dataesr/scientific-paragraphs-categorization huggingface dataset:
Repression of RLuc activity could be mediated through the recruitment of Ago proteins to the splice donor site in the luciferase 3 UTR and inhibition of translation as for the miRNA-dependent binding of Ago. Alternatively, introducing a splicing donor site in 3 UTR of RLuc could lead to an aberrant splice transcript using a downstream cryptic acceptor site, splicing out for instance the polyA tail. The HIV-1 5 splice donor site could also suppress usage of the polyA signal site  (47). Recruitment of the spliceosome without proper splicing could also lead to aberrant transcript and a subsequent degradation. To investigate further these possibilities, the level of RLuc and Fluc transcripts were measured for different constructs by RT-qPCR. Although a decrease in RLuc/FLuc ratio at the transcripts   S2 ) were cloned into psiCHECK-2 vector and relative RLuc/Fluc ratio was calculated as before.
(B) HIV-1 sequences resulting from spliced junctions (Donor/Acceptor: D/A) were inserted into psiCHECK-2 and relative RLuc activity was measured as before. (C) HIV-1 clusters 2, 16 and 21 and truncated sequences encompassing the corresponding 5 splice donor sites (SD), either wild-type (D), mutated on the SD site (Dmut) or in the antisense orientation (AS) were cloned into psiCHECK-2 vector. Splice donor sites are indicated by a black arrow. (D, E and F) PsiCHECK-2 constructs described in (C) were transfected in HEK293T cells and RLuc/Fluc ratio was calculated as before. Values are presented as percentage of the control psiCHECK-2 empty vector (Empty). Data are presented as mean of 3 to 9 replicates ± SD. P-values were calculated using a Student's t-test (****P < 0. 0001, ***P < 0. 001, **P < 0.
01 and *P < 0. 05). Reads that encompass unspliced donor sites were aligned using Bowtie2 and reads that encompass spliced Donor sites were aligned using hyb as described in the Material and Methods section. level could be seen for several splicing site containing constructs, we did not observed a correlation with the decreases in RLuc/Fluc protein activity ratio (Supplementary Figure  S4A ). Furthermore, no chimeric product between RLuc and Fluc transcripts could be amplified from cells transfected with the psiCHECK-2 plasmids containing clusters 2, 13, 16, 21 or the miR-92a binding sequence, suggesting that termination of RLuc and FLuc transcripts was not affected (Supplementary Figure  S4B ). Taken together, our results show that HIV-1 Ago2 binding clusters that are located nearby HIV-1 splice donor sites present on the US HIV-1 RNA have the capacity to downregulate reporter gene expression through the presence of Ago proteins and in a miRNA independent manner.
To validate Ago HITS-CLIP targets, 31 of the HIV-1 clusters identified were tested in a 3 UTR reporter assay. In this assay, miRNA-mediated binding of Ago to the 3 UTR of the reporter gene induces downregulation of its expression. However, most of the clusters identified had either no effect or led to an upregulation of RLuc. Upregulation of transcripts expression through Ago binding to 3 UTR regions have been reported in some cases  (50, 51). However, when tested in Ago depleted cells, clusters leading to RLuc upregulation appeared to function independently of Ago proteins (data not shown). It is likely that the length of the inserted sequence, its secondary structure or the neighboring sequences introduced in 3 UTR of the Luciferase could stabilize the transcript or increase its translation as previously described  (52).
Further experiments will be required to define more precisely the mechanisms involved. A role for Ago and miRNAs during HIV-1 replication is still debated as only mild effects were observed in Ago and Dicer depleted cells  (27, 33). Whisnant et al. identified several Ago binding sites on HIV-1 RNA by PAR-CLIP. However, only few of them could be assigned to cellular miR-NAs suggesting that HIV-1 must be largely refractory to inhibition by the cellular miRNA pathway  (22). On the other hand, our results clearly indicate that simultaneous downregulation of Ago1 and Ago2 diminished by 10-fold the release of CAp24 in the supernatant of transfected cells (Figure  6E ). One hypothesis to explain this difference could be that we used cells depleted for both Ago1 and Ago2. Indeed, downregulation of Ago1 or Ago2 alone was not sufficient to achieve a robust effect on viral particle release (data not shown).
