dataesr/scientific-paragraphs-categorization huggingface dataset:
(h) Correlating transcript and protein expression patterns Based on the processed transcript and protein data, two correlations were investigated: whether absolute transcript values rspb. royalsocietypublishing. org Proc. R. Soc. B 285: 20172654 and absolute protein values were tightly linked, and whether relative changes of transcript expression were similar to that of protein expression under thermal stress. A complication to both analyses was that some detected peptides (approx. 10% of all) could originate from multiple gene models. To reduce ambiguity, these peptides were removed from the dataset, and subsequently plotted with Python.
(i) Functional enrichment of differentially expressed gene models Since some detected peptides could originate from multiple gene models, if all gene models that could give rise to the differentially expressed peptide were included in the functional analysis, it would result in the over-representation of functional categories associated with the peptide. To remove this bias, a representative gene model was chosen per ambiguous peptide via a Python script. This complication was not present for the transcript data. Functional enrichment was carried out with topGO  [34]  with default settings; however, the background gene set (universal set) was set as gene models with detectable expressions. This precaution, which was more applicable to the protein dataset than the transcript dataset, prevents the analysis from being biased towards gene ontology (GO) terms that are associated with expressed gene models.
(l) Typing of the ITS2 regions of Red Sea symbionts Extracted DNA from four RS Aiptasia at 258C were subjected to a PCR targeting the ITS2 regions of Symbiodinium (using the primers SYM_VAR_FWD and SYM_VAR_REV  [35] ). These samples were 4 of 96 samples sequenced on a MiSeq run. Reads from these samples were pooled (757 132 paired-end reads), trimmed with cutadapt v1. 16  [36] , and merged into a single read with bbmerge v37. 93  [37]  to produce 348 488 reads (with length !200 bp). These reads were clustered at 97% similarity with cdhit-est v4. 7  [38]  to produce 73 clusters-the 10 most abundant clusters were parsed with a Python script. Representative sequences of these clusters were subjected to a BLASTN search against nt, and clade information of the best Symbiodinium hits were searched for manually.
(c) Functional enrichment of differentially expressed transcripts and proteins Functional enrichment of the DETs revealed a total of 24 significantly enriched GO terms that overlapped between the strains (electronic supplementary material, table  S3 ). Particular noteworthy terms were related to protein misfolding, oxidative stress, mRNA splicing, endoplasmic reticulum stress and iron binding. When categorized into higher-order terms, protein misfolding pathways appear to be positively enriched in all strains, especially CC7 (electronic supplementary material, supplemental dataset S1). In addition, terms related to oxidative stress response were predominant in RS, showing twice as many than any of the other strains (figure  3 ). Included in these were unique responses related to superoxide dismutase (GO:0004784), regulation of nitric oxide (GO:0045429) and peroxidase activity (GO:0004601).
A possible confounding factor of the ROS assay was that the different strains of Aiptasia could have had different initial expressions of oxidative stress-related genes. Thus, the lower amounts of ROS assayed in RS samples could potentially be attributed to a higher initial level of anti-ROS transcripts in the host, and not owing to its symbiont being more adapted to heat stress. To de-convolute the role of the host further, we performed inter-strain transcriptomic and proteomic comparisons, focusing on a set of 98 genes with oxidative stress-related functions (electronic supplementary material, supplemental dataset S2). We found that CC7 consistently had a higher abundance in antioxidant transcripts under control conditions compared to RS and H2 (24/20 and 17/12 respectively). When exposed to temperature stress, CC7 had even more transcripts at significantly higher abundances compared to both other strains.
