dataesr/scientific-paragraphs-categorization huggingface dataset:
In the north, the climate is Mediterranean transit, marked by seasonal oscillations. The average annual rainfall is estimated at about 600 mm. The minimum rainfall is recorded in the southern regions. It is about 50 mm while the maximum is observed in the Djurdjura massif located in Kabylia and the massif of Edough located a little farther east, where it exceeds 1,500 mm. The study area in this work is located in the north of Algeria, on domain with a radius of 250 km (Fig. 1 ). In Fig. 1 , the red circle shows the domain of radar which coincides with the study area. For this study, MSG/SEVIRI data together with corresponding ground-based radar data are required.
MSG/SEVIRI data The dataset used in this work provided by the SEVIRI radiometer of Meteosat-8 in different frequency bands is collected from November 2006 to March 2007 and November 2009 to March 2010. The MSG is a spinning stabilized satellite that is positioned at an altitude of about 36,000 km above the equator at 3. 4°W. The SEVIRI radiometer gives every 15 min 12 images in the 12 available channels. We selected the channels sensitive to optical and microphysical properties of clouds (optical thickness, droplet size, cloud phase) as well as to the temperature of cloud tops, and those located in the spectral absorption bands mainly affected by the water vapor. These channels correspond to bands: visible (VIS0. 6), near infrared (NIR1. 6), water vapor (WV6. 2, WV7. 3) and infrared (IR3. 9, IR8.
Meteorological radar data The radar data are provided by the ground-based C band radar network of The National Office of Meteorology (ONM). The radar of Setif is installed near to the town of Setif, at 36°11 0 N, 5°25 0 E and 1,700 m of altitude, is one of seven Algerian meteorological radars. This is a Radar AWSR 81C in Cband, its operational frequency is 5. 6 GHz. The displacement in azimuth is between 0°and 360°in continuous and the movement in inclination is of -1°to 90°. Its polarization is linear and horizontal. The effective domain of radar is a radius of 250 km. Meteorological radar data are collected at a temporal resolution of 15 min and a spatial resolution of 1 km in a format of 512 9 512 pixels. Each pixel is coded on four bits. Thus, it consist of 15 classes representing different reflectivity intensities which are all together considered as raining in the comparison with collocated satellite pixels and one class representing no raining.
The physical parameter of the radar is the reflectivity factor, referred to as Z and expressed in (mm 6 /m 3 ). The conversion of reflectivity factor Z into rainfall intensity R (mm/h) is obtained using the Eq. (1) adapted to our Radar and can also be converted into dBZ: Z ¼ 300 R 1:5 ð1Þ The scan interval for both data sets is 15 min. For the spatial comparison the radar data with an original spatial resolution of 1 9 1 km were projected to the viewing geometry of SEVIRI with a spatial resolution of 4 9 5 km in the study area. Because of discrepancies between the SEVIRI data and radar data, due to differences in observation time, parallax errors and collocation errors  (Vicente et al. 2002) , the comparison of these types of data may be hampered. To reduce the imbalances mentioned above and find a better correlation, we performed a repositioning to SEVIRI data to coincide spatially with radar data.
We also applied a resampling to radar data to have the same resolution as resolution of satellite data. The resolution is 4 9 5 km in the study region and is assumed constant due to low overlapped area observed by both sensors. Therefore, each SEVIRI pixel is collocated with 4 9 5 radar pixels. The time lag between the radar and the satellite is about 3 min. This small time difference does not require synchronization between the two data types. Because of the differing information about optical and microphysical cloud properties between day-time and night-time scenes, both data sets are divided into day-and night-time scenes.
Training and testing data generation A representative training data set consisting of the SEVIRI data and corresponding Radar data are needed to develop a multilayer perceptron for the rainfall identification problem. SEVIRI data are applied to the network as the input and the corresponding Radar data are used as the target or desired output (Fig. 5 ). The training procedure for a multilayer perceptron includes two steps, namely forward propagation and backward propagation. The connectional weights are updated during the backward error propagation according to the learning algorithm. This process is repeated until the error between the network output and desired output (radar measurements) meets the prescribed requirement. When the training process is complete, the network is ready for application.
