dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussions and Conclusions The DACCIWA ground-based field campaign, conducted over southern West Africa during the 2016 monsoon season, provided a comprehensive dataset allowing the analysis of the diurnal cycle of the LLC. The most important lessons drawn from the analyzes of Savè supersite dataset  (Adler et al. , 2019; Babić et al. , 2019a; Dione et al. , 2018)  and additional results on the LLC breakup scenarios and their impact on the CBL vertical development on the following day, are used to build a conceptual model for LLC diurnal cycle and are generalized, when possible, to the two other supersites of the DACCIWA ground-based field campaign, Kumasi and Ile-Ife. The conceptual model proposed in this study divides the LLC diurnal cycle in four main phases and addresses, for each of them, the main atmospheric processes involved.
Acknowledgements. The  DACCIWA  project has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 603502. The authors thank also  Laboratoire d'Aérologie ,  Université de Toulouse ,  CNRS ,  UPS, France  and  KIT (Karlsruhe Institute of Technology)  and  UPS  ( Université Toulouse ) for helping to install the equipment as well as the people from 10 INRAB in Savè for allowing the equipment on their ground. We thank the  Aeris data infrastructure  for providing access to the data used in this study.
analysed the C57BL/6 IECs' proteome at 6 DPI. Comparative gene set enrichment analysis of C3H/HeN and C57BL/6 revealed largely conserved changes to IEC processes upon infection (Figure  S3 ). The KEGG pathways, Staphylococcus aureus infection, complement and coagulation cascades, and phagosome were found to be opposingly regulated at 6 DPI; however, the differential response of proteins assigned to these pathways converged by 8 DPI (PRIDE identifier: PXD005004;  Berger et al. , 2017). A comprehensive analysis of the C57BL/6 proteome has recently been reported elsewhere  (Berger et al. , 2017; Hopkins et al. , 2019). We quantified a total of 11,602 proteins in C3H/HeN mice, of which 9,184 were mapped to Mus musculus and 2,418 to C. rodentium genes (peptide false discovery rate [FDR] <1%). Proteins with altered abundance at or above 1.
| Statistical analysis Graphpad Prism 7 was used for all statistical calculations unless otherwise specified. FC values were log transformed prior to analysis of variance being carried out. P values <. 05 were considered significant ( * P ≤. 05, ** P ≤. 01, *** P ≤. 001, and **** P ≤. 0001). Statistical analysis of the microbiome was performed at individual taxon levels. For each taxon, the Mann-Whitney U statistical test was carried out between uninfected and infected groups, correcting P values for FDR using the Benjamini and Hochberg method. Unpaired t test was used for statistical analysis of faecal water content and quantification of total bioluminescent flux from ex vivo tissues. Pathway analysis was performed with the 1D enrichment method in the Perseus software  (Cox & Mann, 2012; Tyanova et al.
, 2016). The enrichment score shows whether the proteins of a pathway tend to be upregulated or downregulated based on Wilcoxon-Mann-Whitney test and is normalised within min = -1 and max = 1. Comparison to C57BL/6 proteins at 8 DPI was carried out using data deposited in PRIDE with identifier PXD005004  (Berger et al. , 2017). Proteins from enriched pathways of interest (Benjamini-Hochberg FDR < 0. 05) were selected based on previous literature on C57BL/6 mice. Proteins were considered significant if abundance changed 1. 5-fold or higher upon infection. supported by a Wellcome Trust studentship. This work was supported by grants from the Medical Research Council, the Wellcome Trust, and the Royal Society.
The pedestrian detection has attracted considerable attention from research due to its vast applicability in the field of autonomous vehicles. In the last decade, various investigations were made to find an optimal solution to detect the pedestrians, but less of them were focused on detecting and recognition the pedestrian's action. In this paper, we converge on both issues: pedestrian detection and pedestrian action recognize at the current detection time (T=0) based on the JAAD dataset, employing deep learning approaches. We propose a pedestrian detection component based on Faster R-CNN able to detect the pedestrian and also recognize if the pedestrian is crossing the street in the detecting time. The method is in contrast with the commonly pedestrian detection systems, which only discriminate between pedestrians and non-pedestrians among other road users.