Waste and biomass valorization can be achieved by biological degradation processes. The resulting biogas can be transformed into liquid fuels or chemicals via reforming processes. This paper aims to study the thermodynamic equilibrium of methane reforming with different oxidants: CO 2 , H 2 O, and O 2 at atmospheric pressure using FactSage software (6. 3. version). The reaction temperature plays crucial role in all cases. High methane conversion together with high selectivity in syngas (H 2 and CO) can be only obtained above 750°C. An excess in oxidants is also required to limit the formation of solid carbon. Taking into account the fact that biogas usually contains more CH 4 than CO 2 , steam addition to biogas reforming medium is recommended to get high methane conversion and to increase the molar ratio of H 2 /CO, which is favorable for liquid fuels or chemical production via Fisher-Tropsch synthesis, methanol synthesis or hydrogen production.
II. METHODS The determination of the equilibrium composition of a given system (mixture of CH 4 and oxidants) is important before any experimental study. In this work, thermodynamic behavior of different initial gas mixtures of CH 4 , CO 2 , H 2 O, and O 2 was performed using FactSage software (6. 3. 1 version). The calculation is based on the principle of the Gibbs free energy minimization method. For each calculation, the starting composition of the mixture of CH 4 , CO 2 , H 2 O, and O 2 , as well as the temperature and pressure of the considered system were well-defined. The formation of liquid hydrocarbons was neglected. Only solid carbon (C s ), water, hydrogen and carbon monoxide were considered as products from methane reforming processes.
Overview Our proposed approach mb-CW-PLS (micro-batch Clusterwise Partial Least Squares) has the following properties: • Nested clustering: we combine the PLS with two levels of clustering (macro-and micro-clustering) and microbatch optimization approaches to create a new Clusterwise method that can find the underlying structure of the observations and provide each macro-cluster of observations with its own set of regression coefficients. • Micro-Batch processing (divide and conquer strategies): rather than move a single observation from a macrocluster to another to calculate the regression models in a cross validation approach, we move an entire microcluster of points, which we call the micro-cluster shift. These micro-clusters are computed using the k-means clustering, where k is set as the ratio of dataset size to the desired micro-cluster size.
Implementations specificities In order to get an efficient distributed implementations, we decided to apply the Clusterwise logic through Spark framework. As explained previously in order to apply a regression in the faster way, data should be inside the machine which perform linked operations. Then rather than to distribute piece of data through nodes in order to avoid shuffling, we put the entire dataset on each node using the sc. broadcast(dataset) function. Once this step done, we distribute simultaneously every initializations which correspond to CV×INIT homogeneously over nodes. It allows to each node to perform sequentially their CV×INIT nodes Clusterwise initializations, via a mapPartitions functions where the number of Spark partitions is CV × INIT. The Map part achieved we aggregateByKey obtained results where the Key is a Cross-Validation index.
For our experiments different datasets listed in Table  1   The evolution of the RMSE in Table  3  with the value of micro-cluster size N k shows that we can efficiently decrease the RMSE with our approach (mb-CW-PLS). We observe a property of Clusterwise methods that increasing number of macro-clusters G reduces the RMSE score. On the other hand, a lower G allows to use larger values of N k. In Table  3 , N/A entry indicates that we cannot execute the algorithms under these conditions due to risk of generate empty class which will falsify quality of results. First we seek for the optimal number of macro-clusters G, then we search for the optimal size of the micro-clusters (buckets) N k. The first performance criterion is the Root Mean Square Error of prediction as evaluated with a ten-fold cross-validation procedure.
The number of clusters K in the k-means clustering is K = N/N k , the number of cross validation classes is CV = 10, the number of initializations is INIT = 20 and the number of k nearest neighbors for PLS model assignation is 20. Note that for N k = 1 is equivalent to the standard CW-PLS. Figure  2  illustrates results with the three output dimensions Y from SimData dataset. We observe that the predicted values (in red) are closer to the true values (in blue) for the mb-CW-PLS than with CW-PLS. The corresponding RMSE is referenced in Table  3.
