dataesr/scientific-paragraphs-categorization huggingface dataset:
Further examination of the CBS data revealed that the quality of reporting was not satisfactory. Not all variables are consistently reported, and depended on district and patient type. For example, the case identification number, a critical value utilised for patient identification for further investigation, was reported in only 59% of cases. Thus, almost half of all reported cases in Ngami, Chobe and Okavango districts were missing this essential information. Without the case identification number, tracking and potential epidemiological linkage of cases is not possible, making elimination challenging, as it is difficult to know which cases notified in the CBS are index cases and which are secondary to the index cases. Correcting this pivotal error in the reporting system will require significant support and follow-up at the provider level.
Ngami district had more missing data than Okavango and Chobe districts. This was surprising, as Ngami has fewer cases than the other two districts. We would argue, however, that reporting in Ngami district is thus even more important, and that this district should be prioritised for receiving targeted enhanced support. The findings from this study may have resulted from the fact that the IDRS and CBS systems are operated under the MoH Department of Public Health by different units, and therefore have different implementation mechanisms. The CBS has not yet been adequately appreciated by health workers, given its recent application countrywide, and health workers may not understand the importance of reporting through both systems, which they may see as duplication. Health workers may not be adequately trained in completing the CBS forms and, finally, the reporting forms may be too cumbersome.
Our findings are consistent with a 2013 study from Botswana, which concluded that to accurately monitor progress towards the goal of elimination, the NMP should strengthen the reporting and capturing of data at household and individual levels. 10 However, no similar studies have been conducted in Africa, and other studies have focused primarily on the timeliness of reporting in an elimination programme and not on the completeness or quality of information. The missing parameters are key to identifying detected cases and ensuring that they are epidemiologically linked. These findings have serious programmatic implications. First, frequent data audits are required to address the discrepancies between the IDSR and CBS data. Second, the NMP needs to conduct intensive training countrywide, with emphasis on correct form completion and exploring options for e-reporting formats, which might lead to improved compliance.
Proteogenomics approaches use customized protein databases and mass spectrometry (MS)-based proteomics to detect translated proteins  (3) (4) (5) (6) (7) (8) (9) (10). Both methods have revealed prevalent translation of ORFs outside of annotated CDSs and of out-of-frame ORFs (altORFs)  (2, 11). These findings call into question the concept of the single CDS in eukaryotic mRNAs  (12). In addition, they also highlight the need to redefine translated sequences  (13) , modernize functional genome annotations with shorter ORFs  (14) , and reassess the translation output of protein codinggenes by considering smaller proteins in addition to larger canonical proteins. The cellular stoichiometry of a canonical protein versus a small protein encoded in the same gene and their respective concentrations is unknown.
nanoLC-MS/MS Analysis Instrument Setup-A total of 12 l of peptide mixture was loaded onto a trap column (Acclaim PepMap100 C18 column, 0. 3 mm id ϫ 50 mm, Thermo Fisher Scientific) at a constant flow rate of 4 l/min. Peptides were separated in a PepMap C18 nano column (75 m ϫ 50 cm, Thermo Fisher Scientific) using a 0 -35% gradient (0 -215 min) of 90% acetonitrile, 0. 1% formic acid at a flow rate of 200 nL/min followed by acetonitrile wash and column re-equilibration for a total gradient duration of 4 h with a RSLC Ultimate 3000 (Thermo Fisher Scientific, Dionex). Peptides were sprayed using an EASYSpray source (Thermo Fisher Scientific) at 2 kV coupled to a quadrupole-Orbitrap (QExactive, Thermo Fisher Scientific) mass spectrometer. Affinity Purification and MS Analysis (AP-MS)-For preliminary AP-MS of GFP-tagged constructs, the mass spectrometer was used in data dependent acquisition mode (DDA).
