dataesr/scientific-paragraphs-categorization huggingface dataset:
The two modules are almost mutually exclusive. The Ca V and BK channels can fine-tune the temporal structure of spike trains elicited by CatSper if present at minute densities. At densities or currents comparable to those of CatSper these channels will disrupt the [Ca 2+ ] i oscillation train. This suggests that the stoichiometry of the channels in sea urchin sperm might be tightly regulated. At least this would be the case, according to the general conviction that the temporal organisation of the [Ca 2+ ] i -spike trains is critical for steering the spermatozoon to the egg. Such constraints on stoichiometry are difficult to reconcile with the pervasive observation that the number of molecules per cell varies in time and across a cell population. This has been observed in free living cells (eg.
[47, 48] ) or in cells of multicellular organisms (eg. [49, 50] ). If sperm cells require tight control of channel stoichiometry as implied by the results with CatSper, one would expect that the coefficient of variation in the number of channels per sperm cell should be on average lower than the coefficient of variation in membrane receptors of somatic cells, such as haematopoietic or epithelial cells. In the same vein, the CatSper module predicts [Ca 2+ ] i -spike trains in agreement with the experimental observations only if BK channel densities are negligible. If one assumes that the unitary conductance of BK channels in marine invertebrates is closer to the estimated in mollusk and crustacean cells  [51] [52] [53]  (under asymmetric conditions that approach physiological gradients) than its counterpart in mammalian cells  [54] , this would be of the order of ≈70 pS.
Under these conditions, the BK conductance densities that allow for oscillatory trains (Fig. 9B , second row) would imply channel counts of less than 1 molecule per flagellum, given the membrane area (Tab. A1). Therefore, although BK is expected to play a key role in a scenario wherein Ca V channels are the ones driving calcium, the role of this channel, if any, should be minimal if CatSper turns out to be the predominant channel. The role for BK in [Ca 2+ ] i responses to SAP was inferred indirectly by the alterations of the period of these oscillations by blockers such as niflumic acid  [55, 56]  and Iberiotoxin  [13] , which were corroborated by simulations of a discrete model. In the present quantitative framework, there are shared effects of BK both on the Ca V +BK and CatSper modules, such as defining a tonic-like minimal value for the nadir of [Ca 2+ ] i oscillations and increasing the period of the oscillations.
This 25/43 notwithstanding, the CatSper module, under the reference parameters, precludes the presence of BK in sea urchin sperm at significant levels. We used bifurcation analysis of a subset of the differential equations of the full system as a heuristic tool to grasp how the temporal evolution of cGMP activity affects the envelop and intervals between [Ca 2+ ] i spikes. It is important to bear in mind that the present analysis involves an asymptotic approximation, i. e. in our case G was set to a fixed value as a way to transform this variable into a parameter and quantify the amplitude and period of the stable limit cycles. The experimental corroboration of such analysis would require development of a cGMP-clamping technique, analogously to classic voltage-clamping experiments or synthetic biology approaches that seem presently unfeasible in sea urchin sperm.
a Taking into account the membrane surface of the flagellum s f , and KCNG unitary conductance (110 pS  [31] ), this implies ∼37 channels per flagellum   e Taking into account the membrane surface of the flagellum s f , and BK unitary conductance (70 pS  [53] ), this implies ∼92 channels per flagellum f Fitted in the complete CatSper + sNHE module to conserve the initial drop of membrane potential, whereas allowing calcium signal to return to levels close to resting value after spike-train finishes g Based on figure  1  of  [72]  h Taking into account the membrane surface of the flagellum s f , and assuming that the Ca 2+ unitary conductance of CatSper might be below 1 pS, this implies ∼1200 channels per flagellum.