Comparison to existing regression methods To compare our PLS and mb-CW-PLS regressions to other regression methods, we took the experimental data sets with a single response variable (q = 1). We then fit models for the OLS, Ridge regression, LASSO and Random Forest using code from the Smile library (https://haifengl. github. io/smile/). 500 replicates of 90% random sampled are taken as training data, then the RMSE based on the remaining 10% test data is computed. The RMSEs are shown in Table  2. We observe that PLS provides at least as well the the OLS, Ridge regression and the LASSO, and less well than the Random Forest for the Forest Fire, Yacht Hydrodynamics and Wine Quality Red. The proposed mb-CW-PLS outperforms these methods, in some cases by a substantial margin, in terms of the RMSE.
Comparison of prediction accuracy and execution times Table  3  shows the RMSE scores and the execution times for CW-PLS and mb-CW-PLS. Let recall that CW-PLS is equivalent to mb-CW-PLS with N k = 1. Figure  4a  illustrates some of these results indicating that the higher we set G the better can be our results with a smaller standard deviation. This result is partially intuitive in that sense that the more specialized cluster we build, the more effective will be model build on them. The counter part of this strategy is a risk of overfitting. For most datasets, mb-CW-PLS presents similar RMSE scores to classical CW-PLS ones. Indeed execution times is much faster as Figure  3c  highlight it, especially on bigger datasets. The micro-batch processing which produces the micro-clusters allows these decrease in execution time without sacrificing too much of the prediction accuracy as it is exposed on Figure  4b-4c.
Discussions and conclusion This work present a new Clusterwise PLS regression algorithm, which brings multiple improvements. First, the micro-batch processing facilitates a drastic reduction in execution times, keeping the same magnitude of prediction accuracy. Second, the distributed implementation enables the test with a large number of initializations in order to find the optimal cross-validated model. One key aspect of the algorithm is the number of regressions needed for one complete run which is 2 × G × CV × INIT × N ≈ 1000 × N leading to a quadratic time complexity. Our micro-cluster approach reduce exclusively the number of regression from N to N N k , but we still compute PLS on the whole dataset preventing better time performance. In our future works we will examine methods to decrease the execution time and also to increase prediction quality.
Unfortunately, an inevitable bottleneck is the impossibility to decrease the inner complexity of a regression and Clusterwise strategy is very regression greedy. One solution we desire to investigate will be to reduce to a computable size datasets keeping their inner structure in order to train the Clusterwise model. We have engage this work but due to number of existing sketching methods we need to further testing to find an optimal solution. Another important aspect holds in the affectation of a new observation to its closest macro-cluster which is solved here by the k-nearest neighbors. However others methods can be tested as for example comparison between the observation with macro-clusters centroid which is faster than k-nearest neighbors but potentially less accurate. As explained previously, an important number of initializations is required to guaranty quality due to their random nature.
CoAP provides (i) proxy (and cache) support; (ii) observation support, which means a client can receive notifications when a resource changes; (iii) resource discovery; and (iv) group communication (through multicast support). Resource discovery is not used in this comparison since it is not in the scope of this document  11. CoAP has a 4-byte header, and two more bytes will be used for the Content-Format option. About the security in this comparison, we have chosen AES 128 bits in Counter CBC-MAC (CCM) mode which is an Authenticated Encryption with Additional Data (AEAD) algorithm with 64 bits of integrity checking value (ICV). Kerberos and SSH-based architectures do not have this algorithm. SSH may be used with another AEAD algorithm such as AES 128 GCM  [15] , but there is no AEAD algorithm for Kerberos, which can instead be used with AES 128 bits in ciphertext stealing (CTS) mode along with keyed-hash message authentication code (HMAC) SHA256 for integrity verification, requiring 16 bytes of integrity checking value.
Keeping a context per client (on the resource server) allows a unique security association between two entities. Such context is composed of the current sequence numbers, negotiated algorithms, current data encryption keys and other pieces of information required for a secure communication. There is no specific context between the client and the resource server in OSCAR since the encryption key of a resource type is the same for every client. The MQTT-SN architecture has a dedicated authentication server on a powerful device, leveraging the cost of authentication on the resource server that then only needs to authenticate itself to the authentication server. End-to-end secure communication means that the data is secured from the endpoint that holds the data to the endpoint that wants it. For example, the data can be encrypted by the resource server and can only be decrypted by the client (intermediaries cannot decrypt it).
