dataesr/scientific-paragraphs-categorization huggingface dataset:
VI. CONCLUSION We proposed a time scheduling based TMO-OFDMA scheme by transmitting group of O-OFDMA users' data in their dedicated time slots for the LiFi-based uplink IoT networks in an indoor scenario. We studied the energy efficiency of ACO and DCO signaling and demonstrated the advantage of ACO signaling in achieving target BER with relatively low transmit power by quite a large margin. Taking into account realistic LED characteristics, we studied the MAI performance of our proposed TMO-OFDMA scheme compared to its O-OFDMA counterparts. In particular, we demonstrated the effectiveness of our scheme in reducing MAI, albeit with the requirement of increased transmission BW. We further showed that our proposed scheme can significantly reduce Tx energy consumption for IoT devices compared to the O-OFDMA scheme, thanks to the reduced computational complexity of TMO-OFDMA.
The bars are arranged in a cylindrical arc on an aluminum frame 104 cm from the implant point, covering 11. 7% of a 4π solid angle. Due to shadowing from a steel support frame, four detectors were not used in this analysis, reducing the solid angle coverage to 10%. INDiE is placed on the opposite side of the implant point from the HPGe detectors to minimize the interactions of neutrons emitted towards INDiE. Each end of a scintillator detector is coupled to a PMT, and each detector is gain matched to ensure a consistent response to neutrons with the same energy across all of INDiE. All β-, γ -, and neutron-detector signals were connected to a digital data acquisition (DDAQ). The details of the DDAQ system are described in Ref. [22]. The 250-MHz digitized waveforms (4-ns period) were recorded for the signals from the two β detectors and 26 INDiE modules, while only the energy filter samplings were stored for the HPGe signals.
Waveforms were then analyzed offline to extract a highresolution time stamp (HRT). This technique achieves timing resolutions below the digitizer sampling period. Using a polynomial constant fraction discriminator (polyCFD) algorithm  [23] , HRTs are calculated to measure the time difference between β and neutron signals. The full width at half-maximum (FWHM) resolution of the γ -flash peak in the time-of-flight (TOF) histogram such obtained is 1. 5 ns. Well-known 17 N βdelayed neutron-emission data  [24]  were used to calibrate the individual distances from the implant position to each INDiE bar. The average distance to the center of each bar in this setup is 104. 2(3) cm. The 134 Cs isomer contamination caused significant background in all detector systems. The isomer half-life is 2. 9 h and predominately decays via a 127.
[36, 42]  for CuAu-ordered CIS and CuIn 5 S 8 respectively, and ref. [36]  for CIGS chalcopyrite. For the latter, theoretical positions are given for CuIn 0. 8 Ga 0. 2 S 2. Pole figures are measured in low resolution mode without Ni Cu-kβ filter. Indexation of reflections on the pole figures has been performed with the help of STEREOPOLE software  [43] , which allows to superimpose the experimental pole figure data and the stereographic projection of candidate crystals. The azimuth corresponds to the Si[110] direction of the miscut's descending steps.
Thermodynamics of the Cu-In-Ga-S system This article deals with epitaxial thin films; therefore, to analyse the data, ab initio refinement or comparison with powder diffraction patterns database is not possible. Consequently, to interpret the data, it is important to have a coherent picture of the candidate phases in the Cu-In-Ga-S. In the following, we review and comment the thermodynamic stability of the possible phases in the latter chemical system. One of the many challenges of pure-sulfide CIGS is its notoriously complex chemistry, which unfolds when the film's Cu content, defined as the CGI=[Cu]/([In]+[Ga]), differs from the ideal Cu(In,Ga)S 2 stoichiometry  [36, 50]. On the Cu-rich side (CGI > 1), the excess in Cu atoms is accommodated by the formation of Cu 2-x S alongside the CH phase  [37].
To a lesser extent, the same holds for P Kα photons, which escape through the CIGS layer with a lower probability than Ga Kα photons. This shows that the precise determination of the composition of our samples is not trivial. We define the chalcogen-to-metals ratio as SCGI=[S]/([Cu]+[In]+[Ga]). We measure a SCGI of 1. 13 and 1. 07 for the Cu-poor and the KCN-etched Cu-rich samples respectively. The slightly larger SCGI of the Cu-poor sample may be related to the anticipated presence of TS-CuIn 5 S 8 , which intrinsically has a SCGI=1. 33 above unity. For the Cu-rich sample, we argue a SCGI different from unity may be related to the complex geometry of the stacks, which introduces quantification errors. In summary, EDX data is compatible with the 1:1:2 of CH-CIGS, although we cannot exclude that the films are marginally S-rich.
