dataesr/scientific-paragraphs-categorization huggingface dataset:
Selection of strains The first MRSA strains isolated from each patient in South-West Sweden have been stored in the Culture Collection University of Gothenburg (CCUG; www. ccug. se) since 1983 together with patient epidemiological data. The strains have been identified as MRSA, according to routine laboratory protocols, and have, until 2016, been typed with PFGE. In addition, representative strains of each PFGE-type have been spa-typed until this method was implemented as routine in 2003 (Supplementary information, Table  S1 ). Altogether, 111 strains representing the 19 most common MRSA PFGE-types were selected for construction of a MALDI-TOF MS database consisting of Superspectra specific for individual PFGE-types. These so-called BSuperspectra strains^represented one to six subtypes within each PFGEtype, 60 spa-types, 10 spa-clonal complexes (CC) and 11 MLST-CC (Table  1 ).
The PFGE-type was determined by cluster analysis using the BioNumerics software version 7. 1 (Applied Maths NV, Sint-Martens-Latem, Belgium) with the Dice coefficient for calculating pair-wise similarities and the UPGMA algorithm for constructing dendrograms of estimated relatedness. For creation of the Superspectra, four to ten strains of a particular PFGE-type, but of different PFGE-subtypes, were selected (Superspectra strains, Table  1 ). For the selected strains the maximal variation within each PFGE-type was set at 70% similarity and inclusion of strains with identical PFGE-profile were avoided. For subsequent testing of the performance of the created database, two sets of strains were tested. These so-called Btest strains^(Table  2 ), included 146 strains isolated from routine clinical and MRSA screened samples from patients in the Gothenburg region between October 2014 and April 2015.
Secondly, 109 MRSA strains of different PFGE-types and subtypes were arbitrarily selected from the CCUG MRSA strain collection in order to test a more diverse collection of strains. MLST data were available for 59 of these 255 strains, for the remainder the most common MLST-CC were deduced from the spa-type, using the Ridom Spaserver (www. spa. ridom. de). The 255 MRSA test strains were of 25 different PFGE-types with 1-17 subtypes/PFGE-type, 102 spa-types, 12 spa-CC and 18 MLST-CC (Table  2 ).
Mean (median) number of peaks for the four tested strains for FA-treated and untreated samples was 160. 1 (160) and 160. 3 (164), respectively. MALDI-TOF MS analysis was performed using a VITEK MS RUO instrument (bioMérieux), in the range of 2000-20,000 m/z, with a laser frequency of 50 Hz, an acceleration voltage of 22 kVand an extraction delay time of 83 ns. Spectra were acquired in automatic mode by accumulating 100 profiles of five laser shot cycles each using the auto quality control of Launchpad 2. 9. Spectral data were automatically processed and exported as peak lists for analysis in SARAMIS. An exclusion list with horse blood agar specific peaks was applied to the spectra to avoid non-bacterial peaks within the spectra. The E. coli strain CCUG 10979 was used as a control strain in each run.
Three of the four replicate spectra of each strain were selected for further analysis in each run. First, all spectra with less than 100 peaks were excluded, the remaining replicate spectra of each strain were then compared using the Pearson correlation in the SARAMIS software. Spectra with less than 85% similarity to at least two of the other replicates of the same strain were considered to be outliers and also removed from further analysis. If all four replicate spectra showed ≥85% similarity, as they did in 75% of the cases, the three most similar spectra were selected for further analyses. If three good quality replicate spectra could not be obtained from one run the strain was retested and analysed according to the above procedure; hence, three replicate with ≥85% similarity were always used for further analyses.
Only 2% of all strains tested had to be retested. Creating Superspectra corresponding to PFGE-types MALDI-TOF MS peak lists of all 111 Superspectra strains were imported from SARAMIS into the BioNumerics version 7. 5 software. The peak lists were compared, using the composite dataset function in BioNumerics, generating a pattern of present and absent peaks for each spectrum as shown in Fig. 1. No account was given for the intensity of the peaks. Peak matching was made with a tolerance of 0. 08%. The peak patterns were imported into an Excel sheet in which present peaks were designated as 'one' and absent peaks as 'zero'. The frequency of respective peaks could then be calculated by summing the peak columns individually. The peak frequencies within each PFGE-type of the Superspectra strains, as well as within the whole MRSA population studied, were calculated.