Application The neural network MLP was created using five spectral parameters that were calculated from SEVIRI radiometer to discriminate raining cloud from no raining cloud. It was created with four layers (input, two hidden, and output) that consist of five input neurons, six neurons in the first hidden layer, five neurons in the second hidden layer and two output neurons in the output layer that represent the two classes (raining and no raining) corresponding to radar data (Fig. 5 ). The information about optical and microphysical cloud properties derived from SEVIRI used as input data to Artificial neural networks MLP are given as follows: Information about ''Cloud Water Path (CWP)'' The effective droplet radius r e and the optical thickness s of clouds both represented by a single parameter referred to as CWP are directly related to rainfall probability of a cloud.
The technique we present for the identification of precipitating clouds is based on these assumptions. It is a technique based on optical and microphysical cloud properties that was originally developed and applied to SSM/I data (Special Sensor Microwave/Imager)  (Wentz and Spencer 1998). We will apply it to the radiances measured in different bands of SEVIRI. Reflectances and brightness temperatures are used during the daytime (in the presence of solar radiation) and only brightness temperatures are used during the nighttime.
7-IR10. 8 , DT IR10. 8-IR12. 0 and T IR10. 8 , which give information about the thermodynamic phase and thickness of cloud. The parameters presented previously will be used as input data, and information about rain from meteorological radar as output data (Fig. 5 ). The MPL rain delineation algorithms during daytime were trained using five spectral parameters (Fig. 5a ) and the MPL during nighttime were trained using five spectral parameters (Fig. 5b ). These parameters were computed from SEVIRI dataset during the period November 2006 to March 2007.
Rainfall identification results and performance evaluation Models are validated against independent rainy days during November 2009 to March 2010, not used for training the rain area delineation algorithms. The evaluation was performed by comparison with instantaneous ground-based radar data collocated with SEVIRI data. The aim is to evaluate the potential of MLP algorithm in the identification of precipitation. The observation scenes made by the radar and satellite at a rhythm of 15 min are 15,880, most of which are non-raining situations. To evaluate the potential improvement by the new scheme, the validation scenes were also classified by the Enhanced Convective Stratiform Technique (ECST)  (Reudenbach et al. 2001)  which is similar to the Convective Stratiform Technique (CST)  (Adler and Negri 1988).
Conclusion A new consistent day and night technique for precipitation process separation and rainfall intensity differentiation using MSG SEVIRI data is proposed. It relies on information about the CWP, the CP in the upper regions, and the cloud top height (CTH). This technique is based on the new conceptual model that precipitation is favored by a large CWP and the presence of ice particles in the upper part of the cloud. The technique considers information about both parameters inherent in the channel differences DT IR3. 9-IR10. 8 , DT IR3. 9-IR7. 3 , DT IR8. 7-IR10. 8 , DT IR10. 8-IR12. 1 Together nighttime and daytime, the new algorithm offers the great potential for a 24-h technique for rain area delineation with a high spatial and temporal resolution. The present study has demonstrated the great potential offered by the increased spectral resolution of MSG/ SEVIRI.
Production and purification of proteins The amylase proteins were produced in the yeast Pichia pastoris. The α-amylases genes of D. melanogaster, D. erecta and D. sechellia were cloned into the expression plasmid pPIC3. 5K (Invitrogen TM ) and integrated to the genome of P. pastoris strain GS115 by electroporation. The Amy sequences of D. melanogaster, D. erecta and D. sechellia that were used in this study have GenBank accessions BAB32511, D17728 and D17732, respectively. In D. erecta the known protein sequences (proximal D17727 and distal D17728) are identical. For D. melanogaster, sometimes only one copy is active or both copies give the same protein or different proteins  (Klarenberg and Scharloo, 1986). The sequence we used here belongs to the widespread electrophoretic class AMY1  (Inomata et al.
Determination of kinetic constants Microcalorimetry records the heat generated by the hydrolysis of glycosidic bonds. Different substrates were prepared in 20 mM Hepes, 20 mM NaCl, 1 mM CaCl2 (pH 7. 5) at a concentration of 20 g/l for starch and 5 mM for maltooligosaccharides. The results were recorded on a VP-ITC microcalorimeter (MicroCal, Northampton, MA) at 25°C and analyzed with MicroCal Origin software (version 7).
