dataesr/scientific-paragraphs-categorization huggingface dataset:
In Sec. III, we discuss the role played by DM halos accreting around PBH, treating them both via semi-analytical toy models and dedicated numerical simulations. Our CMB bounds are reported in Sec. IV, providing an upgrade to the the bounds presented in Ref. [11]  in the light of the last Planck data release and the updated treatment of the energy release via the new ExoCLASS package  [15]. Also, we present an extension of the bounds up to masses of M few × 10 4 M , beyond which some working hypotheses break down. Sec. V briefly reviews the puzzle concerning the origin of SMBH, and discusses the implications of our CMB limits. We can anticipate that, even under Eddington accretion conditions for the PBH surrounded by DM halos, CMB angu-lar power spectra are not capable of testing the hypothesis that the SMBH detected already at high-redshift are (seeded by) PBH.
II. THE LUMINOSITY OF ACCRETING PBH In the following, we assume that the ionization of gas in the dark ages due to accretion onto PBHs and probed by the CMB can be considered homogeneous. A justification is provided in Appendix A. The key input needed to compute the CMB bound is the total energy injection rate per unit volume: d 2 E dV dt = L acc n PBH = L acc f PBH ρ DM M , (1) where L acc (M, t) is the bolometric (in general, time dependent) accretion luminosity onto a PBH of mass M , and is the main unknown. A milder uncertainty (within a factor < ∼ 2) comes from the spectral distribution of the emitted radiation, which in turns determines the energy deposited in the medium-what actually matters-and for which we make use of the transfer functions 1 from Ref. [17, 18]  as implemented in the ExoCLASS package  [15].
IV. CMB CONSTRAINTS A. Impact of accretion onto DM halos We modify the branch ExoCLASS  [15]  of the public code CLASS to include the effect of DM halo. We compare the effect of accretion with and without halos. In practice, our simulations only have the necessary resolution to solve eq. (  13 ) for redshifts 100 < 1 + z < 1000, which encompasses the redshift range at which e. m. energy deposition has the biggest impact on the CMB, 300 < 1 + z < 600  [17]. We extrapolate with constant values of the lower (higher) boundary at lower (higher) redshifts. We checked that this has sub-percent impact by turning off injection at z < 100, while the effect of energy ejection is naturally turned off at higher-z since the plasma is still mostly ionized. We show the effect of the e. m. energy injection from accretion of matter around PBH on the CMB power spectra in Figs.
B. Analysis We run a Markov-chain Monte Carlo (MCMC) using the public code MontePython-v3 4  [42, 43] , interfaced with our modified version of CLASS. We perform the analysis with a Metropolis-Hasting algorithm, assuming flat priors on {ω b , ω cdm , θ s , A s , n s , τ reio , f PBH } at fixed PBH mass M/M = [10, 20, 30, 40, 50, 10 2 , 10 3 , 10 4 , 10 5  ]. We additionally perform a MCMC run at fixed f PBH = 1, with M/M free to vary to determine the minimal PBH mass probed by cosmological data through accretion. In practice we find that M 95% min = 15M > 10M in the spherical case, we therefore did not run with M = 10M in that accretion scenario. We adopt the Planck collaboration convention and model free-streaming neutrinos as two massless species and one massive with M ν = 0. 06 4 https://github.
com/brinckmann/montepython_public FIG. 5 : Same as Fig. 4 , in the spherical accretion case. eV. Our data set includes Planck 2018 high-and low-TT, EE and lensing likelihood  [44, 45] ; the isotropic BAO measurements from 6dFGS at z = 0. 106  [46]  and from the MGS galaxy sample of SDSS at z = 0. 15  [47] ; the anisotropic BAO and the growth function f σ 8 (z) measurements from the CMASS and LOWZ galaxy samples of BOSS DR12 at z = 0. 38, 0. 51, and 0. 61  [48]. Additionally, we use the Pantheon 5 supernovae dataset  [49] , which includes measurements of the luminosity distances of 1048 SNe Ia in the redshift range 0. 01 < z < 2. 3. As usual, we use a Choleski decomposition  [50]  to deal with the numerous nuisance parameters associated with the likelihoods (not recalled here for brevity). We consider chains to be converged using the Gelman-Rubin  [51]  criterion R -1 < 0.