A small shift of the measured position could be due to strain, a small incorporation of Ga into CA-CIS or the presence Cu vacancies. These peaks exhibit extremely small intensities of 3 orders of magnitudes bellow the intensity of the strong CH(004) peak at 2θ = 32. 5°. This suggests that only a small volume of CA is present in the Cu-poor sample. We perform a rough estimation of CA-CuInS 2 peak intensities performed with VESTA Software  [41]  based on lattice parameter of ref. [42]. The presence of the CA(002) CH at 2θ=16. 13° indicate that a CA(004) CH peak of approximatively the same intensity is expected at 2θ=32. 59°. The weak CA(004) CH peak is expected very near the very intense CH(004) CH peak at 2θ = 32. 5°. Thus, the weak CA(004) CH is eclipsed by the CH(004) peak and is therefore not visible.
8 Acknowledgments: This work is supported by the  French National Research Agency  project  EPCIS  ( Grant  no. ANR-20-CE05-0038 ). The authors are grateful to  C. Derouet  and  V. Demange  for their assistance on XRD measurements performed on Osirix platform (ScanMAT,  UAR 2025 University of Rennes-CNRS. ScanMAT, received a financial support from the  European Union  through the  European Regional Development Fund (ERDF) , the  Département d'Ille et Vilaine ,  Rennes Métropole  and  Région Bretagne  (2015-2020  CPER  project  SCANMAT ).
Additionally in each measurement, a pair of baseline images was acquired without preceding inversion pulses. For bSSFP readouts, both baseline images are acquired without any preparation pulses, while for spGRE an additional saturation prepulse is added immediately prior to the readout of the second baseline image. Postprocessing of images including MBF quantification and statistical analysis was performed in MATLAB (MathWorks). The MBF was quantified using Buxton's GKM as described in Equation (  1 ). For bSSFP-based MBF calculation, the baseline signal I BL corresponds to the average of the two baseline images. With spGRE readout, the saturation-baseline image I BL,Sat is subtracted from the original one and the difference image is used as the baseline value in MBF calculations as given in Equation (  6 ).
ACKNOWLEDGMENTS The authors would also like to thank  Joao L. S. C. Tourais  and  Scannexus Imaging Center, Maastricht, The Netherlands , for their support with preliminary phantom and in vivo measurements. This work was supported by the  4TU federation , a  NWO Start- up grant  STU. 019. 024 , and  ZonMW Off -Road 04510011910073. Maša Boži ć-Iven  is funded by a PhD scholarship from the  Landesgraduiertenförderung Baden-Württemberg. George Thornton  is funded by a  BHF   Clinical Research Training Fellowship  ( FS/CRTF/21/24127 ). Thomas A. Treibel  is directly and indirectly supported by the  University College London Hospitals NIHR   Biomedical Research Centre and Biomedical Research Unit at Barts Hospital , respectively. Thomas A. Treibel  is funded by  British Heart Foundation Intermediate Fellowships  ( FS/19/35/34374 ).
These fluorochromes include indocyanine green, pafolacianine, and gleolan, which improve outcomes in various solid organ malignant tumors, including sarcomas, lung cancer, colorectal cancer, breast cancer, intracranial tumors, and ovarian cancer. [12] [13] [14] [15]  A promising target is the glycoprotein carcinoembryonic antigen-related cell adhesion molecule type 5 (CEACAM5), also known as carcinoembryonic antigen (CEA). 5] [26]   In this study, we explored the safety, efficacy, and specificity of SGM-101 during IMI-guided lung nodule resections. First, we explored a large national cancer database for CEACAM5 RNA expression levels in various lung cancers. Second, we assessed glycoprotein expression levels in 33 patients diagnosed with lung adenocarcinoma from our institution to assess local rates of CEACAM5 tumor presence.
Study Design and Patient Selection The current study was an open-label, nonblinded, proof-of-principle, phase 1 nonrandomized controlled trial performed in patients 18 years or older at the Hospital of the University of Pennsylvania in Philadelphia from August 1, 2020, to January 31, 2022. The trial protocol appears in Supplement 1. Details on immunohistochemistry, The Cancer Genome Atlas analysis, inclusion and exclusion criteria, fluorochrome parameters, camera systems used, and outcomes analyzed are detailed in the eMethods in Supplement 2. Data on race and ethnicity were not collected because this was a cellular molecular study and such data would not be informative. The study was reviewed and.