Notes on message counts: msg count at first connection (RS) only includes the messages from the connection, not the actual request and response. A DTLS connection implies at least 10 messages (without certificates) and up to 14 messages before any data being transferred. When the resource server is expected to exchange messages with an authentication or authorization service, this communication channel is considered already open (no DTLS connection), this is why ACE OAuth introspection only adds 2 messages. General aspects. Specific application architecture criterion means that the architecture is bound to a specific application protocol. This is the case for MQTT-SN that defines its own application protocol for instance. Finally, resource discovery mechanism provided directly by the architecture allows a client to save some time and energy finding resources.
The most suitable architectures in this context are the ones harvesting the data in a cache proxy before being requested by users (OSCAR, MQTT-SN): there is no authentication between users and resource servers (fewer generated messages, less memory constraints on resource servers, fewer connections to maintain). Architectures without a cache are the less suitable. Parameters such as the use of asymmetric cryptography and the number of relations between domains are taken into account to choose the right architecture, from the most suitable to the less: ACE DCAF (no asymmetric cryptography), ACE OAuth (no asymmetric cryptography unless DTLS is used to contact RS), then OSCOAP (requires asymmetric cryptography, but only two messages are required per request) and finally DTLS and IPsec which both require asymmetric cryptography.
Deployments with actuators Requesting a data from a resource server is no different from sending a command to an actuator for all architectures except MQTT-SN which does not use CoAP. MQTT-SN is poorly suitable for deployments with actuators since clients are not expected to directly send messages to resource servers. MQTT-SN is designed as a pub-sub system for data harvesting and not as a request-response system: clients ask to receive updates of a resource, they do not send a value. Thus MQTT-SN cannot be used in a deployment with actuators as-is, resource servers with actuators need to register themselves as a client of a topic to the proxy, and vice-versa for the client in order to receive messages, this limitation implies more control messages.
Deployments with heterogeneous network Devices communicating with each other need to use the same cryptographic algorithms: this can be achieved with a negotiation or with a prior agreement for example. Any architecture using a negotiation protocol (DTLS, IPsec) is suitable for heterogeneous networks (eponymous architectures, ACE DCAF, MQTT-SN). OSCAR and OSCOAP require a prior agreement, which may be cumbersome with large networks of devices communicating with each other. ACE OAuth also requires a prior agreement between clients and the authentication entity unless DTLS or EDHOC are used. Besides the security perspective and constraints on devices, MQTT-SN is unsuitable for deployments with heterogeneous softwares working with different representations of the same resource type since there is no negotiation of the resource type representation.
Observations and data reduction We used CanariCam  (Telesco et al. 2003) , the mid-infrared (7. 5 -25 µm) imager with spectroscopic capabilities of the Gran Telescopio CANARIAS in La Palma, Spain. Although the GTC has an equivalent 10. 4-m primary mirror, the diffraction limit achievable with CanariCam is set by a cold circular pupil stop equivalent to 9. 4-m used to optimize the sensitivity. CanariCam holds a Raytheon 320x240 Si:As detector which covers a field of view   1. The observation sequence consisted in the observation of a PSF reference star, followed by the science object, followed again by a second and different PSF reference star. The positions in the sky of the calibrators were chosen to minimize the difference in the parallactic angles at the time of observation. This ensures a similar image quality for the science and calibration targets.
The precipitable water vapor was measured between 7 mm and 8 mm, which is suitable for good-quality observations in the N band. Low-resolution spectroscopic observations were obtained on July 1, 2015, under good weather conditions with ∼0. 8 ′′ seeing and 10 mm precipitable water vapor. The spectroscopic calibration was obtained by observing two different Cohen standard stars before and after the science target. The 0. 36 ′′ slit was used with a 6 ′′ throw. The IDL pipeline iDealCam  (Li 2014) , which was customdesigned to reduce imaging data of Canaricam, was used for the data reduction. After removal of the thermal background, the pipeline produces individual frames (or saveset) of ∼2 s duration that can be combined in a long integration sequence. Savesets can be realigned along their centroid through two-dimensional Lorentzian 3  fit of the PSF prior to the final shift-and-add stacking.