This improvement is due roughly equally to the new Planck 2018 low multipole polarization data and the additional use of BAO and Pantheon data, as well as to the improvements in the treatment of energy deposition, now implemented in ExoCLASS. Accounting for the the halo (light shaded regions) does not lead to significant differences unless f PBH is sufficiently small, i. e. there is sufficient material for growing a sizable DM halo. The threshold to see significant improvements is f PBH < ∼ 0. 01 for the disk accretion case, but already at f PBH < ∼ 0. 2 for the spherical accretion case. For the latter case, the steep improvement of the bound around M ∼ 30 M in presence of a halo is only indicative, since for f PBH > ∼ 0. 01 a non-negligible fraction of the DM can be gravitationally bound to two or more PBH, and the radial profile derived in the isolated-PBH approximation breaks down  [37].
Acknowledgments We acknowledge valuable discussions with  Yacine Ali-Haïmoud. We thank  Nagisa Hiroshima  for collaboration on an early stage of the project and  Nagisa Hiroshima  and  Guillermo Ballesteros  for comments on the manuscript. This work is partially supported by the project Multimessenger avenues in gravitational waves under the program " Initiatives de Recherche Stratégique" -IDEX Univ. Grenoble-Alpes  (PDS); by  JSPS KAKENHI  Grants No. JP17H01131 (K. K. ) ,  MEXT   Grant-in-Aid for Scientific Research on Innovative  Areas  JP15H05889 ,  JP18H04594 ,  JP19H05114  (K. K. ), and by  WPI, MEXT, Japan  (K. K. ), the  Toshiko Yuasa France-Japan Particle Physics Laboratory  " TYL-FJPPL " (VP); by the  National Science Foundation  under Grant No. 1820861  (DI); by the  NYU IT High Performance Computing resources , services, and staff expertise (DI).
Selecting the best design for a methanol assimilation pathway Natural methylotrophs have developed multiple pathways that allow them to grow on methanol as the sole source of carbon and energy  (Chistoserdova, 2011). This metabolic diversity means that in theory, there are more than 1000 unique methanol assimilation pathways from methanol to biomass. To identify the best pathway for E. coli to consume methanol, we used FindPath, a tool that freely recombines a repertoire of existing reactions to create metabolic pathways  (Vieira et al. , 2014). FindPath uses a substrate-associated reaction database and flux balance analysis (FBA) based on a genome scale model (GSM) of the host to (i) find all the possible pathways, and (ii) rank them according to their length and the predicted growth rate on the substrate of interest.
Screening for the best matching methylotrophic enzymes To identify the combination of enzymes that would optimize the in vivo activity of the pathway in E. coli, we built a combinatorial library of mdh and das homologues derived from native and non-native methylotrophs (bacteria and yeasts). Starting with the query protein sequences of Bacillus methanolicus PB1 Mdh2 and Pichia angusta Das, a BLAST search with an identity cut-off at 50% was used with the software CD-HIT to filter out and cluster homologous templates (see Materials and Methods section). This threshold ensured that only functional homologues were identified  (Sangar et al. , 2007). Some of the 12 prokaryotic Mdh sequences and 17 eukaryotic Das sequences selected in this way belong to genera known to contain methylotrophs, such as Bacillus, Burkholderia  (Chistoserdova et al.
Taken together, these data point to the presence of a promiscuous alcohol dehydrogenase that replaces FrmA, but more importantly, confirm the oxidation of methanol into formate. Upregulation of dhaL and dhaM, which encode the dihydroxyacetone kinase (DAK) pathway, was observed in the presence of methanol, particularly at T1, when xylose was still present (Figure  3B ). Because the expression of the dhaKLM operon is induced by DHA  (Bächler et al. , 2005) , this confirms the presence of DHA in the cells and thus the co-assimilation of methanol with xylose. However, the genes encoding alternative DHA assimilation routes (i. e. the glycerol (gldA, glpK and glpD) and the FSA (fsaA and fsaB) pathways) were not transcriptionally activated or even down-regulated (Figure  3B ) at both time points.
