dataesr/scientific-paragraphs-categorization huggingface dataset:
monstrosa ). We detected 56 common taxa between eDNA and trawling ( Figure  1 b ). On average, 4. 2 trawl hauls caught the same taxonomic richness (SR) as a single eDNA site (when pooling both replicates per site; Figure  2  ). The estimated asymptotic taxonomic richness SR max was 93 for eDNA, 75 for trawling, and 108 for the combined dataset. The number of taxa detected per order differed significantly between eDNA and trawling ( χ 2 = 71, df = 35, p < 0. 01; Supplementary Figure  S1  ). eDNA detected fewer Pleuronectiformes, Gadiformes, Carangiformes, and Callionymiformes but more Spariformes and Beloniformes compared with trawling ( Supplementary Figure  S2  ). Considering PD, the detected species had an estimated common ancestor dating back to 446 ± 44 Ma ( Figure  1 c ). The total PD was 10040 ± 250 Ma (9180 ± 220 Ma for eDNA and 7060 ± 150 Ma for trawling).
This result is confirmed by other studies showing that eDNA metabarcoding and traditional methods are complementary  (Polanco-Fernandez et al. , 2020 ; Keck et al. , 2022 ). Such results can be partly explained by the lack of completeness of the genetic reference databases, which prevents a full taxonomic detection by eDNA metabarcoding  (Miya, 2022 ) , even though databases covering European areas are more complete than some other regions  (Marques et al. , 2021 ). In our study, we added to the 12S EMBL reference database a regional custom database, which have improved taxonomic assignment  (Mugnai et al. , 2023 ). Among the 14 taxa detected by trawling and not by eDNA ( Figure  3  ), 5 were missing from the genetic reference database ( Ammodytes marinus , Gaidropsarus macrophthalmus , Microchirus variegatus , Mora moro , and Phycis blennoides ; the last four did not even have a representative of the same genus), and 6 were easy to discriminate with the teleo primer, preventing misassignment at a higher taxonomic level (Hippocampus hippocampus , Lithognathus mormyrus , Phycis blennoides , Atherina presbyter , Hexanchus griseus , and Dasyatis pastinaca ).
However, for three other species detected only by trawling, Merlangius merlangus , Nezumia aequalis , and Lepidotrigla dieuzeide , their detection was not possible with the teleo primer because they shared a similar barcode DNA sequence with other species known to be present in the region of interest. We did not exclude the species without a DNA barcode from the trawl data set to account for current eDNA metabarcoding weaknesses. However, the nine taxa detected by trawling and not by eDNA metabarcoding but present in the genetic reference database were rarely detected in our study (i. e. once or twice). Biodiversity analyses also rely on quantitative data, i. e. estimates of fish abundance. Even though our results indicated a positive and significant relationship between the number of individuals caught by trawling and the number of eDNA metabarcoding reads for several sites, this relationship was weak and very uncertain.
The constant improvement in the completeness of the DNA sequence reference database will help researchers to detect the remaining species currently missed. Further, with other sampling devices targeting the bottom substrate, we could enhance the detectability of benthic species buried in the sediment with eDNA metabarcoding. Finally, further developments should be guided towards a better understanding of eDNA ecology (production, fate, and degradation) to help build hydrodynamic models of eDNA concentrations in the environment to improve both species occurrence detection and quantitative estimates  (Fukaya et al. , 2021 ).
Randomization Patients were randomly assigned 1:1 to BIF or degludec treatment groups using an interactive web-response system. The BIF group used a paper-based algorithm that provided guidance on BIF initiation and titration. Another BIF group that used a digital dosing algorithm was discontinued because of technical issues with the tool caused by missing values and data entry errors. Patients randomly assigned to the digital algorithm (n = 14) were transitioned to the BIF paper-based algorithm, and their data were included in safety analyses but excluded from efficacy analyses.
Procedures During the screening and lead-in period, patients were trained on disease management, study diaries, and procedures. Patients were provided with a glucose meter to collect self-monitoring of blood glucose (SMBG) measurements. Patients were instructed on signs, symptoms, and treatment of hypoglycemia and to document all readings #70 mg/dL (<3. 9 mmol/L) in the electronic diary. Patients were provided with and trained on the Libre Pro continuous glucose monitoring (CGM) system (Abbott), which was used in a blinded mode during three predefined 14-day periods: prior to weeks 0, 12, and 26. BIF was provided as a lyophilized powder and dosed in milligrams immediately following reconstitution. One goal of the phase 2 program was to determine the international unit conversion. Rather than rely on the conventional use data derived from clamp studies to determine the unit definition for ultra-long-acting insulins, which may not be accurate for all diabetes populations (15), phase 2 data were used to determine the BIF unit definition for phase 3 development.