Overview of Input and Ago2 HITS-CLIP libraries analyzed Number of high quality reads obtained from the sequencing of each library. b Number of reads longer than 50 nucleotides. c Number of reads that aligned either to NL4-3 HIV-1 sequence (GenBank ID: AF324493. 2) or to the hg19 UCSC H. sapiens genome. d Number of reads that aligned exclusively to the human genome.
ACKNOWLEDGEMENTS The authors are grateful to  Hugues Parinello  ( MGX Platform, IGF, Montpellier ),  Franck Letourneur (Genom'IC core facility, Institut Cochin, Paris) ,  Marc Lavigne  ( Institut Pasteur, Paris ),  Gordon Langsley  ( Institut Cochin, Paris ) and all members of the  Berlioz and Emiliani lab  for helpful discussions. The authors greatly acknowledge  Karine Bailly ,  Emmanuelle Maillard  and  Muriel Andrieu  at the  Cytometry and Immunobiology core facility ,  Institut Cochin. The authors thank  Lars D ölken  ( Julius-Maximilians-Universität W ürzburg, Germany ) for his valuable input Adam Whisnant ( Julius-Maximilians-Universität Würzburg, Germany ) and  Bryan Cullen  ( Duke University, Durham, NC, USA ) for sharing their data. The following reagents were obtained through the  NIH AIDS Reagent Program, Division of AIDS, NIAID, NIH : TZM-bl from  Dr John C.
Kappes ,  Dr Xiaoyun Wu  and  Tranzyme Inc. , pNL4-3 from  Dr Malcolm Martin. HeLa   P4. 2 cells were obtained from  Dr Marc Alison. LentiCRISPR v2  was a gift from  Feng Zhang  (Addgene plasmid #  52961 ) and  pT7-EGFP-C1-HsAgo2  and pT7-EGFP-C1 were a kind gift of  Elisa Izaurralde. FUNDING Agence Nationale de Recherche sur le Sida et les Hépatites (ANRS)  [to S. G. M. and S. E];  Agence Nationale de Recherche (ANR) Investissement d'Avenir  [ ANR-II-INSB-0014  to M. C. and J-P. C. ];  Association Francaise contre les Myopathies (AFM)  [n • 18566 to M. C. and J-P. C. ];  Action Thématique et Incitative sur Programme (ATIP ) Grant from  CNRS  and  Sanofi  [to H. S. ]; A. E. was a post-doctoral fellowship recipient from  ANRS ; N. P. was a post-doctoral fellowship recipient from  La Ligue Nationale Contre le Cancer ; D.
(2015). Figure  7  shows the infrared column dust absorption optical depth from this dataset, averaged over all longitudes, as a function of latitude and time of year (L s ). The dust absorption optical depth was measured at 9. 3 µm, and then normalized to a consistent reference pressure of 610 Pa. This optical depth should be multiplied by about 2. 6 to get an equivalent broadband visible dust total extinction. Two features are of particular note: the variability in the timing and occurrence of the periods of high dust loading in northern hemisphere winter (L s = 180°-360°), in particular the planet-encircling dust events in MY 25  (2001) and MY 28 (2007) ; and the remarkable repeatability and much lower levels of dust loading in northern hemisphere summer (L s = 0°-180°).
DD inventories There have been several global-scale surveys of martian DDs, each using image datasets of vastly different spatial resolution and temporal coverage (see Table  3 ). The first survey was conducted by  Thomas and Gierasch (1985)  using VIS images from VO during MYs 12-14, corresponding to the late 1970s on Earth (an example is shown in Figure  10a ). Their study required that DDs be transient phenomena for positive detections (i. e. , identified in only one of two overlapping images), to ensure they were not mistaken for landforms. Although DDs typically have a distinctive morphology, small hills can replicate their shape, and so the requirement of transience is often still in use today. Because of the limited amount of overlapping high resolution VO image coverage,  Thomas and Gierasch (1985)  identified only 99 DDs, found in two limited regions in the northern hemisphere lowlands (see Figure  11 ).
