dataesr/scientific-paragraphs-categorization huggingface dataset:
Since the 39S mitoribosome part of all states resembled very well the mature 39S mitoribosome, the mature human 39S mitoribosome model could be fitted as rigid body. The immature region in rRNA domain IV and V were either simply removed or manually adjusted in Coot  58  to better fit the density. The MALSU1 complex was taken from PDB:5OOL and rigid body fitted into every state  31. For state 1, the overall resolution was not sufficient to build a molecular model. DDX28 was fitted as a rigid body using a homology model generated using the SWISS-MODEL server based on PDB: 4W7S  59, 60. After fitting, small adjustments were done manually in Coot. MRM3 and GTPBP10 were also rigid body docked as homology model generated using the SWISS-MODEL server based on PDB: 4X3M and 1LNZ  61  , respectively.
However, due to the low local resolution, no adjustments could be further done, and, in the final model of state 1, only poly-Ala models are provided. In the states 3A and 5A, the E-site tRNA did not have sufficient resolution to build a molecular model, thus a previously observed mitochondrial tRNA was used to rigid body fit into the density  9. The position A73 was manually built in Coot. For state 4, the crystal structure of NSUN4-mTERF4 complex was used to dock into the map, with a small manual adjustment of mTERF4  33, 34. Also here, due to low local resolution of these two factors, only poly-alanine models were provided. Model refinement was carried out using Phenix. real_space_refine, and final models were validated using MolProbity  62, 63  inside of the Phenix software suit. Maps and models were visualized and figures created with ChimeraX 64.
Acknowledgements The authors thank  S. Rieder ,  C. Ungewickell , and  A. Gilmozzi  for technical assistance,  L. Kater  for discussions and critical comments on the manuscript. This research was supported by grants from the  Deutsche Forschungsgemeinschaft  ( GRK1721 ) and by an  European Research Council (ERC)   Advanced Grant  ( HumanRibogenesis)  to R. B.
Data availability The EM density maps have been deposited in the Electron Microscopy Data Bank under accession codes EMD-12919 (state 1), EMD-12920 (state 2), EMD-12921 (state 3A), EMD-12922 (state 3B), EMD-12923 (state 3C), EMD-12924 (state 3D), EMD-12925 (state 4), EMD-12926 (state 5A), EMD-12927 (state 5B), and the coordinates of the EMbased models have been deposited in the Protein Data Bank under accession codes PDB 7OI6 (state 1), PDB 7OI7 (state 2), PDB 7OI8 (state 3A), PDB 7OI9 (state 3B), PDB 7OIA (state 3C), PDB 7OIB (state 3D), PDB 7OIC (state 4), PDB 7OID (state 5A), PDB 7OIE (state 5B). Other coordinates of the used models in this study are available under accession codes: PDB 3J9M, PDB 5OOL, PDB 5OOM, PDB 4W7S, PDB 4X3M, and PDB 1LNZ. A Life Sciences Reporting Summary for this paper is available.
Study design This randomized, multicenter, double-blind, placebocontrolled trial was conducted in 19 ICUs in France (from February 5th, 2014, to January 23rd, 2019) (Trial registration-ClinicalTrials. gov Identifier NCT02152358). The sponsor was the Assistance Publique-Hôpitaux de Marseille, and a grant was obtained from the French Ministry of Health (PHRC 2011). An independent Ethics Committee (Comité de Protection des Personnes Sud Méditerranée 5) approved the protocol and the amendments. Study sites and investigators are listed in the Appendix. The trial protocol has already been published (Additional file 1)  [16]. During the study period, potentially eligible patients (i. e. , those under invasive mechanical ventilation for ≥ 96 h) were screened twice weekly for CMV and herpesvirus simplex (HSV) reactivations, respectively, with quantitative or qualitative polymerase chain reactions (PCR) on whole blood or oropharyngeal swabs collected the same day for as long as the patients remained under invasive mechanical ventilation with a maximum of 30 days of mechanical ventilation.
