dataesr/scientific-paragraphs-categorization huggingface dataset:
As such, we utilise the OFC a single time, adding the static results to the variable output from each scenario generated in the NFC. Fig. 5  illustrates the flow of data through the forest model. The brown rectangles represent entities, mainly conifer and broadleaf, for old and new forest. The purple circles represent processes, while the green rectangle represents a common data store. The old and new forests are kept in separate containers before being aggregated. To estimate the various elements (sequestration from biomass, organic and mineral soil emissions, dead organic matter, etc. ) for the forest estate, a matrix approach is adopted. For each element in the forest model, a value matrix is established based on the age of the forest stand. Stand age is then utilised to establish the total biomass, dead organic matter and emissions from organic soils.
The Clinicaltrials. gov database currently references 15 active clinical studies for ANIA treatment  [74]. For CIA, studies include CC (n = 10), keratinocyte growth factor (n = 1) and LED lights (n = 1). One study is currently evaluating the effect of platelet-rich plasma in EIA and pCIA. For pCIA, ongoing studies include oral minoxidil (n = 1) and CC (n = 1). In addition to new synthetic entities, interesting perspectives are cell therapy and in vitro neogenesis of hair follicles from autologous cells  [75].
Acknowledgments. We are deeply grateful to  Alexander Borichev  and  Alexei Klimenko  for useful discussions and very helpful comments. The research of  A. Bufetov  on this project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  under grant agreement No  647133  (ICHAOS). It has also been funded by the  Gabriel Lamé Chair at the Chebyshev Laboratory of the SPbSU, a joint initiative of the French Embassy in the Russian Federation  and the  Saint-Petersburg State University. Y. Qiu is supported by the grant  IDEX UNITI -ANR-11-IDEX-0002-02 , financed by  Programme "Investissements d'Avenir" of the Government of the French Republic  managed by the  French National Research Agency , and he is also supported by  NSF of China  ( NSFC Y7116335K1 ,  NSFC 11801547  and  NSFC 11688101 ).
The 4LAC catalogue Since 2008 August 4, the vast majority of data taken by the Large Area Telescope (LAT) on board the Fermi satellite has been performed in all-sky-survey mode, whereby the Fermi-LAT detector points away from the Earth and rocks north and south of its orbital plane, on subsequent orbits. This rocking motion, coupled with Fermi-LAT's large effective area, allows the LAT instrument to scan the entire -ray sky in about a few hours. This observational characteristic, coupled with Fermi-LAT's long mission lifetime, allows us to construct a deep exposure of the -ray sky in the 0. 1 -100 GeV photon energy range. The 4LAC catalogue lists all -ray bright AGN detected by the LAT during the first 8 years of the Fermi mission. In particular, the 4LAC reports the position and best-fit spectral parameters for 2863 -ray bright AGN derived from integrating over the entire 8-year data set.
Introduction Myelofibrosis (MF) is characterised by anaemia, splenomegaly, debilitating constitutional symptoms, and shortened survival, often presenting with Janus kinase 2 (JAK2) mutations  1, 2  that result in constitutively activated JAK2 proteins and activation of the JAK-signal transducer and activators of transcription (STAT) pathway,  2  thereby increasing levels of circulating pro-inflammatory cytokines. 3, 4 Fedratinib is an oral JAK2 inhibitor approved in the USA, European Union, UK, Canada, and elsewhere for use in adult patients with MF, as first-line therapy or following prior ruxolitinib treatment. The pivotal phase III, randomised, placebo-controlled JAKARTA (NCT01437787) trial evaluated the efficacy and safety of fedratinib in patients with JAK-inhibitor-na€ ıve MF. In 2013, fedratinib development was placed on clinical hold because of suspected cases of Wernicke encephalopathy, prompting closure of ongoing clinical trials including JAKARTA (initiated in 2011).