This has been accurately measured and is available on the website of the National Institute of Standards and Technology Databank (http://xpdb. nist. gov/enzyme_thermodynamics/ enzyme_thermodynamics_data. html). Here, we used the value of -1083 cal/mol, which corresponds to the hydrolysis of a α-1,4 linkage at 25°C. The catalytic constant k cat (s -1 ) was calculated according to the following equation  (D'Amico et al. , 2006) : k cat =(Pr-Pi)M/ΔHrCV where (Pr-Pi) is the energy of reaction in μcal/sec, M the mass of the enzyme in Da, ΔHr is the bond enthalpy (in calories), C the concentration of the enzyme in the syringe (mg/ml) and V the volume of injection (μl).
Thin-layer chromatography The reaction products for starch and maltooligosaccharides (G3 to G7) produced by α-amylase reaction were identified by thin-layer chromatography (TLC). The products were spotted on a precoated silica gel plate (Sigma-Aldrich ® Silica gel on TLC Al foils) and developed with a solvent of chloroform: acetic acid: water (5:7:1). The sugars developed on the plate were stained by spraying 10% (v/v) sulfuric acid in ethanol followed by heating at 130°C for 10 min (adapted from  Kumagai et al. , 2013)  Alignment of primary structures Amino acid sequences and translated nucleotide sequences of α-amylases were extracted from the GenBank database. A multiple sequence alignment, after removal of the signal peptides, was generated by the MUSCLE program  (Edgar, 2004).
Bacillus massilioanorexius (JX101689) Figure  1. Phylogenetic tree highlighting the position of Bacillus massilioanorexius strain AP8 T relative to a selection of type strains of validly published species of Bacillus genus. GenBank accession numbers are indicated in parentheses. Sequences were aligned using CLUSTALW, and phylogenetic inferences obtained using the maximum-likelihood method within MEGA program. Numbers at the nodes are percentages of bootstrap values obtained by repeating the analysis 500 times to generate a majority consensus tree. Clostridium botulinum was used as outgroup. The scale bar represents a 2% nucleotide sequence divergence. Different growth temperatures (25, 30, 37, 45°C) were tested. Growth was observed between 25 and 45°C, with optimal growth at 37°C after 24 hours of incubation.
Positive reactions were observed for D-glucose, D-fructose, Dsaccharose, ribose, mannose, mannitol and Dtrehalose and weak reactions were observed for L-rhamnose, esculine, salicine, D-cellobiose and gentiobiose. Using an API 20E strip (BioMerieux, Marcy l'Etoile), positive reactions were observed for tryptophane deaminase, acetoin and gelatinase production. Negative reactions were found for urease and indole production. B. massilioanorexius is susceptible to amoxicillin, rifampicin, ciprofloxacin, gentamicin, doxycycline and vancomycin but resistant to trimethoprim/sulfamethoxazole and metronidazole. When compared with representative species from the genus Bacillus, B. massilioanorexius strain AP8 T exhibited the phenotypic differences detailed in Table  2. A score enabled the presumptive identification and discrimination of the tested species from those in a database: a score > 2 with a validated species enabled the identification at the species level; and a score < 1.
Genome project history The organism was selected for sequencing on the basis of its phylogenetic position and 16S rRNA similarity to other members of the Bacillus genus, and is part of a "culturomics" study of the human digestive flora aiming at isolating all bacterial species within human feces. It was the twenty-seventh genome of a Bacillus species and the first genome of Bacillus massilioanorexius sp. nov. A summary of the project information is shown in Table  3. The Genbank accession number is CAPG00000000 and consists of 120 contigs. Table  3  shows the project information and its association with MIGS version 2. 0 compliance  [48].