For endogenous CV analysis, most quantifiable precursor to fragment transitions were measured for each peptide within a 3-ppm precision window, and two replicates were compared. For absolute quantification experiments, protein concentration was determined by comparing the ratio of the endogenous peptide to spiked-in AQUA standard and its concentration with the same precursor to fragment transitions within a 3-ppm mass precision window. Peptide ratios were kept below 25. Spectral similarity was controlled by importing RAW files into Skyline and peptides were validated if their spectral contrast angles  (39)  or ratio dot products were close to 1 as well as their retention times matching AQUA standards. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (40) partner repository with the data set identifier PXD008147.
Determination of MiD51 and altMiD51 Proteotypic Peptides-In addition to the canonical CDS (Consensus CDS CCDS13995; RefSeq NM_019008. 5; Ensembl ENST00000 325301) and associated protein (RefSeq NP_061881, UniProt Q9NQG6; Ensembl ENSP00000327124), human MIEF1 contains a functional and recently annotated altORF (GenBank HF548110) coding for a small protein (Uniprot L0R8F8; Gen-Bank CCO13821. 1; Ensembl ENSP00000490747) (Fig. 1A  and 1B ). Thus, MIEF1 is clearly a prototypical dual-coding gene for which the absolute quantification of the large and small protein products is unknown. We evaluated the ability of MiD51 and altMiD51 to generate proteotypic peptides after trypsin digestion. Proteotypic peptides are specific for each protein and they must be consistently detected with excellent quality precursor and fragment mass transitions  (49 -52).
To facilitate the detection of specific tryptic peptides for both proteins, we used affinity purification coupled with mass spectrometry. MiD51 GFP and altMiD51 GFP were independently overexpressed in HeLa cells. Both proteins were affinity purified and analyzed via data-dependant (DDA) nano capillary liquid chromatography mass spectrometry (nanoLC-MS/ MS). Several proteotypic peptides were detected for a total sequence coverage of 68. 6% and 71. 9% for altMiD51 and MiD51, respectively (Fig. 1B  and supplemental Data S1). After manual evaluation (see method section), best quality proteotypic peptides were selected for parallel reaction monitoring (PRM) optimization  (38, 53). PRM Optimization for MiD51 and altMiD51 Proteotypic Peptides-Selected proteotypic peptides were then validated in low-sensitivity PRM experiments in 3 kDa FASP processed samples  (29).
1B ). A total of 5 y ion transitions starting from the most N-terminal amino acid were measured and both peptides displayed at least one quantifiable transi-tion within a range of 40 amol -250 fmol and a CV Ͻ 20% (supplemental Fig. S4 ). Absolute quantification PRM experiments were performed by spiking AQUA peptides with trypsin into the digestion mixture as described by  (30). After desalting and dephosphorylation with CIP treatment, the resulting peptides were processed using a high sensitivity PRM method  (38). For each peptide, retention times for the corresponding native and AQUA species as well as spectral contrast angles or ratio dot product  (39)  were controlled to ensure correct identification (Fig. 2A , 2B and supplemental Fig. S6 -S12 ). The absolute amount of native peptides were thus determined (supplemental Data S2).
Absolute Amounts and Ratio of altMiD51 to MiD51-Absolute quantification performed in HEK 293, HeLa and colon tissue indicate that altMiD51 is the most abundant protein product of MIEF1 (Fig. 4A , supplemental Data S2). We compared absolute quantities of altMiD51 and MiD51 in HEK 293, HeLa and human colon tissue samples to determine their stoichiometric relationship. The stoichiometry indicated that the most abundant translation product from MIEF1 is alt-MiD51 rather than the canonical MiD51 protein. The ratio of altMiD51 to MiD51 is 2. 71 in HEK 293 cells, 5. 73 in HeLa cells, and 2. 62 in Human colon tissue (Fig. 4B  and supplemental Fig. S13 ). This observation is consistent with our analysis of ribosome occupancy with data extracted from GWIPS (supplemental Fig. S14 ; supplemental Data S3).
Thus, all efforts to find the physiological function or role in the pathology of a specific gene are invariably focused on the protein encoded by this CDS, or one of its variants generated by alternative splicing. Second, in the absence of annotation of non-canonical ORFs, the protein sequence of the corresponding proteins cannot be routinely detected by MS-based proteomics approaches which rely on current protein databases containing the sequences of canonical proteins only. Third, the widely used Western blotting technique relies on specific antibodies, but antibodies have been raised and commercialized for canonical proteins only. Raising novel specific antibodies may take time and several attempts, thus delaying the investigations on small proteins. Fourth, the detection of small proteins by MS-based proteomics is more challenging than for large proteins.