Analysis of a large national cancer database similarly demonstrated NSCLC to have a high messenger RNA expression of CEACAM5 compared with normal and in line with pancreatic, gastric, and esophageal cancers for which glycoprotein is an established clinical diagnostic and prognostic marker (eFigure 3 in Supplement 2). 16, 29 In addition, although the sample size was small, higher CEACAM5 expression was noted in higher-stage disease. In the control group of patients with metastatic pulmonary nodules, SGM-101 allowed for the in vivo and ex vivo localization of CEACAM5-positive tissues (eTable 6 in Supplement 2). Serum CEA levels had a good positive predictive value, but negative or normal values did not necessarily exclude fluorescence detection (Figure  1  and Figure  2 ). These results corroborate previous findings of SGM-101 and indicate the glycoprotein targeting and specificity of the antibody fluorochrome conjugate, which does not change behavior in the pulmonary parenchyma.
Histopathologic Details of Patients in the Control Group With Corresponding SGM-101-Related Intraoperative Findings eTable 5. Primary Malignancy Surgical Characteristics of the Patients in the Metastasis Control Group eTable 6. Characteristics of SGM-101 Guided Resections of the Lung Nodules in the Cohort eFigure 1. CEACAM5 Core Biopsy IHC Staining of 33 Consecutive Lung Adenocarcinoma Patients With (1) Cancer Tissue, (2) Cancer Adjacent Normal, and (3) Distant Normal Lung Demonstrating 22/33 Positive CEACAM5 Presence eFigure 2. Higher-Stage Lung Adenocarcinoma Correlated With an Increased Rate of CEACAM5 Expression (AJCC 8 th ed) eFigure 3. TCGA Database Analysis Demonstrates CEACAM5 Expression in Various Prevalent Solid Organ Malignancies, With Colorectal Adenocarcinoma Demonstrating the Highest mRNA Expression (16 Log2(Value +1)) and Melanoma Demonstrating the Lowest Median Expression (1 (Log2(Value +1)) eFigure 4.
Thus, chromatin remodeling of histone variants by DDM1 underlies the epigenetic inheritance of DNA methylation in plants. Transgenerational inheritance is much less common in mammals  93  , but close parallels with HELLS and Lsh suggest similar mechanisms may operate in the mammalian germline as well. A recent study has indicated that transgenerational inheritance of a methylated promoter in the mouse occurs despite the reprogramming and loss of DNA methylation in the embryo, suggesting a similar placeholder mechanism may be at work in mammals as well  94. • This paper also analyzes existing, publicly available data. These accession numbers for the datasets are listed in the key resources table. • Coordinates and the cryo-electron microscopy map for the DDM1-nucleosome complex have been deposited in the Protein Data Bank and are publicly available as of the date of publication.
qPCR primers are listed in Table  S4. ChIP-sequencing (ChIP-seq) libraries were prepared with ~200 bp insert size using NEBNext Ultra II DNA Library Prep Kit for Illumina (New England Biolabs). The ChIP-seq libraries were sequenced using an Illumina NextSeq platform with 75-cycle single reads for H3K27me1 and HTR5 samples and with paired-end 151 cycles for DDM1 and H4K16ac samples. MGH3 libraries were prepared by Fasteris SA (Switzerland) and sequenced on HiSeq platform to 100bp paired-end reads for WT and 100bp single reads for ddm1/+. Only read1 was processed from the WT sample to compare to single-end ddm1/+. Sequencing metrics on all ChIP-seq libraries are listed in Table  S5. ChIP-seq data were analyzed as previously described 106 , trimming adapters with cutadapt 107 but using bowtie2 108 to map to TAIR10, before filtering primary alignements with samtools 109.
Two independent replicates of ChIP-seq were obtained for each antibody and genotype except for MGH3-GFP, which was compared instead to H3. 3. The Pearson correlation coefficient was at least 0. 8 for the replicates (Figure  S6A ). When available, the IP and control of the two biological replicates were merged after mapping and deduplication with samtools, and then the signal tracks (bigwig) were generated with Deeptools 95 by calculating the log2 fold change of IP over control after count-per-million normalization. MGH3 and H3. 3 ChIP-seq datasets produced in this study have been compared to previously published datasets GSE120664  47  (Figure  S6B ) and from GSE34840  40  (Figure  S6C ), respectively. Figures were generated in R (https://www. rproject. org/) using ggplot2 110 and GViz 111 packages.