Over the last decade, the scientific community and the automobile industry have contributed to the development of different types of ADAS systems in order to improve traffic safety. The Nissan company has developed a system which recognizes the vehicle's environment, including pedestrians, other vehicles, and the road. Lexus RX 2017 has a self-driving system which is linked up to a pedestrian detection system. More recently, the Audi ADAS system accumulates the data of the camera and/or radar sensor to determine the possibility of a collision by detecting pedestrians or cyclists and warns the driver with visual, acoustic and haptic alerts if a crash is coming. These current ADAS systems still have difficulty distinguishing between human beings and nearby objects. In recent research investigations, deep learning neural networks have frequently improved detection performance.
The impediment for those patterns is that they require a large amount of annotated data. This paper proposes a pedestrian detection system which not only discriminates the pedestrians among other road users but also able to recognize the pedestrian actions at the current detection time (T=0). The contribution of this paper concerns detecting and classifying pedestrian actions. To do so, we develop the following methodology relying on a deep learning approach: • Train, all pedestrian samples with the Faster R-CNN-Inception version 2 for pedestrian detection, proposes  [1] ; • Train all pedestrian samples also using the pedestrian actions tags (cross/not cross) with the CNN as mentioned above for detection and action recognition based on the Joint Attention for Autonomous Driving (JAAD)  [2]  dataset; The paper is organized as follows: Section 2 outlines sever existing approaches from the literature and supplies our main contribution.
RELATED WORK A wide variety of methodologies have been proposed with optimization in performance, resulting in the development of detection methods using deep learning approaches  [3, 4, 5]  or combination of features followed by a trainable classifier  [4, 6]. A deep, unified pattern that conjointly learns feature extraction, deformation handling, occlusion handling, and classification evaluated on the Caltech and the ETH datasets for pedestrian detection was proposed in  [7]. An investigation focused on the detection of small scale pedestrians on the Caltech data set connected with a CNN learning of features with an end-to-end approach was presented in  [8]. A combination of three CNNs to detect pedestrians at various scales was introduced on the same monocular vision data set  [9]. A cascade Aggregated Channel Features detector is used in  [10]  to generate candidate pedestrian windows followed by a CNN-based classifier for verification purposes on monocular Caltech and stereo ETH data sets.
In  [11]  is presented a pedestrian detection based on a variation of YOLO network model, (three layers were added to the original one) in order to join the shallow layer pedestrian features to the deep layer pedestrian features and connect the high, and low-resolution pedestrian features. A mixture of CNN-based pedestrian detection, tracking, and pose estimation to predict the crossing pedestrian actions based on JAAD dataset is addressed in  [12]. The authors utilize the Faster R-CNN object detector based on VGG16 CNN architecture for the classification task, use a multi-object tracking algorithm based on Kalman filter, apply the pose estimation pattern on the bonding box predicted by the tracking system and finally handle the SVM/Random Forest to classify the pedestrian actions (Crossing /Not Crossing).
EXPERIMENTS This section presents the set of experiments, including setups and performance assessment of our approaches. 4. 1. Data Setup. The experiments are completed on the JAAD dataset  [2]  because of its data set in typical urban road traffic environments for various locations, times of the day, road and weather conditions. This dataset supplies pedestrian bounding boxes (BB) for pedestrian detection, tagged as non-occluded, partially occluded and heavily occluded BBs. Moreover, it includes the pedestrian actions annotations for several of them, the pedestrian attributes for estimating the pedestrian behavior and traffic scene elements. The experiment employs all the pedestrian samples, including the partially and heavily occluded pedestrians for all training and testing process.