Creating a database of Superspectra corresponding to PFGE-types To use SARAMIS for sub-typing MRSA strains, a database using the Superspectra strains was created, consisting of 19 Superspectra representing the 19 most common MRSA PFGE-types in the region. For each PFGE-type, ten MS peaks were selected, resulting in a combination of peaks in a Superspectrum, which is an artificial spectrum specific for that PFGE-type. The individual peaks comprising a Superspectrum were chosen according to their relative frequencies. For the selected peaks associated with a particular PFGE-type, the frequency of a given peak for strains of the respective PFGE-type varied from 73 to 91% (mean 81%, median 78%), whereas for the whole set of Superspectra strains, the frequencies were 18-42% (mean 31%, median 34%).
Determining threshold peak matching value The matching of a strain's spectra to the created Superspectra was, after fulfilling the initial criteria to generate three replicate spectra (all of which were fulfilling the 85% similarity criteria in the SARAMIS software as outlined in Material and Methods), always dependent on all three replicates. Each replicate spectrum was compared one by one to each Superspectrum in the database. The sum of the weights for each spectrum was calculated by adding the weights of the peaks in the spectrum matching the peaks in the Superspectrum (ranging from 0 to 100, which equals 0-100%). The peak matching values of the three replicates were added together to get the mean peak matching value (%) of the strain to a particular Superspectrum. The definition of a match to a Superspectrum was first set at choosing the Superspectrum with the highest mean peak matching value.
However, this rendered a large number of inconsistent typing results, according to the known PFGE-types. Thus, different thresholds were evaluated using the test strains (data not shown). As a result of this evaluation the final analysis followed the procedure outlined in Fig. 2. If a strain matched only to a single type-specific Superspectrum in the database with a mean peak matching value of ≥65% the strain was considered to be Bidentified^as possibly belonging to this type. If the strain had a peak matching value ≥65% to several Superspectra the threshold was increased up to 80% (however ≥75% was the level generally needed to match a strain to a single Superspectra). If there were more than one possible match above the 80% threshold this was considered BMixed identity^and if no peak matching to a single Superspectrum reached 65% this was considered BNo identity^with respect to any of the 19 Superspectra or MALDI-types, respectively, in the database.
Evaluation and modification of the Superspectra database The accuracy of each Superspectrum using the thresholds as described in Fig. 2  was evaluated in relation to the known PFGE-types of the included Superspectra strains in two steps: first, it was compared to the Superspectra strains used to create it, of the same PFGE-type. Second, all Superspectra strains, regardless of PFGE-type, were analysed. If the Superspectrum failed to identify the strain to the correct PFGE-type or misidentified strains of other PFGE-types to the respective Superspectra, other combinations of peaks were evaluated to generate more specific Superspectra to the respective PFGEtypes. The newly generated Superspectrum was then evaluated with the above described procedure and if better performance was achieved this Superspectrum was used as a PFGE-type specific Superspectrum in the following evaluation.
The five PFGE-specific Superspectra that were included in M7 consisted of 30 peaks (Supplementary information Table  S2 ), of which 18 were present in only one Superspectrum and only two of the peaks were shared among all five Superspectra. In the database the original PFGE-type specific Superspectra were kept and the designation of a strain to a MALDI-type was based on matching of the strain to a PFGE-specific Superspectrum within that MALDI-type.
Evaluation of Superspectra performance with test strains All replicates of the 255 MRSA test strains were first verified to be S. aureus with SARAMIS using standard procedures. As a second step the stored replicate spectra were reanalysed one at a time against the 19 PFGE-type Superspectra in the database and classified into the ten MALDI-types. In total, 172 strains (67. 4%) were assigned to the correct MALDI-types, including 12 strains belonging to a PFGE-type not represented in the database thatcorrectly-did not yield matches to any Superspectrum (Table  3 ). The median value for correct assignment to a MALDI-type was 78% (range 65. 3-100%), whereas the second-best matching Superspectra had a significantly lower value, with a median of 49% (range 23. 3-77. 0%, p < 0. 0001; Fig. 3a  and b ).