Typically, the proteome has to be fractionated to enrich low molecular weight proteins, and the identification often relies on a single tryptic peptide  (5, 9). In addition, there may be no sites for trypsin digestion and peptides exceeding 25 aa are rarely identified in bottom-up proteomics. Fifth, because they are short, small proteins are less likely to have known protein domains discovered in large proteins, or to display a specific structure. Thus, there might exist a biased perception that small proteins have minor functions compared with large proteins in biological mechanisms. Yet, many small proteins have essential functions in prokaryotes and eukaryotes  (14, 61). AltMiD51 was integrated into the automatically annotated UniProtKB/TrEMBL database (identifier L0R8F8) in March 2013, following its detection under the name altSMCR7L (3).
It was integrated into the manually annotated UniProtKB/Swiss-Prot database in March 2017. Like altMiD51, which is now a manually annotated bicistronic gene, it will be important to update genome annotations according to recent proteogenomics studies  (14, 24). Indeed, the function of a dualcoding gene should not be inferred according to the molecular activity of the larger protein product only. In addition, the impact of mutations on gene function should not be analyzed in the conceptual frame of a single CDS, because mutations outside currently annotated CDSs may affect noncanonical ORFs and ultimately, gene function  (62). Finally, our results clearly demonstrate that knocking out the canonical CDS in a gene and leaving altORFs unnaltered does not completely abrogate the translation output of that gene.
Fault diagnosis presents a considerable difficulty to human operators in supervisory control of manufacturing systems. Implementing Internet of Things (IoT) technologies in existing manufacturing facilities implies an investment, since it requires upgrading them with sensors, connectivity capabilities, and IoT software platforms. Aligned with the technological vision of Industry 4. 0 and based on currently existing information databases in the industry, this work proposes a lower-investment alternative solution for fault diagnosis and problem solving. This paper presents the details of the information and communication models of an application prototype oriented to production. It aims at assisting shop-floor actors during a Manufacturing Problem Solving (MPS) process. It captures and shares knowledge, taking existing Process Failure Mode and Effect Analysis (PFMEA) documents as an initial source of information related to potential manufacturing problems.
First, the existing company Process Failure Mode and Effect Analysis (PFMEA) records  [14]  are stored in the case base of the Case-Based Reasoning (CBR) system  [15]. Second, the company's Product Lifecycle Management (PLM) repository contains the PPR data. The Case-Based Reasoning (CBR) system is used to search for and retrieve information concerning similar problems. The similarity determination between the current problem and the problems contained in the case base depends on the PPR data stored in the PLM repository. The architecture of the system is based on SEASALT (Shared Experience using an Agent-based System Architecture LayouT)  [16] , which is a multi-case-based, domain-independent reasoning architecture for extracting, analyzing, sharing, and providing experiences. Camarillo et al.
3. Combining the input from the user and the data collected from the PLM repository, the system creates a global query to search for possible solutions (S2). 4. The system distributes the global query among its agents  [16]  that look for the most similar proposals out of their own case bases by applying CBR. The 10 most similar proposals coming from the agents are presented to the user. Initially, only the proposed containment actions and problem causes are displayed (S2).
6. Once the possible root causes are identified, the system provides the related proposals for corrective and preventive actions (S3). 7. As part of the lessons learned step, the user gives feedback to be analyzed by a Knowledge Engineer. When appropriate, the Knowledge Engineer will update the CBR subsystems to extend the case bases (S4, S5, S6). 1. The user inputs a basic description of a manufacturing problem into the system (S1). 2. Based on the user input, the system searches and collects context information related to the problem from the PLM system repository. The result is shown to the user (S1). 3. Combining the input from the user and the data collected from the PLM repository, the system creates a global query to search for possible solutions (S2). 4. The system distributes the global query among its agents  [16]  that look for the most similar proposals out of their own case bases by applying CBR.