These events may have been less frequent if prophylaxis had been required. The ongoing phase III FREE-DOM (NCT03755518) and FREEDOM2 (NCT03952039) trials, which assess fedratinib clinical outcomes in patients with MF previously treated with ruxolitinib, include mitigation strategies to manage gastrointestinal events during fedratinib therapy. Preliminary results from the FREEDOM trial indicate rates of diarrhoea, nausea, and vomiting are markedly lower with these mitigation strategies. 12 hese updated results of the JAKARTA study further support the clinical efficacy and safety of fedratinib 400-mg daily in JAK-inhibitor-na€ ıve patients with intermediate-2 or highrisk MF and may serve to inform expectations of therapeutic response and the potential toxicity of fedratinib in the clinic.
Acknowledgments We thank the patients, families, co-investigators, and all study personnel who participated in this trial. The original study was supported by  Sanofi S. A. ; this re-analysis was funded by  Celgene , a  Bristol-Myers Squibb company. The authors received editorial assistance on an early draft from  Brian Kaiser  and  Sheila Truten  ( Medical Communication Company, Inc. ), sponsored by  Bristol-Myers Squibb. The authors are fully responsible for all content and editorial decisions, and all approved submission of the final manuscript.
This article presents the results of the COVID Energy Map, a novel, global mapping exercise tracking emergency responses undertaken by governments, regulators, utilities and companies in the Global North and South to mitigate energy poverty by keeping energy affordable and available. The map constitutes a comprehensive open access evidence-based database, so far collating 380+ emergency measures, in 120+ countries. This paper particularly shows and discusses how the response has been developing until early 2021, highlighting various emerging longer-term concerns and strategies across Global North and South. The global COVID-19 response merits close attention in our view, as it reveals both the universal importance of household energy services access and important underlying existing narratives and policy-making questions about securing energy services access as a vital basic need, and even a 'basic right'.
Mapping world-wide responses to secure energy services in times of COVID-19 From March 2020, the authors have researched and collated a wide range of emergency measures via the COVID Energy Map, on an open access Google My Maps platform which can be accessed through www. covidenergymap. com (Fig. 1 ), and is still live. Although the map is extremely comprehensive, it should not be understood as a definitive global account of COVID-19 measures undertaken in the sphere of household energy, largely owing to the methodological justifications and limitations explained below. Specifically, the absence of measures in certain areas does not necessarily mean that no measures are in place: it may well be that measures could not be found (e. g. due to language constraints), that measures to assist and protect households already existed prior to COVID-19, or that measures did not fit our selection criteria.
Acknowledgements This article is based upon work from  EU   COST Action  'European Energy Poverty:  Agenda Co-Creation and Knowledge Innovation'  ( ENGAGER 2017-2021 ,  CA16232 ) supported by  COST (European Cooperation in Science and Technologywww. cost. eu). We kindly thank colleagues and students for additional research assistance and validations of measures on the map. Full acknowledgments and more information about the project are available via www. covidenergymap. com and http://www. engager-energy. net/covid19/.
g. Ref. [486] ). Second, inflation provides a natural way to generate the initial metric perturbations and explain their super-horizon correlations at the time of CMB decoupling. These correlations are usually described in terms of the scalar curvature power spectrum, which at least at the largest physical distance scales is measured to have a power-law form  [13] P ζ (k) = A k k * ns-1 , (38) to a high precision. Here ζ denotes the curvature perturbation and k the wavenumber in a Fourier decomposition. The spectrum  (38)  has the observed amplitude A 2. 1 × 10 -9 and spectral tilt n s 0. 965 at the pivot scale k * = 0. 05 Mpc -1  [13]. The data are consistent with no running, dn s /d lnk, or running of the running of the spectral tilt, d 2 n s /d(lnk) 2  [13] , and therefore we will neglect them here for simplicity.
The results can change substantially in the presence of e. g. a curvaton field (see Sec. 3. 4)  or in models where multiple fields took part in inflationary dynamics, see e. g. Refs. [516, 517, 518]  for some recent work on the topic. Also, we note that the best-fit regime for n s , r shown here was obtained from the Planck / Keck Array CMB data. Including other datasets can change the preferred values for these parameters, especially when the new dataset is in tension with the CMB measurements or when new physics is assumed to be involved. For example, the effects of nonstandard late-time expansion history (related to the so-called H 0 tension) have been discussed in Refs. [374, 519, 520]  and the effects of nonstandard neutrino interactions and presence of extra radiation on inflationary model selection have been discussed in Refs.
