dataesr/scientific-paragraphs-categorization huggingface dataset:
Technological advances in the first two decades of the 21 st century have profoundly impacted medical research in many ways, with large population cohorts, biological sample collections and datasets through biobanks becoming valued global resources to guide biomedical research, drug development, and medical practice. However, in order for biobanks to maximize their impact and scientific reach of their resources, they would need to act within a complex network of infrastructures and activities. Therefore, different ways have emerged in which biobanks, including those for infectious diseases, can emerge as (part of) infrastructures, integrate within existing ones, or become an independent, yet an interoperable component of the existing infrastructural landscape. However, there has been a limited understanding and study of such mechanisms to date.
Introduction Technological advances in the first two decades of the 21st century have profoundly impacted medical research in many ways. Following the completion of the Human Genome Project, genomic research progressed rapidly, with large datasets and reference maps becoming valued global resources to guide scientific and clinical research, drug development, and medical practice  [1]. This push for the availability of scientific reference data sets, often within the framework of open data science, has subsequently led to the creation of a variety of dedicated research infrastructures that gather data and provide services to different research communities. The European Research Infrastructures are one such example, involving research areas such as paediatrics [Paediatric Translational Research Infrastructure (ID-EPTRI); https://eptri.
Centralizing existing infrastructures (top-down approach): A different approach to create infrastructures is to push for consolidated biomedical and research laboratories, where resources and services are centralized and serve a large(r) population for the purposes of enhanced efficiency, increased standardization, and potentially earlier time to results. This approach is often driven by business considerations and scarcities of appropriately qualified personnel, while additional secondary benefits, can include integrated databases, reporting systems as well as biobanks  [15]. In contrast to the UK example above, Estonia has taken a different approach and centralized several different infrastructures into a single one. Estonia's digital national infrastructure, where state services are digital, includes virtually all interactions a citizen can have with the government (e.
Considerations It has to be noted at this point, that field activity in infectious diseases, such as the collection of specimens and/or pathogens, standardized procedures that aim to guarantee the compatibility of samples collected in different conditions. The same holds true for analyses, however here the digital outcome may differ due to different instruments, a different way of interpreting the results, and so on. Thus, standardization relies mainly on shared procedures, which include how to deal with the characteristics of analytical equipment and their digital outcomes: for example, data from instruments of different precision cannot be compared in a straightforward way. Finally, a compatible data organization is necessary to guarantee full interoperability among different (data) infrastructures, in what is usually called 'semantic interoperability': it is necessary that data stored in infrastructure A mean the same as data stored in infrastructure B, to enable A and B to dialogue and effectively co-operate; or, at least, a bi-lingual vocabularyin technical terms, a 'mapping'is required, associating the logical structures of the two data banks  [22, 23].
2. The remaining electrode potential was measured for several pH buffer solutions. A "super" Nernstian behavior was observed and an electrode sensitivity higher than 60 mV/pH was measured. These results agree with the literature data. Several studies have revealed an electrode sensitivity between 60 and 78 mV/pH  [2, 8, 20, 24, 25, 33]. One can observe that there is a greater data discrepancy for the non-heated electrode (Fig. 2a ) than for the heat-treated electrode (Fig. 2b ) -a larger standard error was calculated: 41 mV/pH versus 2 mV/pH for the non-heat-treated and the heat-treated electrodes, respectively. This suggest that the drying makes the Pt/IrOx microelectrode more reliable. Accordingly, for the following experiments, each electrode was dried for.
(2022)  in this issue;  Eddy & O'Connell, 2021b). The PD also included workshops on the three content strands and a workshop on the use of course-level data to investigate participation and success of different demographic groups  (Ormand et al. , 2022). The eight Peer Leaders, including one adjunct, each led one of the one-hour workshop sessions in the Spring Workshop and a session during the culminating June Workshop. We think the following aspects of the SAGE 2YC Cohort 3 and Cohort 3 Extension programs made them both attractive for participation by all faculty, including adjunct faculty, and contributed to their success in supporting all faculty. 1. Participation was open to all instructors, including adjuncts. Participants were not identified on the project website or in the program as adjunct faculty.
