavior of precursor and fragment ions is found to be different. The dependence of the phase for both precursor and fragment ion signals on various parameters (e. g. , modulation frequency, shape of the fragmentation zone) is discussed. Experimental data confirms the theoretical calculations of the phase in each dimension. Understanding the phase relationships in a 2D mass spectrum is beneficial to the development of possible algorithms for phase correction, which may improve both the signal-to-noise ratio and the resolving power of peaks in 2D mass spectra.
for a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR MS) in 1987  [3] [4] [5] [6] [7]. For two decades, the demands of 2D MS in terms of data storage and processing exceeded commonly available computational capacities. The theory of 2D MS, as well as alternative pulse sequences for ion radius modulation such as stored waveform ion radius modulation and Hadamard transformation, was proposed and studied  [8] [9] [10] [11]. In 2010, 2D MS was revived and successfully applied to a commercial FT-ICR mass spectrometer with infrared multiphoton dissociation (IRMPD), electron capture dissociation (ECD), and infrared activated electron capture dissociation (IR-ECD) as fragmentation methods  [12] [13] [14]. Denoising algorithms have been proposed in order to decrease the amount of scintillation noise in 2D mass spectra and the pulse sequence was optimized  [15] [16] [17].
Phasecorrected absorption-mode data from the dynamically harmonized ICR cell has allowed the highest published FT-ICR MS resolving power (R = 32,000,000 at m/z 400)  [37]. The effect of various instrument parameters on phase functions has also been studied  [33]. Apodisation functions have been developed for absorption mode spectra, as well as peak shape distribution models  [38, 39]. Absorption mode FT-ICR MS has shown particular promise for complex mixture analysis such as the study of proteins, crude oil, and lignin  [40] [41] [42] [43]. Phase correction for absorption mode FTMS has also been developed for Orbitraps and electrostatic linear ion traps  [44, 45]. In this article, the behavior of phase in 2D mass spectra is studied. First, the different behavior of the phase of precursor and fragment ion peaks in each transient of the 2D data set is discussed.
Instrument parameters All experiments were performed on a 12 T solariX Fourier transform ion cyclotron resonance mass spectrometer (Bruker Daltonik, GmbH, Bremen, Germany). Substance P and angiotensin 1 were ionized using a home-built nanoelectrospray (nESI) ion source at a rate of approximately 7-8 μL/h. After transfer through a dual ion funnel, two octopoles and a quadrupole ions were accumulated in a hexapole-based collision cell for 0. 1 s and transferred through a transfer hexapole (1. 0-ms transfer period) to the infinity cell for fragmentation and detection  [46]. ECD tandem mass spectra were measured for substance P with the pulse sequence shown in Scheme 1a (data shown in Figures  1  and 2 ). The single scan transients were recorded with 4 Mwords (16 bits) over 1. 6777 s for a mass range of m/z 147.
6777 s for a mass range of m/z 147. 4-1500 (1250--122. 8-kHz frequency range). The first pulse had 10 μs per frequency in 1804 decrements of 625 Hz and an amplitude varying from 0 to 50 V pp. The second pulse had an amplitude of 70 V pp with 20 μs per frequency in 1804 decrements of 625 Hz. IRMPD was performed using a Synrad 48-2 CO 2 25-W laser (Mukilteo, WA, USA) with a 10. 6-μm wavelength at 50% power with a 0. 55-s irradiation period. IRMPD tandem mass spectra were measured for angiotensin 1 with the pulse sequence shown in Scheme 1a (data shown in Figures  4d-f  and 5 ). The precursor ion at m/z 433 was isolated in the front-end quadrupole. The single scan transients were recorded with 4 Mwords (16 bits) over 1. 6777 s for a mass range of m/z 147. 4-1500 (1250--122. 8-kHz frequency range).
Data processing The mass spectra were visualized in the DataAnalysis 4. 0 software (Bruker Daltonik, GmbH, Bremen, Germany). All spectra were externally calibrated with the solariXcontrol software (Bruker Daltonics, Billerica, MA, USA) using Agilent ESI-L Low Concentration Tuning Mix (Agilent Technologies, Stockport, UK). Internal calibration was achieved using a quadratic calibration equation in the Data Analysis 4. 0 software (Bruker Daltonik, GmbH, Bremen, Germany)  [48, 49]. The absorption mode spectra of the ECD tandem mass spectra of substance P were obtained by using a quadratic phase function that was optimized with the Autophaser software with mass exclusions with m/z 1 window around m/z 353. 3, m/z 449. 9, and m/z 781. 1 after asymmetric apodisation (F = 0. 25)  [34, 35, 38, 39].