On the other hand, a combination of CMB and BAO data leads to ∆N fs eff < 0. 30  [33]  at 95% CL. Interestingly, it is possible to completely change this constraint by relaxing the assumption of free-stream. In fact, CMB data are powerful in testing new interactions of neutrinos since these interactions affect the streaming properties of neutrinos. As a striking example, it has been found that the CMB temperature data can be equally well fit by postulating that neutrinos strongly self-interact through a contact interaction  [783, 399, 784, 397]. In this cosmology, the phase shift of the acoustic peaks is completely erased, but the position of the peaks is kept constant by increasing H 0. In fact, Planck 2015 temperature data 27 can fully accommodate 4 species of strongly interacting neutrinos, with H 0 = 72.
Molecular results NGS analyses identified in P1, P2, P3, P4 from family 1 and in P5, P6, P7 from family 2, a heterozygous deletion of TTN exons 11 to 18, with breakpoints in introns 10 and very closed to exon-intron junctions. Genomic Sanger sequencing confirmed the precise breakpoints, c. 1662+15_3101-3del (Fig. 2A ),  8  resulting in a tiny 16 nucleotides long recombinant intron between exons 10 and 19. This CNV is not reported in the database of genomic variants (http://dgv. tcag. ca/dgv/app/home). NGS analyses did not detect other TTN variants in trans nor other shared variants of interest among both families. Comparison of SNPs within and surrounding the TTN locus showed a common haplotype of at least 42 SNP (271 kb) between both families, suggesting a common ancestor shared by the two families (data not shown).
Probably some small CNVs result in a still functioning protein without any clinically relevant phenotype, as reported for deletions in exon 48 in DMD. 15, 16 One published TTN CNV is a recessive large deletion of exons 34 to 41, associated in trans with a frameshift mutation p. (Lys3596Asnfs*), reported in a family with a severe phenotype (loss of ambulation before the age of 40 and marked hyperCKemia). 17 The second CNV previously described is a deletion of exons 346 to 362 (A-band) predicted to cause frameshift with dominant inheritance  9  with early onset skeletal muscle involvement and dilated cardiomyopathy developing in later decades. No WB data were reported to determine if the truncated titin was present or not. In our report, muscle RNAseq studies detected, in addition to the exons 11-18 deletion, aberrant splicing with retention of the tiny 16 nucleotides recombinant intron, which is presumably too small to be recognized by splicing factors and leads to a stop 11 codons downstream from the frameshift.
2a ). An F-DOPA PET-CT and 111 In-octreoscan revealed a weak uptake of the cardiac mass. An immuno-PET-CT using an anti-CEA bispecific antibody and a 68 Ga-labeled peptide (IMP288), performed within a clinical trial (NCT01730638), also revealed a cardiac uptake confirming the metastatic nature of the mass (Fig. 2b ). At that time, there was no cardiac metastasisrelated consequence. The decision to perform a surgical resection was rejected given the unfavorable benefit-risk balance. Vandetanib (300 mg/day) was initiated, and a partial regression of the cardiac metastasis was observed (-30% in 8 months) and a decrease in cervical lymph node and lung metastases. Vandetanib dosage reduction was necessary because of a prolonged QTc interval 9 months after its introduction. Progressively, calcitonin and CEA levels rose again (422 ng/mL and 13.
Traditional Neural Networks (NN) have been popular in the satellite remote sensing community for the last 25 years. For coarse resolution infrared or microwave instruments, NN algorithms have been used at the pixel level. New neural architectures such as Convolutional Neural Networks (CNN) use the Deep Learning (DL) approach to solve complex problems at the image scale. For instance, CNNs have been applied to high resolution instruments (SAR or in the visible domain) to detect surface waters or vegetation. High resolution data is better suited for image processing techniques because spatial features are stronger and pixel noise can be an important issue. CNNs applications are generally related to image classification or segmentation, less for regression problems dealing with the estimation of a variable in each pixel of the image.
