dataesr/scientific-paragraphs-categorization huggingface dataset:
Resulting images have a reduced signal intensity and low SNR. Importantly, having both (i) not been part of the training and evaluation datasets of the aforementioned methods and (ii) having different characteristics, such images represent a yet unsolved challenge for nuclei segmentation. Moreover, the performance of the existing supervised deep-learning methods depends on the amount of high-quality annotated data available for training. Despite the large effort that was put to produce publicly available labels for nuclei segmentation, such as the 2018 Data Science Bowl competition, such data is often partially or even incorrectly labeled  (He et al. , 2020).
Image to Image translation Image quality enhancement has also been approached through image to image translation deep learning methods. The goal is to transform an image having a particular style (source style) into a desired target style. The most efficient models are based on GANs  (Pang et al. , 2021; Wang et al. , 2020). Authors of pix2pix  (Isola et al. , 2017)  were the first to apply a GAN-based architecture to perform the image to image translation. It is a fully supervised method that requires large paired image datasets to train the translation model that transforms the source images to the desired target images. In the context of nuclei segmentation, pix2pix has been used by the authors of nucleAIzer for data augmentation of their training nuclei datasets. Specialized image enhancement models have been since proposed, such as Cycle-CBAM  (You et al.
DATASETS To train and evaluate our SalienceNet enhancement method, we have collected different datasets (see figure  3 ). First, two experimentally acquired and expertly segmented datasets (see section 4. 2), which have been previously extensively used for training segmentation models. Second, we have acquired a dataset of organoid images with low-light conditions that specifically represents the segmentation challenge that we want to address, as well as generated the corresponding synthetic datasets (see sections 4. 1 and 4. 2). These images belong to one of the two styles: 1. Source style with low saliency of nuclei (organoid and synthetic low saliency datasets), 2. Target style with high saliency of nuclei (two experimental datasets and the synthetic high saliency dataset).
Source style datasets To evaluate whether SalienceNet enables precise nuclei segmentation, we acquired a 3D cell culture dataset with the soSPIM technique. soSPIM is a single objective light-sheet microscopy approach capa-ble of streamlining 3D cell cultures with fast 3D liveimaging at speeds up to 300 3D cultures per hour  (Galland et al. , 2015; Beghin et al. , 2022). We selected 11 neuroectoderm organoids exhibiting a wide variety of shapes and densities. These organoids have been differentiated from hESCs, fixed at day 8, immunostained with DAPI and imaged using soSPIM, yielding 1056 2D slices. These 2D image slices are composing the DS org dataset. To augment the source style dataset, in addition to the experimental 1056 2D image slices, we generated synthetic images. First, we performed an expert segmentation of nuclei on each individual 2D slice.
Target style datasets The goal of our network is to learn to transform an image i into e(i), where saliency  (Kim and Varshney, 2006)  at nuclei location is enhanced. To provide the target style dataset for training the SalienceNet network, we have collected two experimental datasets where the nuclei saliency was already satisfactory for segmentation by classical pipelines and for which the nuclei segmentation masks are available. We complemented them by a synthetic high-saliency dataset. In 2018, a Data Science Bowl competition orga- nized by Kaggle released a dataset for a challenge of "Identification and Segmentation of Nuclei in Cells" of images acquired under different conditions and of different cell types and that vary in size, magnification, and imaging method (brightfield and fluorescence).
Nuclei masks have been manually created by specialists and are provided with the dataset. For the purpose of this paper, only grayscale cell culture images were kept, yielding the TS DB with 551 images. Experimentally acquired nuclei images from human cell lines  (Chouaib et al. , 2020)  were used to define the TS LS dataset. It is composed of 568 images from 57 different acquisition conditions of 32 gene expression measured in the study for the purpose of performing a localization screen. Nuclei masks have been acquired with NucleAIzer. The synthetic high-saliency dataset, TS synth , was generated following the same procedure as DS synth (see section 4. 1) with 2000 images, but with enhanced saliency. Nuclei masks are provided by the input generation procedure (elliptical shapes). Taking these 3 datasets together (summarized in table 1), the target style dataset contains 3119 images.