, 2019)  and this is in line with the presence of DHA in the cells. Interestingly, among genes related to transcription regulation, positive regulators of the tdc operon involved in threonine and serine catabolism were up-regulated (Table  2 ). This is in accordance with the previous findings that the activation of the threonine degradation pathway is associated with better methanol assimilation  (Gonzalez et al. , 2018). The presence of methanol had a significant impact on overall cellular organization. The genes encoding ribosomes, translation machinery, amino acid metabolism and other more general processes (Krebs cycle, respiratory chain, fatty acids metabolism, etc. ) were downregulated during the exponential and stationary growth phases (i. e. at T1 and T2) (Table  2 ). Overall, these data demonstrate that methanol has a negative effect on the cellular machinery by down-regulating cell wall and membrane genes, most probably by chemical perturbation of the outer layers.
In silico design of the synthetic pathway The synthetic pathway for methanol assimilation was designed using the software FindPath  (Vieira et al. , 2014). The workflow starts with the creation of a substrateassociated reaction database based on the literature and available metabolic databases. This database consists of reactions involving the target molecule (in our case, methanol). The database is then converted into a model that is subsequently used to compute elementary flux modes (EFMs), i. e. , all the possible flux distributions in a metabolic network under steady state conditions. Among these EFMs, the best pathways are selected and ranked according to their efficiency. Finally, the best module combinations for efficient methanol conversion were identified. In our case, the methanol database encompassed more than 100 reactions steps and 100 metabolic compounds involved in methanol metabolism.
The spectrometer was operated in full-scan mode at a resolution of 60,000 (400 m/z). 13 C-carbon isotopologue distributions were identified by matching masses from the mass spectra (mass tolerance of 5 ppm) and retention times using the software TraceFinder (v. 4. 1). The peaks of different isotopologues were integrated and corrected for the natural abundance and isotopic purity of the tracer using the software IsoCor  (Millard et al. , 2019). Levels of 13 C-isotopic enrichment were then determined as follows: 13 C-enrichement (%) = sum(Mi*i)/n, where n is the number of carbon atoms for the measured fragment and Mi is the corrected abundance of the mass isotopologue.
Supernatant analysis Metabolite utilization and the production of the synthetic methylotroph were analyzed by quantitative 1D 1 H-NMR at 280 K using a zgpr30 sequence with water pre-saturation prior to acquisition on an Avance III 500 MHz spectrometer (Bruker, Rheinstetten, Germany) equipped with a 5 mm QPCI cryogenic probe head. The parameters were as follows: 286°K, 128K points, 8 s relaxation time, 2 dummy scans, 32 scans. Free induction decays (FIDs) were converted into frequency domain spectra by Fourier transform. All spectra were processed using the software TopSpin (v. 3. 5). Phases were adjusted manually, baselines were adjusted automatically, and the spectra were aligned and quantified using 3-trimethylsilylpropionic-2,2,3,3-d4 acid sodium salt (TSP-d4, 1 mM) as a chemical shift and concentration standard.
Transcriptomic analysis Cells were grown in flasks of M9 minimal media containing 15 mM xylose with or without 150 mM MeOH. At T1 (OD600 = 1, exponential phase) and T2 (OD600 = 2, stationary phase), 4 mL of each culture was centrifuged for 90 s at 14000 rpm before discarding the supernatant and immediately freezing the pellets in liquid nitrogen. Total RNA was extracted according to the Qiagen RNAeasy MiniKit procedure and quantified using a Nanodrop® spectrophotometer. Double-stranded complementary DNA (cDNA) synthesis and array processing were performed using the Agilent Technologies One-Color Microarray-Based Gene Expression Analysis protocol. The images were analyzed with the software DEVA (v. 1. 2. 1). All array procedures were performed using the GeT-Biopuces platform (http://get. genotoul.
fr/). For each data set, methanol were divided by the log2 intensities obtained without methanol. These ratios were then normalized by the log median intensity. Genes whose expression level differed by a factor of 2 or more between the two conditions were selected for further analysis. Gene ontology analyses were performed using Ecocyc (https://ecocyc. org/). Gene expression data have been deposited in the ArrayExpress database at EMBL-EBI (www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-8909.
Acknowledgment The Table 2: Functional classification of genes with statistically significant increases (log2 ratio >2) and decreases (log2 ratio <-2) in mRNA levels in E. coli ΔfrmA_pSEVA424-Mdh-Das(opt) strain during growth in M9 minimal medium containing 15 mM xylose compared to M9-glucose medium supplemented with 150 mM methanol. The categories of orthologous genes (COG) were used for grouping. The indicated values are the p-values attributed to each category on Gene Ontology. 23PG ), fructose-6phospohate (F6P), glycerol-3-phosphate (GLYC3P), pool of pentoses-5-phosphate (P5P), phosphoenolpyruvate (PEP) and malate (Mal) within the different strains after 90 min of culture in M9 medium with 655 mM 13 C-methanol. Mean of 3 replicates. B) Table of overexpressed (dark blue) and deleted (light blue) genes within the different strains.