11 13 15 Figure 7. 157 Figure 7. Bifurcation analysis of the model featuring the CatSper module. The analysis was done under conditions wherein cGMP is defined as a constant input (G =constant) and upstream variables ignored reducing the state space to {f o kn ,f o hc ,V , f a nh ,m cs ,h cs ,C,H}. Top: The graphs depict the variable C as a function of the input parameter G. The thick continuous lines are the stable equilibria and the dashed lines indicate unstable equilibria, as obtained by XPPAUT [25]. The thick dotted lines are the maxima and minima of the stable limit cycle obtained by numerical solutions of the system under random initial conditions. The numbered gray dots indicate the maxima and minima of the consecutive spikes obtained by solving numerically the full model, corresponding to those depicted in Fig.
16 Figure 10. 1610 Figure 10. Titration of the module Ca V + BK on the CatSper module. Starting from the model that includes the CatSper module, numerical solutions for calcium are shown under different values of the weighting parameter θ, which controls the percentage of module Ca V + BK that is being added. θ multiplies the reference conductance g cv and g bk , and correspondingly the conductance density of CatSper decreases by the factor (1 -θ). For reference, the original CatSper-only scenario is shown first, i. e. θ = 0, and the subsequent rows correspond to the gradual increase of θ. The effect of this parameter on the channel densities is reported in the coefficient r g , which measures the total conductance ratio CatSper/Ca V , as well as the new values g bk. As in the figure9, the greater the value of r g , the greater the predominance of CatSper on the total calcium conductance with respect to Ca V.
D. Consejo Nacional de Ciencia y Tecnología from Mexico (CONACyT) grants CB-2015-01 255914-F7 to G. M. -M. , Fronteras 71 39908-Q to A. D. G. M. -M. thanks PASPA/DGAPA/UNAM for support during a sabbatical leave at the Ecole Normale Supérieure, Paris. J. C. is funded by the Fundação para a Ciência e Tecnologia and Instituto Gulbenkian de Ciência. Daniel Alejandro Priego-Espinosa was a doctoral student from Programa de Doctorado en Ciencias Biomédicas, UNAM and received fellowships 275795 and CB-2015-01 255914-F7 from CONACyT; this work is part of his PhD Thesis. He also thanks UNAM for the fellowships given by Programa de Apoyo a los Estudios de Posgrado (PAEP) and Movilidad Internacional de la Coordinación de Estudios de Posgrado (CEP). Ana Laura González-Cota thanks CONACyT for the postdoctoral fellowship EPE-2017 291231.
Phylogenetic analysis The obtained 16S rRNA gene sequence was compared with the National Center of Biotechnology information (NCBI) (http://www. ncbi. nlm. nih. gov) database. Nearest relatives and sequence similarities were determined by BLAST search  [27] ). 16S rDNA sequences of representative species of the Chromatiaceae were aligned using the Clustal X program  [28]  and refined manually. Phylogenetic analyses were performed using MEGA version 5. 2 software  [29]. The phylogenetic trees were inferred by the maximum-likelihood method (ML)  [30]. Node robustness was assessed by performing 1000 bootstrap replicates  [31]  and only bootstrap values above 50% are indicated at the nodes of the trees. Chloroflexus aurantiacus strain DSM 637 gij15551748jembjAJ308501. 1j) was used as an outgroup.
Thus, these data strongly suggest that the strain analyzed here belongs to PSB. SEM observations showed that these micro-organisms occur singly and are ovoid with a monopolar tuft of flagella (Figs. 3a-c ). TEM showed a double membrane typical of Gram negative bacterial cell wall (Figs. 3d-e ). Moreover, cells contained intracytoplasmic vesicular membrane systems extending throughout the cell in which the photosynthetic pigments are located. No gas vesicles could be detected. The ''large empty vesicles'' observed inside the cytoplasmic volume presumably represent the location of sulfur inclusions lost during the dehydration and embedding processes. Freshly collected samples were analyzed using an ESEM microscope coupled with an EDX detector to identify chemical content. The white dots observed in each cell indicated internal compounds with higher atomic density (Fig.