" If only I had known! Participating in the SAGE 2YC program during the pandemic was grounding and motivating. It was a concrete goal, a distraction, and a way to connect. It allowed me to flourish (at least in one area of my life) during a time of great challenge. While I was familiar with some of the pedagogical techniques discussed in the workshops, SAGE 2YC allowed me to see these concepts in the broader institutional context and understand them as powerful tools for inclusion and equity. SAGE 2YC allowed me to translate what I already knew into action in this bigger context and facilitated my transition from teaching geology well to being a more inclusive educator. Through my participation in the SAGE 2YC program, and together with my colleague Lorraine Carey, I explored demographic data and communicated patterns I found as a call to action for my colleagues.
After participating in the SAGE 2YC program and the associated Leadership Extension, I have recognized myself as a faculty leader with the capacity and energy to implement changes to better support students, faculty, and administrators with the final goal of improving students' success rates, pass rates, and transfer rates. In addition, after participation in the Leadership Extension program, I was able to practice strategies that let me address some of the faculty's fear to implement change such as understanding others' leadership frames. As part of my CA responsibilities, I have explored disaggregated data on students' pass and transfer rates to recognize their needs according to their race and/or ethnicity. Moreover, after leading different events at LCC, I have been seen as a faculty member interested in implementing change, which has led to my being invited to serve as faculty co-advisor of a STEM Transfer Pathway club and as a member of a search committee for a senior administrator.
Professional organizations and grant-funded projects • Encourage adjunct participation in professional organizations. Create spaces that directly encourage adjunct participation: offer sessions on pedagogy; select adjuncts for committee and leadership positions; include adjuncts in strategic planning for the organization; support efforts to collect data about adjuncts; include adjuncts in communications; set reduced fees for membership and/or conferences; support regional workshops/conferences that are easier for adjuncts to attend  (Maier et al. , 2021). • Develop grant-related PD programs for adjuncts. Sponsor sessions that help demystify the grant-writing and application process to highlight where contributions from adjunct faculty might be most valuable. Host tours of funding agencies or informational sessions for adjuncts.
Acknowledgments A. I. is partially supported by grants from  Takeda   CW2680521 ; CONICET;  ANID/FONDECYT Regular  ( 1210195  and  1210176 );  FONCYT-PICT 2017-1820 ;  ANID/FONDAP/15150012 ;  Sistema General de Regalías  ( BPIN2018000100059 ),  Universidad del Valle  ( CI 5316 );  Programa Interdisciplinario de Investigación Experimental en Comunicación y Cognición (PIIECC) ,  Facultad de Humanidades, USACH; Alzheimer's Association   GBHI ALZ UK-20-639295 ; and the  MULTI-PARTNER CONSORTIUM TO EXPAND DEMENTIA RESEARCH IN LATIN AMERICA [ReDLat , supported by  National Institutes of Health, National Institutes of Aging  ( R01 AG057234 ),  Alzheimer's Association  ( SG-20-725707 ),  Rainwater Charitable foundation -Tau Consortium , and  Global Brain Health Institute )]. C. D. A. is partially supported by  2018-AARG-591107 ,  ANID/FONDEF ID20I10152 ,  ANID/FONDECYT 1210622 , and  ANID/PIA/ANILLOS ACT210096.
Comparison with PED conformations and link with biological activity The sets of Sic1 and pSic1 conformations selected from the fitting of SAXS curves and of Ramachandran probability maps, were compared to the sets of protein conformations deposited in the Protein Ensemble Database proteinensemble. org. 21  The values of the resulting gyration radii were calculated (Table  4 ) from the populations determined by BioEn and RamaMix, and using the individual gyration radii of selected conformations. Globally, the resulting gyration radii display orders of values agreeing with the measurements reported in Figure  2E  of Ref. 17. For the conformations extracted from the data-sets Sic1 1 and Sic1 2 , the resulting gyration radii agree with the measurement of 3. 0 ± 4. 1 Å given in Figure  2E  of Ref.
Origins of the data Three sets of conformations for Sic1 and pSic1 were available from the Protein Ensemble Database (PED) proteinensemble. org: 21 PED159 and PED160 for pSic1 and PED161 for Sic1. 17 The residue numbering used here is the one proposed in the PED. The NMR chemical shifts were downloaded from the Biological Magnetic Resonance Data Bank (BMRB)  27  as entries: 16657 for Sic1  17  and 16659 for pSic1. 19 The SAXS data-sets recorded as triplicate sets in the conditions described in Ref. 17  were provided by Tanja Mittag.