Re-analysis of isolates at different occasions corresponded well; no conflicting results were observed (data not shown). For 39 of the 255 (15. 3%) test strains the mean peak matching value to the expected Superspectrum was 61. 7% (range 45. 3-64. 7%) indicating possible similarity, but still below the threshold of 65%, and for 24 strains (9. 4%) the Fig. 2  Workflow of the analysis of the spectra. Three replicate spectra generated with MALDI-TOF MS were selected for analysis of each strain. The spectra were compared to the PFGEspecific Superspectra database generated in this study and the number of matching peaks was compared to the 65%, 75% and 80% thresholds in order to assign the tested strain to a PFGEspecific Superspectra best matching Superspectrum/MALDI-type were incorrect with regard to PFGE-type.
Therefore, these strains could not be assigned to any of the ten MALDI-types, although the expected MALDI-type was present in the database. Only 14 (5. 5%) of the strains were incorrectly assigned to another MALDI-type than the expected based on the PFGE-type of the strain (Table  4 ). Four of these 14 strains were of a PFGE-type not represented among the Superspectra strains and did thus not have a corresponding Superspectrum. An additional three strains were incorrectly assigned to MALDI-types containing strains with that same spa-type, while the PFGE-type was different and another two strains were assigned to MALDI-types (M1 and M4) that shared MLST and spa-types.
The results could be used to urgently determine the need of initiating an outbreak investigation with further typing or not at all. If strain relatedness is suspected, more indepth molecular typing methods are more suitable to determine the clonality of the isolates  [18] [19] [20]. MALDI-TOF MS typing may also function as a quick complementary test when more conventional tests like spa-typing give inconclusive results. In this study we have compared the MALDI-TOF MS typing primarily to PFGE-typing data of the MRSA strains. To compare the Superspectra database with spa-type or MLST would have based the method on more objective grounds. On the other hand, studies have shown that PFGE-typing is a discriminatory typing method in an outbreak situation  [21]. The Superspectra strains were chosen as representative strains of the most common PFGE-types in the region of South-West Sweden and several subtypes of 70% identity or more within the respective PFGE type were included as well.
Other attempts have been made to establish MALDI-TOF MS-based screening methods for MRSA  [14-16, 22, 23]. However, of the 115 peaks used to construct our Superspectra database, only 12 peaks are shared with these other studies. The specificity of our approach is further ensured since the combination of peaks and their individual relative contribution, reflected by different weights, is considered for each Superspectrum. This also allows particular peaks to be present in more than one Superspectrum and thus the number of studied peaks can be enlarged. Biomarkers presumed to be specific for given subsets of strains often lose their specificities when more strains are introduced into the analyses  [24]. Furthermore, as argued by Lasch et al. [25] , it is unlikely that single-marker peaks will be sufficiently discriminatory to identify a given subset of strains.
In this study no effort has been made to identify the corresponding proteins of the peaks or the functions thereof, hence this method could be set up for any given microorganism without any in-depth information regarding the identity of the proteins. However, the risk of not knowing the identity of the proteins include introduction of non-specific peaks, originating from, for example, the growth media. In this study growth media specific peaks were excluded by applying an exclusion list automatically removing horse blood agar specific peaks. We have constructed the PFGE-specific Superspectra in a similar way that the Superspectra of VITEK MS RUO/ SARAMIS is constructed. As for any database, the coverage depends on how generally applicable the data it is built on is. The database presented in this study is only tested using strains from a limited region.
In its current form it may only be applicable to this local setting. To use the method in other settings, strain types present in those localities need to be included and compared to the types of strain in the current database. A database such as this one also needs to be adjusted over time. We used historical data to identify the most common PFGE-types in the region for the past 20 years when constructing our Superspectra. When evaluating the database, some of the PFGE-types for which a Superspectrum was created were less common or absent among the more recently isolated test strains. On the other hand this demonstrates the specificity of our protocol. Low discriminatory power has serious implications as this may hamper or delay discovery of an outbreak. A combination of tests has been suggested to increase the discriminatory power  [21].