The third pulse is applied in order to excite all ions to higher radius before detection. The pulse sequence is repeated multiple times with regularly incremented values of t 1 to modulate the precursor ion packet radii in and out of the central fragmentation zone in the ICR cell prior to irradiation with an infrared laser (IRMPD) or reaction with electrons (ECD). In contrast with the ion-neutral collision method initially proposed, ECD and IRMPD are applied to ions located near the center of the cell, and in any case, ions rotating with a radius negligible compared with the final radius accessed after the final excitation pulse will interact with the laser or electron beam and fragment  [3, 4, 14, 17]. A scan is acquired and recorded for each value of t 1 to produce the dataset. A signal s(t 1 , t 2 ) is measured as a function of t 1 (delay between the two pulses) and t 2 (transient).
Phases of ions in the horizontal fragment ion dimension Figure  1  shows the ECD scan of substance P obtained with the pulse sequence shown in Scheme 1a with t 1 = 1. 0 μs. No quadrupole isolation was applied before the 2D MS pulse sequence. Figure  1  a shows the spectrum in magnitude mode. Two precursor ions can be assigned in the spectrum MH 3 3+ and MH 2 2+ of substance P, as well as fragments that are characteristic of ECD (see peak assignments in Table  S2  in the Online Resource). The resolving power was 271,000 FWHM at m/z 400. Figure  1  b shows the same data set in phase-corrected absorption mode. The phase function used to correct the phase in the spectrum was calculated with mass exclusions that ensured that the phase function was calculated using only fragment ion peaks in the mass spectrum.
3+ ), the phase correction is inaccurate and the peak shapes are distorted. This result is consistent with the theoretical calculations showing that the phase of the fragment ion peaks in a transient from a 2D MS data set is determined by the third pulse in the pulse sequence (Eq. (  20 )), but that the phase of the precursor ion peaks depends on whole pulse sequence (Eq. (  19 )). To demonstrate the phase dependences of the precursor and fragments on t 1 , Figure  2  shows the phase-corrected absorption mode for ECD tandem spectra of substance P that have been acquired using the 2D pulse sequence shown in Scheme 1a for different values of the delay t 1. The middle column shows the evolution of the intensity of the MH 2 2+ precursor ion, the left column shows the evolution of the intensity of the c 5 fragment, and the right column shows the evolution of the intensity of the c 7 fragment.
The value of t 1 therefore significantly affects the phase of the precursor ion peak, but does not affect the phase of the fragment ion peaks. The phases of precursor ion peaks are dependent on t 1 , and the phases of fragment ion peaks are independent of t 1. In Figure  2 , the intensities of the c 5 and c 7 fragments decrease between t 1 = 1. 0 and 3. 0 μs and both reach a minimum   S3  of the Online Resource, all five spectra were phasecorrected with the same phase coefficients that were optimized in Autophaser using the dataset in Fig. S1 : the precursor and fragment peaks show the same behavior as in Figure  2 )  [61].
Here, T = 0. 7216 ms. The predicted phase difference between the 12 C and 1× 13 C isotopes is therefore 13. 6°and the predicted phase difference between the 1× 13 C and 2× 13 C isotopes is 13. 5°. This phase shift can be clearly observed between the three isotopes in Figure  5a , where the signal intensity modulation is shifted along t 1. The measured time shift is 0. 6 μs, which corresponds to a phase shift of 15°, which is within the expected error for the experimental data. The cyclotron frequency-dependent phase shift for the precursor ion signal intensity modulation and the correlation between precursor ion and fragment ion signal intensity modulation means that the phase information contained in the 2D MS data sets along t 1 can be used to correlate precursor and fragment ions. Figure  5b-d  show the shift between the 12 C and 1× 13 C isotopes of the (HPF-H 2 O) + fragment at m/z 364.
1768, the y 9 3+ fragment at m/z 394. 5575, and the b 9 2+ at m/z 583. 2987 respectively. The same 15°phase shift as the MH 3  3+ precursor can be found between the signals of the isotopes of all three fragments. Within a 360°phase cycle, the phase information in the vertical dimension can therefore be used to increase the accuracy of the correlation between precursor and fragment ion m/z ratios. In the present data set, with a pulse length of T = 0. 7216 ms, the corresponding frequency range is 1. 39 kHz, which corresponds to an m/z 1. 2 range at m/z 400. Within an m/z 1. 2 range in the precursor ion dimension, phase information can be used for fragment ion peaks to match them with precursors that cannot be resolved in the magnitude mode 2D mass spectrum, which is useful in the case of overlapping precursor ion isotopic distributions.
Acknowledgements The authors would like to thank  Dr. Christian Rolando  for helpful conversations. The authors would like to thank the  Centre for Scientific Computing at the University of Warwick  for the use of their server for data processing. The authors would like to thank the  Warwick Impact Fund Proof-of-Concept Award  and the  EPSRC Impact Acceleration Award , the  Engineering and Physical Sciences Research Council  for grant number  EP/J000302/1. andgrant number  EP/N021630/1 , the  Biotechnology and Biological Sciences Research Council  for grant number  P021875/1  and grant number  BB/R022399 , the  Royal Society for the Translation Award , the  Horizon 2020 ,  EU FTICR MS network , (project  731077 ), and the  Innovation to Commercialization of University Research Programme.