Stanzel et al. (2008)  identified 205 DDs, measuring their location as well as translational velocity, diameter, and height. The average measured DD diameter and height were 230 and 660 m, respectively, indicating that the previous studies using MOC WA images were unable to capture DDs smaller than the mean size, highlighting the limitations of image resolution. THEMIS obtains images of the surface in both visible and thermal IR wavelengths. This data set is not optimal for a large-scale DD survey because 1) these images have a relatively low spatial resolution relative to other recent data sets (although it is significantly better than that of MOC WA), and 2) the sun-synchronous orbit typically passes over the surface with local times later than 16:00h, when diurnal DD activity has typically waned.
Each of these data sets has its own advantages, reflecting technological advancements over the last few decades (see Table  2 ). CTX images combine a relatively high spatial resolution similar to that of MOC NA but with an image footprint typically ~100x larger. HRSC images have a moderate spatial resolution, but they cover broad swath areas and produce multiple images that can be used to track DD motion. HiRISE images span only a small area, but they provide astonishing detail in color with image resolutions reaching to 25 cm/px; however, they are unsuitable for broad surveys. Some DD inventories have focused on specific regions, such as Arsia Mons  (Reiss et al. 2009 ) and Amazonis Planitia  (Fenton and Lorenz 2015) ; others specifically searched through temporally-overlapping image data sets to estimate DD velocities and lifetimes  (Reiss et al.
This suggests that errors in measuring DD diameters from orbit can be quite large (up to ~50 %) due to dust clouds surrounding the vortex, causing uncertainties in accurately determining the dust-laden vortex diameter. However, DD diameters measured with higher resolution image data are more accurate. DD heights are calculated by measuring the shadow length and dividing it by the tangent of the solar incidence angle. There are several uncertainties for accurate measurements of shadow lengths, such as detached dust clouds and diffuse shadows. In addition, DDs are not necessarily vertical; they are typically tilted towards the direction of motion (i. e. , downwind) with height (e. g. ,  McGinnigle 1966; Maxworthy 1973). Thus, the error in measuring DD heights is estimated to be between ~20 to ~30 % (e.
g. ,  Reiss et al. 2014a; Fenton and Lorenz 2015). The accuracy in measuring heights is probably higher for relatively short DD, which exhibit sharper shadows  (Fenton and Lorenz 2015). In 2012, the HiRISE team announced the tallest recorded DD, with an estimated height of 20 km (http://www. uahirise. org/ESP_026394_2160; this DD is also shown in Figure  10f  and 10g ). Fenton and Lorenz (2015)  proposed that this height is an overestimate, produced by a combination of DD lean and shear by winds aloft, both of which would artificially stretch the dust column and make it appear taller. Imagers are not the only sensor that can estimate DD height: some DDs may have been dense enough to trigger returns in MOLA data  (Neumann et al. 2003) , raising the question of whether terrestrial lidars such as CALIOP have been able to detect convectively-lofted dust.
25 m/px (although the smallest observed diameter in HiRISE to date is ~10 m). In general, orbital image data reveals larger DDs in comparison to those identified in lander observations. Greeley et al. (2010)  observed ~760 DDs in images taken from MER Spirit on the martian surface, with diameters ranging between 2 and 276 m; the median diameter was 30 m, more than 5x smaller than that obtained from orbital camera images. Full DD heights from lander images could only be determined for 44 DDs (most extended beyond the top of the images), which ranged from 0. 01 to 0. 36 km  (Greeley et al. 2006 ), compared to 0. 03 to 16. 5 km from orbital data. DDs on Mars can be orders of magnitude larger than terrestrial ones. On Earth, in situ measurements of DD diameters and heights are typically in the range of ~1 to < 100 m and ~0.
Translational ground speeds Dynamic processes on Mars can be captured by orbital instruments or platforms acquiring timedelayed imaging data of the same surface area. Hence, the horizontal ground speed and direction of motion of DDs translating across the surface can be directly measured when imaged by such datasets. For example,  Cantor et al. (2006)  used two VO images taken 5 s apart from each other, which showed the same DD in the overlapping area of the two images. The DD moved ~880 m towards the northeast across the surface in the time between the two image observations, resulting in a horizontal ground speed of 17. 6 ± 2. 6 m s -1  (Cantor et al. 2006). However, such observations in acquired image pairs of frame cameras are rare, because the overlapping areas required to contain the same DD are relatively small.