GO:0019752 - 1. 35E-06 carboxylic acid metabolic process GO:0022904 - 1. 64E-04 respiratory electron transport chain Carbon GO:0006090 - 4. 51E-03 xylB, eno, metabolism pyruvate metabolic rhaAB, sucA, process aceEF, gatDZ, Toulouse, France, carbohydrate catabolic GO:0016052 - www. metatoul. fr, ANR-11-INBS-0010, deoC, etc. 4. 52E-03 process www. metabohubmetabohub. fr) and its staff members for technical support and access to MS and NMR facilities. This work was funded by the ANR (ANR-16-CE20-0018-01 source organisms.
Purpose: To assess the performance of deep learning algorithms for different tasks in retinal fundus images: (1) detection of retinal fundus images versus optical coherence tomography (OCT) or other images, (2) evaluation of good quality retinal fundus images, (3) distinction between right eye (OD) and left eye (OS) retinal fundus images,(4) detection of age-related macular degeneration (AMD) and (  5 ) detection of referable glaucomatous optic neuropathy (GON). Patients and Methods: Five algorithms were designed. Retrospective study from a database of 306,302 images, Optretina's tagged dataset. Three different ophthalmologists, all retinal specialists, classified all images. The dataset was split per patient in a training (80%) and testing (20%) splits. Three different CNN architectures were employed, two of which were custom designed to minimize the number of parameters with minimal impact on its accuracy.
5] [16] [17]  In fact, previous studies report accuracy of over 90% with sensitivity and specificity levels over 90% as well, which coincides with roughly the same level as a human ophthalmology expert. We can find similar outcomes with the detection of referable AMD. 18 owever, using such algorithms in real-world situations remains a challenge, as shown by the decreased accuracy (around 80%) achieved in prospective studies. 19 This decrease could be due to inconsistent image quality and other aspects, such as comorbidity. Dataset quality assessment is normally taken for granted, with researchers using trainings and certifications for photographers  17, 18  or manually "cleaning" the datasets;  19  some authors have even used quality filters on algorithms before training the algorithms. 20 tretina is a telemedicine platform which performs general screening for retinal diseases using nonmydriatic cameras and human evaluation by a retinal specialist ophthalmologist.
Methods Datasets Optretina has been carrying out a telemedicine screening program since 2013 in optical centers and since 2017 in workplace offices and private companies. This study is based on the tagged dataset from Optretina, composed of 306,302 retinal images. Labelled with diagnosis, laterality and quality. Ophthalmologists-all of whom are retinal specialists-have evaluated all of these images. The dataset is real-life (Not from clinical trials) and anonymized, composed of approximately 80% cases of normal retinas and 20% abnormal ones. Images are acquired using NMC (color fundus and red free) and optical coherence tomography (OCT). The dataset includes images from different types and brands of cameras. Figure  1  shows the distribution of the different cameras used, by percentage. The dataset includes color fundus photography (CFP), macular OCT's, retinal nerve fiber layer OCT (RNFL), and other added non-medical images trying to fool the AI (such as round objects like pizzas or photos of the full moon).
Of the 306,302 images, over 250,000 correspond to CFP. In 2017, Optretina published the results of three years of screenings,  17  detailing the percentages of diseases found. Classification of image type (CFP, OCT, others) and eye of evaluation (right or left) was performed by one ophthalmologist. Labeling for quality assessment, presence of AMD, or glaucomatous optic neuropathy (GON) was performed by two independent retinal specialists, a third ophthalmologist assessed cases where there was no agreement, in order to establish at least two consistent gradings. Since Optretina performs screenings and patients in whom pathology is detected are subsequently referred to an ophthalmologist, repetition of images of the same patient are very rare. It is estimated that 0. 5% of the dataset images may be of patients already assessed in previous years.
Convolutional Neuronal Networks (CNNs) The first three CNN models (trained from scratch) were used for different image classification tasks usually applied in data-collection, data-selection, display, and data classification. The first model is very useful for cleaning unlabeled datasets, since it can separate the    2  presents the three main CNN. We have also published sample code for these architectures on https://github. com/octavifs/optretina-cnns.
