dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation in independent data sets DE-kupl was applied to eight skin and eight colon libraries from GTEx  [38]  using parameters kmer_length 31, min_recurrence 6, min_recurrence_abun dance 5, pvalue_threshold 0. 05, lib_type unstranded, diff_method Ttest, and refe rence_transcriptome Gencode. DE-kupl contigs were interactively classified using R commands, applying the same rules as in Table  3. Classes antisense RNA and SNV-DU were excluded since identification of antisense RNA is not possible using the unstranded GTEx and HPA libraries, and we had no reason to expect common SNVs with DU in this data set. DE contigs were sorted by fold-change. The k-mer labels of the top 100 DE contigs in each class were extracted (50 for class unmapped due to the fewer events). GTex k-mers were then sought in the six skin and six colon libraries from HPA described above  [29]  (E-MTAB-2836).
The k-mers were counted in each library using Jellyfish with options k = 31 and -C (canonical k-mers) as GTEx data were unstranded. All k-mers selected from the GTEx analysis were queried against the Jellyfish databases using the jellyfish query command. Finally, the extracted k-mers counts were processed with DESeq2  [33]  and the resulting adjusted p values were plotted for each event class (Fig. 8 ).
METHODS This was a retrospective analysis using a prospective database that includes all patients hospitalized in a surgical intensive care unit for POP from September 2006 to August 2011. The data collected included demographics, comorbidities, postoperative severity parameters, bacteriological findings, adequacy of antimicrobial therapy and surgical treatments. Adequate source control was defined based on a midline laparotomy, infection source control and intraoperative peritoneal lavage. The number of reoperations needed was also recorded.
Finally, peritonitis is shown to be a frequent condition related to death due to multiple organ failure. In this context, reoperation and postoperative immune depression may favour sepsis and the development of organ failure  [11] [12] [13] [14]. All of these factors may explain the high mortality observed in association with POP and illustrate the need to evaluate POP separately from other types of intraabdominal infections. Nevertheless, few studies have evaluated the risk factors for mortality in POP, especially in critically ill patients  [8, 15, 16]. We hypothesized that POP may have specific chara cteristics and risk factors for mortality that could help physician in the care of the patients with POP. Accordingly, we performed an analysis using a prospective database to determine the risk factors for mortality associated with POP in patients who required intensive care.
MATERIALS AND METHODS We performed a retrospective analysis from a prospective database that aimed to include all patients with POP. This database was completed from September 2006 to August 2011 in a surgical critical care unit of a university hospital (Rennes -France). All patients older than 18 years of age who were admitted for POP were included. POP was defined as a peritoneal infection developing after intraabdominal surgery. Only the first episode of POP was taken into account. Patients who had focal abscess(es) drained under computed tomography (CT)-scan guidance and/or who had more than one previous episode of POP before intensive care unit (ICU) admission were excluded. Infections were confirmed macroscopically and/or based on the identification of one or several pathogens in peritoneal sample.
Because of the low frequency of methicillin resistant Staphylococcus aureus and Enterococcus faecium, the use of vancomycin was not considered. Effects of antimicrobial therapy on Enterococcus species for ongoing POP and the rates of escalation or de-escalation of antimicrobial therapy were reported. Empirical antimicrobial therapies were re-evaluated based on microbiological data and the susceptibility of the isolated microorganisms. Treatments against fungi were only administered in cases of positive, direct examinations of the peritoneal liquid or positive cultures and included the use of fluconazole or an echinocandin. Bacteremia was recorded and defined based on at least one positive blood culture (2 positive samples in cases of coagulase-negative Staphylococcus) and were linked to the intra-abdominal infection if the same microorganisms were recovered in each sample.
Statistical analysis All statistical analysis were performed with SAS software version 9. 2 (SAS Institute, Cary, NC, United States). Mean values and standard deviations were used to describe quantitative data, and a t-test or Wilcoxon test were used as needed. Numbers, ranges, and percentages were used to describe qualitative data, and a χ 2 test or Fisher's test was used as needed. The multivariate analysis was designed by selecting variables with a P-value < 0. 20 in the univariate analysis to build a logistic regression model. The results are expressed with ORs and 95% confidence intervals (95%CI). Results were considered significant at a P-value < 0. 05.
Controversies exist regarding age as a risk factor for mortality in ICUs, and factors other than age itself, such as previous comorbidities and/ or frailty, may have a better prognostic significance  [34, 35]. This issue has been poorly studied in ICU patients with peritonitis. In 163 patients with secondary peritonitis, excluding patients with POP, Hynninen et al [32] showed that previous functional status was an independent risk factor for mortality overall but not in the ICU patients. Several limitations to the interpretation of our data are worth noting. First, this is a retrospective and monocentric study, but data were prospectively collected, and one third of the patients came from another hospital. Moreover, the management of POP was standardized regardless of whether it was for surgical procedures or postoperative ICU management.