In addition, spa-typing has the advantage of better reproducibility between different laboratories and the nomenclature is uniform in contrast to PFGE, and spa-types can more easily be compared worldwide. In this study, only 5. 5% of the test strains were mistyped, i. e. assigned to a different MALDI-type than expected (Table  4 ). Four of these strains were of a PFGE-type not represented among the Superspectra strains and thus did not have a corresponding Superspectrum. Possibly, by including these PFGE-types to form additional Superspectra in the database, this problem could have been solved. It should however be kept in mind that mistyping of MRSA strains using MALDItyping does not automatically lead to final mistyping of the isolate. MALDI-typing is only to be used as a first step in the typing scheme and the clinical background of the samples in an outbreak situation should always be taken into account when the urge for further typing is considered.
Even though setting the threshold is somewhat subjective, once set, the threshold is fixed for all analysed strains. Furthermore, the MALDItyping is only a first step of typing and can be compared between laboratories if the databases are shared. In order to use MALDI-typing for more in-depth typing means that the resolution would have to be increased. More extensive sample preparations have been shown to increase the resolution and enable typing  [5, 6] ; however, this means that one of the major advantages of whole-cell MALDI-TOF MS, the rapid turnaround time from sample to result, would be lost. PFGE and spa-typing are methods capable of detecting substantial intra-species diversity to a degree that MALDItyping may not  [31]. However, these methods are both labour intensive and costly and while results of spa-typing and to some degree MALDI-typing can easily be shared between laboratories, PFGE results cannot.
PFGE may be useful in outbreak situations because it can differentiate strains with minor genetic changes; however, both PFGE and other sequence-based methods have been shown to suggest close relationship between MRSA isolates while they actually differ when it comes to pathogenesis  [32]. Therefore, we suggest the use of MALDI-typing for a rapid screening and when more indepth typing is prompted for, NGS-based methods should be used to get the most accurate epidemiological typing. The MALDI-TOF MS-based typing in this study has been developed using MRSA as a model system and based on PFGE-data. However, the method for building and using the database could be applied to other microorganisms in need of inexpensive real-time epidemiological surveillance. To conclude, in this study we have developed and applied a method for rapid epidemiological screening of MRSA, using VITEK MS RUO/SARAMIS.
d. d (1) n. d. M9 P 10 CC-345/359(14) 80 M10 T 8 CC-448/690(20) 88 CC-790/005(1) 22 CC-202(1), t11550 c (1), n. d. d (1) n. d. Other f 12 t386 e (1) 1 CC-008(2) 8 CC-091(2) 15 CC-012(1) 30 CC-345/359(3) 97 t314 c (1) 121 t034 c (2) 398 t375 e (1) 509 t991 e (2), t15556 c (1), n. d. d (5) n. d. c (1), n. d. d (1) n. d. a The dominating spa-CC (founder spa-type indicated in the table) within each group is marked with bold letters b The indicated MLST-CC was determined by MLST analysis. The reminders were deducted from the spa-type (Ridom spa server) c Singletons d spa-CC not determined e Excluded from cluster analysis due to less than four repeats f Others refer to strains with PFGE-types for which no Superspectrum were present in the database.
3% M5 15% 008 (t304) R The spa-type of the strain is common in both M5 and M7 M7 78. 7% M5 60. 7% 008 (t304) R The spa-type of the strain is common in both M5 and M7 M5 79% M10 14% - T M5 68. 7% M2 42. 3% t272 b I M7 83% M2 78. 7% 790/005 (t790) BK M9 65% M5 57% t127 b R M7 75. 7% M8 17. 7% 008 (t1476) K M7/M9 71/73. 7% M7 71% 345/359 (t657) J Mixed ID M7/M10 65/66% M7 65% 345/359 (t1839) J Mixed ID M5/M9 68. 3/69% n. a. - 091 (t084) - Mixed ID, no Superspectra available in database M5/M7 65. 3/65% M5 65% 008 (t701) R Mixed ID M4/M5 91. 3/86. 7% M4 91. 3% 012 (t012) SM Mixed ID M9/M10 80. 3/97% M10 97% 448/690 (t690) T Mixed ID a Excluded from cluster analysis due to less than four repeats b Singletons.