The objective of this paper is to better understand how and on which conditions CNNs work, and how beneficial they can be for coarse resolution instruments such as IASI (Infrared Atmospheric Sounding Interferometer). The CNN and DL approaches are tested in a regression mode, to estimate the Sea Surface Temperature (SST) at the image scale. The CNN technique is compared to a traditional pixel-based NN: both have a SST retrieval error of 0. 3 K. An instrument noise and a missing data sensitivity studies are conducted. It is shown that the use of the CNN approach in this simpleexperiment context is beneficial only under some conditions: when the variable to retrieve has enough spatial coherency (simple smoothness or presence of spatial features in the images), and when the instrument noise at the pixel scale is larger than a threshold.
CNNs and DL have been used in Earth observation. Several research institutes or space agencies (see e. g. the ESA Phi-lab 1 or the AI4EO 2 initiative) have prioritised the use of these techniques as a main source of improvement for the next generation of remote sensing algorithms. CNNs have mostly been used on high spatial resolution instruments such as SAR (Synthetic Aperture Radar)  (Ban et al. , 2020)  for fire detection, or in the visible domain to monitor for instance rice fields  (Zhang et al. , 2018). This type of satellite observations is often used to develop algorithms at the regional scale, and historically this was often done using image processing approaches. Image processing techniques are well suited for such high resolution data at the surface because they generally have very important spatial features, contrasted textures, and spatial coherency; and because they often include important noise level at the pixel level (such as the speckle noise in SAR data).
Image databases In the following, machine learning techniques will be used to retrieve SST from the IASI TB measurements. Therefore, a comprehensive database of images including the TBs and the SST is required to calibrate the NN regression models. The sampling process to build a NN training dataset is a difficult task because samples need to be sampled in a highdimensional space whose dimensions can be very different in nature (such as temperature and water vapour or ozone), in order to describe as well as possible all the possibles states in nature but at the same time emphasising on the most frequent states  (Aires and Prigent, 2007; Paul and Aires, 2014). This is the most important step in an artificial intelligence / machine learning experiment  (Zhou et al. , 2017; Shao et al. , 2018).
"Regular" version The database used here is based on three years of IASI data  (2010) (2011) (2012). EUMETSAT (EUropean organisation for the exploitation of METeorological SATellites) data were obtained in the form of orbit files including the geophysical variables (SST, atmospheric profile of temperature and humidity) (including EUMETSAT algorithm versions 4. 3. 2 to 6. 0. 5) and the TBs on the 8461 IASI channels  (Klaes et al. , 2021). A channel selection scheme based on the entropy reduction  (Rabier et al. , 2002; Collard, 2007) was used to extract channels for the retrieval of surface temperature, atmospheric temperature, humidity and ozone. Among these 300 resulting channels, ten were kept for the SST around the 1190 cm -1 spectral region  (Pellet and Aires, 2018). This channel selection was used for instance in  Safieddine et al.
(2020). A traditional NN experiment would gather a large number of pixels sampled in this 3-year dataset. For CNN processing, it is necessary to collect images and not pixels. Therefore, a sampling process was used to obtain images of dimension 22×22 pixels from the IASI orbits. These images are centred at Nadir (i. e. center of the orbit) to avoid inhomogeneities in the image due to the higher incidence angles. Fig. 1  represents three samples of 22×22 images, with one of the selected channels at 2189 cm -1 and the corresponding SST. There is a strong similarity between these two patterns, showing the adequacy of the channel selection. Images are chosen only over sea, between [-50 ∘ ; +50 ∘ ] in latitude, and for clear sky (cloudy pixels introduce missing data that are hard to handle with the CNN).
Cloudy pixels are defined by a cloud fraction >10%. This clear-sky constraint is very selective because most IASI images contain cloudy pixels, this point will be discussed later. From this sampling process, about 150,000 images are selected. Fig. 2  represents the average SST and TB on the resulting database. The number of samples in each 1 ∘ ×1 ∘ is also indicated, the sampling covers well the ±50 ∘ latitudinal band, except for the coastal areas due to the constrain on no-land pixels in the image. The 150,000 image samples are divided in 70% for the learning database, and 30% for the testing database.