Second, we assessed only 3 surgical criteria (the type of laparotomy, the intraoperative management of the lesions and the quality of washing), but many other surgical factors not reported in our database may affect outcomes, such as the experience of the surgeon, the duration of the surgery, the quality of the drainage and the stitching of abdominal wall. Third, our inclusion criteria were stringent because we excluded POP that had been operated on using coelioscopy because we believe that the coelioscopy does not have a sufficiently well-defined role in the surgical management of peritonitis  [6, 7, 36]. Fourth, biological markers of inflammation have not been recorded in our database. It might have allowed a better stratification of the peri tonitis severity, but were not recommended in usual practice in a recent guideline  [36].
The first data interface in ITP is a packet of 84 bytes, using the UDP network protocol for fast transmission. The small size of the ITP packet makes it suitable for high packet rates. In  [6] , the Secure ITP enciphers communications between the master and slave. The communications use TLS (Transport Layer Security), DTLS (Datagram TLS) protocols and the AES (Advanced Encryption Standard) cipher. Analysis of the possibilities for providing real-time services for bilateral teleoperation systems on existing wireless infrastructures, using only application layer measurements without any information from the network layer, is presented in  [7]. Since teleoperation systems should have a video feedback channel, the influence of video transmission on the transmission quality in the other two periodical channels is analysed.
Electron backscatter diffraction (EBSD) analyses For EBSD analysis, each sample was placed on a Gatan copper holder using thermally conductive silicone grease and then frozen onto the holder by injecting water around its edges. Sample preparation was achieved by scraping the surface with disposable Leica microtome blades, following the method of  Piazolo et al. (2008). EBSD was performed at À100 C( ±5 C) using a Gatan liquid nitrogen gas cooling cryogenic stage in the CamScan Crystal Probe X500-FEG SEM-EBSD facility of G eosciences Montpellier (Montpellier, France), which is equipped with Oxford Instruments Nor-dlysNano EBSD detector and Aztec2. 4 data acquisition software (Oxford/HKL). Instrumental settings were 15 kV acceleration voltage, 25 mm working distance, and 1 Pa chamber pressure.
Acquisition conditions were 4 Â 4 binning and low gain with grid steps of 20 mm for overview maps and 5 mm for high-resolution crystal orientation maps. Large area mapping was carried out by combined beam and stage scanning, hence the overview maps are mosaics of smaller maps. Percentage of indexed points in raw maps always exceeded 90%. Post-acquisition data treatment was carried out using the Channel5 algorithms by (1) removing isolated wild spikes, (2) filling the non-indexed pixels that have up to eight identical neighbors with the same orientation and repeating this iterative operation using seven and six identical neighbors, and (3) removing grains defined by less than 10 indexed pixels. For treating and visualizing EBSD data, we used the built-in functions of the MTEX4 Matlab toolbox (https://mtex-toolbox.
github. io/; Bachmann et al. , 2010). For grain recognition, we applied a segmentation angle (i. e. , a minimum threshold misorientation between neighboring pixels that defines a high-angle grain boundary) of 7 following  Chauve et al. (2017) , except for sample CI18Z6 [5. 2/-2]. For this sample, due to the very close initial orientation of the parent grains, the fast multiscale clustering  (FMC; McMahon et al. , 2013)  algorithm with C Maha value e which is a constant scaling factor applied on Mahalanobis distance that controls segmentation sensitivity e in the range of 0. 3e0. 4 provided more reliable boundary definition than the segmentation method with a threshold of 7.
Finally, as approximately the same area was analyzed after each annealing step, we use average values for the whole mapped domain to characterize the global evolution of stored energy and grain parameters during the static heat treatment. In order to put emphasis on the evolution of large grains without filtering smaller grains from the dataset, in a few cases we weighted the averages by the area of the grains, instead of using the 95% confidence intervals of the geometric mean. All calculated parameters are provided in Supplementary Table  S1a. À7 C 3. 0% CI06-Z2 0-1-2-3-5 h CI06-Z10 0-0. 5-1. 5-3-5 h 3. 7% CI02-Z1 0-1-2-3-4-6 h CI02-Z4 0-1-2-3-4-6-25 h CI02-Z9 0-0. 5-2-4 h 5. 2% CI18-Z1 0-1-2-4. 5-9-16-23 h CI18-Z6 0-0. 5-1. 5-3-5-15-78 h Results of annealing experiments are presented for selected representative samples in Figs.