The assembled conformations in which C↵ atoms closer than 1 Å are observed, were pruned from the calculation. The fragment assembly was implemented using python scripting based on the MDAnalysis 34, 35 and numpy 1. 7. 1 36 packages. To scale down the combinatorial explosion of the calculation, a clustering approach based on Self-Organizing Maps (SOM)  [37] [38] [39] [40]  was systematically applied to the generated sets of conformations larger than 100 during the iBP and assembly steps. The details of this approach are described in the section "Clustering of generated conformations" of the Supplementary Material. After the assembly step, the sidechains have been added to the conformation backbones, and the conformations were refined by molecular dynamics simulations as described in the section "Molecular dynamics refinement in implicit solvent" in the Supplementary Material.
( 4 ) For the sake of clarity, let us first introduce a standard, non-periodized Gaussian density p n q ( , ) for the residue n in conformation q: p n q ( , ) = 1 2⇡ det(C n q ) 1/2 exp( V n q ( , )) (5) where V n q ( , ) represents the free energy surface for the basin around the conformation q. The free energy surface is described in the frame of an elastic network model on the backbone dihedral angles:  [42] [43] [44] [45]  V n q ( , ) = 1 2 ✓ t q [C n q ] 1 ✓ q (6) where: ✓ q = ( n q , n q ) t , n q and n q are the values of dihedral angles of the residue n in the conformation q and C n q is the corresponding covariance. The software IMOD  42  was used for determining the full Hessian (N, N) (N is the total number of residues in the protein) matrix H q along the backbone dihedral angles.
By optimization of the log-likelihood, the RamaMix approach will thus produce the Q normalized populations q , the Q ⇥ N couples of backbone angles n q and n q , as well as the The RamaMix approach was implemented in Fortran90, and the software is available at github. com/tmalliavin/RamaMix. Q ⇥ N triplets ( n 1 ,  2 , ⇢ n q ) describing.
Determining the populations from SAXS data The software BioEn 0. 1. 1  18  was used in order to determine the populations from SAXS data. On each considered conformation, theoretical SAXS curves were calculated using CRYSOL  53  available in the package ATSAS 3. 0. 3  54 with 847 points, a maximum scattering vector of 0. 503 nm 1 and a maximum order of harmonics of 18. A 1D cubic interpolation  55  was used to obtained the theoretical SAXS values at the same sets of scattering vectors q than the ones at which the experimental SAXS curve was recorded. The processing with BioEn was performed in the following way. For each TAiBP run and each SAXS curve, the optimization was run for 1000 steps using the GSL library. 56 runs were performed independently on all considered conformations, and the subset of conformations for which the sum of observed populations is larger than 0.
Expectation-Maximization (EM) algorithm The EM algorithm is a reference solution to determine ✓ by iterative local optimization. Each EM iteration consists in solving the following auxiliary problem: ✓ new = arg max ✓ Q(✓; ✓ old ), ( 17 ) where Q is the expectation of the log-likelihood of the "complete" dataset: Q(✓, ✓ old ) = E ⇥ ln (Pr(C; ) p(Y |C; ⇣)) |Y ; ✓ old ⇤ (18) = Q 0 ( , ✓ old ) + Q 1 (⇣, ✓ old ) ( 1 9 ) with Q 0 ( , ✓ old ) = E ⇥ ln Pr(C; )|Y ; ✓ old ⇤ , (20) Q 1 (⇣, ✓ old ) = E ⇥ ln p(Y |C; ⇣)|Y ; ✓ old ⇤. ( 21 ) On the one hand, along classical derivations, we get Q 0 ( , ✓ old ) = Q X q=1 D X d=1 P qd ! ln q (22) where P qd = P q (y d ), with P q (y) = Pr(C = q|y; ✓ old ) = old q p q (y; ⇣ old ) P q 0 old q 0 p q 0 (y; ⇣ old ). ( ) 23 On the other hand, Q 1 (⇣, ✓ old ) = D X d=1 Q X q=1 P qd ln p(y d ; ⇣ L q , ⇣ S q ).
Case of multiple datasets In the case where N residues are available, each conformation is characterized by a unique weight vector, whereas its location and shape parameters are specific to each residue. The identification problem then consists in estimating: • Q normalized weights = ( q ) for the protein conformations (classes), • 5NQ = 3NQ + 2NQ shape and location parameters specific to each conformation and each residue, respectively ⇣ S qn = ( 1qn ,  2qn , qn ) and ⇣ L qn = ( qn , qn ). The log-likelihood then reads L(Y ; ✓) = N X n=1 Dn X d=1 ln Q X q=1 q p(y dn ; ⇣ S qn , ⇣ L qn ) ! where the nth residue corresponds to D n observed pairs of angles y dn. The gradient component relative to each shape or location parameter can still be calculated using the equations (  37 )-(  42 ) and (  44 )- (46), respectively, while a summation of Eq.