Duolink Proximity ligation assay The proximity ligation assay (Duolink in situ PLA; Eurogentec, Angers, France) was performed to visualize and quantify interactions. Cells were fixed with paraformaldehyde 4%, blocked with BSA 5% for 1 h and incubated with primary antibodies against HuR, HnRNPA1, Phospho-Serine or Methyl-Arginine overnight at 4°C. A pair of oligonucleotide-labelled secondary antibodies was used according to the manufacturer's instructions. Each dot corresponds to a close interaction. The signals were counted and assigned to a specific subcellular location based on microscopy images by using the Blob-Finder/MatLab freeware.
6B ). Western blot analysis of MTR in 72 mice revealed that 30% of the animals did not present a significative decrease of the protein while 54% of them expressed less than 10% of the protein compared to controls (data not shown). Immunohistochemical analysis of different regions of the brain indicated that SIRT1 expression was significantly decreased in the CA1 subfield of the hippocampus but not in the dentate gyrus, CA2, CA3, cortex and cerebellum (Fig. 6C ). In order to evaluate the effect of the SIRT1-activating compound, a group of mice was treated with 50 mg/kg of SRT1720 every two days by oral administration during 2 weeks. Our results indicated that this treatment was able to restore the level of SIRT1 in the CA1 region (Fig. 6C, 6D ). Then, we evaluated the activation of SIRT1 by SRT1720 on cellular stress and RBP mislocalization by analyzing the subcellular distribution of HuR.
The Dataset We extracted 46 days worth of engineering note data, spread between the months of May, August, September and October. In total, we extracted approximately 6,800 task notes over 33 unique scenario types (classes). We then removed any class which contained less than 5 examples. It also became clear that a certain scenario, "No New Action Required" (NNR), was fully reliant on external information and not on the contents of the note. This was because the NNR class was only relevant if a scenario had already been organised for a given task. Based upon feedback gained from co-creation with the user group, we decided to remove this class until the external data source was available. The resulting dataset contained 5,343 notes spread between 29 classes. There was notable class imbalance, with the rarest class containing only 20 notes while the most populated class contained 1,200.
Evaluation of Classification Engine We performed an exploratory evaluation to understand the effectiveness of different representation learners and classifiers on the above dataset by creating a classification task where notes were classified according to one of 29 scenarios. The dataset was divided into distinct training and test sets using 5-fold cross-validation. We considered both a distributional (term-frequency/inverse-document-frequency) and a distributed (Document-2-Vector) method of learning representations. We also considered three classification methods -k-Nearest Neighbour (kNN), Logistic Regression (LR) and a Multi-Layer Perceptron (MLP). We used accuracy in a classification task as a proxy measurement for representation goodness. Term frequency-inverse document frequency (tf-idf) is a statistical measure to develop representations for documents in a corpus based upon the terms they contain.
This suggests that the additional information that tf-idf offers about term rarity (in the form of its idf portion) is important. Removing the NNR class offers a much improved performance for the classifiers using representations gained from tf-idf. As above, this is likely due to the focused technical vocabulary of the notes, a mixture of which would be used throughout the NNR class. This is misleading, as the vocabulary used within a note is irrelevant to whether it should be classified NNR or not. Hence the decision to remove the NNR class from our dataset. That said, it is interesting to note that (with the exception of kNN) removing the NNR class did not overly affect the classification accuracy gained from the Doc2Vec representations. We take this as further support that a much larger volume of text would be needed to ensure that Doc2Vec could function effectively on this problem.
LEGI facility Experiments at LEGI were performed in a low-turbulence wind tunnel with a measurement section of 4m long and a square cross-section of 0. 75x0. 75m². For the two freestream velocities considered, background turbulence (measured via hot-wire anemometry and an empty test section) remains always below 0. 5%. The models were placed at 0. 5m from the section's inlet and very close to its centre. Blockage ratios created by the discs within the test section are 5. 6% and 2% for Disc A and Disc B, respectively. Horizontal and vertical traverses within the wake are conducted using a Cobra probe and data are acquired for 180s and with a sampling frequency of 1. 25 kHz at each traverse point.
METU facility METU experiments are conducted at an open-return suction type wind tunnel that has a test section of 1m×1m cross-section. The average inlet turbulence intensity is about 0. 5%. The discs are placed at the centre of the wind tunnel, where the flow is most homogeneous. Blockage ratios created by the discs within the test section are 3. 15% and 1. 15% for Disc A and Disc B, respectively. Horizontal and vertical traverses within the wake are conducted using a single sensor CTA system and data are acquired for 5 s and with a sampling frequency of 10 kHz at each traverse point.