This allows reducing centering and tip-tilt errors that may otherwise lead to unwanted broadening of the PSF. Despite observing in the mid-infrared range with a good ∼0. 5-0. 6 ′′ average seeing, the atmospheric turbulence above a 10-m class telescope still degrades the image quality, resulting in a non-fully diffraction-limited PSF. Depending on the instantaneous strength of turbulence, the image quality of individual savesets can be significantly affected, which appears in the form of distorted and elongated PSFs. For all our targets, we visually inspected each saveset of the CanariCam dataset and discarded the most corrupted images, that is, where the PSF clearly departs from circular shape. We note that in order to avoid biases by selecting only the "best" images, we applied the same visual criterion for frame selection to both the PSF reference and the science targets.
Description of the disk model We developed radiative transfer disk models for HD 179218 aiming at constraining simultaneously the spectral energy distribution (SED) and the imaging data on the source. We used for this purpose the well-established Monte-Carlo code RADMC3D  (Dullemond & Dominik 2004 ) that permits one to synthesize disk images and SEDs. Assuming a disk in vertical hydrostatic equilibrium and perfect gas/dust coupling, the dust density in g. cm -3 is modeled analytically according to ρ(r, z) = Σ(r) H(r) √ 2π exp        - 1 2 z H(r) 2        , (3) where ρ(r, z) is described via the parametrized dust surface density Σ(r)=Σ out (r/r out ) p and dust scale height H(r)=H out (r/r out ) (1+β) , and p is the surface density exponent and β the disk flaring exponent. The subscript out refers to the outer radius of each disk component considered.
2 and were not done in the PAH filters. In a second step, our radiative transfer modeling helped to investigate the nature of the resolved emission. Fitting both the SED data and our imaging data with a model containing a passive disk suggests that the dust thermal emission alone would not be resolved by our CanariCam observations. We suggest that the detected resolved emission is caused by PAH molecules UV-excited by the central star and located near the surface of the flared disk. An interesting comparison can be advanced here with HD 97048, a notable Herbig star for which the PAH emission at 8. 6 and 11. 3 µm is resolved out to several tens of astronomical units  (Lagage et al. 2006). Doucet et al. (2007)  estimated the disk diameter D d of HD 97048 -in the sense of the quadratic subtraction used above -to be ∼40 au at a distance of 180 pc, which is slightly larger than for HD 179218.
6, 11. 3 and 12. 5 µm and found values consistent with published flux densities and without noticeable variability within the measurement errors. The CanariCam low-resolution spectrum matches quite well the shape measured by Spitzer, except in the region of the Earth's ozone band where the spectral calibration is found to be unreliable. -Importantly, the combination of our imaging data with radiative transfer modeling suggests that the spatially resolved emission at 8. 6 and 11. 3 µm is not of thermal equilibrium nature but may originate from UV-excited PAH molecules located at the surface of the flared disk. By taking into account the relative flux ratios between the PAH and thermal component, we find that our observations are best reproduced with a model of PAH "disk" extending out to the physical limits of the dust disk model.
Acknowledgements. This work is fully supported by the  University of Cologne , the  Ministry of Higher Education and Scientific Research in Iraq (MoHERS) , the  Bonn-Cologne Graduate School of Physics and Astronomy (BCGS)  and the  University of Baghdad. We are thankful to all members of the  Gran Telescopio Canarias (GTC) telescope  for their support during the observations. We would also like to express our gratitude to the  Sky Team of the IAC  for providing us with the seeing data. We thank the anonymous referee for assessing the quality of this work.