RESULTS To train the SalienceNet models, we split each of the two source datasets as well as the two experimental target datasets (see figure  3 ) into train and test subsets, in 90% and 10% proportions. The test datasets are denoted DS t org , DS t synth , TS t DB and TS t LS , respectfully. We then performed the hyperparameter search by exploring all possible combinations of α, β, and γ (weights of the loss components, see section 3. 2) with step of 0. 1 in order to estimate which combination of parameters yielded the best model. This resulted in 42 models, denoted by (α, β, γ) combinations in figure  5. Moreover, for comparison purposes we have trained a vanilla CycleGAN model, without any modification with respect to the original CycleGAN network. All the 42 SalienceNet models and the vanilla Cy-cleGAN were applied to the 4 test datasets DS t org , DS t synth , TS t DB and TS t LS to perform saliency enhancement.
The original images and their enhanced coun-terparts were then segmented, without any parameter tuning, using two widely used segmentation methods: 1. the non-parametric version of the classical segmentation Otsu thresholding method with an adaptive threshold, 2. StarDist, a deep-learning based segmentation, with the 2D fluo versatile model as provided by  (Schmidt et al. , 2018) , without re-training on our data or supplementary fine-tuning. The resulting masks were then compared with ground truth. For this purpose, expert ground truth annotation was performed on the DS t org test dataset; nuclei masks (ground truth) were already available for the 3 other test datasets (see sections 4. 1 and 4. 2). To measure the quality of the resulting segmentation, we computed the intersection over union (IOU) for each image to quantify the overlap (in pixel count) between the segmentation and the ground truth: IOU = |S∩G| |S∪G| , where S is the mask resulting from segmentation and G is the ground truth mask.
SalienceNet enables accurate nuclei segmentation First, we determined the most performant model with respect to our goal of segmenting low SNR images from live-cell imaging by looking at the enhancement performance on organoid images. Figure  5  shows the IOU scores for segmentation by StarDist of both enhanced (by 42 models) and non-enhanced images, and that for each image of the experimental organoid test dataset DS t org. Individual model results for Otsu segmentation being very similar (albeit slightly worse), are not shown in this figure. The IOU is shown in figure  5  for each image with respect to the ground truth. The geometric mean of all non-enhanced image's IOU was 0. 49 for StarDist segmentation and 0. 45 for Otsu segmentation, while the geometric mean for IOU after saliency enhancement ranged from 0.
54 to 0. 75 for StarDist and from 0. 48 to 0. 75 for Otsu segmentation. Best results were obtained for segmentation after enhancement by the SalienceNet model with α = 0. 2, β = 0. 2, and γ = 0. 6, with a geometric mean of IOU of 0. 75 for both StarDist and Otsu. We denote this model by M. The impact of this model M on the quality of the downstream segmentation was computed as the ratio of IOU for images enhanced with M over the IOU of non-enhanced images. Impact values range between 1. 08 and 2. 57 for Otsu segmentation and between 1. 09 and 10. 73 for StarDist segmentation; notice that the lower bound is > 1 in both cases. An illustration of StarDist segmentation results for non-enhanced images and for those enhanced by M  for low saliency organoid images is provided in figure  4. Table  2  shows the IOU values for the best model M of SalienceNet applied to the 4 test datasets before and after enhancement by SalienceNet.
On one hand, we observed that SalienceNet indeed improved the accuracy of nuclei segmentation in low-light source datasets. On the other hand, this table shows that for the already salient images, SalienceNet did not degrade the quality of segmentation. Together, these results show that saliency enhancement by SalienceNet enables accurate downstream nuclei segmentation by widely used methods without need for parameter tuning. SalienceNet enhances nuclei saliency To evaluate whether SalienceNet improved saliency, we computed its indirect measure -the Signal to Noise Ratio (SNR) as SNR = (mµ B )/σ , where m is the maximum pixel intensity within the nuclei masks in an image, µ B is the mean value of the background and σ is the standard deviation of the background. The SNR was computed for the source style datasets DS org and DS synth , and the target style datasets TS synth , TS LS and TS DB.
We compared the segmentation results of widely used non-parametric Otsu thresholding and StarDist on both raw images and images enhanced with SalienceNet of our novel organoid live-cell imaging dataset. We have shown that using SalienceNet improved the segmentation quality of both classical and deep learning based nuclei segmentation algorithms in low SNR nuclei images. It should be noted that adding the SalienceNet enhancement step prior to nuclei segmentation did not degrade the quality of results for the already salient datasets. Taken together, these results show that SalienceNet is a useful new step for nuclei segmentation workflows.