Acknowledgements We acknowledge the reviews by  Lars Hansen  and  Mark Peternell , as well as the editorial work of  Cees W. Passchier. Research leading to these results was funded by the  European Union   Framework Programme 7  ( EU-FP7 )  Marie Curie postdoctoral grant  to  K. H. [grant number  PIEF-GA-2012-327226 ]. Financial contribution from the  French 'Agence Nationale de la Recherche ' is also acknowledged [grant number # ANR-13-BS09-0001-01 ; project DREAM]. T. C. and M. M. acknowledge support from the  Grenoble Institute of Technology (Grenoble INP)  and the  Universit e Joseph Fourier  grant  AGIR 2012.
e. , voxels selective for different stimulus variables) contribute to the success of MVPA. Specifically, decoding performance is enhanced when voxels with high vs. low noise correlations (measured during rest or in the background of the task) are selected during classifier training. Conversely, voxels that are strongly selective for one class in a GLM or that receive high classification weights in MVPA tend to exhibit high noise correlations with voxels selective for the other class being discriminated against. Furthermore, we use simulations to show that this is a general property of fMRI data and that selectivity and noise correlations can have distinguishable influences on decoding. Taken together, our findings demonstrate that if there is signal in the data, the resulting above-chance classification accuracy is modulated by the magnitude of noise correlations.
Introduction The development of fMRI has made it possible to observe the human brain noninvasively as it responds to stimuli or engages in cognitive tasks. For example, participants might be presented with a series of stimuli drawn from two or more categories (e. g. , faces and scenes), while the blood oxygenation level-dependent (BOLD) contrast is measured over time from tens of thousands of volumetric pixels (voxels). Different events in the experiment can then be linked to changes in BOLD activity, permitting inferences about the neural basis of cognition (in the example above, about category-selective object perception). However, this is a challenging endeavor because both the physiological processes underlying BOLD activity and the measurement of BOLD activity with fMRI are noisy, and because the resulting datasets can be large and statistically complex  [1, 2].
We find that MVPA decoding performance is influenced not only by the selectivity of individual voxels but also by noise correlations between heterogeneous populations of voxels. Across several analyses of an fMRI dataset, we demonstrate a positive relationship between the magnitude of noise correlations and decoding performance, and we show that as expected with such classifier algorithms  [46, 49] , MVPA exploits noise correlations by assigning higher weights to voxels with higher noise correlations. We also show that selectivity and noise correlations influence decoding in a complementary fashion-as long as there is signal in the data, performance is modulated by the magnitude of noise correlations. Indeed, voxels that were highly selective for one class also exhibited higher noise correlations with voxels selective for the other class.
For instance, when we divided voxels in our dataset into percentiles of raw noise correlations, we observed a monotonic improvement in MVPA decoding performance with an increase in the To answer this question, we took the absolute value of the selectivity scores that had been used to identify face and scene voxels in one localizer run (i. e. , for determining which voxels should count as having opposite selectivity when calculating heterogeneous noise correlations). As a reminder, these scores reflect the face vs. scene contrast from the GLM, specifically the zscored difference of the parameter estimates modeling the average evoked response from face and scene blocks, respectively. Average selectivity increased monotonically (Fig 7 ) as we moved from voxels with low noise correlations to voxels with high noise correlations (slope vs.
0: t 16 = 5. 03, p < 0. 001), and the Spearman rank order correlation between noise correlation and selectivity across voxels was reliable (mean rho = 0. 076; t 16 = 4. 17, p < 0. 001). In other words, voxels with higher selectivity for one of the two categories also had higher noise correlations with voxels selective for the other category. Given the link between selectivity and noise correlations across voxels in our empirical dataset, we next sought to examine their cumulative influence on decoding. We selected voxels with the top vs. bottom 12% of noise correlations, and within each set selected voxels with high vs. low selectivity based on a median split of voxel selectivity from the GLM. We then examined MVPA classification accuracy for the patterns of activity from voxels in each of the resulting four bins with 6% of voxels (Fig  8 ).
Model simulations So far, we have used an existing fMRI dataset to demonstrate that MVPA is highly attuned to noise correlations between voxels, and that decoding performance may be sensitive to the information carried both by the selectivity of individual voxels and the noise correlations between them. We next sought to expand upon these findings in two ways: First, as described above, selectivity and noise correlations were inherently confounded in the empirical dataset. How might we better examine the cumulative contributions of noise correlations and selectivity to decoding performance? Second, all of the findings reported above were based on one fMRI dataset with particular characteristics. To what extent do our conclusions apply to other datasets and reflect a general principle about the computational underpinnings of MVPA? To address these issues, we developed a simple model of selective coding in the presence of noise correlations, wherein we could independently vary voxel selectivity and heterogeneous noise correlations.