0 mmol/L]), and time above range (181-250 mg/dL [10. 0-13. 9 mmol/L]) were calculated. Safety was monitored throughout the study, and analyses were performed on the safety population. The incidence and rate of hypoglycemia were based on the data recorded by the participant. Level 1 hypoglycemia was defined as a glucose <70 mg/dL (3. 9 mmol/L) and $54 mg/dL (3. 0 mmol/L), and level 2 hypoglycemia was defined as a glucose <54 mg/dL (3. 0 mmol/L). Severe hypoglycemia (level 3) was defined as an event requiring assistance because of neurological impairment. Incidence of treatment-emergent adverse events and clinical assessments, including a physical examination, body weight, vital signs, electrocardiograms, and laboratory measures, were assessed.
The MMRM model included treatment (BIF [paper-based, nondigital] algorithm, degludec), strata (country, DPP-4 [yes/no], SGLT2 [yes/no], and baseline BMI [<30, $30 kg/m 2 ]), visit, and treatment-by-visit interaction as the fixed effects and the baseline value of the dependent variable as the covariate. The HbA 1c is reported in the DCCT unit of percent. Other efficacy end points were analyzed using the same MMRM model with the addition term of HbA 1c strata (<8. 5% and $8. 5%). The hypoglycemia event rate was calculated by the total number of patientreported events divided by total treatment exposure. The relative rate was used for treatment comparison. The CI was estimated by an empirical method based on the delta method and assumption of log-normal distribution because the regular method with Poisson distribution may underestimate the variance, especially since the patient-reported hypoglycemia data in this insulin-naive population were sparse.
A B Figure  2 -A: Glucose profile for six-point SMBG monitoring. Data are the least squares mean ± SE. B: TIR parameters for 24-h period collected from assessments performed at baseline and after 12 and 26 weeks of treatment. Data are least squares mean. CGM assessments at week 26 reflect $60% of the BIF group. *P < 0. 1 for BIF vs. insulin degludec. from week 0 to 26 were similar between the treatments (all P $ 0. 496). Similarly, the rate of all documented, nocturnal, and nonnocturnal level 1 hypoglycemia from week 0 to 26 was similar between treatments (all P $ 0. 290). No severe hypoglycemia (level 3) was reported. Reported adverse events were balanced between treatments (Supplementary Table  4 ). One death occurred in the degludec group (as a result of coronavirus 2019 pneumonia). No treatment-emergent hepatic disorders were reported, and both treatments showed reductions in hepatic enzymes with no clinically significant differences between treatments.
The rate of hypoglycemia per year was very low, and there was no significant increase of documented hypoglycemia based on patientreported events with BIF compared with degludec. Despite the theoretical hypoglycemia risk with once-weekly basal insulin treatment and limitations of the CGM data set for this study, passive collection of hypoglycemia using CGM was not concerning between treatment differences. No severe hypoglycemia was reported. Though the glycemic control end points were quite similar between treatments, interpretation of dosing information is confounded by differences in the dosing algorithms; residual uncertainty related to the current unit definition, which will not be confirmed until the end of phase 3; and the likelihood that patients in both groups may have required additional time to complete titration.
BIF's low weekly peak-totrough ratio and long half-life may convey lower between-day glucose variability  (16) , which could influence hypoglycemia risk relative to day of injection and provide some flexibility with regard to the timing of the weekly injection within a range of 2-3 days; however, these hypotheses require confirmation. BIF was well tolerated, and routine safety assessments in this study were unremarkable, but there was an imbalance in hypersensitivity reactions. Though none of these presented as systemic hypersensitivity, hypersensitivity and injection site reactions will be monitored during phase 3. A unit definition for BIF was estimated using data from the BIF phase 1 program, but an important aim of the broader phase 2 program was to refine the unit definition based on glycemic outcomes from all relevant patient populations.