The Aptian was characterized by dramatic tectonic, oceanographic, climatic and biotic changes and its record is punctuated by Oceanic Anoxic Events (OAEs). The timing and duration of these events are still contentious, particularly the age of the Barremian-Aptian boundary. This study presents a cyclostratigraphic evaluation of a high-resolution multiproxy dataset (δ 13 C, δ 18 O, MS and ARM) from the Poggio le Guaine core. The identification of Milankovitch-band imprints allowed us to construct a 405-kyr astronomically-tuned age model that provides new constraints for the Aptian climato-chronostratigraphic framework. Based on the astronomical tuning, we propose: (i) a timespan of ~7. 2 Myr for the Aptian; (ii) a timespan of ~420 kyr for the magnetic polarity Chron M0r and an age of ~120. 2 Ma for the Barremian-Aptian boundary; and (iii) new age constraints on the onset and duration of Aptian OAEs and the 'cold snap'.
Results Magnetic and stable isotope variations. MS and ARM vary between 1. 1 × 10 -9 m 3 /kg and 5. 66 × 10 -7 m 3 /kg and from 4. 51 × 10 -11 Am 2 /kg to 1. 16 × 10 5 Am 2 /kg, respectively. The highest MS and ARM values occur in clay-, marl-, and limestonerich lithofacies of the Marne a Fucoidi Formation. In contrast, the lowest values occur in the white lithofacies of the Maiolica Formation (comprising mainly limestone and marly limestone/calcareous marl). Significant low-and high-wavelength quasiperiodic contents can be observed for both the MS and ARM datasets (Fig. 2a, b ). Rock magnetic measurements reveal magnetite as the main magnetic carrier  33. The δ 13 C values vary significantly in the Aptian (1. 47-4. 82‰; Fig. 2c ). The 95. 99 m (base)-91. 19 m interval displays δ 13 C values between 1.
Therefore, the relatively higher δ 18 O values in our section have been interpreted as a "cold snap" record. Aptian astronomically tuned chronology. We identified spectral patterns suggesting astronomical forcing for all four datasets (MS, ARM, δ 13 C, δ 18 O; Fig. 3a-d ). Spectral analyses (Fig. 3 , top) revealed statistically significant spectral peaks above the 95% confidence level. Comparing these spectral peak frequencies with evolutionary fast Fourier transform (eFFT) results (Fig. 3  ). Due to their lower sampling resolution (~10 cm), the stable isotope records do not show the precession signal, which is below the Nyquist frequency. We interpret the spectral peak bands for the magnetic data (MS and ARM) of 12-13 cm and 14-19 cm as the precession parameters P 2 (~18. 5 kyr) and P 1 (~22.
The short eccentricity cycle is also evident in the MS record through the ~4:1 bundling of short eccentricity to precession (yellow bundles in Fig. 4c ). The spectral content confined to the 191. 3-321. 2 cm (MS), 181. 7-276. 7 cm (ARM), 188. 7-338. 4 cm (δ 13 C), and 190. 8-359. 0 cm (δ 18 O) spectral bands could be related to the ~405-kyr eccentricity cycle (herein referred as "E"). Cyclostratigraphic analysis for the MS dataset (Fig. 3a , top and bottom) indicates a ~243 cm wavelength above the 99. 9% confidence level (c. l. ), which is applied for the astronomical tuning also indicated as red bundles in Fig. 4c. Periodicities of ~60-70 kyr occur in both spectra (herein referred to as "T A " and "T B " spectral ranges). These are particularly evident from the eFFT for the MS dataset (Fig.
3c ) in the upper first (~82-87 m) and last (~65. 5-67. 0 m) thirds of the PLG core interval. Despite the detrending, low-frequency spectral peak ranges in the Myr-scale wavelength (at ~1798-640 cm (MS), ~1635-449 cm (ARM), ~1631-598 cm (δ 13 C), and ~1795-1380 cm (δ 18 O)) were also verified above a 99. 9% confidence level (MS data are shown as black bundles in Fig. 4c ). These features could be linked to non-periodic long-term climatic variations or long-term orbital modulation cycles in sedimentary profiles worldwide. Therefore, they could indicate a significant control on the global climate (e. g. , refs. 37, 38 ). Using a low-pass filter with a cutoff frequency of 0. 4 cycles/m, we isolated the interpreted 405-kyr long-eccentricity component from the MS dataset. Since there are no available radiometric ages for the Marne a Fucoidi Formation, we assigned the stratigraphic position of the Aptian-Albian boundary to ~113.