4. 2. Training, Testing and Evaluation Protocol. We used the first 70% of samples from the whole JAAD dataset for the learning process. The training set consists of first 190 video sequence training samples, and includes even the partially occluded and heavily occluded BBs, in contrast with  [2]  where the authors used just a part of the dataset, omitting samples with partially and heavy occlusion. Moreover, the authors do not give a detailed explanation of how the datasets were divided into training and testing sets; hence, it does not allow a fair comparison. The validation set represents 10% of the learning set. We used the holdout validation method, which held back from training the model. The evaluation of a model skill on the training dataset would result in a biased score. Therefore  The JAAD dataset has three main pedestrian actions tags: • pedestrian completes crossing the street; • pedestrian does not cross the street; • pedestrian does not have any intention of crossing.
We used the pre-trained weights from COCO dataset with the default Faster RCNN loss function which is optimized for a multi-task loss function. The testing set used to assess the CNN model performance is independent of the training dataset. It contains 110 video samples, the last 30% of video samples from the whole JAAD dataset. The evaluation process for all the CNN models is performed with Tensorflow Deep Neural Network Framework. The performances are assessed by the mean average precision (mAP) for the detection part using the TensorFlow metrics tool. We calculate the Confidence Interval (CI) to evaluate whether one model is statistically better than another one. (1) CI = 2 * 1. 96 P (100 -P ) N %. In this formulation, P represents the performance system (e. g. , mAP) and N represents the number of video testing.
4. 3. Results and Discussions. The detection results are presented in Table 1. We observed that the detection performance achieved with the CPDM method (using all samples as pedestrians) a good performs on Jaad dataset since it has to identify the pedestrian among other road users. The CrPDM approach (using multiple pedestrian tags), although it detects the pedestrians, cannot be associated with the first method because it also instantly classifier the action of the pedestrians during the detection step. Therefore its performance is less than the first detection approach. On the other hand, pedestrian detection using the multiple tags approach could be a start point for a deep investigation. This approach estimates the pedestrian actions on the current time (T=0) and could be beneficial for developing a pedestrian prediction system.
CONCLUSIONS This paper presents a classical pedestrian detection system and a pedestrian detection system able to recognize even the pedestrian actions based on deep learning approaches using JAAD dataset. We evaluated the pedestrian detection approach (we called Classical Pedestrian Detection Method (CPDM)), where all sample are tagged as pedestrian and not pedestrian and a pedestrian detection approach using multiple tags (pedestrian, pedestrian cross), which we call Crossing Pedestrian Detection Method (CrPDM). The first method achieved better performance since it has only to distinguish the pedestrians among other road users in contrast with the second one who has to recognize even the pedestrian actions. The second detection approach returned a weaker performance than the classical one.
The XPS measurements were performed in the off-synchrotron analysis chamber at EMIL in ultra-high vacuum (base pressure < 5×10 -10 mbar) employing a non-monochromatized Mg Kα (1253. 56 eV) x-ray tube (PREVAC RS40B1) as excitation source. The photoelectrons were detected by a Scienta Omicron Argus CU electron analyzer. HAXPES was measured at beamline BL15XU at SPring-8 using an excitation energy of 6 keV and a Scienta R4000 electron analyzer (see  [15, 16]  for more details on the experimental setup; same measurement conditions apply for the data shown in Figure  1 ). Figure  4  shows the XPS S 2p/Se 3p (left panel) and HAXPES shallow core level (right panel) spectra of a SOL-Cu 2 ZnGe(S,Se) 4 sample prepared from a solution of Cu-, Zn-and Ge-oxides in water and ammonium thioglycolate. Upon selenization, a clear Se 3d signal in the shallow core level region and the Se 3p core level dominating the S 2p/Se 3p energy region can be observed.