Microscopy Immunofluorescence experiments for leaf nuclei were performed using 3-week-old leaves as previously described  115. Monoclonal antibodies of anti-H3K27me1 (Active Motif; 61015) were used as primary antibodies and goat anti-mouse Alexa Fluor 488 (Thermo Fisher Scientific; A-10680) was used as secondary antibody. DAPI (2 μg/mL final concentration) was used for nuclei staining and the samples were mounted with Prolong Diamond (Thermo Fisher Scientific). DDM1-GFP, DDM1-mCherry, HTR3-GFP, and HTR5-RFP were observed with a Zeiss 710 confocal microscope. Live imaging data of DDM1-GFP was acquired using a Perkin-Elmer UltraVIEW VoX confocal microscope.
Cryo-electron microscopy data acquisition Data were acquired on a Titan Krios transmission electron microscope (ThermoFisher) operating at 300 keV. EPU data collection software version 2. 10. 0. 5 (ThermoFisher) was used to collect micrographs at a nominal magnification of 81,000x (1. 1 Å/pixel) and defocus range of -1. 0 to -2. 2 μm. Dose-fractionated movies were collected using a K3 direct electron detector (Gatan) operating in electron counting mode. In total, 30 frames were collected over a 4. 8 s exposure. The exposure rate was 14. 8 e -/Å 2 /s, which resulted in a cumulative exposure of approximately 71. 2 e -/Å 2. In total, 8,165 micrographs were collected.
Cryo-electron microscopy data processing Real-time image processing (motion correction, CTF estimation, and particle picking) was performed concurrently with data collection using WARP version 1. 0. 9  96. Automated particle picking was performed with the BoxNet pretrained deep convolutional neural network bundle included with WARP that is implemented in TensorFlow. A particle diameter of 180 Å and a threshold score of 0. 6 yielded 3,788,872 particle coordinates. Of the particles collected during cryo-EM acquisition, nearly three-quarters were free nucleosomes. Classification and refinement were carried out in cryoSPARC v3. 2. 0+210831  97. Initial 2D classification showed distinct classes of nucleosomes both bound to and independent of DDM1. To isolate the DDM1-bound nucleosome particles, 2D classes were first manually inspected.
Molecular model building and refinement An atomic model of the SWI/SNF nucleosome complex (PDB: 6UXW)  118  and the AlphaFold prediction of DDM1 119 were used as initial references for model building in Coot version 0. 9. 2-pre 120. After the initial build was generated, density modification was performed using Resolve 121. Subsequent rounds of interactive model building and refinement were performed with Coot and Phenix version 1. 19. 2-4158-000 122 , respectively. Secondary structure restraints for both the protein (helix and β-strand) and DNA (base-stacking and base-pairing) were used throughout refinement. Structure validation was conducted by MolProbity version 4. 5. 1 123. Data collection, processing, and model validation statistics are provided in Tables  S7  and S8.
Acknowledgments We thank  Crisanto Gutierrez  for providing H3. 1-GFP and H3. 3-RFP Arabidopsis reporter lines and  Frédéric Berger  for providing the htr4 htr5 htr8/+ and HTR13-CFP lines. We thank  Eric Richards ,  Vincent Colot  and  Tetsuji Kakutani  for discussions on the genetics of ddm1, and  Cigall Kadoch ,  Filipe Borges  and  Jean-Sebastien Parent  for helpful advice. We thank  Joe Calarco  (in memoriam) for the anti-DDM1 polyclonal antibodies. We acknowledge technical assistance for live imaging from  Nour El-Amine , and informatics help from  Leandro Quadrana  ( Ecole Normale Superieur, France ). Research in the Martienssen laboratory is supported by the  U. S. National Institutes of Health (NIH)  grant.
Initial Corneal Defects In the first EVEIT experiment, formulations were tested on three corneas each, based on previously published results that used the same model  [11]. For the second experiment, the number of tested corneas was increased to five per product in order to increase the statistical significance of the results and to make sure they were homogenous. After 24 h of stabilization within the EVEIT culturing system, the corneas were evaluated by microscopy. Corneas with epithelial defects or opacities were excluded from further experiments. During the experiments, the integrity of both the epithelial and endothelial sides was monitored daily using a phase contrast microscope-integrated camera (KY-F1030U Camera, JVC, Bad Vilbel, Z16 APO Microscope, Wetzlar, Germany) connected to DISKUS software (Hilgers, Koenigswinter, Germany).