In the PLG core, the Kilian Level occurs between 63. 02 and 62. 64 m and is represented by a poorly laminated black shale  32  (Figs. 2  and 4 ). In agreement with the findings at the PLG section  11  , this level falls in the NC8A-B calcareous nannofossil Zone and the lowermost part of the M. renilaevis planktonic foraminiferal Zone. It is likely to be the earliest Albian in age (Figs. 2  and 4 ). According to our results, the Kilian Level, which occurs in the isotopic zone C11 or Ap16-Ap18  7  , is located at the eccentricity bundle E 0 (Fig. 4d ) and has a duration of ~90 kyr (Table  1 ). Based on our floating ATS, the age for this level is ~112. 9 Ma, in close agreement with previous studies (e. g. , ~112 Ma; 21 ~112. 8 Ma; 23 113 Ma 29 ) (Table  3 ). The cyclostratigraphic analysis of all four proxy datasets (Supplementary Fig.
5 ) indicates a clear imprint of 10 3 -10 6 yr periodicities compatible with the Milankovitch spectral peak ratio for Albian-Aptian times  36. We also identified a pervasive, ~3. 0-0. 8 Myr-band above a 99. 9% confidence level which is detectable for all proxy datasets, similarly to those recogniseable in sedimentary records worldwide for distinct geological intervals-the Cenozoic and Mesozoic 6161 , Early and Late Cretaceous (e. g. , refs. 38, 62 ), Carboniferous (early Moscovian)  63  , and Ordovician (Darriwilian)  64. The late-Early Cretaceous was a warm period with supergreenhouse conditions, weak latitudinal temperature gradients, and an absence of ice sheets (e. g. , refs. 54 ). However, in the last few decades, some authors (e. g. , refs. 25, 58, 59 ) suggested that cooler interludes punctuated these conditions.
The ARM has been considered in cyclostratigraphic investigations as an alternative proxy to MS  66. Notably, the ARM obtained at 100 mT (hereafter referred to as ARM for simplification) is useful in providing information on the fine-grained ( < 20 μm) lowcoercivity ferromagnetic minerals 66. Our rock magnetic cyclostratigraphic analyses comprise both MS and ARM datasets to provide a better assessment and comparison of the spectral content and depositional time range  63, 66. The MS measurements were carried out at the Laboratório de Paleomagnetismo of Universidade de São Paulo (USPMag). They were made on an MFK1-FA Multi-Function Kappabridge at an operating frequency of 976 Hz, in a field of 200 A/m. The acquisition of the ARM dataset was carried out by the following protocol: remanence measurements were all made in a SQUID magnetometer model 755 (2G-Enterprises), housed in a magnetically shielded room with internal field < 500 nT at USPMag.
Samples underwent a stepwise alternating field (AF) demagnetization over 17 steps: 0-4 mT (step = 2 mT), 4-10 mT (step = 3 mT), 10-40 mT (step = 5 mT), 40-100 mT (step = 10 mT). After AF demagnetization, the sample was submitted to a stepwise ARM acquisition along the same AF demagnetization steps until 100 mT with a direct current bias field of 0. 05 mT. Stable δ 18 O and δ 13 C isotopes. It is well-known that specific stable isotope ratios are particularly useful for cyclostratigraphic analyses because they are independent of facies and have a global significance  67. Geochemical parameters can be used to analyze the cycles because they are sensitive to climate and environmental changes  68. Therefore, cyclostratigraphic analysis of δ 18 O and δ 13 C datasets are often used for paleoclimatic studies to detect orbitally forced patterns  38.
The powders of 33 samples were analyzed using a Kiel IV carbonate device coupled to Thermo Delta V Advantage mass spectrometer and nine samples were analyzed using a Gas Bench II carbonate device coupled to a Thermo Scientific Delta V mass spectrometer. Samples were reacted with purified phosphoric acid (H 3 PO 4 ) at 70-90 °C in all instruments. The calibration was undertaken using the Oxford in-house Carrara marble standard (NOCZ) and NBS-19 (TS-Limestone). Data are reported relative to the Vienna Pee Dee Belemnite (VPDB) scale. The reproducibilities of replicated standards (1σ) were < 0. 09‰ for δ 13 C and < 0. 10‰ for δ 18 O. The maximum difference between triplicate and duplicate samples from the same depth were 0. 31‰ for δ 13 C and 0. 39 ‰ for δ 18 O. In this study, we compared these results with those provided by the rock magnetic proxies (MS and ARM) to verify a possible astronomical imprint in the PLG succession.