Acknowledgments The project  ANR-19-CE45-0019  (multiBioStruct) is acknowledged for funding, as well as  Institut Pasteur ,  CNRS ,  Ecole Polytechnique  and  University of Rennes. Tanja Mittag  is acknowledged for providing SAXS data recorded as triplicate sets in the conditions described in Ref. 17. Cyprien Bertran  is acknowledged for fruitful discussions.
In these conditions, I A densities were $250 pA/pF in GAD67-GFP -neurons (excitatory neurons) and $140 pA/pF in GAD67-GFP + neurons (inhibitory interneurons), which included a small percentage of neurons with no I A. In interneurons recorded on slices, I A was induced with a +70 mV step after a À120 mV conditioning prepulse  (Yoo et al. , 2021). With this protocol, I A densities were determined for GAD67-GFP -and GAD67-GFP + interneurons selectively located in LIIi or in LIIo. The values obtained were $260 pA/pF for LIIi GAD67-GFP -interneurons, $295 pA/pF for LIIo GAD67-GFP -interneurons, $40 pA/pF for LIIi GAD67-GFP + interneurons, and, finally, $95 pA/pF for LIIo GAD67-GFP + interneurons. The data obtained when using the two different models were remarkably consistent, leading to the conclusion that both types of interneuron express I A in naı ¨ve mice but with striking differences in amplitude.
This increase triggers a dysregulation of macrophage activity, leading to an increase in the concentration of interleukin (IL)-10, which has anti-inflammatory effects, ultimately favoring skin repair  (Hoeffel et al. , 2021). No data are currently available concerning putative changes in TAFA4 production within the dorsal spinal cord after peripheral inflammation or sciatic nerve injury. After SNI, TAFA4 persists in the pro-jection territory of the spared C-LTMRs but disappears from the spinal territory displaying strong microglial activation. I A (together with I h ) has been shown to be modulated in specific populations of interneurons within this highly perturbed area, as described above  (Yoo et al. , 2021). Each SNI-induced modulation can be reversed by incubating the spinal cord slices in an external solution containing TAFA4.
In these conditions, the mechanical pain pathway is activated (black vertical arrow). The stylized traces are based on data from  Zhang et al. (2018)  and  Yoo et al. (2021)  for LIIo excitatory interneurons and are exclusively from  Yoo et al. (2021)  for LIIi inhibitory interneurons. One asterisk means that the data were exclusively obtained by  Yoo et al. (2021). Two asterisks means that the data obtained by  Yoo et al. (2021)  were the same as those obtained by  Zhang et al. (2018). therefore seems possible that, after SNI, ROS increase I A in LIIi GAD67-GFP + interneurons but not in LIIo GAD67-GFP -interneurons, based on the assumption that DPLP6a and/or DPP10a are expressed in the former but not the latter. Interestingly, in the dorsal horn of the spinal cord, the expression of DPPL6 and DPPL10 is heterogeneous, as most LI, LIII, and LV dorsal horn projection neurons express DPPL10 whereas LII neurons express a strong DPPL6 immunoreactivity co-localized with Kv4.
ACKNOWLEDGMENTS This work was funded by the  French National Research Agency  ( ANR-CE16-Myochronic grant ) and by the '' Fondation pour la Recherche Medicale (FRM) '' Equipe  FRM-EQP202003010192  (grants awarded to A. M. ). This work was also supported by institutional funding from the  CNRS  and  Aix-Marseille-Universite  ´(awarded to IBDM).
Recordings were obtained by means of a Multiclamp 700B ampli er, Digidata 1440 digitized and pCLAMP 8. 0 software (Molecular Devices). Access resistance was continuously monitored for each cell. Only the cells with access resistance less than 20 MΩ were recorded, and recordings were terminated/discarded when a signi cant (>10%) increase occurred. Data were analyzed using Clamp t 10. 5 (Molecular Devices) and Mini Analysis (Synaptosot).
Pole test In the pole test, which evaluated motor coordination, mice were trained for 2 days (in the morning and in the afternoon) to descend a vertical pole (90 cm length, 1 cm diameter). Every training consisted of three trials. Mice were accustomed to the room 20 minutes before trials and test day. The test was performed the third day and the time necessary to the mice to descend the pole in ve trials was recorded. A cut-off of 60 seconds was given  (55) , with minor modi cations). Data were shown as mean of 5 trials evaluated during the test day.