Genome annotation Open Reading Frames (ORFs) were predicted using Prodigal  [49]  with default parameters but the predicted ORFs were excluded if they were spanning a sequencing gap region. The predicted bacterial protein sequences were searched against the GenBank database  [50]  and the Clusters of Orthologous Groups (COG) databases using BLASTP. The tRNAScanSE tool  [51]  was used to find tRNA genes, whereas ribosomal RNAs were found by using RNAmmer  [52]  and BLASTn against the GenBank database. Lipoprotein signal peptides and the number of transmembrane helices were predicted using SignalP  [53]  and TMHMM  [54]  respectively. ORFans were identified if their BLASTP E-value was lower than 1e were identified using the Proteinortho software (version 1. 4)  [55]  using a 30% protein identity and 1e -05 E-value.
Dglucose, D-fructose, D-saccharose, D-trehalose, ribose, mannitol, mannose were used as carbon source. Positive reactions were observed for tryptophane deaminase, acetoin and gelatinase production. Weak reactions were obtained for Lrhamnose, esculine, salicine, D-cellobiose and gentiobiose. Cells are susceptible to amoxicillin, rifampicin, ciprofloxacin, gentamicin, doxycycline and vancomycin but resistant to trimethoprim/sulfamethoxazole and metronidazole. The G+C content of the genome is 34. 10%. The 16S rRNA and genome sequences are deposited in GenBank under accession numbers JX101689 and CAPG00000000, respectively. The type strain AP8 T (= CSUR P201 = DSM 26092) was isolated from the fecal flora of a female suffering from anorexia nervosa in Marseille, France.
Computer-simulated ligand binding (docking) Protein input file preparation. A clean Estradiol receptor a input file (1ERE pdb, referred to in this paper as ER-E2) was generated using the protein preparation wizard from Maestro software (Maestro 8. 5, academic campaign, Schrodinger website. Available: http://www. schrodinger. com, Accessed 2013  March 11). The protein was cleaned by removing water molecules, ligands and subunits b, c, d and f from the original ER-E2 pdb file. Next, the bond orders were assigned, and hydrogen atoms were added. The resulting receptor (ERa) was saved as a PDB file. Ligand input file preparation. The ligand input structure was generated and 3D optimized with MarvinSketch Academic Package (MarvinSketch 5. 10. 0, 2012, ChemAxon website, http:// www. chemaxon. com, Accessed 2013  March 11).
The ligand structures were saved as a mol2 file. GOLD docking protocol. For the study, the binding pocket of the receptor was defined from the crystallographic coordinates of the hydroxyl of estradiol. Dockings were performed using the GOLDScore fitness function under the 'Standard default settings' of GOLD software: a population size of 100, number of islands was 5, number of operations was 100,000, with a niche size of 2 and a selection pressure of 1. 1.
As a control, we measured the level of ERa protein by Western Blot in untreated MCF-7 cells or cells treated with E2 (10 28 M) or ICI (10 27 M) and the different BPs (10 26 M) for 24 h. As shown in Fig. 2. D, the protein levels of ERa is drastically decreased after exposure to ICI but remained similar to the control after the exposure of MCF-7 cells to the different BPs. This indicates that proliferative effects observed upon BP treatments are not due to a consequence of changes in the level of intracellular ERa expression. Altogether, these data are evidence of a proliferative effect of several BP derivatives in the MCF-7 ERa-positive breast cancer line, with the maximum mitogenic potential exhibited by 4BP (Table  2 ).
They complement the main user's guide in the field of registries for evaluating patient outcome  [9]. A natural extension is to determine the metrics that could be used to measure the successful adoption of some of these principles. A review of rare disease literature raises some important questions about RDR. First, there are semantic issues. For instance, what, if any, is the difference between a patient registry compared to a disease registry? How does this relate to a clinical registry? Is there any difference between a disease repository, disease registry, contact registry and a disease or patient database? What is the difference between a research 'cohort' (eg EuroCYST  [10] ) and an audit registry (eg the UK Renal Registry  [11] )? What about their relatedness to national and ethnic mutation databases (NEMBDs) or locus-specific databases  [12] [13] [14] ? Second, what defines successful and sustainable interoperability between registries? Third, does the fact that a RDR system is available and permitted for download satisfy the term open source software or should other criteria be considered? Fourth, does the choice of software environment in which registries are implemented affect the ability of a RDR to be customised, extended or modified for evolving requirements? Fifth, what levels of security are employed, are they appropriate and is it possible to modify permissions and access privileges dynamically according to changing stakeholder needs? There are clearly some pre-conceived notions of disease registry development and in this paper we dispel three of these myths.