Therefore, BIF was dosed in milligrams during the current study. As was done during development of basal insulin peglispro, efficacy outcomes from the phase 2 studies were used to inform a meta-analysis of a unit definition for all patient populations (15) rather than relying on a unit definition obtained from data derived from clamp studies that may not be sufficiently accurate for all intended patient populations. As all phase 2 trials are now complete, the current unit definition for weekly dosing is set at 1 mg of BIF being equivalent to 35 IU and will be confirmed when phase 3 is complete. This study has several limitations. One limitation, common for phase 2 research, was the lack of generalizability to the broader insulin-naive T2D population. At the time of study conduct, there was limited phase 1 experience and no outpatient experience in this patient population or on a background therapy of GLP-1 analogs.
These formulation-dependent issues will not be applicable in phase 3 when a prefilled pen formulation with dosing in international units will be used. Compliance with collection of CGM was limited, and the system's reduced accuracy in the hypoglycemic range limited the strength of interpretations from the CGM data. Although $60% of patients provided valid data for the week 24-26 CGM session, improvements in the ambulatory glucose profiles were evident from baseline to week 26, which were supported by other indices of glycemic control. The BIF dose adjustment algorithm was unusually complex compared with those commonly used with daily basal insulin because it was intended to serve as a manual, paper-based backup for dose adjustment in the event of technical issues with the digital algorithm.
It used an individualized starting dose strategy based on both body weight and FBG and a loading dose approach. This resulted in a range of BIF starting doses that was necessary to achieve an appropriate therapeutic concentration more quickly and to achieve FBG goals within the first 12-14 weeks for assessment of HbA 1c at 26 weeks. Ultimately, this approach resulted in effective titration to treatment target with minimal hypoglycemia risk. Data from this study will be used to develop a phase 3 dosing algorithm that is more consistent with commonly used approaches for daily basal insulin adjustment and less individualized. BIF is currently in phase 3 development, referred to as the Once Weekly Insulin Therapy (QWINT) program, and two studies are focusing on insulin-naive patients (QWINT-2 [ClinicalTrials.
gov reg. no. NCT05362058] and QWINT-1 [ClinicalTrials. gov reg. no. NCT05662332]). This study demonstrated the clinical utility and potential of BIF as a promising once-weekly basal insulin that will reduce the injection burden in insulinnaive patients with T2D. Once-weekly BIF achieved excellent glycemic control, with low rates of hypoglycemia similar to degludec titrated in a manner consistent with standard of care. Available from https://www. globenewswire. com/ news-release/2022/07/29/2488672/0/en/Novo -Nordisk-achieves-primary-objectives-of-ONWARDS -3-and-4-trials-with-once-weekly-insulin-icodec -demonstrating-superior-reduction-in-HbA1c-vs -insulin-degludec-in-ONWARDS-3. html 14. Novo Nordisk. Once-weekly insulin icodec demonstrates superior reduction in HbA1c in combination with a dosing guide app versus once-daily basal insulin in people with type 2 diabetes in ONWARDS 5 phase 3a trial [press release].
Acknowledgments. The authors thank all study participants and  Kristen Syring, PhD , and  Alastair Knights, PhD  ( Eli Lilly and Company ), for medical writing assistance. Funding. This study and medical writing assistance was funded by  Eli Lilly and Company. Duality of Interest. J. M. B. -V. , C. M. K. , C. M. , J. C. , Q. Z. , E. C. , W. L. , and A. H. are employees and shareholders of  Eli Lilly and Company. J. P. F. declares research support from  Akero ,  Astra-Zeneca ,  Boehringer Ingelheim ,  Bristol-Myers Squibb ,  89bio ,  Eli Lilly and Company ,  Intercept ,  IONIS, Janssen, Madrigal, Metacrine ,  Merck ,  North-Sea Therapeutics ,  Novartis ,  Novo Nordisk, Oramed ,  Pfizer ,  Poxel , and  Sanofi  and participation in speakers bureaus of  Eli Lilly and Company and Sanofi  and advisory boards or as a consultant for Akero, Altimmune,  Axcella Health ,  Becton Dickenson ,  Boehringer Ingelheim ,  Carmot Therapeutics, Echosens ,  89bio ,  Eli Lilly and Company ,  Gilead ,  Intercept ,  Metacrine ,  Merck ,  Novo Nordisk ,  Pfizer , and  Sanofi.