[20]  and Reus et al. [21]. Chiu et al. [20]  propose the use of CBR together with genetic algorithms to enhance dynamic weighting and the design of non-similarity functions. With this approach, the proposed CBR system is able to achieve superior learning performance. As in the previous case, the approach presented in this paper remains at a simpler AI level, but proposes knowledge sharing among different MPS units. Reus et al. [21] , as in this work, propose the use of SEASALT as a multi-agent architecture to share knowledge among multiple units. Nevertheless, the use of extended context-related information to enrich the similarity calculation is not addressed. Therefore, the link to a PLM system to enrich the similarity calculation and the search for solutions is outside its scope. The next section introduces the developed information models and their link to the data sources, with special focus on the data related to PFMEA and the PPR concepts to be supported by the PLM system repository.
Information Models and Sources of Data The proposed knowledge-based system uses data from four different sources (Figure  3 ):  User query: through the developed Graphical User Interface (GUI), the user introduces a rough definition of the problem that occurs in the production facility. Taking the Kepner-Tregoe method  [22]  as a reference, the user should provide an answer to the questions 'What?' (a brief description of the problem), 'When?' (date and time), 'How often?' (frequency), 'Where?' (this question is divided into three different attributes related to the line and station where the problem happens and the product that is being produced), 'Who?' (operator name), and 'Why?' (a brief description of why it is a problem).  Problem context: based on the user query, the system receives from the PLM system repository multiple details about the associated context of the problem.
Based on the input description, a GUI agent will send a query to the PLM system to receive the contextual information related to the data input by the user. Then, a comprehensive query, comprising both the user's input data plus the retrieved context data, is sent to the CBR system (Coordination Agent). The Coordination Agent will need to collect a first set of possible solved cases. The Coordination Agent communicates with the different topic agents to request proposals for similar problems. The case base of each topic agent needs to be populated with an initial set of cases (i. e. , already solved problems). To do so, the company's PFMEAs are taken as the initial set of cases. The case base will be continuously extended with new solved problems. A solved problem is analyzed by an expert to decide whether it is included as a new case in the CBR case bases or not.
A deeper insight to this process is given in the next sections. The next subsections focus on the developed information models used to collect cases from PFMEA and to collect context information from the PLM system. Figure  3  shows the top-level view of the knowledge-based system. The user manages the system through the GUI. The GUI represents a Problem-Solving Sheet (PSS) divided into the corresponding areas of the 8D method. The user inputs the description of the problem through the GUI as described at the beginning of this section. Based on the input description, a GUI agent will send a query to the PLM system to receive the contextual information related to the data input by the user. Then, a comprehensive query, comprising both the user's input data plus the retrieved context data, is sent to the CBR system (Coordination Agent).
PFMEA Data The initial source of cases for the knowledge-based system is the PFMEA documentation of the company. Figure  4  shows the information mapping between PFMEA and the knowledge model of the system  [5]. As is shown in this figure, the PFMEA does not contain all the needed information to match totally the knowledge model of the system. Thus, the support of experts and the PLM system of the company are needed to translate the identified failures from the PFMEA into useful cases for the knowledge-based system. This situation derives from the fact that a PFMEA document represents a detailed analysis of all possible modes of failure associated with a specific production process  [14]. However, the PFMEA document does not contain explicit technical information about the context of the process under analysis, and that information needs to be found in other documents associated with the PFMEA.
• The context information is retrieved based on the query defined by the user. As was mentioned in Section 3, the user inputs data about the affected production line, the station, the product, the user, and the date when the problem happened. Taking these data, the PLM system needs to access all the context-related data. This means that the system needs to move automatically from element to element, collecting as much data as possible. For this reason, specific relationships to link all the related items in the PLM system have to be created. In the case study presented in this work, the selected company had no prior PLM system available. The software Aras Innovator (version 11. 0) was selected as a PLM system and its installation and customization were part of the case study. On top of the two customization activities defined above, an experimental novel PLM information model was developed with the aim of accelerating the retrieval process of context data.