DEVICE SIMULATIONS Numerical simulations were carried out using the SCAPS software  [30]  to determine the best device design for Cu 2 ZnGe(S,Se) 4 -based top cells in a tandem configuration, with a focus on suggesting suitable TBCs. The parameters used in the simulations are listed in Table  S2  [  31, 32]. Most of the parameters related to the absorber material, such as thickness, doping concentration, band gap, and absorption coefficient, were extracted from (our own) electrical and optical measurements. Parameters related to MoSe 2 and MoO 3 were taken from literature, and those related to CdS and ZnO:Al layers are already available in the SCAPS software. Two configurations were tested in the simulations: Cu 2 ZnGe(S,Se) 4 substrate or superstrate solar cells, as shown in Figure  S5. In the standard configuration [substrate, Figure  S5(a) ], a thin layer of MoSe 2 was inserted in the model between the Cu 2 ZnGe(S,Se) 4 absorber layer and the Mo contact.
ACKNOWLEDGEMENTS This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement N o 640868. The synchrotron radiation experiments were performed at the SPring-8 beamline  BL15XU  with the approval of the  NIMS Synchrotron X-ray Station  (Proposals  2016A4600 ,  2016B4601 , and  2017A4600 ) and at  BESSY II  with the approval of HZB. B. Vermang has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement n°  715027 ).
2010a; Vidal et al. 2016a ). An overview about the NFI sampling methods relevant for growing stock estimation is given in the Appendix Table  7. In total, the implementation of stem volume harmonisation is based on the sample tree data collected at approximately 390,000 NFI plot locations. In most cases, the plot design for sample tree selection are concentric circular plots and less frequently angle count samples or singular circular plots. In recent years, many NFIs have augmented their field data collection by integrating specific assessments about small trees (0. 0 cm < dbh < dbh-threshold). Usually, these assessments are stem counts by species and dbh-classes on additional small and often circular plots. In several instances, also height assessments are made for small trees.
Components and workflow The implementation of harmonisation measures by the individual NFIs has three basic components, the NFI data-bases, the  program codes containing the volume models and up-scaling procedures, and the bridging functions that have to be integrated into the program codes (Fig. 3 ). The NFI data-bases contain sample tree-, stand-and site-specific data collected on the plots during the different NFI campaigns. An overview about the variables assessed by NFIs is available from the National Forest Inventory reports presented by  Vidal et al. (2016a). The volume models of NFIs and also the bridging functions require mostly sample tree-specific data such as species, dbh and tree height as input for calculating stem volumes (Appendix Tables  8  and 10 ). The program codes contain the algorithms for calculating NFI estimates and include the volume models used by NFIs for stem volume estimation.
The bridging functions were integrated in a separate set of program codes which process the NFI data. Un-harmonised and harmonised sample tree volumes were predicted and then upscaled to obtain growing stocks according to the country-level definitions, the reference definition of Cost Action E43 (2010) and the alternative reference definitions. The bridging functions can have different forms depending on the type of volume model used by an NFI, the kind of existing NFI data and other available data sources. The bridging functions can originate from already existing models, the re-parameterisation of available models or the development of new models. The harmonisation in the DIABOLO (2015) project was facilitated and supported by a mutual exchange of bridging functions between NFIs. Consequently, the same deviation from the reference definition could be solved by more than one bridging function and required the choice of the most reliable option.
Partly, this applies also to complementary models. However, several complementary models have been newly developed or were transferred from one NFI to another NFI with similar forest conditions. For the reason of such initial applications, the bridging functions were examined by the NFIs before integration into the estimation procedures to avoid biased volume predictions and to choose the most appropriate model among available options. The model examination includes comparisons with an independent data set, comparison with other models or expert knowledge if appropriate data are absent. According to  Ståhl et al. (2012) , the uncertainty of harmonised estimates depends on the harmonisation method applied. The different approaches of bridging functions applied in the presented work have their own specifics regarding the error of predicted sample tree volumes.