(2014a)  measured translational ground speeds of 44 DDs, which ranged from 4 -25 m s -1 with a mean of 12 m s -1 (median: ~11. 5 m s -1 ). A further method measuring DD translational ground speeds is the usage of the HiRISE bluegreen, red, and infrared color swaths, which cover the same surface area with a time delay of ~0. 1 seconds. However, such measured horizontal ground speeds should be seen as an estimate due to the very short time interval between the color swaths, which introduces significant uncertainties. Choi and Dundas (2011)  and  Reiss et al. (2014b)  estimated DD translational ground speeds in the range of ~5-20 m s -1 using HiRISE color swaths. The comparison of some DD translational ground speeds and directions of motion with predicted wind speeds and directions from the Mars Climate Database (MCD;  Forget et al.
2012). As pointed out by  Balme et al. (2012)  and shown by  Reiss et al. (2014a) , orbital measurements of DD translational ground speeds and directions of motion can therefore be used as a proxy for local regional wind regimes within the PBL on Mars. Table  5  summarizes horizontal ground speed measurements from both orbiting and landed missions on Mars. Greeley et al. (2010)  measured horizontal ground speeds of nearly 500 DDs at the MER Spirit landing site in Gusev crater based on time sequential lander images. Although the range of translational ground speeds of the large dataset obtained by  Greeley et al. (2010)  is in agreement with orbitally-derived ranges of DD translational ground speeds, the median speeds are lower than those measured from orbit. Because orbital data can only resolve larger DDs, in contrast to lander missions, this might suggest that larger DDs move faster.
Incorporating models of human perception into the process of scene interpretation and object recognition in visual content is a strong trend in computer vision. In this paper we tackle the modeling of visual perception via automatic visual saliency maps for object recognition. Visual saliency represents an efficient way to drive the scene analysis towards particular areas considered 'of interest' for a viewer and an efficient alternative to computationally intensive sliding window methods for object recognition. Using saliency maps, we consider biologically-inspired independent paths of central and peripheral vision and apply them to fundamental steps of the so-called Bag-of-Words (BoW) paradigm, such as features sampling, pooling and encoding. Our proposal has been evaluated addressing the challenging task of active object recognition, and the results show that our method not only improves the baselines, but also achieves stateof-the-art performance in various datasets at very competitive computational times.
The benefits of our approach are multiple: i) the computation of saliency maps is category-independent and a common step for any object detector, ii) compared to sliding window methods, by looking at the salient area we can avoid much of the computational overhead caused by an exhaustive scanning process, iii) our automatic saliency maps not only focus on the object of interest of a scene but usually contain some context around the object, iv) an object recognition method working with saliency maps does not need ground-truth bounding boxes for training, which dramatically reduces the human resources devoted to the database annotation. In contrast, a known limitation of the use of saliency is that, as it focuses on the objects/area of interest of the scene, it may prevent systems from detecting those objects located outside these areas and that belong to the background of the scene.
In order to assess these benefits, we have selected an experimental benchmark composed of both video and image datasets containing scenes in which just a few objects are considered and have been manually labeled as active. The video datasets are 1st-person camera view (egocentric videos), which have recently gained a lot of attention due to the emerging end-user applications involving the use of wearable cameras in scenarios such as robotics, telemedicine or life-logging  [21]. Furthermore, as this kind of content fits well with the problem being addressed, we can find previous works in the literature that have previously applied visual saliency to egocentric video analysis  [22, 8, 23, 24]. On the contrary, the image datasets are 3rd-person camera view and demonstrates that our method is not restricted to egocentric contents.
K in a visual vocabulary by following a vectorquantization process. The visual vocabulary B, computed using a k-means algorithm over a large set of descriptors in the training dataset (we use about 1M descriptors), has a size of K visual words. The vector-quantization process allows the generation of image signatures as L1-normalized histograms H of word occurrences. Finally, to detect the presence of a category in the image, we use an SVM with a nonlinear χ 2 kernel, which has shown good performances working with normalized histograms  [36]. In parallel, our system generates a saliency map S of the frame which is used to model two differentiated pathways found in retinal vision: foveal or central vision, and peripheral vision. It is known that, due to the varying morphology of neurons in the retina, the human eye simultaneously allows for a high-resolution and detailed perception in the visual field associated with the fovea, and a low-resolution one in the peripheral visual field  [37].