"Structured" version SST fields at the spatial resolution of IASI over images of size 22×22 pixels are rather smooth. On smooth images and under low instrumental noise, it is expected that a pixel scale processing might be enough and that no spatial filters are necessary. In order to test the sensitivity of the CNN approach on the presence of spatial features, a "synthetic" database is built. It includes the same 22×22 images as in the regular database, but pixels in each image are ordered following a simple pattern. The pixel values are kept identical, but their location is changed, so the distributions in one image do not change. This reorganisation of the pixels inside an image does not perturb the SST/TB relationship from the radiative transfer, so the physics of the problem is preserved in this new structured database, only the surface patterns are modified.
The goal is to enhance the spatial features inside the images to analyse the behaviour of the CNN when spatial features are present at the surface. Strong spatial structures should be stronger for other variables than the SST that is relatively smooth. For instance, Land Surface Temperature (LST) is more heterogeneous with complex spatial patterns due to contrasted surface emissivities over land  (Paul et al. , 2012). The pixel ordering inside each image of the new "structured" database follows simple orientations: north-south, east-west, and diagonal. Fig. 1  (third and fourth column) represents from top to bottom three samples of SST and their corresponding reorganised TB, following these simple orientations.
Spatial filtering PCA can also be used on the spatial domain. It will be seen in the following that the CNN will use 3×3 pixels window filters, so it is interesting to see what the PCA filters are on these same 3×3 windows. For that purpose, 3×3 window samples are extracted from the image database, and a PCA is then performed on the corresponding 9-dimensional vectors. Such local filters have been used to analyse larger scale images using PCA or independent component analysis, see for instance  Nadal et al. (2000). This spatial filtering using PCA is performed on the regular database but results (not shown) are extremely similar for the structured database. Resulting filters are presented in Fig. 3. The percentage of explained variance by each component is respectively: 99. 8649, 0. 0686, 0.
Neural networks NN techniques have proven to be very successful for remote sensing applications. The Multi-Layered Perceptron (MLP) model  (Rumelhart et al. , 1986)  is selected here (also called feedforward network). It is defined by the number of input neurons, the number of outputs, and the number of neurons in the hidden layers that controls the complexity of the model. A balance needs to be found: Too many free parameters in the model can conduct to the over-learning (over-parameterisation) leading to degraded generalisation properties. On the contrary, too few free parameters will yield under-parameterisation and bias error of the model. The NN is trained to reproduce the behaviour described by a database of samples composed of inputs (the TBs) and their associated outputs (the SSTs).
When enough samples are available, any continuous relationship, as complex as it is, can be represented by a MLP  (Cybenko, 1989; Hornik et al. , 1989)  providing that enough hidden neurones are present, but the quality of the NN retrieval is ultimately limited by the information content of its inputs. The learning algorithm used to train the NN is the classical back-propagation algorithm. The NN are trained on a learning dataset, and the ability of the model to generalise to independent samples is monitored on a testing dataset. The learning is stopped when this generalisation error ceases decreasing  (Bishop, 1996). The inputs of such a model can be anything: a point measurement, a time series or an image. More complex architectures (such as CNNs presented in the next section) are actually based on the same principles of MLPs.
The convolutional layer is the basic building block of CNNs. Each layer uses one or more filters on the space of the data of the previous layer(s). Filters can be of size 3×3, 5×5 or much bigger, depending on the spatial shapes that are expected in the processed images. CNNs use these filters to recognise spatial features. These window filters are moving along the image following some specifications. There are three hyper-parameters that control the output volume of the convolution Finally, the network usually ends with a loss layer, which specifies how the network penalises the differences between the network and the desired outputs in the training dataset. CNNs have been used a lot for classification: each input image is classified based on the presence of an object in the image. CNNs are also very useful for segmentation in which each pixel of the image is classified.