partially protected or unprotected zones) or estimated the effect of protection in MPAs by comparing data acquired before and after the protection started (e. g. ,  study ID 4, 27, 54). Few studies (~20%) considered the effects of global climate change, either correlating one or more environmental variables to the biotic response variable as a proxy of change (8 studies out of 18) or suggesting the potential influence of climate change in the discussion of the article. The most cited process influencing natural assemblages was global warming (e. g. , study ID 43, 68), followed by extreme events (e. g. , study ID 61, 80), variation in the rainfall regime, variations in the North Atlantic Ocean Index (representing different phases of the jet-stream that affect weather patterns; e. g. , study ID 32), and introduction of invasive species  (e.
g. , study ID 35, 62). However, no study citing climate change adopted a sampling design comparing protected and nonprotected areas in an attempt to disentangle the potential effects of protection from the effects of climate change, thus lacking the means to separate the effects of local and global stressors. Practical constraints often limit the potential to adopt rigorous experimental designs in contexts like this one; nonetheless, so far, in most of the cases, heat stress events reported within MPAs have been linked to climate change. We stress that applying rigorous experimental designs using MPAs to assess one of the most important processes occurring at basin scale should be a priority. In summary, the lack of long-term monitoring data poses serious limits to our potential to assess the role of MPAs in reaching GES, including their ability to help in assessing or mitigating the effects of climate change impacts (as suggested by  Roberts et al.
Adaptive conservation planning in marine spatial planning The implementation of MSP provides not only challenges but also a series of opportunities for conservation   , 1994, 2008, 2017, (17)  no plan, preplanning, (18) no plan, analysis for planning, (19) no plan, preplanning, (  20  globally. For example, if systematic conservation planning is applied and implemented in the framework of MSP, advances can be made in marine conservation by extending MPAs, in particular fully protected areas or zones, and by creating MPA networks that are coherent, representative, and more robust at multiple spatial scales. Effective design of networks will rely on advances made in mapping population connectivity, by combining genetic and oceanographic data and models  (Hodgson et al. 2009 , Andrello et al.
In this context, working to incorporate change in marine spatial plans is crucial, since currently only a few studies on marine spatial plans incorporate climate change in MSP processes  (Gissi et al. 2019). Finally, long-term monitoring will be of paramount importance to assess the effects of climate change and to disentangle global and human-induced changes in marine ecosystems contrasting time series of data collected inside and outside MPAs (McQuatters-Gollop 2012).
ACKNOWLEDGMENTS Author contributions:  G. Rilov  conceived the idea for the study,  S. Fraschetti ,  E. Gissi ,  E. Menini , and L. Tamburello performed the analysis;  G. Rilov ,  S. Fraschetti ,  S. Katsanevakis ,  C. Pipitone ,  F. Badalamenti ,  E. Gissi ,  E. Menini , and  L. Tamburello  wrote the first draft of the manuscript and all authors contributed to later stages of the manuscript. This article is based upon ideas developed in two workshops in Naples in November 2017 and November 2018 organized as part of the  COST Action 15121  'Advancing marine conservation in the European and contiguous seas (MarCons; www. marcons-cost. eu;Katsanevakis et al. [2017]) supported by  COST (European Cooperation in Science and Technology ,  CA15121 ). It is also partly supported by an  Israel Science Foundation  grant to GR (grant no.
The distinct origins of NAP genes in eukaryotes Previous studies proposed a bacterial origin for the transporter and the two nitrite reductases  [14]. However, which particular bacterial group(s) were the possible donors of these three NAP genes was not determined. We investigated the origin of Fd-nir, NAD(P)H-nir and nrt2 in eukaryotes using a comprehensive and taxonomically representative prokaryotic dataset  (Fig 3). The bacterial donors of Fd-nir, NAD(P)H-nir and nrt2. The reconstructed phylogenies show in all cases a well-supported monophyletic clade that includes all eukaryotic sequences (see dark green and light blue clades in  Fig 3) , suggesting that each eukaryotic NAP descends from a single acquisition from prokaryotes. In particular, the three NAPs would have been transferred from Bacteria, given the distal branching of archaeal sequences (dark purple clades) respect to eukaryotes.