This paper firstly describes the development and evaluation of a project course which yields university students building a complete hardware and software chain for a digital image processing application. To arouse the interest and learning initiative of students, we propose them to build a setup including a Raspberry Pi and image processing programmes. This inexpensive single board computer answers today's issues in energy saving and permits to review fundamental hardware and software principles. Secondly, we propose a low-cost setup for a time-limited practical work: a Raspberry Pi is shared and controlled remotely by several student pairs: we emphasize the interest of collaborative work, and we provide knowledge and skills about micro-computers to a large number of students simultaneously.
However, information and communication technologies are not reduced to algorithms, and it is important that students understand the link between hardware and software. The technology acceptance model (TAM) is considered a successful model in studying learners' behaviour regarding technology use in education  [4]. One conclusion of TAM is that people are eager for novel technologies if they grasp their usefulness  [5]. Hence the need for an application when teaching informatics. The main contribution of this paper is as follows: To teach micro-computers and their interest for digital signal processing applications, we propose an approach that uses a low-cost single board computer. We also demonstrate its interest for a collaborative practical work. We propose a setup including a Raspberry Pi (RPi) to perform somehow advanced digital signal processing algorithms [6],  [7].
We notice that, although the cost of the RPi itself is relatively low, the whole budget is up to 180 US $. Owing to the cost of the peripherals, a faster single board computer such as the Banana Pi Pro (provided with a quad-core 1GHz processor, and 2GB of RAM) could be used despite a slightly higher cost compared to the RPi. Still for cost purposes an advantage of RPi is its ability to run freeware softwares. The students are asked to write a short report on the structure of the RPi, with compulsory keywords such as 'RAM', 'processor'. As all hardware elements are visible on the RPi, the educational target of this task is that the students get familiar with these elements.
1) Operating system: The first task for the students consists in installing the operating system on the micro-SD card by a PC computer through a software such as Win32Disk manager or Linux Live USB creator. They need an 'image file' to install the 'Raspbian' operating system. Raspbian is the RPi foundation's official operating system [15], and is based on Debian. The educational target of this task is the following: letting the students perform the installation starting from scratch is relevant so that they understand that the operating system is placed on the micro-SD card which is plugged into the RPi, and neither on the Samsung memory chip, nor on the "ARM" processor placed underneath. They are asked to run the following command lines, for the purpose of software update or installation: $ sudo apt-get update, $ sudo apt-get upgrade, $ sudo apt-get install.
This can be done by the students in a project setup, where the time constraint is not strong, but should be performed by the teacher in the framework of timelimited practical works. Last but not least, we emphasize the difference between the two modes for users to interact with their RPi: graphical user interface (GUI) and command line interface (CLI). This allows them to understand the difference between the "Windows"type GUI and the "Linux" or "MS-DOS" -type CLI. On a RPi, you start the GUI from the CLI with the following command line: sudo startx. The CLI takes far less system ressources but the GUI is more convenient to create program files, and toggle through program files and folders. 2) Programmes: We aim at showing to the students that they develop programmes based on free softwares and free libraries of programming functions.
Although the mathematics concerning signal processing may be easier for 1-D than 2-D, we decided to consider images as processed data: • students find it easier to grasp the consequences of some processings while looking at result images than while hearing sounds; • students appreciate to process data that they have acquired themselves, such as photographies acquired with a camera connected to the RPi at USB or a specific port; • a large panel of image processing and computer vision functions are available for free, as OpenCV [6] for C programming or SimpleCV  [7]  for Python language. To implement image processing algorithms, the students use Octave and Python programming languages. The students are assumed to have basic knowledge of programming languages: instruction list such as assembly, structured text such as C or Octave.