The main analyses were conducted on an intention-to-treat basis. All analyses were computed with IBM SPSS statistics 20, R software, version 3. 5. 1 (R Foundation) at a two-sided, 5% alpha level. Non-prespecified metanalyses were finally performed for three endpoints taking into account two already published randomized clinical trials  [14, 15]  and the present one. Day 28 mortality and hospital mortality were analyzed using odd ratios (95% CI), defined as the ratio of the probability of an event occurring between two groups. Ventilator-free days to day 28 were analyzed using standardized means difference (standard error), expressing the size of the intervention effect (means and standard deviations were estimated from the medians and interquartile ranges)  [25, 26]. We used fixed effects  [27]  and random effects models  [28] , which account for the between-study heterogeneity by weighting studies similarly.
Acknowledgements The authors thank  Jean-Marie Chrétien  and  Lucie Van Eeckhoutte  for their help in the data management;  Camille Pinglis ,  Céline Sanz  and  Sabine Valera  for their help in data acquisition;  Patrick Sudour  and  Séverine Le Roux  for their administrative support. This manuscript was edited for proper English language, grammar, punctuation, spelling, and overall style by American Journal Experts ( FBC7-532D-97CC-9B31-5618 ).
Phantom Experiments The CZT camera was previously calibrated for clinical routine reconstruction parameters using a cylinder filled in with a homogeneous solution of 99m Tc, as previously described  (15). A NEMA IEC body phantom (Data Spectrum Corporation, Durham, NC, USA) was filled in within the background compartment using a 99m Tc solution with an activity concentration of 16. 50 kBq ml -1 at scan time and with an 8. 3-fold higher activity concentration (137. 05 kBq ml -1 ) within six spherical inserts having respective volumes of 0. 6, 1. 1, 2. 6, 5. 6, 11. 5, and 26. 5 ml. This phantom was recorded at the center of the camera field of view using the routine protocol recommended for bone scintigraphy, although with recording time reduced to 2 min in a single bed position. Accordingly, the total number of recorded counts was in the range observed for the thoraco-abdominal bed positions of our routine bone scintigraphy recordings  (1.
Statistical Analysis Qualitative variables were expressed with frequencies and quantitative values, with means ± standard deviations. Mann-Whitney tests and Wilcoxon signed-rank tests were used, respectively, for the unpaired and paired comparisons of quantitative variables. For all tests, a p-value <0. 05 was considered as indicative of a significant difference. This statistical analysis was performed with SPSS 25. 0 software (IBM Corp R , Armonk, NY, USA).
Introduction: Tajikistan is scaling up molecular diagnostic and digital technologies to strengthen its fight against drug-resistant TB (DR-TB). The study aimed to document national scale-up GeneXpert/GxAlert and Open MRS from 2012-2019 and compare time taken from TB diagnosis to treatment and quality of data recording before and after the introduction of GxAlert. Methodology: This was a longitudinal study that included a comparison of historical cohorts. Continuous variables were compared using Wilcoxon Rank-Sum test and categorical variables using the chi square test. Results: GeneXpert was introduced in 2011 and scaled up to 46 instruments in 43 (51%) diagnostic laboratories by May 2019. GxAlert was introduced in August 2018 and connected with all GeneXpert instruments by February 2019. Open MRS was introduced in 2014 and implemented in all 108 treatment centers by mid-2018.
Time from diagnosis to treatment pre-GxAlert (range 0-749, median 3, days) was significantly longer than with GxAlert (range 0-273, median 3, days) (p <0. 001). The proportion of patients whose time from diagnosis to treatment was > 2 weeks was 16% (282/1740) pre-GxAlert and 11% (206/1902) with GxAlert (p < 0. 001). Between 31%-34% of patients with DR-TB results in Open MRS did not have results available in GeneXpert/GxAlert systems. Where results were present in both systems, there were discrepancies in 8. 2% of patients pre-GxAlert and 4. 3% with GxAlert (p = 0. 25). Conclusions: The scale-up of GeneXpert and digital technologies in Tajikistan was associated with a reduction in the proportion of patients with delays more than 2 weeks between diagnosis and treatment, but data quality recording improved only slightly.
Unfortunately, this promise has yet to be realized, and in Africa and Asia there continues to be long turn-around times between diagnosis and treatment initiation as well as high pre-treatment losses to follow when the Xpert MTB/RIF assay is used  [3] [4] [5]. GxAlert (SystemOne, Boston, MA, USA) is a remote, electronic cloud-based data monitoring system that allows the automatic connection of the Xpert instrument to the network and mobile channels, thus monitoring instrument status and removing the need for a paper-based interface to communicate the results of the Xpert MTB/RIF assay. The monitoring functionality provides visibility on readiness of the 59S instrument, calibration status, cartridge usage and supply and this information is used for planning and procurement. Additionally, the test results can be transmitted in real time over the internet as emails or through mobile channels as short message services (SMS) to the country's existing health information systems, treatment centers, referring clinicians and even patients themselves.