UOLD facility The experiments at ForWind, University of Oldenburg, have been carried out in the 30 m long closed test section of Oldenburg's Large Wind Tunnel (OLWiT), that has an outlet of 3 m x 3 m  [8]. The freestream turbulence intensity is lower than 0. 4%. Blockage ratios created by the discs within the test section are 1. 3% and 0. 5% for Disc A and Disc B, respectively. Data was collected using a Dantec StreamLine with 1D hot-wire anemometry, and the sensor length was 1. 25 mm. A hardware low-pass filter with a cut-off frequency of 10 kHz was used, and the sampling frequency was 20 kHz. 4. 10 5 data points were collected at each position.
The paper is organized as follows. Section 2 presents the fluorescence and bioluminescence datasets, a modeling exercise used to investigate the impact of concentration and flash kinetics on the bioluminescence signal, the proxy calculation method, and its application to three field campaigns during which in situ plankton counts were collected alongside matching fluorescence/bioluminescence time series. Section 3 validates the proxies against in situ counts and presents results during the AOSN-2003 field campaign. Section 4 discusses situations when proxies may or may not be valid, and conclusions are offered in Section 5.
Species identified as Ceratium lineatum during MUSE-2000 were removed from the Ceratium counts shown here because that species is non bioluminescent; most Ceratium species were not identified beyond genus. Small jellies include Physonect, Calycophoran, Hydromedusa, and Beroe. Additional counts (not represented here) are available for nonbioluminescent zooplankton and dinoflagellates as well as diatoms. photons originating from an external source  (Haddock et al. , 2010). Because coastal bioluminescence is dominated by dinoflagellates  (Lapota et al. , 1988; Moline et al. , 2009; Marcinko et al. , 2013b)  whose bioluminescence is strongly inhibited during the day  (Marcinko et al. , 2013a) , all datasets were restricted to nighttime (1 h after sunset to 1 h before sunrise). Bathyphotometers (BPs) measure the "bioluminescence potential" of organisms (hereafter bioluminescence) by pumping seawater into a detection chamber, mechanically stimulating bioluminescent plankton, and detecting their light emission with a photomultiplier tube.
1). We consider that bg_BL is a proxy for all dinoflagellates as well as bioluminescent dinoflagellates, because both are highly correlated (r = 0. 52, p < 0. 01 for the dataset displayed in Fig. 1 ; r > 0. 8 for SPOKES-2002 and AOSN-2003 when considered separately). Zooplankton proxies are based on flashes: the number of flashes per liter is used as a proxy for larvaceans (low intensity flashes) and copepods (high intensity flashes) while the maximum flash intensity is used as a proxy for small jellies. The rationale is that (1) larvacean flashes are dimmer than other types of zooplankton including copepods, (2) larvacean and copepods are common enough where a flash intensity threshold can be determined from sensitivity analysis (see Appendix B), and (3) while jellies are brightest and could theoretically be determined using another flash intensity threshold, they are too rare for this threshold to be determined but their presence should be indicated by very bright flashes.
By contrast, diatoms and dinoflagellates succeed each other, and are favored by different environmental conditions and less likely to cohabitate  (Margalef, 1978; Smayda and Trainer, 2010). Proxies for heterotrophic dinoflagellates (h-dinos), autotrophic dinoflagellates (a-dinos), and other phytoplankton (a-other, mostly diatoms in our datasets) are computed for a given fluorescence/ bioluminescence dataset by assuming that the autotrophic dinoflagellate population (bioluminescent or not) is characterized by a constant bg_BL / fluo ratio, termed ratioAdinos. Details and equations are provided in Appendix B. Briefly, ratioAdinos is estimated from histograms as the most frequently observed bg_BL / fluo ratio. The adino, h-dino and a-other proxies are defined based on their location in the bg_BL, fluo space relative to ratioAdinos (Fig.
Application to field campaigns Fluorescence and bioluminescence datasets, as well as in situ plankton counts, were collected during three field campaigns. These took place in Monterey Bay, California in Aug 24-Sep 2, 2000  (MUSE-2000) ,  Aug 21-26, 2002 (SPOKES-2002 ), and Aug 10-16, 2003 (AOSN-2003). The objective of these field campaigns was to use a variety of platforms to obtain bay-wide distributions of biooptical parameters, along with corresponding plankton distributions, so they could be mapped to oceanographic features. As part of these efforts, fluorescence and bioluminescence sensors were deployed onboard ships, towfish and AUVs. Ship-based water samples for phytoplankton and zooplankton were collected using a CTD-rosette for phytoplankton and a 160 L Schindler trap for zooplankton (Fig.