To this end, we carried out siRNA-mediated knock down (KD) of KAT2A/KAT2B or TADA2A HAT module subunits, in HeLa cells followed by an anti-ZZZ3 IPs on nuclear extract prepared from either non-treated, or siRNA treated cells (siKAT2A/KAT2B or siTADA2A). IP purified complexes were subjected to mass spectrometric (MS) analyses. The MS data indicated that the KD of KAT2A/KAT2B or TADA2A subunits resulted in the loss of the whole HAT module incorporation, and that the six-subunit ATAC core module could still form (Fig. 1E ). Taken together, these experiments indicate that ATAC is composed of two functional modules: the core and the HAT modules.
6D ). In acetylated-lysine IP fractions from mock treated cytoplasmic extracts (siNonT), 257 acetylated peptides were identified (Supplemental Table  6 ). To find potential targets of the SAGA acetyl transferase module in the cytoplasm, we examined the datasets for the loss of acetylated peptides when comparing acetylation in control siNonT cytoplasmic extracts either to the siTADA2B extracts, or to the corresponding siKAT2A/KAT2B extracts. The resulting MA plots indicated that in both KD CE extracts the acetylation of 89 and 65 peptides were significantly down-regulated, respectively, with a log2fold change above -0. 5 and below a 5% FDR (Fig. 6B  and Supplemental Table  6 ). There is an overlap (40 peptides) between the down-regulated acetylated peptides of which the acetylation is either TADA2B-and KAT2A/KAT2B-dependent (highlighted in the MA plot with black circles, Fig.
e. polyubiquitylation-driven proteasome activities) may exist to avoid accumulation of ATAC in the cytoplasm. In agreement, importin proteins ( and ) may play a key role in the rapid import of the ATAC complex, or its functional modules to the nucleus  83. Overall, our study contributes to the general understanding of the basis of subcellular building and distribution of large multiprotein complexes. In this context, our data unveil the co-translational assembly pathways of the mammalian transcriptional coactivator complexes, ATAC and SAGA, and their differential subcellular distributions. In addition, our findings argue for a strong functional link between the biogenesis of holo-complexes and their distinct function in subcellular compartments. Whilst the mammalian SAGA complex forms as a holocomplex in the cytoplasm, where it has an acetyl transferase activity towards non-histone targets, the ATAC complex does not seem to have a cytoplasmic function.
cDNA preparation and RT-qPCR For cDNA synthesis, 5 µl of purified RIP-RNA and 5 µl of 1:10 diluted input RNA samples were used. cDNA was synthesised using random hexamers and SuperScript IV (ThermoFischer Scientific) according to the manufacturer's instructions. Quantitative PCR was performed with primers on a Roche LightCycler 480 instrument with 45 cycles. Enrichment relative to input RNA was calculated using the formula 100 × 2 [(Cp(Input) - 3,322) - Cp(IP)] and expressed as "% input RNA". Enrichment values were expressed as "mRNA fold enrichment" relative to the mock IP using the formula ΔΔCp [IP/mock]. All experiments were performed with a minimum of three biological and three technical replicates and values are represented as mean ±SD. Figures panels were prepared with taking in account all these data points using Prism.
Microscopy image acquisition Confocal imaging of cells processed for IF-smiFISH was performed on Leica SP8-UV microscope. A 63× oil immersion objective (NA 1. 4) was used and images were taken by using the hybrid detector photon-counting mode. For excitation of DAPI, AF488 (IF) and Cy3 (smiFISH), 405 nm, 488 nm and 561 nm laser lines were used, respectively. The laser power for all acquisitions and laser lines was set to 10%. 8-bit images were acquired with a xy pixel size of 0. 081 μm and a z step size of 0. 3 μm (~30-40 optical slices). Image processing was performed using the Fiji/Image J software  87. All images were processed the same way.
Image analysis of IF-smiFISH and dual color smiFISH data Nuclei were segmented starting from maximum intensity projections of DAPI channel images and by applying a gaussian blur filter followed by Otsu-algorithm thresholding and analyze particles commands in Fiji. The resulting nuclei contours were saved as ROI selections. The total population of RNA smiFISH spots was detected by using TrackMate Fiji plugin using DoG detector  89. Object diameter and quality threshold were determined for each image separately. The coordinates of total FISH spots were saved as ROI selections. To measure the total number of cytoplasmic FISH spots for each image, nuclear RNA FISH spots selections were removed from the total using the combine (OR) and subtract (XOR) commands in ROI Manager tool in Fiji using the nuclei selections as reference.