Experiments and results As discussed in the introduction, we aim to solve the problem of 'active object recognition' in visual scenes. For that end, we have selected several video and image datasets in which, although each frame/image may contain several of objects, only a few (in most cases one) are considered as 'active ones'. In particular, we have used four datasets which will be described in-depth in the next section: three video datasets (GTEA, ADL, Dem@care) with 1st-person camera view in which the user recording the scene is interacting with it, and a still image dataset (PPMI) with 3rd-person camera view contents, in which users manipulate musical instruments.
Scenarios, Datasets and Evaluation Metrics We consider two different scenarios: constrained and unconstrained. We call constrained a scenario in which all the subjects perform actions in the same room and, therefore, interact with the same objects in the same context, e. g. a hospital scenario in which patients perform several activities. Here, the limited intra-class variation is only due to natural conditions: occlusions, lighting, etc. We have used two video datasets to model this scenario: GTEA and Dem@care. GTEA is a publicly available dataset for Object Recognition  [22]  in egocentric video, that contains cap-mounted videos showing 7 types of daily activities, each performed by 4 different subjects, and comprising 16 categories of manipulated objects. Dem@acare is a dataset generated under the Dem@care 1  research project.
It contains 27 egocentric videos, captured by a shoulder-mounted GoPro camera with real Alzheimer patients performing various instrumental daily activities in a controlled hospital environment. This dataset contains 18 categories of active objects. In contrast, in an unconstrained scenario the recordings are made at different locations and users are interacting with different instances of the same object categories. The intra-class variation here is strong and the amount of training data is small (just a few instances of each object category). For this scenario, we have used two datasets: ADL and PPMI. ADL is a publicly available egocentric video dataset  [45] , which contains videos captured by a chest-mounted GoPro camera on users performing various daily activities at their homes, showing objects from 44 categories.
We have just considered objects labeled as 'active' in both training and testing. This dataset was used for two purposes: a) a reduced version was utilized to validate the free parameters of our proposal. Here a strong temporal sub-sampling was applied and the data was split into training and test sets with 1464/1251 frames respectively, ensuring that frames of the same video were not contained in both sets. And b) for the final evaluation, the 20 videos were divided into 5 sets of 4 videos each, so that a leave-one-out assessment was performed at this subset level. The People Playing Musical Instruments (PPMI) dataset  [46]  contains images of humans interacting with 12 different musical instruments in two different ways: simply holding or playing the instrument. Therefore, each image contains an object considered as active: the instrument.
In this dataset we aim to address the problem of fine-grained activity recognition with the set of 24 categories (12 instruments and 2 manipulations) and, as we will describe later in the manuscript, we have followed the experimental setup of the authors  [46] , which slightly differs from the rest of the experiments. As evaluation metrics, again following the setup of the original authors, mean Average Precision (mAP) was used for the Dem@acare, ADL and PPMI, and multiclass accuracy was applied in the GTEA. We finally isolate the case of PASCAL VOC 2010 dataset  [47] , as a widely used for performance comparison in object recognition problem. Generally speaking it fits to the unconstrained scenario. Nevertheless, we do not follow the active object scheme and consider all objects present in the scene instead for the sake of comparison.
For some categories that are highly correlated with their spatial context, small values of k are preferred, as they draw descriptors in both active and non-active areas. In contrast, those categories of objects that may appear in many different scenarios or locations, are better represented with very high values of k. In average, it seems that the later are dominant in the ADL dataset. This makes high values of k more interesting (we get an optimal value of k = 5). For these values, the sampling process is highly unbalanced so that many more points are selected in high-saliency areas than in low ones.
1). This is something that does not occur with Saliency Pooling which, although by itself provides good performance, when combined with other modules weighs in excess the foveal with respect to the peripheral path and therefore cancels the influence of the context in the recognition process. Even more, by comparing the NUS+VSR+LLC and NUS+VSR+SC alternatives, it is worth noting how the last term in eq. (  8 ) notably contributes to the system performance. Other interesting comparisons, such as the one between NUS+SC and NUS+VSR+SC, offer coherent results with the previous observations about VSR. From these results, in the following we will assess the best performing approach NUS+VSR+SC in other scenarios, and in comparison with various meth- ods that have reported state-of-the-art results in the considered datasets.