This systematic approach should lead to patients being diagnosed and treated in a timely manner with potentially better treatment outcomes. The few published studies to date on GxAlert, however, have shown mixed results. Two studies in Mozambique and Papua New Guinea showed that GxAlert was associated with decreased time from Xpert testing to treatment initiation and a reduced pre-treatment loss to follow-up  [6, 7]. In Tanzania, the installation of Xpert MTB/RIF and GxAlert enhanced the rapid diagnosis of TB but did not improve linkage to care because of health system inefficiencies, inaccurate or incomplete patient data entry and communication breakdown among health care providers  [8]. The potential effectiveness of this new technology is still unresolved. In 2012, Tajikistan received its first GeneXpert instrument.
Scale up then occurred to diagnostic laboratories in the country, the main expansion taking place from 2017 onwards. From mid-2018, the instruments deployed around the country were connected to the GxAlert diagnostic system. The scale up of GeneXpert in Tajikistan coincided with the rollout of the country's new Open MRS medical information system which potentially allows for better recording and reporting of TB diagnosis and treatment. Because TB-electronic medical record systems have met with mixed success in Asian and African countries  [9] [10] [11] [12] , the Open MRS system was first piloted only in the capital city, Dushanbe, in 2011. Three years later, Open MRS was rolled out country-wide in a phased approach and full coverage was achieved in all TB treatment centers by 2018. All centers were mandated to back-enter their TB data to 2014, so that those initiating Open MRS later than 2014 had updated historical data back to that time.
Data sources and variables Data variables included year and month for all objectives. For Objective 1, specific variables included names of TB treatment facilities, type of the facility (treatment, diagnostic or both) availability and dates of implementations of OpenMRS, GeneXpert and GxAlert. The source of data was National TB Control Programme records from 2012 to 2019. For Objective 2, specific variables included date of GeneXpert test result and date of start of TB treatment. The source of data was Open MRS. For Objective 3, specific variables included GeneXpert test results in GxAlert / GenXpert system print-outs and the Open MRS system. Sources of data were GeneXpert/GxAlert and Open MRS systems.
First, additional GeneXpert instruments must be procured and deployed to all diagnostic facilities in the country. The WHO in 2020 updated its guidance recommending that Xpert MTB/RIF and Xpert Ultra be used as the initial diagnostic tests in all adults and children with symptoms of pulmonary TB  [19]. The Xpert MTB/RIF Ultra is an assay with higher sensitivity than Xpert MTB/RIF, is run on the same instrument as Xpert, and requires only a software upgrade  [20]. Sufficient funding, technical maintenance and support and good health system infrastructure must accompany the further scale up and deployment of GeneXpert instruments around the country. Regular supervision is 64S also essential to ensure that the instruments are fully functional and utilized to their maximum capacity  [21, 22]. Second, it is important that GxAlert is rapidly linked to the new GeneXpert instruments and further work conducted on how to optimize its utilization.
Acknowledgements This research was conducted through the  Structured Operational Research and Training Initiative (SORT IT ), a global partnership coordinated by  TDR  (the  UNICEF/UNDP / World Bank/WHO Special Programme for Research and Training in Tropical Diseases). The specific SORT IT programme that led to these publications included a partnership between  TDR  and the  European Tuberculosis Research Initiative  ( ERI-TB ) at the  WHO Regional Office for Europe , and was implemented by: the  Tuberculosis Research and Prevention Center, Armenia ;  The Alliance for Public Health, Ukraine; The Centre for Operational Research, The Union, France ; the  Operational Research Unit (LuxOR) ,  Médecins Sans Frontières, Brussels Operational Centre , Luxembourg; Sustainable Health Systems Sierra Leone; and  TDR.