Data were processed by database searching against Homo Sapiens Uniprot Proteome database (www. uniprot. org) using Proteome Discoverer 2. 4 software (Thermo Fisher Scientific). Precursor and fragment mass tolerance were set at 7 ppm and 0. 6 Da respectively. Trypsin was set as enzyme, and up to 2 missed cleavages were allowed. Oxidation (M, +15. 9949), Acetyl (K, +42. 0367) were set as variable modification and Carbamidomethylation (C, +57. 0215) as fixed modification. Proteins and peptides were filtered with False Discovery Rate <1% (high confidence). Lastly quantitative values were obtained from Extracted Ion Chromatogram (XIC) and exported in Perseus 1. 6. 15. 0 to produce heatmap and Volcano plot 93. Statistical AnalysesStatistical analyses were performed using unpaired two tailed t-tests between two different experimental condition (CHX and PURO) in IF-smiFISH confocal microscopy image quantification.
Acknowledgements We are grateful to the  IGBMC Proteomics and Photonic Microscopy platforms and cell culture facility  for their assistance and instrumentation. We thank the members of the  Tora lab  for helpful discussions and comments, and  S. Bour  for help with the drawings. This work was financially supported by  Agence Nationale de la Recherche (ANR)   ANR-19-CE11-0003-02 ,  ANR-20-CE12-0017-03 ,  ANR-22-CE11-0013-01_ACT ;  Fondation pour la Recherche Médicale  ( EQU-2021-03012631 );  NIH MIRA  ( R35GM139564 ); and  NSF  (Award Number:1933344) grants. The work from the lab of L. T. and H. T. M. T. was financed by the  ANR-PRCI-19-CE12-0029-01  grant. A. B. has been supported by the  Fondation ARC pour la recherche sur le cancer  ( ARCPOST-DOC2021080004113 ). The research of P. K. M. S.
and H. T. M. T. is financially supported by the grants from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) with the project-IDs  192904750-SFB 992  and  TI688/1-1. This work, as part of the  ITI 2021-2028 program  of the  University of Strasbourg , was also supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ), and by  SFRI-STRAT ' US  project ( ANR 20-SFRI-0012 ) and  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program.
FMCW Radar System Figure  1a  shows the Synview radar architecture used in the measurements  [23]. The first stage of the system, working at low frequencies, features a voltage control oscillator (VCO) combined with a phase locked loop (PLL) for stabilization in order to generate fast saw tooth-like frequency sweeps. A multiplier chain is then inserted for the up-conversion to reach the desired working frequency, corresponding to the J band (220 to 330 GHz), with a 275 GHz center frequency. The output power is about 60 µW and the dynamic range is 60 dB. The signal is then demodulated and measured with a national instrument data acquisition card. Figure  1b  shows the generated chirp signal, with frequency as a function of time. The continuous line represents the emitted signal and the dashed line represents the reflected signal.
Measurements The radar system was set to image a surface of around 11 × 11 cm with a step of 1 mm. Acquired an image took around 15 min. In all measurements, the chosen distance from the Teflon lens to the first rock face was optimized to collect as much as possible the reflected beams. Assuming that the objects were larger than the wavelengths involved and neglecting interference and diffraction, we simulated the system (radar + rocks + artificial fractures) through ray tracing, using the free open-source web application software Ray Optics Simulation. We used the refractive indices of the rocks as presented in the bibliography  [25, 26]. We found a distance of around 8 cm in all cases. The experiments were rather robust since changing the distance to 7 or 9 cm had no impact on the detected signals.
The reflections from the second rock face were inclined with respect to the boundaries (see the white rectangle in slide 9) because the thickness of the first rock increased at the bottom (see the blue rectangle in Figure  7a ). Therefore, the reflected signals from the lower part of the second rock face arrived later. The sequence of images from slide 3 to 15 were utilized to make a 3D reconstruction. We used the open-source image processing software FIJI TM. We first imported the raw images into FIJI and then subtracted any background noise using the subtract background tool. Next, we used the FIJI plug-in '3D viewer' to obtain the 3D image results. We did not interpolate the images. To help with the visualization, the distances used between the slides were 1. 4 mm instead of 500 µm. Figure  7c  presents an upper view of the 3D image.