However, the few attempts to culture such bacteria in liquid media (under aerobic or anaerobic conditions) have failed  [37] , making their characterization difficult. Indeed, Halochromatium-like PSB from Guadeloupe was unable to grow photo-lithoautotrophically [anaerobic, aerobic, daylight exposure, Na 2 S. 9H 2 O (1 mM), Na 2 S 2 O 3 Á5H 2 O (5 mM) and NaHCO 3 (0. 1% w/v)] or photo-organotrophically (using citrate or glucose). In order to ascertain the phylogenetic positions of this purple bacterial isolate, we amplified and partially sequenced the 16S rRNA gene (848 bp fragment, accession number KX714823). Phylogenetic analyses revealed that this environmental PSB novel phenotype belongs to the family Chromatiaceae and is related to a group of marine and halophilic species genera including Halochromatium and Thiohalocapsa (Fig.
d-i: Chlorine and sulfur are only located within the bacterial cell (f, h), whereas carbon (e) is present everywhere (biological sample and specimen holder). However, iron clearly comes from the specimen holder (g), as no iron can be located within the bacterial cell analyzed (g). The sulfur map (h) clearly demonstrates that sulfur is located inside the bacteria as confirmed by the mix-map image (i). genera (with identity < 93%). In summary, the molecular and phenotypic data support the affiliation of Halochromatium-like PSB from Guadeloupe with various clades within the genus Halochromatium, but not to the genus Thiorhodovibrio. Thus, the strain collected from the marine mangrove environment may correspond to a new species of Halochromatium. The sulfur metabolism of this new species was confirmed by EDX analysis.
In addition, a weighting scheme that reflects how important a visual word is with respect to a given image is introduced. We speed up the learning process by using histogram intersection kernel by Support Vector Machine to learn a discriminative classifier. The efficiency of the proposed algorithm is compared with standard bag of visual words method and with bag of visual words method with spatial pyramid. Extensive experiments on the CK+, the MMI and the JAFFE databases show good average recognition rates. Likewise, the ability to recognize spontaneous and non-basic expressive states is investigated using the DynEmo database.
The utilization of IDF(vw) is based on its ability to distinguish between visual words with some semantical meanings and simple visual words. The IDF(vw) measures how unique a vw is and how infrequently it occurs across all training facial expression images. IDF(vw) = log( T n vw ) T : total number of training images. n vw : number of occurrences of vw in the whole training database T. However, if we assume that certain visual words may appear a lot of times but have little importance, then we need to weight down the most frequent visual words while scaling up the rare ones, by computing the term-frequency-inverse-document-frequency referred as T F. IDF. Where: T F. IDF(vw) = T F(vw) • log( T n vw ) (1) The TF. IDF vw,I assigns to visual word vw a weight in image I, where I ∈ T , such that: high weight when vw occurs frequently within a small number of images, thus lending high discriminating power to those images.
Data Exploration For effective and fair comparison, four different databases are used: three with Ekman's caricatured facial expressions (the JAFFE database  (Lyons et al. , 1998) , the extended Cohn Kanade database (CK+)  (Kanade et al. , 2000)  and the MMI facial expression databases  (Pantic et al. , 2005) ) and one with spontaneous expressions (the DynEmo database  (Tcherkassof et al. , 2013) ). The JAFFE Database: it is a well-known database made of acted facial expressions. It contains 213 facial expression images with 10 different identities. It includes: "happy", "anger", "sadness", "surprise", "disgust", "fear" and "neutral". The head is in frontal pose. The number of images corresponding to each is roughly the same (around 21). Seven identities are used during the training phase while three other identities are used during the test phase.
The CK+ Database: it is a widely used database containing acted Ekman's expressions. It is composed of 123 different identities. In our study, we pick out the last frame which represents the peak of emotion. Each subject performed different sessions corresponding to different emotions. In total we collected 306 images associated with its ground truth label. The MMI Database: it is composed of more than 1500 samples of both static images and image sequences of faces in frontal and in profile views displaying various facial expressions. It is performed by 19 different people both genders, ranging in age between 19 and 62, having a different ethnic background. Images are given a single label that belongs to one of six Ekman emotion. In this study, we collect 600 static frontal images and we exploit the 900 sequences to extract from each sequence other static expressive images.