Funding TDR and partners are able to conduct their work thanks to the commitment and support of a variety of funders. These include long-term core contributors from national governments and international institutions, as well as designated funding for specific projects within current priorities. A full list of TDR donors is available on our website at: https://www. who. int/tdr/about/funding/en/. This SORT IT program was funded by the United States Agency for International Development (USAID) and supported by implementing partners. Funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Weight, BMI, and levodopa equivalent daily dose (LEDD) were collected at baseline, 6 months and 12 months. At baseline, neuropsychological tests (either MMSE or Mattis Dementia Rating Scale) were done for all the patients, while depression and dysautonomia were assessed through a standardized interview. The data were extracted from the NS-PARK national cohort which gathers clinical data from all French Parkinson Expert Center, collected as a clinical care routine procedure, with the oral consent of the patients. It was registered by each center at the French National Commission for Data Protection and Liberties. Quantitative parameters were expressed as mean AE stanstandard deviation, and qualitative parameters as percentage. Demographic and clinical characteristics were compared between the three groups of patients using the Kruskal-Wallis and Chi 2 tests.
Purpose: Digital PET involving silicon photomultipliers (SiPM) provides an enhanced time-of-flight (TOF) resolution as compared with photomultiplier (PMT)-based PET, but also a better prevention of the count-related rises in dead time and pile-up effects mainly due to smaller trigger domains (i. e. , the detection surfaces associated with each trigger circuit). This study aimed to determine whether this latter property could help prevent against deteriorations in TOF resolution and TOF image quality in the wide range of PET count rates documented in clinical routine. Methods: Variations, according to count rates, in timing resolution and in TOFrelated enhancement of the quality of phantom images were compared between the first fully digital PET (Vereos) and a PMT-based PET (Ingenuity). Single-count rate values were additionally extracted from the list-mode data of routine analog-and digital-PET exams at each 500-ms interval, in order to determine the ranges of routine PET count rates.
5 Mcps for the Rubidium-82 PET exam recorded on the Ingenuity camera, based on the upper limit observed for this exam on the Vereos camera (S Ver = 29. 5 Mcps. For this purpose, the following equation was used: S Ing estimated ¼ f Ing A→S f Ver S→A S Ver ð Þ À Á ð7Þ where f Ver S→A is the double-exponential function predicting activity concentrations based on the single-count rates of the Vereos camera (29. 5 Mcps) (Fig. 2b ), and f Ing A→S is the double-exponential function predicting the single-count rates of the Ingenuity camera based on activity concentration (Fig. 2a ). The validity of these estimations was strengthened by comparisons with actual measurements, although these comparisons could not be obtained with human data (no patient was injected in this instance with 5 MBq. kg -1 of tracer and no patient was investigated with Rubidium-92 with the Ingenuity camera) and only on the IEC phantom data obtained on a wide activity range (see Additional file 5: Appendix 1).
We explored the dataset structure by multivariate and statistical analyses, seeking to identify the underlying factors. Results: For cells, the 5-mer profiles were inconsistent with the phylogeny of archaea. At a finer taxonomic level, the influence of the taxonomy and the environmental constraints on 5-mer profiles was very strong. These two factors were interdependent to a significant extent, and the respective weights of their contributions varied according to the clade. A convergent adaptation was observed for the class Halobacteria, for which a strong 5-mer signature was identified. For mobile elements, coevolution with the host had a clear influence on their 5-mer profile. This enabled us to identify one previously known and one new case of recent host transfer based on the atypical composition of the mobile elements involved.
Many archaeal plasmids have not yet been classified into well-defined families; however, several families of plasmids have been defined according to plasmid size, replication mode, and genomic content (reviewed in  [25] ). Among archaea, there are no known pathogens for humans, plants or animals, so there is no overrepresentation bias linked to pathogens in the databases. Other biases are, however, present: the mobile elements from several archaeal taxonomic groups (orders or even phyla, ) are very poorly represented in public databases, so the view on global diversity remains incomplete. In addition to the diversity of their mobile elements, archaea constitute an interesting case in terms of adaptation or loss of adaptation to extreme environments, which has played an important role in their evolutionary history  [29].
They observed a higher variability in AT-rich and hostassociated genomes compared to GC rich or free-living archaea and bacteria  [33]. Currently, the number of publicly available genomes has greatly increased, warranting a new study of signatures across the domain Archaea. Selecting close to 600 cellular, viral and plasmid genomes, we applied metrics based on short k-mer profiles to understand how mobile elements are distributed with respect to their hosts in the profile landscape. We used multivariate and statistical analyses to explore the dataset structure and identify some key structuring factors, namely, the taxonomic classification, the genomic GC content, the ecological niche and, for mobile elements, the taxonomy of the host. Moreover, we examined whether 5-mer profiles enable the detection of singular evolutionary trajectories, such as host transfers, among mobile elements.