With the pole test we measured the latency to turn downwards and descend and we observed that the Scn1a+/-mice took longer time than WT mice while the Scn1a+/-/eEF2K-/-mice behaved as WT mice (Figure  4D ). Finally, in the rotarod test the Scn1a+/-mice showed a strong impairment in motor coordination that was rescued in the Scn1a+/-eEF2K-/-mice (Figure  4E ). All these data clearly demonstrated that deletion of eEF2K was able to recover all the motor de cits found in the Scn1a+/-mice. eEF2K deletion rescue the stereotyped behavior of Scn1a+/-mice Dravet syndrome patients are affected also by important and diverse patient-related co-morbidity such as stereotyped behaviors and anxiety We measured stereotyped behaviors by self-grooming test, counting the time spent in this activity. We observed that EF2K deletion in Scn1a+/-mice was able to reduce the time occupied in self-grooming of Scn1a+/-mice to value similar to the WT mice (Figure  5A ).
High levels of phosphorylated eEF2 have been described in Alzheimer disease  (31, 32) , Parkinson disease (28) and genetic epilepsy (18) and it has also been reported that inhibition of eEF2K activity ameliorate the pathology (18,  27, 31, 32). We found that phosphorylation of eEF2, the only substrate of eEF2K, is higher in the hippocampus and cerebral cortex of Scn1a+/-mice compared with WT, while no difference was found in the liver, kidney and heart. We measured a higher level of phosphorylated eEF2 in adult Scn1a+/ mice (3 months old mice) when the high number of spikes and the behavioral de cit are clearly displayed. The increased level of eEF2 phosphorylation was maintained also in 9 month old mice. These data suggest eEF2K/eEF2 pathway alterations might contribute to the inhibition/excitation unbalance that leads to epileptic phonotype and behavioral de cit of Scn1a+/-mice.
In order to facilitate the identi cation of these variants, we developed a test to quickly identify these mutations without sequencing. To this end, we performed two similar SARS-CoV-2 Mutation Discrimination assay (SARS-MD) for the detection of spike protein mutations on 501 and 484 positions. This novel SARS-MD assay is based on TaqMan 5'nuclease chemistry for amplifying and detecting speci c variants in puri ed genomic RNA samples. Each assay contained sequence-speci c forward and reverse primers to amplify the target sequence region mutation and two TaqMan MGB probes, i. e. one VIC dye-labeled probe to detect the reference sequence and one FAM dye-labeled probe to detect the mutation sequence. The oligonucleotide primers and probes used in the study were designed from the spike gene of the Wuhan Strain (GenBank : NC_045512).
Forward and reverse primers designed to cover both sides of the N501Y and E484K mutations had resulted in a product size of 94pb and 74pb respectively. The sequences for each assay provided by ThermoFisher Scienti c are presented in Table  1. Brie y, 5µl of the total nucleic acid eluated on the MGISP-960 Platform (BGI) was added to 20µl total volume reaction mixture (1X SuperScript III Platinium One-Step qRT-PCR Reaction Mix, 4. 0mM of MgSO4, 500nM of Rox reference Dye, 0. 9µM of each primer, 0. 2µM of each probe and 0. 5µl of Superscript III RT/platinum Taq mix). The RT-qPCR was carried out using the QuantStudio TM 5 Real Time PCR instrument (QS5) and the QuantStudio TM Design and Analysis Software V2. 5 (Applied Biosystems) under the following conditions : a pre-read at 60°C for 30 sec, then 55°C for 15 min, followed by 2 min at 95°C, 40 cycles for 95°C for 15 sec and 60°C for 30 sec, terminated by a post-reading at 60°C for 30 sec.
28/b. 1. 160) in France. We then observed a signi cant increase in these cases in our laboratory during the rst quarter of 2021 (January to May). According to the data reported in GISAID's EpiCoV database, this A. 28 VOC was found into 24 countries of the world with highest prevalence in the Persian Gulf countries. France was the European country where the most cases of A. 28 were found. In the present study, two molecular assays that detect SARS-CoV-2 mutations were developed. All assays were precise and robust, completely consistent with the Sanger sequencing. During the rst quarter of 2021, the combination of the TaqPath™ COVID-19 RT-PCR kit with SARS-MD assays allowed our laboratory to quickly identify the United Kingdom variant 20I/501Y. V1 and the South-African variant 20H/501Y. V2 that were circulating in our region.