From this database we create a collection of 1900 static images. This database is used to check the scalability of the proposed approach on a large databse. The DynEmo Database: it is a database containing elicited facial expressions. It is made of six spontaneous expressions which are: "irritation", "curiosity", "happiness", "worried", "astonishment", and "fear". The database contains a set of 125 recordings of facial expressions of ordinary Caucasian people (ages 25 to 65, 182 females and 176 males) filmed in natural but standardized conditions. 480 expressive images that correspond to 65 different identities are extracted from the database. The head is not totally in frontal pose. The number of images corresponding to each of the six categories of expressions is roughly the same (80 images per class).
Experimental setup and Results We focus the experimental evaluation of the proposed method on the following four questions: What is the best feature detector that locates salient and reliable feature points for facial expression recognition? Does each of the proposed novelties improve the performance of the SBoVW? What is the influence of using k-means++? Is the proposed approach efficient for facial recognition and scalable for larger databases? The proposed model has many parameters that influence its classification performance: the usage of weighting scheme T F. IDF, the usage of Relative Conjunction Matrix (RCM), the combination of T F. IDF weighting scheme along with RCM, the us-age of K-mean and K-mean++ as clustering methods, the choice of the best feature detector and descriptor. Thereby, in order to answer the first three questions, we report performance of facial expression recognition with SBoVW representation along with each novelty.
The JAFFE (caricatured facial expressions) and the DynEmo (non-caricatured facial expressions) databases are used for the method evaluation and setting and to figure out the best feature detector that suit facial expression recognition. The final question is addressed using the CK+ and the MMI databases to establish the performance of ImpBoVW model and to compare it with SBoVW and SP BoVW. Multi-class classification is done using SVM trained using one-versus-all rule. The histogram intersection kernel presented in equation 2 is used. Compared to RBF kernel, we experience faster computation while accuracy rate has a smaller variance. One-hold-out cross-validation method is used over the training set in order to tune the algorithm hyperparameters such as the regularization parameter C ( the optimal value is 10.
0), the gamma parameter which stands for Gaussian kernel to handle non-linear classification if considered. We fix the vocabulary size to 2000 visual words, since experimentally it shows the best classification performance. For spatial pyramid representation, we notice that at level-2 and level-3 same performance is achieved. Thus to reduce the complexity of feature computation, we only consider two levels. Figure  2  represent the performance of each novelty and its contribution to the final ImpBoVW over the JAFFE database. ImpBoVW represents the best final model which is a combination of SBoVW representation along with RCM and T F. IDF weighting scheme (representd as a star in the figure  2  and 3 ). Its quantization is based on K-mean++. Its features are located using 2D-Harris detector and described using SIFT descriptor.
The final average recognition rate obtained over the JAFFE database is 92%. If we compare ImpBoVW model using DoG feature detector, we got 84% accuracy while 89,5% is achieved using dense features. 2D-Harris is less computationally expensive than dense features due to the fact that it produces less redunadat features. In addition, figure  2  shows that if each novelty stand alone along with SBoVW, a noticed increase in the recognition rate is achieved. More importantly, the figure shows that the usage of K-means++ increases the classification rate significantly. In order to fairly estimate the performance of ImpBoVW model and to get a fair judgment about the choice and the best feature detector and clustering method, we evaluate the same steps over the  DynEmo database. DynEmo represents naturalistic static images where subtle deformations occur.
CONCLUSION In this paper, we introduced an improved BoVW approach for automatic emotion recognition. We examined several aspects of the SBoVW approach that are linked directly to gain classification performance, speed and scalability. It has been proved that Harris detector is suitable for emotion recognition. It selects adaptable and reliable salient keypoints. We improve the codebook generation process through employing k-means++ as a clustering method, wherein we gain speed and accuracy. The importance of spatial organization of the data has been examined, and we optimized the feature representation by introduc-ing a relative conjunction matrix to preserve the spatial order. We properly weighted the visual words after preserving the spatial order using TF. IDF based on their occurrences.