C = Common, 1 specimen in 50/fields of view. F = Few, 1 specimen in 100/fields of view. R = Rare, 1 specimen in 200/fields of view. 300 fields of view were scanned in each slide. Cyclostratigraphy. Cyclostratigraphic analyses for MS, ARM, δ 13 C, and δ 18 O datasets were performed with the MATLAB ® -based Acycle software version 2. 4. 1  69. All datasets were log-transformed to harmonize fluctuation 66 before undergoing linear interpolation and uniformly resampling every 2 cm (for MS and ARM data) and 10 cm (for δ 13 C and δ 18 O data), which correspond to the mean spacing of the magnetic and geochemical series, respectively. The resulting datasets were then linearly detrended. Spectral analysis was carried out with the prolate multitaper spectral estimator against a robust red noise null model  70  with mean, 90%, 95%, 99%, and 99.
Hence, it has been considered in the literature as a suitable metronome in astrochronology investigations along the Phanerozoic  66. A low-pass filter allowed us to isolate the interpreted long-eccentricity component sinusoidal curve from the datasets tuned according to the g2-g5 target curve from La2004 astronomical solution  39  , which does not differ considerably from the La2011 solution for Aptian-Albian times  62. Astronomical tunings based on the ~100 kyr short eccentricity could be unreliable for time intervals older than 50-60 Ma due to the chaotic evolution of the Solar System 39,63 and hence it was not considered here for astronomical tuning. In order to find the optimal sedimentation rate based on our astrochronological interpretation for the PLG core, we performed correlation coefficient (COCO) and its evolutionary variant (eCOCO) analyses  40  with 5000 Monte Carlo simulations on tested sedimentation rates ranging from 0.
Acknowledgements The Poggio le Guaine core is an integral part of the  FUSP (Fundação de Apoio à Universidade de São Paulo)-Petrobras   2405  project. The paper is an integral part of the Project:  Processamento e interpretação de dados magnetoestratigráficos do Cretáceo das Bacias Brasileiras , which is financially supported by  Petróleo Brasileiro S. A. -Petrobras  ( FAURGS 8368 ). The authors are grateful to  Professors Hugh C. Kenkyns  and  Stuart Robinson  ( University of Oxford, U. K. ) for the valuable discussions that contributed to this study. We all are grateful to  São Paulo State Research Foundation (FAPESP)  to academically support this work through the  CORE  project ( 16/24946-9 ). Support for this work came from the  National Council for Scientific and Technological Development (CNPq- grants  141093/2018-8  to C.
Bioinformatics Analysis and Differentially Expressed Genes Analysis Quality control (QC) was performed on the primary sequencing data generated by RNA-Seq (raw reads). Table  1  summarizes the details of total readings and mapping ratio reads. By deleting the adaptor, low-quality bases, low-quality reads, and reads with unknown N bases, Trimmomatic software  (Bolger et al. , 2014)  filtered the raw reads into high-quality clean reads. HISAT2 (version 2. 2. 1. 0) (Kim et al. , 2015)  was used to align the clean reads to the reference genome and the RSeQC (version 2. 6. 4) (Wang et al. , 2012)  technique was used to count the proportions of various comparison types and evaluate the comparison results. Stringtiew2 software (version 1. 3. 3b) (Pertea et al. , 2015)  was used to assemble the reads and splice the new transcript after getting the SAM file with the comparison findings.
The candidate lncRNA transcripts were then chosen by comparing the gene annotation information of the reference sequence obtained by Cuffcompare software (version 2. 2. 1) to the gene annotation information of the candidate lncRNA transcripts  (Trapnell et al. , 2012). Finally, to acquire lncRNA predicted sequences, transcripts with coding potential were screened out using CPC  (Kong et al. , 2007) , Pfam (version v30)  (Finn et al. , 2006) , and PLEK (version 1. 2)  (Li et al. , 2014). The sequencing reads were aligned with the sequence of mRNA transcript sequences using Bowtie (version 2. 2. 9)  (Langmead and Salzberg, 2012). And then, eXpress (version 1. 5. 1) (Roberts and Pachter, 2013)  was applied to make quantitative gene analysis, the FPKM value, and counts value. Finally, differential expression analysis was done using DESeq2 (version 1.
Protein-Protein Interaction Network Analysis The DEGs' interactive network was built using the STRING database (http://string-db. org/). The interactions score >0. 4 was used as the reliability threshold value  (Otasek et al. , 2019). The Cytoscape program (version 3. 8. 2) was used to view further and analyze the PPI network  (Shannon et al. , 2003). Finally, the relevance of this network was determined by calculating the connectivity degree of each protein, which is the number of proteins with which it interacts.