Before corneal healing experiments started, four small corneal abrasions measuring 2. 9 ± 0. 9 mm 2 were induced on each cornea by an abrasive drill, which was placed on the cornea in a square pattern. Defect sizes were monitored by fluorescein sodium stains (0. 17% an aqueous solution), with yellow-green fluorescence indicating the areas of epithelial defects which were circumscribed using the DISKUS software tool. The erosion sizes (sum of four erosions of each cornea) are given in square millimeters.
Histology Regarding the corneal microstructure at the end of both long-term studies over 3 days (Fig. 7 ) or 5 days (Fig. 8 ), histological findings confirmed microscopic data for all test substances. Formulations A and B and CT show equal results after 3 days as well as after exposure and observation period extended to 5 days with complete healing of the epithelial layer, dense stroma, and regularly arranged keratocytes. In both Fig. 2  Representative fluorescein-stained microscopical images of the corneal healing process during experimental period of 5 days with drug application six times daily (n = 5 corneas per test item). All five corneas per test item are shown in detail in Figs. S1-S5 in the supplementary material. Healing was observed from day 1 for the control reference CT, completed by day 2, closely followed by latanoprost formulations A and B with stable epithelial closure at day 3.
Methods The Pacific Islands Regional Fisheries Observer (PIRFO) programme standardizes the data collection protocols and training framework adopted by observer programmes of the Pacific Islands Countries and Territories  [37]. The regional minimum data standards and collection formats are used for independent fisheries monitoring data collection, including a set of forms aimed at gathering information for the management of the stocks, for monitoring ecosystem impacts, and the implementation of WCPFC CMMs. One form, GEN-5 (Appendix 1) has been specifically designed to collect information related to FAD configuration (dFADs, aFADs and natural floating objects). Information recorded on GEN-5 includes the nature of the FAD (artificial or natural, anchored or drifting), the dimensions (length, width and depth) and the materials used (for both the raft and for submerged appendages), as well as the unique ID number from the satellite buoy attached to a dFAD.
Number of records of dFAD materials The number of records related to dFAD activities ranged between 50,000 and 65,000 per year between 2011 and 2019 (Fig. 4 ), with most records corresponding to dFAD visits and sets. Note that the decline in observations in 2020 and 2021 reflects both the impact of COVID-19 restrictions on observer deployments and data entry patterns. Information related to the materials of dFADs was recorded in less than 42. 7 % of all records. Nonetheless, a general increase through time was detected in terms of information related to dFAD materials (Fig. 4 ), with records in less than 5. 8 % of the instances in 2011 to 55. 1 % in 2019 and 58. 7 % in 2020. It should be noted however, that the dFAD-related form was rarely filled in by the observers during certain activities, such as deployments, retrievals and servicing, potentially due to the observers not typically observing these activities, not considering it being a priority or their occurrence when observers are busy with other duties or resting.
9. Mesh size of netting used to cover rafts (left) and as appendages (right) of dFADs, as recorded by observers per year (top) and fleet (bottom) (2011-2021). The grey dotted line indicates the 7 cm mesh size, used to classify dFADs as high or low entanglement risk by WCPFC (see Fig. 2 ). Country abbreviations same as Fig. 6. materials that present high entanglement risk was introduced by the WCPFC  [38] , which would be expected to lead to a reduction in the use of netting in dFADs. While a reduction in the presence of netting was observed in 2021, the data are limited due to COVID related low observer coverage and additional data are needed in coming years to confirm a reduction in the use of netting. Analyses presented in this paper were guided by an approach proposed recently in the WCPFC and IATTC FAD working groups to transition to Biodegradable dFADs.
For instance, some observers might not record all dFAD materials, but focus only on the materials that make up most of the dFAD, such as the raft and appendages, without recording components such as ropes or attractors. Analyses accounting for the experience level of the observer could be considered, while training of fisheries observers related to dFAD data collection should continue, given the increase in dFADrelated management measures. Further, the priority of dFAD data collection by observers needs to be considered in relation to other tasks, and if dFAD data collection is considered a low priority, alternative data collection approaches will need to be considered. The estimates of dFAD depth presented here may be biased, as they are based only on the observer's estimate, either when dFADs are lifted from the water or dragged by the speedboat close to the vessel's side.