Analysis of Double-Stranded RNAs by High-Throughput Sequencing and Completion of the PM14 Isolate Genome Sequence Double-stranded RNAs (dsRNAs) were extracted from symptomatic leaves of PM14 according to the protocol of Gentit et al. [14]  with the modifications introduced by Candresse et al. [15]. The purified dsRNAs were then reverse-transcribed, randomly amplified, and submitted to Illumina sequencing in a multiplexing scheme as described by Candresse et al. [15]. After demultiplexing and quality trimming steps, the reads were assembled using CLC Genomics Workbench 6. 5 (http://www. clcbio. com) or a pipeline integrating the Newbler assembler. Contigs were then annotated by BlastN and BlastX comparison with the Genbank database (cut-off e-value = 10 -3 ). Mapping on reference genomes allowed the ordering of the contigs and the reconstruction of a unique scaffold spanning the viral genome.
Phylogenetic Analyses Multiple alignments of nucleotide (nt) or amino acid (aa) sequences were performed using the ClustalW program  [18]  implemented in MEGA version 6. 0  [19]. Genetic distances (p-distances using strict nt or aa identity) were calculated using MEGA 6. 0. Phylogenetic reconstructions were performed using the neighbor-joining technique and randomized bootstrapping (1000 replicates) to evaluate the statistical significance of branches.
These contigs, tentatively identified as belonging to a capillovirus, were manually assembled by mapping to CVA genome, in order to create a scaffold of 6542 nt missing the 5 and 3 genome ends and containing five internal gaps. The genome sequence of the PM14 isolate was completed by targeted PCR as described in the Materials and Methods section. The assembled complete genome sequence was deposited in the GenBank database under accession number MG783575.
Biological Characterization and Incidence of Mume virus A Despite significant efforts to transmit MuVA to various herbaceous plants, none of the plant species tested could be successfully inoculated. No local or systemic symptoms appeared on inoculated plants, and RT-PCR assays performed to detect MuVA were all negative. MuVA was, however, successfully graft-transmitted to peach seedlings (GF305 indicator). However, no symptoms could be identified on leaves or in growth habit of the infected peach plants (data not shown). The initial tree in which MuVA was detected showed diffuse chlorotic spots on leaves. However, it is co-infected by a potentially novel luteovirus (currently under characterization), so that it is difficult to assess the role of MuVA in the symptomatology observed. Moreover, due to the dsRNA sequencing strategy used in the present study, the potential contribution in the observed symptoms of another agent such as a DNA virus or a viroid cannot be excluded.
, 2017)  previously structurally characterized by our group  (Diallo et al. , 2009). This can also explain the modest yield recorded for 1 (37%). Further work is nevertheless in progress to experimentally verify this hypothesis. With regard to compound 1, the C, H, and Se contents, experimentally determined by elemental analysis and inductively coupled plasma-optical emission spectrometry (ICP-OES) assay, respectively, corroborate its composition (see details in the Experimental section). The structure of 1 was definitively solved by an X-ray diffraction study from a suitable single crystal (colorless, prism shaped). Selected crystallographic data and refinement details are reported in the Experimental section. An Ortep view with selected bond lengths and angles (Å, °) is shown in Figure  3.
The 77 Se NMR spectrum recorded for 1 shows a characteristic resonance of selenate derivatives (δ = 1040 ppm, Figure  S5 )  (Diallo et al. , 2017). The electrospray mass spectrum (positive mode) of 1, also measured in methanol, corroborates the previous NMR data and provides complementary indices on its stability in solution. Several intense mass clusters displaying the characteristic isotope pattern distributions of species containing Sn-O-Se moieties are observed in the range of 0-1500 Da (Figure  S6 ). The major mass cluster (100%) is detected at m/z = 634. 8, and can be assigned to [M-OH] +. The calculated spectrum, shown in Figure  S7 , is well in agreement. The second most intense mass cluster at m/z = 454. 8 (47%) can be assigned to [M-OH-(CH 3 ) 3 Sn-O] +. The mass cluster at m/z = 699.