Myth 2: professional software developers are not required to develop Disease Registries There is a significant difference between developing RDR that are grounded in professional software development processes versus under-resourced pilot projects that are undertaken to meet discrete internal user requirements with little, if any, engagement with external requirements/stakeholders. Professional software development is a complex undertaking that includes: i) appropriate software project management; ii) team-based software development; iii) well-structured, commented code; iv) version control; v) issue tracking; vi) documentation; and vii) software deployment instructions. In order to ensure value is delivered to the client, skilled software developers need to collaborate with end-users to produce working software which is technically excellent and builds in flexibility for modification should needs change  [17].
Stakeholders undertaking RDR development should consider these issues so they can deliver viable software solutions while mitigating technical risk. In addition, it is instructive to examine a number of important considerations, such as whether the RDR should be a desktop application or an Internet-based application; developed on an open source or proprietary software platform; the use of a relational database management system or an alternative (eg. unstructured) data storage system; and the decision to deploy in a cloud environment or on physical ICT infrastructure. Other considerations are to ensure systems are capable of extensibility, interoperability, and security that are supported in a sustainable way. Once these decisions are made, a critical question becomes whether the chosen professional software development team possess the requisite skills and experience to adequately support the decisions made.
The software development process requires expertise and it is costly and time consuming. It is interesting to note that in a self reported survey, undertaken by TREAT-NMD (http://www. treat-nmd. eu), the costs associated with developing national Spinal Muscular Atrophy registries in more than 30 countries, using a defined set of common data elements, were widely variable with some registries being established with ≤3000€, while others had funds in excess of 250,000€. The median amount of money invested to set up a registry was 20,000€ (Blanden, personal communication 2013).
Modular rare disease registry framework The RDRF has been designed and implemented so that common features can be shared between registries. These common features include common data elements within what is referred to as base modules. Base modules might include: Patient Details, Medical History, Diagnosis Information, Genetic Variation, Working Groups and have agreed Common Data Elements (CDEs) providing conformity/interoperability of the data fields across platforms. Patient Details and Medical History have been devised in consultation with patient advocacy groups. The CDEs conform to international standards such as TREAT-NMD. Base modules can be extended upon and customised for individual registries. For example, diagnostic information is tailored to each registry, since the required information varies significantly.
In a similar way other modules created such as 2 factor authentication secure log-on, web-enabled consent and phenotyping approaches can be 'plugged-in' as required. During the development of the RDRF, via professional agile software development processes, a refactoring process has now created a number of common modules that are shared between individual registries. Because of this flexible modular design and thanks to a collaboration with the Universal Mutation Database team, we will now be able to add specific genetic modules such as: predictions of the pathogenicity of reported exonic  [19]  or intronic  [20, 21]  variations; genotype-phenotype correlations  [22] ; or even methods to facilitate new genotype based therapeutic approaches such as exon-skipping  [23, 24]. The RDRF graphical user interface is also modular so it can be easily customised for a given RD.
A specific example is in the neuromuscular domain where, even as the national NMD registry grew, patient advocates from Spinal Muscular Atrophy (SMA) wanted a different user interface from Duchenne Muscular Dystrophy (DMD) or Myotonic Dystrophy. For SMA, the interface was modified to reflect real-time practice, i. e. it was aligned with how motor function is clinically captured by patients and their clinicians. These nuances were able to be accommodated without the need to modify the underlying architecture of the RDRF. Both national and international RD registries have now been built using this framework and they all have been informed by fundamental stakeholders such as patients and clinicians. The RDRF has been developed to be able to automatically de-identify data when exported. The Australian DMD registry feeds into the TREAT-NMD international registry and additionally, where appropriate, we have designed interoperability to connect the Myotonic Dystrophy registry to both the TREAT-NMD core data and the Rochester Registries with equal degrees of interoperability for data exchange.