By performing MVPA over artificial BOLD activity generated from this model, we could then simulate the influence of different parameters. The model included a set of voxels roughly matched in number to the 1% bin size in our earlier analyses. By construction, half of the voxels responded preferentially to face stimuli and the other half to scene stimuli. The mean responses, variances, and correlations of all voxels in the model were drawn from the range observed in our empirical dataset, ensuring that the simulated voxels produced physiologically realistic activity. Following Azeredo da Silveira & Berry (2014), we used a Gaussian approximation in each of 100 model "participants" to sample data for time points from face and scene blocks (matched to the number of face and scene TRs in the empirical dataset).
To ensure that the resulting timecourses were temporally autocorrelated like real BOLD activity  [58] , we convolved them with a canonical hemodynamic response function (HRF). Finally, we performed cross-validated MVPA over the artificial patterns of activity obtained from the simulated voxels. We first sought to examine the influence of heterogeneous noise correlations on decoding performance. Noise correlations across pairs of voxels varied according to whether the voxels were drawn from the pool of face-selective voxels, the pool of scene-selective voxels, or one from each of the pools. We performed 20 simulations manipulating the magnitude of acrosspool noise correlations linearly between 0 and 0. 22 (i. e. , the range of positive noise correlations in the empirical dataset), while holding all other parameters constant.
There was a monotonic increase in classification accuracy as the magnitude of heterogeneous noise correlations increased (blue curve in Fig  9A ). This is precisely the pattern predicted by the computational theory on which our study was based  [36] , and is similar to the pattern of results observed in our empirical dataset. Notably, by allowing noise correlations to vary while the selectivity of the voxels in the two pools was held constant, these results show that noise correlations are sufficient to influence above-chance decoding performance. To examine the influence of voxel selectivity, we repeated the analysis above but further manipulated the strength of face and scene selectivity in the mean responses of voxels from the two pools, over a fixed range of noise correlations. As expected, when voxel selectivity decreased across three levels, overall decoding performance also decreased (Fig  9A ).
However, at all levels, we observed the same monotonically increasing relationship between classification accuracy and the magnitude of noise correlations. Notably, selectivity affected classification accuracy even with near-zero noise correlations, but the effect of selectivity was stronger in the regime of stronger noise correlations. We next sought to examine the extent to which these results depend on the specific parameters used in our simulations. For instance, in the simulations described thus far, the variance of all voxels was matched to the median variance observed in our empirical dataset. Given that overall noise in the system, correlated or otherwise, is ultimately a function of the variability in the activity of individual voxels, we examined the extent to which our results depended on the magnitude of voxelwise variance.
Repeating our analysis across three levels of variance, spanning the range observed in our empirical dataset, we found a similar influence of noise correlations on classification accuracy (Fig  9B ). Specifically, as voxel variance increased, thereby increasing noise in the system, overall decoding performance went down; however, at every level of variance, we observed the same relationship between classification accuracy and the magnitude of noise correlations. Another modeling choice we made was to sample activity within the face-and scene-selective voxel pools based on homogeneous mean, variance, and correlation values matched to population averages from our empirical dataset. We next examined the influence of introducing heterogeneity in the response properties of the simulated population of voxels.
We generalized our model to include greater voxelwise diversity by randomly varying the population covariance matrix according to a Gaussian distribution with SD equal to 10% of the original value. We similarly varied the mean responses of individual voxels (while maintaining selectivity) in each population according to a Gaussian distribution with SD matched to the mean within-population SD from our empirical dataset. We obtained the same pattern of results from MVPA, with classification accuracy increasing monotonically as the magnitude of noise correlations increased (Fig  9C ). Indeed, greater population diversity led to a steeper increase in classification accuracy, consistent with the notion that heterogeneity can be beneficial, especially at higher levels of noise correlation.
In other words, the weight assigned to a given "channel" (or voxel in the case of fMRI) need not only reflect how well it represents the signal of interest-it may be assigned a high weight if the structure of noise in this channel also contributes to the classifier's effectiveness. Our study complements and builds upon this and other prior work. Inspired by a computational theory in the literature  [36] , and in line with  [46] , we argue that MVPA is effective precisely because it is sensitive to both the signal and noise (i. e. , selectivity and noise correlations) in patterns of activity across populations of voxels, and the weights assigned to voxels are functions of both of these variables. Our findings go beyond these prior theoretical proposals by providing empirical evidence from a real fMRI dataset and by simulating fMRI data with a range of characteristics.
We demonstrate that if a voxel has high noise correlations with voxels selective for the other class, then considering this voxel's activity allows the classifier to find a better decision boundary (by providing an excellent marker for the noise), thus resulting in the classifier assigning this voxel a higher weight. Furthermore, our analysis scheme highlights a previously overlooked empirical result: The representation of information in human ventral temporal cortex seems to be dominated by a small subset of voxels that are both highly selective for one of the task-relevant categories, and also exhibit high noise correlations with voxels selective for the other category. Thus, at least with the dataset considered here, sensitivity to both selectivity and noise correlations makes MVPA particularly effective at extracting the relevant information.