Train set: 70% of randomly shuffled images per class are picked out as training sets. Therefore, for the JAFFE dataset we have 143 training images (20 images per class), for the CK+ dataset we have 216 training images (36 images per class), for the MMI dataset we have 1330 images (around 221 images per class) and finally for the DynEmo we have 360 training images (60 images per class). Development set: Leave-one-out cross validation is considered over the training set to tune the algorithm hyper-parameters. Test set: 30% images per class are picked out as test set, that is 70 test images from the JAFFE set (10 images per class), 90 test images (15 images per class) for the CK+ set, 570 test images (95 images per class) for the MMI set and 120 test images from the DynEmo set (20 images per class), randomly shuffled, to test the performance of the proposed method.
The algorithm does not target any specific type of genomic variations but instead systematically reports all positions (with a z-score) that have depth departing from the overall distribution. The algorithm normalizes the genome coverage using a running median (RM) and then calculates a robust statistic (z-score) for each base position based on the parameter estimation of the underlying distribution. This allows us to obtain robust and nonconstant thresholds at each genome position. Various types of clustering or filtering can then be implemented to focus on specific categories of variations. In the Data Description section, we describe the datasets used throughout as test-case examples. In the Methods section, we describe the RM used to detrend the genome coverage, the statistical methods used to characterize the central distribution from which outliers can be identified, and a double thresholds method proposed to cluster the ROIs.
Data Description Three test cases of genome coverage are presented here, covering representative organisms and sequencing depths. The genome coverage datasets are in BED (Browser Extensible Data) format, a tabulated file containing the coverage, reference (e. g. , chromosome number, contig), and position on the reference. BED files can be created from binary alignment (BAM) files (mapped reads) using bedtools  [21] , in particular, the genomecov tool. We first considered bacteria from a study of methicillinresistant Staphylococcus aureus  [22]. One circular chromosome of 3 Mbp is present. The sequencing depth is 450X and the genome coverage exhibits a nonconstant trend along the genome (see Fig. 1 ). This pattern, often observed in rapidly growing bacteria, is the result of an unsynchronized population where genome replication occurs bidirectionally from a single origin of replication  [23, 24].
The proportion of outliers (see Table  1 ) is about 2. 5% of the total bases. The original datasets (Illumina sequencing reads, paired-end, 100 bp) are available at the European Nucleotide Archive (ENA)  [25]  under study accession number PR-JEB2076 (ERR036019). The accession number of the reference is FN433596. The second organism is a virus with a sequencing depth of 1000X  [26]. A circular plasmid containing the virus chromosome is 19,795 bp long. About 13% of the genome coverage contains large or low coverage regions (outliers). It also contains two large undercovered regions (one partially undercovered and one region that is not covered at all), as shown in the Notebook 1 of  [18] ). The accession number of the reference is JB409847. The third test case is a fungus (Schizosaccharomyces pombe)  [27].
We have applied the EM algorithm on the normalized genome coverage vector on various real NGS datasets, including the three test cases in Fig. 4. The EM retrieves the parameters of the central distribution (in particular, μ0 = 1) and the outliers. Note that the choice of the RM parameter, W, does not significantly affect the parameter estimation. In each case, the mean of the central distribution is very close to unity. The standard deviation varies significantly and is a function of the sequencing depth only (since the outliers are now incorporated in C 1 b ). Finally, we can confirm that the proportion of outliers is small compared to the central distributions by inspection of parameters π 0 and π 1 : π 0 >> π 1. Once we have identified the parameters of the central distribution C 0 , we can assign statistics for C b in terms of z-score: z(b) = C (b) -μ 0 σ 0.
Stand-alone and computational time Although the algorithm described here is quite simple per se, each of the three steps requires optimization in order to handle HTS datasets. We provide an implementation within the Sequana project  [34] , which is a Python library that also provides HTS pipelines based on the workflow management system called Snakemake  [35]  (Makefile-like with a Python syntax). Stand-alone applications are provided, including sequana coverage. In addition to the algorithm described above, the stand-alone application has several additional features as explained hereafter. The input file can be either a BAM or a BED file  [21]  encoded as a three-column tab-delimited file (chromosome, position, coverage). Consider this command:.