Model 1: Image Type Selection CFP/OCT/Other Images The CNN-1 performs a binary classification, separating CFP from all other image types (OCT/RNFL/other images). The training process was done from scratch, using RGB images resized to 128x128 pixels with geometric data augmentation over the training dataset. It should be noted that the CNN-1 has only three convolution layers, three max pooling layers, and three dense layers; as a result, the CNN-1 has fewer parameters than very deep models (such as Inception, VGG-19, and RESNET50, among others). Its shallow architecture makes it computationally inexpensive, which is beneficial for increasing the processing throughput of our CADx pipeline. CNN-1, along with the rest of the models, have been implemented using Keras,  22  a software library that facilitates the creation of neural networks with access to specialized hardware resources, such as graphics processing units (GPUs), in order to speed up the training and execution of such algorithms.
We propose a novel architecture for AMD binary classification which will be referred to hereinafter as AMDNET. The architecture of AMDNET allows us to extract small details and features while also making predictions with relatively few trainable parameters (around 241,000) in comparison with other well-known architectures such as RESNET50  24  or InceptionV3  25  which have approximately 25 million trainable parameters. The AMDNET architecture is presented in Table  2. We trained AMDNET from scratch with the data specifications presented in Table  1.
Ethics and Data Protection Given that requesting informed consent for the Optretina evaluation is mandatory, patients are informed of the pros and cons of screening, including the spectrum of diseases that can and cannot be detected by a retinal fundus image. As part of giving consent, patients also agreed to their images being used anonymously for science and research purposes. The dataset was completely anonymized. No Optretina or BSC employee has access to patient data except for the Optretina technical director (Didac Royo), solely for emergency purposes. The keys for reversing the anonymization are physically stored in a bank. The study was approved by the ethics committee of the Hospital Vall d'Hebron in Barcelona, the research followed the tenets of the Declaration of Helsinki.
Retinal landmarks have traditionally been used for recognizing a retinal image and evaluating its quality,  30, 31  but we used two CNNs without any intervention or segmentation, thereby obtaining high levels of AUC for the algorithms. To guarantee the quality of the images, some authors perform a manual evaluation of their databases;  16, 29  while this system is suitable for retrospective studies, using it in prospective studies is not ideal. Other authors point out the need for training and certifying photographers,  19  which certainly substantially improves the image quality. We believe, however, that the use of quality algorithms may be a more appropriate measure: it has a short feedback circle, even during the image taking process, thus offering a greater guarantee of quality and more cost-effectiveness.
Other authors have also used quality algorithms, as was the case with Varadarajan et al,  20  who had to exclude 12% of the images of one of their datasets due to poor quality. In the case of multiple acquisition devices and different types of images-red free, color fundus photography (CFP), autofluorescence, OCT, non-medical images-AI could be used as a cascade of filters. First, we could evaluate what kind of image we have, then the eye (in.
Also AI has detected patterns that are invisible to the human eye in CFP such as gender, age, and smoking status. Detection of advanced AMD in retinal fundus images should therefore be studied. Screening for glaucoma is much more complicated than doing so for other ophthalmological diseases. Authors have had the best outcomes with a combination of the slit lamp, OCT retinal nerve fiber layer (RNFL), and visual field tests, but this combination is not the modality of choice when we want to consider devices that are low cost, widely available, and easy to use. 29 In the latter case, we are limited to detecting suspected glaucoma by visualizing only the optic disc, similar to Ting et al, who obtained a 94% accuracy for possible glaucoma in a dataset of 125,000 diabetic patients. 15 In our case, with only 4720 images in the suspected glaucoma algorithm, we achieved levels of accuracy of over 80%.
Further research with a better-defined glaucoma database would be desirable in order to reach the maximum potential for AI in screening for this disease. We believe the kind of technology discussed in this paper could be used not only for prospective purposes, but also in retrospective studies, for classification in non-tagged big datasets such as hospitals or ophthalmological clinics. We use AI for classification, but one of the most exciting types of future implementation of CNNs could be the prediction of disease progression or clinical data. 29 In the near future, application of big data will require a well-tagged database, and AI could be very helpful in this arena-for massive retrospective classifications of image datasets The accuracy of CNNs appears to be related primarily to two factors.