28% ± 4. 29 SD on average in our dataset. At the other extreme, the less GC-rich cluster (Fig. 1a , letter b) comprised only Group I methanogens (Methanococcales and Methanobacteriales), except for one Group II Methanosarcinales genome. We also identified taxonomy as an important factor, and many clusters were dominated by a single taxonomic group (Fig. 1a ). In particular, all members of the class Halobacteria were located in a single cluster (Fig. 1a , letters c) with only two exceptions, corresponding to the two Haloquadratum walsbyi genomes (order Haloferacales). Similarly, 33 out of 37 members of the order Methanosarcinales were gathered in a single cluster (Fig. 1a, letter d ). Members of the order Sulfolobales were divided into a major cluster (31 genomes out of 39) and a minor cluster (8 genomes out of 39) (Fig.
We therefore assumed that major properties of the environmental niches could be another important factor underlying the 5-mer composition among archaea. We focused on salinity and temperature and defined 8 "Niche" categories. All Halobacteria members were categorized as "halophile". The remaining archaea were labeled according to 7 qualitative growth temperature categories, ranging from "weak mesophile" to "extreme hyperthermophile" (Additional File 2), based on the BacDive database  [36]  and on the literature, e. g. [37]. The clustering pattern was clearly influenced by the "Niche" categories (Fig. 2 a ). Among the 6 main clusters of the dendrogram for cells (Fig. 2  a, clusters a to f), cluster b was largely dominated by thermophiles to extreme hyperthermophiles. Cluster c was dominated by extreme thermophiles, corresponding mostly to Sulfolobales members.
Based on PERMANOVA, the "Niche" categories explained 64. 17% of the dataset variance (D 5_cells ~Niche). Although this percentage is lower than that explained by the taxonomic rank of order (namely, 75. 94%), it is still very high. As anticipated, the GC content, taxonomic rank and "Niche" had a high level of dependency (Additional file 1, D 5_cells ~Niche*Order*GC%). In particular, the last two factors explained 60. 56% of the cell profile dissimilarity variance in an indistinguishable manner (D 5_cells ~Order*Niche), consistent with the strong links between the ecological niche and the evolutionary history in Archaea. Finally, we noticed that a model combining the genomic GC content, ecological niche and taxonomy (order rank) explained almost all the cell dataset variance, namely, 95. 48% (Additional file 1, D 5_cells ~Niche*Order*GC%).
Interestingly, the taxonomic classification of viruses and plasmids was by far the most influential factor, alone explaining 68. 30% of the extrachromosomal element dissimilarity variance (Additional File 3, D 5_mobile Family). This could be due partly to the high number of viral and plasmid families in the dataset (60 compared to only 11 different host orders), which must support a better fit of the model. However, this finding also suggests that individual viral and plasmid families could have a specific 5-mer composition. The extrachromosomal element family and the taxonomy of their hosts at the order level were strongly dependent, since 51. 90% of the extrachromosomal element dissimilarity variance was explained indistinguishably by one of the factors (Additional File 3, D 5_mobile ~Host Order*Family and D 5_mobile ~Family*Host Order).
As we observed for cells, the information about the "Niche" was almost fully included in the host taxonomic classification, since the "Niche" explained only 1. 16% of the extrachromosomal element dataset variance when the influence of host taxonomy was first removed (Additional File 3, D 5_mobile ~Host Order*Niche). A statistical model combining the genomic GC content, the ecological niche and the taxonomy of the host explained 70. 85% of the profile dissimilarity variance (Additional File 3, D 5_mobile ~Niche*Host Order*GC%); adding the extrachromosomal element family as a variable to the model enabled us to reach 89. 29% of explained variance (Additional File 3, D 5_mo- bile ~Niche*Host Order*GC% and D 5_mobile ~Niche*-Host Order*Family*GC%). A clear 5-mer signature for halophily and a weaker signature for hyperthermophily Considering the strong association between the ecological niche and the 5-mer profile distribution, we decided to identify some of the most discriminant 5-mers between halophilic and nonhalophilic entities on the one hand, and between hyperthermophilic versus nonhyperthermophilic entities on the other.